Methods for determining the risk of age-related functional decline

Abstract

Aging progresses at different rates among individuals, underscoring the need for better indicators of functional decline. The inventors showed that the T cell energy metabolism in the elderly reveals that high mitochondrial oxidative phosphorylation, especially in CD4 T regulatory cells, is associated with a reduced probability of being frail / prefrail, independent of age, sex, or comorbidities. When mitochondrial respiration drops below a critical threshold, it adversely affects intrinsic capacity. Furthermore, a low level of oxidative phosphorylation in CD4 T helper cells, particularly in central and effector memory subsets, was significantly associated with a faster decline in intrinsic capacity over time, suggesting that T cell mitochondrial bioenergetics represents a key biomarker for managing functional decline in older adults. Accordingly, the present invention relates to a method for determining the risk of age-related functional decline in a subject comprising the step of measuring the mitochondrial dependency in a population of T cells wherein the mitochondrial dependency (MitoDep) correlates with the age-related functional decline.

Description

[0001] METHODS FOR DETERMINING THE RISK OF AGE-RELATED FUNCTIONAL DECLINE

[0002] FIELD OF THE INVENTION:

[0003] The present invention is in the field of medicine, in particular immunology and ageing.

[0004] BACKGROUND OF THE INVENTION:

[0005] Modem medicine has increased life expectancy by treating diseases, but with about 25% of life still spent in poor health and disability, efforts should focus on extending healthy aging. People age at different rates, which leads to the concepts of physical frailty and intrinsic capacity (IC). Physical frailty is characterized by reduced physiological reserves and functional capacity while IC measures physical and mental abilities across key domains2. As frailty and IC assessments become more common in geriatric care, there is a pressing need for biological markers to improve early risk stratification and help evaluate responses to treatments or interventions.

[0006] Mitochondrial dysfunction, marked by impaired energy metabolism, is a hallmark of aging3and a major contributor to immune decline in in older adults, especially in T cells4Naive (N) and memory (M) T cells rely mainly on mitochondrial oxidative phosphorylation (OXPHOS), fueled by fatty acid oxidation (FAO) and glucose-derived pyruvate oxidation, while activated effector T cells show increased aerobic glycolysis5’6. Age-related disruption in these energy pathways affects T cell differentiation, function, and homeostasis7 l 2. Furthermore, a large body of evidence suggests that T cell aging may contribute to overall systemic aging13,14Strikingly, T cells with impaired mitochondrial function promote multiple aging-related features in mouse models15. However, the hypothesis that impaired mitochondrial energy metabolism in T cells is linked to age-related functional decline in humans remains underexplored16 18and has not been examined at single-cell level.

[0007] SUMMARY OF THE INVENTION:

[0008] The present invention is defined by the claims. In particular, the present invention relates to a method for determining the risk of age-related functional decline. DETAILED DESCRIPTION OF THE INVENTION:

[0009] Aging progresses at different rates among individuals, underscoring the need for better indicators of functional decline. The inventors demonstrated that in elderly individuals, T cell energy metabolism, particularly the high cellular dependence on mitochondrial oxidative phosphorylation in CD4 regulatory T cells, is associated with a reduced probability of being frail / prefrail, independent of age, sex, or comorbidities. When cellular dependence on mitochondrial oxidative phosphorylation falls below a critical threshold, it impacts intrinsic capacity, suggesting that T cell mitochondrial bioenergetics represents a key biomarker for managing functional decline in older adults. Furthermore, low cellular dependence on mitochondrial oxidative phosphorylation in CD4 T helper cells, particularly within central and effector memory subsets, is associated with a significant more rapid decline in intrinsic capacity over time.

[0010] Accordingly, the present invention relates to a method for determining the risk of age-related functional decline in a subject comprising the following steps of: i) obtaining a blood sample from the patient comprising one or more population(s) of T cells, and ii) measuring the mitochondrial dependency (MitoDep) in the population(s) of T cells wherein the mitochondrial dependency correlates with the risk of age-related functional decline.

[0011] As used herein, the term "subject" refers to an individual being assessed within the scope of the present invention. Specifically, the subject is an elderly person, typically over 50, and preferably over 60 or even more over 70. This definition acknowledges the particular relevance of the invention to an aging population, which is more vulnerable to age-related functional decline and could significantly benefit from early detection and intervention.

[0012] As used herein, the term "age-related functional decline" refers to the progressive reduction in an individual's ability to perform everyday activities and maintain optimal physiological function as a result of aging. This decline includes both physical and mental deterioration, characterized by reduced muscle strength, endurance, mobility, sensory and cognitive functions, and an overall loss of vitality. The inventors show that the age-related functional decline is influenced by various biological factors, such as mitochondrial dysfunction, impaired energy metabolism, and immune system weakening, particularly in T cells. The present invention aims to provide a method for early detection and risk stratification of this decline, facilitating timely interventions to promote healthier aging. In particular, the inventors found that measuring mitochondrial dependency in T cell populations correlates with the Intrinsic Capacity (IC) as defined by the World Health Organization (WHO). IC is a comprehensive concept introduced by WHO to assess and promote healthy aging. It encompasses the combined physical and mental capabilities that an individual can draw upon at any given time. Unlike traditional measures that focus on disease and disability, IC emphasizes a holistic approach to understanding and enhancing the functional abilities of older adults.

[0013] IC is measured across five core domains: locomotion: the ability to move, including walking, balance, and coordination.

[0014] - vitality: physical and mental energy levels, including aspects like muscle strength, nutrition and metabolic health. cognition: mental functions such as memory, attention, and problem-solving, psychological: mental health aspects including emotional well-being and resilience, sensory: functions related to vision, hearing, and other sensory perceptions.

[0015] As used herein, the term “risk" refers to the likelihood that a specific event, such as in the onset of age-related functional decline, will occur over a specific period of time. This can refer to either a subject's "absolute" risk or "relative" risk. Absolute risk is measured either through direct observation after the measurement within the relevant time cohort, or by using reference to index values derived from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which may vary depending on how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used. The ratio is calculated using the formula p / (l-p) where p represents the probability of the event and (1- p) the probability of no event to no- conversion. "Risk evaluation," or "evaluation of risk" in the context of the present invention refers to predicting the probability, odds, or likelihood that a certain event or disease state will occur. This could involve estimating the rate of occurrence of the event or conversion from one disease state to another, i.e., from a normal condition to an age-related functional decline or to one at risk of developing an age-related functional decline. Risk evaluation may also include the prediction of future clinical parameters, traditional laboratory risk factor values, or other indicators of an age-related functional decline. These predictions can be made in absolute or relative terms by comparing the results to a previously measured population. The methods of the present invention can be used to make continuous or categorical measurements of the risk of conversion to an age-related functional decline, thus helping to diagnose and define the risk spectrum of a category of subjects identified as being at risk for age-related functional decline. In the categorical scenario, the invention can be used to distinguish between low risk subjects and those at higher risk for age-related functional decline.

[0016] As used herein, the term “T cell” refers to a population of lymphocytes that plays a vital role in cell-mediated immunity and is distinct from other lymphocytes, such as B cells, by the presence of a T-cell receptor on the cell surface. Several subsets of T cells have been described, including CD4 T cells and CD8 T cells. CD4 T cells, also known as helper T cells, assist in orchestrating the immune response by activating other immune cells. CD8 T cells, also known as cytotoxic T cells, are responsible for directly attacking and destroying infected or cancerous cells. Additionally, these subsets can further be categorized into memory T cells, regulatory T cells (Treg cells), natural killer T cells, gamma-delta (y5) T cells, and auto-aggressive T cells (e.g., Th40 cells), unless otherwise indicated by context. In some embodiments, the term "T cell" refers specifically to a helper T cell. More particularly, subsets of CD4 and CD8 T cells include naive (N), central memory (CM), and effector memory (EM) T cells, based on the expression of CD27, CD28, CD45RA, and CCR7. EM cells range from early-differentiated (P2, expressing both CD27 and CD28) to terminally differentiated states (P4, lacking CD27 and CD28). CD4 T cells are classified into conventional helper (CD4Th) or regulatory (CD4Treg) cells based on CD127 and CD25 expression. CD4Treg cells are further subdivided into N, M, and activated memory (AM) subsets using HLA-DR and CD45RA expression.

[0017] In particular, the population of T cells is selected from Table 1.

[0018] Table 1:

[0019]

[0020] In some embodiments, the mitochondrial dependency is measured in one or more population(s) selected from the group consisting of CD4Treg, CD4Treg M, or CD8 Pl subsets.

[0021] In some embodiments, the mitochondrial dependency is measured in one or more population(s) selected from the group consisting of CD4Th CM, CD4Th total EM, CD4Th P2 EM.

[0022] In some embodiments, the mitochondrial dependency is measured in one or more population(s) comprising:

[0023] 1) at least one T cell subset in any one group of the following groups:

[0024] Group 1 : CD4 Th naive (N), central memory (CM), effector memory P2 and P3, along with CD4 Treg total (T), naive (N), memory (M), and CD8 effector memory (EM) Pl.

[0025] Group 2: CD4 Treg total (T), naive (N), memory (M), and CD8 effector memory (EM) Pl

[0026] Group 3 : CD4 Th central memory (CM), and CD4 Treg memory (M)

[0027] Group 4: CD4 Th central memory (CM), effector memory (EM), and EM P2 2) and at least one T cell subset from either Group 2 and Group 3 and one subset selected from the other.

[0028] As used herein, the term “biological sample” refers to any sample of biological origin potentially containing one or more population(s) of T cell. Examples of biological samples include tissue, organs, or bodily fluids such as whole blood, plasma, serum, tissue, lavage or any other specimen. Typically, the population of T cells is prepared from a PBMC sample. As used herein, the term “PBMCs” or “peripheral blood mononuclear cells” or “unfractionated PBMCs”, refers to populations of white blood cells with round nuclei that has not been enriched for any specific sub-population. Cord blood mononuclear cells are also included in this definition. PBMC samples, according to the invention, cover lymphocytes (B cells, T cells, NK cells, NKT cells), monocytes, and their precursors. These cells can typically be extracted from whole blood using Ficoll, a hydrophilic polysaccharide that separates layers of blood, with the PBMCs forming a cell ring under a layer of plasma and red blood cells at the bottom. Alternatively, PBMC can be extracted from whole blood using a hypotonic lysis buffer, which will preferentially lyse red blood cells. Such procedures are well known in laboratory practices. For example, the initial cell preparation may consist of PBMCs from fresh or frozen (blood cytopheresis). Isolated immune cell populations (T cells, NK cells, B...) can be analyzed by flow-cytometry.

[0029] As used herein, the term "mitochondrial dependency" or “MitoDep” refers to the process of oxidative phosphorylation in the mitochondria, which is crucial for adenosine triphosphate (ATP) production, the primary energy currency of cells. This process involves the transfer of electrons from electron donors to oxygen through a series of protein complexes located in the inner mitochondrial membrane, known as the electron transport chain. This chain creates a proton gradient that drives the synthesis of ATP by the ATP synthase. Measuring mitochondrial dependency in T cell subpopulations provides insight into their metabolic health and functionality, which are essential for immune cell responses and homeostasis. The inventors suggest that variations in mitochondrial dependency can serve as a biomarker for assessing the risk of age-related functional decline, allowing for more accurate predictions and tailored interventions. Mitochondrial dependency is inversely related to glycolytic capacity. The formula used to calculate glycolytic capacity is Glycolytic capacity (GlycoDep) = 100 - % MitoDep. Thus, by determining the glycolytic capacity, one can also assess mitochondrial dependency. According to the present invention, the mitochondrial dependency can be measured by any method well-known in the field.

[0030] In some embodiments, the mitochondrial dependency is measured using the Seahorse MitoStress Kit, which assesses the Oxygen Consumption Rate (OCR) as an indicator of mitochondrial respiration. Alternatively, the glycolytic capacity can be measured using the Seahorse Glycolysis Stress Test Kit, which measures the Extracellular Acidification Rate (ECAR). Additionally, the Seahorse XF Real-Time ATP Rate Assay simultaneously measures both OCR and ECAR, providing a comprehensive understanding of cellular energy metabolism.

[0031] In some embodiments, the mitochondrial dependency is measured by the SCENITH® method as described in WO2020-212362 and in the study by Arguello, R. J. et al. SCENITH: A Flow Cytometry-Based Method to Functionally Profde Energy Metabolism with Single-Cell Resolution. Cell Metab. 32, 1063-1075. e7 (2020).

[0032] In particular, the method comprises the following steps: i) providing four subsamples [SI], [S2], [S3] and [S4] of said population of cells ii) measuring the level of protein synthesis [LCo] in subsample [SI] in absence of any inhibitor; iii) exposing the subsample [S2] to an inhibitor [A] of energy production resulting from glycolysis and the oxidative phosphorylation of glucose-derived pyruvate, and measuring the protein synthesis level [LA]; iv) exposing the subsample [S3] to an inhibitor [B] of the production of the energy resulting from TCA cycle and oxidative phosphorylation comprising pyruvate oxidation, oxidation of fatty acids and oxidation of amino acids and measuring the protein synthesis level [LB]; and

[0033] - v) exposing the subsample [S4] cells with both inhibitors [A] sand [B] and measuring the protein synthesis level [L(A+B)].

[0034] Then, the mitochondrial dependency of the cells is assessed by calculating the formula (I): mitochondrial dependency = ([LCo]-[LB]) / ([LCo]-[L(A+B)]) x 100 (I)

[0035] In some embodiments, the inhibitor [A] is selected from the group consisting of 2-Deoxy- Glucose, 2-[N-(7-Nitrobenz-2-oxa-l,3-diaxol-4-yl)amino]-2-deoxyglucose / 2-NBDG,

[0036] Phloretin, 3-Bromophyruvic acid, lodoacetate, Fluoride and 6- Aminonicotinamide. In some embodiments, step iii) is performed in presence of pyruvate.

[0037] In some embodiments, the inhibitor [B] is selected from the group consisting of Oligomycin (A / B / C / D / E / / F and derivates), Rotenone, Carbonyl cyanide-p- trifluoromethoxyphenylhydrazone / FCCP, Trimetazidine / TMZ, 2[6(4- chlorophenoxy )hexyl]oxirane-2-carboxylate / Etamoxir, Bis-2-(5-phenylacetamido-l,3,4- thiadiazol-2-yl)ethyl sulfide / BPTES, and enasidenib.

[0038] In some embodiments, the measurement of protein synthesis levels is determined using any well-known in the field. For instance, protein synthesis level can be assessed as described by Schmidt, E.K., Clavarino, G., Ceppi, M., and Pierre, P. (2009). SUnSET, a nonradioactive method to monitor protein synthesis. Nat Methods 6, 275-277. Briefly, this is a nonradioactive fluorescence-activated cell sorting-based assay that allows for monitoring and quantification of global protein synthesis in individual mammalian cells and in heterogeneous cell populations. Specifically, this method involves the use of monoclonal antibodies against puromycin (puro) to directly monitor translation using standard immunochemical methods. Puro, an antibiotic that mimics the structure tRNA-AA, is efficiently incorporated into the nascent polypeptide chains during the process of mRNA translation by the Ribosomes.

[0039] In some embodiments, the method of the present invention is carried out by flow cytometry. Flow cytometry is a well-accepted tool in research that allows a user to rapidly analyse and sort components in sample fluid. Flow cytometers use a carrier fluid (e.g., a sheath fluid) to pass the sample components one at a time through a zone of illumination. Each component is illuminated by a light source, such as a laser, and the light scattered by the components is detected and analysed. The sample components can be separated based on their optical and other characteristics as they pass through the illumination zone. Fluorescence activated cell sorting (FACS) may be employed, typically using a flow cytometer capable of simultaneous excitation and detection of multiple fluorophores. These systems may include cytometric sample fluidic subsystems, as described below. In addition, the cytometric systems include a cytometer fluidically coupled to the cytometric sample fluidic subsystem. Systems of the present disclosure may include a number of additional components, such as data output devices, e.g., monitors, printers, and / or speakers, data input devices, e.g., interface ports, a mouse, a keyboard, etc., fluid handling components, power sources, etc. Preferred methods typically involve the permeabilization of the cells preliminary to flow cytometry. Any convenient means of permeabilizing cells may be used in practicing the methods.

[0040] Thus, in some embodiments, the method of the present invention comprises the step of contacting the subsample of T cells with a panel of antibodies comprising antibodies specific for puromycin and antibodies specific for one or more cell surface marker(s) specific for the population(s) of T cells.

[0041] Typically the antibodies are conjugated with a detectable label. Suitable detectable labels include, for example, a heavy metal, a fluorescent label, a chemiluminescent label, an enzyme label, a bioluminescent label or colloidal gold. Methods of making and detecting such detectably-labeled immunoconjugates are well-known to those of ordinary skill in the art, and are described in more detail below.

[0042] In some embodiments, the mitochondrial dependency is compared to a predetermined reference value, wherein differential between the determined mitochondrial dependency and the predetermined reference value indicates the risk of having an age-related functional decline.

[0043] In some embodiments, the method further comprises the steps of i) determining the mitochondrial dependency in population of T cells, ii) comparing the mitochondrial dependency with a predetermined reference value and iii) determining the risk of having an age-related functional decline from said comparison.

[0044] Typically, when the mitochondrial dependency is lower than the predetermined value, it is concluded that the subject is at risk of having an age-related functional decline and conversely when the mitochondrial dependency is higher than the predetermined reference value, it is concluded that the subject is not at risk of having an age-related functional decline.

[0045] Typically, the predetermined reference value is a threshold value or a cut-off value. A "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and / or clinical conditions, as would be recognized by a person of ordinary skilled in the art. For example, retrospective measurement of mitochondrial dependency in properly banked historical subject samples may be used in establishing the predetermined reference value. The threshold value has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit / risk balance (clinical consequences of false positive and false negative). Typically, the optimal sensitivity and specificity (and so the threshold value) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after determining the mitochondrial dependency in a group of reference, one can use algorithmic analysis for the statistic treatment of the measured mitochondrial dependency in samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is receiver operator characteristic curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1-specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and 1 -specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer. Existing software or systems in the art may be used for the drawing of the ROC curve, such as: MedCalc 9.2.0.1 medical statistical software, SPSS 9.0, ROCPOWER.SAS, DESIGNROC.FOR, MULTIREADER POWER. SAS, CREATE-ROC.SAS, GB STAT VIO.O (Dynamic Microsystems, Inc. Silver Spring, Md., USA), etc. This method has been extended for censored data. Here, the objective is to discriminate between those who will have the event in the future from those who will not.

[0046] The predetermined reference value can also be relative to a number or value derived from population studies, including without limitation, subjects adjusted for age, sex, BMI, physical activity, smoking status, LDL and HDL-cholesterol, SBP, Lipid lowering and BP lowering drugs, T2D, renal function (CKD-EPI) and / or having the same IC score. Such predetermined reference values can be derived from statistical analyses and / or risk prediction data of populations obtained from mathematical algorithms and computed indices. In some embodiments, the predetermined reference values are derived from the mitochondrial dependency in a control sample derived from one or more subject who do not develop an age- related functional decline. Furthermore, retrospective measurement of the mitochondrial dependency in properly banked historical subject samples may be used in establishing these predetermined reference values.

[0047] In some embodiments, a cut-off value thus consists of a range of quantification values, e.g. centered on the quantification value for which the highest statistical significance value is found. For example, on a hypothetical scale of 1 to 10, if the ideal cut-off value (the value with the highest statistical significance) is 5, a suitable (exemplary) range may be from 4-6. For example, a subject may be assessed by comparing values obtained by measuring the mitochondrial dependency, where values lower than 5 reveal that the subject is at risk of having an age-related functional decline and values higher than 5 reveal that the subject is not at risk of having an age-related functional decline. In some embodiments, a subject may be assessed by comparing values obtained by measuring the mitochondrial dependency and comparing the values on a scale, where values below the range of 4-6 indicate that the subject is at risk of having an age- related functional decline and values above the range of 4-6 indicate that the subject is not at risk of having an age-related functional decline, with values falling within the range of 4-6 indicate that further explorations are needed to conclude whether the subject is at risk of having an age-related functional decline.

[0048] In some embodiments, the method of the present invention comprises the steps of a) assessing at least one parameter that is mitochondrial dependency, b) implementing an algorithm on data comprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having an age-related functional decline from the algorithm output obtained at step b).

[0049] As used herein, the term “algorithm” is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous parameters and calculates an output value, sometimes referred to as an “index” or “index value”. As used herein, the term “parameter” refers to any characteristic assessed when carrying out the method according to the invention. As used herein, the term “parameter value” refers to a value (a number for instance) associated to a parameter.

[0050] In some embodiments, the algorithm implements one or more additional parameters. Typically, the additional parameters are selected from the group consisting of age, sex, BMI, physical activity, smoking status, LDL and HDL-cholesterol levels, systolic blood pressure level, status regarding lipid lowering, status regarding blood pressure lowering drugs, T2D status, renal function such as eGFR, hepatic function such as alanine transaminase (ALT) level, cardiac function such as NT-proBNP level, cognitive function such as Tau protein level, age-related biomarker such as GDF15 level, inflammatory markers such as C-reactive protein level (CRP).

[0051] In some embodiments, the mitochondrial dependency measured in the population of T cells is implemented in an existing predictive algorithm for predicting the risk of age-related functional decline, such as aging and IC clocks algorithms, which are used to predict a person’s biological age or IC across its several domains. These include epigenetic clocks (e.g. Horwath’s epigenetic clock algorithm (Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol 14, 3156 (2013)'), DNAm IC clock (Fuentealba M, et al. A Novel Blood-Based Epigenetic Clock for Intrinsic Capacity Predicts Mortality and is Associated with Clinical, Immunological and Lifestyle Factors. bioRxiv. 2024; 2024.08.09.607252), inflammatory clocks (e.g. iAge (Sayed, N., Huang, Y., Nguyen, K. et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging 1, 598-615 (2021)) , or multi-omics clocks that integrate on various -omics data, such as epigenomic, proteomic and metabolomic measures (e.g. OMICmAge) (Chen Q, et al. OMICmAge: An integrative multi-omics approach to quantify biological age with electronic medical records. bioRxiv. 2023 ;2023.10.16.562114).

[0052] Non other limiting examples of algorithms include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Non- limiting examples of algorithms thus include logistic regression, linear regression, random forests, classification and regression trees (C&RT), boosted trees, neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feedforward networks, support vector machines (e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss-Newton algorithms, mixtures of Gaussians, gradient descent algorithms, learning vector quantization (LVQ), and combinations thereof. Of particular use in combining parameters are linear and non-linear equations and statistical classification analyses to determine the relationship between levels of said parameters and the objective response to the preoperative adjuvant therapy. Of particular interest are structural and syntactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELDA), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art.

[0053] In some embodiments, the method of the present invention comprises the use of a machine learning algorithm. The machine learning algorithm may comprise a supervised learning algorithm. Examples of supervised learning algorithms may include Average One-Dependence Estimators (AODE), Artificial neural network (e.g., Backpropagation), Bayesian statistics (e.g., Naive Bayes classifier, Bayesian network, Bayesian knowledge base), Case-based reasoning, Decision trees, Inductive logic programming, Gaussian process regression, Group method of data handling (GMDH), Learning Automata, Learning Vector Quantization, Minimum message length (decision trees, decision graphs, etc.), Lazy learning, Instance-based learning Nearest Neighbor Algorithm, Analogical modeling, Probably approximately correct learning (PAC) learning, Ripple down rules, a knowledge acquisition methodology, Symbolic machine learning algorithms, Subsymbolic machine learning algorithms, Support vector machines, Random Forests, Ensembles of classifiers, Bootstrap aggregating (bagging), and Boosting. Supervised learning may comprise ordinal classification such as regression analysis and Information fuzzy networks (IFN). Alternatively, supervised learning methods may comprise statistical classification, such as AODE, Linear classifiers (e.g., Fisher's linear discriminant, Logistic regression, Naive Bayes classifier, Perceptron, and Support vector machine), quadratic classifiers, k-nearest neighbor, Boosting, Decision trees (e.g., C4.5, Random forests), Bayesian networks, and Hidden Markov models. The machine learning algorithms may also comprise an unsupervised learning algorithm. Examples of unsupervised learning algorithms may include artificial neural network, Data clustering, Expectation-maximization algorithm, Self-organizing map, Radial basis function network, Vector Quantization, Generative topographic map, Information bottleneck method, and IBSEAD. Unsupervised learning may also comprise association rule learning algorithms such as Apriori algorithm, Eclat algorithm and FP -growth algorithm. Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering, may also be used. Alternatively, unsupervised learning may comprise partitional clustering such as K-means algorithm and Fuzzy clustering. In some embodiments, the machine learning algorithms comprise a reinforcement learning algorithm Examples of reinforcement learning algorithms include, but are not limited to, temporal difference learning, Q-leaming and Learning Automata. Alternatively, the machine learning algorithm may comprise Data Preprocessing.

[0054] The interest of the present invention lies in its method of evaluating an individual's abilities within these domains to provide a comprehensive understanding of their overall functional potential. This approach allows healthcare providers to identify early signs of decline, tailor interventions, and support the maintenance or improvement of functional ability. By promoting strategies that enhance quality of life, independence, and well-being among older adults, the method of the present invention aims to support healthier aging populations globally.

[0055] The invention will be further illustrated by the following figures and examples. However, these examples and figures should not be interpreted in any way as limiting the scope of the present invention.

[0056] FIGURES:

[0057] Figure 1. Analyses of energy metabolism profile in T cell subsets by frailty status. a) Volcano plot showing differential metabolic activities between frail / prefrail and robust individuals. The plot shows differences (fold-change, x-axis) when comparing frail / prefrail to robust individuals versus significance (y-axis) for energy metabolism variables in CD8, CD4Th, and CD4Treg cell subsets. Significance (non-adjusted p-value) and difference are plotted as -Logic p-value and Log2 fold-change, respectively. Dark dots indicate significantly lower variables in frail / prefrail individuals (Log2 fold-difference < -0.05, p<0.05) and grey dots represent non-significant variables. b) Forest plot of odd ratios for being frail / prefrail in relation to MitoDep in CD8, CD4Th and CD4Treg subsets. MitoDep was considered as a continuous variable. Only subsets with significant odd ratio (p<0.05) are shown. c) Reduction in the odds of being frail / prefrail in relation to MitoDep in CD8, CD4Th and CD4Treg subsets. MitoDep was considered as a continuous variable. Only subsets with significant odd ratio (p<0.05) are shown.

[0058] Figure 2. Relationship between IC score and energy metabolism variables in T cell subsets.

[0059] Circles represent men and triangles represent women, with grey gradient indicating IC score levels. a) Partial least squares (PLS) regression of IC score based on energy metabolism variables in T cell subsets. The plot displays the PLS score and the loading coefficients for the seven variables most strongly correlated with the first two components, tl and t2. Predictive performance (Q2Y), goodness of model fit (R2Y), and root mean square error (RMSE) values are provided in the table on the right. b-c) Segmented regression models examining the relationship between IC score and energy metabolism variables in T cell subsets. Scatter plots show piecewise linear relationships between IC score and MitoDep in central memory CD4Th (b) and memory CD4Treg (c) subsets, with two regression lines connected at a breakpoint. Only plots with significant 0 estimates (p < 0.05) for the slope of the left line and the slope change at the breakpoint are shown. Analyses were adjusted for age, sex, and comorbidities. Model-related values are reported in the table on the right.

[0060] Figure 3. Intrinsic Capacity (IC) score evolution over time according to MitoDep levels (%) in T-cell subsets.

[0061] Models based on CD4Th CM (a), CD4Th EM Total (b), and CD4Th EM P2 (c) demonstrated a significant time-by-MitoDep interaction, indicating that lower MitoDep levels were associated with a faster decline in IC score over time. For illustrative purposes, MitoDep values were set at ±1 SD from the sample mean reported in Table 6, corresponding to low MitoDep (dashed line) and highMitoDep (solid line). Slopes represent estimates for individuals of average age, adjusted for sex and comorbidities. The IC score is reflected such that the y- axis is reversed, with higher positions indicating better scores.

[0062] Abbreviations: CCI, Charlson Comorbidity Index; CD4Th, CD4+T helper cells; CM, central memory; EM, effector memory; IC, Intrinsic Capacity; MitoDep, mitochondrial dependency; SD, standard deviation.

[0063] EXAMPLE:

[0064] Methods:

[0065] Data source and study population

[0066] This longitudinal, 3 -year follow-up study included participants from the INSPIRE-T study, part of the INSPIRE program. Details on the study’s objectives, design, recruitment, and procedures have been reported previously37,38. The INSPIRE program focuses on geroscience and healthy aging, with the ongoing INSPIRE-T cohort designed to investigate biomarkers of aging, age- related diseases, and immune system changes over a planned 10-year follow-up. Since 2019, the cohort has recruited individuals aged 20 to 102 from the Toulouse area (southwest France) and has collected biological, clinical, digital, and imaging data. Participants could be at any level of functional capacity but needed to have a life expectancy of at least 5 years (or 1 year if already disabled and elderly).

[0067] There was no upper age limit. As of 2023, the cohort included 1,014 adults. The INSPIRE-T cohort protocol has been approved by the French Ethical Committee in Rennes (CPP Ouest V, France) and registered at ClinicalTrials.gov (NCT04224038). All participants signed informed consent.

[0068] In this study, participants were excluded if they were outside the 70-89 range, lacked complete information on IC or frailty status, had missing peripheral blood mononuclear cell (PBMC) samples, had PBMC viability below 70%, or had a PBMC count of less than 5 x 10A6. As a result, the final study population consisted of 187 participants (not shown).

[0069] Intrinsic capacity (IC)

[0070] The main outcome was the composite Intrinsic Capacity (IC) score, encompassing five domains — cognition, locomotion, psychology, vitality, and sensory — assessed at all time points over the three-year follow-up, from the INSPIRE-T baseline visit (year 0) to the third- year follow-up visit (year 3).

[0071] IC was operationalized as a five-domain construct37,38:

[0072] • Locomotion — assessed by the Short Physical Performance Battery (SPPB), which includes walking, chair-stand, and balance tests, summarized on a 0-12 scale (higher scores indicate better performance).

[0073] • Cognition — measured using the 30-item Mini -Mental State Examination (MMSE; range 0-30, higher is better).

[0074] • Psychology — assessed with the nine-item Patient Health Questionnaire for depression (PHQ-9; range 0-27, higher is worse).

[0075] • Vitality — evaluated by dominant-hand grip strength using a hydraulic dynamometer (Jamar; unit: kg).

[0076] • Sensory — composed of vision, measured by the WHO simple eye chart (range 0-3, higher is better), and hearing, assessed by the whisper test (range 0-2, higher is better).

[0077] Each domain measurement was rescaled from 0 to 100 points, with 100 indicating the best performance in the original instruments (for example, 30 for MMSE) and 0 showing the worst performance. Regarding handgrip strength, 100 points was defined as the maximum observed value in our cohort for women and men separately. The sensory domain was determined by the average score of vision and hearing. Finally, IC was defined as the arithmetic mean of the five domain scores.

[0078] Peripheral blood mononuclear cells (PBMC) preparation

[0079] PBMC were obtained from blood samples by density gradient centrifugation using Ficoll®- Paque Plus (Sigma-Aldrich) and were cryopreserved for further analysis37

[0080] Flow cytometry and measurement of energy metabolism in T cells.

[0081] Cryopreserved PBMC were thawed, plated in 96-well plates, and rested for 1 hour at 37°C with 5% CO2 in DMEM medium supplemented with 10% fetal bovine serum. Cellular energy metabolism was measured using a validated multiparameter flow cytometry method17, which involved treating cell with metabolic drugs and analyzing protein translation via puromycin incorporation as a surrogate marker of metabolic activity (not shown). Briefly, cells were treated for 15 minutes with either Dimethyl sulfoxide (DMSO, Sigma-Aldrich), 2 -Deoxy -D- Glucose (DG, 100 mM, Sigma-Aldrich), Oligomycin (O, 1 pM, Sigma-Aldrich), or a combination of both drugs (DG and O). 2 -Deoxy -D-Glucose is known to block glycolysis while oligomycin interfere with OXPHOS. The use of both drugs completely abolish energy metabolism highlighted by a decrease in puromycin incorporation. Puromycin (10 pg / mL, Sigma-Aldrich) was then added and incubated for 20 minutes at 37°C. Following incubation, cells were washed with cold PBS and stained using the Live / Dead Fixable Blue Dead Cell Stain Kit (ThermoFisher Scientific). Nonspecific binding was blocked with the human TruStain FcX (BioLegend). Surface staining of T cell subsets (not shown) was performed using stain buffer (BD Pharmingen) and antibodies from Table 2. Intracellular puromycin staining was done after fixation and permeabilization with the Ebioscience FOXP3 Transcription Factor kit, followed by staining with anti -puromycin Alexa Fluor 647 antibody (Clone 12D10, Merck Millipore). Cell analysis was conducted on a Symphony A5 cytometer (BD Biosciences). Reproducibility was ensured through standard procedures, including Cytometer Setup and Tracking Research and 8-peak rainbow beads (BD Biosciences).

[0082] Table 2: List of antibodies used in flow cytometry experiments.

[0083] Cellular energy metabolism parameters, expressed as a percentage, were calculated using geometric mean fluorescence intensity (MFI) of puromycin staining under different conditions as follow17:

[0084] - Mitochondrial dependence (MitoDep) = 100 * (puromycin MFI levels in DMSO-treated cells - puromycin MFI levels in oligomycin-treated cells) / (puromycin MFI levels in DMSO-treated cells - puromycin MFI levels in cells treated with both drugs) Glucose Dependence = 100 * (puromycin MFI levels in DMSO-treated cells - puromycin MFI levels in 2-DG-treated cells) / (puromycin MFI levels in DMSO-treated cells - puromycin MFI levels in cells treated with both drugs)

[0085] Glycolytic capacity = 100 - % MitoDep

[0086] - Fatty acid and amino acid oxidation (FAAO) capacity = 100 - % glucose Dependence Since glycolytic capacity and glucose dependence are derived from the percentage of MitoDep and FAAO capacity, respectively, this study solely analyzed MitoDep and FAAO capacity. Cell subsets represented by more than 100 cells were analyzed. Accordingly, Pl CD4Th and activated memory CD4Treg subsets were excluded.

[0087] Statistical analysis

[0088] Graphs represent mean values with error bars indicating the standard error of mean. Statistical analyses and graphical representations were performed using GraphPad Prism v9.3.0 software. One-way analysis of variance (ANOVA) with the Tukey post-hoc test was used to compare metabolic parameter across cell subsets.

[0089] The relationship between energy metabolism profile and frailty status was examined using multiple logistic regressions on IBM SPSS statistics Version 23 (IBM Corp., Armonk, NY, USA). The analyses were first performed without adjustment, and then performed with adjustment for age and sex (model 1) and age, sex, and CCI (model 2). All analyses were conducted using a significant threshold set at p < 0.05.

[0090] The relationships between IC and energy metabolism were analysed using R-4.3.2 by principal component analysis (PCA), partial least square-regression (PLS-R) and broken-line model analysis (R packages: ropls and segmented, respectively). PLS-R is a technique that generalizes and combines features from principal component analysis and multiple regression method. It has been developed for constructing predictive models of phenotypical data (predictor space) explaining the IC score (response space)31. Broken-line relationship assesses threshold value where the effect of the covariate changes39. The IC score was log transformed in order to obtain better distribution of the residuals Results and discussion:

[0091] Life expectancy has significantly increased over the past century due to improved living conditions and better management of chronic diseases, but 25% of life is still spent in poor health and disability. Thus, increasing health expectancy and promoting healthy longevity are key public health priorities.

[0092] People age at different rates — some remain healthy and resilient into old age, while others experience early decline and frailty, a condition characterized by reduced physiological reserves and functional capacity1, as well as decline of intrinsic capacity (IC), a compound measure of physical and mental abilities across key domains2. Despite common use of frailty and IC assessments in older adults, the biological mechanisms behind these conditions remain unclear. Therefore, there is an urgent need to identify biomarkers to detect those at high risk of frailty and evaluate the effectiveness of therapeutics or interventions.

[0093] Mitochondrial dysfunction, which among other issues causes impaired energy metabolism, is a hallmark of aging3and has been identified as a major contributor to immune function decline, particularly in T cells, in the elderly4. Age-related disruptions of mitochondrial health affect T cell differentiation, function, and homeostasis5 l0, and evidence suggests that T cell aging may also contribute to systemic aging11,12. Strikingly, T cells deficiency in mitochondrial oxidative phosphorylation (OXPHOS) instigated multiple aging-related trait in mice13. However, the hypothesis that impaired mitochondrial energy production in T cells is related to age-related traits in humans, including functional decline, remain underexplored, especially at the singlecell level14 l6.

[0094] In a longitudinal cohort of 187 individuals aged 70-89 (mean age 78.7 [SD 5.3], 57.7% [n = 108] women, not shown), we characterized mitochondrial energy metabolism in T cell subsets. We hypothesized that the degree of reliance of T-cells on OXPHOS would be related to immune function and, through this mechanism to declining physical health operationalized as IC.

[0095] We employed a multiparameter flow cytometry method that combine the SCENITH approach17that explore energy metabolism in circulating cells with a broad panel of antibodies that are suitable to characterize the population of Table 1. We analyzed different subsets of CD4 and CD8 T cells, including naive (N), central memory (CM), and effector memory (EM) cells, classified from Pl to P4 according their differentiation stage based on the expression of CD27, CD28, CD45RA, and CCR7. CD4 T cells were categorized as conventional helper (CD4Th) or regulatory (CD4Treg) cells based on CD127 and CD25 expression. CD4Treg cells were further subdivided into naive, memory, and activated memory (AM) subsets using HLA-DR and CD45RA expression18 20.

[0096] We found that that CD8 T cells were predominantly composed of effector memory subsets, with a high proportion of terminally differentiated (P4) cells21 24(data not shown). In contrast, CD4Th cells predominantly consisted of naive cells and low differentiated memory cell subsets, mainly central memory and P2 effector memory (data not shown), consistent with previous studies showing that CD8 T cells exhibit more pronounced age-associated changes than CD4 T cells4,25. Aging features were also observed within the CD4Treg population with a shift toward a memory phenotype rather than naive one (data not shown).

[0097] Energy metabolism in the T cell subsets was analyzed by measuring cellular dependence on OXPHOS (mitochondrial dependency, MitoDep) and the capacity for fatty acid and amino acid oxidation (FAAO capacity), expressed as percentages relative to glycolytic capacity and glucose dependence, respectively9(data not shown). In line with previous reports26,27 26,28, naive cells were highly dependent on mitochondrial respiration regardless of cell subsets, while central memory showed higher MitoDep than effector memory cells. Highly differentiated effector memory (P4) CD8 and CD4Th cells shifted to FAAO and glycolysis (data not shown), a pattern associated with differentiation4,8,9,28. Naive and memory CD4 Treg cells primarily relied on MitoDep, with naive cells being more dependent on FAAO and memory cells on glycolysis (data not shown), aligning with optimal suppressive function29,30. Overall, analyses of energy metabolism in the cohort revealed heterogeneity across T cell subsets, especially among the differentiation states of effector memory cells.

[0098] Table 3 presents individual data at recruitment for the entire cohort, categorized into "robust" and "frail / prefrail” groups based on Fried frailty criteria1. Frail and prefrail individuals were combined due to the small number of frail participants (n = 12). The frail / prefrail group, comprising 47% of the cohort, was older, had more comorbidities as indicated by a higher Charlson Comorbidity Index (CCI), and had lower IC scores compared to the robust group. Table 3: Baseline characteristics for the whole population, and categorized by frailty status. For continuous variables, values are expressed as mean with standard deviation (SD) in parentheses. For categorical variables, values are expressed as number with frequency (%) in parentheses. Between-group differences were determined using Students’ t -test for unpaired series (*Welch test in case of heteroscedasticity).aAnalyses performed on log transformed data Abbreviations: CCI, Charlson comorbidity index; F / PF, frail / prefrail; IC, intrinsic capacity; N / A, not available; R, Robust.

[0099] Significant differences in energy metabolism variables were observed between robust and frail / prefrail individuals. MitoDep was lower in frail / prefrail individuals across the CD4 Th naive, central memory, P2, P3 subsets, as well as in the total, naive, and memory CD4 Treg subsets, and in the Pl CD8 subset. However, FAAO capacity did not differ between the two groups across all T cell subsets (data not shown). Consistently, MitoDep in most of these T cell subsets showed a significant negative fold-change when comparing frail / prefrail to robust population (Fig.la), Logistic regression analyses further revealed that higher MitoDep in total, naive and memory CD4 Treg, and Pl CD8 subsets was markedly associated with a reduced likelihood of being classified as frail / prefrail, independently of age, sex, and comorbidity (Fig.lb and Table 4). For instance, in a model adjusted for these factors, a 10% increase in MitoDep in memory CD4 Treg correspond to a 35% reduction in the probability of being frail / prefrail (Fig.lc), Overall, individuals at high MitoDep percentile in total, naive and memory CD4 Treg, and Pl CD8 subsets showed a lower probability of being frail / prefrail (Table 5), revealing the potential value of MitoDep in these specific T cell subsets as biomarkers of frailty.

[0100] Table 4: Baseline energy metabolism variables for the whole population, and categorized by frailty status. MitoDep and FAAO capacity were measured in T cell subsets at baseline (year 0) from 187 participants aged 70-89 years (57.7% women) in the Inspire-T cohort. Values are expressed as percentage (%) with standard deviation (SD) in parentheses. Between-group differences were determined using Students’ t -test for unpaired series (*Welch test in case of heteroscedasticity).aAnalyses performed on log transformed data. Abbreviation: IC, intrinsic capacity; FAAO, fatty acid and amino acid oxidation; F / PF, frail / prefrail; MitoDep, mitochondrial dependency; N / A, not available; N, naive; P2, P3, and P4 subsets, as defined in Table 1; R, robust ; Th, T helper; Treg, regulatory T cells.

[0101] Table 5: Estimated probability of being F / PF according to different MitoDep percentiles in T cell subsets. MitoDep was measured in T cell subsets at baseline (year 0) from 187 participants aged 70-89 years (57.7% women) in the Inspire-T cohort. Probability was calculated from logistic regression analyses adjusted for age, sex and CCI and only T cells subsets with statistical significance (p < 0.05) are reported.

[0102] Abbreviations: CCI, Charlson Comorbidity Index; CM, central memory; F / PF, frail / pre-frail; MitoDep, mitochondrial dependency.

[0103] Probability of being F / PF, % (MitoDep percentile value, %)

[0104] MitoDep MitoDep MitoDep MitoDep ercenti e CD4Treg CD4Treg N CD4Treg M CD48 Pl p20 56.1 (64.9) 54.7 (75.7) 55.1 (61.5) 55.3 (41.6) p40 49.7 (70.6) 49.3 (79.7) 49.7 (67.2) 50.8 (48.5) p6O 45.4 (75.3) 45.2 (82.9) 44.3 (71.8) 44.8 (55.8) p80 39.1 (80.9) 41.1 (85.7) 40.1 (76.4) 39.0 (64.3)

[0105] We then analyzed the energy metabolism variables of T cell subsets in relation to the IC scores. All analyses were adjusted for age, sex and comorbidities.

[0106] Cross-sectional analyses, based on frailty status, IC composite score and energy metabolism variables at baseline (year 0), were first performed. No correlation was found between IC and either MitoDep or FAAO capacity in any CD4 T cells subset (data not shown). MitoDep in naive and central memory CD4 Th, and total CD8 cells was negatively correlated with age. MitoDep variables were positively correlated with each other across various T cell subsets, FAAO variables were similarly positively correlated within subsets, but MitoDep and FAAO were negatively correlated with each other across most subsets suggesting that OXPHOS rely mostly on glucose-derived pyruvate in these cells (data not shown). To address multicollinearity among these energy metabolism variables, we performed Partial Least Squares (PLS) regression to assess whether the T cell energy metabolism could differentiate individuals based on their IC score31(Fig.2a), The first two components, tl and t2, explained 48% of variability. PLS revealed a moderate prediction of IC score (R2Y = 0.30 and Q2Y = 0.16, where R2Y represents the cumulative variation explained by tl and t2, and Q2Y indicates the variation explained by the model according to cross-validation31). The loading coefficients for the most correlated variables on the PLS score plot showed that MitoDep and FAAO capacity display opposite patterns: MitoDep mainly was associated with tl, FAAO capacity with t2, while age associated with both. However, the limited predictive ability of the PLS model suggests that the full set of metabolic variables moderately contributes to IC score prediction. Considering, the lack of association between energy metabolism variables of T cell subsets and the IC score (data not shown), we further analyzed each metabolic variable within each T cell subset using segmented regression models, adjusted for age, sex, and comorbidities. Notably, MitoDep in central memory CD4 Th and memory CD4 Treg cells showed a significant piecewise linear relationship with the IC score. In the scatter plots, this relationship is depicted by two distinct linear segments, indicating a significant change in slope at an estimated breakpoint corresponding to a MitoDep threshold values of 46.6% [95% CI, 42.7-50.9] in central memory CD4Th and 58.5% [95% CI, 50.7-67.5] in memory CD4 Treg cells (Fig.2b,c). The significant positive linear relationship for values before the breakpoints (P estimates = 1.22 [p = 0.0069] and 0.40 [p = 0.0104], respectively) and the lack of significance after the breakpoint (P estimates = -0.07 [p = 0.2036] and -0.11 [p = 0.1731], respectively), suggest that the loss of OXPHOS activity (MitoDep) in central memory CD4 Th and memory CD4 Treg cells have minimal impact on IC up to a certain threshold, as resilience processes might intervene32. However, once below such threshold, IC is affected. These findings could help define a target population for gerotherapeutic drugs aimed at enhancing mitochondrial bioenergetics to improve IC.

[0107] To further evaluate the predictive value of MitoDep variables in T-cell subsets for IC outcomes over the three-year follow-up period, longitudinal analyses were conducted using annual IC data. Multilevel regression models were fitted with a random intercept at the participant level and a random slope for time. The models were adjusted for age, sex, and comorbidities (Charlson Comorbidity Index, CCI).

[0108] Models based on central memory CD4Th cells (CD4Th CM; Fig. 3a), total effector memory CD4Th cells (CD4Th total EM; Fig. 3b), and CD4Th P2 effector memory cells (CD4Th EM P2; Fig. 3c) demonstrated significant time-by-MitoDep interactions, indicating that lower MitoDep levels were associated with a faster decline in IC over time. Specifically, IC declined significantly over the three-year period when MitoDep values were below 74% in CD4Th CM, below 58% in CD4Th Total EM, and below 63% in CD4Th P2 EM, independently of age, sex, and CCI (Table 6).

[0109] Table 6: MitoDep thresholds in T cell subsets below which IC decline over 3 years becomes significant, independently of age, sexe and comorbidities. MitoDep was measured at baseline (year 0) in T cell subsets from 187 participants aged 70-89 years (57.7% women) in the Inspire-T cohort. IC score was assessed annually over the three-year follow-up. Probabilities were calculated from multilevel regression analyses adjusted for age, sex and CCI and only T cells subsets with statistical significance (p < 0.05) are reported.

[0110] Abbreviations: CCI, Charlson comorbidity index; CD4Th, CD4+T helper cells; IC, intrinsic capacity, MitoDep, mitochondrial dependency; SD, standard deviation. Mean IC declines

[0111] CD4th substet MitoDep SD significantly

[0112] (%) when MitoDep is

[0113] Central memory 69.0 11.1 < 74

[0114] Total effector memory 52.3 14.7 < 58

[0115] P2 effector memory 57.4 14.9 < 63

[0116] This study is the first to validate in humans the link between mitochondrial OXPHOS activity in specific T cell subsets, particularly CD4Th and CD4Treg, and two distinct clinical measures of functional decline. Notably, in cross-sectional analyses, high rate of MitoDep in CD4Treg M cells is associated with a reduced likelihood of being F / PF, and IC could be affected when their levels reach below a certain threshold, independently of age, sex and comorbidities. In longitudinal analysis, lower MitoDep rate in CD4th CM, total EM and P2 EM subsets were significantly associated with a faster decline in IC over time.

[0117] Mitochondrial function in T cells, including OXPHOS activity, declines with age and leads to a low-grade chronic inflammatory state known as inflammaging, a condition associated to increased frailty traits, potentially due to heightened vulnerability to stressors and reduced functional reserves33 35. Inflammaging can be promoted by inefficient control of inflammation due to mitochondria-associated CD4 Treg dysfunction, but also by increased secretion of proinflammatory cytokines from CD4 Th cells with impaired mitochondria13,36. Therefore, the higher rate of MitoDep in CD4Treg cells and other CD4Th cell subsets observed in robust individuals, compared to frail / prefrail ones, along with the relationship with IC, suggests that mitochondrial OXPHOS activity in these cells might contribute to healthier aging by moderating ongoing inflammation.

[0118] Our findings represent a significant advancement in using MitoDep in T cells as a mitochondrial function biomarker to assess and manage functional health and decline in older adults.

[0119] REFERENCES:

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Claims

CLAIMS:

1. A method for determining the risk of age-related functional decline in a subject comprising the following steps of: i) obtaining a blood sample from the patient comprising one or more population(s) of T cells, and ii) measuring the mitochondrial dependency (MitoDep) in the population(s) of T cells wherein the mitochondrial dependency correlates with the risk of age-related functional decline.

2. The method according to claim 1 wherein the subject is an elderly person.

3. The method according to claim 1 or 2 wherein the population of T cells is selected from Table 1.

4. The method according to any one of claims 1 to 3 wherein the mitochondrial dependency is measured in one or more population(s) selected from the group consisting of CD4Treg, CD4Treg M, or CD8 Pl subsets.

5. The method according to any one of claims 1 to 3 wherein the mitochondrial dependency is measured in one or more population(s) selected from the group consisting of CD4Th CM, CD4Th total EM, CD4Th P2 EM.

6. The method according to any one of claims 1 to 5 that comprises the following steps: i) providing four subsamples [SI], [S2], [S3] and [S4] of said population of cells ii) measuring the level of protein synthesis [LCo] in subsample [SI] in absence of any inhibitor; iii) exposing the subsample [S2] to an inhibitor [A] of energy production resulting from glycolysis and the oxidative phosphorylation of glucose-derived pyruvate, and measuring the protein synthesis level [LA]; iv) exposing the subsample [S3] to an inhibitor [B] of the production of the energy resulting from TCA cycle and oxidative phosphorylation comprising pyruvate oxidation, oxidation of fatty acids and oxidation of amino acids and measuring the protein synthesis level [LB]; andv) exposing the subsample [S4] cells with both inhibitors [A] sand [B] and measuring the protein synthesis level [L(A+B)].Wherein the mitochondrial dependency of the cells is assessed by calculating the formula (I): mitochondrial dependency = ([LCo]-[LB]) / ([LCo]-[L(A+B)]) x 100 (I)7. The method according to claim 6 wherein the inhibitor [A] is selected from the group consisting of 2-Deoxy-Glucose, 2-[N-(7-Nitrobenz-2-oxa-l,3-diaxol-4-yl)amino]-2- deoxyglucose / 2-NBDG, Phloretin, 3-Bromophyruvic acid, lodoacetate, Fluoride and 6- Aminonicotinamide.

8. The method according to claim 6 wherein step iii) is performed in presence of pyruvate.

9. The method according to claim 6 wherein the inhibitor [B] is selected from the group consisting of Oligomycin (A / B / C / D / E / / F and derivates), Rotenone, Carbonyl cyanide- p-trifluoromethoxyphenylhydrazone / FCCP, Trimetazidine / TMZ, 2[6(4- chlorophenoxy )hexyl]oxirane-2-carboxylate / Etamoxir, Bis-2-(5-phenylacetamido- l,3,4-thiadiazol-2-yl)ethyl sulfide / BPTES, and enasidenib.

10. The method according to claim 6 wherein the protein synthesis level is determined by contacting the sample with an amount of puromycin and then after with an amount of monoclonal antibodies specific for purmoycin that are typically conjugated with a detectable label.

11. The method according to any one of claims 1 to 10 that is carried out by flow cytometry.

12. The method according to claim 13 that comprises the step of contacting population(s) of T cells with a panel of antibodies comprising antibodies specific for puromycin and antibodies specific for one or more cell surface marker(s) specific for the population(s) of T cells.

13. The method according to any one of claims 1 to 12 that comprises the steps of i) determining the mitochondrial dependency in the population(s) of T cells, ii) comparing the mitochondrial dependency with a predetermined reference value and iii) determining the risk of having an age-related functional decline from said comparisonwherein when the mitochondrial dependency is lower than the predetermined value, it is concluded that the subject is at risk of having an age-related functional decline and conversely when the mitochondrial dependency is higher than the predetermined reference value, it is concluded that the subject is not at risk of having an age-related functional decline.

14. The method according to any one of claims 1 to 13 that comprises the steps of a) assessing at least one parameter that is mitochondrial dependency, b) implementing an algorithm on data comprising or consisting of the parameter assessed at step a) as to obtain an algorithm output, the implementing step being computer-implemented; and c) determining the risk of having an age-related functional decline from the algorithm output obtained at step b).

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