Estimation of intrinsic capacity using blood epigenetics

Abstract

Disclosed herein is a novel DNA methylation-based tool designed to predict intrinsic capacity, which encompasses the sum of an individual's physical and mental capacities. A computational model was developed that, given the DNA methylation levels of 91 Cytosine-phosphate-Guanine (CpGs) sites in the blood, calculates an estimate of intrinsic capacity.

Description

ESTIMATION OF INTRINSIC CAPACITY USING BLOOD EPIGENETICSCross-Reference to Related Application(s)

[0001] This application claims the benefit under 35 U.S.C. § 119(e) to U.S. Provisional Application 63 / 680,845, filed August 8, 2024 and entitled “Estimation of Intrinsic Capacity Using Blood Epigenetics,” which is hereby incorporated herein by reference in its entirety.Field

[0002] The various embodiments herein relate to methods of diagnosis, treatment and prevention through the use of biomarkers of aging that can be utilized to estimate a person’s intrinsic capacity (IC) to guide and preserve functionality.Background

[0003] In 2015, the World Health Organization (WHO) introduced the concept of intrinsic capacity (IC), defined as the sum of all physical and mental capacities that an individual can draw on at any point in their life. This concept promotes healthy aging by shifting the healthcare focus from treating acute illnesses toward measuring and preserving functional ability. Although IC varies between individuals, it peaks in early adulthood, declines after midlife, and can be improved at any age through lifestyle The International Classification of Diseases (11th Revision) (ICD-11 ) recently added ‘age-related decline in IC’ under code MG2A, standardizing the clinical use of IC globally as a metric of functional aging. Since the inception of IC, many studies have developed IC scores and demonstrated its association with health-related factors, including linking low IC to higher comorbidity, frailty, difficulties in activities of daily living, and increased falls.

[0004] Age-related decline in intrinsic capacity (IC), defined as the sum of an individual's physical and mental capacities, is a cornerstone for promoting healthy aging by prioritizing maintenance of function over disease treatment. However, assessing IC is resource-intensive, and the molecular and cellular bases of its decline are poorly understood.

[0005] Despite the advantages of using IC to assess functional ability, current methods to quantify it require equipment and trained personnel, and the molecular and cellular mechanisms underlying the age-associated decline are still poorly understood. There is a need in the art for accurate, simple methods to diagnose, measure and preserve functionality.Brief Summary

[0006] Discussed herein are various embodiments relating to a biological clock for age-related decline in intrinsic capacity, which can be estimated from blood and saliva samples and tracks multiple clinical, functional, immune and inflammatory components, as well as lifestyle choices, and can be used to diagnose and treat age-related issues for the improvement of a patient’s quality of life. This biomarker of aging represents a metric for health that can be utilized to estimate a person’s IC to guide and track aging interventions.#3277290

[0007] In Example 1 , a method of diagnosing a patient’s DNAm intrinsic capacity (IC) and guiding treatment to preserve functional ability comprises obtaining a sample from the patient: isolating DNA from the sample and determining DNA methylation (DNAm) markers, wherein the DNAm markers comprise methylation levels at predefined CpG sites associated with aging and physiological resilience; comparing the DNAm markers to a reference DNAm matrix to determine a biological age of the patient, wherein the reference DNAm matrix correlates specific methylation patterns with chronological and biological age ranges in healthy populations; and assessing DNAm IC of the patient by comparing the biological age to the patient’s chronological age and evaluating deviations across one or more physiological domains related to mobility, cognition, psychological well-being, sensory function, or vitality.

[0008] Example 2 relates to the method according to Example 1 , wherein the sample is a blood sample.

[0009] Example 3 relates to the method according to Example 1 , wherein the sample is a saliva sample.

[0010] Example 4 relates to the method according to Example 1 , wherein the method further comprises prescribing a treatment or intervention regimen to preserve or improve functional ability based on the assessment of the DNAm IC, the treatment selected from the group consisting of pharmacologic agents, nutritional plans, physical activity protocols, cognitive training, or lifestyle modifications.

[0011] Example 5 relates to Examples 1 through 4 wherein the method further comprises repeating steps (a) through (e) at two or more time intervals and tracking changes in the DNAm IC over time to assess treatment or intervention regimen.

[0012] Example 6 relates to Examples 1 through 5 wherein the method further comprises communicating the DNAm IC to a digital proxy, wherein the digital proxy comprises a remote server, electronic health care system, clinical decision support system, mobile health application, or monitoring device configured to store, display or act upon the DNAm IC for clinical use or patient management.

[0013] Example 7 relates to Example 6 wherein the digital proxy is further configured to generate alerts to clinicians or authorized users when the DNAm IC exceeds a predefined threshold; display longitudinal trends in the DNAm IC across time for clinical evaluation; integrate the DNAm IC into a clinical decision support system for guiding diagnosis, treatment, or escalation of care; and secure the communication and storage of the DNAm IC using encryption and authentication protocols to protect patient data integrity and privacy in compliance with applicable data protection regulations

[0014] In Example 8 a method of creating a DNAm intrinsic capacity score for a subject comprises receiving a DNAm signature derived from a sample of the subject; creating input vectors based on the DNAm signature; inputting the input vectors into a machine learning platform; and generating a predicted intrinsic capacity score of the sample based on the input vectors by the machine learning platform, wherein the intrinsic capacity score is specific to the sample; and preparing a report that includes the intrinsic capacity score that identifies a predicted intrinsic capacity of the sample

[0015] Example 9 relates to the method of Example 8 wherein the method further comprises using the DNAm intrinsic capacity score to determine levels of gene expression of specific genes, the method further comprising comparing the DNAm IC to a reference DNAm IC matrix, wherein the referenceDNAm matrix correlates with gene expression levels in healthy populations; assessing gene expression by comparing the DNAm IC of the sample and assigning a value to deviations from the reference DNAm matrix; and assigning a value to gene expression levels based on the DNAm IC value.

[0016] Example 10 relates to the method of Example 9 wherein the sample is a blood sample.

[0017] Example 11 relates to the method of Example 9 wherein the sample is a saliva sample.

[0018] Example 12 relates to the method of Example 9 wherein the gene expression levels correspond to levels of Cluster of Differentiation 28 (CD28), PFTAIRE Protein Kinase 1 (PFTK1 / CDK14) or Mucolipin 2 (MCOLN2).

[0019] In Example 13 a method for determining a biological age of a patient sample comprises selectively detecting a presence of methylation corresponding to a set of methylation markers in genomic DNA of the patient sample, said set of methylation markers comprising at least 60% of CpG methylation markers listed in Table 1; and comparing the detected methylation markers to reference markers for the methylation markers to determine the age of the sample based on said methylation levels.

[0020] Example 14 relates to the method of Example 13 wherein the set of methylation markers comprises at least 80% of the CpG methylation markers listed in Table 1.

[0021] Example 15 relates to the method of Example 13 wherein the set of methylation markers comprises at least 90% of the CpG methylation markers listed in Table 1.

[0022] Example 16 relates to the methods of Examples 13-15 wherein the method further comprises prescribing a treatment or intervention regimen to preserve or improve functional ability based on the assessment of the DNAm IC, the treatment selected from the group consisting of pharmacologic agents, nutritional plans, physical activity protocols, cognitive training, or lifestyle modifications.

[0023] Example 17 relates to the methods of Examples 13-15, further comprising repeating steps (a) through (e) at two or more time intervals; and tracking changes in the DNAm IC over time to assess treatment or intervention regimen.

[0024] Example 18 relates to the methods of Examples 13-17, further comprising communicating the DNAm IC to a digital proxy, wherein the digital proxy comprises a remote server, electronic health care system, clinical decision support system, mobile health application, or monitoring device configured to store, display or act upon the DNAm IC for clinical use or patient management.

[0025] Example 19 relates to Example 18 wherein the digital proxy is further configured to generate alerts to clinicians or authorized users when the DNAm IC exceeds a predefined threshold; display longitudinal trends in the DNAm IC across time for clinical evaluation; integrate the DNAm IC into a clinical decision support system for guiding diagnosis, treatment, or escalation of care; and secure the communication and storage of the DNAm IC using encryption and authentication protocols to protect patient data integrity and privacy in compliance with applicable data protection regulations

[0026] While multiple embodiments are disclosed, still other embodiments will become apparent to those skilled in the art from the following detailed description, which shows and describes various illustrative implementations. As will be realized, the various embodiments herein are capable of modifications in various obvious aspects, all without departing from the spirit and scope thereof.Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictiveBrief Description of the Drawings

[0027] FIG. 1A is a diagram showing the domains of IC and clinical assessment tools used for the derivation of the IC score.

[0028] FIG. 1B is a set of graphs showing the correlation between the scores in each domain(and overall) and chronological age. Due to its non-linear relationship with age, correlation values were calculated using Spearman’s method. P-values were approximated via t-distribution (two-sided). The regression line was estimated using the Locally Estimated Scatterplot Smoothing (LOESS) method Shaded regions represent the 95% confidence intervals around the smoothed line.

[0029] FIG. 1C shows the average scores for each IC domain in male (right) and female (left) participants. Lines indicate the mean value for each sex in each domain The two-sided p-values for the mean differences were calculated using a Wilcoxon's rank-sum test.

[0030] FIG. 1 D charts the estimated age of decline in each domain obtained from a continuous two-phase model regression analysis.

[0031] FIG 1E shows Spearman’s correlation between each IC domain’s score MMSE: Mini¬Mental State Examination; SPPB: Short Physical Performance Battery; PHQ-9: Patient Health Questionnaire for depression disorder.

[0032] FIG. 1F is a set of graphs illustrating the correlation between domains for each sex which were analyzed, and it was observed that in male individuals, there is a higher correlation between locomotion and the psychological or sensory domains, whereas in female individuals, locomotion is correlated with vitality and cognition.

[0033] FIG 1G also shows the correlation between domains for each sex that were analyzed and observed that in male individuals (top), there is a higher correlation between locomotion and the psychological or sensory domains, whereas in female individuals (bottom), locomotion is correlated with vitality and cognition

[0034] FIG. 2A shows ranked models with different elastic net mixing parameters (alpha) based on the correlation between observed and cross-validated predicted values, the model's error, and the number of CpGs used.

[0035] FIG 2B is a graph of a Spearman’s Correlation between IC and the DNA methylationbased estimate of IC (DNAm IC).

[0036] FIG. 2C shows DNA methylation levels of the 91 CpGs in the best predictive model.Samples were sorted by IC values, and CpGs were sorted by their correlation with IC.

[0037] FIG. 2D is a graph depicting Spearman’s correlation between DNAm IC and chronological age.

[0038] FIG. 2E is a graph showing that the CpGs with the highest coefficients displayed nearly zero correlation with chronological age

[0039] FIG. 2F shows Spearman’s correlations between age acceleration from epigenetic clocks (i.e. age-adjusted epigenetic age) and age-adjusted DNAm IC

[0040] FIG. 2G also shows Spearman’s correlations between age acceleration from epigenetic clocks (i.e. age-adjusted epigenetic age) and age-adjusted DNAm IC.

[0041] FIG. 2H illustrates the overlaps between CpGs included in the IC clock (left circles) and epigenetic clocks (right circles)

[0042] FIG. 3A shows 4 data sets where DNAm IC is calculated using epigenetics from saliva.In the 4 datasets, DNAm IC displayed a strong age-related decline (mean rs= -0.74).

[0043] FIG. 3B shows blood-derived predictions in 27 external datasets with the same correlation as saliva (mean rs= -0.74).

[0044] FIG. 3C shows the correlations between estimations of DNAm IC from blood and saliva(rs= 0.64, p = 1.23e-04).

[0045] FIG. 3D shows genes whose expression is significantly associated with ageindependent changes in DNAm IC (FDR < 0 05, non-shaded discreet dots) Labels indicate the top 10 genes with the largest changes in each direction after multiple testing adjustment. Two-sided p-values were calculated from the t-statistics obtained from the linear regression.

[0046] FIG. 3E shows the relationship between the expression of the 578 genes associated with DNAm IC and the methylation of the 91 CpGs in the IC clock. CpGs in the IC clock showed a correlation of 0.21 with the expression of at least one significantly associated gene, whereas an average correlation of 0.1 is expected by chance (permutation p < 0.0001 ).

[0047] FIG 3F correlates the genes with the levels of multiple CpGs and found that the expression of Mucolipin 2 (MCOLN2) correlated with the methylation of 61 out of 91 CpGs (67%).

[0048] FIG. 3G the similarity between gene expression signatures associated with epigenetic clocks and DNAm IC.

[0049] FIG. 3H list the top 20 biological processes enriched (FDR < 0 05) in genes significantly associated with DNAm IC.

[0050] FIG. 3I shows the enrichment for gene sets of the hallmarks of aging. Nominal p-values were calculated using a one-signed gene set enrichment analysis

[0051] FIGs. 3J-L show the cell frequency changes with age and DNAm IC The top panels showthe relationship between the DNAm-based cell count estimates and chronological age. The middle and bottom panels display the relationship between DNAm IC and cell count, adjusted or unadjusted by chronological age. The lines indicate the linear regression between the variables. The correlations were calculated using the Spearman’s method, and nominal p-values were derived from the two-sided test of the correlation coefficient (t-distribution approximation).

[0052] FIG 3M lists biological processes and shows the IC score for each The higher vitality scores were associated with up-regulation of mitochondrial electron transport chain genes (FDR = 8.09e-03), while higher locomotion scores were linked to increased expression of genes regulating muscle adaptation and the Notch signaling pathway.

[0053] FIG. 3N is a correlation analysis between cell composition changes and predicted IC scores. Higher cognitive scores were associated with fewer granulocytes (rs= -0.27) and more CD4+ T cells (rs= 0.38), while higher psychological scores correlated with fewer B cells (rs= -0.28) and more plasmablasts (rs= 0 31)

[0054] FIG. 4A is a set of Forest plots summarizing the results from the Cox proportional hazard models adjusted by chronological age and sex for DNAm IC and first- and second-generation epigenetic clocks. Hazard ratio (HR) is calculated for a one-unit increase in standard deviation units of age acceleration. Error bars represent the 95% confidence intervals for the HR estimate from the Cox model. Nominal p-values for the predictor variable were derived from two-sided Wald tests.

[0055] FIG. 4B is a set of Kaplan-Meier survival estimates for individuals with high and lowDNAm IC (age-adjusted). P-values were calculated using a log-rank test.

[0056] FIG. 4C is the principal components generated from physical and mental health parameters from 637 individuals. The sample shades represent the DNAm IC estimations.

[0057] FIG. 4D is a set of graph plots overall indicating that health is positively correlated withDNAm IC (rs= -0.44), even more strongly than with chronological age (rs= -0.37).

[0058] FIG. 4E lists the significance of the association between health measurements and high or low DNAm IC using logistic regression. Nominal p-values were derived from two-sided Wald tests. Dot shading and lines indicate the data types in which the variable was measured. The background color for each variable indicates the Pearson’s correlation with age. The list shows the positive association set.

[0059] FIG. 4F lists the significance of the association between health measurements and high or low DNAm IC using logistic regression. Nominal p-values were derived from two-sided Wald tests. Dot shading and lines indicate the data types in which the variable was measured. The background color for each variable indicates the Pearson’s correlation with age. The list shows the negative association set.

[0060] FIG. 4G shows correlations between DNAm IC and consumption of different foods and derived flavonoid intake Diamonds indicate significant correlations after multiple testing correction (FDR < 0.05).

[0061] FIG. 4H shows correlations between DNAm IC and consumption of fatty acid concentrations in the blood and dietary adherence. Diamonds indicate significant correlations after multiple testing correction (FDR < 0.05).

[0062] FIG. 4I outlines the high association between DNAm IC and all-cause mortality risk and how it is conserved in individuals with different disease subgroups.

[0063] FIG. 5 shows how DNA methylation beta values and IC scores were used to build a predictive model for IC. To ensure robust predictions, the IC score was split into 20 bins of 0.05 and only bins with at least 10 individuals were considered for the prediction.Detailed Description

[0064] In various embodiments herein, a biological clock for age-related decline in intrinsic capacity, which can be estimated from blood and saliva samples and tracks multiple clinical, functional, immune and inflammatory components, as well as lifestyle choices, and can be used to diagnose and treat age- related issues for the improvement of a patient’s quality of life This biomarker of aging represents a metric for health that can be utilized to estimate a person’s IC to guide and track aging interventions.

[0065] To address these, DNA methylation (DNAm) data was collected from participants in the INSPIRE-T cohort to construct an epigenetic predictor of IC (IC clock). Then the IC clock was applied to the Framingham Heart Study to evaluate associations between DNAm IC and mortality, clinical markers of health and lifestyle, and explore the molecular and cellular mechanisms of IC using transcriptomics data and cell composition changes.

[0066] In one exemplary embodiment, an IC clock was constructed using the INSPIRE-T cohort (1,014 individuals aged 20 to 102 years). The embodiment includes a DNA methylation (DNAm)-based predictor of IC, trained on the clinical evaluation of cognition, locomotion, psychological well-being, sensory abilities, and vitality. In the Framingham Heart Study, DNAm IC outperforms first- and second- generation epigenetic clocks in predicting all-cause mortality, and it is strongly associated with changes in molecular and cellular immune and inflammatory biomarkers, functional and clinical endpoints, health risk factors, and lifestyle choices. The IC clock can be used as a validated tool bridging molecular readouts of aging and clinical assessments of IC.

[0067] In various other embodiments herein, a methylation-based clock was constructed to monitor age-related decline in IC, which predicts mortality, tracks cardiovascular risk factors and functional resilience, and is strongly associated with immune function and inflammatory health. As humans age, both CD4+and CD8+T-cells gradually lose their ability to express CD28, a gene essential for T-cell activation and proliferation, which ultimately results in immunosenescence and a reduced immune response in older adults. Notably, it was observed that individuals with high DNAm IC displayed increased expression of CD28, higher CD8+naive T cells and lower CD8+exhausted T cells, demonstrating that maintaining DNAm IC levels is an effective strategy for preserving immune function with age.

[0068] Individuals with high DNAm IC displayed significantly lower iAge, a metric for systemic chronic age-related inflammation. Consistently, it was observed that negative associations between DNAm IC and the levels of inflammatory markers CRP and IL-6, which have been previously linked with IC. This could be explained by the loss of CD28 on T-cells, which has been associated with increased levels of CRP and inflammatory cytokines such as IL-6.

[0069] At the domain-specific level, a novel association between IC domains and hallmarks of aging pathways was demonstrated. Examples include vitality with mitochondrial function, psychological with the DNA damage response, and sensory with chronic inflammation. These links between specific aging hallmarks and IC domains help explain how molecular aging can lead to IC decline. In one embodiment of the invention, targeted interventions are provided to maintain IC as we age

[0070] It was observed that individuals with high DNAm IC display improved pulmonary function, consistent with the observation that lower levels of IC are associated with an increased risk of respiratory disease mortality. Similarly, individuals with high DNAm IC exhibited improved bone mineral density and engaged in more physical activity, contributing to overall physical resilience. Regarding mental capacities, individuals with high DNAm IC had better constructive praxis and reading comprehension in the earliest version of the MMSE test. Also, individuals with high DNAm IC had fewer depressive symptoms (CES-D scale) and lower levels of Tau in plasma.

[0071] It was observed that individuals with higher DNAm IC consumed more fish and had elevated levels of marine-origin omega-3 fatty acids in their blood. A recent randomized, controlled trial conducted on 138 sedentary, overweight, middle-aged participants (n = 93 women, n = 45 men) receiving >1 g / d of omega-3 for 4 months, reported positive effects on inflammation and telomere length.

[0072] The INSPIRE-T cohort is carried out in accordance with the declaration of Helsinki, which is the accepted basis for clinical study ethics, and must be fully followed and respected by all engaged in research on human beings. The INSPIRE-T cohort protocol has been approved by the French Ethical Committee located in Rennes (CPP Quest V) in October 2019. In the Framingham Heart Study cohort, the research protocols are reviewed annually by the Observational Studies Monitoring Board of the National Heart, Lung, and Blood Institute and by the Institutional Review Board of Boston University Medical Center. All participants are required to provide written consent before each examination.

[0073] Data Availability: The INSPIRE-T cohort de-identified data is available under controlled access due to privacy, and ethical and legal requirements. Researchers requiring access should contact the INSPIRE Data Access Committee (guyonnet s@chu-toulouse.fr) and submit a research proposal for approval. Access will be granted once the proposal is approved and a Data Use Agreement has been signed. The data used to validate the study observations come from the Framingham Heart Study cohort, which requires a data access request via dbGaP (https: / / www ncbi nlm nih gov / projects / gap / cgi- bin / study.cgi?study_id=phs000007). Controlled access is required to protect participant privacy and comply with ethical and legal requirements. Researchers must submit a research proposal along with Institutional Review Board (IRB) approval documentation. Access is granted after review by the dbGaP Data Access Committee and execution of a Data Use Certification Agreement co-signed by a designated institutional signing official. All custom code used for the analyses is available on GitHub: https: / / github.com / msfuentealba / IC_clock

[0074] No data were excluded from generating the IC scores in INSPIRE-T, except for samples that did not measure all relevant IC domains. For the model training, individuals with low IC scores were excluded from bins (0.05 increments) containing fewer than 10 participants to ensure robust predictions. For the FHS cohort validation analysis, the Applicant only excluded samples whose DNA methylation data did not pass quality control.EXAMPLE 1

[0075] The following example describes the calculation of the Intrinsic Capacity (IC) score.

[0076] To calculate the IC score, data was used from the INSPIRE-T cohort (version 1 0) The INSPIRE-T cohort is an ongoing 10-year follow-up study investigating IC changes and biomarkers of aging and age-related diseases. Participants ranged from 20 to 102 years old and covered all levels of functional capacity. Assigned sex was obtained from identity cards (ID), and no gender information was collected. An overall IC score was calculated based on the following variables describing five health domains: Cognition: Mini-Mental State Examination (MMSE; score range 0-30, higher is better). Locomotion: Short Physical Performance Battery (SPPB; score range 0-12, higher is better). Psychology: Nine-item Patient Health Questionnaire for depression (PHQ-9; score range 0-27, higher is worse). Sensory: Visual acuity measured by the WHO simple eye chart (score range 0-3, higher is better) and hearing measured by the whisper test (score range 0-2, higher is better). Although the WHOICOPE Handbook recommends MMSE, SPPB, PHQ-9, WHO simple eye chart, and whisper test as tools to approximate domains of IC, there is no agreed-upon measure for vitality in the literature. According to the WHO, vitality can include factors related to energy, metabolism, neuromuscular function, and immune response. Although vitality can be measured in multiple ways, in some embodiments of the invention, handgrip strength was used as a measure of vitality because it is a marker of physiological reserve, which is strongly associated with negative health outcomes, mortality across all ages and disability. Handgrip strength has also been suggested to serve as a vital sign for healthy aging throughout the lifespan. Additionally, unlike other potential measures of vitality, it is an indicator without fixed minimum and maximum values, thereby limiting the risk of ceiling and floor effects, which is particularly relevant in a lifespan cohort such as INSPIRE-T with participants aged 20 to 102. In contrast to self-reported data, handgrip strength offers an objective, performance-based measure of vitality, and has been demonstrated to be sensitive to age-related changes. It has been used in literature as a measure of vitality, allowing comparability with previous intrinsic capacity studies. From 1 ,014 individuals in INSPIRE-T, 973 were assessed in all five domains of intrinsic capacity. Raw scores for each individual were re-scaled from 0 to 1 , where higher is better (PHQ-9 scores were reversed to match the direction). Given the different score distributions, the values were z-transformed. The overall IC score was defined as the average of the z-transformed values across domains. The sensory domain score was calculated by averaging the visual and hearing scores. Finally, min-max normalization was performed on the overall IC score to provide an interpretable metric.

[0077] Intrinsic capacity declines with age: Using clinical assessment tools, an IC score was developed that represents the combined age-related decline in five domains: cognition, locomotion, sensory (vision and hearing), psychological, and vitality (FIG. 1A) The IC scores ranged from 0 to 1 , with 1 indicating the best possible health outcome and 0 representing the worst. Correlations between chronological age and each domain were examined. All IC domains correlated negatively with age, with the strongest correlation observed for overall IC (rs= -0.65, p = 9.97e-117) (FIG. 1 B) and the weakest with the psychological domain (rs= -0.07, p = 1 ,94e-02).

[0078] Given the well-established differences in health span and lifespan between sexes, sex differences in the levels and age at decline of IC domains were investigated. It was found that male subjects have higher scores in the psychological and vitality domains (p = 6 81e-09 and 5.17e-07, respectively), while female subjects have higher scores in the sensory domain (p = 6.11 e-04) (FIG. 1 C, FIG. 1 E). Cognition and locomotion showed no statistically significant differences between sexes (p > 0.05). Using a continuous two-phase model regression analysis, we found that female participants exhibit an earlier sensory decline (42 vs 67 years) (FIG. 1D), whereas male participants exhibited earlier cognitive decline (72 vs 86 years), demonstrating that female subjects tend to maintain cognitive resilience for longer.

[0079] The contribution of each domain was assessed by calculating their correlation with overall IC. It was observed that the overall IC score had a stronger positive correlation with each domain (rsranging from 0.48 to 0.77) than the correlations between domains, confirming the integrative nature of the IC score (FIG. 1 E). The sensory and vitality domains showed the highest inter-domain correlation (rs= 0.4), while the psychological and sensory domains had the lowest (rs= 0.1 ). The correlation betweendomains for each sex were analyzed and observed that in male individuals, there is a higher correlation between locomotion and the psychological or sensory domains, whereas in female individuals, locomotion is correlated with vitality and cognition (FIG. 1F-G). This result might reflect sex-specific effects on other domains when locomotion is impairedEXAMPLE 2

[0080] The following example describes the collection of DNA methylation data and using the data as a predictor of IC

[0081] DNA methylation profiling (EPIC array) was performed on 1 ,002 individuals in the INSPIRE-T cohort. The raw methylation data was read using the read. metharray. exp function from minfi v1.42. Detection p-values were computed to evaluate sample quality and filtered out samples with a high proportion of failed probes (average p > 0.01 ). Initial preprocessing was performed using the preprocessRaw function and identified samples with outlier methylation profiles using QC plots CpG sites with poor detection p-values (p > 0.01 ) were removed from any sample. The methylation dataset was then converted into a genomic ratio set and mapped to the genome. Probes were removed at CpG sites with SNPs and cross-reactive probes, and sex chromosome probes were excluded. Beta values representing methylation levels were normalized using the BMIQ method implemented in the R package ChAMP v2.28 DNA methylation data was processed from the FHS using the same method.EXAMPLE 3

[0082] The following example describes the model generation for DNA-methylation based prediction of intrinsic capacity.

[0083] DNA methylation beta values and IC scores were used to build a predictive model for IC To ensure robust predictions, the IC score was split into 20 bins of 0.05 and bins with at least 10 individuals were only considered for the prediction (FIG. 5). This criterion included individuals with an IC between 0.55 and 1. Therefore, samples with an IC below 0.55 were excluded, which accounted for 2.9% of the total data. Using the glmnet v4.1 package, the Applicant performed a 10-fold cross-validated elastic net regression with alpha parameters ranging from 0.1 to 1 , using mean absolute error (MAE) as the performance metric. To ensure cross-platform usability, data was used from 361,080 cytosine- phosphate-guanine sites (CpGs) that passed quality control tests and overlapped with the 450K array. Using the beta values from the CpGs, the elastic net regression algorithm selected sets of CpG sites at different levels of feature sparsity, with stronger penalties (e.g., alpha = 1) selecting fewer features and weaker penalties (e.g., alpha = 0 1 ) including more features. The best-performing model based on the lowest mean absolute error, highest correlation with IC, and the smallest number of features, used an alpha of 0.91 and included 91 CpG sites. To facilitate calculation of the IC clock, in addition to the inclusion of the model coefficients as supplementary material, a simple web application was developed (https: / / mfuentealba.shinyapps io / icclock / ) that allows users to calculate the DNAm IC from an uploaded file containing DNA methylation beta values (i.e., cpg identifier as the first column and sample beta values are the remaining columns)

[0084] DNA methylation data (Infinium EPIC array) was used from 933 INSPIRE-T participants to predict IC. The predictive model was built using elastic-net regression and 10-fold cross-validation. The Applicant ranked models with different elastic net mixing parameters (alpha) based on the correlationbetween observed and cross-validated predicted values, the model's error, and the number of CpGs used (FIG. 2A). The model with the best ranking across all three metrics (highest correlation, lowest error, and fewer CpGs) resulted in a correlation of 0.61 between IC and predicted values based on DNAm (FIG. 2B) and was composed of 91 CpGs (FIG 2C, Table 1 ). The IC estimations based on DNAm data showed a strong correlation with age (rs= -0.92, FIG. 2D), although the CpGs with the highest coefficients displayed nearly zero correlation with chronological age (FIG. 2E).Table 1: DNAm IC model coefficients feature coefficient feature coefficient (Intercept) 0.7852838 cg21449170 -0.017275 cg06846752 -0.023062 cg07955995 -0.109255 cgl9916364 0.0283663 cgl2526474 0.0260831 cg05781609 0.0669768 cg09015433 -0.012092 cgl5726426 -0.005531 cgl7131553 0.1498339 cgl0389771 -0.020453 cg07691793 0.0349776 cgl6269733 -0.003388 cg25653947 -0.122239 cg03890680 0.0042793 cglll72629 -0.003095 cgl6052388 -0.00794 cgl8240400 -0.002758 cgl3022624 -0.196481 cgl9939077 0.0027201 cgl2387232 0.0256995 cg26579838 -0.003124 cglll76990 -0.02246 cgl9158754 -0.00114 cg22158769 -0.080367 cgl8231614 -0.032421 cg00740914 0.003676 cg21990700 0.0020122 cgll807280 0.0088097 cgl5996644 -0.051188 cg07337544 -0.038983 cg25161889 0.0003619 cg00528640 0.0161968 cg06647068 0.013814 cg22454769 -0.074185 cgl5125438 0.024288 cgl9074170 -0.016918 cg06648759 -0.002562 cgl7165841 -0.008945 cg23118561 -0.001171 cgl4435073 -0.005051 cg22720431 0.0032752 cg00870633 -0.00202 cg03808001 -0.001761 cgl2340144 0.0694639 cgl6188984 -0.020201 cg03607117 -0.176135 cg03035871 0.0971606 cg07144720 0.0228401 cg02018089 0.0193885 cg25265234 0.0064045 cgl4299483 -0.010123 cg27182476 0.0174105 cg01763090 -0.056409 cg24850932 0.0188953 cg23479586 0.0702249 cg03915012 0.0326313 cg20809087 -0.080571 cg03799405 0.0379184 cg02010347 0.0696642 cg25144207 -0.003134 cgl6480692 -0.005333 cg03194122 0.0026953 cg06470626 0.0418122 cg25993931 -0.043926 cg07285209 0.012551 cg02891686 -0.008096 cg03890691 -0.005406 cgl0183150 -0.005246 cgl8693704 0.0331557 cgl4071179 -0.015349 cg06959205 0.0072581 cgl4874742 0.0292653 cg03832440 0.0068371 cg02204637 0.1048997 cgl8026225 -0.011869 cgl9206146 -0.027724 cg00002749 0.0114314cgl9398269 -0.019455 cg22353329 -0.001539 cg26910594 0.1761926 cgl2989560 -0.013596 cgl6113650 0.023171 cgl3383153 0.1008531 cg23760574 -0.000517 cg00539386 -0.031133 cg25278663 -0.003182 cg25970491 0.0041498 cgl3903548 -0.014349 cgl6722536 -0.000103 cg21429551 0.0087672 cgl3003054 0.0430132

[0085] We compared the IC clock to established first- and second-generation clocks (Horvath, Hannum, PhenoAge and GrimAge). The comparison used IC predictions based on DNA methylation (DNAm IC) and epigenetic age estimates calculated with the methylclock package v1 .2.1 , including the Horvath, Hannum, and Levine clocks. Age acceleration was computed as the residuals from the linear model between chronological age and DNAm age or DNAm IC. Spearman’s correlation coefficients were computed to evaluate the relationships between the IC clock and each epigenetic clock. It was demonstrated that a negative correlation existed between DNAm IC and epigenetic clocks, with PhenoAge showing the strongest correlation (rs= -0.37, p < 2.2e-16), followed by the Hannum clock (rs= -0.36, p < 2.2e-16) (FIG. 2F-G). Furthermore, the absolute magnitude of the correlation was higher between epigenetic clocks compared to DNAm IC (absolute mean rs= 0.38 vs. 0.29), demonstrating that the IC clock captures a distinct aspect of the biology of aging. In line with this finding, no major overlaps between the CpG sites included in the epigenetic clocks and DNAm IC were found (FIG. 2H).

[0086] It was also tested as to whether DNAm IC could be calculated using epigenetics from saliva. In 4 datasets, DNAm IC displayed a strong age-related decline (mean rs= -0.74) (FIG. 3A), which was equivalent to blood-derived predictions in 27 external datasets (mean rs= -0.74) (FIG. 3B). In addition, methylation data from blood and saliva samples of 19 patients (aged 13-73 years) was used to directly evaluate the degree of similarity. The methylation levels of the 91 CpGs in the DNAm IC model are highly correlated (mean rs= 0.96, p = 1.54e-50), as were the estimations of DNAm IC from blood and saliva (rs= 0.64, p = 1.23e-04) (FIG. 3C). These findings highlight the novel use of saliva as a non- invasive alternative to blood for calculating DNAm IC.EXAMPLE 4

[0087] The following example demonstrates the use of differential expression analysis to show how DNAm IC is associated with immune response alterations.

[0088] To better understand the molecular components and biological processes associated with DNAm IC, the Applicant calculated the IC clock in the Framingham Heart Study (FHS) and performed differential expression analysis using DNAm IC (age-adjusted) as outcome. Using the generated model, the Applicant predicted IC for individuals in the Offspring cohort of the FHS using DNA methylation data (obtained during the 8th exam) and investigated its association with gene expression changes, followed by enrichment analysis. Differential gene expression analysis was performed using linear models to identify genes associated with age-adjusted DNAm IC. Genes with an FDR < 0.05 were filtered. Enrichment analysis was conducted using the clusterprofiler package v4.6.2 to identify biological processes associated with differentially expressed genes. The Applicant visualized enriched Gene Ontology (GO) terms using the CellPlot package v1.0 (https: / / github.com / dieterich-lab / CellPlot).

[0089] It was found that the expression of 578 genes (286 up-regulated and 292 down-regulated) was significantly associated with changes in DNAm IC (Fig. 3D, Supplementary Table 2) Among the top genes, higher DNAm IC was strongly associated with a significant increase in expression of Cluster of Differentiation 28 (CD28) (False Discovery Rate (FDR) = 1.07e-32), a surface molecule highly expressed in CD4+and CD8+T-cells whose loss of expression is a hallmark of immunosenescence. In contrast, poor IC clock levels tracked with elevated expression of PFTAIRE Protein Kinase 1 (PFTK1 / CDK14) (FDR = 2.77e-29), a regulator of the Wnt signal transduction pathway, a proinflammatory mediator associated with Parkinson's disease and a variety of cancers, and sensitive to changes in diet.

[0090] To confirm the relevance of this signature, the relationship between the expression of the 578 genes associated with DNAm IC and the methylation of the 91 CpGs in the IC clock was analyzed. CpGs in the IC clock showed a correlation of 0.21 with the expression of at least one significantly associated gene, whereas an average correlation of 0.1 is expected by chance (permutation p < 0.0001 ) (FIG. 3E). This analysis confirms a strong connection between the IC clock and the identified gene expression signature of DNAm IC.

[0091] It was also identified which genes showed correlation with the levels of multiple CpGs and found that the expression of Mucolipin 2 (MCOLN2) correlated with the methylation of 61 out of 91 CpGs (67%) (FIG. 3F, Supplementary Table 3). It was observed that lower expression of MC0LN2 was associated with higher DNAm IC. Studies have found that MCOLN2 promotes virus entry and infection. Also, CD28 displayed a correlation with the methylation of more than half (53 of 91 CpGs) of the CpGs in the IC clock. The similarity between gene expression signatures associated with epigenetic clocks and DNAm IC (FIG. 3G) was also examined. It was observed that DNAm IC showed a moderate correlation with PhenoAge and Hannum (rs= 0.68 and 0.62, respectively), but lower correlation with GrimAge and Horvath (rs= 0.49 and 0.31 , respectively).

[0092] To further investigate the gene expression signature of DNAm IC, gene ontology enrichment analysis was performed to identify associated biological processes. Genes associated with age- adjusted DNAm IC were mostly involved in the immune response, particularly T-cell activation (FIG. 3H), Supplementary Table 4), consistent with the observation that the T-cell co-stimulatory molecule CD28 was among the top correlated genes.

[0093] To validate the associations between the IC expression signature and immune system or inflammatory processes, gene sets were compiled for each aging hallmark by leveraging large language models and 36 million journal abstracts from PubMed To identify genes linked to the 12 hallmarks of aging, we used a corpus of 36 million abstracts from PubMed (https: / / huggingface.co / datasets / ncbi / pubmed). First, the Applicant identified 71,129 abstracts including the word “aging” or “ageing” in the title or abstract. Then, large language models were used (GPT-4o mini) to analyze each abstract using the following query: "Your task is to identify genes associated with the hallmarks of aging from the following scientific abstract For each gene mentioned in the abstract, annotate it with the corresponding hallmark of aging (genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, disabled autophagy, chronicinflammation, dysbiosis)”. To perform enrichment analysis using these gene sets, the genes in the query signature were ranked by -loglO(p-value) and then performed a one-signed gene set enrichment analysis using the package fgsea v1.27. We found that the IC expression signature is strongly enriched in genes involved in cellular senescence and chronic inflammation (FIG. 3I).EXAMPLE 5

[0094] The following example demonstrates the relationship between DNAm IC and cell populations.

[0095] The Applicant estimated the proportions of CD8+T-cells, CD4+T-cells, natural killer cells, B cells, and granulocytes using Houseman's estimation method via the meffilEstimateCellCountsFromBetas function implemented in the methylclock package v1.2.1. The Applicant also estimated the number of CD8+naive and CD8+cytotoxic T-cells, and plasmablasts using blood cell count predictors developed by Horvath et al. The Applicant integrated the age data and calculated correlations between cell counts and age using Spearman’s correlation coefficients. Correlation analyses between the age-adjusted cell counts and the age-adjusted DNAm IC were also conducted.

[0096] The Houseman's method was used to estimate blood cell counts from epigenetic data and examine the relationship between DNAm IC and cell populations (FIG. 3J-L). While CD4+T-cell frequency decreased significantly with age (rs= -0.26, p = 1.46e-39), higher DNAm IC was associated with a greater number of CD4+T-cells (rs= 0.34, p = 2.65e-70). Similarly, CD8+naive cell numbers decreased with age (rs= -0 36, p = 3.18e-79) and increased in individuals with high DNAm I C ( rs= 0.22, p - 7.43e-30). A negative correlation was found between the number of CD8+exhausted and cytotoxic cells (defined as CD2& and CD45RA') with DNAm IC (rs= -0 18, p = 5.9e-19), which could explain why individuals with higher DNAm IC have higher levels of CD28 expression. Overall, these findings indicate that the IC clock detects aspects of immunosenescence in the blood that are associated with functional immune aging changes.

[0097] Next, the Applicant identified molecular correlates and distinguished potential mechanisms underlying the different domains of IC. DNA methylation-based predictors for each IC domain in the INSPIRE-T cohort were generated and the transcriptomic and cell composition changes associated in the FHS cohort were evaluated It was found that higher vitality scores were associated with upregulation of mitochondrial electron transport chain genes (FDR = 8.09e-03), while higher locomotion scores were linked to increased expression of genes regulating muscle adaptation and the Notch signaling pathway (FDR = 0.01 , FIG. 3M-N, Supplementary Table 5). Higher cognitive scores were associated with lower expression of pathways involved in neuron development and projection (FDR = 5.19e-04), while individuals with higher psychological scores showed down-regulation of the transcriptional response to DNA damage (FDR = 7.75e-08). Higher sensory scores were associated with up-regulation of ribosome biogenesis and down-regulation of immune response pathways (FDR = 1.67e-08). In a correlation analysis between cell composition changes and predicted IC scores, higher cognitive scores were associated with fewer granulocytes (rs= -0 27) and more CD4+ T cells (rs= 0.38), while higher psychological scores correlated with fewer B cells (rs= -0.28) and more plasmablasts (rs= 0.31 ) (Extended Data Fig. 5b) Additionally, higher sensory scores were associated with increased CD4+ and CD8+ naive T cells (rs= 0.38 and 0.25) and fewer CD8+ cytotoxic T cells (rs= -0.24). Vitalityand locomotion showed small correlations with cell counts (|rs| < 0.16) These results link genomic instability, mitochondrial dysfunction and loss of proteostasis to the function of specific IC domainsEXAMPLE 6

[0098] DNAm IC links to mortality, health markers and lifestyle: Although the IC clock was not trained on mortality data, it was hypothesized that the DNAm estimate of IC could also predict mortality, given previous findings showing that IC is a mortality risk factor. Using mortality data from the 1 ,680 individuals in the FHS, it was investigated as to whether DNAm IC was associated with an increased risk of mortality from all causes or age-related conditions. Age- and sex-adjusted residuals were computed for DNAm IC and each clock. The subjects were grouped into quintiles based on DNAm IC residuals, comparing the top and bottom 20%. Using the R package survival v3.5.7, Cox proportional hazards models were performed, adjusted for age and sex, to calculate hazard ratios (HRs) and 95% confidence intervals (Cis) associated with mortality from all causes, cardiovascular diseases, congestive heart failure, and stroke / TIA. Kaplan-Meier survival curves were plotted to visualize survival differences between high and low IC groups, and calculated p-values using a log-rank test.

[0099] It was found that DNAm IC was more strongly associated with all-cause mortality risk than PhenoAge, Horvath, and Hannum clocks (HR = 1.38, p = 1.67e-24) (FIG. 4A). This higher association between DNAm IC and all-cause mortality risk was conserved in individuals with different disease subgroups (FIG. 4I). In addition, DNAm IC was also more significantly associated with an increased risk of death from age-related diseases such as cardiovascular disease (HR = 1.29, p = 2.37e-08), congestive heart failure (HR = 1.33, p = 1.62e-06), and stroke or transient ischemic attack (TIA) (HR = 1.21 , p = 7.71e-03). Kaplan-Meier survival curves were examined for various causes of death comparing the quintiles with the highest and lowest DNAm IC. Based on the survival curves for allcause mortality, it was estimated that a person with high DNAm IC lives on average 5.5 years longer than someone with low DNAm IC (FIG. 4B).

[0100] The association between DNAm IC and assessments of physical and mental health, daily living activity questionnaires, overall health and biomarkers, and clinical measurements was calculated. First, all markers of health were analyzed using an integrative approach to derive a healthy aging score. Dimensionality reduction (PCA) was performed to extract a single variable representing overall health (i.e. Principal Component 1). It was found that overall health is positively correlated with DNAm IC (rs= -0.44), even more strongly than with chronological age (rs= -0 37) (FIG. 4C, FIG. 4D), and individuals within the highest and lowest 20% of overall health displayed significant differences in physical and mental health parameters (Supplementary Table 6). When individual parameters were analyzed, it was observed that individuals with high DNAm IC tended to display better pulmonary function, faster walk time, greater bone mineral density, and better self-reported health perception (FIG. 4E, Supplementary Table 7). A negative association was also observed between high DNAm IC and inflammatory age (iAge), C-reactive protein (CRP), lnterleukin-6 (IL-6), markers of neurodegeneration such as Tau, and smoking status (FIG 4F)

[0101] Using the comprehensive food frequency questionnaire from the FHS, the relationship between DNAm IC and specific food consumption was explored. It was found that individuals with higher DNAm IC tend to consume more beer (FDR = 5.07e-06) and dark meat fish (i.e. mackerel, salmon, sardines,bluefish, and swordfish) (FDR = 9.39e-03), but fewer calcium supplements (FDR = 7.59e-06) (Fig. 4G- H, Supplementary T able 8). The consumption of most flavonoids was associated with higher DNAm IC, but none reached statistical significance after multiple testing correction. Also, elevated blood levels of docosahexaenoic acid (FDR = 9.32e-10), docosapentaenoic acid (FDR = 3.71e-03), and eicosapentaenoic acid (FDR = 5.24e-03), all three long-chain omega-3 fatty acids from marine origin, were associated with higher DNAm IC. Lastly, the dietary guideline adherence questionnaire was analyzed and found that consuming sugar at the recommended level (<=5% of total energy) was associated with a higher DNAm IC (FDR = 3.82e-02) suggesting that deviations from recommended sugar intake guidelines significantly impact functional aging. Overall, these results suggest that consuming fish rich in long-chain omega-3 fatty acids and adhering to recommended sugar intake guidelines are associated with IC maintenance.

[0102] The correlation between age-adjusted IC and various physiological and clinical measurements were analyzed using data from the FHS, including clinic lab assays, inflammatory markers, Tau levels, cardiovascular risk factors, and dietary data. Spearman’s correlation coefficients and p-values were calculated for the relationship between IC and each feature, applying false discovery rate (FDR) adjustments for multiple comparisons. Significant correlations (FDR < 0.05) were visualized using the ComplexHeatmap package v2.1257, and scatter plots were created for dietary factors (flavonoids, fatty acids, food frequency, and dietary guidelines adherence).

[0103] All the analyses were performed using R v4.2.3, run on RStudio server V2022.02.3 build 492. Data handling and visualization also included the use of the following R packages: tidyverse v2.0, ggpubrv0.4, RColorBrewerv1.1.3, circlize vO.4.15, khroma v1.10, gridExtra v2.3, ggrepel vO.9.1 , viridis vO.6.2, ggplot2 v3.5.1 , ggh4x vO.2.8.9, and ggsci v2.9.

[0104] While the various systems described above are separate implementations, any of the individual components, mechanisms, or devices, and related features and functionality, within the various system embodiments described in detail above can be incorporated into any of the other system embodiments herein.

[0105] The terms “about” and “substantially,” as used herein, refers to variation that can occur (including in numerical quantity or structure), for example, through typical measuring techniques and equipment, with respect to any quantifiable variable, including, but not limited to, mass, volume, time, distance, wave length, frequency, voltage, current, and electromagnetic field. Further, there is certain inadvertent error and variation in the real world that is likely through differences in the manufacture, source, or precision of the components used to make the various components or carry out the methods and the like. The terms “about” and “substantially” also encompass these variations The term “about” and “substantially” can include any variation of 5% or 10%, or any amount - including any integer - between 0% and 10%. Further, whether or not modified by the term “about” or “substantially,” the claims include equivalents to the quantities or amounts.

[0106] Numeric ranges recited within the specification are inclusive of the numbers defining the range and include each integer within the defined range. Throughout this disclosure, various aspects of this disclosure are presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on thescope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible sub-ranges, fractions, and individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed sub-ranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 3, 4, 5, and 6, and decimals and fractions, for example, 1.2, 3.8, 11 , and 4% This applies regardless of the breadth of the range. Although the various embodiments have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

[0107] Although the various embodiments have been described with reference to preferred implementations, persons skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope thereof.

Claims

ClaimsWhat is claimed is:

1. A method of diagnosing a patient’s DNAm intrinsic capacity (IC) and guiding treatment to preserve functional ability, the method comprising: obtaining a sample from the patient; isolating DNA from the sample and determining DNA methylation (DNAm) markers, wherein the DNAm markers comprise methylation levels at predefined CpG sites associated with aging and physiological resilience; comparing the DNAm markers to a reference DNAm matrix to determine a biological age of the patient, wherein the reference DNAm matrix correlates specific methylation patterns with chronological and biological age ranges in healthy populations; and assessing DNAm IC of the patient by comparing the biological age to the patient’s chronological age and evaluating deviations across one or more physiological domains related to mobility, cognition, psychological well-being, sensory function, or vitality.

2. The method of claim 1 wherein the sample is a blood sample3 The method of claim 1 wherein the sample is a saliva sample4. The method of claim 1 wherein the method further comprises prescribing a treatment or intervention regimen to preserve or improve functional ability based on the assessment of the DNAm IC, the treatment selected from the group consisting of pharmacologic agents, nutritional plans, physical activity protocols, cognitive training, or lifestyle modifications.5 The method of claims 1 or 4, further comprising: repeating steps (a) through (e) at two or more time intervals; and tracking changes in the DNAm IC over time to assess treatment or intervention regimen.

6. The method of claims 1-5, further comprising: communicating the DNAm IC to a digital proxy, wherein the digital proxy comprises a remote server, electronic health care system, clinical decision support system, mobile health application, or monitoring device configured to store, display or act upon the DNAm IC for clinical use or patient management.

7. The method of claim 6, wherein the digital proxy is further configured to:generate alerts to clinicians or authorized users when the DNAm IC exceeds a predefined threshold; display longitudinal trends in the DNAm IC across time for clinical evaluation; integrate the DNAm IC into a clinical decision support system for guiding diagnosis, treatment, or escalation of care; and secure the communication and storage of the DNAm IC using encryption and authentication protocols to protect patient data integrity and privacy in compliance with applicable data protection regulations.

8. A method of creating a DNAm intrinsic capacity score for a subject, the method comprising: receiving a DNAm signature derived from a sample of the subject; creating input vectors based on the DNAm signature; inputting the input vectors into a machine learning platform; and generating a predicted intrinsic capacity score of the sample based on the input vectors by the machine learning platform, wherein the intrinsic capacity score is specific to the sample; and preparing a report that includes the intrinsic capacity score that identifies a predicted intrinsic capacity of the sample.

9. The method of claim 8, the method further comprising using the DNAm intrinsic capacity score to determine levels of gene expression of specific genes, the method further comprising: comparing the DNAm IC to a reference DNAm IC matrix, wherein the reference DNAm matrix correlates with gene expression levels in healthy populations; assessing gene expression by comparing the DNAm IC of the sample and assigning a value to deviations from the reference DNAm matrix; and assigning a value to gene expression levels based on the DNAm IC value.

10. The method of claim 9 wherein the sample is a blood sample11 . The method of claim 9 wherein the sample is a saliva sample.

12. The method of claim 9 wherein the gene expression levels correspond to levels ofCluster of Differentiation 28 (CD28), PFTAIRE Protein Kinase 1 (PFTK1 / CDK14) or Mucolipin 2 (MCOLN2).

13. A method for determining a biological age of a patient sample, the method comprising:selectively detecting a presence of methylation corresponding to a set of methylation markers in genomic DNA of the patient sample, said set of methylation markers comprising at least 60% of CpG methylation markers listed in Table 1; and comparing the detected methylation markers to reference markers for the methylation markers to determine the age of the sample based on said methylation levels.

14. The method of claim 13 wherein the set of methylation markers comprises at least 80% of the CpG methylation markers listed in Table 1.

15. The method of claim 13 wherein the set of methylation markers comprises at least 90% of the CpG methylation markers listed in Table 1.

16. The method of claims 13-15 wherein the method further comprises prescribing a treatment or intervention regimen to preserve or improve functional ability based on the assessment of the DNAm IC, the treatment selected from the group consisting of pharmacologic agents, nutritional plans, physical activity protocols, cognitive training, or lifestyle modifications.

17. The method of claims 13-15, further comprising: repeating steps (a) through (e) at two or more time intervals; and tracking changes in the DNAm IC over time to assess treatment or intervention regimen.

18. The method of claims 13-17, further comprising: communicating the DNAm IC to a digital proxy, wherein the digital proxy comprises a remote server, electronic health care system, clinical decision support system, mobile health application, or monitoring device configured to store, display or act upon the DNAm IC for clinical use or patient management.

19. The method of claim 18, wherein the digital proxy is further configured to: generate alerts to clinicians or authorized users when the DNAm IC exceeds a predefined threshold; display longitudinal trends in the DNAm IC across time for clinical evaluation; integrate the DNAm IC into a clinical decision support system for guiding diagnosis, treatment, or escalation of care; and secure the communication and storage of the DNAm IC using encryption and authentication protocols to protect patient data integrity and privacy in compliance with applicable data protection regulations

Images