Methods for determining biological age and / or biological age gap

Abstract

The present invention is in the field of biomedical sciences. The present invention relates to in vitro and / or ex vivo methods of determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. Specifically, the present invention relates to a method of determining an age gap between chronological age and biological age based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample. The method of the invention may be computer-implemented. Furthermore, the present invention relates to a method of determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) in cells derived from a bodily fluid sample from an individual and determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level(s) of said one or more marker(s) is / are indicative of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. The present invention further relates to a method for determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological sections of at least one tissue of said individual, and ii) chronological age of said individual, (b) extracting morphological features from said histological sections of step (a) i), (c) analyzing the extracted morphological features of step (b) and correlating said morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and (d) applying the associations determined in step (c) to histological sections to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age. The present invention also relates to a method of determining the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to a bodily fluid sample, and determination of biological age from said bodily fluid sample using associated marker expression level. In particular, the present invention also relates to methods of determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age using the analysis of histopathological tissue samples and / or marker expression levels in cells derived from bodily fluids. The present invention is particularly useful for determining tissue-specific and / or systemic biological age and / or tissue-specific and / or systemic biological age gaps. The invention also relates to a method of determining areas in a histological section that are affected by aging. Furthermore, the present invention is particularly of value for research, diagnostics, and personalized medicine, in particular personalized aging assessment and development of corresponding treatment options. Furthermore, the present invention allows, inter alia, for detection, in particular early detection or prediction of diseases such as age-related pathologies and / or diseases, as well as the determination of the health state or fitness state. The present invention also provides for monitoring treatment responses.

Description

[0001] New PCT patent application based on EP 24 204 785.0 CeMM - Forschungszentrum fur Molekulare Medizin GmbH Vossius Ref.: AJ3371 PCT S3

[0002] METHODS FOR DETERMINING BIOLOGICAL AGE AND / OR BIOLOGICAL AGE GAP

[0003] The present invention is in the field of biomedical sciences. The present invention relates to in vitro and / or ex vivo methods of determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. Specifically, the present invention relates to a method of determining an age gap between chronological age and biological age based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample. The method of the invention may be computer-implemented. Furthermore, the present invention relates to a method of determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) in cells derived from a bodily fluid sample from an individual and determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level(s) of said one or more marker(s) is / are indicative of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. The present invention further relates to a method for determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological sections of at least one tissue of said individual, and ii) chronological age of said individual, (b) extracting morphological features from said histological sections of step (a) i), (c) analyzing the extracted morphological features of step (b) and correlating said morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and (d) applying the associations determined in step (c) to histological sections to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age. The present invention also relates to a method of determining the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to a bodily fluid sample, and determination of biological age from said bodily fluid sample using associated marker expression level. In particular, the present invention also relates to methods of determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age using the analysis of histopathological tissue samples and / or marker expression levels in cells derived from bodily fluids. The present invention is particularly useful for determining tissue-specific and / or systemic biological age and / or tissue-specific and / or systemic biological age gaps. The invention also relates to a method of determining areas in a histological section that are affected by aging. Furthermore, the present invention is particularly of value for research, diagnostics, and personalized medicine, in particular personalized aging assessment and development of corresponding treatment options. Furthermore, the present invention allows, inter alia, for detection, in particular early detection or prediction of diseases such as age-related pathologies and / or diseases, as well as the determination of the health state or fitness state. The present invention also provides for monitoring treatment responses.

[0004] Aging is a fundamental biological process with far-reaching implications for human health and disease (Flatt (2018), BMC Biol., 16, 93). Despite its inevitability, the mechanisms underlying aging and the resultant age-related changes display a striking diversity across tissues (Shock (1956), Bull. N. Y. Acad. Med., 32, 268-283; Khan (2017), Aging Cell, 16, 624-633). This heterogeneity presents a considerable challenge in elucidating the pathways through which aging influences tissue function decline and disease onset. Contributions from evolutionary biology (Haldane (1942), George Allen & Unwin); Medawar (1964), Modern Quarterly, 1, 30-56; Charlesworth (2000), Genetics, 156, 927-931), cellular biology and molecular genetics (Flatt (2009), Biochim. Biophys. Acta, 1790, 951-962; Lemoine (2021), Front. Genet. 12, 693071; Lopez-Otin (2023), Cell, 186, 243-278) have enriched the understanding of the genetic and environmental factors that influence the aging process and human lifespan. However, translating these insights to a coherent understanding of the aging process, aligned with changes in physiological function at the organism level, remains a considerable challenge (Kirkland (2016), Cold Spring Harb. Perspect. Med., 6, a025908; Albert (2020), Innov Aging, 4, igaa003; Guo (2022), Signal Transduct Target Ther, 7, 391).

[0005] Research into human aging has historically faced several limitations. Animal models, despite their value, fall short of capturing the complexity of human aging due to physiological and lifespan differences, as well as divergent environmental exposures (Rice (2012), Nature 484, S9; Calado (2013), Semin. HematoL, 50, 165-174). Longitudinal studies in humans tend to focus on readily accessible organs (Lassen (2023), Aging Cell, 22, el3813; Tian (2023), Nat. Med., 29, 1221-1231), which may overlook the full spectrum of aging in the body. Moreover, the current predominant focus on cellular and molecular aspects of aging (Tabula Muris Consortium (2020), Nature, 583, 590-595), while essential, may neglect the intricate interplay between cellular, microanatomical and organ- and organism-level changes along the human lifespan. Furthermore, researching aging in humans has the added challenge of distinguishing pathological alterations (Gladyshev (2016), Trends MoL Med., 22, 995-996) from the normative aging process, making it difficult to pinpoint the initiation processes of age-related diseases.

[0006] To address these challenges, it is desired to move beyond a gene, or cell, or focus to a holistic view of the aging organism (Cohen (2022), Nat Aging, 2, 580-591). Among this view, epigenetic, and plasma proteomics predictors of biological age have emerged as promising approaches that leverage multivariate measurements (i.e., multiple DNA sites, or multiple proteins) to provide measurements of biological age that try to reflect more than one dimension of the process of aging.

[0007] Epigenetic predictors of biological age, primarily using DNA methylation (Horvath (2013), Genome Biol., 14, R115; Lin (2016), Aging, 8, 394^401; Zhang (2019), Genome Med., 11, 54; de Lima Camillo (2022), npj Aging, 8, 1-15; Belsky (2022), Elife, 11; Higgins-Chen (2022), Nat Aging, 2, 644-661), while groundbreaking, have shown limited physiological relevance and tissue specificity, as they predominantly rely on stochastic DNA changes (Meyer (2024), Nat Aging, 4, 871-885). Despite their low error rates, different types of DNA methylation clocks have been found to lack correlation with each other (Rutledge (2022), Nat. Rev. Genet., 1-13). This discrepancy highlights the fundamental differences between epigenetic clocks and other assessment methods, with DNA methylation clocks reflecting accumulating stochastic changes in DNA (Meyer (2024), Nat Aging, 4, 871-885), that may not be tightly linked to changes in tissue physiology.

[0008] Plasma proteomics clocks have emerged as another multivariate alternative to DNA methylation clocks for minimally- or non-invasive aging biomarkers, offering some degree of tissue specificity by analyzing the origin of circulating proteins in plasma (Oh (2023), Nature 624, 164-172; Goeminne (2024), bioRxiv; Oh (2024), bioRxiv). In plasma proteomics clocks, tissue-specificity can be conferred by prior knowledge on the tissue-specificity expression of proteins derived from studies of gene and protein expression. However, internal physiological processes, as well as external factors such as stress, diet, and disease, can significantly and undesirably alter proteomic results.

[0009] Therefore, a main challenge of the current methods for predicting biological age is the robustness and precision in spatial and / or temporal resolution of the biological age determination. Accordingly, there is still a need for improved means and methods for the determination of biological age and in particular, aging rates, like tissue-specific biological age and aging rates.

[0010] Furthermore, there is still a need for earlier and / or more precise detection of tissue -specific aging and / or for more precise monitoring said tissue-specific aging, in particular, in relation to diseases that are associated with tissue-specific aging.

[0011] The above technical problem is solved by the provisions of the embodiments as characterized in the claims and as described herein below.

[0012] Accordingly, as will be further explained herein below, the present invention relates to a method of determining an age gap between chronological age and biological age comprising a step of determining the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age.

[0013] Specifically, an age gap determined according to the invention represents a deviation of the biological age from the chronological age. While the age gap determined according to the invention may be specified by a time unit (e.g. in years or months), the deviation from the chronological age is not merely numerical but also represents a biologically or physiologically relevant deviation from normal (i.e. average) aging. For example, when a tissue appears biologically older than expected based on the chronological age), the reason can be biological / physiological processes which may be linked, for example, to disease and / or lifestyle (cf. Examples 3, 4, 6, 7 and 9, and Figures 6, 7, 11, 12, 15(d)-(e), 16, 17 and 25).

[0014] As further explained below, herein and in context of the invention, the age gap (i.e., the value thereof) may be considered as "positive", "above average", "above zero" "increased", "high" or "higher", when the biological age is higher than the chronological age, and / or the age gap (i.e. the value thereof) may be considered as "negative", "below average", "below zero", "low" or "lower", when the biological age is lower than the chronological age. When the biological age is about the same as the chronological age, it may be considered that an age gap is absent, or that the age gap is "around average" or "around zero".

[0015] Aging is a progressive process. A positive or high / increased age gap corresponds, in particular, to an accelerated aging, as further described below. In particular, a physiological state associated with a higher chronological age may be reached faster than normal (and the normal state appropriate for the chronological state may be gone earlier). By the same logic, a negative or low age gap corresponds, in particular, to a decelerated or resilient aging, as further described below. In particular, physiological state associated with a younger chronological age may be present later or for longer than normal (and the normal state appropriate for the chronological age may be not reached yet). Therefore, as illustrated in the present invention, the age gap between the chronological age and the biological age according to the invention corresponds, in particular, to the rate of aging (i.e. the aging rate). Specifically, a high rate of aging (indicated by a positive age gap) may be also considered as accelerated aging, and / or a low rate of aging (indicated by negative age gap) may be also considered as decelerated aging, as described herein, and as illustrated in the appended Examples (see, e.g., Example 5 and Figure 13). Furthermore, the predicted / determined age gaps based on marker expression level(s) in a sample according to the invention may correspond, in particular, to the current rate of aging at the time-point when the sample was obtained. As further detailed herein below, the age gap (i.e. rate of aging) may be indicated as a percentile rank. Specifically, the percentile rank may be calculated based on the distribution of age gaps (i.e. aging rates) in the individual's age cohort and / or reflect the individual's position within said age cohort. In particular, a higher percentile rank than the 50thor 60thpercentile of the age cohort may indicate an accelerated aging, a lower percentile rank than the 50thor 40thpercentile of the age cohort may indicate a decelerated aging, and / or a rank at around the 50thor between the 40thand 60thpercentile of the age cohort may indicate normal aging.

[0016] In practice (e.g. for detecting or predicting disease or assessing the health state of an individual), the biological age determined by an age prediction method is often awarded much of its value when put in relation to the chronological age, i.e., when the age gap between the biological age and the chronological is determined. For example, merely determining that a tissue has a biological age of 60 years may not allow to conclude whether the issue is normal (which may the case when it is from a 60 yr old) or abnormal (which may be the case when it is, e.g., from a 40 yr old or an 80 yr old). In contrast, determining a positive age gap for a tissue (e.g. when the biological age is 20 years higher than the chronological age) means, in particular, that the tissue ages faster than normal which may be indicative of the presence of or susceptibility for a disease (irrespective of the normal deterioration of the body occurring generally during aging), as described herein and as illustrated in the appended Examples (see, e.g., Examples 6 and 7 and Figures 15, 16 and 17, as further confirmed in Example 9 and Figure 25). Determining the age gap between the chronological age and the biological age according to the invention thus allows to delimit abnormal (potentially pathological) biological / physiological processes from normal aging which can be very useful, e.g., for the overall assessment of the health state of an individual or the early detection or prediction (and possible prevention) of a disease.

[0017] While the prior art typically uses molecular markers (e.g. DNA methylation of certain CpGs) for determining biological age and then, optionally, calculating an age gap by subtracting the chronological age, the present inventors took a completely different route:

[0018] As illustrated in the appended non-limiting Examples, the present invention allows to determine the presence and / or magnitude of an age gap (which corresponds to the rate of aging) directly based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid (e.g. blood) sample from an individual. Unexpectedly, and in contrast to prior art approaches, the age gap can be determined (i.e. is determinable), according to the present invention, independently of the chronological age. In particular, the present inventors found markers whose expression levels are indicative of the presence and / or magnitude of an age gap, as illustrated in the appended Examples. The markers according to the invention therefore allow to directly determine the rate of aging of one or more specific tissues and / or of the entire organism (i.e. systemically).

[0019] These surprising findings are, inter alia, the result of the inventive approach to first determine biological age and respective age gaps based on histological sections, and to then train a model to directly predict the histology-derived age gaps from gene expression levels in a bodily fluid such as blood, as further described below and as illustrated in the appended Examples. Training a model to directly predict age gaps can be advantageous in comparison to conventional models of the prior art which predict biological age, inter alia, for the following reasons:

[0020] During the training process, a model for predicting age gaps (i.e. aging rates) learns various features associated with the speed of physiological decline, which may be different in various stages of adult life. In contrast, learning (and outputting) biological age, as is done in the prior art, forces the assumption that accelerated (or decelerated) aging in early life is similar to a stage of older age which may not be true. Moreover, the training process for learning age gaps directly (as provided by the present invention and as illustrated in the appended Examples) is unconfounded by factors such as chronological age, survival biases, or pre-existing diseases that can distort cross-sectional biological age estimates (see, e.g., Examples 7-9). By directly outputting age gaps, the methods and models according to the present invention can provide a more intuitive and universal metric that can be directly related to persons of any chronological age (cf. Example 8 and Figure 20d). Therefore, the methods of the present invention for determining an age gap between chronological age and biological age is more robust, versatile and useful in practice as compared to prior art models which output biological age and need a further step of subtracting the chronological age from the output to indirectly calculate age gaps.

[0021] Another advantage of the present invention is that age gaps (i.e. aging rates) have been learned from histological sections: Tissue histology captures directly several morphological hallmarks of aging such as loss of epithelial proliferation, atrophy, fibrosis, and microvascular rarefaction, as illustrated in the appended Examples (see, e.g., Example 3 and Figures 8 and 22). These reflect cumulative, microanatomical alterations in tissue integrity. In addition, tissue morphology provides an organ- and tissue-specific context, directly tied to organ function and health status, making biological age / age gap estimates more physiologically interpretable than clocks based on stochastic molecular changes such as DNA methylation (see, e.g. Example 3 and Figure 9). Furthermore, histology-derived age gaps can be visualized in tissues (see Example 10 and Figure 35) and connected to known, interpretable pathological features. This is in contrast with DNA methylation signatures which are often abstract CpG combinations that change stochastically. In sum, histology integrates the structural and functional state of tissues, giving a more direct and interpretable measure of physiological aging. Therefore, determining histology-derived age gaps according to the present invention based on marker expression levels in a bodily fluid such as blood is not only extremely convenient and applicable in routine diagnostics. Histology-derived gaps also provide a physiologically particularly meaningful measure of the health state of individual tissues or the entire organism which is more useful and interpretable than biological age predicted from stochastic molecular changes such as DNA methylation.

[0022] Therefore, as will be further explained herein below, determining the presence and / or magnitude of the age gap according to the invention, preferably, comprises applying (an) association coefficient(s) on the expression level(s) of the marker(s) derived from a bodily fluid sample from an individual, wherein the association coefficient(s) reflect(s) associations between a histology-derived age gap and the expression level(s) of said marker(s). In other words, determining the presence and / or magnitude of the age gap according to the invention, preferably, comprises applying a model on the expression level(s) of said marker(s), wherein the model was trained to map expression level(s) in bodily fluid samples to histology-derived age gaps, as described herein.

[0023] Moreover, as illustrated in the appended Examples and described herein, the present invention allows to determine tissue-specific age gaps (i.e. tissue specific aging rates). This has many advantages: the sensitivity of detecting an abnormal physiological state which may be associated with a disease in a specific tissue (by determining tissue-specific age gaps) is much higher in comparison to determining a general biological age or a general age gap is as often done in the prior art, where tissue-specific aberrations may be drown in noise. Moreover, as illustrated in the appended Examples, determining tissue-specific age gaps provides a much more complete and finely resolved picture of the health state of an individual and makes it easier to pinpoint underlying reasons for abnormal / accelerated aging and / or potential diseases, as compared to providing only one general biological age or age gap for the entire organism.

[0024] Therefore, in preferred embodiments of the invention, the age gap is a tissue-specific age gap. As further explained herein below, the method of the invention may be fully or partly computer-implemented. Hence, the method of the invention may be a computer-implemented method or a computer-assisted method.

[0025] Furthermore, the method may comprise a step of determining the expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual. Therefore, the present invention further relates to a method of determining an age gap between chronological age and biological age comprising the steps of

[0026] (i) determining expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, and

[0027] (ii) determining the presence and / or magnitude of said age gap, wherein the expression level(s) of the one or more marker(s) of step (i) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age.

[0028] Accordingly, the present invention also relates to a method of determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) in cells derived from a bodily fluid sample from an individual and determining said biological age and / or presence and / or magnitude of said age gap between chronological age and biological age, wherein the expression level(s) of said one or more marker(s) is / are indicative of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age, in particular of the biological age and / or the presence and / or magnitude of said age gap in a tissue and / or organ to be assessed. Preferably, in context of the invention, the tissue(s) and / or organ(s) is / are of mammalian origin, more preferably the tissue(s) and / or organ(s) is / are of human origin.

[0029] As mentioned above, in context of the present invention, the age gap may be determinable or may be determined independently of the chronological age.

[0030] Further provided herein are specific markers to be measured or determined in accord with the present invention. The one or more markers according to the present invention may comprise, but are not limited to, JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and / or KDM5D (see also the non-limiting Examples, e.g., Tables 3 and 4).

[0031] Accordingly, the present invention further provides a method of determining an age gap between chronological age and biological age comprising a step of determining the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age, and wherein the one or more marker(s) is / are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6- 57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0032] Furthermore, the present invention relates to a method of determining presence and / or magnitude of an age gap between chronological age and biological age comprising a step of determining expression level(s) of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of an age gap between chronological age and biological age.

[0033] The present invention also relates to a method of determining biological age comprising a step of determining expression level(s) of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, wherein the biological age is determined based on (i) the expression level(s) of the one or more marker(s) and, optionally, (ii) chronological age.

[0034] Furthermore, the present invention relates to a method (in particular, a computer-implemented method) of determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age comprising a step of determining biological age or the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the biological age and / or the presence and / or magnitude of the age gap between chronological age and biological age, as described herein.

[0035] The present invention also relates to a method (in particular, a computer-implemented method) of determining biological age, comprising a step of determining biological age based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the biological age, as described herein.

[0036] The present invention further provides a method for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3- 15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S1OOB, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, and determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level of the one or more marker(s) of step (i) is indicative of biological age, preferably tissue-specific biological age, and / or the presence and / or magnitude of an age gap between chronological age and biological age, preferably a tissuespecific age gap.

[0037] Preferably, the presence and / or magnitude of an age gap between chronological age and biological age is determined, and the expression level of the one or more marker(s) of step (i) is, preferably, indicative of the presence and / or magnitude of an age gap between chronological age and biological age. Furthermore, the biological age and / or the age gap is, preferably, tissue-specific, as described herein.

[0038] According, the invention also relates to a method of determining presence and / or magnitude of a tissue-specific age gap between chronological age and biological age comprising the steps of

[0039] (i) determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual and

[0040] (ii) determining presence and / or magnitude of said tissue-specific age gap between chronological age and biological age, wherein the expression level(s) of the one or more marker(s) of step (i) is / are indicative of the presence and / or magnitude of a tissue-specific age gap between chronological age and biological age.

[0041] Suitable marker and marker combinations (in particular, "favorable markers") for determining tissue -specific age gaps and / or age gaps in specific tissues are further described herein below and in the illustrative Examples appended herewith, e.g., in Example 7 and Table 4.

[0042] Furthermore, the present invention relates to a method (in particular, a computer-implemented method) of determining an age gap, the method comprising:

[0043] (a) receiving data comprising expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, as described herein; and

[0044] (b) applying a model that was trained to map expression level(s) of one or more marker(s) in cells derived from a bodily fluid to age gaps inferred from images of histological sections of training subjects, as described herein, to output, for at least one tissue of the individual, an age gap. Furthermore, the present invention relates to a method (in particular, a computer-implemented method) of determining at least one tissue-specific age gap, the method comprising:

[0045] (a) receiving data comprising expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, as described herein; and

[0046] (b) applying a model that was trained to map expression level(s) of one or more marker(s) in cells derived from a bodily fluid to tissue-specific age gaps inferred from images of histological sections of training subjects, as described herein, to output, for at least one tissue of the individual, a tissue-specific age gap.

[0047] The present invention for the first time advantageously utilizes morphological features extracted from a large-scale histopathological image dataset to predict the tissue-specific biological age. As illustrated in the appended non-limiting examples provided herein, it could convincingly be shown that morphological features extracted from said histopathological images when associated with a corresponding chronological age can effectively be used to determine biological age and / or the presence / magnitude of an age gap between chronological age and biological age, which is enriched in samples with orthogonally / independently annotated tissue-specific age-associated pathologies and disease. It was further shown that the histology-based determination of the biological age and / or age gap is, in particular, tissue-specific. Accordingly, the present invention, in particular the herein disclosed means and methods, provide for improved robustness and precision in spatial and / or temporal resolution of the determination of tissue-specific biological age and / or age gaps.

[0048] Histopathological images are a rich and standardized resource, providing a direct window into the pathophysiological states of tissues. The large-scale availability of these images presents a unique opportunity to quantify the histopathological hallmarks of aging and to link these morphological insights with molecular data, demographic information, and clinical outcomes. Recent advancements in deep learning (LeCun (2015), Nature, 521, 436-444) have significantly enhanced the ability to analyze and interpret tissue morphology and architecture (Chen (2024), Nat. Med., 30, 850-862; Xu (2024), Nature, 630, 181-188), as well as to detect and classify diseases with unprecedented accuracy (Esteva (2017), Nature, 542, 115-118; Coudray (2018), Nat. Med., 24, 1559-1567).

[0049] These advancements allow histopathological images to be transformed into highly informative, multivariate measures that accurately reflect tissue-specific aging processes. Histological measures of biological age, as presented in the present application, capture the complex interplay of genetic, environmental, and physiological factors that influence tissue morphology over time. As illustrated in the appended examples provided herein, the present application, corroborated by associations with shorter telomeres and the presence of pathological features, demonstrates that tissue-specific aging is more precisely captured through morphological assessments than through other methods, such as DNA methylation or plasma proteomics.

[0050] Accordingly, the present invention further provides a method (in particular a computer-implemented method) of generating a model for determining biological age or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of

[0051] (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section of at least one tissue of said individual, preferably, (an) image(s) of the histological section(s) and ii) chronological age of said individual, and

[0052] (b) extracting morphological features from said histological sections of step (a) i), preferably from the image(s) thereof, (c) correlating the extracted morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and

[0053] (d) applying the associations determined in step (c) to (a) histological section(s), preferably (an) image(s) thereof, to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age. The extracted morphological features may be further analyzed, as described herein, before correlating with the chronological age.

[0054] In preferred embodiments, said training dataset further comprises for each individual iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual, and the method further comprises a step (e) of correlating the histology-derived biological age and / or age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived biological age and / or age gap of step (d) are determined. The marker expression level(s) may be further analyzed, as described herein, before correlating with the histology-derived biological age and / or age gap.

[0055] Preferably, said method is for generating a model for determining presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of

[0056] (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section (in particular an image thereof) of at least one tissue of said individual, and ii) chronological age of said individual, and, optionally, iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual,

[0057] (b) extracting morphological features from said histological sections of step (a) i),

[0058] (c) correlating the extracted morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and

[0059] (d) applying the associations determined in step (c) to histological sections to determine the presence and / or magnitude of an age gap between the chronological age and the biological age, and, optionally,

[0060] (e) correlating the histology-derived age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived age gap of step (d) are determined. The extracted morphological features may be further analyzed, as described herein, before correlating with the chronological age. Furthermore, the marker expression level(s) may be further analyzed, as described herein, before correlating with the histology-derived age gap.

[0061] In particular, the model comprises at least one association coefficient for each of said marker(s), as described herein. Furthermore, determining the presence and / or magnitude of the age gap according to the invention may comprise applying (an) association coefficient(s) on the expression level(s) of said marker(s). Preferably, the association coefficient(s) reflect(s) associations between a histology-derived age gap and the expression level(s) of said marker(s), as described herein. The association coefficient(s) may be obtained or obtainable by a method as described herein. Specifically, said method may comprise the steps of:

[0062] (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section (in particular an image thereof) of at least one tissue of said individual, ii) chronological age of said individual, and iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual,

[0063] (b) extracting morphological features from the histological sections of step (a) i) (in particular the images thereof), (c) correlating the extracted morphological features of step (b) with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined,

[0064] (d) applying the associations determined in step (c) to histological sections (in particular images thereof) to determine the presence and / or magnitude of an age gap between the chronological age and the biological age, and

[0065] (e) correlating the histology-derived age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived age gap of step (d) are determined. The extracted morphological features may be further analyzed, as described herein, before correlating with the chronological age. Furthermore, the marker expression level(s) may be further analyzed, as described herein, before correlating with the histology-derived age gap.

[0066] Determining the presence and / or magnitude of the age gap according to the invention may further comprises applying a model on the expression level(s) of the marker(s), wherein the model was trained to map expression level(s) in bodily fluid samples to histology-derived age gaps. Specifically, said model may have been trained using paired data of a plurality of individuals comprising (i) gene expression levels in bodily fluid samples and (ii) histology-derived age gaps determined based on histological sections (in particular, images thereof). The histology-derived age gaps may have been determined or may be obtainable by a method comprising the steps of

[0067] (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section (in particular an image thereof) of at least one tissue of said individual, and ii) chronological age of said individual,

[0068] (b) extracting morphological features from the histological sections of step (a) i) (in particular the images thereof),

[0069] (c) correlating the morphological features of step (b) with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and

[0070] (d) applying the associations determined in step (c) to histological sections (in particular images thereof) to determine the presence and / or magnitude of an age gap between the chronological age and the biological age.

[0071] Furthermore, the present invention provides a method for determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of step (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) (an) histological section(s) of at least one tissue (in particular (an) image(s) thereof) of said individual, and ii) chronological age of said individual, step (b) extracting morphological features from said histological sections of step (a) i), step (c) analyzing the extracted morphological features of step (b) and correlating said morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and step (d) applying the associations determined in step (c) to histological sections to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age. In particular, said method is a computer-implemented method.

[0072] In the methods of the present invention, the biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age may be determined, in particular, for the tissue(s) of which the histological section(s) is / are derived from. The histological sections (in particular the images thereof) may be whole slide images, as described herein. The histological section may be of different tissues or tissue types. Furthermore, the morphological features are, preferably, indicative of aging-related characteristics. Moreover, the morphological features may be represented numerically, as described herein.

[0073] As already indicated above and as will be further explained below, the inventors have surprisingly found links between the biological age and, in particular, age gaps derived from histopathological images and blood-based mRNA expression level measurements. As illustrated in the appended non-limiting examples provided herein, it could convincingly be shown that associations between the biological age and / or age gap, derived from histopathological images, and expression level(s) of one or more marker(s) in cells, derived from bodily fluid samples of the same individuals from which the histopathological images were taken, can be made. It was further shown that these associations can be used to determine the biological age and / or age gap for an individual based on the expression level(s) of one or more marker(s) in cells from a bodily fluid sample. In other words, the presence and / or magnitude of an age gap between chronological age and biological age (i.e the rate of aging) can be determined based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age, as described herein, e.g. in context of the computer-implemented method of the invention. The bodily fluid-based determination of the biological age and / or age gap may be tissue-specific and / or systemic ( e.g., indicative for the mean of all tissues).

[0074] Accordingly, one of the aspects of the present invention is a method that enables the prediction of biological age and / or age gaps, in particular tissue-specific biological age and / or age gaps, using easily accessible samples, for example bodily fluid samples, such as blood samples, combining the depth of histology-based tissue-level insights with the practicality of non-invasive, or at least minimal-invasive, sampling, and the advantages of a high-sensitivity, high- specificity, and high-throughput analysis method. In other words, the present invention further provides a method of determining the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to expression levels of one or more marker(s) in cells derived from a bodily fluid sample, and determination of biological age and / or presence of an age gap between the chronological age and the biological age from said bodily fluid sample using associated marker expression level(s). Accordingly, the present invention also relates to a method (in particular a computer-implemented method) of determining an age gap between chronological age and biological age comprising a step of applying (an) association coefficient(s) on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) in combination with the association coefficient(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age, and, preferably, wherein the association coefficient(s) reflect(s) associations between a histology- derived age gap and the expression level(s) of said marker(s), and / or wherein the association coefficient(s) reflect(s) associations were determined or are obtainable by a method of the invention, as described herein.

[0075] As will be described herein below, the term bodily fluid sample is known to the person skilled in the art, and such samples comprise, inter alia, peripheral blood mononuclear cell (PBMC) sample, whole blood sample, and dried blood spot sample. Since the marker expression level from cells derived from bodily fluid samples or cells comprised in bodily fluid samples harbor such strong correlation with pathologies that can also be linked to aging process, as illustrated in the appended non-limiting example and as found herein, it is envisaged that not only cells from bodily fluid samples (even if preferred in context of this invention and in the experimentally herein disclosed embodiments) may comprise the relevant marker information necessary to determine the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. It is therefore envisaged that, in a further embodiment of the present invention, also other samples such as, for example, hair follicle samples allow the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age based on the expression level of one or more marker(s), for example as disclosed herein in Example 7 and Table 3. Hair follicle samples are envisaged in certain embodiments of the present invention and as a source of the herein described markers, since hair follicle samples contains, inter alia, immune cells and stem cells, that are useful as cell samples for the determination of marker expression as disclosed herein and in context of the present invention, i.e., in methods for the analysis of and / or the determination, prediction of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. Hair follicle cells have a well-established role in skin homeostasis, regeneration, and repair, all of which are linked to aging. Furthermore, hair follicles undergo regular cycles of growth and regression (anagen, catagen, telogen), which may be coupled to systemic aging processes. Accordingly, also in context of the present invention, hair follicle samples / hair follicle cells are furthermore useful in the herein disclosed means and methods for the detection / determination of age-related diseases and / or age-related pathologies. Finally, their use is non-invasive and easily accessible.

[0076] Illustratively, the present invention relates to means and methods for determining the biological age and / or the presence of an age gap of chronological age and biological age on basis of the determination of specific marker(s) derived from a bodily fluid sample from an individual or from hair follicle samples from an individual as disclosed herein. In other words, the present invention also relates to a method of determining an age gap between chronological age and biological age comprising a step of determining the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid or hair follicle sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age. Said method may be a computer-implemented method, as described herein.

[0077] As already mentioned above, the invention is, inter alia, based on the surprising finding that tissue-specific and systemic biological age gaps can be determined with highly specific markers. Accordingly, the invention relates to a method for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. Illustratively the means and methods provided herein can be based on the following technical information:

[0078] Accordingly, the corresponding means and methods can comprise, for example and non-limiting, the following determination / detection steps:

[0079] Step 1: Determining expression level of one or more marker(s) selected from the group consisting of JUP,

[0080] MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual.

[0081] Step 2: Determining said biological age or presence and / or magnitude of said age gap between chronological age and biological age, wherein the expression level(s) of the one or more marker(s) of step 1 is / are indicative of the biological age or the presence and / or magnitude of an age gap between chronological age and biological age.

[0082] A further method for determining biological age or the presence and / or magnitude of an age gap can, for example and non-limiting, comprise, the following steps: Step 1: Providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological section(s) of at least one tissue (in particular (an) image(s) thereof), and ii) chronological age, and optionally iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample of said individual.

[0083] Step 2: Extracting morphological features from said histological sections of step 1 i).

[0084] Step 3: Analyzing the extracted morphological features of step 2 and correlating said morphological features with the chronological age of step 1 ii) of the same individual, wherein associations between the morphological features and the chronological age are determined.

[0085] Step 4: Applying the associations determined in step 3 to (a) histological section(s) (in particular (an) image(s) thereof) to determine the biological age or the presence and / or magnitude of an age gap between the chronological age and the biological age.

[0086] The method can, for example and non-limiting, further comprise the following steps:

[0087] Step 5: Analyzing the marker expression level of step 1 iii) and correlating the biological age or age gap derived of step 4 with said marker expression level(s), wherein associations between the marker expression level and the biological age are determined.

[0088] Step 6: Determining one or more marker(s) underlying the associations determined in step 5 based on the specific marker expression level of said one or more marker(s).

[0089] Furthermore, the method can, for example and non-limiting, further comprise the following steps:

[0090] Step 7 Providing a test dataset of one or more individual(s) which is / are not comprised in the training dataset of step 1 comprising for each individual iv) chronological age and v) marker expression level(s) in cells derived from a bodily fluid sample.

[0091] Step 8: applying the associations determined in step 5 of the one or more marker(s) determined in step 6 on the marker expression level(s) of step 7 v) to determine biological age or presence and / or magnitude of an age gap.

[0092] Step 9: Determining the biological age or presence and / or magnitude of an age gap between the chronological age and the biological age.

[0093] Furthermore, the method can, for example and non-limiting, further comprise the following step:

[0094] Step 10: Contextualizing the biological age or age gap value in light of the distribution of a population of the same chronological age.

[0095] In one embodiment, the present invention further relates to a method of determining the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to expression levels of one or more marker(s) in cells derived from a bodily fluid sample, and determination of biological age and / or presence of an age gap between the chronological age and the biological age from said bodily fluid sample using associated marker expression level.

[0096] The here non-limiting described methods for determining biological age and / or the presence of an age gap can also be employed in the herein disclosed means and methods for the detection of age-related pathologies and / or age- related diseases and can readily be adapted thereto. The present invention offers several distinct advantages over plasma proteomics and DNA methylation clocks. As already indicated above, the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age based on histological images captures the complex interplay of genetic, environmental, and physiological factors that influence tissue morphology over time.

[0097] Using plasma proteomics, as provided in the prior art, to determine biological age is an indirect method. It never truly observes the actual phenotype of tissue-specific biological age or age gaps (i.e., aging rates), as it fails to consistently reflect the complex and dynamic nature of aging across different tissues and conditions. Instead, it only infers the actual phenotype of biological age. Regardless of the conceptual approach to derive biological age, in particular tissuespecific biological age, plasma proteomics as a method faces several limitations. These include lack of temporal resolution due to reliance of measurement of proteins continuously released from cells, which hinders the capture of real-time changes crucial for early detection and monitoring of aging processes. Additionally, protein detection is complicated by post-translational modifications, and the sensitivity for low-abundance proteins is limited. Furthermore, plasma proteomics lacks a comprehensive coverage useful to discover novel biomarkers and therapeutic targets, with severe tradeoffs in terms of number of proteins covered and the rates of error associated with their measurement. The technical reproducibility and stability of protein assays are also challenging, potentially affecting their reliability in large- scale studies, biobanking efforts, and implementation of personalized medicine.

[0098] Biological age and / or age gap predictions in accordance with the present invention show unexpected stronger correlations with pathology incidence compared to chronological age, see illustratively Example 3 Figure 7c. This stronger correlation with pathologies emphasizes that biological age and / or age gap determined in accordance with the present invention is a more sensitive marker for the presence of certain pathologies, capturing further molecular and physiological factors besides chronological aging that contribute to tissue dysfunction.

[0099] Accordingly, the present application allows for a more precise determination of tissue -specific aging through morphological assessments than through other methods, such as DNA methylation or plasma proteomics. As illustrated in the appended non-limiting examples provided herein, this determination of biological age and / or age gap is confirmed to be associated with the physiological state of the tissue, including the presence of age-associated pathologies and disease, see e.g., Example 8 and as further confirmed in Example 9.

[0100] The present invention is particularly advantageous over the prior art, when combining the advantages of the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age based on histological images with the determination of marker expression level(s). This allows for even higher precision and robustness of the determination of tissue-specific aging.

[0101] By determining the expression level of one or more marker(s), as disclosed herein, for example in Example 7 and Table 3, in cells derived from a bodily fluid sample near real-time transcriptional changes can be captured, providing an immediate snapshot of cellular responses. This allows, for example, for the detection, in particular the early detection, of age-related disease and / or the monitoring of treatment responses with greater precision. In contrast, DNA methylation reflects longer-term, cumulative changes and lacks the ability to capture dynamic, real-time cellular processes. Plasma proteomics, on the other hand, is similarly limited, as protein levels can lag behind transcriptional changes and are influenced by various post-translational processes, reducing their temporal sensitivity. Accordingly, the present invention provides methods for determining biological age and / or the presence of an age gap between chronological age and biological age comprising the determination of the expression level of one or more marker(s) in cells derived from bodily fluid sample from an individual, thereby providing temporal and / or spatial resolution that allows for earlier and / or more precise detection of tissue-specific aging and / or for more precise monitoring said tissuespecific aging.

[0102] Furthermore, the present invention allows to bypass the complexities introduced by post-translational modifications, which can significantly alter protein structure and function. These modifications make it challenging to detect proteins reliably across different tissues and conditions using proteomic approaches. DNA methylation, while not subject to these modifications, does not provide information about protein expression or function. The universality of the determination of the expression level of a marker, preferably the RNA expression level of a marker, more preferably the mRNA expression level of a marker, focusing on conserved exon sequences and optionally capturing every transcript that are used by all protein isofbrms, can ensure consistent detection across diverse tissue types and cellular states, offering a more stable and accurate measure of biological processes. Accordingly, the present invention provides a method for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age that allows reliable detection of markers across different tissues and conditions, independently of post-translational modifications.

[0103] Yet another advantage of the present invention is its sensitivity to low-abundant markers. For example, the mRNA expression level of a marker can be detected more readily even when the corresponding proteins are present at very low levels, thanks to the amplification capabilities of RNA sequencing. In contrast, traditional protein assays, as used in tissue clocks based on plasma proteomics, often struggle with detecting low-abundance proteins due to limited sensitivity, potentially overlooking critical biomarkers. Because DNA first must be transcribed into RNA which only then becomes translated into proteins, DNA methylation offers very limited direct insight into protein levels, further limiting its utility in capturing the nuances of protein expression - the active biochemical components that effect work in the cells. Accordingly, the present invention provides a method for determining biological age and / or the presence of an age gap between chronological age and biological age that allows detection even of (very) low abundant markers.

[0104] The present invention advantageously enables a direct link to individual marker expression level(s) thereby allowing higher accuracy and reliability of marker level prediction models. According to the invention, marker expression level data, in particular mRNA data, can be integrated into predictive models to forecast protein levels and functional outcomes. This provides a more comprehensive understanding of gene expression changes and allows, for example, for more accurate predictions in personalized treatment strategies. DNA methylation lacks the direct link to marker expression, in particular protein expression, and its kinetic properties in the cell are very slow, reducing its effectiveness in predictive modeling. Plasma proteomics, while directly measuring proteins measures a pool of proteins released from all the cells in the body, which, for example, have highly heterogeneous rates of degradation in the blood, mixing both newly released proteins and long existing ones, constraining analysis and making it less reliable for predictive purposes. mRNA sequencing offers comprehensive coverage of the transcriptome, enabling the detection of all transcripts, both known and novel. This extensive coverage is very helpful for uncovering new biomarkers and therapeutic targets that may not be detectable at the protein level. The detection of novel biomarkers is illustrated in the appended non-limiting examples provided herein, e.g., in Example 7 and Table 3, where at least six novel transcripts of uncategorized genes are identified as relevant markers for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. In contrast, plasma proteomics approaches are often limited by a tradeoff detecting previously known proteins with sensitivity (e.g., using antibodies) or all proteins with low sensitivity and reproducibility (e.g., with mass spectrometry) with currently no method able to detect all proteins present in plasma with high sensitivity. DNA methylation analysis focuses on a narrow aspect of gene regulation, missing the broader spectrum of gene expression changes. Accordingly, the present invention provides a method for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age that enables comprehensive coverage of the transcriptome, irrespectively of whether the marker (or the respective base sequence) is known or not.

[0105] RNA sequencing techniques are generally more reproducible and exhibit lower variability compared to protein assays, ensuring more consistent and reliable data. Additionally, mRNA can be stabilized more effectively in bodily fluid samples, such as for example blood samples, using appropriate reagents, enhancing the reliability of measurements in large-scale studies, biobanking scenarios, or personalized medicine. Plasma proteomics, on the other hand, is prone to variability due to factors like sample handling and the inherent instability of certain protein types (e.g., membrane proteins). Accordingly, the present invention provides a method for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age that enables higher reproducibility and lower variability thereby ensuring more consistent and reliable data.

[0106] The age gap, determined in accordance with the present invention, is confirmed to be associated with the physiological state of the tissue. This confirmation is based on robust evidence, including the presence of age-associated pathologies (e.g., tissue fibrosis, atrophy, or congestion; in GTEx cohort) and disease (enrichment in the number of comorbidities in individuals with higher age gap; in the GTEx cohort, and clear separation between age gap of tissue of healthy and disease-affected individuals; also in independent external cohorts), as illustratively shown in the appended non-limiting examples provided herein, e.g., Examples 8 - 10. By leveraging the observed changes in tissue morphology to train a marker expression level-based predictor of tissue-specific aging, the present invention harnesses the strengths of both histological and molecular data, providing a more accurate and practical solution than plasma proteomics or DNA methylation clocks. The enrichment of tissue-specific pathologies, as illustrated in the appended non-limiting examples provided herein, e.g., Example 3, and validated across independent cohorts, underscores the robustness of the present invention. The present invention represents a significant advancement in personalized aging monitoring. It offers a realistic and valuable tool for reliable, non-invasive, or at least minimal-invasive assessment of tissue-specific aging rates, ultimately enhancing the ability to monitor and manage aging and age-related diseases in humans.

[0107] As is described herein and illustrated in the appended non-limiting examples, the present invention, inter alia, provides methods to determine the biological age gap for more than 30 different tissues, in particular tissue-specific age gaps, as well as a systemic age gap (which may be indicative for the mean of all tissues). Surprisingly, this biological age gap (also referred to herein simply as "age gap") determination may be based on the expression level of one or more markers in cells derived from a bodily fluid sample, as described herein. This determination of the biological age gap allows for the creation of highly differentiated tissue-specific age gap profiles on an individual basis. As has been shown, the tissue-specific and systemic age gaps are significantly different for disease-affected individuals compared to healthy individuals. By identifying clear patterns of tissue-specific and systemic age gap combinations, it becomes possible to distinguish between healthy and disease-affected individuals, to detect diseases and / or predict the likelihood of occurrence of a disease, and to determine the health state of an individual (e.g. via a multi-organ landscape of accelerated aging) see, in particular, Examples 8 and 9, and Figures 17e-f, 20 and 25-29.

[0108] As illustrated in the appended Examples (cf. Example 9), the inventors used blood-based tissue-specific aging predictors according to the invention on 1,205 samples (healthy and diseased) to further assess how and to what extent predicted age gaps correlate with organ -specific disease effects. The inventors found distinct aging signatures for each investigated disease (systemic lupus erythematous, Crohn's disease, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, Alzheimer's disease and stroke), forming a multi-organ landscape, wherein chronic and acute conditions showed elevated tissue-specific age gaps, particularly in organs relevant to the disease. Stroke patients exhibited the strongest brain-specific aging acceleration, while chronic diseases like Crohn's and vasculitis showed organ -specific patterns (e.g., gastrointestinal tract, kidney, liver, heart). Some associations involved secondary or systemic effects, such as brain aging in cystic fibrosis or kidney / liver aging in stroke, revealing broader physiological impacts beyond primary disease sites. Predictive models using thresholded age gaps achieved moderate-to-strong classification performance, further suggesting that the means and methods of the present invention provide for an early disease detection and / or population-level screening (e.g. of the general health state and / or specific diseases / pathologies) from minimally invasive blood samples.

[0109] Accordingly, the distinction between patterns of significantly different age gaps, in accordance with the present invention, can help in identifying and differentiating specific pathologies and / or diseases, in particular age-related pathologies and / or diseases, to detect diseases and / or predict the likelihood of occurrence of a disease, and / or to determine the health state of an individual. Therefore, the present invention allows, inter alia, for detection, in particular early detection, of age-related pathologies and / or diseases.

[0110] Accordingly, the present invention also relates to a method of detecting (or diagnosing) one or more diseases in an individual, comprising a step of determining an age gap between chronological age and biological age of at least one tissue of the individual according to a method of the invention, wherein a positive age gap of at least one tissue of the individual indicates that the individual has at least one disease. In particular, the disease may be associated with and / or occur in at least one tissue for which a positive age gap (i.e accelerated aging) is determined, as described herein.

[0111] Hence, the present invention further relates to a use (e.g. a computer-implemented use) of a model for detecting at least one disease or predicting the likelihood of occurrence of at least one disease in an individual, wherein (i) said model is obtained or obtainable by a method of the invention, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

[0112] Furthermore, the present invention relates to a method of determining a health status of an individual, comprising a step of determining an age gap between chronological age and biological age of at least one tissue of the individual according to a method of the invention, wherein

[0113] (i) a positive age gap of a tissue indicates that said tissue is unhealthy, affected by a disease or prone to become affected by a disease, and / or

[0114] (ii) the absence of an age gap or a negative age gap indicates that said tissue is healthy.

[0115] Accordingly, the present invention further relates to a use (in particular, a computer-implemented use) of a model for determining a health state of an individual, wherein (i) said model is obtained or obtainable by a method the invention, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S1OOB, HIST1H2AD, HLA-L, LRRC32, MALAKI, KLF11, TNFSF14, ACKBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52- AS1, HES6, 1TGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8- AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

[0116] The present invention also relates to a method of predicting the likelihood of occurrence of at least one disease, comprising a step of determining an age gap between chronological age and biological age of at least one tissue of the individual according to the present invention, wherein

[0117] (i) a positive age gap of at least one tissue indicates a high likelihood that a disease will occur in said tissue(s), and / or

[0118] (ii) the absence of an age gap or a negative age gap of at least one tissue indicates a low likelihood that a disease will occur in said tissue(s).

[0119] As described herein, a positive age gap of at least one tissue may be indicative of the presence of at least one disease associated with and / or occurring in said tissue(s).

[0120] In context of the invention, e.g. in the above methods, the disease(s) may be selected from the group consisting of: inflammatory immunological disorders such as Crohn's disease, ulcerative colitis, vasculitis, rheumatoid arthritis and lupus erythematous, genetic disorders such as cystic fibrosis, renal failure, Barret's Oesophagus, cancer such as esophageal, prostate, gastric, colorectal, endometrial, and cervical cancer, kidney disease, heart attack, infarction, acute coronary state, Diabetes mellitus type 2, Diabetes mellitus type 1, Hypertension, Ischemic heart disease, liver disease, chronic respiratory disease, ascites, cerebrovascular disease, heart disease, cellulitis, systemic lupus, multiple sclerosis, Alzheimer's, dementia, chronic lower respiratory disease, arthritis, post-menopausal syndrome, osteoarthritis, osteoporosis, Parkinson's disease, amyotrophic lateral sclerosis, atrial fibrillation, chronic kidney disease, venous thromboembolism, peripheral artery disease, hyperlipidemia, congestive heart failure, sarcopenia, frailty syndrome, urinary incontinence, benign prostatic hyperplasia, chronic venous insufficiency, and fibromyalgia, as described herein. For example, the disease(s) may comprise systemic lupus erythematous, Crohn's disease, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, Alzheimer's disease and / or stroke. In preferred embodiments, the disease(s) comprise(s) at least one age-related disease or pathology, as described herein.

[0121] The means and methods of the invention may be also used in non -therapeutic or non-diagnostic contexts, for example, lifestyle and / or biohacking contexts, e.g., to determine and / or monitor a fitness state.

[0122] Therefore, the present invention also relates to a use (in particular a computer-implemented use, which may be also non-therapeutic) of a model for determining or monitoring a fitness state of an individual, wherein (i) said model is obtained or obtainable by the method of the invention, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD- 2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAKI, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, KKGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

[0123] Furthermore, the means and method of the invention may be used to analyse changes of biological age or aging rates over time, e.g., before and after an invention such as a treatment or a lifestyle change. Hence, the means and methods of the invention may be used for monitoring responses to a treatment or a lifestyle change.

[0124] Accordingly, the present invention further relates to a use (e.g. a computer-implemented) use of a model for monitoring treatment responses, wherein (i) said model is obtained or obtainable by a method of the invention, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

[0125] The present invention also relates to a use (e.g. a computer-implemented) use of a model for detection and / or monitoring of aging processes and / or tissue-specific aging, wherein (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

[0126] Accordingly, the present invention further relates to a use (e.g. a computer-implemented which may be further non- therapeutic) use of a model for monitoring responses to a lifestyle change or biohacking, wherein (i) said model is obtained or obtainable by a method of the invention, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD- 2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IF1TM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker. In context of the above (computer-implemented) uses of the invention, the association coefficient(s) may be as described herein, and / or the model may be as described herein, in particular, in context of the determination of the presence and / or magnitude of age gaps.

[0127] Furthermore, the present invention relates to a method of determining or monitoring the efficacy and / or adverse side effect(s) of a treatment, comprising the steps of determining an age gap between chronological age and biological age of at least one tissue of the individual according to a method of the invention before the treatment and after the treatment, wherein

[0128] (i) a decrease of the biological age of a tissue relative to the chronological age after the treatment is indicative of an effective treatment of said tissue,

[0129] (ii) no alteration of the biological age of a tissue relative to the chronological age after the treatment indicates that the treatment has no effect on said tissue, and / or

[0130] (iii) an increase of the biological age of a tissue relative to the chronological age after the treatment indicates that the treatment has an adverse side effect on said tissue.

[0131] The present invention also relates to a method of determining or monitoring the effect(s) of biohacking, comprising the steps of determining an age gap between chronological age and biological age of at least one tissue of the individual according to a method of the invention before the biohacking and after the biohacking, wherein

[0132] (i) a decrease of the biological age of a tissue relative to the chronological age after the biohacking is indicative that the biohacking has a positive effect on said tissue,

[0133] (ii) no alteration of the biological age of a tissue relative to the chronological age after the biohacking indicates that the biohacking has no effect on said tissue, and / or

[0134] (iii) an increase of the biological age of a tissue relative to the chronological age after the biohacking indicates that the biohacking has a negative effect on said tissue.

[0135] As described above and will be elucidated further below, the present invention provides methods for predicting biological age and / or age gap, in particular tissue-specific or systemic biological age and / or age gap, by using physiological meaningful features reflecting the tissue's actual state that are associated to the biological age gap and can be translated to expression level(s) of one or more marker(s).

[0136] As indicated above, in one aspect, the present invention provides a method of determining biological age and / or presence of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) in cells derived from a bodily fluid sample from an individual and determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level(s) of said one or more marker(s) is indicative of biological age and / or presence of an age gap between chronological age and biological age. In particular, the present invention provides a method of determining biological age and / or presence of an age gap between chronological age and biological age comprising the steps of (i) determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from bodily fluid sample from an individual and (ii) determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level of the one or more marker(s) of step (i) is indicative of biological age and / or presence of an age gap between chronological age and biological age.

[0137] The present invention, in a further aspect, provides a method of determining biological age and / or presence of an age gap between chronological age and biological age comprising the steps of determining expression level of one or more marker(s) in cells derived from a bodily fluid sample from an individual and determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level(s) of said one or more marker(s) is indicative of biological age and / or presence of an age gap between chronological age and biological age. In particular, a method of determining biological age and / or presence of an age gap between chronological age and biological age comprising the steps of (i) obtaining a sample from an individual, wherein the sample is a bodily fluid sample; (ii) determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3- 23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from the bodily fluid sample from the individual and (iii) determining said biological age and / or presence of said age gap between chronological age and biological age, wherein the expression level of the one or more marker(s) of step (i) is indicative of biological age and / or presence of an age gap between chronological age and biological age.

[0138] In context of the present invention, the terms "individuum" and "individual" may be used interchangeably. Herein, the individual may be a mammal such as, inter alia, a human, a horse, a monkey, an ape, a cow, a pig, a sheep, a rodent, a tiger or a polar bear, preferably, a human.

[0139] In context of the present invention, "chronological age" refers to the time that has passed since an individual's birth. The chronological age can be determined by means and methods known to the person skilled in the art. For example, the chronological age of an individual can be determined in accordance with data collected or available on the individuals date of birth, such as insurance data or data on the birth certificate, passport, or personal identification card of the individual, but also information provided by the individual or relatives of the individual on his or her data of birth. Chronological age may be given in decades, years, months, weeks, days, and / or hours, preferably in decades, years, months and / or weeks, as described herein.

[0140] Furthermore, in context of the present invention, "biological age" is a measure of the accumulation of (the) physiological state(s), i.e., cellular and tissue damage, as well as the rate of biological processes, such as metabolism, inflammation, and DNA methylation. Accordingly, "predicted biological age", in context of the present invention, is the prediction of the relative age of a physiological state of an individual, preferably of a specific tissue or the systemic age (e.g. of the mean of all tissues), reflecting the condition of the biological systems and overall health state. Accordingly, the predicted biological age may be indicative of the health state of an individual (e.g. in medical / therapeutic / diagnostic contexts) or of the fitness state of an individual (e.g. in non -therapeutic, lifestyle or performance enhancement / biohacking contexts). In context of the present invention, "health state", in particular, relates to the state of at least one ageing-related disease, at least one phenotype associated with at least one ageing-related disease, and / or cancer, wherein the state indicates the absence, presence, or stage of the disease or the phenotype associated with a disease. Thus, the health state, as used herein, is preferably related to aging. Thus, biological age may be seen as a more accurate indicator of age-related health span, or the number of years lived in good health, than chronological age. In context of the present invention, "fitness state", in particular, relates to a physiological state associated with well-being and / or performance capacity including cognitive performance and / or physical performance capacity / capacities. In context of the present invention, "biohacking", in particular, relates to deliberate, often self-directed interventions to optimize physical, cognitive, and / or overall human performance, typically through lifestyle changes, nutritional strategies, wearable technology, or biological enhancements, without involving clinical or therapeutic procedures.

[0141] Biological age may be given in decades, years, months, weeks, days, and / or hours, preferably in decades, years, months and / or weeks. In context of the present invention "predicted" (and grammatical variations thereof, such as "predict" or "prediction") may be used interchangeably with the term "determined" (and grammatical variations thereof, such as "determine" or "determination").

[0142] Herein, a "phenotype associated with an ageing-related disease" refers preferably to at least one symptom of an ageing-related disease. Furthermore, an aging-related disease or cancer or a phenotype associated therewith usually progresses in certain stages. Thus, herein, an aging-related disease or cancer or a phenotype associated therewith, can be absent or present, or be in a certain stage.

[0143] In context of the present invention, the biological age and / or age gap may be determined for one or more tissue(s), i.e., tissue-specific biological age(s) and / or age gap(s), or for the whole body (e.g. for the mean of all tissues), of an individual. In context of the present invention, if the biological age and / or age gap is determined for the whole body (e.g., for the mean of all tissues) of an individual, , it is also referred to as the systemic biological age and / or systemic (biological) age gap.

[0144] In preferred embodiments of the invention, the age gap is a tissue -specific age gap. In some embodiments of the invention, the age gap is a systemic age gap.

[0145] Preferably, the mean of all tissue comprises at least the tissues of subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, pancreas tissue, pituitary tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin such as lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, and / or thyroid tissue. Therefore, the systemic age or age gap may be based on the mean of a plurality or of all tissues selected from the group consisting of: subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, pancreas tissue, pituitary tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin such as lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, and thyroid tissue.

[0146] For female individuals the mean of all tissues or the above group of tissues may further comprise ovary tissue, uterus tissue, and / or vagina tissue, whereas for male individuals the mean of all tissues or the above group of tissues may further comprise prostate tissue and / or testis tissue.

[0147] In context of the present invention, "age gap" is the deviation (difference) of the predicted or determined age from the (known) chronological age. Age gap may be given in decades, years, months, weeks, days, and / or hours, preferably in decades, years, months and / or weeks. Consequently, in context of the present invention, the terms "age gap" or "biological age gap" (which may be used interchangeably herein) refer to the difference between biological age and chronological age. Usually, an individual's determined biological age and chronological age are different, and it is very rare, or even a coincidence, when they are the same. As described above and as illustrated in the appended Examples, tissues with positive age gaps (esp. higher positive age gaps) may reflect accelerated aging processes and / or accumulated damage. Inversely, tissues with negative age gaps (esp. higher negative age gaps) may reflect decelerated aging process and / or reduced tissue damage. As used herein, "determination of the presence of an age gap between chronological age and biological age" also comprises the quantification of the age gap value, i.e., deviation (distance) between chronological age and biological age. Accordingly, the present invention also concerns determining the magnitude (i.e. size or extent) of an age gap between chronological age and biological age. The deviation (distance) between chronological age and biological age may be given in decades, years, months, weeks, days, and / or hours, preferably in decades, years, months and / or weeks.

[0148] The person skilled in the art knows that decades may be given in years such as one decade is about 10 years. Similarly, they know that decades and years may be given in months, such as one year is about 12 months, and that decades, years, and months may be given in weeks, such as one year equals about 52 weeks. Furthermore, they know that decades, years, months, and weeks may be given in days, such as one week is about 7 days and one year equals about 365 days, and that decades, years, months, weeks, and days can also be given in hours, such as a day is about 24 hours and a week is about 168 hours. If the chronological age of an individual is known, the biological age can be deduced from the biological age gap and vice-versa. However, in context of the present invention the age gap can be determined directly and independently of the chronological age, i.e., the age gap is determinable independently of the chronological age, as described herein.

[0149] In context of the present invention, the "expression level of one or more marker(s)", also referred herein as "marker expression level", or "gene expression level", refers to the RNA expression level(s) of said one or more marker(s), preferably the mRNA expression level of said one or more marker(s). The expression level can readily be determined, for example, by employing RNA sequencing methods, such as RNA-seq, or other expression profiling methods, such as RT-qPCR, known in the art to quantify the expression level(s) of (the) marker(s). Unless stated otherwise, in context of the present invention the terms marker and genes may be used interchangeably, or may be used together, as "marker gene".

[0150] Herein, a gene refers to a genomic DNA sequence which encodes a protein (coding sequence; CDS), or a microRNA or long non-coding RNA. Herein, a genomic DNA sequence which encodes a protein also encodes the mRNA for the translation of said protein. A microRNA (miRNA) is a small non-coding RNA molecule (containing about 22 nucleotides) that functions in RNA silencing and post-transcriptional regulation of gene expression. Long noncoding RNAs (long ncRNAs, IncRNAs) are a type of transcripts with typically more than 200 nucleotides which are not translated into proteins (but possibly into peptides). Still, the majority of long non-coding RNAs are likely to be functional, i.e., in transcriptional regulation.

[0151] Herein, a genomic DNA sequence refers to the sequence as described and / or the reverse complementary sequence thereof. The skilled person can easily judge if the sequence as described, or the reverse complementary sequence thereof, should be used. By default, and for most applications, the sequence as described is to be used, but for some applications the complementary sequence thereof is used.

[0152] In context of the present invention, "bodily fluid sample" is not particularly limited, as long as the bodily fluid sample comprises cells that can be derived from said sample. Non-limiting examples of bodily fluid samples may be peripheral blood mononuclear cell (PBMC) sample, whole blood sample, dried blood spot sample, amniotic fluid sample, bone marrow aspirate sample, lymph fluid sample, buffy coat sample, saliva sample, sputum sample, mucus sample, cerebrospinal fluid (CSF) sample, pleural / peritoneal fluid sample, synovial fluid sample, urine sample, lacrimal fluid sample, and sweat sample. However, in some preferred embodiments of the present invention the bodily fluid sample may be selected from the group consisting of peripheral blood mononuclear cell (PBMC) sample, whole blood sample, dried blood spot sample, amniotic fluid sample, bone marrow aspirate sample, lymph fluid sample, buffy coat sample, saliva sample, sputum sample, mucus sample, cerebrospinal fluid (CSF) sample, pleural / peritoneal fluid sample, and synovial fluid sample, more preferably from the group of peripheral blood mononuclear cell (PBMC) sample, whole blood sample, and dried blood spot sample, and most preferably from peripheral blood mononuclear cell (PBMC) sample. In particularly preferred embodiments of the inventions the bodily fluid sample is a blood sample. Preferably, said blood sample comprises PBMCs.

[0153] In context of the present invention, "bodily fluid-based marker expression" refers to marker expression of cells derived from bodily fluids.

[0154] In context of the present invention, "cells derived from bodily fluid sample" are not particularly limited, and examples thereof may comprise circulating immune cells, such as T cells, B cells, monocytes, natural killer (NK) cells, dendritic cells, or neutrophils, circulating endothelial cells, such as mature endothelial cells or endothelial progenitor cells, hematopoietic stem cells (HSCs), mesenchymal stem cells (MSCs), such as bone marrow-derived MSCs, adipose-derived MSCs, or circulating fibrocytes, and adipose-derived cells, such as adipocytes or adipose-derived mesenchymal stem cells, cartilage-derived cells, and / or circulating tumor cells (CTCs), such as epithelial tumor cells or mesenchymal tumor cells. Further examples may be, depending on the origin of the bodily fluid sample, peripheral blood mononuclear cells (PBMC), circulating endothelial cells (CECs), and exfoliated epithelial cells, such as oral epithelial cells, urinary tract epithelial cells, or cervical epithelial cells. In particularly preferred embodiments, the "cells derived from bodily fluid sample" comprise PBMCs.

[0155] The methods of the present invention are, in particular, in silico or computer-implemented methods, ex wVoor in vitro methods, or a combination thereof, more specifically computer-implemented methods or ex vivo methods or a combination thereof. In particular, ex vivo methods may be performed in vitro with a bodily fluid sample from an individual.

[0156] Determining the expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample allows for detecting near real-time transcriptional changes. This advantage in temporal and / or spatial resolution that allows for earlier and / or more precise detection of tissue-specific aging and / or for more precise monitoring said tissue-specific aging can be achieved, inter alia, because in the central dogma of molecular biology, RNA transcription precedes translation of RNA into proteins. Proteins often further undergo post-translational modifications (PTMs) and degradation, adding further complexity to proteomics analysis. Furthermore, as circulating cells may originate from or travel through various tissues their respective RNA profile(s) (representing the expression level(s) of the genes within the cell) provide an immediate snapshot of tissue-specific gene expression enabling the reflection of the current state(s) of the tissue(s).

[0157] Accordingly, measurements of expression level of one or more marker(s) in cells derived from a bodily fluid sample provide specific insights into gene expression within the cells from which they were determined, and hence also from the tissue said cells originated from and / or are in contact with. This can give a direct view of what marker are being actively expressed in cells, offering a more precise understanding of cellular function or pathology. As shown in the appended non-limiting examples provided herein, in particular Example 3 and Figure 7, the inventors have demonstrated that the age gap predictions in accordance with the present invention show unexpected strong correlations with disease or pathology incidence, also as compared to chronological age. This correlation has been further confirmed in independent cohorts of healthy and disease-affected cohorts, see, for example, Examples 8-10 and Figures 20, 32 and 34. In contrast, proteins present in a bodily fluid sample may come from a variety of sources, including leakage from damaged cells, secretion from multiple tissues, or even external factors, making it difficult to pinpoint their cellular origin or biological relevance.

[0158] One of the main challenges of the field of age determination is to find methods to determine biological age, in particular tissue-specific and / or systemic biological age, that are able to infer biological age in a way that corresponds to, or reflects, the functional integrity and physiological condition of the tissue and / or the mean of all tissues. This requires a shift towards metrics that evaluate how well predicted biological age aligns with the tissue's physiological characteristics such as the manifestation of pathology and / or diseases, rather than purely statistical measures like error relative to chronological age.

[0159] As illustrated in the appended examples provided herein, for example, Examples 2 and 3, the inventors have surprisingly found image-based predictors of biological age. These predictors have been found by correlating morphological features, which were identified and extracted from a large-scale collection of histopathological whole slide images (WSIs), with observed chronological age of the same individual that the respective WSI was taken from. The collection of WSI, analyzed by the inventors, comprised samples from 40 different tissues in 29 organs making up more than 25,000 WSIs. By employing this unique correlating strategy, the inventors were able to capture physiologically relevant features that reflect the tissue's actual state and could thus determine the biological age for the tissue of which the histological sections were taken. From the inferred biological age and the difference to the observed chronological age the biological age gap could also be determined.

[0160] Accordingly, the present invention relates in another aspect to a method for determining biological age and / or presence of an age gap between chronological age and biological age comprising the steps of providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological section(s) of at least one tissue of said individual (in particular images thereof), and ii) chronological age of said individual, extracting morphological features from said histological sections (in particular the images thereof) of the training dataset and analyzing the extracted morphological features and correlating said morphological features with the chronological age of the same individual of the training dataset, wherein associations between the morphological features and the chronological age are determined, and applying the associations, determined by correlation of said morphological features with the chronological age of the same individual, to histological sections (in particular images thereof) to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age. In other words, the present invention may be used to derive histology-based biological age from extracted tissue features and known chronological age. By comparing the histology-based biological age to the known chronological age, also a histological age gap (i.e. a histology-derived age gap) can be determined.

[0161] The above method may be a computer-implemented method. It should be noted that, in particular, the steps of providing a training dataset, extracting morphological features, analyzing the extracted morphological features, and / or applying the associations to histological sections (in particular images thereof) may preferably be performed as a computer-implemented method and / or may comprise one or more machine learning model(s). For example, these steps, in particular the steps of extracting morphological features, analyzing the extracted morphological features and / or applying the associations to histological sections (in particular images thereof), may comprise applying a computer model such a foundation model and / or a graph neural network (GNN). Said computer model may be, e.g., a vision model, a multimodal model and / or a slide encoder. Furthermore, said computer model may be a vision encoder coupled to a regression method such as linear regression, Ridge, Lasso or ElasticNet regression, support vector regression, ensemble or boosting regressors such as random forest or xgboost. Said model (e.g., a foundation model) may also have an architecture selected from the group consisting of: self-supervised pretrained models such as CHIEF (Wang (2024), Nature, 634, 970-978) , Swin Transformer such as CTransPath, iBOT such as Phikon, CLIP such as PLIP, CONCH and Titan, DINOv2 or DINOv3 such as UNI, GigaPath, Virchow, Virchow2, UNI2, Midnight, HibouB, HibouL and PhikonV2, Perceiver in combination with BioGPT such as Prism, Vision transformer such as HOptimusO, HOptimusl, and HOMini (Example 10 and Figures 30, 31, 32 and 34).

[0162] In context of the present invention, "morphological features" may also be referred to as "histopathological features" or "histological feature(s)". These morphological features refer to the structural and organizational characteristics of biological tissues that can be observed under a microscope after staining and processing. These features are essential or helpful for identifying and classifying different tissue types, as well as for diagnosing various diseases and disorders. Morphological features of a tissue are highlighted through various staining techniques known in the art, such as hematoxylin and eosin (H8iE), which are commonly used in conjunction with each other to provide a comprehensive view of tissue structure and organization. The interpretation of these features is crucial in pathology, enabling the diagnosis of various diseases and disorders, and guiding treatment decisions. In context of the present invention the step of "extracting morphological features" may be considered an analyzing step, wherein "morphological features" are detected and quantified in an automated process such as with deep learning models.

[0163] In context of the present invention, "histological age" refers to a measure of the accumulation of cellular and tissue damage, as well as the rate of biological processes, that is determined through histopathological features, preferably extracted from WSIs. Accordingly, histological age is a subclass of the biological age and is an indicator of biological age associated with pathologies. Histological age may also be referred to as "histologically-derived biological age", "histology-based biological age", or "tissue-derived biological age". "Predicted histological age", as used herein, is the prediction of the biological age based on histopathological features, taking into account the physiological and morphological state of the respective tissue and / or the whole body of an individual. Accordingly, in context of the present invention, the difference between biological age based on histological pathologies and chronological age is referred to as "histological age gap", "histologically-derived biological age gap", "histology-derived age gap", "histologybased biological age gap", or "tissue-derived biological age gap", terms which may be used interchangeably herein.

[0164] Furthermore, as illustrated in the appended Examples (cf. Example 10 and Figure 35), the inventors implemented graph neural networks (GNNs) to predict biological age from histopathological images by modeling tissue patches as graph nodes connected based on spatial proximity. The GNN models successfully learned to predict age with good performance metrics confirming their suitability for biological age estimation. Attention mechanisms within GNNs allowed to produce heatmaps that highlight tissue regions most associated with aging signals. This approach demonstrates that GNNs not only match the predictive capability of other architectures described and employed herein in context of the present invention but also provide interpretability by localizing aging-related features within tissue images.

[0165] Accordingly, the present invention further relates to a computer-implemented method of determining areas in a histological section that are affected by aging, said method comprising the steps of

[0166] (a) applying a model to an image of the histological section, wherein said model was trained to predict biological age or presence and / or magnitude of an age gap between chronological age and biological age from morphological features extracted from images of histological sections, as described herein, and

[0167] (b) determining the contribution of areas of the histological section to the determination of the biological age and / or age gap, wherein a contribution of an area above average and / or above a threshold indicates that said area is affected by aging. Said method may comprise before said step (a) the following steps to generate said model:

[0168] (a7) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) at least one image of (a) histological section(s) of at least one tissue of said individual, and ii) chronological age of said individual,

[0169] (a") extracting morphological features from said histological section(s) of step (a7) i), and

[0170] (a'") correlating the extracted morphological features with the chronological age of step (a') ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, as described herein.

[0171] In particular, in step (a'"), the associations between the morphological features and the chronological age may be determined in a spatially resolved manner in the image(s) (i.e. location-wise). Moreover, said model may compute spatially resolved attribution scores for contribution to the age gap. In preferred embodiments, said model employs a graph neural network with an attention mechanism, preferably, for (i) determining the associations between the morphological features and the chronological age in a spatially resolved manner and / or for (ii) spatially resolving the attribution scores for contribution to the age gap.

[0172] Furthermore, said method may comprise a step of visualizing areas that are affected by aging in an image of the histological section. This is illustrated, e.g., in Example 10 and Figure 35.

[0173] In context of the present invention, a training dataset may comprise at least about 1 sample per tissue, at least about 10 sample per tissue, at least about 100 samples per tissue. Accordingly, the training dataset may comprise at least about 100 samples per tissue, at least about 150 samples per tissue, at least about 200 samples per tissue, at least about 300 samples per tissue, at least about 400 samples per tissue, at least about 500 samples per tissue, at least about 600 samples per tissue, at least about 700 samples per tissue, at least about 800 samples per tissue, at least about 900 samples per tissue, at least about 1000 samples per tissue, at least about 1500 samples per tissue, at least about 2000 samples per tissue, or more than about 2000 samples per tissue.

[0174] As used herein, the term "about" indicates and encompasses an indicated value and a range above and below that value. For example, the term "about" may indicate the designated value ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, or ±1% without substantial loss of effect or activity. According to the present invention, the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age may be determined for the tissue from which the histological section is derived from. According to the present invention, the histological sections may be images of histological sections, in particular, whole slide images, preferably of different tissues or tissue types. In context of the present invention, the tissues may be derived from any organs of the individual's body, in particular the organs may be one or more selected from the group consisting of adipose, adrenal gland, artery, brain, breast, bladder, colon, esophagus, heart, kidney, liver, lung, minor salivary gland, muscle, nerve, pancreas, pituitary, skin, small intestine, spleen, stomach, thyroid, and, in case of a female individual, cervix, fallopian tube, ovary, uterus, and vagina, or in case of a male individual, prostate and testis.

[0175] As mentioned above, the inventors have found that higher (in particular above 0, i.e., positive) age gaps, predicted based on morphological features, strongly correlate with pathologies, as illustrated in the appending non-limiting examples provided herein. In particular, higher or positive age gaps are associated with tissue-specific pathology and comorbidities related to aging characteristics, see e.g., Example 3, Figures lf-j, and Figure 7. Accordingly, in the present invention the morphological-features may be indicative of aging-related characteristics.

[0176] As illustrated in the appended non-limiting examples provided herein, individual morphological features in a whole slide image (WSI) were identified and extracted using vision models. These features were represented as numerical values, which in turn were combined into a single feature vector representing the whole tissue area imaged in the WSI. To capture histological features better, in accordance with the present invention, fine-tuned vision models may preferably be used to extract a feature vector that numerically represents the morphological features, see e.g., Example 2 and Figure 3a-c. This allows for numerical quantification of the morphological features. In other words, features may be extracted from whole slide images to numerically represent tissue morphological features that are indicative of aging- related characteristics and / or the pathophysiological state of the tissue. Accordingly, in the present invention the morphological-features may be represented numerically. Preferably, the steps of extracting and numerically representation may be performed as a computer-implemented method and / or may comprise one or more machine learning model(s), as described herein and as illustrated in the appended Examples.

[0177] As described herein, the extracted morphological features were correlated with the chronological age of the individual, of which the respective tissue slide was obtained from. Therefore, the association between the morphological features and the chronological age, of the individual of which the respective tissue slide was obtained from, allows to determine the biological age (and with this also the biological age gap) when applied to WSI that may be part of the training dataset but also to WSIs that are not part of the training dataset. This process of determining the association between the morphological features and the chronological age, of the individual of which the respective tissue slide was obtained from, may also involve building a statistical model. This statistical model may be used to predict the biological age of a tissue sample, that is not part of the training dataset, based on its morphological features, as illustrated in the appending non-limiting Examples 2 and Example 3. Accordingly, in the present invention the associations determined between the morphological features and the chronological age may be applied to histological sections that are not part of the training dataset. Accordingly, the associations, determined by correlation of morphological features with the chorological age of the individual of which the respective tissue slide was obtained from, may also be used to determine the biological age of any test dataset comprising for each individual only histological sections of at least one tissue. In other words, the present invention allows for the determination of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age based on morphological features extracted from histological sections, in particular images thereof, such as histological whole slide images (WSIs). In context of the present invention the dataset which is used for generating an age predictor is also called the "training dataset". A "validation dataset" refers to a data set which can be used for evaluating or validating the derived association(s) from the training dataset, i.e., as a control measurement. Usually, said training dataset and validation dataset have the same structure. In particular, the validation dataset and the training dataset comprise the same set of marker(s) whose expression level is determined. As essential difference however, the validation dataset only contains data of individuals who have not contributed data to the respective training dataset. In context of the present invention a "control dataset" is a completely independent dataset used to assess the mode's final performance after training is complete. It is only used once the model is fully trained and tuned, to provide an unbiased estimate of how the model will perform on unseen data in real-world scenarios.

[0178] As described herein and as illustrated in the appended non-limiting examples provided herein, the inventors could combine the wealth of molecular data available for the same samples of which the histological tissue slides were available, see e.g., Example 4 and Figure lOa-b, with the morphological insights gained from the morphological featurebased determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. This allows to determine molecular basis of tissue aging by integrating the histologically inferred age gaps with the gene expression profiles of the same histological tissue sections. Accordingly, the expression of specific genes, also herein referred to as markers, could be associated with the varying degree (i.e. magnitude) of deviation from chronological age in the same samples. This allowed the identification of molecular-determinants of histological aging, in particular tissue-specific histological-aging, as illustrated in the appended non-limiting Example 4. Accordingly, the age gaps, e.g., tissue-specific age gaps, according to the present invention are, in particular, indicative of tissue aging, especially, aging rates of tissues, as described herein.

[0179] As shown in the appended non-limiting examples provided herein, the inventors could further determine which tissues are most similar in their aging rates throughout the human lifespan. In particular, the inventors used a cross -inference approach, where one tissue's clock was used to predict the age of all others. This process was repeated for every tissue type. The inventors were able to register deviations in predicted age across the population, in particular age gaps, between tissues, which indicated differential aging rates across tissues, see, e.g., Example 5 and Figure 13a. The resulting matrix of age or age gap predictions provided a landscape of relative age acceleration or deceleration across tissues, with some tissues appearing to age faster - predicting others to be years older, while others showed the opposite effect - predicting others to be years younger, see, e.g., Example 5 and Figure 14. The inventors identified a hierarchy of changes across tissues, by leveraging dimensionality reduction methods to derive an axis of rates of change between tissues across the human lifespan. Surprisingly, few organs had a constant rate of aging, which highlights the extremely dynamic nature of the aging process across tissues and the need of biological age and age gap prediction methods with improved robustness and precision in spatial and / or temporal resolution.

[0180] In context of the present invention, "tissue clock" refers to biological measurement system that estimates the biological age of specific tissues. If the systemic biological age (e.g. the biological age of the mean of all tissues) is determined the clock is referred to as "systemic clock". In context of the present invention, the term "systemic" may refer, in particular, to the mean of all tissues (e.g. of the individual), preferably wherein the tissues are subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, pancreas tissue, pituitary tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin, preferably lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, and / or thyroid tissue. For female individuals the group of tissues may further comprise ovary tissue, uterus tissue, and / or vagina tissue, whereas for male individuals the group of tissues may further comprise prostate tissue and / or testis tissue. In accordance with the present invention, these tissue clocks may be histopathological image-based predictors of biological age of a respective tissue, i.e., histology-based tissue clock. In accordance with the present invention, these tissue clocks may also be bodily fluid sample-based predictors of biological age of a respective tissue, for example, blood-based tissue clock.

[0181] The inventors were further able to quantify the degree to which aging acceleration in relation to individuals of the same chronological age may be different for different organs within a single individual. They identified several different patterns of cross-tissue aging withing individuals: consistently below average (i.e. negative) age gaps across tissues (resilient agers'); consistently average age gaps ('average agers'); single- and multiple tissue agers - one or a few tissues with higher age or age gaps, than others; and a group of individuals with most tissues consistently predicted with high (esp. above average, i.e., positive) age gap Csystemic agers'). The inventors were furthermore able to identify factors potentially underlying a specific deviation in the aging of one organ in comparison with others. Thereby several new associations that were tissue- and sex-specific have been uncovered and other less well-known associations that have not yet been linked to an explicit morphological manifestation of the aging process have been found, see, e.g., Example 6 of the appended non-limiting examples provided herein.

[0182] As illustrated in the appended non-limiting examples provided herein, the inventors could convincingly show that gene expression could be associated with tissue-specific biological age, and, in particular, age gaps derived from images, see, e.g., Example 4, Example 7, and Figure 11, and that aging reflects in a systemic manner in many individuals, see, e.g., Example 6, Example 7, and Figure 15. Based on these observations, the inventors, for the first time in the field of age determination, developed a predictor of tissue-specific biological age, and, in particular, tissue-specific age gap(s), i.e., driven by morphological features in tissue, from a gene expression sample of blood in the Genotype -Tissue Expression (GTEx) cohort, see e.g., Example 7 and Figure 17.

[0183] Accordingly, the training dataset, in context the present invention, for determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age may further comprise for each individual: iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual. Preferably the one or more marker(s) is / are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52- AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8- AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0184] The present invention is particularly advantageous when combining the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age based on morphological features extracted from histological images (histology-based) with the advantages of marker expression level determination. Advantages that the determination of marker expression level in cells derived from bodily fluid samples adds to the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age include, for example and as further elaborated elsewhere herein, higher sensitivity, earlier detection of cellular changes, a more direct reflection of gene activity, and the ability to avoid complexities related to protein degradation and post-translational modifications.

[0185] By linking blood-based gene expression profiles with the histology-derived tissue-specific age gaps of the same individuals, the inventors were able to derive blood-based predictors of tissue age gaps across tissues, see, e.g., Example 7 and Figure 17b and in particular Table 2. As is illustrated in the appended non-limiting Example 7, the marker expression level in cells derived from bodily fluid samples, in the present case whole blood sample, was further analyzed and correlated with biological age, or, specifically, age gap, to determine associations between marker expression and the biological age, in particular the age gap. In other words, the gene expression in bodily fluid samples, such as blood samples, of the same individuals of which the histological sections were taken, can be analyzed to determine an association between marker expression and biological age, in particular, the age gap. This effectively means that while the bodily fluid samples, such as blood samples, are traditionally seen as systemic, in context of the present invention, they are indicative of both systemic and / or tissue-specific biological age, and, in particular, systemic and / or tissue-specific age gaps.

[0186] In context of the present invention, the term "bodily fluid-based biological age" may refer to a prediction of the relative age of a physiological state, preferably of a specific tissue (tissue-specific) or systemic (e.g. mean of all tissues)including cellular and tissue damage, as well as morphological state, that is derived by analysis of a bodily fluid sample. The term "blood-based biological age gap", in context of the present invention, may refer to a prediction of the relative age of a physiological state, preferably of a specific tissue (tissue -specific) or systemic (e.g. the mean of all tissues), including cellular and tissue damage, as well as morphological state, that is derived by analysis of a blood sample.

[0187] Accordingly, the present invention may further comprise a step of analyzing the marker expression level of the training dataset and correlating the histology-based biological age and / or age gap with said marker expression level, wherein associations between the marker expression level and the biological age and / or age gap are determined.

[0188] In other words, the present invention may be used to derive a (universal) mapping between histology-based age and / or age gap and bodily fluid-based features, i.e., marker expression level(s). The mapping is a vector of association coefficient(s) for each marker, also referred to herein as p.

[0189] Preferably, the step of analyzing the marker expression level and correlating the biological age or age gaps with said marker expression may be performed as a computer-implemented method and / or may comprise one or more machine learning model(s). For example, a regularized linear regression model (e.g., Ridge regression, Lasso regression, ElasticNet regularization, or methods with implicit regularization such as Random Forest regression) may be used to learn a mapping between marker expression data derived from bodily fluids, in particular blood marker expression data, and tissue-derived biological age gap. Further models which may be used include, inter alia, Polynomial regression, Support vector regression, Decision tree regressor, Gradient boosting regressor, XGBoost regressor, LightGBM regressor, CatBoost regressor, AdaBoost regressor, K-nearest neighbors regressor, Multilayer perceptron, Convolutional neural network, Recurrent neural network, Residual network (Resnet), UNET, Long short-term memory, Gated recurrent unit, Transformer, Graph neural network (GNN), Graph convolutional network (GCN), Graph attention network (GAT), Autoencoder, Bayesian linear regression, Gaussian process regression, Generalized additive model, Kernel ridge regression, Huber regression, Quantile regression, Ordinal regression, Isotonic regression, Theil-Sen regressor, Passive aggressive regressor, Stochastic gradient descent regressor, Neural ordinary differential equations, Reservoir computing, Deep forest, Naive bayes, Bayesian network, Mixture density network, Extreme learning machine, Probabilistic neural network, and / or Radial basis function network. Such a universal mapping may be learned by estimating a value (i.e., a coefficient) for each gene which represents the rate of increase or decrease of each marker expression level with the age gap. In other words, the model is a mapping between bodily fluid sample marker expression, in particular blood sample marker expression, and histologically-derived biological age gap, in particular systemic or tissue-specific age gaps, wherein each marker has a different value since it contributes differently or is differential relevant to the age gaps of each tissue. The formulation through which the set of coefficients may be calculated is not particularly limited, but an example thereof is through the least squares formulation, which is given by the following Formula I:

[0190] Formula I P=(X'X+AI)1X'Y

[0191] Herein, p is a vector of association coefficients for each marker, X is the matrix of marker expression level(s) of cells derived from bodily fluids of the shape n,m, where n is the number of samples, and m the number of marker. X' denotes the transpose of X, and Y denotes the histologically-derived biological age gap variable. Al is a regularization factor, where A is a regularization parameter (a scalar value) and I is the identity matrix of size mxm, where m is the number of markers. Alternative formulations to arrive at the association coefficient(s), such as Least Absolute Deviations, Huber regression, Quantile regression, Bayesian regression, Support Vector regression, Principal Component regression, or Tree-Based Methods such as Decision Trees, Random Forests, and Gradient Boosting are known to the person skilled in the art. The estimated, i.e., learned, coefficient values thus represent numerically a measure of association of each gene with biological age gaps and can therefore also be used to determine which genes contribute the most to enable the prediction. In other words, the associations determined between the marker expression level and the biological age or, in particular the biological age gaps, can be represented as an association coefficient that is tissue-specific or specific for the systemic biological age or age gap (e.g. for the mean of all tissues of the individual).

[0192] A coefficient, as used herein, refers to the weight of an independent variable, which herein is the expression level of the respective marker. For predicting or determining the age or age gap of an individual, the coefficient is multiplied with the expression level of the respective marker, or in other words, a weight is put on marker expression level. Preferably, the expression level values are normalized expression values, such as the logarithm of counts per million (log(CPM), or centered and scaled values. Centering may be done by subtracting the mean expression value of each gene across samples, and scaling by dividing by the standard deviation, so that all genes have a mean of zero and a standard deviation of one.

[0193] A regression method, as used herein, refers to a statistical process for estimating the relationships among variables, in particular the relationship between a dependent variable and one or more independent variables. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. Preferably, the regression method comprises a linear regression, more preferably the linear regression model is a Ridge regression model.

[0194] Accordingly, an association coefficient may be a value determined by fitting a regression model to a training dataset comprising marker expression levels and corresponding biological age gaps. It may quantify the relationship between the expression level of a specific marker and the biological age gap, particularly the histology-based biological age gap. The association coefficient can provide a numerical weight for predicting biological age or the biological age gap from the marker expression data.

[0195] As illustrated in the appended non-limiting examples provided herein, the inventors have, based on the associations identified, determined those markers that are indicative for tissue-specific biological age gap and / or systemic biological age gap. This process, inter alia and as illustrated in the appended non-limiting Examples provided herein, involved analyzing the coefficients generated by the model and identifying those genes that have the strongest influence on the difference between biological age (as predicted by the model that works with tissue features) and chronological age (i.e., the actual age of the subject) and can thus function as potential markers for predicting biological age gap. Accordingly, the present invention may further comprise a step of determining one or more marker(s) underlying the associations determined between the marker expression level and the biological age and / or age gap based on the specific marker expression level of one or more marker(s). Preferably the one or more marker(s) are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0196] According to the present invention, each marker expression level detected in a bodily fluid sample, in particular in a blood sample, has a corresponding coefficient in the model. This association coefficient represents the marker's influence on the predicted age gap. Furthermore, an association coefficient according to the invention may be specific and / or distinct for the prediction of the age gap of different specific tissues (Table 4). In context of the present invention, markers may be ranked based on the absolute value of their association coefficients. A higher absolute value indicates a stronger influence on the age gap, regardless of whether it increases or decreases the predicted age. The sign of the association coefficient determines the direction of the influence: A positive coefficient means that among the samples under analysis, samples with higher expression level of the marker, tend to have higher (in particular above 0, i.e., positive) age gaps (i.e., an older biological age compared to the chronological age of the subject). A negative coefficient means that among the samples under analysis, samples with higher expression level of the marker tend to have lower (in particular below 0, i.e., negative) age gaps (i.e., a younger biological age compared to the chronological age of the subject). Since association coefficient determination is a regression task, the inverse is also true, e.g., decreased expression level of a gene with positive coefficient may be also indicative of lower or negative age gaps.

[0197] As illustrated in the appended non-limiting examples provided herein, in particular in Example 7, the inventors have identified a list of genes that are likely to play a significant role in determining the difference between biological and chronological age, i.e., age gap; see, e.g., Example 7 and Table 3. When analyzing the genes underlying the predictive process, the inventors overall found that each tissue-specific predictor had a specific set of unique genes, but also found genes positively associated with age gaps in one tissue and negatively in another, see. e.g., Example 7, Figure 19a-b. Surprisingly, the inventors also found several unknown genes, i.e., uncategorized genes, to be expressed in blood to be among the top predictors of tissue-specific age gaps, see e.g., Example 7, Figure 19b and Table 4). Accordingly, it has been shown by the inventors that markers, in particular tissue-specific expression markers, for predicting biological age, and, especially, age gaps (e.g. tissue-specific age gaps), can be identified. As illustrated in the appended non-limiting examples provided herein, said markers have been measured as mRNA expression level determined from cells derived from whole blood samples. Determining markers predictive for biological age or age gap on a mRNA level is advantageous over other methods, such as, for example, protein detection, as mRNA measurements can, inter alia, provide comprehensive coverage of the transcriptome and can detect marker that are present in very low quantities (i.e., low-abundance markers). Comprehensive transcriptome coverage allows detection not only of known markers but also of novel marker transcripts, as Table 4 convincingly shows. Low-abundance markers may evade traditional protein detection assays whereas mRNA corresponding to these proteins can easily be detected due to the use of conserved binding motifs and amplification techniques used in RNA sequencing. Accordingly, for the first time, tissue-specific markers are provided that allow the determination of biological age and / or age gap from a bodily- fluid sample, preferably a blood sample, of an individual.

[0198] Having established the associations between biological age or age gap, in particular histology-derived biological age gap, and the marker expression level(s) of cells derived from bodily fluids, i.e. association coefficients, the present invention allows for the determination of the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age solely based on the expression level of one or more marker(s) of cells derived from bodily fluid samples without the need for histological sections. This brings, amongst others, the advantage of eliminating the need for invasive tissue biopsy sampling, making the determination of age and / or aging, for example, suitable for accessible healthcare, in particular preventive healthcare or longitudinal monitoring. Furthermore, by removing the need for preparing and analyzing the histological tissue samples, the process of determining tissuespecific and / or systemic biological age and / or age gap without histological section, will also remove the risk of sampling bias, increase reproducibility and standardization. The bodily-fluid-based biological age and / or age gap determination can thereby, inter alia, be more-cost effective and easier implemented into clinical or research settings, thereby increasing accessibility for routine, in particular personalized, aging assessments.

[0199] In context of the present invention, a "test dataset", also known as "production dataset", refers also to a completely independent dataset on which the trained model makes predictions after the model has been fully developed, validated, and tested. As used herein, the test dataset may comprise data from one or more individual(s) and may comprise data on marker expression level in cells derived from a bodily fluid sample of the respective individual and optionally the chronological age of said individual. Because the associations, i.e., association coefficients, have been determined between the biological age gap, in particular histology-derived biological age gap, and the marker expression levels, in context of the present invention, for the determination of tissue-specific and / or systemic biological age gap a test dataset may, for example, only comprise the expression level of one or more marker(s) in cells derived from bodily fluid sample of one or more individual(s). However, since the biological age can be deduced from the biological age gap when the chronological age of the respective individual is known, for the determination of tissue -specific and / or systemic biological age the test dataset may optionally also comprise the chronological age of said individual. Therefore, the chronological age of the individual is not necessary to derive at the biological age gap of said individual, however it may be necessary for determining the biological age of said individual. Accordingly, in context of the present invention, the age gap may be determinable or determined independently of the chronological age, as described herein.

[0200] Accordingly, the present invention may further comprise a step of providing a test dataset of one or more individual(s) which is not comprised in the training dataset used to determine the associations between biological age or age gap, in particular histology-derived biological age gap, and the marker expression level(s), wherein said test dataset comprises for each individual iv) expression level of one or more marker(s) in cells derived from bodily fluid sample, and optionally v) chronological age of said individual. Preferably the one or more marker(s) are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S1OOB, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52- AS1, HES6, 1TGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8- AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0201] The present invention may even further comprise a step of applying the associations, determined between marker expression level of cells derived from bodily fluids and histology-based biological age and / or biological age gap of the one or more marker(s) determined to underly said associations, on the marker expression level of the test dataset to determine biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age. In other words, the association coefficient(s) of one or more markers that are indicative of biological age and / or biological age gap is applied to the marker expression level in cells from bodily fluids provided in the test dataset in order to determine tissue-specific and / or systemic biological age and / or biological age gap. Preferably the one or more marker(s) are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D. Accordingly, the present invention may further comprise a step of determining the biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age. In other words, determining the presence and / or magnitude of the age gap according to the invention, may comprise applying (an) association coefficient(s) on the expression level(s) of the marker(s), wherein the association coefficient(s) reflect(s), in particular, associations between a histology-derived age gap and the expression level(s) of said marker(s).

[0202] Furthermore, the association coefficient(s) may be determined, may have been determined or may be obtainable by a method according to the invention, in particular, a method comprising the steps of:

[0203] (a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section (in particular an image thereof) of at least one tissue of said individual, ii) chronological age of said individual, and iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual,

[0204] (b) extracting morphological features from the histological section(s) of step (a) i), in particular from the image(s) thereof,

[0205] (c) correlating the extracted morphological features of step (b) with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined,

[0206] (d) applying the associations determined in step (c) to (an) histological section(s) (in particular (an) image(s) thereof) to determine the presence and / or magnitude of an age gap between the chronological age and the biological age, and

[0207] (e) correlating the histology-derived age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived age gap of step (d) are determined. Furthermore, the extracted morphological features may be analyzed, as described herein, before correlating with the chronological age. Furthermore, the marker expression level(s) may be analyzed, as described herein, before correlating with the histology-derived age gap.

[0208] Given the determined association coefficient(s) p, the present invention allows to derive an estimate for the tissuespecific and / or systemic biological age or age gap in the test dataset. Importantly, this can be performed without the need for reference samples, since the coefficients are designed to be universal and robust (due to the use of techniques such as cross-validation) and the output of the procedure (estimation of biological age gap) produces a numerically interpretable result - the number of years that a sample is older (in the case of a positive value, i.e., positive age gap) or younger (in the case of a negative value, i.e., negative age gap) than the chronological age of the individual from which the sample was derived. Specifically, for a test sample with bodily fluid-derived cell marker expression data represented by the vector xnew (of length m, corresponding to the number of markers), the estimate for the target variable y new, i.e., the bodily fluid-based biological age gap, can be determined as given by Formula II:

[0209] Formula II: y new=xnew' p

[0210] Herein, xnew' is the transpose of the sample's marker expression vector (xnew), and is the vector of association coefficients for each marker previously obtained, for example, from the least squares formulation (see, Formula I). Formula II provides a linear prediction for the age gap variable based on the marker expression profile of a new sample. In other words, the vector xnew may be determined by measuring the expression level of one or more marker(s) (denoted as m) in a test dataset. As described herein, the marker expression level can readily be determined, for example, by employing RNA sequencing methods, such as RNA-seq, or other expression profiling methods, such as RT-qPCR, known in the art to quantify the expression level(s) of the marker(s). In context of the present invention, the units of xnew may be normalized expression values, such as the logarithm of counts per million (log(CPM)), or centered and scaled values. Centering may be done by subtracting the mean expression value of each gene across samples, and scaling by dividing by the standard deviation, so that all genes have a mean of zero and a standard deviation of one.

[0211] Formula II teaches on the extent to which a tissue's (or whole-body systems) biological aging process is accelerated or decelerated relative to the individual's chronological age. A positive age gap value (yAnew) suggests accelerated aging, where the tissue appears biologically older than expected, while a negative age gap suggests decelerated aging, where the tissue appears biologically younger.

[0212] Accordingly, the present invention also relates to a (e.g. computer-implemented) method of determining whether aging is accelerated or decelerated, comprising a step of determining an aging rate based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the rate of aging. As described herein, the rate of aging corresponds, in particular, to the age gap between chronological age and biological age, as described herein. An accelerated aging corresponds, in particular, to a positive age gap between chronological age and biological age, as described herein. A decelerated aging, corresponds, in particular, to a negative age gap between chronological age and biological age, as described herein. Accordingly, the present invention relates also to an (e.g. computer-implemented) method of determining an age gap between chronological age and biological age comprising a step of determining the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age, as described herein. In some embodiments of the invention, the age gap between the chronological age and the biological age is determined by inputting the expression level of the one or more marker(s) into the equation: yAnew = xnew' p, wherein y new = the age gap, xnew = vector of the determined marker expression level(s) of the length m, wherein m = number of markers, xnew' = the transpose of xnew, p = vector of an association coefficient(s) for each marker.

[0213] Furthermore, the present invention relates to a method of determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of (i) determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, and (ii) determining said biological age and / or presence and / or magnitude of said age gap between chronological age and biological age, wherein the expression level of the one or more marker(s) of step (I) is indicative of biological age and / or age gap, and wherein, the biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age may be determined by inputting the expression level of the one or more marker(s) in cells derived from a bodily fluid sample from an individual into the equation y'new = xnew'p, wherein, y^new denotes the age gap, in particular the bodily fluid-based biological age gap, xnew represents a vector of the determined marker expression level of the length m, wherein m is the number of markers, Awen / 'is the transpose of xnew, and p represents a vector of an association coefficient(s) for each marker.

[0214] In context of the invention, yAnew may be the predicted biological age gap in years.

[0215] Furthermore, it is envisaged that the value of the biological age and / or biological age gap, estimated in accordance with the present invention and provided as an output, can be contextualized, for example, in light of the distribution of a population of the same chronological age. According to the present invention this process of contextualization involves taking the predicted biological age gap for an individual, for a given tissue, or a systemic age gap (e.g., the mean of all tissues), and placing it within the context of a larger population to provide a more meaningful interpretation.

[0216] This means, for example, that instead of dividing a reference population into age groups, e.g., in 10-year intervals based on chronological age, which is a common approach, a gaussian process can be used to model the distribution of age gaps in the reference populations along the range of chronological age. This approach acknowledges explicitly the concept of heteroskedasticity, meaning that the variability of biological age gaps is expected to differ across different chronological age groups. It is envisaged, that the predicted biological age gap for an individual can be compared to the corresponding gaussian process distribution at their chronological age. The percentile rank can, for example, be calculated based on this age-specific distribution, providing a more accurate reflection of the individual's position within their specific age cohort. It is envisaged that the final output of a contextualization in accordance with the present invention can be, for example, a predicted biological age gap along with its corresponding percentile rank calculated within the context of an age-specific distribution of biological age gaps. Accordingly, this approach offers a more nuanced and reliable interpretation of the individual's biological aging trajectory. Therefore, by incorporating, for example, a Bayesian model that considers heteroskedasticity, the present invention is able to provide a robust and informative assessment of an individual's biological age or age gap relative to their peers.

[0217] Accordingly, it is envisaged that the herein described method for determining biological age and / or an age gap between chronological age and biological age may further comprise a step of contextualizing the estimated biological age and / or age gap value in light of the distribution of a population of the same chronological age. Preferably, the contextualization is indicated as a percentile rank reflecting the individual's position within their specific age cohort. Accordingly, in context of the present invention, the age gap (corresponding to the aging rate) may be indicated as a percentile rank. In particular, the percentile rank may be calculated based on the distribution of age gaps (or corresponding aging rates) in the individual's age cohort and / or reflect the individual's position within said age cohort, as described herein. Hence, accelerated aging (which corresponds to a positive age gap) or decelerated aging (which corresponds to a negative age gap) may be also indicated as a percentile rank. Again, said percentile rank may be calculated based on the distribution of aging rates (or corresponding age gaps) in the individual's age cohort and / or reflect the individual's position within said age cohort. In particular, a higher percentile rank than the 50thor 60thpercentile of the age cohort may indicate an accelerated aging, a lower percentile rank than the 50thor 40thpercentile of the age cohort may indicate a decelerated again, and / or a rank at around the 50thor between the 40thand 60thpercentile of the age cohort may indicate normal aging.

[0218] Therefore, the present invention may be used to predict bodily fluid-based, tissue-specific and / or systemic, biological age gaps by applying the previously learned association coefficient p mapping to new, i.e., previously unseen from the model, bodily fluid-based marker expression.

[0219] The method of the present invention may be a computer-implemented method, as described herein, which may comprise and / or employ one or more machine learning model(s), as described herein.

[0220] Accordingly, the steps of applying the associations determined between the marker expression level and the histologybased biological age and / or age gap, for example by utilization of Formula II, the step of determining the biological age and / or presence and / or magnitude of an age gap between the chronological and the biological age, and the step of contextualizing the determined biological age value may be, preferably, performed as a computer-implemented method and / or may comprise one or more machine learning model(s).

[0221] The present invention, in a further aspect, provides a method of determining the biological age and / or the presence of an age gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to a bodily fluid sample, and determination of biological age from said bodily fluid sample using associated marker expression level, the method comprising the steps of: a. extracting features from whole slide images and numerically representing said features, preferably wherein said features are tissue morphological features that are indicative of aging-related characteristics; b. determining correlations of the extracted features with chronological age associated with the whole slide image of which said features were extracted from, wherein the biological age of the tissue and / or the presence of an age gap between the chronological age and the biological age can be predicted based on the features of the histological sections; c. analyzing the marker expression level of one or more marker(s) in cells derived from a bodily fluid sample, wherein said bodily fluid sample is derived from the same individual as the whole slide images used in step a and b, to determine associations between said marker expression level and said biological age or age gap; d. determining one or more marker(s) underlying the associations determined in step c based on the specific marker expression of said one or more marker(s); e. applying the associations determined in step c with the one or more marker(s) determined in step d on the marker expression level in cells derived from a bodily fluid sample, of one or more individual(s) from which tissue was not collected, to determine biological age and / or the presence of an age gap between chronological age and biological age; f. determining the biological age and / or presence of an age gap between the chronological age and the biological age as an output and contextualizing that age gap value in light of the distribution of a population of the same chronological age, wherein said contextualizing is indicated as a percentile rank reflecting the biological age position within the specific chronological age cohort. Preferably, the one or more marker(s) are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3- 23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0222] The present invention, in yet a further aspect, provides a computer-implemented method for determining the gap between chronological age and biological age in tissue from whole slide images, application of the age gap analysis to a blood-based sample, and prediction of biological age from blood using associated gene expression, a method comprising: a. Extracting relevant features from vision models for image tiles in whole slide images to numerically represent tissue morphological features that are indicative of aging-related characteristics. b. Applying a machine learning model (regression models) trained on a dataset of known tissue sample features with associated chronological age, wherein the model is configured to output a predicted biological age of the tissue and an age gap which is the difference between the predicted biological age and the observed chronological age; c. Analyzing the gene expression in a blood sample of the same individuals as the tissue images used above (a-b) to determine an association between blood gene expression and tissue-specific age gaps using machine learning models; d. Determining the most important genes underlying the associations in c) based on the specific gene expression of one or more marker. e. Applying the machine learning models from c) with the genes in d) on blood samples of individuals from which tissue was not collected to predict tissue-specific biological age gaps. f. Providing the estimated biological age as an output and contextualizing that age gap value in light of the distribution of a population of the same chronological age.

[0223] Accordingly, the present invention further relates to a computer-implemented method of determining the biological age or the presence and / or magnitude of an age gap between chronological age and biological age, the method comprising the steps of: a. extracting features from whole slide images and numerically representing said features, preferably wherein said features are tissue morphological features that are indicative of aging-related characteristics; b. determining correlations of the extracted features with chronological age associated with the whole slide image of which said features were extracted from, wherein the biological age of the tissue and / or the presence and / or magnitude of an age gap between the chronological age and the biological age can be predicted based on the features of the histological sections; c. analyzing the marker expression level of one or more marker(s) in cells derived from a bodily fluid sample, wherein said bodily fluid sample is derived from the same individual as the whole slide images used in step a and b, to determine associations between said marker expression level and said biological age and / or age gap; d. determining one or more marker(s) underlying the associations determined in step c based on the specific marker expression of said one or more marker(s); e. applying the associations determined in step c with the one or more marker(s) determined in step d on the marker expression level in cells derived from a bodily fluid sample, of one or more individual(s) from which tissue was not collected, to determine biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age as an output; and, optionally, f. contextualizing that age gap value in light of the distribution of a population of the same chronological age, wherein said contextualizing is indicated as a percentile rank reflecting the biological age position within the specific chronological age cohort.

[0224] Preferably the one or more marker(s) are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

[0225] Preferably, any of the herein described steps of the methods of the present invention for determining biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age may be performed as a computer-implemented method and / or may comprise one or more machine learning model(s). Accordingly, the present invention relates to a method wherein the method is a computer-implemented method and / or comprises the use of one or more machine learning model(s). Accordingly, the method also relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps necessary to determine the biological age and / or the presence of an age gap between chronological age and biological age. In other words, the present invention further relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out a computer-implemented method according to the invention. Furthermore, the present invention relates to a computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out a computer-implemented method of the invention. It has already been indicated herein above and illustrated in the appended non-limiting examples provided herein, that numerous steps listed above will require extensive calculations. Therefore, implementing these steps in a semi- automated or automated manner to be executed by a computer is advantageous. Also, such calculations would neither be affordable in view of costs of computation done by a human being nor acceptable by any individual having to wait for a result. Therefore, executing at least one and preferably all of the calculation and evaluation steps by computers are considered vital. Therefore, it should be noted that the present invention, preferably, related to methods having computer-implemented steps and that at least some steps may be executed using a computer.

[0226] In context of the present invention the use of specific hardware to perform the computer-implemented methods according to the present claim and / or to run the one or more machine learning model(s) in not particularly limited, as long as the methods can be performed and / or the machine learning model(s) run. Non-limiting examples of hardware to be used may be High-Performance Computing (HPC), tensor processing units (TPUs) or graphical processing units (GPUs), such as the NVIDIA RTX 4080, 4090, A6000, A100, or H100, central processing units (CPUs). The person skilled in the art will be acquainted with the necessary hardware requirements required to perform the computer- implemented methods and / or run the one or more machine learning model(s) of the present invention.

[0227] As illustrated in the appended non-limiting examples provided herein, for example as illustrated in Example 7 and Table 4, it is evident that specific markers, in particular tissue-specific markers, for predicting biological age or age gap, in particular tissue-specific and / or systemic biological age or age gap, have been identified by the inventors. Accordingly, for the first time, tissue-specific markers are provided that allow the determination of predicted biological age and / or age gap from a bodily fluid sample of an individual. As is evident for the appended Table 4, for example, the marker "junction plakoglobin (JUP) has an association coefficient of 0,003341708 for subcutaneous tissue, in particular subcutaneous adipose tissue, an association coefficient of 0,011487038 for skin tissue, which is sun or UV exposed, such as leg skin, preferably lower leg skin, an association coefficient of 0,00976784 for small intestine tissue, in particular terminal ileum tissue, and an association coefficient of 0,015977369 for the mean of all tissues (systemic).

[0228] The association coefficients as shown in the appended Table 4 have the advantage of being very precise, allowing for very precise determination of biological age gap. However, even rounded values of or close approximation to these specific values still provide(s) association coefficients, albeit rounded / approximated, that can provide for a meaningful determination of a biological age or age gap, as described herein.

[0229] According to the present invention and as described herein above, the biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age may be a systemic biological age and / or age gap. For example, the biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age may be determined, inter alia, for the mean of all tissues.

[0230] Accordingly, the method of the invention may comprise determining the systemic biological age and / or systemic age gap between the chronological age and the biological, wherein the systemic biological age and / or systemic age gap may be indicative for the mean of all tissues of the individual. In particular, the systemic biological age and / or age gap may correspond to the mean biological age and / or age gap of all or a plurality of (e.g., at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30 or all) tissues of the individual selected from the group consisting of: subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, ovary tissue, pancreas tissue, pituitary tissue, prostate tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin such as lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, testis tissue, thyroid tissue, uterus tissue, and vagina tissue.

[0231] As is evident from Table 4, the present invention provides markers JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MYOM2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B that are favorable markers for predicting systemic biological age or age gap, e.g., the biological age or age gap of the mean of all tissues.

[0232] Herein, and in context of the present invention, any marker(s) according to the invention may be combined with the association coefficient for the respective marker(s), in particular, for determining the presence and / or magnitude of a tissue-specific or systemic age gap, as described herein. In particular, the expression level of one or more marker(s), when combined with the association coefficient for the respective favorable marker(s), may be indicative of the age gap of the respective tissue(s) or systemic age gap, as described herein, and as illustrated in the appended Examples (e.g., Example 7 and Table 4).

[0233] Accordingly, the method of the invention may comprise determining the presence and / or magnitude of a systemic age gap, wherein the one or more marker(s) according to the invention are selected from the group consisting of JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MY0M2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MY0M2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the systemic age gap of said individual.

[0234] In context of the present invention, a standard or control may be the distribution of expression level of the same marker in all samples of the same tissue (e.g. of a representative reference population or age cohort). In particular, the expression level of a marker may be considered increased when it is in the upper part of said distribution, for example, above the average or median of the distribution. Correspondingly, the expression level of a marker may be considered decreased when it is in the lower part of said distribution, for example, below the average or median of the distribution. In other words, in context of the present invention, the standard or control may correspond to the average expression level of the respective marker(s) in corresponding samples of a reference population or age cohort.

[0235] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of JUP, NIPAL2, ZFAS1, IFI27, AFAP1, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B, as compared to a standard or control, is indicative of the systemic biological age (e.g. of the mean of all tissues) of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MYO7B, UPK3BL, PRUNE2, MY0M2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, CDYL2, VDR, and ATP8B3, as compared to a standard or control, is indicative of the systemic biological age (e.g. of the mean of all tissues) of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0236] As described herein, by utilizing Formula II, the systemic biological age and / or age gap can be also determined without the need for a reference sample, such as the standard or control as mentioned herein above, since the association coefficient(s) are designed to be universal and robust and the output, i.e., estimated biological age gap, produces a numerically interpretable result.

[0237] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MYOM2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B, when combined with the association coefficient for the respective marker, is indicative of the systemic age gap (e.g. the age gap of the mean of all tissues) of said individual.

[0238] According to the present invention and as described herein above, the biological age and / or presence and / or magnitude of an age gap between the chronological age and the biological age may also be determined for one or more tissue(s), i.e., tissue-specific biological age(s) and / or age gap(s). Preferably said one or more tissue(s) is / are selected from the group consisting of subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, pancreas tissue, pituitary tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin, preferably lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, and thyroid tissue. For female individuals the group of tissues may further comprise ovary tissue, uterus tissue, and vagina tissue, whereas for male individuals the group of tissues may further comprise prostate tissue and testis tissue. In context of the present invention, the tissues may be derived from any organ of the individual's body, in particular the tissues may be selected from the group consisting of adipose, adrenal gland, artery, brain, breast, bladder, colon, esophagus, heart, kidney, liver, lung, minor salivary gland, muscle, nerve, pancreas, pituitary, skin, small intestine, spleen, stomach, and thyroid. For female individuals the group of organs may further comprise ovary, uterus, and vagina, whereas for male individuals the group of organs may further comprise prostate and testis.

[0239] As is evident from Table 4, the present invention provides markers JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2 that are favorable marker(s) for predicting biological age or age gap in small intestine tissue, in particular terminal iieum tissue.

[0240] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of JUP, CAV1, GPNMB, CSF1, PTRF, WASH7P, KRT5, ANKRD6, COL1A1, NPIPB15, APOE, and COL1A2, as compared to a standard or control, is indicative of the biological age of small intestine tissue, in particular terminal ileum tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of F8A1, TCL6, ZMAT2, NBPF26, OCEL1, TBC1D3L, and MYO7B, as compared to a standard or control, is indicative of the biological age of small intestine tissue, in particular terminal ileum tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0241] As is evident from Table 4, the present invention provides markers TBC1D3L, GLI1, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C that are favorable marker for predicting biological age or age gap in spleen tissue.

[0242] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of IGHG3, ADAT3, CD99P1, KRT13, F12, C5orf66, PPP1R35, ARHGEF19, TREML4, SGSH, BBC3, and CDKN2C, as compared to a standard or control, is indicative of the biological age of spleen tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of TBC1D3L, GUI, TBC1D30, B4GALNT3, PRX, UPK3BL, FAM134B, NEB, SHISA4, LAPTM4B, CLCN4, and C16orf45, as compared to a standard or control, is indicative of the biological age of spleen tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0243] As is evident from Table 4, the present invention provides markers C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1 that are favorable marker for predicting biological age or age gap in esophageal muscularis tissue.

[0244] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of TBC1D7, CHRNE, GTPBP2, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1, as compared to a standard or control, is indicative of the biological age of esophageal muscularis tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of C4BPA, S100B, NBPF3, HTRA1, CETP, FAM13A, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, and WASH6P, as compared to a standard or control, is indicative of the biological age of esophageal muscularis tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0245] As is evident from Table 4, the present invention provides markers FEM1A, AC144831.1, IFITM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and LINC02019 that are favorable marker for predicting biological age or age gap in sigmoid colon tissue.

[0246] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of FEM1A, AC144831.1, IFTTM10, KRT18, KCNK7, EPHX1, CMKLR1, SERPINF1, C3, IGF2, C1R, and UNC02019, as compared to a standard or control, is indicative of the biological age of sigmoid colon tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MYO7B and CBX8, as compared to a standard or control, is indicative of the biological age of sigmoid colon tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue. As is evident from Table 4, the present invention provides markers APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3 that are favorable marker for predicting biological age or age gap in prostate tissue.

[0247] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, APOE, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3, as compared to a standard or control, is indicative of the biological age of prostate tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of NT5DC4, AC116366.5, and ZP3, as compared to a standard or control, is indicative of the biological age of prostate tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0248] As is evident from Table 4, the present invention provides markers PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20 that are favorable markers for predicting biological age or age gap in visceral tissue, in particular viscera! adipose tissue.

[0249] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of PLPP3, GJB6, DCN, HEIH, COL3A1, CIS, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20, as compared to a standard or control, is indicative of the biological age of visceral tissue, in particular visceral adipose (Omentum) tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of RSPH3, AOC3, FUZ, and SOWAHD, as compared to a standard or control, is indicative of the biological age of visceral tissue, in particular visceral adipose (Omentum) tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0250] As is evident from Table 4, the present invention provides markers ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, ULRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100 that are favorable marker for predicting biological age or age gap in gastroesophageal junction tissue.

[0251] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of EPAS1, CSF1, IGKJ5, DTX2P1, RAB15, ACSS2, ULRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100, as compared to a standard or control, is indicative of the biological age of gastroesophageal junction tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of ESAM, SPARC, AP003068.23, CTTN, ATP2C2, TMEM119, PTGS1, TMEM204, ZNF429, GNAZ, ITGB3, and PIGC, as compared to a standard or control, is indicative of the biological age of gastroesophageal junction tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0252] As is evident from Table 4, the present invention provides markers NBPF3, KRT5, IBA57, AKR1C1, MTCO1P12, NOTCH2NL, PRUNE2, GSTM1, LRRC37A2, TNFRSF9, EMBP1, ZC3HAV1, TREML4, AOC3, FBXL14, COL9A3, NIPAL2, ZFAND2A, ASL, LCN10, HSPA7, SLC25A29, DCLRE1B, and MEG3 that are favorable marker for predicting biological age or age gap in pancreas tissue.

[0253] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of KRT5, NOTCH2NL, GSTM1, LRRC37A2, ZC3HAV1, FBXL14, NIPAL2, ZFAND2A, LCN10, HSPA7, DCLRE1B, and MEG3, as compared to a standard or control, is indicative of the biological age of pancreas tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of NBPF3, IBA57, AKR1C1, MTCO1P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29, as compared to a standard or control, is indicative of the biological age of pancreas tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0254] As is evident from Table 4, the present invention provides markers SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, TTGAD, S100A13, SPINT1, and GKAP1 that are favorable marker for predicting biological age or age gap in Over tissue.

[0255] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of MS4A14, MMP17, RNF122, POLR1D, IGHM, IGKJ1, IGHA2, FLT1, TTGAD, S100A13, SPINTl, and GKAP1, as compared to a standard or control, is indicative of the biological age of liver tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of SHISA4, KRT72, CTDP1, PRDM8, ARPIN, CCDC71L, SORBS1, ESPN, FBXL16, Clorfll5, AKR1C1, and CALD1, as compared to a standard or control, is indicative of the biological age of liver tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0256] As is evident from Table 4, the present invention provides markers TBC1D30, WASHCI, VPREB3, GLI1, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7 that are favorable marker for predicting biological age or age gap in thyroid tissue.

[0257] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of TBC1D30, WASHCI, VWCE, PPDPF, SLC9A3R2, GADD45G, SREBF1, RAB33A, FSD1, CAPN5, GLMP, and TBC1D7, as compared to a standard or control, is indicative of the biological age of thyroid tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of VPREB3, GLI1, RGMA, SNX22, EMBP1, COL9A3, RTN2, TG, GUCD1, FNDC10, and SVBP, as compared to a standard or control, is indicative of the biological age of thyroid tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0258] As is evident from Table 4, the present invention provides markers TG, TBXA2R, THAP8, ERRFI1, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3 that are favorable marker for predicting biological age or age gap in muscle tissue, in particular skeletal muscle tissue.

[0259] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of THAP8, ERRFI1, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, ADAMTS1, ACP5, ACCS, and PLPP3, as compared to a standard or control, is indicative of the biological age of muscle tissue, in particular skeletal muscle tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of TBXA2R, TBC1D3L, WASF2, and GP1BA, as compared to a standard or control, is indicative of the biological age of muscle tissue, in particular skeletal muscle tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0260] As is evident from Table 4, the present invention provides markers ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDIT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH 1, and VEGFA that are favorable marker for predicting biological age or age gap in mucosa tissue, in particular esophageal mucosa tissue.

[0261] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of ACP6, HCFC1R1, TBC1D7, DDIT3, GLB1L, AC068580.6, IFTTM10, SDCBP2, MGP, FAM20A, ERICH1, and VEGFA, as compared to a standard or control, is indicative of the biological age of mucosa tissue, in particular esophageal mucosa tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of FUZ, BIK, GID8, SURF1, GUCD1, C4BPA, and RNASE6, as compared to a standard or control, is indicative of the biological age of mucosa tissue, in particular esophageal mucosa tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0262] As is evident from Table 4, the present invention provides markers TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTT1, TEN1, TMTC2, SLED1, MX2, LINC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1 that are favorable marker for predicting biological age or age gap in stomach tissue.

[0263] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of IGHG1, UBE2F, TMTC2, SLED1, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1, as compared to a standard or control, is indicative of the biological age of stomach tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of TREML2, TCL6, FAM69B, MIR600HG, MOB3B, TCL1A, AC011899.9, SLC18B1, IFTT1, TEN1, MX2, and UNC00282, as compared to a standard or control, is indicative of the biological age of stomach tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0264] As is evident from Table 4, the present invention provides markers MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, ITGB4, PNKD, EIF1B, RFXANK, LINC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4 that are favorable marker for predicting biological age or age gap in left ventricle tissue.

[0265] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of ZBTB16, NT5DC4, PTGES, SYNGR1, SH3TC1, SDHAP2, PNKD, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4, as compared to a standard or control, is indicative of the biological age of left ventricle tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MAP3K6, SLC18B1, COL6A3, NAMPTP1, COL6A1, SPEG, ADGRE1, ITGB4, EIF1B, RFXANK, LINC00954, and LTBP2, as compared to a standard or control, is indicative of the biological age of left ventricle tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0266] As is evident from Table 4, the present invention provides markers LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20 that are favorable markers for predicting biological age or age gap in adrenal gland tissue.

[0267] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of, LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, CTTN, CD86, and MUC20, as compared to a standard or control, is indicative of the biological age of adrenal gland tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of the marker RIMS3, as compared to a standard or control, is indicative of the biological age of adrenal gland tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0268] As is evident from Table 4, the present invention provides markers IFI27, MXRA7, RSAD2, SIGLEC1, IF1T1, MX1, IFI6, IFTT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5 that are favorable marker for predicting biological age or age gap in testis tissue.

[0269] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of IFI27, RSAD2, SIGLEC1, IFTT1, MX1, IFI6, IFTT3, GBP4, FBXO6, IFTT5, CMKLR1, and HERC5, as compared to a standard or control, is indicative of the biological age of testis tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MXRA7, TUBB2A, RILPL1, and TNFRSF9, as compared to a standard or control, is indicative of the biological age of testis tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0270] As is evident from Table 4, the present invention provides markers DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1 that are favorable marker for predicting biological age or age gap in skin tissue which is not sun or UV exposed (e.g., suprapubic skin).

[0271] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of DRAXIN, C3, NNMT, SLC9A3R2, NOXA1, TREML4, ClOorflO, DNAJC4, AQP1, AC144831.1, PTRF, and IL1RL1, as compared to a standard or control, is indicative of the biological age of skin tissue, which is not sun or UV exposed, e.g., suprapubic skin, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of PARM1, MTND4P12, UBB, BANK1, TCL6, EPHB4, VPREB3, IGHD, FAM129C, TCL1A, and HCP5, as compared to a standard or control, is indicative of the biological age of skin tissue which is not sun or UV exposed (e.g., suprapubic skin) of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue. As is evident from Table 4, the present invention provides markers ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CISC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3 that are favorable marker for predicting biological age or age gap in uterus tissue.

[0272] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of CCDC9, BBC3, LRRC75B, GTF2IP13, GGT1, CLASRP, F12, TUBG2, CRAT, ZFAS1, CD99P1, and ASIC3, as compared to a standard or control, is indicative of the biological age of uterus tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of ALPK3, SETD7, GCNA, CTSC, ARPIN, SLC2A9, TG, CTSK, TMEM81, and SYNPO2, as compared to a standard or control, is indicative of the biological age of uterus tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0273] As is evident from Table 4, the present invention provides markers MYO7B, HES6, LINC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAP1 that are favorable marker for predicting biological age or age gap in transverse colon tissue.

[0274] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of HES6, LINC01451, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, XCL1, UBE2M, and TSPOAP1, as compared to a standard or control, is indicative of the biological age of transverse colon tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MYO7B, AOC3, AP3B2, MOB3B, IGHD, and TCN2, as compared to a standard or control, is indicative of the biological age of transverse colon tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0275] As is evident from Table 4, the present invention provides markers ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, TOMM7, and PLIN2 that are favorable markers for predicting biological age or age gap in subcutaneous tissue, in particular subcutaneous adipose tissue.

[0276] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of JUP, KLF11, SPATA20, PITPNC1, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, TOMM7, and PLIN2, as compared to a standard or control, is indicative of the biological age of subcutaneous tissue, in particular subcutaneous adipose tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, MYL2, GUI, FRMD3, SCN1B, and MYH11, as compared to a standard or control, is indicative of the biological age of subcutaneous tissue, in particular subcutaneous adipose tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0277] As is evident from Table 4, the present invention provides markers GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, HOXB2, and RNF122 that are favorable marker for predicting biological age or age gap in breast tissue, in particular mammary tissue. Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of KLF11, ZFAS1, HEIH, SNHG7, SPG20, SHB, NT5DC3, PLD1, EPOR, ACHE, HOXB2, and RNF122, as compared to a standard or control, is indicative of the biological age of breast tissue, in particular mammary tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of GFAP, ABCG1, IGKJ4, and BIK, as compared to a standard or control, is indicative of the biological age of breast tissue, in particular mammary tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0278] As is evident from Table 4, the present invention provides markers TREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, NOTCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20 that are favorable marker for predicting biological age or age gap in vagina tissue.

[0279] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, TMTC1, NOTCH2NL, RHD, DNLZ, PLVAP, and SPATA20, as compared to a standard or control, is indicative of the biological age of vagina tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of TREML4, FHL3, EIF4EBP2, SLC22A5, FAM134B, MRPL23, GSTM1, GGT1, and BCL2L11, as compared to a standard or control, is indicative of the biological age of vagina tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0280] As is evident from Table 4, the present invention provides markers JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MY0M2, PAQR6, GAPDHP1, CACFD1, IL1RL1, LINC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1 that are favorable marker for predicting biological age or age gap in skin tissue which is sun or UV exposed (e.g., leg skin, preferably lower leg skin).

[0281] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of JUP, NBPF26, IL1RL1, UNC00623, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1, as compared to a standard or control, is indicative of the biological age of skin tissue, which is sun or UV exposed, e.g., leg skin, preferably lower leg skin, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MY0M2, PAQR6, GAPDHP1, CACFD1, S100B, and DBNDD2, as compared to a standard or control, is indicative of the biological age of skin tissue which is sun or UV exposed (e.g., leg skin, preferably lower leg skin) of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0282] As is evident from Table 4, the present invention provides markers ITGA1, MTND4P12, LILRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4 that are favorable marker for predicting biological age or age gap in atriai appendage tissue.

[0283] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of XIST, SRD5A3, TGM3, TMEM63C, IER5L, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4, as compared to a standard or control, is indicative of the biological age of atrial appendage tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of ITGA1, MTND4P12, LILRA4, BANK1, PKIG, RHD, NBPF3, SLC18B1, USP9Y, C19orf71, BLK, and EEF1A2, as compared to a standard or control, is indicative of the biological age of atrial appendage tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0284] As is evident from Table 4, the present invention provides markers WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, TTGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, UNC01963, and MARCO that are favorable marker for predicting biological age or age gap in cerebellum tissue.

[0285] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of SPSB1, APOE, DUSP18, AREG, TTGAD, LGMN, FOLR2, C1QC, TNFRSF10A, XCL1, EIF1AY, and MARCO, as compared to a standard or control, is indicative of the biological age of cerebellum tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of WDR97, BSCL2, PODXL2, COL5A3, ARSD, GPX7, and LINC01963, as compared to a standard or control, is indicative of the biological age of cerebellum tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0286] As is evident from Table 4, the present invention provides markers SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2 that are favorable marker for predicting biological age or age gap in salivary gland tissue, in particular minor salivary gland tissue.

[0287] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of SERPINF1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ZP3, SLC48A1, IGKC, SNHG17, PIWIL4, and SPNS2, as compared to a standard or control, is indicative of the biological age of salivary gland tissue, in particular minor salivary gland tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MEG3, KAZN, B4GALNT3, USP32P1, ACCS, MYO7B, and GSTM1, as compared to a standard or control, is indicative of the biological age of salivary gland tissue, in particular minor salivary gland tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0288] As is evident from Table 4, the present invention provides markers WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDTT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH that are favorable marker for predicting biological age or age gap in coronary arteria tissue.

[0289] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of SGSH, IGLC2, HILPDA, SLC3A2, DDTT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH, as compared to a standard or control, is indicative of the biological age of coronary arteria tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of WASF2, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, and PPP2R3B, as compared to a standard or control, is indicative of the biological age of coronary arteria tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0290] As is evident from Table 4, the present invention provides markers MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1 that are favorable marker for predicting biological or age gap age in nerve tissue, in particular tibia! nerve tissue.

[0291] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, MT1E, SGSH, CLN8, PIM3, and GPSM1, as compared to a standard or control, is indicative of the biological age of nerve tissue, in particular tibial nerve tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of TRIQK, and EIF4EBP2, as compared to a standard or control, is indicative of the biological age of nerve tissue, in particular tibial nerve tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0292] As is evident from Table 4, the present invention provides markers MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN that are favorable marker for predicting biological age or age gap in brain cortex tissue.

[0293] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of IGHA2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN, as compared to a standard or control, is indicative of the biological age of brain cortex tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of MTND4P12, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, and FTH1P2, as compared to a standard or control, is indicative of the biological age of cortex tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0294] As is evident from Table 4, the present invention provides markers FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1 that are favorable marker for predicting biological age or age gap in lung tissue.

[0295] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of HES1, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, LGALS2, TNXB, ASS1, MRAS, and ADCY9, as compared to a standard or control, is indicative of the biological age of lung tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of FAM134A, PRRT3, VSIG10, TCL6, PHF23, ITGB4, and NDST1, as compared to a standard or control, is indicative of the biological age of lung tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue. As is evident from Table 4, the present invention provides markers MALAT1, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100 that are favorable marker for predicting biological age or age gap in aorta tissue.

[0296] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of MALAT1, IGHA2, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100, as compared to a standard or control, is indicative of the biological age of aorta tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of the marker ELOVL7, as compared to a standard or control, is indicative of the biological age of aorta tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0297] As is evident from Table 4, the present invention provides markers SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTCO1P12, SMIM1, LGALS2, PLAU, PLA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB that are favorable marker for predicting biological age or age gap in ovary tissue.

[0298] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of RGCC, MDGA1, VEGFA, SLC2A14, SMIM1, LGALS2, PLAU, TRIB3, LRRC75B, HCAR3, RLF, and DTNB, as compared to a standard or control, is indicative of the biological age of ovary tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of SSPO, TCN2, GSTM1, TPTEP1, GLMP, TMEM119, SIGLEC16, MTCO1P12, PLA2G15, and HNRNPCP2, as compared to a standard or control, is indicative of the biological age of ovary tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0299] As is evident from Table 4, the present invention provides markers ACTBP8, VPREB3, HOTAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, UNC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS that are favorable marker for predicting biological age or age gap in tibial artery tissue.

[0300] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of ACTBP8, H0TAIRM1, FAM20A, FSTL1, SPARCL1, UNC01001, NPIPB15, TOMM40L, MRPL18, A2M, COL3A1, and CIS, as compared to a standard or control, is indicative of the biological age of tibial artery tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of VPREB3, RIMS3, IGHJ3, AC125232.1, PMEPA1, RHD, BCL7A, and IGHJ6, as compared to a standard or control, is indicative of the biological age of tibial artery tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0301] As is evident from Table 4, the present invention provides markers USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1 that are favorable marker for predicting biological age or age gap in pituitary tissue.

[0302] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1, as compared to a standard or control, is indicative of the biological age of pituitary tissue of an individual being higher than the chronological age of said individual, and / or an increased expression level of the marker USP32P1, as compared to a standard or control, is indicative of the biological age of pituitary tissue of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0303] As is evident from Table 4, the present invention provides markers PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, ALPK3, KAZN, CSTB, ZNRF1, HEIH, CNTLN, FKBPL, ESAM, MBOAT2, SERINC2, CDKN2C, FOLR2, and POMC that are favorable marker for predicting biological age or age gap in kidney tissue, in particular renal cortex tissue.

[0304] Accordingly, in context of the present invention, an increased expression level of one or more marker(s) selected from the group consisting of KAZN, CSTB, ZNRF1, HEIH, CNTLN, FKBPL, ESAM, MBOAT2, SERINC2, CDKN2C, FOLR2, and POMC, as compared to a standard or control, is indicative of the biological age of kidney tissue, in particular renal cortex tissue, of an individual being higher than the chronological age of said individual, and / or an increased expression level of one or more marker(s) selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, as compared to a standard or control, is indicative of the biological age of kidney tissue, in particular renal cortex tissue, of an individual being lower than the chronological age of said individual, optionally, wherein said standard or control is the distribution of expression level of the same marker(s) in all samples of the same tissue.

[0305] As described herein, by utilizing Formula II, the tissue-specific biological age and / or age gap can also be determined without the need for a reference sample, such as the standard or control also mentioned herein above, since the association coefficient(s), as illustrated in Example 7 and Table 4, are designed to be universal and robust and the output, i.e., estimated biological age gap, produces a numerically interpretable result.

[0306] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2, when combined with the association coefficient for the respective marker, is indicative of the age gap of small intestine tissue, in particular terminal ileum tissue, of said individual.

[0307] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of TBC1D3L, GUI, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C, when combined with the association coefficient for the respective marker, is indicative of the age gap of spleen tissue of said individual.

[0308] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1, when combined with the association coefficient for the respective marker, is indicative of the age gap of esophageal muscularis tissue of said individual.

[0309] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of FEM1A, AC144831.1, IFTTM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and LINC02019, when combined with the association coefficient for the respective marker, is indicative of the age gap of sigmoid colon tissue of said individual.

[0310] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3, when combined with the association coefficient for the respective marker, is indicative of the age gap of prostate tissue of said individual.

[0311] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20, when combined with the association coefficient for the respective marker, is indicative of the age gap of visceral tissue, in particular visceral adipose (Omentum) tissue of said individual.

[0312] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, ULRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100, when combined with the association coefficient for the respective marker, is indicative of the age gap of gastroesophageal junction tissue of said individual.

[0313] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of NBPF3, IBA57, AKR1C1, MTC01P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29, when combined with the association coefficient for the respective marker, is indicative of the age gap of pancreas tissue of said individual.

[0314] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, ITGAD, S100A13, SPINT1, and GKAP1, when combined with the association coefficient for the respective marker, is indicative of the age gap of liver tissue of said individual.

[0315] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of TBC1D30, WASHCI, VPREB3, GUI, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7, when combined with the association coefficient for the respective marker, is indicative of the age gap of thyroid tissue of said individual.

[0316] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of TG, TBXA2R, THAP8, ERRFI1, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3, when combined with the association coefficient for the respective marker, is indicative of the age gap of muscle tissue, in particular skeletal muscle tissue of said individual.

[0317] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDTT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH 1, and VEGFA, when combined with the association coefficient for the respective marker, is indicative of the age gap of mucosa tissue, in particular esophageal mucosa tissue of said individual. Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTT1, TEN1, TMTC2, SLED1, MX2, UNC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1, when combined with the association coefficient for the respective marker, is indicative of the age gap of stomach tissue of said individual.

[0318] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, ITGB4, PNKD, EIF1B, RFXANK, UNC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4, when combined with the association coefficient for the respective marker, is indicative of the age gap of left ventricle tissue of said individual.

[0319] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20, when combined with the association coefficient for the respective marker, is indicative of the age gap of adrenal gland tissue of said individual.

[0320] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of IFI27, MXRA7, RSAD2, SIGLEC1, IFTT1, MX1, IFI6, IFTT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5, when combined with the association coefficient for the respective marker, is indicative of the age gap of testis tissue of said individual.

[0321] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1, when combined with the association coefficient for the respective marker, is indicative of the age gap of skin tissue, which is not sun or UV exposed, e.g., suprapubic skin, of said individual.

[0322] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CISC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3, when combined with the association coefficient for the respective marker, is indicative of the age gap of uterus tissue of said individual.

[0323] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of MYO7B, HES6, LINC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAP1, when combined with the association coefficient for the respective marker, is indicative of the age gap of transverse colon tissue of said individual.

[0324] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, T0MM7, and PLIN2, when combined with the association coefficient for the respective marker, is indicative of the age gap of subcutaneous tissue, in particular subcutaneous adipose tissue of said individual.

[0325] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, H0XB2, and RNF122, when combined with the association coefficient for the respective marker, is indicative of the age gap of breast tissue, in particular mammary tissue of said individual.

[0326] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of TREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, NOTCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20, when combined with the association coefficient for the respective marker, is indicative of the age gap of vagina tissue of said individual.

[0327] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MY0M2, PAQR6, GAPDHP1, CACFD1, IL1RL1, UNC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1, when combined with the association coefficient for the respective marker, is indicative of the age gap of skin tissue, which is sun or UV exposed, e.g., leg skin, preferably lower leg skin, of said individual.

[0328] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ITGA1, MTND4P12, LILRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4, when combined with the association coefficient for the respective marker, is indicative of the age gap of atrial appendage tissue of said individual.

[0329] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, ITGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, UNC01963, and MARCO, when combined with the association coefficient for the respective marker, is indicative of the age gap of cerebellum tissue of said individual.

[0330] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2, when combined with the association coefficient for the respective marker, is indicative of the age gap of salivary gland tissue, in particular minor salivary gland tissue of said individual.

[0331] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDIT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH, when combined with the association coefficient for the respective marker, is indicative of the age gap of coronary arteria tissue of said individual.

[0332] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1, when combined with the association coefficient for the respective marker, is indicative of the age gap of nerve tissue, in particular tibial nerve tissue of said individual.

[0333] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN, when combined with the association coefficient for the respective marker, is indicative of the age gap of brain cortex tissue of said individual.

[0334] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1, when combined with the association coefficient for the respective marker, is indicative of the age gap of lung tissue of said individual.

[0335] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of MALAT1, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100, when combined with the association coefficient for the respective marker, is indicative of the age gap of aorta tissue of said individual.

[0336] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTCO1P12, SMIM1, LGALS2, PLAU, PLA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB, when combined with the association coefficient for the respective marker, is indicative of the age gap of ovary tissue of said individual.

[0337] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of ACTBP8, VPREB3, HOTAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, UNC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS, when combined with the association coefficient for the respective marker, is indicative of the age gap of tibial artery tissue of said individual.

[0338] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and H0TAIRM1, when combined with the association coefficient for the respective marker, is indicative of the age gap of pituitary tissue of said individual.

[0339] Accordingly, in context of the present invention, the expression level of one or more marker(s) selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, when combined with the association coefficient for the respective marker, is indicative of the age gap of kidney tissue, in particular renal cortex tissue of said individual.

[0340] Accordingly, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of small intestine tissue, wherein the one or more marker(s) are selected from the group consisting of JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of small intestine tissue of said individual; preferably, wherein the small intestine tissue is terminal ileum tissue.

[0341] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of spleen tissue, wherein the one or more marker(s) are selected from the group consisting of TBC1D3L, GUI, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C, and wherein the expression level(s) of the marker(s) selected from the group consisting of TBC1D3L, GLI1, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of spleen tissue of said individual.

[0342] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of esophageal muscularis tissue, wherein the one or more marker(s) are selected from the group consisting Of C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1, and wherein the expression level(s) of the marker(s) selected from the group consisting of C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of esophageal muscularis tissue of said individual.

[0343] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of sigmoid colon tissue, wherein the one or more marker(s) are selected from the group consisting of FEM1A, AC144831.1, IFTTM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and LINC02019, and wherein the expression level(s) of the marker(s) selected from the group consisting of FEM1A, AC144831.1, IFTTM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and UNC02019, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of sigmoid colon tissue of said individual.

[0344] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of prostate tissue, wherein the one or more marker(s) are selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3, and wherein the expression level(s) of the marker(s) selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of prostate tissue of said individual.

[0345] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of visceral tissue, wherein the one or more marker(s) are selected from the group consisting of PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20, and wherein the expression level(s) of the marker(s) selected from the group consisting of PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of visceral tissue of said individual; preferably wherein the visceral tissue is visceral adipose (Omentum) tissue.

[0346] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of gastroesophageal junction tissue, wherein the one or more marker(s) are selected from the group consisting Of ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, ULRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100, and wherein the expression level(s) of the marker(s) selected from the group consisting of ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, LILRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of gastroesophageal junction tissue of said individual.

[0347] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of pancreas tissue, wherein the one or more marker(s) are selected from the group consisting of NBPF3, IBA57, AKR1C1, MTCO1P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29, and wherein the expression level(s) of the marker(s) selected from the group consisting of NBPF3, IBA57, AKR1C1, MTCO1P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of pancreas tissue of said individual.

[0348] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of liver tissue, wherein the one or more marker(s) are selected from the group consisting of SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, ITGAD, S100A13, SPINT1, and GKAP1, and wherein the expression level(s) of the marker(s) selected from the group consisting of SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, ITGAD, S100A13, SPINT1, and GKAP1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of liver tissue of said individual.

[0349] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of thyroid tissue, wherein the one or more marker(s) are selected from the group consisting of TBC1D30, WASHCI, VPREB3, GUI, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7, and wherein the expression level(s) of the marker(s) selected from the group consisting of TBC1D30, WASHCI, VPREB3, GUI, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of thyroid tissue of said individual.

[0350] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of muscle tissue, wherein the one or more marker(s) are selected from the group consisting of TG, TBXA2R, THAP8, ERRFI1, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3, and wherein the expression level(s) of the marker(s) selected from the group consisting of TG, TBXA2R, THAP8, ERRFU, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of muscle tissue of said individual; preferably, wherein the muscle tissue is skeletal muscle tissue.

[0351] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of mucosa tissue, wherein the one or more marker(s) are selected from the group consisting of ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDIT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH 1, and VEGFA, and wherein the expression level(s) of the marker(s) selected from the group consisting of ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDTT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH 1, and VEGFA, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of mucosa tissue of said individual; preferably, wherein the mucosa tissue is esophageal mucosa tissue.

[0352] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of stomach tissue, wherein the one or more marker(s) are selected from the group consisting of TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTTl, TEN1, TMTC2, SLED1, MX2, UNC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1, and wherein the expression level(s) of the marker(s) selected from the group consisting of TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTTl, TEN1, TMTC2, SLED1, MX2, LINC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of stomach tissue of said individual.

[0353] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of left ventricle tissue, wherein the one or more marker(s) are selected from the group consisting of MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, TTGB4, PNKD, EIF1B, RFXANK, UNC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4, and wherein the expression level(s) of the marker(s) selected from the group consisting of MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, TTGB4, PNKD, EIF1B, RFXANK, LINC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of left ventricle tissue of said individual.

[0354] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of adrenal gland tissue, wherein the one or more marker(s) are selected from the group consisting of LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20, and wherein the expression level(s) of the marker(s) selected from the group consisting of LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of adrenal gland tissue of said individual.

[0355] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of testis tissue, wherein the one or more marker(s) are selected from the group consisting of IFI27, MXRA7, RSAD2, SIGLEC1, IFTTl, MX1, IFI6, IFTT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5, and wherein the expression level(s) of the marker(s) selected from the group consisting of IFI27, MXRA7, RSAD2, SIGLEC1, IFTTl, MX1, IFI6, IFTT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of testis tissue of said individual.

[0356] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of skin tissue, wherein the one or more marker(s) are selected from the group consisting of DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1, and wherein the expression level(s) of the marker(s) selected from the group consisting of DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of skin tissue of said individual; preferably, wherein the skin tissue is not sun or UV exposed such as suprapubic skin.

[0357] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of uterus tissue, wherein the one or more marker(s) are selected from the group consisting of ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CTSC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3, and wherein the expression level(s) of the marker(s) selected from the group consisting of ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CTSC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of uterus tissue of said individual.

[0358] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of transverse colon tissue, wherein the one or more marker(s) are selected from the group consisting of MYO7B, HES6, UNC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAP1, and wherein the expression level(s) of the marker(s) selected from the group consisting Of MYO7B, HES6, UNC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAP1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of transverse colon tissue of said individual.

[0359] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of subcutaneous tissue, wherein the one or more marker(s) are selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, T0MM7, and PLIN2, and wherein the expression level(s) of the marker(s) selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, T0MM7, and PLIN2, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of subcutaneous tissue of said individual; preferably, wherein the subcutaneous tissue is subcutaneous adipose tissue.

[0360] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of breast tissue, wherein the one or more marker(s) are selected from the group consisting of GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, HOXB2, and RNF122, and wherein the expression level(s) of the marker(s) selected from the group consisting of GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, HOXB2, and RNF122, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of breast tissue of said individual; preferably, wherein the breast tissue is mammary tissue.

[0361] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of vagina tissue, wherein the one or more marker(s) are selected from the group consisting of TREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, N0TCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20, and wherein the expression level(s) of the marker(s) selected from the group consisting of TREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, NOTCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of vagina tissue of said individual.

[0362] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of skin tissue, which is sun or UV exposed, e.g., leg skin, wherein the one or more marker(s) are selected from the group consisting of JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MY0M2, PAQR6, GAPDHP1, CACFD1, IL1RL1, LINC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MY0M2, PAQR6, GAPDHP1, CACFD1, IL1RL1, UNC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of skin tissue, which is sun or UV exposed, e.g., leg skin, of said individual.

[0363] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of atrial appendage tissue, wherein the one or more marker(s) are selected from the group consisting of ITGA1, MTND4P12, ULRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4, and wherein the expression level(s) of the marker(s) selected from the group consisting of ITGA1, MTND4P12, ULRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of atrial appendage tissue of said individual.

[0364] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of cerebellum tissue, wherein the one or more marker(s) are selected from the group consisting of WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, TTGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, LINC01963, and MARCO, and wherein the expression level(s) of the marker(s) selected from the group consisting of WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, TTGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, UNC01963, and MARCO, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of cerebellum tissue of said individual.

[0365] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of salivary gland tissue, wherein the one or more marker(s) are selected from the group consisting of SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2, and wherein the expression level(s) of the marker(s) selected from the group consisting of SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of salivary gland tissue of said individual; preferably, wherein the salivary gland tissue is minor salivary gland tissue. Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of coronary arteria tissue, wherein the one or more marker(s) are selected from the group consisting of WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDIT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH, and wherein the expression level(s) of the marker(s) selected from the group consisting of WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDIT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of coronary arteria tissue of said individual.

[0366] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of nerve tissue, wherein the one or more marker(s) are selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1, and wherein the expression level(s) of the marker(s) selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of nerve tissue of said individual; preferably, wherein the nerve tissue is tibial nerve tissue.

[0367] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of brain cortex tissue, wherein the one or more marker(s) are selected from the group consisting of MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN, and wherein the expression level(s) of the marker(s) selected from the group consisting of MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of brain cortex tissue of said individual.

[0368] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of lung tissue, wherein the one or more marker(s) are selected from the group consisting of FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1, and wherein the expression level(s) of the marker(s) selected from the group consisting of FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of lung tissue of said individual.

[0369] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of aorta tissue, wherein the one or more marker(s) are selected from the group consisting of MALAT1, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100, and wherein the expression level(s) of the marker(s) selected from the group consisting of MALAT1, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of aorta tissue of said individual.

[0370] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of ovary tissue, wherein the one or more marker(s) are selected from the group consisting of SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTC01P12, SMIM1, LGALS2, PLAU, PLA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB, and wherein the expression level(s) of the marker(s) selected from the group consisting of SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTCO1P12, SMIM1, LGALS2, PLAU, PLA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of ovary tissue of said individual.

[0371] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of tibial artery tissue, wherein the one or more marker(s) are selected from the group consisting of ACTBP8, VPREB3, H0TAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, LINC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS, and wherein the expression level(s) of the marker(s) selected from the group consisting of ACTBP8, VPREB3, HOTAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, UNC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of tibial artery tissue of said individual.

[0372] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of pituitary tissue, wherein the one or more marker(s) are selected from the group consisting of USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1, and wherein the expression level(s) of the marker(s) selected from the group consisting of USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of pituitary tissue of said individual.

[0373] Furthermore, the method of the present invention may comprise determining the presence and / or magnitude of an age gap of kidney tissue, wherein the one or more marker(s) are selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, and wherein the expression level(s) of the marker(s) selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, in particular, when combined with the association coefficient(s) for the respective marker(s), is / are indicative of the presence and / or magnitude of the age gap of kidney tissue of said individual; preferably, wherein the kidney tissue is renal cortex tissue.

[0374] The present invention further provides for means that are useful in the determination of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. Such means may comprise primers and / or probes that are suitable for the detection and / or quantification of the expression level of one or more marker(s) that are indicative of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. These means, like primers and / or probes, should be capable and / or useful in the detection of the presence and / or the amount (e.g., "expression level") of the corresponding marker(s) in a sample derived from an individual, in particular, in a bodily fluid sample and / or in cells derived from a bodily fluid sample as described herein. Such primers and / or probes can also be used in a further embodiment and mutatis mutandis in other samples such as, for example, hair follicle samples, in the detection of specific marker(s) that allow the determination of biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age. In other words, such primers and / or probes may be useful in molecular biological methods for the assessment and / or measurement of the expression level of one or more marker(s) that are indicative of the biological age and / or presence and / or magnitude of an age gap between chronological age and biological age, in particular of a given tissue and / or organ. As documented herein, the present invention provides for relevant markers for the determination of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. These markers are preferably selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D. Accordingly, the present invention also provides for reagents that are specifically useful in the detection and / or quantification of these markers, like specific primers and / or probes for known molecular biological techniques to measure these markers, for example in cells derived from bodily fluid samples or cells comprised in bodily fluid samples. Such reagents, like primers and / or probes, may also be provided in form of specific kits useful in the means and methods of the present invention.

[0375] Accordingly, the present invention also relates to kits that are particularly suitable in the determination of biological age and / or presence of an age gap between chronological age and biological age of a tissue of an individual. Such kits may comprise primers and / or probes suitable for the detection and / or quantification of the expression level of one or more markers, that when combined with the association coefficient for the respective marker, is indicate of the age gap of the given tissue. Specific markers that are indicative for biological age and / or presence of an age gap between the chronological age and the biological age of e.g., tissues and / or organs are set out herein above. A non -limiting example may be primers and / or probes suitable for the detection and / or quantification of the expression level of one or more markers selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, suitable for the determination of biological age and / or presence and / or magnitude of an age gap between chronological age and biological age of kidney tissue, in particular renal cortex tissue of an individual.

[0376] The nature and sequence of the primers and / or probes that may be comprised in the herein provided kits is not particularly limited as long as the primers and / or probes allow for the sensitive and specific detection and / or quantification of the above-mentioned markers. The skilled person is readily in the position to design, test, and employ such primers and probes.

[0377] As further detailed herein below, presence of an age gap, in particular a positive age gap, between chronological age and biological age, as described herein, may be indicative of the presence of one or more pathologies and / or diseases, like one or more age-related pathologies and / or age-related diseases. Accordingly, the present invention also provides for means and methods of medical intervention of such pathologies / diseases. These means and methods may comprise timely medical intervention for example with pharmaceutical compositions for the relevant pathology / disease, like age- related pathologies and / or age-related diseases but also, inter alia, of pathologies and / or diseases that are related to a premature ageing of a specific tissue and / or organ in an individual. Accordingly, the present invention also relates to pharmaceutical composition(s), pharmaceutical drug(s) and / or supplement(s). Such pharmaceutical composition(s), pharmaceutical drug(s) and / or supplement(s) may also be comprised in kits.

[0378] In accordance with the present invention, the pharmaceutical composition(s), pharmaceutical drug(s) and / or supplement(s) may positively influence the biological age or age gap and / or may treat and / or prevent said pathology and / or disease. The skilled person can readily identify pharmaceutical composition(s), pharmaceutical drug(s) and / or supplement(s) known in the art that are suitable for treating and / or preventing age-related pathologies and / or age- related diseases, as determined by the inventive means and methods provided herein.

[0379] Accordingly, the present invention further envisages diagnostic and therapeutic uses of the herein provided means and methods and also said kits, as also described herein above. Further, the herein provided means and methods and also kits (comprising, inter alia, primers and / or probes for the herein disclosed specific markers) may also be employed in the herein provided methods of determining biological age and determining biological age and / or presence and / or magnitude of an age gap between chronological age and biological age. Therefore, the herein provided means and methods and also kits may also be used in the assessment and / or determination of pathologies and / or diseases, like one or more age-related pathologies and / or age-related diseases. Furthermore, the herein provided means and methods and also kits may also be used for detecting one or more diseases in an individual, determining a health status of an individual, predicting the likelihood of occurrence of at least one disease, detecting and / or monitoring, aging processes and / or tissue-specific aging, monitoring treatment responses, and / or monitoring the efficacy and / or adverse side effect(s) of a treatment, as described herein. For example, the herein provided means and methods (e.g., the herein provided kits) may be employed in the detection, in particular the early detection, of age-related diseases and / or of age-related pathologies and / or inflammatory diseases, such as, for example, Crohn's disease, systemic lupus erythematous, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, Alzheimer's disease and / or stroke (see, e.g., Examples 8 and 9). As discussed herein below, esophagus, small intestine, and colon are organs that may be directly affected by Crohn's disease. Accordingly, the presence of an age gap between chronological age and biological age for the esophagus, the small intestine, and / or the colon may be indicative of an age-related disease (such as, e.g., Crohn's disease) and / or an age-related pathology of said organs. Accordingly, the herein provided means and methods (such as the herein provided kits) may be particularly useful in the determination of, e.g., Crohn's disease and other age- related diseases, as described herein, such as rheumatoid arthritis. As mentioned above, the herein provided kits may comprise primers and / or probes capable of detecting and / or quantifying the herein found markers (such as the markers provided in Table 3). Accordingly, and based on the present invention, the skilled person can readily identify markers that are indicative of e.g., the presence and / or magnitude of an age gap between chronological age and biological age in a given organ or tissue (e.g., the esophagus, the small intestine, and / or the colon) and can readily employ primers and / or probes (as for example comprised in the herein provided kits) for the detection and / or quantifications of such markers. Non-limiting examples of markers that may be targeted (e.g., with the primers and / or probes that may be comprised in the herein provided kits) in order to identify for example the presence of an age gap between chronological age and biological age of a specific organ directly affected by Crohn's disease (i.e., the esophagus, the small intestine, and / or the colon) are detailed herein above. For example, illustratively and non-limiting, in order to detect Crohn's disease in the small intestine, e.g., markers JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2 may be selected. Similarly, the skilled person can readily employ herein identified markers suitable for the determination of biological age and / or the determination of an age gap between chronological age and biological age of the esophagus in the detection of an age-related disease of the esophagus (such as Crohn's disease). Similarly, the skilled person can readily employ herein identified markers suitable for the determination of biological age and / or the determination of an age gap between chronological age and biological age of the colon in the detection of an age-related disease of the colon (such as Crohn's disease).

[0380] The marker(s) disclosed herein are specifically linked as favorable marker(s) for predicting the biological age or age gap of specific tissues / organs. Accordingly, these tissue- / organ-specific marker(s) can also be employed in the determination of a disease status / disease / pathology of the corresponding tissue / organ, i.e., in the determination of age-related diseases and / or age-related pathologies. The present invention provides methods for determining the biological age and / or age gap based on histological tissue samples and / or on marker expression in cell derived from bodily fluid samples. The present invention, as for example illustrated in the appended non-limiting examples, also provides direct links, as well as means to derive at such links, to age-related pathologies and diseases. Accordingly, the present invention also envisages the prevention and / or treatment of age-related pathologies and / or diseases when an increased or positive age gap is determined. As used herein, the biological age and / or age gap is determined increased or positive when the predicted biological age is greater than the chronical age.

[0381] Accordingly, the present invention furthermore envisages that an individual is to be treated for increased age gap, wherein said age gap is determined increased when the biological age is greater than the chronological age. Preferably, the increased age gap may be determined in accordance with the present invention.

[0382] As illustrated in the appended non-limiting examples, the present invention is not only able to detect / determine the biological age and / or age gap but also to differentiate disease -affected individuals from healthy individuals, see, e.g., Example 8 and Figures 17 e-f and 20. Accordingly, it is envisaged that the detection / determination of biological age and / or age gap, in accordance with the present invention, furthermore allows for the detection of age-related pathologies and / or diseases, in particular the detection, in particular earlier detection, of age-related pathologies and / or diseases. Example 8, illustratively and non-limiting, documents that the present invention is not only useful in determining the biological age and / or the presence and / or magnitude of an age gap between biological age and chronological age but also, inter alia, in the detection of diseases such as age-related diseases and / or age-related pathologies (see the below examples on rheumatoid arthritis, Crohn's disease, systemic lupus erythematous, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, Alzheimer's disease, and / or stroke).

[0383] The present invention also provides for means and methods for the detection of age-related diseases and / or age- related pathologies and / or to means and methods to differentiate disease-affected individuals from healthy individuals, detecting one or more diseases in an individual, determining a health status of an individual, predicting the likelihood of occurrence of at least one disease, detecting and / or monitoring, aging processes and / or tissue -specific aging, monitoring treatment responses, and / or monitoring the efficacy and / or adverse side effect(s) of a treatment, as described herein. These means and methods are also based on the determination of one or more marker(s) as identified herein in cells that are derived from a bodily fluid sample and / or from a hair follicle sample from an individual.

[0384] As described herein above and as illustrated in the appended Examples, the inventors utilized the observed changes in tissue morphology to train a blood-based predictor of tissue-specific age gaps (i.e. aging rates), which was enriched in tissue-specific pathologies and which the inventors were able to validate in independent cohorts of patients with eight different diseases (systemic lupus erythematous, Crohn's disease, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, Alzheimer's disease, stroke), see e.g., illustrative Example 9. This analysis uncovered specific signals for affected organs in both acute events (stroke) but even in remarkably heterogeneous chronic diseases such as significantly higher age gaps for brain in Alzheimer's disease patients. To assess the clinical utility of blood-based predictors, the predictors classification performance was evaluated, achieving moderate to strong predictive power with positive predictive values (PPV) exceeding 0.3 in some cases — a threshold relevant for population-level screening. Surprisingly, significant associations of tissue-specific age gaps were found in organs beyond primary disease sites, such as the pervasive accelerated aging of kidney and liver across multiple chronic diseases in comparison with healthy individuals. The ability to detect both tissue / organ-specific and systemic aging patterns from a minimally invasive test underscores the potential of the present invention not only as a tool for early disease detection in a future prognostic marker, but also for disease and treatment monitoring, by leveraging the multi-organ landscape of accelerated aging.

[0385] In context of the present invention, the age-related pathologies are not particularly limited, but examples thereof are fibrosis, hyperplasia, calcification, atherosis, congestion, atrophy, ischemic changes, glomerulosclerosis, tissue scarring, nodularity, hyalinization, prostatitis, steatosis, gynecomastia, nephrosclerosis, dysplasia, pancreatitis, amylacea, hyperosinophilia, hepatitis, gastritis, desquamation, diabetic, edema, metaplasia, cyst, altered pigmentation, atelectasis, reduced spermatogenesis, elastosis, amyloidosis, calcinosis, hyperkeratosis, granuloma, mucinous degeneration, cholestasis, emphysema, bronchiolitis, sclerosis, hyperchromasia, ulceration, telangiectasia.

[0386] In context of the present invention, the age-related diseases are not particularly limited, but examples thereof are inflammatory immunological disorders, such as Crohn's disease, rheumatoid arthritis ulcerative colitis, vasculitis, or systemic lupus (erythematous) (in this context, see also the appended examples, in particular Examples 8 and 9, and corresponding Tables and Figures), cystic fibrosis, Alzheimer's disease, Diabetes mellitus type 2, Diabetes mellitus type 1, renal failure, Barret's Oesophagus, Esophageal carcinoma, cancer, such as prostate, gastric, colorectal, endometrial, or cervical cancer, kidney disease, heart attack, infarction, acute coronary state, Hypertension, Ischemic heart disease, liver disease, chronic respiratory disease, ascites, cerebrovascular disease, heart disease, cellulitis, multiple sclerosis, or dementia, dementia with unknown cause, chronic lower respiratory disease, arthritis, post-menopausal syndrome, osteoarthritis, osteoporosis, Parkinson's disease, amyotrophic lateral sclerosis, atrial fibrillation, chronic kidney disease, venous thromboembolism, peripheral artery disease, hyperlipidemia, congestive heart failure, sarcopenia, frailty syndrome, urinary incontinence, benign prostatic hyperplasia, chronic venous insufficiency, or fibromyalgia.

[0387] In context of the present invention, an inflammatory immunological disease relates to a pathological condition in which the immune system triggers an abnormal and excessive inflammatory response. This inappropriate and dysregulated inflammation may lead to tissue damage, organ dysfunction, or chronic disease. In context of the present invention, inflammatory immunological disease is not particularly limited, but preferred examples thereof are Crohn's disease, rheumatoid arthritis, ulcerative colitis, vasculitis, or systemic lupus (erythematous).

[0388] In context of the present invention, rheumatoid arthritis is a chronic, progressive, and systemic autoimmune disease where the immune system attacks the synovial lining of joints, leading to chronic inflammation, pain, and eventual joint damage, such as joint deformities, loss of function, and significant disability. Rheumatoid arthritis is categorized in the ICD-10 (International Classification of Diseases, 10thRevision) as M05 to M06, subcategories include M05.0, M05.9, M06.0, or M06.9.

[0389] In context of the present invention, Crohn's disease is a chronic inflammatory bowel disease characterized by inflammation of the gastrointestinal (GI) tract. It can affect any part of the GI tract from the mouth to the anus, but it most commonly affects the end of the small intestine (ileum) and the beginning of the colon. The inflammation in Crohn's disease is often transmural, meaning it can affect the entire thickness of the bowel wall, leading to complications such as strictures, fistulas, and abscesses. Crohn's disease is categorized in the ICD-10 (International Classification of Diseases, 10thRevision) as K50, subcategories include K50.0, K50.1, K50.8, or K50.9.

[0390] In context of the present invention, ulcerative colitis is a chronic inflammatory bowel disease characterized by continuous inflammation and ulceration limited to the mucosal lining of the colon and rectum. It presents with symptoms such as abdominal pain, bloody diarrhea, weight loss, and periods of flare-ups alternating with remission phases. The disease is immunologically mediated, with an unclear etiology involving genetic, environmental, and immune dysregulation factors. Ulcerative colitis is classified in the ICD-10 (International Classification of Diseases, 10th Revision) under code K51, including subcategories such as K51.0 to K51.9.

[0391] In context of the present invention, systemic lupus erythematosus (SLE) is a chronic, multisystem autoimmune disease characterized by the production of autoantibodies directed against nuclear and cytoplasmic antigens, leading to inflammation and damage of multiple organs including skin, joints, kidneys, heart, lungs, and the nervous system. SLE manifests with a diverse clinical presentation including skin rash (e.g., butterfly rash), arthritis, nephritis, hematologic abnormalities, and neurologic symptoms. The etiology is multifactorial involving genetic, environmental, and hormonal factors. SLE is classified in the ICD-10 (International Classification of Diseases, 10th Revision) under code M32, including subcategories such as M32.0 (drug-induced SLE), M32.1 (SLE with organ involvement), and M32.9 (unspecified SLE).

[0392] In context of the present invention, vasculitis is an inflammatory disease characterized by immune-mediated inflammation of blood vessels, including arteries, arterioles, capillaries, venules, and veins. This inflammation can cause damage to vessel walls leading to vessel narrowing, occlusion, or aneurysm formation and may result in impaired blood supply to tissues and organs. Vasculitis can be classified as primary (occurring as an independent disorder) or secondary (associated with other diseases such as autoimmune disorders or infections). The Chapel Hill Consensus Conference classification system is commonly used to categorize vasculitis by the size of affected vessels (large, medium, or small vessels). Vasculitides include a broad spectrum of diseases with variable clinical manifestations depending on the organs involved. Vasculitis is classified in the ICD-10 under various codes depending on the subtype (e.g., M30-M31, M35, L95).

[0393] In context of the present invention, Alzheimer's disease is a progressive neurodegenerative disorder characterized by the accumulation of beta-amyloid plaques and neurofibrillary tangles composed of hyperphosphorylated tau protein in the brain, leading to synaptic dysfunction, neuronal loss, and cognitive decline, including memory impairment and behavioural changes. Alzheimer's disease primarily affects elderly individuals and is the most common cause of dementia worldwide. It is classified in the ICD-10 (International Classification of Diseases, 10th Revision) under code G30, with subcategories such as G30.0 and G30.1 depending on the disease's stage and onset.

[0394] In context of the present invention, cystic fibrosis is a hereditary autosomal recessive disorder caused by mutations in the CFTR gene, leading to defective chloride ion transport across epithelial cells. This defect results in thick, viscous secretions affecting multiple organs, predominantly the lungs and pancreas, causing chronic respiratory infections, inflammation, pancreatic insufficiency, and progressive organ damage. Cystic fibrosis is classified in the ICD-10 under code E84, including subcategories for different manifestations.

[0395] In context of the present invention, diabetes mellitus type 1 is a chronic autoimmune disease characterized by absolute insulin deficiency due to immune-mediated destruction of pancreatic beta cells, resulting in hyperglycaemia and requiring lifelong insulin replacement therapy. Type 1 diabetes most commonly manifests in childhood or adolescence. It is classified under ICD-10 codes E10, with further subcategories defining complications and manifestations.

[0396] In context of the present invention, diabetes mellitus type 2 is a chronic metabolic disorder characterized by insulin resistance and relative insulin deficiency, leading to chronic hyperglycaemia. It is associated with obesity, lifestyle factors, and genetic predisposition and can result in microvascular and macrovascular complications. Type 2 diabetes is classified in the ICD-10 under codes Ell and its subcategories. In accordance with the present invention, it is in particular envisaged that the present invention allows for the detection of inflammatory immunological disorders, in particular Crohn's disease, rheumatoid arthritis, systemic lupus erythematous, diabetes, cystic fibrosis, ulcerative colitis, vasculitis, and / or Alzheimer's disease.

[0397] Furthermore, the present invention in a further aspect provides a method of intervention, in particular medical intervention, for an individual in need of such intervention, wherein said method comprises the steps of a) the determination of biological age and / or presence and / or magnitude of a gap between chronological age and the biological age according to the present invention, as described herein, b) administering to the individual a pharmaceutical composition, pharmaceutical drug and / or supplement that positively influences said biological age or age gap. The method may involve a step of obtaining a bodily fluid sample or hair follicle sample, from an individual.

[0398] In context of the present invention, the step of administering to the individual a pharmaceutical composition, pharmaceutical drug and / or supplement, may further comprise the administration of n...

Claims

1. Claims1. A computer-implemented method of determining an age gap between chronological age and biological age comprising a step of determining the presence and / or magnitude of said age gap based on expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age.

2. A method of determining an age gap between chronological age and biological age comprising the steps of(i) determining expression level(s) of one or more marker(s) in cells derived from a bodily fluid sample from an individual, and(ii) determining the presence and / or magnitude of said age gap, wherein the expression level(s) of the one or more marker(s) of step (i) is / are indicative of the presence and / or magnitude of the age gap between chronological age and biological age.

3. The method according to claim 1 or 2, wherein the age gap is determinable or determined independently of the chronological age.

4. The method according to any one of claims 1 to 3, wherein the age gap corresponds to a rate of aging; and, optionally, wherein the rate of aging is indicated as a percentile rank calculated based on the distribution of aging rates in the individual's age cohort and / or reflecting the individual's position within said age cohort.

5. The method according to any one of claims 1 to 4, wherein (i) a positive age gap, wherein the biological age is higher than the chronological age, corresponds to an accelerated aging, and / or (ii) a negative age gap, wherein the biological age is lower than the chronological age, corresponds to a decelerated aging; and, preferably, wherein the accelerated or decelerated aging is indicated as a percentile rank calculated based on the distribution of aging rates in the individual's age cohort and / or reflecting the individual's position within said age cohort.

6. The method according to any one of claims 1 to 5, wherein the age gap is a tissue-specific age gap.

7. The method according to any one of claims 1 to 6, wherein the age gap is a systemic age gap.

8. The method according to any one of claims 1 to 7, wherein determining the presence and / or magnitude of said age gap comprises applying (an) association coefficient(s) on the expression level(s) of said marker(s).

9. The method according to claim 8, wherein the association coefficient(s) reflect(s) associations between a histology-derived age gap and the expression level(s) of said marker(s).

10. The method according to claim 8 or 9, wherein the association coefficient(s) were determined or are obtainable by a method comprising the steps of:(a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section of at least one tissue of said individual, preferably (an) image(s) of the histological section(s),ii) chronological age of said individual, and iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual,(b) extracting morphological features from the histological sections of step (a) i), preferably from the images thereof,(c) correlating the extracted morphological features of step (b) with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined,(d) applying the associations determined in step (c) to (a) histological section(s), preferably (an) image(s) thereof, to determine the presence and / or magnitude of an age gap between the chronological age and the biological age, and(e) correlating the histology-derived age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived age gap of step (d) are determined.

11. The method according to any one of claims 1 to 10, wherein determining the presence and / or magnitude of said age gap comprises applying a model on the expression level(s) of said marker(s), wherein the model was trained to map expression level(s) in bodily fluid samples to histology-derived age gaps.

12. The method according to claim 11, wherein said model was trained using paired data of a plurality of individuals comprising (i) gene expression levels in bodily fluid samples and (ii) histology-derived age gaps determined based on images of histological sections.

13. The method according to any one of claims 9 to 12, wherein the histology-derived age gaps were determined or are obtainable by a method comprising the steps of(a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) a histological section of at least one tissue of said individual, preferably (an) image(s) of the histological section(s), and ii) chronological age of said individual,(b) extracting morphological features from the histological sections of step (a) i), preferably the image(s) thereof,(c) correlating the morphological features of step (b) with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and(d) applying the associations determined in step (c) to images of histological sections to determine the presence and / or magnitude of an age gap between the chronological age and the biological age.

14. The method according to any one of claims 1 to 13, wherein the one or more marker(s) is / are selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3- 15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1,MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D.

15. A method of determining presence and / or magnitude of an age gap between chronological age and biological age comprising a step of determining expression level(s) of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, wherein the expression level(s) of the one or more marker(s) is / are indicative of the presence and / or magnitude of an age gap between chronological age and biological age.

16. A method of determining biological age comprising a step of determining expression level(s) of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA- W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB- 50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual, wherein the biological age is determined based on (i) the expression level(s) of the one or more marker(s) and, optionally, (ii) chronological age.

17. A method of determining biological age or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of(i) determining expression level of one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D in cells derived from a bodily fluid sample from an individual and(ii) determining said biological age or presence and / or magnitude of said age gap between chronological age and biological age, wherein the expression level of the one or more marker(s) of step (i) is indicative of biological age or presence and / or magnitude of an age gap between chronological age and biological age.

18. The method according to claim 17, wherein the biological age and / or the age gap is tissue-specific.

19. The method according to claim 17 or 18, wherein the presence and / or magnitude of an age gap between chronological age and biological age is determined, and wherein the expression level of the one or more marker(s) of step (i) is indicative of the presence and / or magnitude of an age gap between chronological age and biological age.

20. The method according to any one of claims 1 to 19, wherein the age gap between the chronological age and the biological age is determined by inputting the expression level(s) of the one or more marker(s) into the equation: yAnew = xnew' p, wherein y new = the age gap, xnew = vector of the determined marker expression level(s) of the length m, wherein m = number of markers, xnew' = the transpose of xnew, p = vector of an association coefficient(s) for each marker; optionally, wherein the expression level(s) of the one or more marker(s) are those determined in step (i).

21. A computer-implemented method for determining biological age or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of(a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological sections of at least one tissue of said individual, and ii) chronological age of said individual,(b) extracting morphological features from said histological sections of step (a) i),(c) analyzing the extracted morphological features of step (b) and correlating said morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and(d) applying the associations determined in step (c) to histological sections to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age.

22. The method according to any one of claims 10 to 21, wherein the biological age or presence and / or magnitude of an age gap between the chronological age and the biological age is determined for the tissue(s) of which the histological section(s) is / are derived from.

23. The method according to any one of claims 10 to 22, wherein the histological sections are whole slide images, preferably of different tissues or tissue types.

24. The method according to any one of claims 10 to 22, wherein the morphological features are indicative of aging-related characteristics.

25. The method according to any one of claims 10 to 24, wherein the morphological features are represented numerically.

26. The method according to any one of claims 10 to 25, wherein the associations of step (c) are applied to histological sections that are not part of the training dataset.Zl. The method according to any one of claims 21 to 26, wherein the training dataset of step (a) further comprises for each individual: iii) dataset comprising marker expression level of one or more marker(s) in cells derived from bodily fluid sample of said individual.

28. The method according to claim Zl, wherein the method further comprises a step of(e) analyzing the marker expression level of step (a) iii) and correlating the biological age and / or age gap derived of step (d) with said marker expression level, wherein associations between the marker expression level and the biological age and / or age gap are determined.

29. The method according to claim 28, wherein the method further comprises a step of(f) determining one or more marker(s) underlying the associations determined in step (e) based on the specific marker expression level of said one or more marker(s).

30. The method according to claim 29, wherein the method further comprises a step of(g) providing a test dataset of one or more individual(s) which is not comprised in the training dataset of step(a), wherein said test dataset comprises for each individual iv) expression level of said one or more marker(s) of cells derived from bodily fluid sample of said individual and optionally v) the chronological age of said individual.

31. The method according to claim 30, wherein the method further comprises a step(h) of applying the associations determined in step (e) of the one or more marker(s) determined in step (f) on the marker expression level of step (g) iv) to determine biological age or presence and / or magnitude of an age gap between the chronological age and the biological age.

32. The method according to claim 31, wherein the method further comprises a step(i) of determining the biological age or presence and / or magnitude of an age gap between the chronological age and the biological age; and optionally further comprises a step(j) of contextualizing that biological age or age gap value in light of the distribution of a population of the same chronological age, wherein said contextualizing may be indicated as a percentile rank reflecting the individual's position within their specific age cohort.

33. A computer-implemented method of determining the biological age or the presence and / or magnitude of an age gap between chronological age and biological age, the method comprising the steps of: a. extracting features from whole slide images and numerically representing said features, preferably wherein said features are tissue morphological features that are indicative of aging-related characteristics;b. determining correlations of the extracted features with chronological age associated with the whole slide image of which said features were extracted from, wherein the biological age of the tissue and / or the presence and / or magnitude of an age gap between the chronological age and the biological age can be predicted based on the features of the histological sections; c. analyzing the marker expression level of one or more marker(s) in cells derived from a bodily fluid sample, wherein said bodily fluid sample is derived from the same individual as the whole slide images used in step a and b, to determine associations between said marker expression level and said biological age and / or age gap; d. determining one or more marker(s) underlying the associations determined in step c based on the specific marker expression of said one or more marker(s); e. applying the associations determined in step c with the one or more marker(s) determined in step d on the marker expression level in cells derived from a bodily fluid sample, of one or more individual(s) from which tissue was not collected, to determine biological age and / or the presence and / or magnitude of an age gap between chronological age and biological age as an output; and, optionally, f. contextualizing that age gap value in light of the distribution of a population of the same chronological age, wherein said contextualizing is indicated as a percentile rank reflecting the biological age position within the specific chronological age cohort.

34. The method according to any one of claims 1 to 33, wherein said bodily fluid sample is selected from the group consisting of peripheral blood mononuclear cell (PBMC) sample, whole blood sample, dried blood spot sample, amniotic fluid sample, bone marrow aspirate sample, lymph fluid sample, buffy coat sample, saliva sample, sputum sample, mucus sample, cerebrospinal fluid (CSF) sample, pleural / peritoneal fluid sample, synovial fluid sample, urine sample, lacrimal fluid sample, and sweat sample, preferably from the group consisting of peripheral blood mononuclear cell (PBMC) sample, whole blood sample, dried blood spot sample, amniotic fluid sample, bone marrow aspirate sample, lymph fluid sample, buffy coat sample, saliva sample, sputum sample, mucus sample, cerebrospinal fluid (CSF) sample, pleural / peritoneal fluid sample, and synovial fluid sample, more preferably from the group consisting of peripheral blood mononuclear cell (PBMC) sample, whole blood sample, and dried blood spot sample, most preferably from peripheral blood mononuclear cell (PBMC) sample.

35. The method according to any one of claims 1 to 34, wherein the bodily fluid sample is a blood sample, preferably, comprising PBMCs.

36. The method according to any one of claims 1 to 20 and 27 to 35 wherein the expression level of one or more marker(s) is the RNA expression level, preferably the mRNA expression level, of said one or more marker(s).

37. The method according to any one of claims 1 to 36, wherein the biological age is in decades, years, months, and / or weeks, wherein the chronological age is in decades, years, months, and / or weeks, and wherein the age gap between the chronological age and the biological age is in decades, years, months, and / or weeks.

38. The method according to any one of claims 1 to 37, wherein the method is a computer-implemented method and / or comprises the use of one or more machine learning model(s).

39. The method according to any one of claims 1 to 38, comprising determining the systemic biological age and / or systemic age gap between the chronological age and the biological, wherein the systemic biological age and / or systemic age gap may be indicative for the mean of all tissues of the individual.

40. The method according to claim 39, wherein the systemic biological age and / or age gap corresponds to the mean biological age and / or age gap of all or a plurality of tissues of the individual selected from the group consisting of: subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, ovary tissue, pancreas tissue, pituitary tissue, prostate tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin such as lower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, testis tissue, thyroid tissue, uterus tissue, and vagina tissue.

41. A method according to claim 39 or 40, comprising determining the presence and / or magnitude of a systemic age gap, wherein the one or more marker(s) are selected from the group consisting of JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MYOM2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, MYO7B, UPK3BL, PRUNE2, NIPAL2, MYOM2, GFAP, CETP, MTND4P12, CDHR1, NINJ2, ZFAS1, IFI27, AFAP1, CDYL2, VDR, ATP8B3, GBAP1, SLC16A6, MIATNB, TMTC1, LOXL3, DRAXIN, and FCGR2B is / are indicative of the presence and / or magnitude of the systemic age gap of said individual.

42. The method according to any one of claims 1 to 41, comprising determining biological age or presence and / or magnitude of an age gap between the chronological age and the biological age for one or more tissue(s), preferably, selected from the group consisting of subcutaneous tissue, in particular subcutaneous adipose tissue, visceral tissue, in particular visceral adipose tissue, adrenal gland tissue, aorta tissue, coronary arteria tissue, tibial artery tissue, brain tissue, in particular cerebellum tissue and / or cortex tissue, breast tissue, in particular mammary tissue, colon tissue, in particular sigmoid colon tissue and / or transverse colon tissue, gastroesophageal junction tissue, mucosa tissue, in particular esophageal mucosa tissue, esophageal muscularis tissue, heart tissue, in particular atrial appendage tissue and / or left ventricle tissue, kidney tissue, in particular renal cortex tissue, liver tissue, lung tissue, salivary gland tissue, in particular minor salivary gland tissue, muscle tissue, in particular skeletal muscle tissue, nerve tissue, in particular tibial nerve tissue, ovary tissue, pancreas tissue, pituitary tissue, prostate tissue, skin tissue, in particular skin tissue which is not sun or UV exposed (e.g., suprapubic skin), and / or skin tissue which is sun or UV exposed (e.g., leg skin such aslower leg skin), small intestine tissue, in particular terminal ileum tissue, spleen tissue, stomach tissue, testis tissue, thyroid tissue, uterus tissue, and vagina tissue.

43. The method according to any one of claims 1 to 20 and U to 42, comprising determining the presence and / or magnitude of an age gap of small intestine tissue, wherein the one or more marker(s) are selected from the group consisting of JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, CAV1, GPNMB, F8A1, TCL6, CSF1, ZMAT2, PTRF, WASH7P, KRT5, ANKRD6, NBPF26, COL1A1, NPIPB15, OCEL1, APOE, TBC1D3L, MYO7B, and COL1A2 is / are indicative of the presence and / or magnitude of the age gap of small intestine tissue of said individual; preferably, wherein the small intestine tissue is terminal ileum tissue.

44. The method according to any one of claims 1 to 20 and 27 to 43, comprising determining the presence and / or magnitude of an age gap of spleen tissue, wherein the one or more marker(s) are selected from the group consisting of TBC1D3L, GUI, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C, and wherein the expression level(s) of the marker(s) selected from the group consisting of TBC1D3L, GUI, IGHG3, ADAT3, TBC1D30, B4GALNT3, PRX, CD99P1, KRT13, F12, UPK3BL, C5orf66, FAM134B, PPP1R35, NEB, ARHGEF19, SHISA4, LAPTM4B, TREML4, SGSH, CLCN4, BBC3, C16orf45, and CDKN2C is / are indicative of the presence and / or magnitude of the age gap of spleen tissue of said individual.

45. The method according to any one of claims 1 to 20 and 27 to 44, comprising determining the presence and / or magnitude of an age gap of esophageal muscularis tissue, wherein the one or more marker(s) are selected from the group consisting of C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1, and wherein the expression level(s) of the marker(s) selected from the group consisting of C4BPA, TBC1D7, S100B, NBPF3, HTRA1, CETP, FAM13A, CHRNE, B4GALNT3, GCAT, FAM26F, AC074289.1, FAM43A, GTPBP2, WASH6P, SLC3A2, AC068580.6, HMOX1, DIAPH2, NUCB1, MAFK, LDHAP4, TPPP3, and RAB3IL1 is / are indicative of the presence and / or magnitude of the age gap of esophageal muscularis tissue of said individual.

46. The method according to any one of claims 1 to 20 and 27 to 45, comprising determining the presence and / or magnitude of an age gap of sigmoid colon tissue, wherein the one or more marker(s) are selected from the group consisting of FEM1A, AC144831.1, IFTTM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and UNC02019, and wherein the expression level(s) of the marker(s) selected from the group consisting of FEM1A, AC144831.1, IFTTM10, MYO7B, KRT18, KCNK7, EPHX1, CBX8, CMKLR1, SERPINF1, C3, IGF2, C1R, and UNC02019 is / are indicative of the presence and / or magnitude of the age gap of sigmoid colon tissue of said individual.

47. The method according to any one of claims 1 to 20 and 27 to 46, comprising determining the presence and / or magnitude of an age gap of prostate tissue, wherein the one or more marker(s) are selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2, TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3, and wherein the expression level(s) of the marker(s) selected from the group consisting of APP, IGHA1, GATA2, TACSTD2, NT5DC4, APOE, AC116366.5, ZP3, IGLC6, IGHA2,TBC1D3L, IGKJ2, TMEM56, FHL2, and FSTL3 is / are indicative of the presence and / or magnitude of the age gap of prostate tissue of said individual.

48. The method according to any one of claims 1 to 20 and U to 47, comprising determining the presence and / or magnitude of an age gap of visceral tissue, wherein the one or more marker(s) are selected from the group consisting of PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20, and wherein the expression level(s) of the marker(s) selected from the group consisting of PLPP3, GJB6, RSPH3, DCN, HEIH, AOC3, COL3A1, FUZ, CIS, SOWAHD, CALD1, SLC2A14, LRRC32, G0S2, PC, and RAB20 is / are indicative of the presence and / or magnitude of the age gap of visceral tissue of said individual; preferably wherein the visceral tissue is visceral adipose (Omentum) tissue.

49. The method according to any one of claims 1 to 20 and 27 to 48, comprising determining the presence and / or magnitude of an age gap of gastroesophageal junction tissue, wherein the one or more marker(s) are selected from the group consisting of ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, ULRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100, and wherein the expression level(s) of the marker(s) selected from the group consisting Of ESAM, SPARC, AP003068.23, EPAS1, CTTN, ATP2C2, TMEM119, PTGS1, CSF1, TMEM204, ZNF429, IGKJ5, GNAZ, ITGB3, PIGC, DTX2P1, RAB15, ACSS2, LILRB1, MAST4, GTPBP2, HSBP1L1, FAM20A, and PET100 is / are indicative of the presence and / or magnitude of the age gap of gastroesophageal junction tissue of said individual.

50. The method according to any one of claims 1 to 20 and 27 to 49, comprising determining the presence and / or magnitude of an age gap of pancreas tissue, wherein the one or more marker(s) are selected from the group consisting of NBPF3, IBA57, AKR1C1, MTCO1P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29, and wherein the expression level(s) of the marker(s) selected from the group consisting of NBPF3, IBA57, AKR1C1, MTCO1P12, PRUNE2, TNFRSF9, EMBP1, TREML4, AOC3, COL9A3, ASL, and SLC25A29 is / are indicative of the presence and / or magnitude of the age gap of pancreas tissue of said individual.

51. The method according to any one of claims 1 to 20 and 27 to 50, comprising determining the presence and / or magnitude of an age gap of liver tissue, wherein the one or more marker(s) are selected from the group consisting of SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, ITGAD, S100A13, SPINT1, and GKAP1, and wherein the expression level(s) of the marker(s) selected from the group consisting of SHISA4, MS4A14, MMP17, KRT72, RNF122, CTDP1, POLR1D, PRDM8, ARPIN, CCDC71L, SORBS1, IGHM, IGKJ1, IGHA2, ESPN, FBXL16, FLT1, Clorfll5, AKR1C1, CALD1, ITGAD, S100A13, SPINT1, and GKAP1 is / are indicative of the presence and / or magnitude of the age gap of liver tissue of said individual.

52. The method according to any one of claims 1 to 20 and 27 to 51, comprising determining the presence and / or magnitude of an age gap of thyroid tissue, wherein the one or more marker(s) are selected from the group consisting of TBC1D30, WASHCI, VPREB3, GUI, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7, and wherein the expression level(s) of the marker(s) selected from the group consisting of TBC1D30, WASHCI, VPREB3, GUI, VWCE, PPDPF, RGMA, SNX22, SLC9A3R2, EMBP1, COL9A3, RTN2, GADD45G, SREBF1, TG, GUCD1, FNDC10, RAB33A, FSD1, CAPN5, SVBP, GLMP, and TBC1D7 is / are indicative of the presence and / or magnitude of the age gap of thyroid tissue of said individual.

53. The method according to any one of claims 1 to 20 and U to 52, comprising determining the presence and / or magnitude of an age gap of muscle tissue, wherein the one or more marker(s) are selected from the group consisting of TG, TBXA2R, THAP8, ERRFI1, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3, and wherein the expression level(s) of the marker(s) selected from the group consisting of TG, TBXA2R, THAP8, ERRFI1, TBC1D3L, IL1RL1, LOXL3, MT1E, SPARCL1, CALD1, WASF2, ADAMTS1, GP1BA, ACP5, ACCS, and PLPP3 is / are indicative of the presence and / or magnitude of the age gap of muscle tissue of said individual; preferably, wherein the muscle tissue is skeletal muscle tissue.

54. The method according to any one of claims 1 to 20 and 27 to 53, comprising determining the presence and / or magnitude of an age gap of mucosa tissue, wherein the one or more marker(s) are selected from the group consisting of ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDIT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH1, and VEGFA, and wherein the expression level(s) of the marker(s) selected from the group consisting of ACP6, HCFC1R1, TBC1D7, FUZ, BIK, DDIT3, GLB1L, GID8, AC068580.6, SURF1, IFTTM10, GUCD1, SDCBP2, C4BPA, RNASE6, MGP, FAM20A, ERICH 1, and VEGFA is / are indicative of the presence and / or magnitude of the age gap of mucosa tissue of said individual; preferably, wherein the mucosa tissue is esophageal mucosa tissue.

55. The method according to any one of claims 1 to 20 and 27 to 54, comprising determining the presence and / or magnitude of an age gap of stomach tissue, wherein the one or more marker(s) are selected from the group consisting of TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTT1, TEN1, TMTC2, SLED1, MX2, UNC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1, and wherein the expression level(s) of the marker(s) selected from the group consisting of TREML2, TCL6, FAM69B, IGHG1, MIR600HG, MOB3B, TCL1A, UBE2F, AC011899.9, SLC18B1, IFTT1, TEN1, TMTC2, SLED1, MX2, LINC00282, CCL3, HSPA5, TOX, DRAXIN, IGHG3, HSPA1B, PLD1, and GFI1 is / are indicative of the presence and / or magnitude of the age gap of stomach tissue of said individual.

56. The method according to any one of claims 1 to 20 and 27 to 55, comprising determining the presence and / or magnitude of an age gap of left ventricle tissue, wherein the one or more marker(s) are selected from the group consisting of MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, ITGB4, PNKD, EIF1B, RFXANK, UNC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4, and wherein the expression level(s) of the marker(s) selected from the group consisting of MAP3K6, SLC18B1, COL6A3, ZBTB16, NAMPTP1, NT5DC4, PTGES, COL6A1, SYNGR1, SPEG, SH3TC1, SDHAP2, ADGRE1, ITGB4, PNKD, EIF1B, RFXANK, UNC00954, LTBP2, HMOX1, FCGR2C, GFOD1, MAP3K8, and FUT4 is / are indicative of the presence and / or magnitude of the age gap of left ventricle tissue of said individual.

57. The method according to any one of claims 1 to 20 and 27 to 56, comprising determining the presence and / or magnitude of an age gap of adrenal gland tissue, wherein the one or more marker(s) are selected from the group consisting of LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20, and wherein the expression level(s) of the marker(s) selected from the group consisting of LRRC32, AP003068.23, RGS6, CRACR2B, KCNMB1, VWF, GNAZ, MYL9, SPARC, RIMS3, CTTN, CD86, and MUC20 is / are indicative of the presence and / or magnitude of the age gap of adrenal gland tissue of said individual.

58. The method according to any one of claims 1 to 20 and U to 57, comprising determining the presence and / or magnitude of an age gap of testis tissue, wherein the one or more marker(s) are selected from the group consisting of IFI27, MXRA7, RSAD2, SIGLEC1, IFTT1, MX1, IFI6, IFTT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5, and wherein the expression level(s) of the marker(s) selected from the group consisting of IFI27, MXRA7, RSAD2, SIGLEC1, IFTT1, MX1, IFI6, IFIT3, GBP4, TUBB2A, FBXO6, IFTT5, RILPL1, TNFRSF9, CMKLR1, and HERC5 is / are indicative of the presence and / or magnitude of the age gap of testis tissue of said individual.

59. The method according to any one of claims 1 to 20 and 27 to 58, comprising determining the presence and / or magnitude of an age gap of skin tissue, wherein the one or more marker(s) are selected from the group consisting of DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1, and wherein the expression level(s) of the marker(s) selected from the group consisting of DRAXIN, PARM1, C3, NNMT, SLC9A3R2, MTND4P12, NOXA1, UBB, TREML4, BANK1, TCL6, ClOorflO, EPHB4, DNAJC4, VPREB3, AQP1, AC144831.1, PTRF, IGHD, FAM129C, TCL1A, HCP5, and IL1RL1 is / are indicative of the presence and / or magnitude of the age gap of skin tissue of said individual; preferably, wherein the skin tissue is not sun or UV exposed such as suprapubic skin.

60. The method according to any one of claims 1 to 20 and 27 to 59, comprising determining the presence and / or magnitude of an age gap of uterus tissue, wherein the one or more marker(s) are selected from the group consisting of ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CTSC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3, and wherein the expression level(s) of the marker(s) selected from the group consisting of ALPK3, SETD7, GCNA, CCDC9, BBC3, LRRC75B, CTSC, ARPIN, SLC2A9, GTF2IP13, GGT1, TG, CLASRP, CTSK, TMEM81, F12, TUBG2, CRAT, ZFAS1, CD99P1, SYNPO2, and ASIC3 is / are indicative of the presence and / or magnitude of the age gap of uterus tissue of said individual.

61. The method according to any one of claims 1 to 20 and 27 to 60, comprising determining the presence and / or magnitude of an age gap of transverse colon tissue, wherein the one or more marker(s) are selected from the group consisting of MYO7B, HES6, UNC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAP1, and wherein the expression level(s) of the marker(s) selected from the group consisting of MYO7B, HES6, UNC01451, AOC3, ACHE, FGFR1, ALB, ZFAND2A, KEL, HSBP1L1, HSPB1, AP3B2, XCL1, UBE2M, MOB3B, IGHD, TCN2, and TSPOAPl is / are indicative of the presence and / or magnitude of the age gap of transverse colon tissue of said individual.

62. The method according to any one of claims 1 to 20 and 1 to 61, comprising determining the presence and / or magnitude of an age gap of subcutaneous tissue, wherein the one or more marker(s) are selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, TOMM7, and PLIN2, and wherein the expression level(s) of the marker(s) selected from the group consisting of ALPK3, MYO7B, GFAP, VSIG10, MYH7, RTN2, JUP, MYL2, GUI, KLF11, SPATA20, FRMD3, SCN1B, PYGM, PITPNC1, MYH11, WDR97, BCL2A1, CPT1A, SNHG5, CNTLN, ZFAS1, TOMM7, and PLIN2 is / are indicative of the presence and / or magnitude of the age gap of subcutaneous tissue of said individual; preferably, wherein the subcutaneous tissue is subcutaneous adipose tissue.

63. The method according to any one of claims 1 to 20 and U to 62, comprising determining the presence and / or magnitude of an age gap of breast tissue, wherein the one or more marker(s) are selected from the group consisting of GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, HOXB2, and RNF122, and wherein the expression level(s) of the marker(s) selected from the group consisting of GFAP, KLF11, ABCG1, ZFAS1, HEIH, SNHG7, SPG20, SHB, IGKJ4, NT5DC3, PLD1, BIK, EPOR, ACHE, HOXB2, and RNF122 is / are indicative of the presence and / or magnitude of the age gap of breast tissue of said individual; preferably, wherein the breast tissue is mammary tissue.

64. The method according to any one of claims 1 to 20 and 27 to 63, comprising determining the presence and / or magnitude of an age gap of vagina tissue, wherein the one or more marker(s) are selected from the group consisting of TREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, NOTCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20, and wherein the expression level(s) of the marker(s) selected from the group consisting ofTREML4, FHL3, ACCS, TMEM176A, TMEM176B, SEC14L2, NMRK1, ETFBKMT, EIF4EBP2, SLC22A5, FAM134B, MRPL23, TMTC1, NOTCH2NL, RHD, GSTM1, DNLZ, GGT1, PLVAP, BCL2L11, and SPATA20 is / are indicative of the presence and / or magnitude of the age gap of vagina tissue of said individual.

65. The method according to any one of claims 1 to 20 and 27 to 64, comprising determining the presence and / or magnitude of an age gap of skin tissue, which is sun or UV exposed, e.g., leg skin, wherein the one or more marker(s) are selected from the group consisting of JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MYOM2, PAQR6, GAPDHP1, CACFD1, IL1RL1, LINC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1, and wherein the expression level(s) of the marker(s) selected from the group consisting of JUP, NBPF26, MTND4P12, TP53INP2, RHD, GFAP, SPHK1, ITGA1, MYOM2, PAQR6, GAPDHP1, CACFD1, IL1RL1, LINC00623, S100B, DBNDD2, POMZP3, TMEM63C, TBC1D7, OSGIN1, DRAXIN, C19orf71, SPATC1L, and EPAS1 is / are indicative of the presence and / or magnitude of the age gap of skin tissue, which is sun or UV exposed, e.g., leg skin, of said individual.

66. The method according to any one of claims 1 to 20 and 27 to 65, comprising determining the presence and / or magnitude of an age gap of atrial appendage tissue, wherein the one or more marker(s) are selected from the group consisting of ITGA1, MTND4P12, ULRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4, and wherein the expression level(s) of the marker(s) selected from the group consisting of ITGA1, MTND4P12, ULRA4, XIST, BANK1, PKIG, RHD, NBPF3, SLC18B1, SRD5A3, USP9Y, C19orf71, TGM3, BLK, TMEM63C, IER5L, EEF1A2, FGD6, RAB3A, PIM3, C17orf49, TMEM176B, XBP1, and IGHG4 is / are indicative of the presence and / or magnitude of the age gap of atrial appendage tissue of said individual.

67. The method according to any one of claims 1 to 20 and 27 to 66, comprising determining the presence and / or magnitude of an age gap of cerebellum tissue, wherein the one or more marker(s) are selected from the group consisting of WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, ITGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, UNC01963, and MARCO, and wherein the expression level(s) of the marker(s) selected from the group consisting of WDR97, SPSB1, APOE, BSCL2, DUSP18, AREG, PODXL2, ITGAD, COL5A3, LGMN, FOLR2, C1QC, ARSD, TNFRSF10A, XCL1, GPX7, EIF1AY, LINC01963, and MARCO is / are indicative of the presence and / or magnitude of the age gap of cerebellum tissue of said individual.

68. The method according to any one of claims 1 to 20 and U to 67, comprising determining the presence and / or magnitude of an age gap of salivary gland tissue, wherein the one or more marker(s) are selected from the group consisting of SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2, and wherein the expression level(s) of the marker(s) selected from the group consisting of SERPINF1, MEG3, KAZN, B4GALNT3, USP32P1, IGHA2, SIGLEC14, NOTCH4, SLC2A14, PCBP3, ACCS, ZP3, SLC48A1, IGKC, MYO7B, SNHG17, PIWIL4, GSTM1, and SPNS2 is / are indicative of the presence and / or magnitude of the age gap of salivary gland tissue of said individual; preferably, wherein the salivary gland tissue is minor salivary gland tissue.

69. The method according to any one of claims 1 to 20 and 27 to 68, comprising determining the presence and / or magnitude of an age gap of coronary arteria tissue, wherein the one or more marker(s) are selected from the group consisting of WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDIT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH, and wherein the expression level(s) of the marker(s) selected from the group consisting of WASF2, SGSH, TNFSF14, GPER1, KLF16, TCL6, RASL11A, PLEC, GTF2IP4, FUZ, FAM43A, TREML2, PPP2R3B, IGLC2, HILPDA, SLC3A2, DDIT3, DDTL, CLK3, AC068580.6, BHLHE40, AGAP9, H1F0, and HEIH is / are indicative of the presence and / or magnitude of the age gap of coronary arteria tissue of said individual.

70. The method according to any one of claims 1 to 20 and 27 to 69, comprising determining the presence and / or magnitude of an age gap of nerve tissue, wherein the one or more marker(s) are selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1, and wherein the expression level(s) of the marker(s) selected from the group consisting of MT1X, USP32P1, MT1F, MRPL41, UBALD1, MT2A, NNMT, TRIQK, MT1E, SGSH, CLN8, EIF4EBP2, PIM3, and GPSM1 is / are indicative of the presence and / or magnitude of the age gap of nerve tissue of said individual; preferably, wherein the nerve tissue is tibial nerve tissue.

71. The method according to any one of claims 1 to 20 and 27 to 70, comprising determining the presence and / or magnitude of an age gap of brain cortex tissue, wherein the one or more marker(s) are selected from the group consisting of MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN, and wherein the expression level(s) of the marker(s) selected from the group consisting of MTND4P12, IGHA2, AC005301.9, ESPN, YBEY, FTH1P8, FTLP3, SHISA4, CNN3, FTH1P20, APOD, Clorfll5, FTH1P2, GPR15, GPR55, ACP5, FBXL8, CES4A, SIL1, E2F1, B4GALNT3, CDCA7, HSPA7, and JCHAIN is / are indicative of the presence and / or magnitude of the age gap of brain cortex tissue of said individual.

72. The method according to any one of claims 1 to 20 and 27 to 71, comprising determining the presence and / or magnitude of an age gap of lung tissue, wherein the one or more marker(s) are selected from the group consisting of FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1, and wherein the expression level(s) of the marker(s) selected from the group consisting of FAM134A, HES1, PRRT3, PTRF, CD9, SERPING1, IFI27, BAHCC1, TPPP3, VSIG10, TCL6, PHF23, LGALS2, TNXB, ASS1, MRAS, ITGB4, ADCY9, and NDST1 is / are indicative of the presence and / or magnitude of the age gap of lung tissue of said individual.

73. The method according to any one of claims 1 to 20 and 27 to 72, comprising determining the presence and / or magnitude of an age gap of aorta tissue, wherein the one or more marker(s) are selected from the groupconsisting of MALATl, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100, and wherein the expression level(s) of the marker(s) selected from the group consisting of MALATl, IGHA2, ELOVL7, IGLC2, U2AF1L4, NEAT1, ZFAS1, MIATNB, IL1RL1, EFNA1, IGHG2, ZMYND15, and PET100 is / are indicative of the presence and / or magnitude of the age gap of aorta tissue of said individual.

74. The method according to any one of claims 1 to 20 and 27 to 73, comprising determining the presence and / or magnitude of an age gap of ovary tissue, wherein the one or more marker(s) are selected from the group consisting of SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTCO1P12, SMIM1, LGALS2, PLAU, PIA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB, and wherein the expression level(s) of the marker(s) selected from the group consisting of SSPO, RGCC, TCN2, GSTM1, MDGA1, TPTEP1, GLMP, VEGFA, TMEM119, SLC2A14, SIGLEC16, MTCO1P12, SMIM1, LGALS2, PLAU, PLA2G15, TRIB3, LRRC75B, HCAR3, HNRNPCP2, RLF, and DTNB is / are indicative of the presence and / or magnitude of the age gap of ovary tissue of said individual.

75. The method according to any one of claims 1 to 20 and 27 to 74, comprising determining the presence and / or magnitude of an age gap of tibial artery tissue, wherein the one or more marker(s) are selected from the group consisting of ACTBP8, VPREB3, HOTAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, LLNC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS, and wherein the expression level(s) of the marker(s) selected from the group consisting of ACTBP8, VPREB3, HOTAIRM1, FAM20A, RIMS3, IGHJ3, FSTL1, SPARCL1, LINC01001, NPIPB15, AC125232.1, TOMM40L, MRPL18, PMEPA1, RHD, A2M, BCL7A, COL3A1, IGHJ6, and CIS is / are indicative of the presence and / or magnitude of the age gap of tibial artery tissue of said individual.

76. The method according to any one of claims 1 to 20 and 27 to 75, comprising determining the presence and / or magnitude of an age gap of pituitary tissue, wherein the one or more marker(s) are selected from the group consisting of USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1, and wherein the expression level(s) of the marker(s) selected from the group consisting of USP32P1, IGHA2, IGHA1, BACE2, IGLC2, CA2, IGLC3, FAM65C, IGHG2, GFOD2, AC006547.13, UROD, and HOTAIRM1 is / are indicative of the presence and / or magnitude of the age gap of pituitary tissue of said individual.

77. The method according to any one of claims 1 to 20 and 27 to 76, comprising determining the presence and / or magnitude of an age gap of kidney tissue, wherein the one or more marker(s) are selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3, and wherein the expression level(s) of the marker(s) selected from the group consisting of PYGM, SHISA4, ATP2A1, FLNC, ACTA1, IQCG, MB, TCAP, CKM, TNNT3, RYR1, and ALPK3 is / are indicative of the presence and / or magnitude of the age gap of kidney tissue of said individual; preferably, wherein the kidney tissue is renal cortex tissue.

78. The method according to any one of claims 1 to 77, wherein the age gap is considered positive when the biological age is higher than the chronological age, and / or wherein the age gap is considered negative when the biological age is lower than the chronological age.

79. A method of detecting one or more diseases in an individual, comprising a step of determining an age gap between chronological age and biological age according to the method of any one of claims 1 to 78 of at least one tissue of the individual, wherein a positive age gap of at least one tissue of the individual, indicates that the individual has at least one disease.

80. The method of claim 79, wherein the disease is associated with and / or occurs in at least one tissue for which a positive age gap is determined.

81. A method of determining a health status of an individual, comprising a step of determining an age gap between chronological age and biological age according to the method of any one of claims 1 to 78 of at least one tissue of the individual, wherein(i) a positive age gap of a tissue indicates that said tissue is unhealthy, affected by a disease or prone to become affected by a disease, and / or(ii) the absence of an age gap or a negative age gap indicates that said tissue is healthy.

82. A method of predicting the likelihood of occurrence of at least one disease, comprising a step of determining an age gap between chronological age and biological age according to the method of any one of claims 1 to 78 of at least one tissue of the individual, wherein(i) a positive age gap of at least one tissue indicates a high likelihood that a disease will occur in said tissue(s), and / or(ii) the absence of an age gap or a negative age gap of at least one tissue indicates a low likelihood that a disease will occur in said tissue(s).

83. The method according to any one of claims 1 to 78, wherein a positive age gap of at least one tissue is indicative of the presence of at least one disease associated with and / or occurring in said tissue(s).

84. A method of determining or monitoring the efficacy and / or adverse side effect(s) of a treatment, comprising the steps of determining an age gap between chronological age and biological age according to the method of any one of claims 1 to 78 of at least one tissue of the individual before the treatment and after the treatment, wherein(i) a decrease of the biological age of a tissue relative to the chronological age after the treatment is indicative of an effective treatment of said tissue,(ii) no alteration of the biological age of a tissue relative to the chronological age after the treatment indicates that the treatment has no effect on said tissue, and / or(iii) an increase of the biological age of a tissue relative to the chronological age after the treatment indicates that the treatment has an adverse side effect on said tissue.

85. A computer-implemented method of generating a model for determining biological age or presence and / or magnitude of an age gap between chronological age and biological age comprising the steps of(a) providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) an a histological section of at least one tissue of said individual, preferably (an) image(s) of the histological section(s), and ii) chronological age of said individual, and(b) extracting morphological features from said histological sections of step (a) i), preferably from the image(s) thereof,(c) correlating the extracted morphological features with the chronological age of step (a) ii) of the same individual, wherein associations between the morphological features and the chronological age are determined, and(d) applying the associations determined in step (c) to (a) histological section(s), preferably (an) image(s) thereof, to determine the biological age and / or the presence and / or magnitude of an age gap between the chronological age and the biological age.

86. The method of claim 85, wherein the training dataset further comprises for each individual iii) a dataset comprising marker expression level(s) of one or more marker(s) in cells derived from bodily fluid sample of said individual, and wherein the method further comprises a step (e) of correlating the histology-derived biological age and / or age gap of step (d) with the marker expression level(s) of step (a) iii), wherein associations between the marker expression level(s) and the histology-derived biological age and / or age gap of step (d) are determined.

87. The method of claim 86, wherein said model comprises at least one association coefficient for each of said marker(s).

88. The method of any one of claims 85 to 87, wherein a model for determining presence and / or magnitude of an age gap between chronological age and biological age is generated, wherein in step (d) the presence and / or magnitude of an age gap between the chronological age and the biological age is determined, and, optionally, wherein in step (e) the histology-derived age gap of step (d) is correlated with said marker expression level(s), and associations between the marker expression level(s) and the histology-derived age gap of step (d) are determined.

89. Computer-implemented use of a model for determining a health state of an individual, wherein (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, ITGA1, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1- 16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

90. Computer-implemented use of a model for detecting at least one disease or predicting the likelihood of occurrence of at least one disease in an individual, wherein (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD- 2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3- 15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32,MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

91. Computer-implemented use of a model for determining a fitness state of an individual, (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1- 16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

92. Computer-implemented use of a model for monitoring treatment responses, wherein (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFITM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB- 50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

93. Computer-implemented use of a model for detection and / or monitoring of aging processes and / or tissuespecific aging, wherein (i) said model is obtained or obtainable by the method of any one of claims 86 to 88, and / or wherein (ii) said model comprises one or more marker(s) selected from the group consisting of JUP, MYO7B, IGKV1-27, MTND4P12, IFI27, PYGM, GFAP, VPREB3, CTD-2006K23.1, ALPK3, SLC18B1, NBPF3, GUI, KRT5, HLA-J, TBXA2R, NBPF26, SHISA4, CLCN4, IGHV3-49, SGSH, AC144831.1, FHL3, HIST1H1E, TREML2, HCFC1R1, FAM134A, IFTTM10, HIST1H3D, HLA-W, COL6A3, IGHV3-15, TBC1D3L, C4BPA, HIST1H2BG, TBC1D7, CH17-472G23.2, WASF2, S100B, HIST1H2AD, HLA-L, LRRC32, MALAT1, KLF11, TNFSF14, ACTBP8, IGLV6-57, IGHG3, ACP6, AP003068.23, IGHV1-69D, IGHA2, TG, PLPP3, GJB6, TREML4, HES1, HIST1H2BD, WASHCI, USP32P1, FEM1A, IGHV3-23, TRIM52-AS1, HES6, TTGAl, MAP3K6, EIF1AY, HLA-DQA2, MXRA7, RSPH3, RSAD2, RGCC, IGKV1-16, CTB-50L17.8, TCN2, PAX8-AS1, TBC1D30, MT1X, ESAM, WDR97, IGKV1-5, SSPO, AC005301.9, and KDM5D, and at least one association coefficient for each selected marker.

94. The use of claim 93, wherein the association coefficient(s) are as defined in claim 9 or 10, and / or said model is as defined in claim 11 or 12.

95. The method of any one of claims 79 to 83 or the use of claim 90, wherein said disease(s) comprise(s) at least one age-related disease or pathology.

96. The method of any one of claims 79 to 83 and 95 or the use of claim 90 or 95, wherein said disease(s) is / are selected from the group consisting of: inflammatory immunological disorders such as Crohn's disease, ulcerative colitis, vasculitis, rheumatoid arthritis and lupus erythematous, genetic disorders such as cystic fibrosis, renal failure, Barret's Oesophagus, cancer such as esophageal, prostate, gastric, colorectal, endometrial, and cervical cancer, kidney disease, heart attack, infarction, acute coronary state, Diabetes mellitus type 2, Diabetes mellitus type 1, Hypertension, Ischemic heart disease, liver disease, chronic respiratory disease, ascites, cerebrovascular disease, heart disease, cellulitis, systemic lupus, multiple sclerosis, Alzheimer's, dementia, chronic lower respiratory disease, arthritis, post-menopausal syndrome, osteoarthritis, osteoporosis, Parkinson's disease, amyotrophic lateral sclerosis, atrial fibrillation, chronic kidney disease, venous thromboembolism, peripheral artery disease, hyperlipidemia, congestive heart failure, sarcopenia, frailty syndrome, urinary incontinence, benign prostatic hyperplasia, chronic venous insufficiency, and fibromyalgia.

97. A computer-implemented method of determining areas in a histological section that are affected by aging, said method comprising the steps of(a) applying a model to a histological section, preferably an image thereof, wherein said model was trained to predict biological age or presence and / or magnitude of an age gap between chronological age and biological age from morphological features extracted from histological sections, preferably images thereof, and(b) determining the contribution of areas of the histological section to the determination of the biological age and / or age gap, wherein a contribution of an area above average and / or above a threshold indicates that said area is affected by aging.

98. The method of claim 97, comprising before said step (a) the following steps to generate said model:(a') providing a training dataset of a plurality of individuals, wherein said training dataset comprises for each individual: i) histological section of at least one tissue of said individual, preferably (an) image(s) of the histological section(s), and ii) chronological age of said individual,(a") extracting morphological features from said histological section(s) of step (a') i), and(a'") correlating the extracted morphological features with the chronological age of step (a') ii) of the same individual, wherein associations between the morphological features and the chronological age are determined.

99. The method of claim 98, wherein in step (a'"), wherein the associations between the morphological features and the chronological age are determined in a spatially resolved manner in the image(s).

100. The method of any one of claims 97 to 99, wherein said model computes spatially resolved attribution scores for contribution to the age gap.

101. The method of any one of claims 97 to 100, wherein said model employs a graph neural network with an attention mechanism, preferably, for (i) determining the associations between the morphological features andthe chronological age in a spatially resolved manner and / or for (ii) spatially resolving the attribution scores for contribution to the age gap.

102. The method of any one of claims 97 to 101, further comprising a step of visualizing areas that are affected by aging in a histological section, preferably an image thereof.

103. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the computer-implemented method according to any one of claims 1, 3 to 14, 20 to 77, 85 to 88 and 97 to 102.

104. A computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the computer-implemented method according to any one of claims 1, 3 to 14, 20 to 77, 85 to 88 and 97 to 102.

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