Improved methods of determining immune age

Abstract

Methods of determining the immunological age of a subject comprising measuring relative abundance of all immune cell populations in an immune cell panel in a blood sample from the subject are provided. Methods of determining the percentage of stenosis in a subject suffering from coronary artery disease and predicting response to anti-IgE immunotherapy based on the subject's immune age are also provided.

Description

IMPROVED METHODS OF DETERMINING IMMUNE AGECROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority of U.S. Provisional Patent Application No. 63 / 650,552, filed May 22, 2024, the content of which is incorporated herein by reference in its entirety.FIELD OF INVENTION

[0001] The present invention is in the field of immune aging.BACKGROUND OF THE INVENTION

[0002] Although the immune system’s state has a strong effect on the outcome of a wide range of medical conditions and life-saving treatments, existing tools do not provide a simple metric for measuring its strength. Thereby making it difficult for physicians to make informed medical decisions for each patient.

[0003] Previous work tracked the longitudinal movement of ~ 140 healthy individuals across 9 years and identified a high-dimensional trajectory that shed light on the dynamics of how the immune system ages (Alpert et al., “A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring”, 2019, Nature Medicine, 25, 487- 495; and International Patent Application PCT / IL2019 / 050523) - a phenomenon known as immunosenescence. Critically, the position of a given patient at a given time along this trajectory defines a single-score metric termed immune age (IMM-AGE). IMM-AGE is an integrative metric that summarizes environmental exposures, dietary habits, age, genetic factors, and lifestyle into a single value. The IMM-AGE score reflects the state and the strength of one’s immune system, and it outperformed current clinical criteria for predicting all-cause mortality but an association between common causes of mortality, such as cardiovascular diseases, and IMM-AGE were left uncharacterized.

[0004] To date, IMM-AGE is assessed by whole transcriptome sequencing or by measuring the frequencies of 18 different bloodborne immune cells by Cytometry by time of flight (CyTOF) - expensive, technically challenging methods unavailable to the clinical world. Therefore, even though IMM-AGE outperforms existing mortality predictors, its potentialhas yet to be realized. Novel methods for IMM-AGE assessment that can be performed in clinical setting are greatly needed.SUMMARY OF THE INVENTION

[0005] The present invention provides methods of determining the immunological age of a subject comprising measuring relative abundance of all immune cell populations in an immune cell panel in a blood sample from the subject. Methods of determining the percentage of stenosis in a subject suffering from coronary artery disease and predicting response to anti-IgE immunotherapy based on the subject’s immune age are also provided. Kits comprising detecting molecules for determining the relative abundance of all immune cell populations in an immune cell panel are also provided.

[0006] According to a first aspect, there is provided a method of determining the immunological age of a subject, comprising: a. measuring relative abundance of all immune cell populations in an immune cell panel in a blood sample from the subject, wherein the panel comprises at least one of: i. at least 6 of 7 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and all CD3+ cells; ii. at least 7 of 8 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CD25+, CD127- cells; and all CD3+ cells; and iii. at least 7 of 8 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells;CD3+, CD8+, CD4-, PD1+ cells; and all CD3+ cells; and b. determining an immunological age of the subject by at least one of: i. comparing the measurement of relative abundance of immune cell populations in the immune cell panel to a dataset of measurements of immune cell population relative abundance in subjects with predetermined immunological ages; ii. combining the measurement of relative abundances with data of measurements of relative abundance from at least 19 other subjects to produce a database, and calculating from the database a trajectory for all at least 20 subjects based on the measurements of relative abundance; and iii. determining an immune age score, wherein the determining comprises: a. for all immune cell populations whose abundance correlates with immune age, summing the abundance of cells in the blood sample and subtracting their mean in the sample to produce an UP- score; b. for all immune cell populations whose abundance negatively correlates with immune age, summing the abundance of cells in the blood sample and subtracting their mean in the sample to produce a DOWN-score; and c. subtracting the DOWN-score from the UP- score to produce an immune age score, wherein the immune age score is proportional to the subject’s immunological age; thereby determining the immunological age of the subject.

[0007] According to some embodiments, the panel comprises of at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; and CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells;ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8-, CD4+, CD25+, CD127- cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells

[0008] According to some embodiments, the panel consists of at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and all CD3+ cells; ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CD25+, CD127- cells; and all CD3+ cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells; and all CD3+ cells.

[0009] According to some embodiments, the panel further comprises all CD3+ cells.

[0010] According to some embodiments, the measuring relative abundance comprises flow cytometry analysis of relative abundance of each cell population within the blood sample.[Oi l] According to some embodiments, the comparing comprises employing a distance metric with respect to immune cell population abundance in the sample and samples of the dataset.

[0012] According to some embodiments, the comparing comprises selecting from the dataset individuals with a smallest distance with respect to relative abundance of the immune cell populations of the panel and averaging an immunological age of the selected individuals.

[0013] According to some embodiments, the calculating a trajectory comprises applying a non-linear dimensionality reduction algorithm to the population relative abundancies.

[0014] According to some embodiments, an immune age score above a predetermined threshold indicates the subject’s immune age is greater than the subject’s biological age, an immune age score below a predetermined threshold indicates the subject’s immune age is less than the subject’s biological age, or both.

[0015] According to some embodiments, the measuring immune cell relative abundance comprises estimating immune cell relative abundance by measuring gene expression in the blood sample.

[0016] According to some embodiments, immune cell populations whose abundance correlates with immune age are selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CD57+ cells; CD3+, CD8+, CD4-, PD1+ cells and CD3+, CD8-, CD4+, CD25+, CD 127- cells and immune cell populations whose abundance negatively correlates with immune age are selected from the group consisting of: CD3+ cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells.

[0017] According to another aspect, there is provided a method of determining the immunological age of a subject, comprising: a. measuring the ratio of expression levels of a panel of gene pairs in a blood sample from the subject, wherein the panel comprises the gene pairs provided in Table 7, provided in Table 6 or provided in Table 2; b. determining an immunological age of the subject by at least one of: i. comparing the measurement of gene pair expression level ratio to a dataset of measurements of gene pair expression level ratios in subjects with predetermined immunological age;ii. applying an algorithm to the measured gene pair expression level ratios and all possible gene pair ratios for all genes measured to determine the enrichment of the panel of gene pairs within all possible gene pairs, wherein the enrichment is proportional to immunological age; and iii. determining a gene ratio score wherein the determining comprises:1. for all ratios that correlate with immune age, summing the ratio of expression for each gene pair and subtracting the average ratio of expression for all gene pairs to produce an UP-score;2. for all ratios that negatively correlate with immune age summing the ratio of expression for each gene pair and subtracting the average ratio of expression for all gene pairs to produce a DOWN-score;3. subtracting the DOWN-score from the UP-score to produce a gene ratio score, wherein the gene ratio score is proportional to immunological age; thereby determining the immunological age of the subject.

[0018] According to some embodiments, the algorithm is Single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm.

[0019] According to some embodiments, the comparing comprises employing a distance metric with respect to gene pair expression level ratios in the sample and samples of the dataset.

[0020] According to some embodiments, the comparing comprises selecting from the dataset individuals with a smallest distance with respect to gene pair expression level ratios and averaging an immunological age of the selected individuals.

[0021] According to some embodiments, ratios that correlate with immune age are selected from MPO / NOG, MXRA7 / SIRPG, DIS3L2 / NOG, JAKMIP1 / ZNF84, MXRA7 / PLXDC1, MXRA7 / ZFP82, Clorf21 / ZNF264, EOXE3 / NOG, MXRA7 / SATB1, GAB3 / ZNF264, Clorf21 / FCRE1, and MXRA7 / POU2AF1 and ratios that negatively correlate with immune age are selected from ARHGEF18 / MXRA7, CD96 / GZMH, BACH2 / PJA1, ITGA6 / MXRA7, PAICS / RASGEF1A, VPREB3 / ZNF319, ABEIM1 / MXRA7, BZW2 / Clorf21, GNG7 / EOXE3, RAB3O / RGS3, BACH2 / FCRE6, NOG / TTC38,FBLN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and N0G / TTC16.

[0022] According to some embodiments, the ratio FCRL6 / TRAF5 correlates with immune age and ratios that negatively correlate with immune age are selected from BACH2 / JAKMIP1, EPHX2 / FCRL6, NT5E / PPP2R2B, NT5E / TGFBR3, ABLIM1 / GPR68, CR2 / FCRL6, LRRN3 / PRKCA and LRRN3 / ZNF563.

[0023] According to another aspect, there is provided a method of determining the immunological age of a subject, comprising: a. measuring the methylation status of a panel of CpG dinucleotides in a blood sample from the subject, wherein the panel comprises Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552 the CpGs provided in Table 3; and b. determining an immunological age of the subject by at least one of: i. comparing the measured methylation statuses to a dataset of measurements of methylation statuses in subjects with predetermined immunological age; and ii. providing a weight to each CpG dinucleotide in the panel, wherein the weight is a measure of that CpG dinucleotide’s contribution to immunological age, to provide a CpG score and combining the CpG score of all CpG dinucleotides of the panel to produce a total CpG score and wherein the total CpG score is proportional to immunological age; thereby determining the immunological age of the subject

[0024] According to some embodiments, the weights are determined by applying stability selection model to a dataset of methylation measurement statuses of the panel of CpG dinucleotides in subjects with known immunological age.

[0025] According to another aspect, there is provided a method of determining the immunological age of a subject, comprising:a. measuring the ratio of immune cell population abundance pairs in a blood sample from the subject, wherein the population pairs comprise at least one of the following panels: i. CD28- CD8+ T cells / T follicular helper (TFH) CD4+ T cells, CD28- CD8+ T cells / naive CD8+ T cells,CD57+ NK cells / naive CD8+ T cells, CD57+ CD8+ T cells / TFH CD4+ T cells, CD57+ CD8+ T cells / naive CD8+ T cells, TFH CD4+ T cells / effector CD8+ T cells, effector CD 8+ T cells / naive CD4+ T cells, effector CD8+ T cells / naive CD8+ T cells and effector memory CD4+ T cells / naive CD8+ T cells; ii. effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD28- CD8+ T cells; and iii. effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / CD57+ CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD57+ CD8+ T cells; b. determining an immunological age of the subject by at least one of: i. determining an immune population ratio score, wherein the determining comprises applying a weight to each immune cell population abundance ratio to produce a product and summing all the products to produce an immune population ratio score; ii. comparing the measurement of population pair ratio to a dataset of measurements of population pair ratios in subjects with predetermined immunological age; and iii. applying an algorithm to the measured population pair ratios and all possible population pair ratios for all populations measured to determine the enrichment of the panel of population pair within allpossible population pair, wherein the enrichment is proportional to immunological age; thereby determining the immunological age of the subject.

[0026] According to some embodiments, the weight is the loading of the ratio on the leading axis of a low dimensional representation of each ratio’s contribution to immune age, optionally wherein the low dimensional representation is principle component analysis (PCA).

[0027] According to some embodiments, the blood sample is a peripheral blood sample.

[0028] According to some embodiments, immunological age is relative immunological age as compared to the subjects biological age.

[0029] According to some embodiments, the method further comprises diagnosing the subject with increased risk of illness when the subject’s immunological agent age is greater than the subject’s chronological age.

[0030] According to some embodiments, the method further comprises diagnosing the subject with a relative increased risk of illness when the subject’s immunological age is greater than an immunological age of at least one other subject of the same chronological age.

[0031] According to some embodiments, the illness is cardiovascular disease.

[0032] According to some embodiments, the cardiovascular disease is coronary artery disease (CAD).

[0033] According to some embodiments, the subject has suffered from a myocardial infarction and the cardiovascular disease is heart failure (HF).

[0034] According to some embodiments, the increased risk of illness is increased risk of allcause mortality.

[0035] According to some embodiments, the method further comprises providing to the subject a prophylactic regimen for the illness or more frequent illness -related screening procedures.

[0036] According to some embodiments, the subject suffers from CAD and further comprising determining the percentage stenosis in the subject wherein the percentage stenosis is proportional to the subject’s immune age.

[0037] According to some embodiments, the method further comprises predicting response of the subject to an anti-IgE immunotherapy, wherein an immune age above a predetermined threshold indicates the subject will not respond to the anti-IgE immunotherapy and an immune age below a predetermined threshold indicates the subject will respond to the anti- IgE immunotherapy.

[0038] According to some embodiments, the anti-IgE immunotherapy is an anti-IgE monoclonal antibody.

[0039] According to some embodiments, the anti-IgE monoclonal antibody is omalizumab.

[0040] According to some embodiments, the subject suffers from a disease treatable by the anti-IgE immunotherapy.

[0041] According to some embodiments, the disease is asthma.

[0042] According to another aspect, there is provided a method of determining the percentage stenosis in a subject suffering from CAD, the method comprising determining the subject’s immune age based on a blood sample from the subject, wherein the subjects immune age is proportional to the percentage stenosis in the subject, thereby determining the percentage stenosis in a subject suffering from CAD.

[0043] According to another aspect, there is provided a method of predicting response of a subject suffering from a disease treatable with an anti-IgE immunotherapy to the anti-IgE immunotherapy, the method comprising determining the subject’s immune age based on a blood sample from the subject, wherein an immune age above a predetermined threshold indicates the subject will not respond to the anti-IgE immunotherapy and an immune age below a predetermined threshold indicates the subject will respond to the anti-IgE immunotherapy, thereby predicting response of a subject to an anti-IgE immunotherapy.

[0044] According to some embodiments, the anti-IgE immunotherapy is an anti-IgE monoclonal antibody.

[0045] According to some embodiments, the anti-IgE monoclonal antibody is omalizumab.

[0046] According to some embodiments, the disease is asthma.

[0047] According to some embodiments, the immune age is determined by a method of any one of the invention.

[0048] According to some embodiments, the immune age is determined by a method of the invention.

[0049] According to another aspect, there is provided a kit comprising detecting molecules for determining the relative abundance of all immune cell populations in an immune cell panel, wherein the panel comprises at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; and CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8-, CD4+, CD25+, CD127- cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells.

[0050] According to some embodiments, the kit comprises antibodies against: a. CD3, CD8, CD4, CD28, CCR7, and CD45RA; b. CD3, CD8, CD4, CD28, CCR7, CD45RA, CD25 and CD127; or c. CD3, CD8, CD4, CCR7, CD45RA, CD57 and PD1.

[0051] According to some embodiments, each antibody comprises a distinct fluorophore label enabling separate detection of each antibody.

[0052] Further embodiments and the full scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figures 1A-1D: Reduce IMM-AGE panels outperform the original one. (1A) To replace the need for CyTOF when estimating IMM-AGE, we modelled reduced cell panels compatible with a clinical flow cytometer. We identified panels that recapitulate the features of the original CyTOF panel. (1B-1D) Recapturing of the original ordering of the samples on the IMM-AGE trajectory achieved by the reduced combination (IB) 55, (1C) 83 and (ID) 114.

[0054] Figure 2: Negligible error rates are observed when IMM-AGE is assessed by the reduced combinations. To further assess how robust clinical estimation of IMM-AGE using a reduced panel of antibodies would be, we approximated 95% confidence intervals and observed negligible error rates.

[0055] Figures 3A-3C: Line graphs of percent of the original signal (correlation) of the IMM-AGE prediction made when one cellular population is left out of the (3A) 55 combination, (3B) 83 combination and (3C) 114 combination.

[0056] Figures 4A-4B: IMM-AGE assessment of newly incoming patients. (4A) We utilized newly generated CyTOF data, measured on the same samples as in the original study, and omitted cell populations not chosen by our optimization algorithm. We projected the simulated data onto a flow-cytometry based trajectory. (4B) We show high preservation of the original trajectory by leveraging the spearman correlation between the original IMM- AEG score and the projected IMM-AGE score.

[0057] Figures 5A-5N: Platform independent IMM-AGE gene-expression-ratio based signature for further implementation of IMM-AGE in clinics and research. (5A-B) 31 gene-ratio panel based signature recapitulates the IMM-AGE scores of the individuals in the Framingham Heart Study when (5A) analyzed by enrichment analysis and (5B) by subtraction analysis. (5C-F) 163 gene-ratio panel based signature recapitulates the IMM- AGE scores based on (5C-D) expression and (5E-F) CyTOF when (5C, 5E) analyzed by enrichment analysis and (5D, 5F) by subtraction analysis. (5G-J) 17 gene-ratio panel based signature recapitulates the IMM-AGE scores based on (5G-H) expression and (5I-J) CyTOF when (5G, 51) analyzed by enrichment analysis and (5H, 5J) by subtraction analysis. (5K- N) 9 gene-ratio panel based signature recapitulates the IMM-AGE scores based on (5K-L) expression and (5M-N) CyTOF when (5K, 5M) analyzed by enrichment analysis and (5L, 5N) by subtraction analysis.

[0058] Figure 6: Prediction of IMM-AGE based on DNA methylation data. DNA methylation-based prediction of IMM-AGE scores of the individuals in the Framingham Heart Study.

[0059] Figures 7A-7D: Platform independent IMM-AGE gene-expression-ratios allow broad assessment of IMM-AGE relevance across a broad range of clinical conditions. (7A) We leveraged RNA-seq data of patients admitted for elective invasive coronary catheterization (GSE221911) and showed that IMM-AGE predicts stenosis percentage. (7B) Box plot of IMM-AGE by patient stenosis severity. Control patients have the lowest IMM- AGE, significantly lower than patients with intermediate or severe disease. (7C) Box plot of IMM-AGE of subjects with myocardial infarction separated by whether they developed heart failure (HF) following the infarction. (7D) We leveraged whole-genome blood gene expression data of Asthma patients treated with Omalizumab (GSE134544). We showed that non-responders have higher IMM-AGE scores at baseline (before treatment) than responders (Wilcoxon, p=0.05).

[0060] Figures 8A-I: (8A) (Top) Dimension reduction algorithm (e.g., PCA) of the top 9 selected cell type ratios shows a clear trajectory from low to high pseudotime values, colored from blue (young immune age) to red (old immune age). The major component of the reduction, which captures greater than 80% of the total variance, serves as the new immune pseudotime axis. (Bottom) The loadings of the leading axis in the low dimensional representation for each of the 9 selected ratios reveal their contributions to the pseudotime axis. (8B) Correlation between the original and newly computed pseudotime axes, with samples colored by age group (dark = young, light = old). (8C) The new pseudotime axis (PCI) shows a strong positive correlation (Pearson r = 0.89) with the original IMM-AGE score, with a linear regression line and confidence interval shown. (8D-E) The loadings of the leading axis in the low dimensional representation for (8D) cell ratio Panel 1 and (8E) cell ratio Panel 2 reveal their contributions to the pseudotime axis. (8F-G) Dimension reduction algorithm (e.g., PCA) of (8F) cell ratio Panel 1 and (8G) cell ratio Panel 2 show a clear trajectory from low to high pseudotime values, colored from blue (young immune age) to red (old immune age). (8H-I) The new pseudotime axis (PCI) for (8H) cell ratio Panel 1 and (81) cell ratio Panel 2 show a strong positive correlation (Pearson r = 0.87 for each) with the original IMM-AGE score, with a linear regression line and confidence interval shown.DETAILED DESCRIPTION OF THE INVENTION

[0061] The present invention, in some embodiments, provides methods of determining the immunological age of a subject comprising measuring relative abundance of all immune cell populations in an immune cell panel in a blood sample from the subject. The present invention further concerns methods of determining the percentage of stenosis in a subject suffering from coronary artery disease and predicting response to anti-IgE immunotherapy based on the subject’s immune age. Kits comprising detecting molecules for determining the relative abundance of all immune cell populations in an immune cell panel are also provided.

[0062] The invention is based on the surprising finding that a subject’s immunological age (which need not correspond to the subject’s biological age) can be determined by measuring the relative abundance of specific immune cell populations in the blood and producing an immune age score based on those populations. It was determined that amongst these specific populations certain populations positively correspond with immune age and certain populations negatively correlate. By comparing the abundance of each population to a control or by generating UP and DOWN scores for the positively and negatively correlating populations an immune age score can be generated. These populations can all be measured (e.g., by FACS) using a small panel of surface protein markers (eight or fewer) which enables simple age calculation using standard equipment without a high-level multiplexing device of deep omics analysis.

[0063] By a first aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the abundance of immune cell populations in an immune cell panel in a sample from the subject; and b. determining an immunological age of the subject by comparing the measurement of abundance of the immune cell populations in the immune cell panel to a dataset of measurements of immune cell population abundance in subjects with predetermined immunological ages, thereby determining the immunological age of the subject.

[0064] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the abundance of immune cell populations in an immune cell panel in a sample from the subject;b. combining the measurement of abundance of the immune cell populations in the immune cell panel with data of measurements of relative abundances from other subjects to produce a database; and c. calculating from the database a trajectory for all subjects based on the measurements; thereby determining the immunological age of the subject.

[0065] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the abundance of immune cell populations in an immune cell panel in a sample from the subject; and b. determining an immunological age of the subject by determining an immune age score, wherein the immune age score is proportional to the subject’s immunological age; thereby determining the immunological age of the subject.

[0066] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of expression levels of a panel of gene pairs in a sample from the subject; and b. determining an immunological age of the subject by comparing the measured gene pair expression level ratios of the panel of gene pairs to a dataset of measurements of gene pair expression level ratios in subjects with predetermined immunological ages, thereby determining the immunological age of the subject.

[0067] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of expression levels of a panel of gene pairs in a sample from the subject; and b. determining the enrichment of the panel of gene pairs within all possible gene pairs, wherein the enrichment is proportional to immunological age; thereby determining the immunological age of the subject.

[0068] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of expression levels of a panel of gene pairs in a sample from the subject; and b. determining an immunological age of the subject by determining a gene ratio score, wherein the gene ratio score is proportional to the subject’s immunological age; thereby determining the immunological age of the subject.

[0069] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the methylation status of a panel of CpG dinucleotides in a sample from the subject; and b. determining an immunological age of the subject by comparing the measured methylation statuses of the panel of CpG dinucleotides to a dataset of measurements of methylation statuses in subjects with predetermined immunological ages, thereby determining the immunological age of the subject.

[0070] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the methylation status of a panel of CpG dinucleotides in a sample from the subject; and b. determining an immunological age of the subject by determining a total CpG score, wherein the total CpG score is proportional to the subject’s immunological age; thereby determining the immunological age of the subject.

[0071] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of abundance of immune cell population pairs in a sample from the subject; andb. determining an immunological age of the subject by comparing the measured immune population ratios to a dataset of measurements of immune population ratios in subjects with predetermined immunological ages, thereby determining the immunological age of the subject.

[0072] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of abundance of immune cell population pairs in a sample from the subject; and b. determining the enrichment of the immune population ratios within all possible immune population ratios, wherein the enrichment is proportional to immunological age; thereby determining the immunological age of the subject.

[0073] By another aspect, there is provided a method of determining the immunological age of a subject, the method comprising: a. measuring the ratio of abundance of immune cell population pairs in a sample from the subject; and b. determining an immunological age of the subject by determining a immune population ratio score, wherein the immune population ratio score is proportional to the subject’s immunological age; thereby determining the immunological age of the subject.

[0074] As used herein, the term “immunological age” refers to the approximate age of a subject’s immune system. In some embodiments, immunological age is predictive of future incidence of illness. In some embodiments, immunological age is predictive of future mortality. In some embodiments, mortality is all-cause mortality. In some embodiments, immunological age is predictive of future illness. In some embodiments, immunological age is predictive of increased risk of illness. In some embodiments, immunological age is predictive of cytokine response score (CRS). In some embodiments, immunological age corresponds to CRS. In some embodiments, illness is cardiovascular disease. In some embodiments, the cardiovascular disease is coronary artery disease (CAD). In some embodiments, the cardiovascular disease is heart failure (HF). In some embodiments, the subject has suffered from myocardial infarction (MI) and the cardiovascular disease is HF.In some embodiments, the subject does not suffer from cardiovascular disease. In some embodiments, the subject suffers from cardiovascular disease and immunological age is predictive of developing HF. In some embodiments, the immunological age is predictive of percentage stenosis in the subject. In some embodiments, the immunological age is predictive of percentage HF in the subject. In some embodiments, the immunological age diagnoses stenosis. In some embodiments, the immunological age diagnoses a risk of HF. In some embodiments, immunological age is predictive of response to anti-IgE therapy. In some embodiments, the therapy is immunotherapy. In some embodiments, immunological age is more predictive and / or corresponding than chronological age. In some embodiments, immunological age is more predictive and / or corresponding than methylation age. In some embodiments, immunological age is at least 5, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 100, 150, 200, 250, 300, 400, 500, 600, 700, 750, 800, 900, or 1000% more predictive. Each possibility represents a separate embodiment of the invention. In some embodiments, greater predictivity is greater accuracy.

[0075] In some embodiments, immunological age is a relative measure. In some embodiments, immunological age is an absolute measure. In some embodiments, immunological age is as compared to the immunological age of another individual of the same chronological age. In some embodiments, immunological age is as compared to the immunological age of another individual of the same methylation age. In some embodiments, immunological age is as compared to the subject’s actual chronological age. In some embodiments, determining immunological age is estimating immunological age. In some embodiments, determining immunological age is determining approximate immunological age. In some embodiments, immunological age is relative immunological age. In some embodiments, relative immunological age is as compared to the subject’s biological age. In some embodiments, biological age is chronological age. It will be understood that a subject with a high immune age score has an immunological age that is higher than the subjects biological age and a low immune age score indicates an immune age that is younger than the subj ect’ s biological age. In some embodiments, an immune age score above a predetermined threshold indicates the subject has an immune age older than their biological age. In some embodiments, the threshold is the immune age score in control subjects found to have an immune age equal to their biological age. In some embodiments, control subjects are healthy subjects. In some embodiments, control subjects are young subjects.

[0076] In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the subject is a veterinary animal. In some embodiments, the subject is in need of determining its immunological age. In some embodiments, the subject is elderly. In some embodiments, elderly is at least 30, 35, 40, 45, 50, 55, 60, 65, 70, 76, 80, 85 or 90 years old. Each possibility represents a separate embodiment of the invention. In some embodiments, the subject is at least 40 years old. In some embodiments, the subject is at least 60 years old. In some embodiments, the subject is at risk for developing an illness. In some embodiments, the illness is cardiovascular disease. In some embodiments, the illness is death. In some embodiments, the death is all cause mortality. In some embodiments, the subject is healthy. In some embodiments, the subject is ill. In some embodiments, the subject is at risk of a compromised immune system. In some embodiments, the subject suffers from cardiovascular disease. In some embodiments, the subject suffers from CAD. In some embodiments, the subject suffers from myocardial infarction. In some embodiments, the subject has had a myocardial infarction.

[0077] In some embodiments, the panel is a panel of immune cell populations. In some embodiments, the immune cell populations are identified by surface protein expression. In some embodiments, the immune cell populations are T cell populations. In some embodiments, the panel is selected from a panel provided in Table 4. In some embodiments, the panel is selected from combination 55, 83 and 114.

[0078] In some embodiments, the panel is combination 55. In some embodiments, the panel comprises combination 55. In some embodiments, the panel consists of combination 55. In some embodiments, combination 55 comprises: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, combination 55 comprises 6 of 7 cell populations selected from the group consisting of: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, naive CD4 T cells, naive CD8 T cells and all CD3+ cells. In some embodiments, CD28 negative CD8 T cells are CD3+, CD8+, CD4-, and CD28- cells. In some embodiments, CD8 effector T cells are CD3+, CD8+, CD4-, CCR7-, and CD45RA+ cells. In some embodiments, CD8 effector memory T cells are CD3+, CD8+, CD4-, CCR7-, and CD45RA- cells. In some embodiments, CD4 effector memory T cells are CD3+, CD8-, CD4+, CCR7-, and CD45RA- cells. In some embodiments, naive CD4 T cells are CD3+, CD8-, CD4+, CCR7+, and CD45RA+ cells. In some embodiments, naive CD8 T cells are CD3+, CD8+, CD4-, CCR7+, and CD45RA+ cells. In some embodiments, the panel comprises the following six populations: 1) cells thatare CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8-, CD4+, CCR7- , and CD45RA-, and 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+. In some embodiments, the panel comprises six populations. In some embodiments, the panel further comprising all CD3+ cells. In some embodiments, the panel comprises seven populations. In some embodiments, combination 55 comprises: all T cells, CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, the panel consists of the following six populations: 1) cells that are CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, and 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+. In some embodiments, the panel consists of the following seven populations: 1) cells that are CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8- , CD4+, CCR7-, and CD45RA-, 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, and 7) cells that are CD3+.

[0079] In some embodiments, the panel is combination 83. In some embodiments, the panel comprises combination 83. In some embodiments, the panel consists of combination 83. In some embodiments, combination 83 comprises: CD57 positive CD8 T cells, effector CD8 T cells, effector memory CD4 T cells, effector memory CD8 T cells, PD-1 CD8 T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, combination 83 comprises 7 of 8 cell populations selected from the group consisting of: CD57 positive CD8 T cells, effector CD8 T cells, effector memory CD4 T cells, effector memory CD8 T cells, PD-1 CD8 T cells, naive CD4 T cells, naive CD8 T cells, and all CD3+ cells. In some embodiments, CD57 positive CD8 T cells are CD3+, CD8+, CD4-, and CD57+ cells. In some embodiments, PD- 1 CD8 T cells are CD3+, CD8+, CD4-, and PD1+ cells. In some embodiments, the panel comprises the following seven populations: 1) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 3) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 4) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, 5) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, 6) cells that are CD3+, CD8+, CD4-, and CD57+, and 7) cells that are CD3+, CD8+, CD4-,and PD1+. In some embodiments, the panel comprises seven populations. In some embodiments, the panel further comprising all CD3+ cells. In some embodiments, the panel comprises eight populations. In some embodiments, combination 83 comprises: all T cells, CD57 positive CD8 T cells, effector CD8 T cells, effector memory CD4 T cells, effector memory CD8 T cells, PD-1 CD8 T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, the panel consists of the following seven populations: 1) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 3) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 4) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, 5) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, 6) cells that are CD3+, CD8+, CD4-, and CD57+, and 7) cells that are CD3+, CD8+, CD4-, and PD1+. In some embodiments, the panel consists of the following eight populations: 1) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 3) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 4) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, 5) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, 6) cells that are CD3+, CD8+, CD4-, and CD57+, 7) cells that are CD3+, CD8+, CD4-, and PD1+, and 8) cells that are CD3+.

[0080] In some embodiments, the panel is combination 114. In some embodiments, the panel comprises combination 114. In some embodiments, the panel consists of combination 114. In some embodiments, combination 114 comprises: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, T regulatory (Treg) cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, combination 114 comprises 7 of 8 cell populations selected from the group consisting of: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, T regulatory (Treg) cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, Tregs are CD3+, CD8-, CD4+, CD25+, CD127- cells. In some embodiments, the panel comprises the following seven populations: 1) cells that are CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, and 7) cells that are CD3+, CD8-, CD4+, CD25+, and CD127-. In some embodiments, the panel comprises seven populations. In some embodiments, the panel further comprising all CD3+ cells. In some embodiments, the panel comprises eight populations. In some embodiments, combination 114 comprises: all T cells, CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8effector memory T cells, T regulatory (Treg) cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, Tregs are CD3+, CD8-, CD4+, CD25+, CD127- cells. In some embodiments, the panel consists of the following seven populations: 1) cells that are CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA- , 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, and 7) cells that are CD3+, CD8-, CD4+, CD25+, and CD127-. In some embodiments, the panel consists of the following eight populations: 1) cells that are CD3+, CD8+, CD4-, and CD28-, 2) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA-, 3) cells that are CD3+, CD8+, CD4-, CCR7-, and CD45RA+, 4) cells that are CD3+, CD8+, CD4-, CCR7+, and CD45RA+, 5) cells that are CD3+, CD8-, CD4+, CCR7-, and CD45RA-, 6) cells that are CD3+, CD8-, CD4+, CCR7+, and CD45RA+, and 7) cells that are CD3+, CD8-, CD4+, CD25+, and CD127-, and 8) cells that are CD3+.

[0081] In some embodiments, measuring the ratio of abundance of immune cell population pairs is measuring the ratio of abundance of a panel of immune cell population pairs. In some embodiments, the panel is a panel of immune cell population pairs. In some embodiments, the panel is a panel of immune cell population ratios. In some embodiments, the panel is the panel provided in Table 8. In some embodiments, the panel is the panel provided in Table 10. In some embodiments, the panel is the panel provided in Table 11.

[0082] In some embodiments, the panel comprises the panel in Table 8. In some embodiments, the panel consists of the panel in Table 8. In some embodiments, the panel comprises the ratios of: CD28- CD8+ T cells / T follicular helper (TFH) CD4+ T cells, CD28- CD8+ T cells / naive CD8+ T cells, CD57+ NK cells / naive CD8+ T cells, CD57+ CD8+ T cells / TFH CD4+ T cells, CD57+ CD8+ T cells / naive CD8+ T cells, TFH CD4+ T cells / effector CD8+ T cells, effector CD8+ T cells / naive CD4+ T cells, effector CD8+ T cells / naive CD8+ T cells and effector memory CD4+ T cells / naive CD8+ T cells. In some embodiments, the panel comprises at least 9 ratios. In some embodiments, the panel comprises 9 ratios.

[0083] In some embodiments, the panel comprises the panel in Table 10. In some embodiments, the panel consists of the panel in Table 10. In some embodiments, the panel comprises the ratios of: effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD28- CD8+ T cells. Insome embodiments, the panel comprises at least 5 ratios. In some embodiments, the panel comprises 5 ratios.

[0084] In some embodiments, the panel comprises the panel in Table 11. In some embodiments, the panel consists of the panel in Table 11. In some embodiments, the panel comprises the ratios of: effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / CD57+ CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD57+ CD8+ T cells. In some embodiments, the panel comprises at least 6 ratios. In some embodiments, the panel comprises 6 ratios. In some embodiments, the panel comprises the ratios of: effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, and naive CD4+ T cells / effector CD8+ T cells. In some embodiments, the panel comprises the ratios of: effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and either naive CD8+ T cells / CD28- CD8+ T cells or naive CD4+ T cells / CD57+ CD8+ T cells and naive CD8+ T cells / CD57+ CD8+ T cells.

[0085] In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, and CXCR5+. In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, CXCR5+ and are not CCR7 and CD45RA double positive. In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA-. In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, CXCR5+, CCR7+ and CD45RA-. In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA+. In some embodiments, T follicular helper (TFH) CD4+ T cells are CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA-; CD3+, CD8-, CD4+, CXCR5+, CCR7+ and CD45RA-; or CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA+. In some embodiments, T follicular helper (TFH) CD4+ T cells are a mix of CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA- cells; CD3+, CD8-, CD4+, CXCR5+, CCR7+ and CD45RA- cells; and CD3+, CD8-, CD4+, CXCR5+, CCR7- and CD45RA+ cells.

[0086] In some embodiments, abundance is relative abundance. In some embodiments, relative abundance is within the sample. In some embodiments, relative is relative to the other populations in the panel. In some embodiments, abundance is absolute abundance. In some embodiments, absolute abundance is abundance is a sample of a predetermined size. In some embodiments, size is volume. In some embodiments, the sample is from the subject.In some embodiments, the sample is not a tumor sample. In some embodiments, the sample does not comprise cancer cells. In some embodiments, the sample is a blood sample. In some embodiments, the blood is peripheral blood. In some embodiments, sample is selected from whole blood, serum and plasma. In some embodiments, the sample is whole blood. In some embodiments, the sample is serum. In some embodiments, the sample is plasma. In some embodiments, the sample is peripheral blood mononuclear cells (PBMCs). In some embodiments, the sample comprises PBMCs.

[0087] In some embodiments, measuring a population’s abundance comprises measuring abundance of the epitopes defining the population. In some embodiments, the measuring is on a single cell level. In some embodiments, the measuring comprises extracting cells from a blood sample. In some embodiments, the measuring comprises contacting the cells or blood sample with an agent that binds to at least one of the epitopes that is indicative of and / or defines the population. In some embodiments, the epitope is a protein. In some embodiments, the protein is a surface protein. In some embodiments, the surface proteins are the proteins provided hereinabove. In some embodiments, a plurality of agents that bind to all identifying epitopes are contacted. In some embodiments, the agent is an antibody to the epitope. In some embodiments, the agent is conjugated to a detectable moiety and the measuring comprises measuring the moiety. In some embodiments, the measuring comprises immunodetection. In some embodiments, the immunodetection is flow cytometry. In some embodiments, the flow cytometry is fluorescence activated cell sorting (FACS). In some embodiments, the immunodetection is single-cell mass cytometry analysis (CyTOF). In some embodiments, the measuring comprises CyTOF. In some embodiments, a population is gated based on expression of the defining epitopes. In some embodiments, more than one population are gated in the same measuring and relative abundance is measured. In some embodiments, the immunodetection is immunostaining. In some embodiments, the detectable moiety is a fluorescent moiety. In some embodiments, the measuring comprises cell counting. Any methods of population detection, such as but not limited as are described herein, may be employed for the methods of the invention. Examples of antibodies that can be used for measuring can be found in Alpert et al., 2019, A clinically meaningful metric of immune age derived from high-dimensional longitudinal monitoring, Nature Medicine, 25: 387-495, herein incorporated by reference in its entirety.

[0088] In some embodiments, the measuring comprises flow cytometry. In some embodiments, the flow cytometry is FACS. In some embodiments, the measuring comprises surface staining of the marker surface protein that identifies the population. In someembodiments, the staining is with a plurality of detecting molecules. In some embodiments, the detecting molecules are specific to the marker surface proteins. In some embodiments, the detecting molecules are antibodies. In some embodiments, the antibodies are against CD3, CD8, CD4, CD28, CCR7 and CD45RA. In some embodiments, the antibodies are against CD3, CD8, CD4, CD28, CCR7, CD45RA, CD25 and CD127. In some embodiments, the antibodies are against CD3, CD8, CD4, CCR7, CD45RA, CD57 and PD1.

[0089] In some embodiments, the antibodies are FACS antibodies. In some embodiments, each antibody comprises a distinct label. In some embodiments, the label is a fluorophore. In some embodiments, the distinct label enables separate detection of each antibody. Distinct fluorophore-labeled antibodies within a panel (for detecting a panel) are labeled with distinct (non-equivalent) fluorophores, enabling the separation of cells bound by said antibodies (e.g. by flow cytometry) in a single measurement step. In some embodiments, distinct fluorophores are non-equivalent fluorophores. In some embodiments, equivalent is substantially equivalent. In some embodiments, equivalent is equivalent fluorescence yield. In some embodiments, equivalent is equivalent staining index. In some embodiments, equivalent is equivalent maximal emission (Em) wavelength. In some embodiments, equivalent is equivalent excitation / emission maxima (Ex / Em). The term equivalence with respect to fluorophores refers to comparable relative fluorescent strength and wavelength of emission, typically determined by comparing the potency (quantum yield or staining index) and color (typically defined by emission maxima and / or full width half maxima). For example, substantially equivalent fluorophores having similar emission potency (e.g. ±20% or 25%) and emission maxima (e.g. ±50 nm) may be used in some embodiments. In some embodiments, the term equivalence with respect to fluorophores further refers to comparable wavelength of excitation and wavelength of emission typically defined by the excitation and emission maxima (±50 nm) and / or full width half maxima. Fluorescent dyes may be classified as potent or less potent based on their relative fluorescent strength according to staining index scales known in the art.

[0090] Examples of antibodies for detecting and quantifying the populations are well known in the art and any antibodies can be used. Flow cytometry antibodies can be purchased from Sigma Aldrich, Abeam, Thermo Fisher Scientific and BD Biosciences to name but a few. Determination of antibodies to combine in a panel can be done using websites such as fluorofmder.com and BD’s Spectrum Viewer (bdbiosciences.com / en-us / resources / bd- spectrum-viewer). A skilled artisan will be able to select antibodies and fluorophores for the unique identification of the populations of the panels.

[0091] In some embodiments, the measuring is in blood of the subject. In some embodiments, the blood is a blood sample. In some embodiments, the blood is peripheral blood. In some embodiments, the relative abundance in peripheral blood is measured. In some embodiments, the measuring is performed ex vivo. In some embodiments, the measuring is performed in vitro. In some embodiments, the sample is a routine blood sample. In some embodiments, cells are isolated from the blood sample. In some embodiments, the relative abundance is measured in the blood. In some embodiments, nom-immune cells are removed before the measuring. In some embodiments, the non-immune cells are blood cells. In some embodiments, the blood cells are selected from red blood cells and platelets. In some embodiments, non-immune cells are left in the sample, but not included in the measuring. In some embodiments, the non-immune cells are gated out of the measuring. In some embodiments, CD3 expression is used to gate T cells and exclude all non-T cells.

[0092] In some embodiments, the measurement of relative abundance of a population is compared to the relative abundance of that population in subjects with a known and / or predetermined immunological age. In some embodiments, all measured populations are compared to the relative abundance of those populations in subjects with known and / or predetermined immunological age. In some embodiments, the comparison is to a dataset of measurements from subjects with known and / or predestined immunological age. In some embodiments, the subjects in the dataset have known immunological ages. In some embodiments, the subjects in the dataset have predetermined immunological ages. In some embodiments, the dataset comprises data from at least 20, 30, 40, 50, 60, 70, 80, 90 or 100 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the dataset comprises data from at least 100 subjects.

[0093] In some embodiments, the comparing comprises employing a distance metric. In some embodiments, the distance metric is with respect to population abundance in the sample and in the subjects in the dataset. In some embodiments, the distance metric determines the subject and / or subjects from the dataset with the closest relative abundances. In some embodiments, the immunological age of the subject or subjects from the dataset with the shortest distance from the measured subject’s data is the determined immunological age of the subject. In some embodiments, the trajectory is along a temporal axis. In some embodiments, the subjects in the dataset are placed along a temporal axis of immunological age and the distance metric provides the location on the axis that is the shortest distance from the subject, thereby determining the subject’s immunological age. In some embodiments,the distance metric is applied to each population separately. In some embodiments, the distance metric is applied to all populations simultaneously.

[0094] In some embodiments, comparing comprises selecting from the dataset individuals with relative population abundancies similar to the measured subject and averaging the immunological age of the selected individuals. In some embodiments, similar comprises a difference of less than 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, or 50%. Each possibility represents a separate embodiment of the invention. In some embodiments, similar comprises the 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45 or 50 individuals with the most minimal distance based from the subject. In some embodiments, the most minimally distant individuals are determined by the distance metric. In some embodiments, the similar individuals are the least distant with respect to the relative abundance of the measured immune cell populations. In some embodiments, the differences from each population are summed to determine similarity. In some embodiments, each population must be below a predetermined threshold for a subject to be considered similar. In some embodiments, the predetermined threshold is a difference of less than 0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, or 50%. Each possibility represents a separate embodiment of the invention. In some embodiments, the geometric mean of the similar subject’s immunological ages is the immunological age of the measured subject. In some embodiments, the arithmetic mean of the similar subject’s immunological ages is the immunological age of the measured subject.

[0095] In some embodiments, the measurements from the subject are combined with measurements from other subjects to produce a database. In some embodiments, the subject’s measurements are combined with data from at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 121, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25, 29, 30, 34, 35, 39, 40, 44, 45, 49 or 50 other subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the subject’s measurements are combined with data from at least 19 other subjects. In some embodiments, the other subjects are of different ages. In some embodiments, the subjects in the dataset span ages of at least 5, 10, 15, 20, 25, 30, 35, 40, 45, or 50 years. Each possibility represents a separate embodiment of the invention. In some embodiments, the other subjects were measured concomitantly to the subject. In some embodiments, the other subjects were measured concomitantly to each other. In some embodiments, the database is used to determine the immunological age of the subject and the other subjects in the database. In some embodiments, the method of determining theimmunological age of the subjects in the database is as described herein below for the cohort of 135 individuals.

[0096] In some embodiments, a trajectory for each population’s relative abundance is calculated from the measurements in the database. In some embodiments, the trajectory is calculated from the database of measurements. In some embodiments, the trajectory is calculated for all subjects. In some embodiments, for all 20 subjects. In some embodiments, for the measured subject and the other subjects combined. In some embodiments, the trajectory is calculated based on the measurements of relative abundance. In some embodiments, the trajectory is calculated for each subject. In some embodiments, the trajectory is calculated for each population. In some embodiments, the trajectory is calculated for all populations together. In some embodiments, the trajectory is selected from asymptotic, linear or fluctuating. In some embodiments, the trajectory is calculated with measurements at different chronological ages. In some embodiments, the trajectory is calculated with measurements that span at least 5 years of chronological age. In some embodiments, the trajectory indicates which populations are to be used for calculating immunological age. In some embodiments, the trajectory is calculated along a temporal axis. In some embodiments, the trajectory indicates the immunological age of a given relative abundance of a population. In some embodiments, calculating a trajectory comprises applying a dimensionality reduction methodology to the population relative abundances. In some embodiments, the dimensionality reduction methodology is a non-linear dimensionality reduction algorithm. In some embodiments, the dimensionality reduction methodology is a pseudotime algorithm. In some embodiments, the non-linear dimensionality reduction algorithm is a pseudotime algorithm. In some embodiments, a pseudotime algorithm is a diffusion pseudotime algorithm. In some embodiments, the nonlinear dimensionality reduction algorithm is a trajectory analysis. In some embodiments, a trajectory analysis is a diffusion trajectory analysis. In some embodiments, the dimensionality reduction methodology is principal component analysis. In some embodiments, positioning of the subject along the temporal axis indicates the subject’s immunological age. In some embodiments, the subject is positioned along the axis to determine immunological age. In some embodiments, generation of the trajectory by default places the subject along the axis.

[0097] In some embodiments, an immune age score is determined. In some embodiments, determining an immune age score comprises summing the abundance of all cells from immune cell populations whose abundance correlates with immune age. In someembodiments, correlates is positively correlates. In some embodiments, the populations that correlate with immune age are: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8+, CD4- , CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CD57+ cells; CD3+, CD8+, CD4-, PD1+ cells and CD3+, CD8-, CD4+, CD25+, CD127- cells. In some embodiments, the populations that correlate with immune age are: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells, CD57 CD8 T cells, PD-1 CD8 T cells and T regulatory (Treg) cells. In some embodiments, CD3+, CD8+, CD4-, CD28- cells correlate with immune age. In some embodiments, CD28 negative CD8 T cells correlate with immune age. In some embodiments, CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells correlate with immune age. In some embodiments, CD8 effector T cells correlate with immune age. In some embodiments, CD3+, CD8-, CD4+, CCR7-, CD45RA- cells correlate with immune age. In some embodiments, CD4 effector memory T cells correlate with immune age. In some embodiments, CD3+, CD8+, CD4-, CCR7-, CD45RA- cells correlate with immune age. In some embodiments, CD8 effector memory T cells correlate with immune age. In some embodiments, CD3+, CD8+, CD4-, CD57+ cells correlate with immune age. In some embodiments, CD57 CD8 T cells correlate with immune age. In some embodiments, CD3+, CD8+, CD4-, PD1+ cells correlate with immune age. In some embodiments, PD-1 CD8 T cells correlate with immune age. In some embodiments, CD3+, CD8-, CD4+, CD25+, CD127- cells correlate with immune age. In some embodiments, Tregs correlate with immune age. In some embodiments, T regulatory cells are regulatory T cells.

[0098] In some embodiments, for combination 55 the populations that correlate with immune age are: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; and CD3+, CD8+, CD4-, CCR7-, CD45RA- cells. In some embodiments, for combination 55 the populations that correlate with immune age are: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, and CD8 effector memory T cells. In some embodiments, for combination 83 the populations that correlate with immune age are: CD3+, CD8+, CD4-, CD57+ cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells and CD3+, CD8+, CD4-, PD1+ cells. In some embodiments, for combination 83 the populations that correlate with immune age are: CD57 CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells and PD-1 CD8 T cells. In some embodiments, for combination 114 thepopulations that correlate with immune age are: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells and CD3+, CD8-, CD4+, CD25+, CD 127- cells. In some embodiments, for combination 114 the populations that correlate with immune age are: CD28 negative CD8 T cells, CD8 effector T cells, CD4 effector memory T cells, CD8 effector memory T cells and T regulatory (Treg) cells. In some embodiments, the sum is the UP sum. In some embodiments, determining comprises calculating the mean abundance of all populations whose abundance correlates with immune age. In some embodiments, abundance is the frequency of a cell from a parent population. In some embodiments, frequency is percentage. For example, the abundance of CD4 positive T cells can be represented as the percentage of CD4+ cells out of all T cells (all CD3+ cells). The abundance is thus given as a percentage rather than an absolute number. This can be done for every population. In some embodiments, determining comprises calculating the mean abundance of all populations. In some embodiments, determining comprises calculating the mean abundance of all populations of the panel. In some embodiments, the mean is subtracted from the sum to produce a score. In some embodiments, the mean of all UP cells is subtracted from the sum of all UP cells. In some embodiments, the score is the UP-score. The UP-score refers to the score contribution of populations whose abundance positively correlates with immune age. That is populations that when found to be upregulated / high indicate an increased immune age.

[0099] In some embodiments, determining an immune age score comprises summing the abundance of all cells from immune cell populations whose abundance negatively correlates with immune age. In some embodiments, negatively correlates is inversely correlates. In some embodiments, the populations that negatively correlate with immune age are: CD3+ cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, CD3+ cells are all CD3+ cells. In some embodiments, all CD3+ cells are all T cells. In some embodiments, the populations that negatively correlate with immune age are: all T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, CD3+ cells negatively correlate with immune age. In some embodiments, total T cells negatively correlate with immune age. In some embodiments, CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells negatively correlate with immune age. In some embodiments, naive CD4 T cells negatively correlate with immune age. In some embodiments, CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells negatively correlate withimmune age. In some embodiments, naive CD8 T cells negatively correlate with immune age.

[0100] In some embodiments, for combination 55 the populations that negatively correlate with immune age are: CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 55 the populations that negatively correlate with immune age are: CD3+ cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 55 the populations that negatively correlate with immune age are: naive CD4 T cells and naive CD8 T cells. In some embodiments, for combination 55 the populations that negatively correlate with immune age are: all T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, for combination 83 the populations that negatively correlate with immune age are CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 83 the populations that negatively correlate with immune age are: CD3+ cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 83 the populations that negatively correlate with immune age are: naive CD4 T cells and naive CD8 T cells. In some embodiments, for combination 83 the populations that negatively correlate with immune age are: all T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, for combination 114 the populations that negatively correlate with immune age are CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 114 the populations that negatively correlate with immune age are: CD3+ cells; CD3+, CD8- , CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells. In some embodiments, for combination 114 the populations that negatively correlate with immune age are: naive CD4 T cells and naive CD8 T cells. In some embodiments, for combination 114 the populations that negatively correlate with immune age are: all T cells, naive CD4 T cells and naive CD8 T cells. In some embodiments, the sum is the DOWN sum. In some embodiments, determining comprises calculating the mean abundance of all populations whose abundance negatively correlates with immune age. In some embodiments, abundance is the frequency of a cell from a parent population. In some embodiments, frequency is percentage. For example, the abundance of CD4 positive T cells can be represented as the percentage of T cells (all CD3+ cells) out of all cells (or alternatively all immune cells). The abundance is thus given as a percentage rather than an absolute number. This can be done for every population. In some embodiments, determiningcomprises calculating the mean abundance of all populations. In some embodiments, determining comprises calculating the mean abundance of all populations of the panel. In some embodiments, the mean is subtracted from the sum to produce a score. In some embodiments, the mean of all DOWN cells is subtracted from the sum of all DOWN cells. In some embodiments, the score is the DOWN-score. The DOWN-score refers to the score contribution of populations whose abundance negatively correlates with immune age. That is populations that when found to be downregulated / low indicate an increased immune age.

[0101] In some embodiments, determining an immune age score comprises subtracting the DOWN-score from the UP-score. In some embodiments, the immune age score is the difference between the UP-score and the DOWN-score. In some embodiments, the immune age score is a measure of the contribution of populations that correlate with immune age reduced by the contribution of populations that inversely correlate with immune age.

[0102] In some embodiments, the ratio of immune populations from Table 8 that correlate with immune age are: CD28- CD8+ T cells / T follicular helper (TFH) CD4+ T cells, CD28- CD8+ T cells / naive CD8+ T cells, CD57+ NK cells / naive CD8+ T cells, CD57+ CD8+ T cells / TFH CD4+ T cells, CD57+ CD8+ T cells / naive CD8+ T cells, effector CD8+ T cells / naive CD4+ T cells, effector CD8+ T cells / naive CD8+ T cells and effector memory CD4+ T cells / naive CD8+ T cells. In some embodiments, the ratio of immune populations from Table 8 that inversely correlates with immune age is TFH CD4+ T cells / effector CD8+ T cells.

[0103] In some embodiments, the ratio of immune populations from Table 10 that correlate with immune age are: effector memory CD4+ T cells / naive CD8+ T cells and effector CD8+ T cells / naive CD8+ T cells. In some embodiments, the ratio of immune populations from Table 10 that inversely correlate with immune age are: naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD28- CD8+ T cells.

[0104] In some embodiments, the ratio of immune populations from Table 11 that correlate with immune age are: effector memory CD4+ T cells / naive CD8+ T cells and effector CD8+ T cells / naive CD8+ T cells. In some embodiments, the ratio of immune populations from Table 10 that inversely correlate with immune age are: naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / CD57+ CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD57+ CD8+ T cells.

[0105] In some embodiments, an immune age score above a predetermined threshold indicates the subject’s immune age is greater than the subject’s biological age. In some embodiments, an immune age score below a predetermined threshold indicates the subject’s immune age is less than the subject’s biological age. In some embodiments, an immune age score below a predetermined threshold indicates the subject’s immune age is essentially the same as said subject’s biological age. In some embodiments, an immune age score above a predetermined threshold indicates the subject is at increased risk of illness. In some embodiments, an immune age score above a predetermined threshold indicates the subject is at increased risk of mortality. In some embodiments, mortality is all-cause mortality.

[0106] In some embodiments, measuring relative immune cell abundance comprises estimating immune cell relative abundance. In some embodiments, estimating comprises measuring gene expression in the blood sample. In some embodiments, estimating comprises measuring enrichment of a gene signature. In some embodiments, measuring gene expression comprises measuring a gene signature. In some embodiments, measuring a gene signature is measuring enrichment of a gene signature. In some embodiments, the gene signature is a signature of immunological age. In some embodiments, gene expression is correlated to relative population abundance. In some embodiments, gene expression is correlated to immunological age. In some embodiments, gene expression from a limited number of genes can estimate relative abundance of the immune cell populations of the invention. In some embodiments, gene expression from a limited number of genes correlates to immunological age. Examples of informative genes can be found for example in International Patent Publication WO2019 / 215740, the contents of which are hereby incorporated by reference in their entirety.

[0107] In some embodiments, estimating comprises measuring the ratio of expression of gene pairs. In some embodiments, the method comprises measuring the ratio of expression of gene pairs. In some embodiments, expression is expression levels. In some embodiments, the gene pairs are a panel of gene pairs. In some embodiments, the measuring is in a sample from the subject. In some embodiments, estimating comprises measuring enrichment of specific genes in the gene pairs. In some embodiments, ratio of expression in gene pairs is correlated to relative population abundance. In some embodiments, ratio of expression in gene pairs is correlated to immunological age.

[0108] In some embodiments, estimating comprises measuring the ratio of abundance of immune cell populations. In some embodiments, the method comprises measuring the ratio of abundance of immune cell populations. In some embodiments, abundance is the numberof cells of the population. In some embodiments, the immune cell population pairs are a panel of immune cell population pairs. In some embodiments, the measuring is in a sample from the subject. In some embodiments, estimating comprises measuring enrichment of specific populations in the population pairs. In some embodiments, ratio of abundance of immune cell populations is correlated to immunological age.

[0109] In some embodiments, the gene pairs are selected from the gene pairs provided in Table 1. In some embodiments, the panel comprises gene pairs selected from the gene pairs provided in Table 1. In some embodiments, the panel consists of gene pairs selected from the gene pairs provided in Table 1. In some embodiments, the panel comprises or consists of a plurality of gene pairs provided in Table 1. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275 or 278 gene pairs provided in Table 1. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all gene pairs provided in Table 1.

[0110] In some embodiments, the gene pairs are selected from the gene pairs provided in Table 2. In some embodiments, the panel comprises gene pairs selected from the gene pairs provided in Table 2. In some embodiments, the panel consists of gene pairs selected from the gene pairs provided in Table 2. In some embodiments, the panel comprises or consists of a plurality of gene pairs provided in Table 2. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 gene pairs provided in Table 2. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all gene pairs provided in Table 2.

[0111] In some embodiments, upregulation of a gene pair selected from: MPO / NOG, MXRA7 / SIRPG, DIS3L2 / NOG, JAKMIP1 / ZNF84, MXRA7 / PLXDC1, MXRA7 / ZFP82, Clorf21 / ZNF264, LOXL3 / NOG, MXRA7 / SATB1, GAB3 / ZNF264, Clorf21 / FCRL1, and MXRA7 / POU2AF1 correlates with immune age. In some embodiments, ratios that correlate with immune age are selected from ratios of MPO / NOG, MXRA7 / SIRPG, DIS3L2 / NOG, JAKMIP1 / ZNF84, MXRA7 / PLXDC1, MXRA7 / ZFP82, Clorf21 / ZNF264, LOXL3 / NOG, MXRA7 / SATB 1, GAB3 / ZNF264, Clorf21 / FCRL1, and MXRA7 / POU2AF1. In some embodiments, upregulation of the gene pair MPO / NOG correlates with immune age. In some embodiments, upregulation of the gene pair MXRA7 / SIRPG correlates with immune age. In some embodiments, upregulation of the gene pair DIS3L2 / NOG correlates with immuneage. In some embodiments, upregulation of the gene pair JAKMIP1 / ZNF84 correlates with immune age. In some embodiments, upregulation of the gene pair MXRA7 / PLXDC1 correlates with immune age. In some embodiments, upregulation of the gene pair MXRA7 / ZFP82 correlates with immune age. In some embodiments, upregulation of the gene pair C lorf21 / ZNF264 correlates with immune age. In some embodiments, upregulation of the gene pair L0XL3 / N0G correlates with immune age. In some embodiments, upregulation of the gene pair MXRA7 / SATB1 correlates with immune age. In some embodiments, upregulation of the gene pair GAB3 / ZNF264 correlates with immune age. In some embodiments, upregulation of the gene pair Clorf21 / FCRL1 correlates with immune age. In some embodiments, upregulation of the gene pair MXRA7 / POU2AF1 correlates with immune age.

[0112] In some embodiments, down-regulation of a gene pair selected from: ARHGEF18 / MXRA7, CD96 / GZMH, BACH2 / PJA1, ITGA6 / MXRA7, PAICS / RASGEF1A, VPREB3 / ZNF319, ABLIM1 / MXRA7, BZW2 / Clorf21, GNG7 / LOXL3, RAB3O / RGS3, BACH2 / FCRL6, NOG / TTC38, FBLN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and NOG / TTC16 correlates with immune age. In some embodiments, ratios that negatively correlate with immune age are selected from ARHGEF18 / MXRA7, CD96 / GZMH, BACH2 / PJA1, ITGA6 / MXRA7, PAICS / RASGEF1A, VPREB3 / ZNF319, ABLIM1 / MXRA7, BZW2 / Clorf21, GNG7 / LOXL3, RAB3O / RGS3, BACH2 / FCRL6, NOG / TTC38, FBLN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and NOG / TTC16. In some embodiments, down-regulation of the gene pair ARHGEF18 / MXRA7 correlates with immune age. In some embodiments, down-regulation of the gene pair CD96 / GZMH correlates with immune age. In some embodiments, down-regulation of the gene pair BACH2 / PJA1 correlates with immune age. In some embodiments, down-regulation of the gene pair ITGA6 / MXRA7 correlates with immune age. In some embodiments, downregulation of the gene pair PAICS / RASGEF1A correlates with immune age. In some embodiments, down-regulation of the gene pair VPREB3 / ZNF319 correlates with immune age. In some embodiments, down-regulation of the gene pair ABLIM1 / MXRA7 correlates with immune age. In some embodiments, down-regulation of the gene pair BZW2 / Clorf21 correlates with immune age. In some embodiments, down-regulation of the gene pair GNG7 / LOXL3 correlates with immune age. In some embodiments, down-regulation of the gene pair RAB3O / RGS3 correlates with immune age. In some embodiments, down-regulation of the gene pair BACH2 / FCRL6 correlates with immune age. In some embodiments, down-regulation of the gene pair NOG / TTC38 correlates with immune age. In some embodiments, down-regulation of the gene pair FBLN5 / MXRA7 correlates with immune age. In some embodiments, down-regulation of the gene pair ATXN10 / MXRA7 correlates with immune age. In some embodiments, down-regulation of the gene pair ANKRD13C / JAKMIP1 correlates with immune age. In some embodiments, downregulation of the gene pair BACH2 / Clorf21 correlates with immune age. In some embodiments, down-regulation of the gene pair BCL11A / Clorf21 correlates with immune age. In some embodiments, down-regulation of the gene pair FAIM3 / MXRA7 correlates with immune age. In some embodiments, down-regulation of the gene pair NOG / TTC16 correlates with immune age. In some embodiments, correlation of downregulation to immune age is negative correlation to gene age. In some embodiments, gene pairs whose downregulation correlates with immune age are gene pairs that negatively correlate with immune age.

[0113] In some embodiments, the gene pairs are selected from the gene pairs provided in Table 5. In some embodiments, the panel comprises gene pairs selected from the gene pairs provided in Table 5. In some embodiments, the panel consists of gene pairs selected from the gene pairs provided in Table 5. In some embodiments, the panel comprises or consists of a plurality of gene pairs provided in Table 5. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 125, 130, 140, 150, 160 or 163 gene pairs provided in Table 5. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all gene pairs provided in Table 5.

[0114] In some embodiments, the gene pairs are selected from the gene pairs provided in Table 6. In some embodiments, the panel comprises gene pairs selected from the gene pairs provided in Table 6. In some embodiments, the panel consists of gene pairs selected from the gene pairs provided in Table 6. In some embodiments, the panel comprises or consists of a plurality of gene pairs provided in Table 6. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17 gene pairs provided in Table 6. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all gene pairs provided in Table 6.

[0115] In some embodiments, the gene pairs are selected from the gene pairs provided in Table 7. In some embodiments, the panel comprises gene pairs selected from the gene pairsprovided in Table 7. In some embodiments, the panel consists of gene pairs selected from the gene pairs provided in Table 7. In some embodiments, the panel comprises or consists of a plurality of gene pairs provided in Table 7. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, or 9 gene pairs provided in Table 7. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all gene pairs provided in Table 7.

[0116] In some embodiments, upregulation of the gene pair FCRL6 / TGFBR3 correlates with immune age. In some embodiments, down-regulation of a gene pair selected from: BACH2 / JAKMIP1, EPHX2 / FCRE6, NT5E / PPP2R2B, NT5E / TGFBR3, ABEIM1 / GPR68, CR2 / FCRE6, ERRN3 / PRKCA and ERRN3 / ZNF563 correlates with immune age. In some embodiments, ratios that negatively correlate with immune age are selected from BACH2 / JAKMIP1, EPHX2 / FCRE6, NT5E / PPP2R2B, NT5E / TGFBR3, ABEIM1 / GPR68, CR2 / FCRE6, ERRN3 / PRKCA and ERRN3 / ZNF563. In some embodiments, down-regulation of the gene pair BACH2 / JAKMIP1 correlates with immune age. In some embodiments, down-regulation of the gene pair EPHX2 / FCRE6 correlates with immune age. In some embodiments, down-regulation of the gene pair NT5E / PPP2R2B correlates with immune age. In some embodiments, down-regulation of the gene pair NT5E / TGFBR3 correlates with immune age. In some embodiments, down-regulation of the gene pair AB EIM 1 / GPR68 correlates with immune age. In some embodiments, down-regulation of the gene pair CR2 / FCRE6 correlates with immune age. In some embodiments, downregulation of the gene pair ERRN3 / PRKCA correlates with immune age. In some embodiments, down-regulation of the gene pair LRRN3 / ZNF563 correlates with immune age.

[0117] In some embodiments, the measurement of gene pair expression level ratio is compared to the gene pair expression level ratio in subjects with a known and / or predetermined immunological age. In some embodiments, all measured gene pair expression level ratios are compared to the gene pair expression level ratio in subjects with known and / or predetermined immunological age. In some embodiments, the comparison is to a dataset of measurements from subjects with known and / or predestined immunological age. In some embodiments, the subjects in the dataset have known immunological ages. In some embodiments, the subjects in the dataset have predetermined immunological ages. In some embodiments, the dataset comprises data from at least 20, 30, 40, 50, 60, 70, 80, 90 or 100 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the dataset comprises data from at least 100 subjects.

[0118] In some embodiments, the determining an immunological age comprises applying an algorithm to the measured gene pair expression level ratios. In some embodiments, the determining an immunological age comprises applying an algorithm to all possible gene pair ratios. In some embodiments, all possible gene pair ratios is for all genes in the panel. In some embodiments, all possible gene pair ratios is for all measured genes. In some embodiments, all possible gene pair ratios is for all genes provided in Table 1. In some embodiments, all possible gene pair ratios is for all genes provided in Table 2. In some embodiments, all possible gene pair ratios is for all genes that correlate with the immune cell population abundance. In some embodiments, the algorithm determines the enrichment of the panel of gene pairs within all possible gene pairs. In some embodiments, enrichment is the level of enrichment. In some embodiments, the enrichment of the panel of gene pair expression level ratios is determined. In some embodiments, the method comprises determining the enrichment of panel of gene pair expression level ratios among all gene pair expression level ratios. In some embodiments, enrichment is proportional to immunological age.

[0119] In some embodiments, the measurement of immune cell abundance ratio is compared to the immune cell abundance ratio in subjects with a known and / or predetermined immunological age. In some embodiments, all measured immune cell abundance ratios are compared to the immune cell abundance ratio in subjects with known and / or predetermined immunological age. In some embodiments, the comparison is to a dataset of measurements from subjects with known and / or predestined immunological age. In some embodiments, the subjects in the dataset have known immunological ages. In some embodiments, the subjects in the dataset have predetermined immunological ages. In some embodiments, the dataset comprises data from at least 20, 30, 40, 50, 60, 70, 80, 90 or 100 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the dataset comprises data from at least 100 subjects.

[0120] In some embodiments, the determining an immunological age comprises applying an algorithm to the measured immune cell abundance ratios. In some embodiments, the determining an immunological age comprises applying an algorithm to all possible immune cell abundance ratios. In some embodiments, all possible immune cell abundance ratios is for all immune populations in the panel. In some embodiments, all possible immune cell abundance ratios is for all measured immune cell populations. In some embodiments, all possible immune cell abundance ratios is for all populations provided in Table 8. In some embodiments, all possible immune cell abundance ratios is for all populations provided inTable 10. In some embodiments, all possible immune cell abundance ratios is for all populations provided in Table 11. In some embodiments, all possible immune cell abundance ratios is for all populations that correlate with the immune age. In some embodiments, the algorithm determines the enrichment of the panel of immune cell abundance ratios within all possible ratios. In some embodiments, the algorithm determines the enrichment of each population ratio within the panel. In some embodiments, enrichment is the level of enrichment. In some embodiments, the enrichment of the panel of immune cell populations ratios is determined. In some embodiments, the method comprises determining the enrichment of a panel of immune cell popualtions ratios among all population ratios. In some embodiments, enrichment is proportional to immunological age.

[0121] In some embodiments, determining an immune population ratio score comprises applying a weight to each immune cell population ratio. In some embodiments, applying the weight is multiply by the weight. In some embodiments, apply the weight produces a product. In some embodiments, all the products are added together. In some embodiments, all the products are summed. In some embodiments, the sum of the products is the immune population ratio score. In some embodiments, the weight is the loading of each ratio on to leading axis of a low dimensional representation of the immune age. In some embodiments, the weight is the loading of each ratio on to leading axis of a low dimensional representation of the panel of ratios contribution to immune age. In some embodiments, the weight is the loading of each ratio on to leading axis of a low dimensional representation of each ratio’s contribution to immune age. In some embodiments, the low dimensional representation is principle component analysis (PCA). PC A and similar low dimensional representations are well known in the art and any can be used for determining the weight for each ratio. It will be understood that ratios that are inversely correlated with immune age will have a negative loading and thus a negative weight. Positively correlated ratios will have positive loading and positive weights. Examples of the weights applied to the 3 ratio panels can be found in Figures 8A, 8D and 8E, though the exact weight can be determined by a skilled artisan.

[0122] In some embodiments, the algorithm is the Single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Essentially, the ssGSEA sums the ranks of the gene pairs (gene ratios) provided in Table 2 within the “background”, which consists of -40,000 gene ratios (not chosen randomly - but rather the gene ratios which are derived from the 283 genes that were highly correlated with the abundances of at least one immune cell population as disclosed in International Patent Application PCT / IL2019 / 050523 (WO2019 / 215740), the contents of which are hereby incorporated by reference in their entirety. Given that some ofthe gene pairs provided in Table 2 are positively correlated with IMM-AGE scores (“up- regulated-ratios”) and others correlate negatively with IMM-AGE scores, we subtract the “negatively” (“down-regulated-ratios”) ranking component from the “positively” ranking component. In some embodiments, the enrichment comprises the enrichment of down- regulated gene pair ratios from the enrichment of up-regulated gene pair ratios.

[0123] In some embodiments, the gene pairs are selected from Clorf21 / SH3YL1,ARHGEF18 / EMILIN2, GAL3ST4 / MXRA7, CCL24 / NOG, ARHGEF18 / GAB3,GNG7 / TTC38, DTX2 / PASK, ARHGEF18 / MXRA7, PDE9A / VENTX, MPO / NOG, ARHGEF18 / PSTPIP2, PRIM1 / PATL2, MXRA7 / TRIB2, CD22 / ZSWIM5, RAB3O / RGS3, MXRA7 / TXK, CD22 / ZBTB49, SH3YL1 / PRR5L, CABLES2 / ZNF264,CHMP7 / MICALL2, ANKRD13C / PRSS23, CCDC66 / E2F5, FAM129C / LRRC48, BACH2 / FCRL6, CCNB1IP1 / E2F5, POU2AF1 / SLC16A1, C17orf48 / TTC38, DNASE 1L1 / P2RX5, POU2AF1 / ZFP14, GIMAP6 / GZMH, DTX3 / NOG, RHOH / LOC 154761, NOG / TTC38, EMILIN2 / SERTAD2, RPL4 / TTC38, PAICS / PRSS23, LOXL3 / NCRNA00287, VPREB3 / ZFP14, PRIM1 / PRR5L, MICALL2 / NOG,ZEB1 / ZBTB49, RCAN3 / LOC 100129637, MXRA7 / SIRPG, CD22 / FGFBP2, ATF7IP2 / RASSF1, MXRA7 / TPR, CD22 / GZMH, C17orf48 / GPR56, RGS3 / ZEB1, CD96 / GZMH, CCDC66 / RASSF1, PATE2 / TTC39C, E2F5 / FCRE6, GPAM / PRSS23, Clorf21 / CLN5, FCRE1 / PRR5E, ID3 / NBEAE2, Clorf21 / ERPPRC, KIAA0020 / MXRA7,RBM26 / TBX21, CABLES2 / GNG7, NUCB2 / RASSF1, SCME1 / SYTE3,CCNB1IP1 / CCR6, PAICS / PATL2, TSGA14 / TTC38, DIS3E2 / NOG,ANKRD13C / RASGEF1A, ZFP14 / PATL2, DNASE1E1 / TPR, ATF7IP2 / Clorf21,CCR6 / NOLC1, GLA / PPA1, BACH2 / SIDT1, CCR6 / RRS1, GLA / SATB1,BCE11A / MXRA7, CD96 / FGFBP2, HDAC1 / IGHM, BTEA / EOC154761FBEN5 / MXRA7, JAKMIP1 / ZNF84, BZW2 / MXRA7, FCRE1 / PMPCBMXRA7 / SERTAD2, CD96 / FCRE6, NOG / ZNF440, MXRA7 / TNFRSF25CYBASC3 / TTC38, NOL9 / RGS3, RASSF1 / SATB1, DENND2D / GZMH, RAB30 / PATL2, Clorf21 / NUCB2, FAIM3 / GPR56, ATXN10 / MXRA7, Clorf21 / RBM26, ID3 / MXRA7, BACH2 / RBM45, Clorf21 / TCTN1, BACH2 / PJA1, BLNK / PJA1, GPR56 / TCF7, CLN5 / ORM1, FAIM3 / TTC38, GZMH / IL7R, NOG / ANOIO, FBLN5 / TTC16, JAKMIP1 / PAICS, PASK / PSTPIP2, IGJ / SH2D1B, JAKMIP1 / QRSL1, SCML1 / ZNF683, LEF1 / MXRA7, MXRA7 / PAQR8, TCF4 / PATL2, MS4A1 / MXRA7, RASGEF1A / STRBP, ZNF264 / PATL2, NOG / PLTP, ATXN10 / BACH2, BACH2 / RRP1B, PIK3IP1 / TTC38,DNASE1E1 / RAB30, C17orf48 / MXRA7, POU2AF1 / EOC154761, DNASE1E1 / TSGA14,CCDC66 / PRSS23, RAB30 / SDK2, DOCK2 / TCF4, FANCF / PSTPIP2, RAB30 / ZSWIM5, GAB3 / KIAA0020, FBLN5 / FH0D1, SATB1 / TTC38, GAB3 / P0LR1E, GPR171 / GZMH, TSGA14 / ZBTB49, GAB3 / UHRF1BP1, N0G / SYTL3, VPREB3 / NCRNA00287, MXRA7 / PLXDC1, PIGP / SYTL3, BLNK / PRSS23, MXRA7 / ZFP82, RBM26 / PRR5L, C2orf3 / JAKMIP1, PSTPIP2 / UHRF1BP1, TCTN1 / PATL2, CCT4 / GZMH, PATL2 / ZNF84, ANKRD13C / FCRL6, CD96 / PRR5L, Clorf21 / FANCF, BACH2 / GZMH, FAM113B / MXRA7, Clorf21 / TCF4, CCR6 / FGFBP2, LEF1 / RASSF1, Clorf21 / ZNF264, CDCA7L / FGFBP2, NOG / SLC5A9, CABLES2 / P2RX5, E2F5 / FGFBP2, ANKRD13C / JAKMIP1, CYBASC3 / MAL, E2F5 / GZMH, BACH2 / Clorf21, EMILIN2 / SATB1, FAM153B / TTC38, CD22 / RGS3, EPB41L5 / RAB30, FCRL1 / GZMH, CD8A / FCRL6, GAB3 / RH0H, GCET2 / GZMH, CHMP7 / MXRA7, GZMH / RPL4, FAM129C / NBEAL2, E2F5 / GSPT2, IRAK1 / RH0H, FCRL1 / GPR56, E2F5 / TCEAL1, LOXL3 / NOG, FCRL1 / HDAC1, FOXO1 / MXRA7, MXRA7 / SATB1, FCRL2 / GPR56, IGJ / PATL2, PSTPIP2 / SATB1, LEF1 / RPL14, VPREB3 / LOC154761, PATL2 / SEL1L3, PAICS / RASSF1, BACH2 / KIAA0020, APOBEC3G / BCL11A, BACH2 / SH2D1B, BLNK / Clorf21, APOBEC3G / SLC7A6, BACH2 / PATL2, E2F5 / ZBTB49, C6orf48 / LEF1, CCR6 / RGS3, IRF4 / LOXL3, DAPK2 / IRF4, CDCA7L / FCGR1A, ITGA6 / PRSS23, DNASE1L1 / FBLN5, FCRL1 / MPO, NOG / SERPINF2, DTX2 / TSGA14, GCET2 / GRB10, NOG / VENTX, FAM134A / P2RX5, ITGA6 / MXRA7, ZNF264 / ZNF683, FAM50A / TCF4, PAICS / RASGEF1A, ABLIM1 / FAM50A, GAB3 / NOG, VPREB3 / ZNF319, BCL11A / Clorf21, GAB3 / RBM26, ABLIM1 / MXRA7, BLNK / KIAA0020, LOXL3 / PDE9A, BCL11A / LOXL3, BLNK / RBM45, VENTX / ZNF354C, BCL11B / ZEB1, FAIM3 / MXRA7, PATL2 / ZFP82, BZW2 / Clorf21, FBLN5 / ZBTB49, CD22 / EPHX2, CD22 / VENTX, FCRL1 / ZFP14, COL1A1 / PDE9A, FAIM3 / GAB3, ITM2C / MICALL2, DAPK2 / ITM2C, GNG7 / LOXL3, NOG / TTC16, DNAI1 / MGC29506, GPR18 / PATL2, NUCB2 / ZBTB49, DTX2 / FBLN5, IGJ / NUDT16, ERAP1 / POU2AF1, LEF1 / PATL2, GAB3 / POU2AF1, BACH2 / MXRA7, GAB3 / ZNF264, CCR6 / MXRA7, PIK3C2B / VPREB3, CDCA7L / PRR5L, PRSS23 / SH3YL1, E2F5 / MXRA7, TBX21 / TXK, FCRL1 / PATL2, APOBEC3G / IGJ, VPREB3 / TTC38, Clorf21 / FCRL1, ZNF264 / TTC38, Clorf21 / NOL9, BZW2 / CEBPA, GPR56 / SATB1, CCDC66 / PATL2, GZMH / LBH, CCR6 / GAB3, GZMH / RASGRP1, DPP4 / MXRA7, MXRA7 / POU2AF1, E2F5 / PMPCB, MXRA7 / SLC7A6, FCRL1 / SH2D1B, MXRA7 / UBASH3A, and FOXO1 / GPR56. In some embodiments, the gene pairs are selected from MPO / NOG, ARHGEF18 / MXRA7, MXRA7 / SIRPG, CD96 / GZMH, DIS3L2 / NOG, BACH2 / PJA1, JAKMIP1 / ZNF84, ITGA6 / MXRA7, MXRA7 / PLXDC1, PAICS / RASGEF1A, MXRA7 / ZFP82,VPREB3 / ZNF319, Clorf21 / ZNF264, ABLIM1 / MXRA7, L0XL3 / N0G, BZW2 / Clorf21, MXRA7 / SATB1, GNG7 / LOXL3, GAB3 / ZNF264, RAB3O / RGS3, Clorf21 / FCRL1, BACH2 / FCRL6, MXRA7 / POU2AF1, NOG / TTC38, FBLN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and N0G / TTC16.

[0124] In some embodiments, upregulated gene pairs that correlate with immune age are selected from: CMKLR1 / FCGBP; CMKLR1 / NOSIP; CST7 / SUSD3; FCRL6 / PLAG1; FCRL6 / SH3YL1; FCRL6 / SLC4A10; FCRL6 / TXK; FCRL6 / UBASH3A; FGFBP2 / TSPAN13; GAB3 / P2RX5; GPR56 / NOSIP; GPR68 / P2RX5; GPR68 / SPTBN1; GPR68 / SGK223; GZMB / PIK3IP1; GZMB / TXK; GZMH / MS4A1; GZMH / POU2AF1; GZMH / SLC4A10; JAKMIP1 / PAICS; JAKMIP1 / TRAF5; LLGL2 / NOSIP; MXRA7 / PDE9A; MXRA7 / SLC4A10; NKG7 / SGK223; PPP2R2B / ZNF154; PRSS23 / SATB1; TBX21 / TNFRSF25; Clorf21 / ITGA6; Clorf21 / NELL2; Clorf21 / PIK3IP1; CMKLR1 / IMPDH2; CMKLR1 / NOG; CMKLR1 / SATB1; FCRL6 / TRAF5; FHIT / LRRN3; GZMB / NT5E; GZMB / C2orf89; GZMH / THEMIS; LLGL2 / SGK223; PPP2R2B / STRBP; RAB11FIP5 / C2orf89; and TBX21 / SGK223. In some embodiments, downregulated gene pairs that correlate with immune age are selected from: TBX21 / SGK223; ; ABLIM1 / HOXC4; ABLIM1 / IL23A; ABLIM1 / 0RM2; AQP3 / FCRL6; ARHGEF18 / CMKLR1; ARHGEF18 / LLGL2; BACH2 / HOXC4; BACH2 / JAKMIP1; BCL7A / GPR56; BCL7A / MXRA7; BCL7A / TTC38; BLK / TTC38; BTLA / Clorf21; BTLA / MY06; BTLA / PRR5L; C2orf40 / JAKMIP1; C2orf40 / LGR6; C2orf40 / MY06; CAMK4 / JAKMIP1; CCR6 / CST7; CCR7 / RAB11FIP5; CD22 / FGFBP2; CD27 / TTC16; CD27 / FAM179A; CD3G / GZMH; CD8A / GZMH; CDCA7L / JAKMIP1; CDCA7L / LGR6; CDCA7L / PATL2; CHMP7 / GPR68; CR2 / JAKMIP1; CR2 / PATL2; EPHX2 / FCRL6; FAIM3 / LGR6; FAM102A / TTC16; FAM113B / GPR68; FAM129C / GPR56; FAM153B / FCRL6; FAM153B / GZMH; FCGBP / GZMB; FCGBP / LGR6; FCGBP / LLGL2; FCGBP / FAM179A; GPR18 / MY06; GPR18 / PRR5L; GPRASP1 / JAKMIP1; ID3 / LGR6; ID3 / LLGL2; ID3 / FAM179A; IL23A / LLGL2; ITGA6 / JAKMIP1; LCK / MY06; LRRN3 / PCDH9; MAL / TTC38; NELL2 / 0RM2; NOG / 0RM2; NOG / TTC16; NOSIP / TTC38; NT5E / PPP2R2B; NT5E / RAB 11FIP5; NT5E / TGFBR3; NT5E / TTC16; NT5E / PATL2; NT5E / FAM179A; P2RX5 / RAB11FIP5; PAICS / PRR5L; PAQR8 / TTC38; PDE7A / PPP2R2B; PDE7A / PRSS23; PDE7A / PRR5L; PLAG1 / PPP2R2B; PLAG1 / TGFBR3; POU2AF1 / S1PR5; RCAN3 / S1PR5; SATB1 / FAM179A; SCML1 / PRR5L; TCF7 / TTC16; TCF7 / FAM179A; TRAF5 / PRR5L; TXK / PRR5L; ZNF154 / S1PR5; ABLIM1 / GPR68; AQP3 / TBX21; BZW2 / FCRL6; BZW2 / FGFBP2; BZW2 / MY06; BZW2 / PRR5L; C2orf40 / CMKLR1; CDCA7L / FCRL6; CR2 / FCRL6; CR2 / LGR6; FAM102A / FAM179A; FAM134B / JAKMIP1; FCGBP / GPR68; FCGBP / MXRA7; FCGBP / MY06; FCRL2 / FCRL6; GPR18 / MXRA7; GPR18 / TGFBR3; LRRN3 / NELL2; LRRN3 / PRKCA; LRRN3 / SH3YL1; LRRN3 / ZNF563; LRRN3 / STAP1; NOG / FAM179A; P2RX5 / TGFBR3; P2RX5 I TTC16; P2RX5 / PRR5L; ZNF154 / PRR5L; BANK1 / JAKMIP1; EPHX2 / JAKMIP1; FCGBP / FCRL6; FCGBP / PRR5L; LBH / PRR5L; LCK / MXRA7; LCK / PRR5L; MS4A1 / MY06; SLC7A6 / TBX21; and SUSD3 / FAM179A.

[0125] In some embodiments, gene pair ratios that correlate with immune age are up- regulated gene pairs ratios. In some embodiments, gene pair ratios that negatively correlate with immune age are down-regulated gene pair ratios. In some embodiments, the up- regulated gene pairs are: MPO / NOG, MXRA7 / SIRPG, DIS3L2 / NOG, JAKMIP1 / ZNF84, MXRA7 / PLXDC1, MXRA7 / ZFP82, Clorf21 / ZNF264, LOXL3 / NOG, MXRA7 / SATB1, GAB3 / ZNF264, Clorf21 / FCRL1, and MXRA7 / POU2AF1. In some embodiments, the down-regulated gene pairs are: ARHGEF18 / MXRA7, CD96 / GZMH, BACH2 / PJA1, ITGA6 / MXRA7, PAICS / RASGEF1A, VPREB3 / ZNF319, ABLIM1 / MXRA7, BZW2 / Clorf21, GNG7 / LOXL3, RAB3O / RGS3, BACH2 / FCRL6, NOG / TTC38, FBLN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and NOG / TTC16.

[0126] In some embodiments, a gene ratio score is determined. In some embodiments, the gene ratio score is an immune age score. In some embodiments, determining a gene ratio score comprises for all ratios of expression that correlate with immune age summing the ratio of expression for each gene pair. In some embodiments, determining a gene ratio score comprises summing the ratio of expression of each gene pair whose ratio of expression correlates with immune age. In some embodiments, correlates is positively correlates. In some embodiments, the sum is the UP sum. In some embodiments, determining comprises calculating the average ratio of expression for all gene pairs. In some embodiments, all gene pairs is all gene pairs that are up-regulated gene pairs. In some embodiments, all gene pairs is all gene pairs in the panel. In some embodiments, the mean is subtracted from the sum to produce a score. In some embodiments, the score is the UP-score. The UP-score refers to the score contribution of gene pair expression ratios that positively correlate with immune age. That is gene pairs whose ratio when found to be upregulated / high indicate an increased immune age.

[0127] In some embodiments, determining a gene ratio score comprises for all ratios of expression that negatively correlate with immune age summing the ratio of expression for each gene pair. In some embodiments, determining a gene ratio score comprises summing the ratio of expression of each gene pair whose ratio of expression negatively correlates with immune age. In some embodiments, negatively correlates is inversely correlates. In some embodiments, the sum is the DOWN sum. In some embodiments, determining comprises calculating the average ratio of expression for all gene pairs. In some embodiments, all gene pairs is all gene pairs that are downregulated gene pairs. In some embodiments, all gene pairs is all gene pairs in the panel. In some embodiments, the mean is subtracted from the sum to produce a score. In some embodiments, the score is the DOWN-score. The DOWN-score refers to the score contribution of gene pair expression ratios that negatively correlate with immune age. That is gene pairs whose ratio when found to be downregulated / low indicate an increased immune age.

[0128] In some embodiments, determining a gene ratio score comprises subtracting the DOWN-score from the UP-score. In some embodiments, the gene ratio score is the difference between the UP-score and the DOWN-score. In some embodiments, the gene ratio score is a measure of the contribution of gene pair ratios that correlate with immune age reduced by the contribution of gene pair ratios that inversely correlate with immune age.

[0129] In some embodiments, a gene ratio score above a predetermined threshold indicates the subject’s immune age is greater than the subject’s biological age. In some embodiments, a gene ratio score below a predetermined threshold indicates the subject’s immune age is less than the subject’s biological age. In some embodiments, a gene ratio score below a predetermined threshold indicates the subject’s immune age is essentially the same as said subject’s biological age. In some embodiments, a gene ratio score above a predetermined threshold indicates the subject is at increased risk of illness. In some embodiments, a gene ratio score above a predetermined threshold indicates the subject is at increased risk of mortality. In some embodiments, mortality is all-cause mortality.

[0130] In some embodiments, estimating comprises measuring methylation status of a CpG dinucleotide. In some embodiments, the method comprises measuring methylation status of a CpG dinucleotide. In some embodiments, CpG dinucleotides are CpGs. In some embodiments, the CpG dinucleotides are a plurality of CpG dinucleotides. In some embodiments, the CpG dinucleotides are a panel of CpG dinucleotides. In some embodiments, each CpG dinucleotide is a site. In some embodiments, methylation status is methylation or unmethylation. In some embodiments, methylation status is the presence orabsence of methylation. In some embodiments, measuring methylation status is measuring methylation. In some embodiments, the CpG dinucleotides are a plurality of CpG dinucleotides. In some embodiments, the CpG dinucleotides are a panel of CpG dinucleotides. In some embodiments, the measuring is in a sample from the subject. In some embodiments, the measuring is in DNA extracted from the sample. In some embodiments, the DNA is bisulfite converted DNA. In some embodiments, the method further comprises bisulfite converting the extracted DNA. In some embodiments, methylation of the CpG dinucleotide is correlated to relative population abundance. In some embodiments, methylation of the CpG dinucleotide is correlated to immunological age. In some embodiments, unmethylation of the CpG dinucleotide is correlated to relative population abundance. In some embodiments, unmethylation of the CpG dinucleotide is correlated to immunological age.

[0131] In some embodiments, the CpGs are one CpG. In some embodiments, the one CpG is selected from the CpGs provided in Table 3. In some embodiments, the CpGs are selected from the CpGs provided in Table 3. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Table 3. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Table 3. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Table 3. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, or 22 CpGs provided in Table 3. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all CpGs provided in Table 3.

[0132] In some embodiments, the CpGs are a panel of CpGs. In some embodiments, a panel is a signature. In some embodiments, the CpGs are selected from the CpGs provided in Appendix lof U.S. Provisional Patent Application No. 63 / 650,552, the content of which is incorporated herein by reference in its entirety. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, or 22 CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. Each possibility represents a separate embodiment of the invention. In someembodiments, the panel comprises or consists of all CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises at least one of the alternative CpGs provided in Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the alternative CpG replaces the listed CpG within the panel. In some embodiments, at least 1 alternative CpG is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, or 22 alternative CpGs. Each possibility represents a separate embodiment of the invention.

[0133] In some embodiments, the CpGs are selected from the CpGs provided in Appendix2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or 28 CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises at least one of the alternative CpGs provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the alternative CpG replaces the listed CpG within the panel. In some embodiments, at least 1 alternative CpG is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or 28 alternative CpGs. Each possibility represents a separate embodiment of the invention.

[0134] In some embodiments, the CpGs are selected from the CpGs provided in Appendix3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or 28 CpGs provided in Appendix 3 ofU.S. Provisional Patent Application No. 63 / 650,552. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all CpGs provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises at least one of the alternative CpGs provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the alternative CpG replaces the listed CpG within the panel. In some embodiments, at least 1 alternative CpG is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 or 28 alternative CpGs. Each possibility represents a separate embodiment of the invention.

[0135] In some embodiments, the CpGs are selected from the CpGs provided in Appendix4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or 19 CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises at least one of the alternative CpGs provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the alternative CpG replaces the listed CpG within the panel. In some embodiments, at least 1 alternative CpG is at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or 19 alternative CpGs. Each possibility represents a separate embodiment of the invention.

[0136] In some embodiments, the CpGs are selected from the CpGs provided in Appendix5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises CpGs selected from the CpGs provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel consists of CpGs selected from the CpGs provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of a plurality of CpGs provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises or consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11 CpGsprovided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. Each possibility represents a separate embodiment of the invention. In some embodiments, the panel comprises or consists of all CpGs provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the panel comprises at least one of the alternative CpGs provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. In some embodiments, the alternative CpG replaces the listed CpG within the panel. In some embodiments, at least 1 alternative CpG is at least 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11 alternative CpGs. Each possibility represents a separate embodiment of the invention.

[0137] In some embodiments, the measured methylation status is compared to the methylation status in subjects with a known and / or predetermined immunological age. In some embodiments, all measured methylation statuses are compared to the methylation statuses in subjects with known and / or predetermined immunological age. In some embodiments, the comparison is to a dataset of measurements from subjects with known and / or predestined immunological age. In some embodiments, the subjects in the dataset have known immunological ages. In some embodiments, the subjects in the dataset have predetermined immunological ages. In some embodiments, the dataset comprises data from at least 20, 30, 40, 50, 60, 70, 80, 90 or 100 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the dataset comprises data from at least 100 subjects.

[0138] In some embodiments, methylation of the CpG correlates with immune age. In some embodiments, unmethylation of the CpG correlates with immune age. In some embodiments, methylation of cg26692003 on chromosome 3 correlates with immune age. In some embodiments, methylation of cg21248060 on chromosome 7 correlates with immune age. In some embodiments, methylation of cgl5227911 on chromosome 17 correlates with immune age. In some embodiments, methylation of cg08362785 on chromosome 22 correlates with immune age. In some embodiments, methylation of cg01534871 on chromosome 1 correlates with immune age. In some embodiments, methylation of cg03771282 on chromosome 2 correlates with immune age. In some embodiments, methylation of cg23336905 on chromosome 5 correlates with immune age. In some embodiments, methylation of cg27506442 on chromosome 13 correlates with immune age.

[0139] In some embodiments, methylation of cgl0922280 on chromosome 16 correlates with immune age. In some embodiments, unmethylation of cg04528720 on chromosome 16 correlates with immune age. In some embodiments, unmethylation of cgl3001844 on chromosome 5 correlates with immune age. In some embodiments, unmethylation ofcgl7540192 on chromosome 7 correlates with immune age. In some embodiments, unmethylation of cg08729908 on chromosome 12 correlates with immune age. In some embodiments, unmethylation of cg26326621 on chromosome 14 correlates with immune age. In some embodiments, unmethylation of cg24704287 on chromosome 19 correlates with immune age. In some embodiments, unmethylation of cg00101629 on chromosome 1 correlates with immune age. In some embodiments, unmethylation of cgl2591668 on chromosome 1 correlates with immune age. In some embodiments, unmethylation of cgl8147543 on chromosome 1 correlates with immune age. In some embodiments, unmethylation of cgl9459094 on chromosome 5 correlates with immune age. In some embodiments, unmethylation of cg26668042 on chromosome 5 correlates with immune age. In some embodiments, unmethylation of cg02492279 on chromosome 10 correlates with immune age. In some embodiments, unmethylation of cgl0243855 on chromosome 17 correlates with immune age.

[0140] In some embodiments, the determining an immunological age comprises providing a weight to each CpG in the panel. In some embodiments, the determining comprises providing a weight to all CpGs in the panel. In some embodiments, the weight is a unique weight. In some embodiments, the weight is CpG site specific. In some embodiments, the weight is a measure of the CpG’s contribution to immunological age. In some embodiments, the weight of the site is its CpG score. In some embodiments, the weight of the site times the measured methylation is the sites CpG score. In cases where the presence of methylation correlates with immune age it can be that present methylation is considered “1” while absence of methylation is “0” such that the weight is applied when methylation is present. In cases where the absence of methylation (unmethylation) correlates with immune age it can be that absence of methylation is “1” and presence of methylation is considered “0” such that the weight is applied when methylation is absent. Alternatively, a %methylation or %unmethylation is determined for a site (based on multiple sequencings of the site). This will not usually be 100% or 0% due to the mix of cells in the sample. This percentage can also be scaled to be from 0 to 1. The weight can be applied to the percent methylation / unmethylation and this is the CpG score. In some embodiments, the method further comprises totaling (summing) all CpG scores to produce a total CpG score. In some embodiments, CpGs whose methylation correlates with immune age are given a percent methylation score or a score based on methylation being present. In some embodiments, CpGs whose unmethylation correlates with immune age are given a percent unmethylationor a score based on methylation being absent. In some embodiments, the CpG score is proportional to immunological age.

[0141] In some embodiments, the weights arc determined by an algorithm. In some embodiments, the algorithm is a regression model. In some embodiments, the regression model is a feature selection model. In some embodiments, the algorithm is a feature selection model. In some embodiments, the regression model is a least absolute shrinkage and selection operator (LASSO) regression model. In some embodiments, the feature selection model is a LASSO regression model. In some embodiments, the feature selection is based on stability. In some embodiments, the feature selection is stability-based feature selection. In some embodiments, the model is applied to a dataset of methylation measurement statuses of the panel of CpG dinucleotides in subjects with known immunological ages. In some embodiments, the subjects have predetermined immunological ages.

[0142] In some embodiments, the CpGs are selected from cg04528720 on chromosome 16, cg26692003 on chromosome 3, cgl3001844 on chromosome 5, cgl7540192 on chromosome 7, cg21248060 on chromosome 7, cg08729908 on chromosome 12, cg26326621 on chromosome 14, cgl5227911 on chromosome 17, cg24704287 on chromosome 19, cg08362785 on chromosome 22, cg00101629 on chromosome 1, cg01534871 on chromosome 1, cgl2591668 on chromosome 1, cgl8147543 on chromosome 1, cg03771282 on chromosome 2, cgl9459094 on chromosome 5, cg23336905 on chromosome 5, cg26668042 on chromosome 5, cg02492279 on chromosome 10, cg27506442 on chromosome 13, cgl0922280 on chromosome 16, and cgl0243855 on chromosome 17. In some embodiments, chromosomal positions are given with respect to human genome build hgl9. In some embodiments, hgl9 is hgl9 / GRCh37.

[0143] In some embodiments, a CpG score above a predetermined threshold indicates the subject’s immune age is greater than the subject’s biological age. In some embodiments, a CpG score below a predetermined threshold indicates the subject’s immune age is less than the subject’s biological age. In some embodiments, a CpG score below a predetermined threshold indicates the subject’s immune age is essentially the same as said subject’s biological age. In some embodiments, a CpG score above a predetermined threshold indicates the subject is at increased risk of illness. In some embodiments, a CpG score above a predetermined threshold indicates the subject is at increased risk of mortality. In some embodiments, mortality is all-cause mortality.

[0144] In some embodiments, a method of the invention further comprises making a prognostication on the subject’s future health based on the subject’s immunological age. In some embodiments, a higher than expected immunological age is indicative of poor future health. In some embodiments, a lower than expected immunological age is indicative of good future health. In some embodiments, the expected immunological age is the subject’s chronological age. In some embodiments, the expected immunological age is the average immunological age of a group of control subjects of the same chronological age as the subject. In some embodiments, the control group comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, expected immunological age is the immunological age of at least one other subject of the same chronological age as the subject. In some embodiments, the at least one other subject is at least 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 subjects. Each possibility represents a separate embodiment of the invention. In some embodiments, the average of the immunological ages of the at least one other subject is the expected immunological age. In some embodiments, higher than expected is greater than the subject chronological age. In some embodiments, higher than expected is greater than a control group or at least one other subject of the same chronological age. In some embodiments, the control group is the same chronological age as the subject. In some embodiments, the control group is the same as the subject by a particular biological measure. In some embodiments, the other subjects are the same as the subject by a particular biological measure. In some embodiments, the biological measure is selected from age, sex, CMV status, overall health, ethnicity, methylation age and health history. In some embodiments, the biological measure is sex.

[0145] In some embodiments, prognosticating future health comprises diagnosing the subj ect with an increased risk of illness when the subj ect’ s immunological age is greater than expected. In some embodiments, the increased risk is relative increased risk as compared to the control group of the at least one other subject. In some embodiments, the illness is cardiovascular disease. In some embodiments, the illness is an immune related illness. In some embodiments, the illness is an illness that is perturbed or prevented by a healthy immune system. In some embodiments, the illness is selected from cancer, cardiovascular disease, neurological disease, metabolic disease, infectious disease and musculoskeletal disease. In some embodiments, the cancer is an immune checkpoint treatable cancer. In some embodiments, the infectious disease is a bacterial, viral or fungal disease. In some embodiments, the neurological disease is selected from Huntington’s disease, Alzheimer’s disease, dementia and Parkinson’s disease. In some embodiments, the illness is death. Insome embodiments, death is all-cause mortality. In some embodiments, immunological age higher than expected indicates an increased risk of death before others of an expected immunological age.

[0146] In some embodiments, the method further comprises providing to the subject a prophylactic regimen for the illness. In some embodiments, the prophylactic regimen is a prophylactic treatment. In some embodiments, the method further comprises providing to the subject a prophylactic regimen for the poor health. In some embodiments, the prophylactic treatment is a medication. In some embodiments, the prophylactic regimen is a diet. In some embodiments, the prophylactic regimen is an exercise regimen. In some embodiments, the prophylactic regimen is a recommendation for an alteration in lifestyle. In some embodiments, the alteration in lifestyle is an alteration in diet. In some embodiments, the alteration in lifestyle is an alteration in exercising. In some embodiments, the medication is a cardiovascular medication. Examples of cardiovascular medications include, but are not limited to statins, aspirin, anticoagulants, antiplatelet agents, angiotensin receptor blockers, beta blockers, calcium channel blockers, diuretics and vasodilators.

[0147] In some embodiments, the method further comprises providing the subject with an illness-related screening procedure. In some embodiments, increased illness-related screenings are provided. In some embodiments, the screening is increased screening. In some embodiments, the method further comprises provided the subject with increased health screenings. In some embodiments, health screening are general health screenings. In some embodiments, screenings are continued until the subject develops the illness or dies. In some embodiments, the illness-related screenings are provided in combination with prophylactic treatment. In some embodiments, the screening are routine checkups. In some embodiments, the screening is illness specific. Screening procedures are well known in the art, and examples include, but are not limited, to triglyceride measurements, weight measurement, BMI measurement and blood pressure readings for heart disease, mammograms for breast cancer, pap smears for cervical cancer, blood sugar tests for diabetes, cognitive exams for neurological disorders, blood panels and regular doctor visits for general health screening.

[0148] By another aspect, there is provided a method of determining percentage stenosis in a subject, the method comprising determining the subjects immune age, wherein the subjects immune age is proportional to the percentage stenosis in the subject, thereby determining the percentage stenosis in the subject.

[0149] By another aspect, there is provided a method of predicting response of a subject to anti-IgE therapy, the method comprising determining the subject immune age, wherein an immune age above a predetermined threshold indicates the subject will not respond to the anti-IgE therapy, thereby predicting response of a subject to an anti-IgE therapy.

[0150] By another aspect, there is provided a method of predicting response of a subject to anti-IgE therapy, the method comprising determining the subject immune age, wherein an immune age below a predetermined threshold indicates the subject will respond to the anti- IgE therapy, thereby predicting response of a subject to an anti-IgE therapy.

[0151] In some embodiments, the subject suffers from CAD. In some embodiments, the subject suffers from a disease treatable with anti-IgE therapy. Examples of diseases treatable with anti-IgE therapy include, but are not limited to asthma, urticaria, allergic reaction, rhinosinusitis, allergic rhinitis, dermatitis and mast cell activation disorders. Examples of asthmas include, but are not limited to: allergic asthma, atopic asthma, occupational asthma, nonatopic asthma and viral-induced asthma and asthma exacerbations. Urticaria may be chronic or non-chronic urticaria including chronic inducible urticaria and chronic spontaneous urticaria. Rhinosinusitis may be chronic or non-chronic and may be with or without nasal polyposis. Dermatitis may be atopic dermatitis. Allergic reactions include food allergies, systemic allergic reactions, allergic rhinitis, allergic reactions during aeroallergen, venom or food immunotherapy and during drug desensitization. In some embodiments, the treatable disease is asthma. In some embodiments, immune age is immunological age. In some embodiments, determining immune age is by a method of the invention. In some embodiments, therapy is immunotherapy. In some embodiments, anti-IgE therapy comprises administering an anti-IgE antibody. In some embodiments, the antibody is a monoclonal antibody. In some embodiments, the antibody is omalizumab. In some embodiments, the antibody is ligelizumab. In some embodiments, the antibody is UB-221. In some embodiments, the antibody is selected from the group consisting of: omalizumab, ligelizumab and UB-221.

[0152] By another aspect, there is provided a kit comprising detecting molecules for determining the abundance of all immune cell populations in an immune cell panel.

[0153] In some embodiments, the panel is a panel of the invention described hereinabove. In some embodiments, the panel is combination 55. In some embodiments, the panel is combination 83. In some embodiments, the panel is combination 114. In some embodiments, the panel is an immune cell population ratio panel. In some embodiments, the panel is thepanel provided in Table 8. In some embodiments, the panel is the panel provided in Table 10. In some embodiments, the panel is the panel provided in Table 11. In some embodiments, the detecting molecules are antibodies. In some embodiments, the antibodies are FACS antibodies. In some embodiments, the antibodies are fluorophore conjugated antibodies. In some embodiments, the kit comprises antibodies against CD3, CD8, CD4, CD28, CCR7, and CD45RA. In some embodiments, the kit comprises antibodies against CD3, CD8, CD4, CD28, CCR7, CD45RA, CD25 and CD 127. In some embodiments, the kit comprises antibodies against CD3, CD8, CD4, CCR7, CD45RA, CD57 and PD1. In some embodiments, each antibody comprises a distinct fluorophore label. In some embodiments, each antibody is each antibody of the kit. In some embodiments, a distinct fluorophore label enables separate detection of each antibody. In some embodiments, detection is by a flow cytometer. In some embodiments, detection is by a cell sorter.

[0154] As used herein, the term "about" when combined with a value refers to plus and minus 10% of the reference value. For example, a length of about 1000 nanometers (nm) refers to a length of 1000 nm+- 100 nm.

[0155] It is noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a polynucleotide" includes a plurality of such polynucleotides and reference to "the polypeptide" includes reference to one or more polypeptides and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as "solely," "only" and the like in connection with the recitation of claim elements, or use of a "negative" limitation.

[0156] In those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and / or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B."

[0157] It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all subcombinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.

[0158] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents, unless the context clearly dictates otherwise. The terms “a” (or “an”) as well as the terms “one or more” and “at least one” can be used interchangeably.

[0159] Furthermore, “and / or” is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term “and / or” as used in a phrase such as “A and / or B” is intended to include A and B, A or B, A (alone), and B (alone). Likewise, the term “and / or” as used in a phrase such as “A, B, and / or C” is intended to include A, B, and C; A, B, or C; A or B; A or C; B or C; A and B; A and C; B and C; A (alone); B (alone); and C (alone).

[0160] Wherever embodiments are described with the language “comprising,” otherwise analogous embodiments described in terms of “consisting of’ and / or “consisting essentially of’ are included.

[0161] Additional objects, advantages, and novel features of the present invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. Additionally, each of the various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples.

[0162] Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.EXAMPLES

[0163] Generally, the nomenclature used herein and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, "Molecular Cloning: A laboratory Manual" Sambrook et al., (1989); "Current Protocols in Molecular Biology" Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols in Molecular Biology", John Wiley and Sons, Baltimore, Maryland (1989); Perbal, "A Practical Guide to Molecular Cloning", John Wiley & Sons, New York (1988); Watson et al., "Recombinant DNA", Scientific American Books, New York; Birren et al. (eds) "Genome Analysis: A Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; "Cell Biology: A Laboratory Handbook", Volumes I- III Cellis, J. E., ed. (1994); "Culture of Animal Cells - A Manual of Basic Technique" by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; "Current Protocols in Immunology" Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), "Basic and Clinical Immunology" (8th Edition), Appleton & Lange, Norwalk, CT (1994); Mishell and Shiigi (eds), "Strategies for Protein Purification and Characterization - A Laboratory Course Manual" CSHL Press (1996); all of which are incorporated by reference. Other general references are provided throughout this document.Materials and Methods:

[0164] Reduction to flow cytometry: We defined all possible combinations of 8 markers, using the markers from the original CyTOF marker panel. As cell type identification requires a specific marker combination, we chose only those combinations which define ‘complete cell types’. Each cell type-associated combination that met this requirement was subsequently used for trajectory assembly and construction of an IMM-AGE gene signature, as described in Alpert et al. 2019, herein incorporated by reference in its entirety.

[0165] Next, we leveraged the Stanford-Ellison longitudinal aging study (SELA) cohort’s original IMM-AGE scores (referred to here as - “pseudo time score”), and correlated, for each combination, the original pseudo time score with the pseudotime scores stemming from each reduced combination using the Kendall correlation.

[0166] We tested the reduced marker combination on a larger scale of data, specifically the Framingham Heart Study. The Framingham Study lacks cell cytometry data, which is whywe needed to leverage the existing gene expression data. This limitation required us to define the cognate gene combination for each reduced marker combination.

[0167] Then, we used an adjusted IMM-AGE gene signature, which was defined only by a subset of cells, to estimate the IMM-AGE of participants in the Framingham Heart Study using ssGSEA.

[0168] To estimate the performance of each cell combination, we split the data into training and test sets; the training set was used to estimate the coefficients of the survival model while the test set was used to estimate the survival itself. We performed 100 iterations: in each iteration (during which the training and the test sets are randomly recreated) we calculated the concordance index (CI) between the estimated survival and the actual survival and summed this difference in order to compare it to a CI derived from a survival model where IMM-AGE was no longer a co-variate. If the CI of the cell combination in the survival model in which IMM-AGE was included was greater than the CI of the concordant cell combination in which IMM-AGE was not included, it was considered a successful iteration - otherwise not.

[0169] Subsequently, we divided the summed CI by the number of iterations to yield a single value quantifying the performance of each combination.

[0170] Finally, the Kendall correlation coefficient of each combination was plotted against each sample’s mean CI score. Combinations whose performance resided in the upper 10th percentile for both tests were chosen for further analysis.

[0171] Single-sample IMM-AGE estimation (Projection): Today’s state of the art for determining the immune age of a sample is analyzing an entire cohort and assembling an immune aging trajectory using non-linear dimensionality reduction analysis (pseudo-time analysis). Thus, it is not possible to determine the immune aging score for a single measurement from the blood sample of an isolated individual. To address this problem, we established a model for single-sample IMM-AGE estimation using cytometry data acquired from flow cytometry analysis. The current procedure for IMM-AGE estimation relies on non-linear dimensionality reduction analysis of the entire cohort and as such there are confounding factors that need to be considered, such as the Age-heterogeneity of the cohort - young people are very similar to each other while elderly tend to have higher interindividual variability. We devised an algorithm to determine IMM-AGE based solely on the relative frequencies of specific cellular subsets, locating them on the original immune aging trajectory and defining their IMM-AGE by the position of their nearest neighbors.

[0172] Flow cytometry panel validation: Finally, we validated our results on a cohort of 53 samples that were originally analyzed on CyTOF between the years 2012 and 2015. We applied the projection algorithm and demonstrated that by using the reduced combination, we were able to recapitulate the original scores and maintain the order of the samples in the immune aging cohort.

[0173] Cross-gene expression platform IMM-AGE estimation: To significantly aid investigation into the role of immune aging in additional diseases, we revamped the approach taken in order to estimate IMM-AGE using gene expression data. Specifically, we designed a platform-independent IMM-AGE gene-expression-ratio-based signature. To optimize the signature, we leveraged transcriptomics data generated on SELA individuals between 2012 and 2015 (262 samples). Out of 18868 genes, 283 genes were highly correlated with the abundances of at least one immune cell population out of the 18 immune cell populations that define the original IMM-AGE trajectory (absolute Pearson correlation coefficient > 0.45). Following a reciprocal calculation of gene ratios and clustering (these gene ratios will be later served as the reference gene -ratio set for ssGSEA which is a ranking algorithm that scores the ranking of the signature genes compared to the reference provided), we identified 42 clusters correlated with IMM-AGE (cluster medoid, absolute Pearson correlation coefficient > 0.6). Following another iteration of clustering of the highly correlated clusters (each cluster was clustered into 10 sub-clusters), we identified 83 sub-clusters correlated with IMM-AGE (cluster medoid, absolute Pearson correlation coefficient > 0.7). To define the novel gene-ratio-based signature, we implemented a feature selection model (LASSO) to select gene ratios that best predict IMM-AGE among the highly correlated gene ratios of each sub-clusters. Initial testing showed high preservation of the IMM-AGE score of the Framingham Heart Study consortium following estimation of the IMM-AGE scores with the original IMM-AGE signature consisting of 56 genes, and the new, robust signature of 278 gene ratios (Table 1). Selected gene ratios were classified as either up-regulated or down- regulated (i.e., positively and negatively correlated with the original IMM-AGE respectively).

[0174] The geneRatio signature was refined and reduced to approximately 31 ratios (Table 2). This was achieved by applying feature selection analysis to all sub-clusters, identifying gene ratios with a probability of selection of at least 0.75 (i.e., these geneRatios are included in the optimal models >=75% of the time). As a result, we achieve even higher overall accuracy.

[0175] Finally, we successfully adapted our scoring algorithm to be independent of the reference gene ratios. For each sample, we compute the sum of the up and down-regulated gene ratios. Subsequently, we subtract the mean ratio values (up-down) for each sample. This vastly improves computational efficiency and allows us to calculate geneRatio-based IMM-AGE in mere seconds. The list of the 278 gene ratios and the list of the refined 31 gene ratios are provided in Tables 1 and 2, respectively.

[0176] Table 1: 278 gene ratios

[0178] Improved gene ratios for IMM-AGE estimation: An improved geneRatio signature was subsequently designed. First samples and years with incomplete values were removed as were genes that did not meet an expression threshold (expression in enough samples). For each year of the dataset a Spearman correlation between each gene and each cell type was calculated. This allowed for the identification of gene-cell pairs with a consistent correlation (same direction, up or down) across all years. Paris with a consistent correlation direction and an absolute mean correlation of > 0.4 were retained. All possible gene expression ratioswere then calculated for the retained genes. For each gene pair the Gene A / Gene B ratio was calculated across all samples. For each ratio and year, the Spearman correlation with the original IMM-AGE score was calculated, and if the correlation direction is consistent across all years the gene ratio was kept. The kept ratios were filtered for a mean correlation of > 0.6 and adjusted p < 0.01. For each year, LASSO was used to select ratios with a stability probability > 0.05, and gene ratios selected in at least 2 years were kept. This resulted in 163 ratios of which 44 are positively correlated with IMM-AGE (up) and 119 are negatively correlated with IMM-AGE (down) (Table 5).

[0179] Table 5: New 163 gene ratio

[0180] The top 10% of the ratios (17 ratios, Table 6) and the top 5% (9 ratios, Table 7) were found to recapitulate the results of the full 163 ratio signature and thus were considered superior gene ratio signatures for predicting IMM-AGE.

[0181] Table 6: New 17 gene ratio

[0182] Table 7: New 9 gene ratio

[0183] Methylation: For the purpose of predicting IMM-AGE based on DNA methylation data, we conducted several steps in order to determine a signature based on the methylation status of known CpG sites across the human genome. Our dataset consisted of DNA methylation data obtained from the Framingham-heart study. Methylation data was generated using the Illumina Infinium HumanMethylation450 BeadChip platform. Initially, and akin to the generation of the IMM-AGE geneRatio signature, we narrowed down the analysis to CpG sites exhibiting a significant linear relationship with IMM-AGE (adjusted p-value < 0.05, BH correction). Employing ak-fold cross validation feature selection model, we computed co-correlated CpGs for various correlation thresholds based on each model’s CpG list. Subsequently, we compiled a final set of CpGs that were common to all models within this iteration for each threshold. To enhance reliability, we repeated this procedure five more times, generating CpGs list for each correlation threshold. By identifying the threshold that yielded the highest overlap across the different models, we identified a list of -10,000 CpGs, out of a total of -250,000 CpGs, for further examination. Finally, and critically, we refined and optimized five signatures by using stability- selected selection model to identify the CpG predictors with the highest robustness. Signature 1 contains 22selected CpG sites. The selected CpGs are provided in Table 3 and Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552. Signature 2 contains 28 selected CpG sites and is provided in Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552. Signature 3 contains 28 selected CpG sites and is provided in Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552. Signature 4 contains 19 selected CpG sites and is provided in Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552. Signature 5 contains 11 selected CpG sites and is provided in Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552. For each CpG of the signature a coefficient is given which is the weight the CpG provides to the signature. Positive coefficients indicate CpGs whose methylation correlates with Immune age and negative coefficients indicate CpGs whose unmethylation correlates with Immune age (or whose methylation inversely correlates with Immune age). CpGs were highly redundant and many of the CpGs from the various signatures could be replaced with alternative CpGs. The optional alternative CpGs for signatures 1-5 are provided in Appendixes 1-5, respectively.

[0184] Table 3: Informative CpGs. Positions are given with genome build hgl9 / GRCh37. Methylation or unmethylation of the CpG correlates with immune age

[0185] Dimensional reduction: In order to create a cell population ratio-based IMMAGE from CyTOF data via dimension reduction 3 cell panels (Table 9) were established. Given the 3 cell panels, the raw dataset of CyTOF data was filtered according to each set of cell subsets according to each panel.Define Xmxn— raw dataset; m — features (cell subsets'), n — samples.Panel 1-. X1= X6xnPanel 2\ X2= X7xnPanel 3-. X3= X7xnFor each pair of features in XpEet the pair be (fltf2). fFor each sample, compute the log ratio: ratio = log(— )ANow, Xi contains not the cell frequencies but the cell frequencies ratios.If one lets q be the panel size (q=6 for panel 1 etc.) then: XtG

[0186] Table 9: Cell panels from CyTOF data

[0187] Next, the ratios are filtered to retain only those with a correlation score of 0.8 or higher with the original IMMAGE score. From step 2 we get the next 2 sets of ratios that are reduced datasets for calculating refined IMMAGE score: (Panel 1 and Panel 2 used the same filtered set of ratios). The panels are provided in Tables 10 and 11.

[0188] Table 10: Panel 1+2 ratios for IMM-AGE calculation] PANEE 1+2 - ratiosCorrelation with original IMM-AGE] effector CD8+ T cells / naive CD8+ T cells 0.846

[0189] Table 11: Panel 3 ratios for IMM-AGE calculation] PANEE 3 - ratiosCorrelation with original IMM-AGEExample 1: Immune population combinations that correlate with IMM-AGE

[0190] We constructed and utilized a two-step computational simulation to identify the minimal number of cell markers needed to be measured by flow -cytometry machines but still yield high accuracy in IMM-AGE estimation. We successfully performed computational optimization of the IMM-AGE cellular markers panel. We applied a set of criteria for the selection of the cell markers, among them (a) each set of cellular markers specifies a ‘defined’ cell population, (b) each set of markers is limited to 8 markers, and (c) chosen markers must define at least 3 different immune cell populations. We then validated the preservation of the ordering of the samples on the immune aging axis by evaluating the Kendall correlation coefficient (Fig. IB ID). This was followed by survival analysis and concordance estimation to ensure the ability to predict mortality in the Framingham heart study was retained. The Framingham study constitutes a gold-standard dataset in the field of cardiology, comprising -2000 patients with varying levels of cardiovascular disease (CVD). From this, we identified potential cell combinations, many of them even outperforming the original combination (Cumulative C-index score of 0.171 in the original combination vs. Cumulative C-index score of 0.78 in the new combination) (Fig. 1A). To avoid overfitting to the training data we decided to utilize the combinations that fall within the upper highest 10th quantile both in terms of the preservation of the ordering and the predictiveness in the Framingham heart study.

[0191] This analysis produced 3 very high performing combinations of populations that produced high predictiveness and had a high Kendall Correlation Coefficient (Fig. 1A). These three combinations are provided in Table 4.

[0192] Table 4: Predictive cell population combinations

[0193] Ultimately, our end goal is to incorporate IMM-AGE assessment into routine clinical assessment. Clinical utilization requires a very precise measurement of IMM-AGE scores which in our case may be affected by technical and algorithmic noise which in turn might hold the metric from being incorporated into routine clinical assessment. To overcome this barrier, we approximated the 95% confidence interval error for each measured IMM-AGE bin (10 bins in total). Specifically, we leveraged machine learning algorithms and classic statistical approaches to quantify the possible deviations of the measured IMM-AGE scores from the true scores. We observed negligible error rates when IMM-AGE is assessed by the reduced combinations (Fig. 2).

[0194] The full combinations produced the most accurate prediction of IMM-AGE, however, it was tested if even smaller panels could be used. To this end, for each combination, a single population was left out and IMM-AGE was calculated. The preservation of the signal (i.e., IMM-AGE accuracy) was calculated for each combination with each population left out. This was done in two ways. In the first the IMM-AGE trajectory that had already been calculated was used, but the patient input data lacked one of the populations (called “subtraction”). In the second the IMM-AGE trajectory was recalculated using only the reduced number of populations and the patient input data alsoonly included the reduced number of populations. As can be seen in Figure 3A-C, each population had a distinct contribution to the projection, but removal of any population never reduced the signal below 80%. As such, it is clear that even smaller panels using one less population, are still useful for predicting IMM-AGE, although clearly removal of certain, less important populations, is preferred.Example 2: IMM-AGE assessment with gene ratios

[0195] To facilitate the IMM-AGE assessment of newly incoming patients we suggest an algorithmic approach to integrate new samples onto the immune aging trajectory and obtain for them a robust IMM-AGE score. To achieve a robust estimate, we developed a novel technique that utilizes the existing immune aging trajectory and following a quantification of limited amounts of cell populations projects the new samples on the original trajectory (Fig. 4A-B).

[0196] With the objective of securing a major advancement to aid investigation into the role of immune aging in additional diseases, we revamped the approach towards gene expression data analysis. Specifically, we yield a platform-independent IMM-AGE gene-expression- ratio-based signature. This signature, initially used a 278 gene ratio panel (Table 1), but this was optimized to a 31 gene ratio panel. This optimized panel, whether analyzed by an enrichment analysis (Fig. 5A) or analyzed by subtracting the “down” gene ratios from the “up” gene ratios (Fig. 5B), recaptures the dynamics of the cells which describe the immune aging process.

[0197] Following this an improved method of selecting gene ratios was designed. In this new method only ratios that consistently showed the same correlation to IMM-AGE (positive / up correlation or negative / down correlation) in every year of the Framingham study examined were included (see Materials and Methods). This resulted in a new set of 163 gene ratio (Table 5). Using this whole gene set to predict IMM-AGE produced a very strong correlation with the results produced using the expression based IMM-AGE prediction (Fig. 5C-5D) and the CyTOF based IMM-AGE prediction (Fig. 5E-5F). However, the use of 163 ratios can be unwieldy and so two reduced sets containing only the top 10% of ratios (17 ratios, Table 6) or only the top 5% of ratios (9 ratios, Table 7) were tested. A strong correlation to expression based IMM-AGE (Fig. 5G-5H) and CyTOF based IMM-AGE (Fig. 5I-5J) was still observed with the 17 ratios, though it was slightly inferior to the full set of ratios. Surprisingly, however, reducing the set to only 9 ratios showed just as strong acorrelation as the set of 17 (Fig. 5K-5N), indicating that using just the very top ratios (top 5%) is sufficient to predict IMM-AGE.Example 3: IMM-AGE assessment with DNA methylation signature

[0198] To further support this goal, we constructed 5 distinct CpG-based signatures using DNA methylation data obtained from the Framingham Heart Study, which included -2000 patients and the measurement of -460,000 CpG sites. IMM-AGE had been previously calculated for these patients using gene-expression. By using a k-fold cross validation stability selection model we identified -7000 CpG sites that significantly and accurately predict IMM-AGE - forming the basis to a novel signature. However, due to a high degree of redundancy found in these sites, we further refined and optimized this signature by using stability selection to identify the CpG predictors with the highest robustness. This yielded 5 unique signatures that all accurately predicted IMM-AGE. Signature 1 contains 23 selected CpG sites and was highly predictive (Fig. 6, Table 3, Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552). The other 4 signatures are provided in Appendixes 2-5, respectively. It should be noted that the many of the CpGs had redundant, alternative CpGs that could be used in their place while retaining most of the predictive accuracy. Any alternative CpG with a correlation of at least 0.7 (retains 70% of the predictability) is provided in Appendixes 1-5. These alternative CpGs could be used as substitutes for the listed CpG of the signature and still be able to accurately predict IMM-Age.Example 4: Use of IMM-AGE to predict disease development and drug response

[0199] Finally, to demonstrate the impact of our invention we leveraged (1) data of patients with diagnosed coronary artery disease (CAD) (clinical study GSE221911) at different stages and showed that a routine assessment the IMM-AGE serves as a biomarker for the percentage of stenosis. These findings potentially revolutionize the standard assessment that relies on a long costly management algorithm which includes coronary computed tomography angiography (CCTA) and catheter angiography (CA) test. Instead, using IMM- AGE, one simple blood test would be capable of separating those with critical blockage from those with more viable coronary artery state (Fig. 7A).

[0200] In another data set we leveraged expression profiling of whole blood cells isolated from 195 patients prior to undergoing cardiac catheterization. Subjects were divided into 3 groups: patients with >70% stenosis in more than one major vessel or at least 50% stenosis in more than two vessels (severe, group 2); patients with greater the 25% luminal stenosisbut less than 50% stenosis (intermediate, group 1); and patients with less than 25% luminal stenosis (control, group 0). IMM-AGE scores of the control group were significantly lower than those of both the intermediate group (permutation test: p values: 0.046) and the severe group (permutation test: p values: 0.047) (Fig. 7B). Paired analysis comparing the control subjects to all disease subjects (groups 1 and 2) found that the IMM-AGE scores of patients with high stenosis as defined by catheterization were significantly higher than that of the control (p=0.0434).

[0201] Next, peripheral blood samples from subjects with ST-segment elevation myocardial infarction (STEMI) were examined and IMM-AGE was calculated. Blood samples were collected at the time of patient presentation with STEMI and heart failure (HF) status was monitored throughout the post myocardial infarction (MI) follow up period. The IMM-AGE of patients with MI that did not develop HF was significantly lower (p=0.0111) than those that did develop HF (Fig. 7C) indicating that IMM-AGE can be used to predict HF in these patients.

[0202] Finally, we also leveraged (2) whole-genome blood gene expression data of Asthma patients treated with Omalizumab (clinical study GSE134544). Samples were available before and after treatment and segregated into responders and non-responders. The IMM- AGE score of all participants was assessed based on the adjusted IMM-AGE gene-ratios- based signature. We showed that non-responders have higher IMM-AGE scores at baseline (before treatment) than responders (Wilcoxon, p=0.05) (Fig. 7D).Example 5: IMM-AGE derived from immune cell population ratios

[0203] In order to further improve the IMM-AGE calculation methods a new approach was tested that makes use of ratios of immune cell populations. Starting with the original immune cell types, 156 pairwise ratios were calculated and then filtered for those most highly correlated with IMM-AGE. This resulted in a set of 9 top -performing population ratios which rely on just 8 cell types total (Table 8). To further enhance interpretability, we replaced the previous dimension reduction method with a linear approach, allowing us to examine the loadings of each ratio and understand their contributions to the final metric which is a clinically important ability (Fig. 8A). The loadings of each ratio (see Figure 8A) are used as a weight which is applied to the ratio calculated. The products of the ratios and their weights are then summed to produce an IMM-AGE score that correlates with the subject’s IMM-AGE.

[0204] Table 8: Top cell type ratios most strongly correlated with IMMAGE.

[0205] The newly constructed pseudo time metric showed a 90% correlation with IMM- AGE, indicating strong preservation of the biological signal (Fig. 8B). Importantly, the distribution of time bins was now nearly uniform, greatly enhancing the clinical utility of the metric (Fig. 8C).

[0206] However, this 8 ratio panel was based on CyTOF data and was not necessarily amenable to FACS analysis. Therefore, the CyTOF data was filtered and dimensional reduction applied to produce two new panels of cell population ratios that could be readily analyzed by FACS (see Materials and Methods). The two panels are provided in Tables 10 and 11. As before, a dimensional reduction algorithm was applied to both panels and the leading axis in the low-dimensional representation was used as the new IMM-AGE trajectory. The trajectory of each panel was constructed of similar features, and this allows us to directly examine the loadings of each ratio (Fig. 8D-8E). This offers insight into each ratio’s contribution to the final pseudo-time score — an important feature for clinical translation. Again, the loading of each ratio was used as a weight when calculating the IMM- AGE score form the population ratios. Both trajectories capture gradual signal (Fig. 8F-8G). Once again, the distribution of time bins was now nearly uniform, greatly enhancing the clinical utility of the metric (Fig. 8H-8I).

[0207] This revised 1MMAGE pipeline which makes use of cell population ratios provides a generalizable, interpretable, and normalization-free method for assessing immune age. Further, it inherently removes any batch effects that may arise from the data collection. By relying on a reduced set of immune cell types and leveraging linear dimension reduction on carefully selected ratios, the new approach maintains biological relevance while offering clear advantages in usability and robustness.

[0208] Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent tothose skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims.

Claims

CLAIMS:

1. A method of determining the immunological age of a subject, comprising: a. measuring relative abundance of all immune cell populations in an immune cell panel in a blood sample from said subject, wherein said panel comprises at least one of: i. at least 6 of 7 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and all CD3+ cells; ii. at least 7 of 8 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CD25+, CD127- cells; and all CD3+ cells; and iii. at least 7 of 8 cell populations selected from the group consisting of: CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; CD3+, CD8+, CD4-, PD1+ cells; and all CD3+ cells; and b. determining an immunological age of said subject by at least one of: i. comparing said measurement of relative abundance of immune cell populations in said immune cell panel to a dataset of measurements of immune cell population relative abundance in subjects with predetermined immunological ages; ii. combining said measurement of relative abundances with data of measurements of relative abundance from at least 19 other subjects to produce a database, and calculating from said database a trajectory for all at least 20 subjects based on said measurements of relative abundance; andiii. determining an immune age score, wherein said determining comprises: a. for all immune cell populations whose abundance correlates with immune age, summing the abundance of cells in said blood sample and subtracting their mean in said sample to produce an UP- score; b. for all immune cell populations whose abundance negatively correlates with immune age, summing the abundance of cells in said blood sample and subtracting their mean in said sample to produce a DOWN-score; and c. subtracting said DOWN-score from said UP-score to produce an immune age score, wherein said immune age score is proportional to said subject’s immunological age; thereby determining the immunological age of said subject.

2. The method of claim 1, wherein said panel comprises of at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; and CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8-, CD4+, CD25+, CD127- cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells3. The method of claim 1 or 2, wherein said panel consists of at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-,CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and all CD3+ cells; ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CD25+, CD127- cells; and all CD3+ cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells; and all CD3+ cells.

4. The method of claim 2, wherein said panel further comprises all CD3+ cells.

5. The method of any one of claims 1 to 4, wherein said measuring relative abundance comprises flow cytometry analysis of relative abundance of each cell population within said blood sample.

6. The method of any one of claims 1 to 5, wherein said comparing comprises employing a distance metric with respect to immune cell population abundance in said sample and samples of said dataset.

7. The method of any one of claims 1 to 6, wherein said comparing comprises selecting from said dataset individuals with a smallest distance with respect to relative abundance of said immune cell populations of said panel and averaging an immunological age of said selected individuals.

8. The method of any one of claims 1 to 7, wherein said calculating a trajectory comprises applying a non-linear dimensionality reduction algorithm to said population relative abundancies.

9. The method of any one of claims 1 to 8, wherein an immune age score above a predetermined threshold indicates said subject’s immune age is greater than said subject’s biological age, an immune age score below a predetermined threshold indicates said subject’s immune age is less than said subject’s biological age, or both.

10. The method of any one of claims 1 to 9, wherein said measuring immune cell relative abundance comprises estimating immune cell relative abundance by measuring gene expression in said blood sample.

11. The method of any one of claims 1 to 10, wherein immune cell populations whose abundance correlates with immune age are selected from the group consisting of: CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CD57+ cells; CD3+, CD8+, CD4-, PD1+ cells and CD3+, CD8-, CD4+, CD25+, CD 127- cells and immune cell populations whose abundance negatively correlates with immune age are selected from the group consisting of: CD3+ cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells.

12. A method of determining the immunological age of a subject, comprising: a. measuring the ratio of expression levels of a panel of gene pairs in a blood sample from said subject, wherein said panel comprises the gene pairs provided in Table 7, provided in Table 6 or provided in Table 2; b. determining an immunological age of said subject by at least one of: i. comparing said measurement of gene pair expression level ratio to a dataset of measurements of gene pair expression level ratios in subjects with predetermined immunological age; ii. applying an algorithm to said measured gene pair expression level ratios and all possible gene pair ratios for all genes measured to determine the enrichment of said panel of gene pairs within all possible gene pairs, wherein said enrichment is proportional to immunological age; and iii. determining a gene ratio score wherein said determining comprises:

1. for all ratios that correlate with immune age, summing the ratio of expression for each gene pair and subtracting the average ratio of expression for all gene pairs to produce an UP-score;2. for all ratios that negatively correlate with immune age summing the ratio of expression for each gene pair and subtracting the average ratio of expression for all gene pairs to produce a DOWN-score;3. subtracting said DOWN-score from said UP-score to produce a gene ratio score, wherein said gene ratio score is proportional to immunological age;thereby determining the immunological age of said subject.

13. The method of claim 12, wherein said algorithm is Single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm.

14. The method of claim 11, wherein said comparing comprises employing a distance metric with respect to gene pair expression level ratios in said sample and samples of said dataset.

15. The method of claim 12 or 14, wherein said comparing comprises selecting from said dataset individuals with a smallest distance with respect to gene pair expression level ratios and averaging an immunological age of said selected individuals.

16. The method of any one of claims 12 to 15, wherein ratios that correlate with immune age are selected from MPO / NOG, MXRA7 / SIRPG, DIS3L2 / NOG, JAKMIP1 / ZNF84, MXRA7 / PLXDC1, MXRA7 / ZFP82, Clorf21 / ZNF264, E0XE3 / N0G, MXRA7 / SATB 1, GAB3 / ZNF264, Clorf21 / FCRE1, and MXRA7 / POU2AF1 and ratios that negatively correlate with immune age are selected from ARHGEF18 / MXRA7, CD96 / GZMH, BACH2 / PJA1, ITGA6 / MXRA7, PAICS / RASGEF1A, VPREB3 / ZNF319, ABEIM1 / MXRA7, BZW2 / Clorf21, GNG7 / EOXE3, RAB30 / RGS3, BACH2 / FCRE6, NOG / TTC38, FBEN5 / MXRA7, ATXN10 / MXRA7, ANKRD13C / JAKMIP1, BACH2 / Clorf21, BCL11A / Clorf21, FAIM3 / MXRA7, and N0G / TTC16.

17. The method of any one of claims 12 to 15, wherein the ratio FCRE6 / TRAF5 correlates with immune age and ratios that negatively correlate with immune age are selected from BACH2 / JAKMIP1, EPHX2 / FCRE6, NT5E / PPP2R2B, NT5E / TGFBR3, ABEIM1 / GPR68, CR2 / FCRE6, ERRN3 / PRKCA and ERRN3 / ZNF563.

18. A method of determining the immunological age of a subject, comprising: a. measuring the methylation status of a panel of CpG dinucleotides in a blood sample from said subject, wherein said panel comprises Appendix 1 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 2 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 3 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 4 of U.S. Provisional Patent Application No. 63 / 650,552 Appendix 5 of U.S. Provisional Patent Application No. 63 / 650,552 the CpGs provided in Table 3; and b. determining an immunological age of said subject by at least one of:i. comparing said measured methylation statuses to a dataset of measurements of methylation statuses in subjects with predetermined immunological age; and ii. providing a weight to each CpG dinucleotide in the panel, wherein the weight is a measure of that CpG dinucleotide’s contribution to immunological age, to provide a CpG score and combining said CpG score of all CpG dinucleotides of the panel to produce a total CpG score and wherein said total CpG score is proportional to immunological age; thereby determining the immunological age of said subject19. The method of claim 18, wherein said weights are determined by applying stability selection model to a dataset of methylation measurement statuses of said panel of CpG dinucleotides in subjects with known immunological age.

20. A method of determining the immunological age of a subject, comprising: a. measuring the ratio of immune cell population abundance pairs in a blood sample from said subject, wherein said population pairs comprise at least one of the following panels: i. CD28- CD8+ T cells / T follicular helper (TFH) CD4+ T cells, CD28- CD8+ T cells / naive CD8+ T cells,CD57+ NK cells / naive CD8+ T cells, CD57+ CD8+ T cells / TFH CD4+ T cells, CD57+ CD8+ T cells / naive CD8+ T cells, TFH CD4+ T cells / effector CD8+ T cells, effector CD 8+ T cells / naive CD4+ T cells, effector CD8+ T cells / naive CD8+ T cells and effector memory CD4+ T cells / naive CD8+ T cells; ii. effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD28- CD8+ T cells; and iii. effector CD8+ T cells / naive CD8+ T cells, effector memory CD4+ T cells / naive CD8+ T cells, naive CD8+ T cells / effector memory CD8+ T cells,naive CD4+ T cells / CD57+ CD8+ T cells, naive CD4+ T cells / effector CD8+ T cells and naive CD8+ T cells / CD57+ CD8+ T cells; b. determining an immunological age of said subject by at least one of: i. determining an immune population ratio score, wherein said determining comprises applying a weight to each immune cell population abundance ratio to produce a product and summing all the products to produce an immune population ratio score; ii. comparing said measurement of population pair ratio to a dataset of measurements of population pair ratios in subjects with predetermined immunological age; and iii. applying an algorithm to said measured population pair ratios and all possible population pair ratios for all populations measured to determine the enrichment of said panel of population pair within all possible population pair, wherein said enrichment is proportional to immunological age; thereby determining the immunological age of said subject.

21. The method of claim 20, wherein said weight is the loading of said ratio on the leading axis of a low dimensional representation of each ratio’s contribution to immune age, optionally wherein said low dimensional representation is principle component analysis (PCA).

22. The method of any one of claims 1 to 21, wherein said blood sample is a peripheral blood sample.

23. The method of any one of claims 1 to 22, wherein immunological age is relative immunological age as compared to said subjects biological age.

24. The method of any one of claims 1 to 23, further comprising diagnosing said subject with increased risk of illness when said subject’s immunological agent age is greater than said subject’s chronological age.

25. The method of any one of claims 1 to 24, further comprising diagnosing said subject with a relative increased risk of illness when said subject’s immunological age is greater than an immunological age of at least one other subject of the same chronological age.

26. The method of claim 24 or 25, wherein said illness is cardiovascular disease.

27. The method of claim 26, wherein said cardiovascular disease is coronary artery disease (CAD).

28. The method of claim 26, wherein said subject has suffered from a myocardial infarction and said cardiovascular disease is heart failure (HF).

29. The method of claim 24 or 25, wherein said increased risk of illness is increased risk of all-cause mortality.

30. The method of any one of claims 24 to 29, further comprising providing to said subject a prophylactic regimen for said illness or more frequent illness-related screening procedures.

31. The method of any one of claims 1 to 23, wherein said subject suffers from CAD and further comprising determining the percentage stenosis in said subject wherein said percentage stenosis is proportional to said subject’s immune age.

32. The method of any one of claims 1 to 23, further comprising predicting response of said subject to an anti-IgE immunotherapy, wherein an immune age above a predetermined threshold indicates said subject will not respond to said anti-IgE immunotherapy and an immune age below a predetermined threshold indicates said subject will respond to said anti-IgE immunotherapy.

33. The method of claim 32, wherein said anti-IgE immunotherapy is an anti-IgE monoclonal antibody.

34. The method of claim 33, wherein said anti-IgE monoclonal antibody is omalizumab.

35. The method of any one of claims 32 to 34, wherein said subject suffers from a disease treatable by said anti-IgE immunotherapy.

36. The method of claim 35, wherein said disease is asthma.

37. A method of determining the percentage stenosis in a subject suffering from CAD, the method comprising determining the subject’s immune age based on a blood sample from said subject, wherein said subjects immune age is proportional to said percentage stenosis in said subject, thereby determining the percentage stenosis in a subject suffering from CAD.

38. A method of predicting response of a subject suffering from a disease treatable with an anti-IgE immunotherapy to said anti-IgE immunotherapy, the method comprising determining the subject’s immune age based on a blood sample from said subject, wherein an immune age above a predetermined threshold indicates said subject will not respond to said anti-IgE immunotherapy and an immune age below a predetermined threshold indicates said subject will respond to said anti-IgEimmunotherapy, thereby predicting response of a subject to an anti-IgE immunotherapy .

39. The method of claim 38, wherein said anti-IgE immunotherapy is an anti-IgE monoclonal antibody.

40. The method of claim 39, wherein said anti-IgE monoclonal antibody is omalizumab.

41. The method of any one of claims 38 to 40, wherein said disease is asthma.

42. The method of any one of claims 37 to 41, wherein said immune age is determined by a method of any one of claims 1 to 23.

43. The method of claim 42, wherein said immune age is determined by a method of any one of claims 12 to 17.

44. A kit comprising detecting molecules for determining the relative abundance of all immune cell populations in an immune cell panel, wherein said panel comprises at least one of: i. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; and CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; ii. CD3+, CD8+, CD4-, CD28- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; and CD3+, CD8-, CD4+, CD25+, CD127- cells; and iii. CD3+, CD8+, CD4-, CCR7-, CD45RA- cells; CD3+, CD8+, CD4-, CCR7-, CD45RA+ cells; CD3+, CD8+, CD4-, CCR7+, CD45RA+ cells; CD3+, CD8-, CD4+, CCR7-, CD45RA- cells; CD3+, CD8-, CD4+, CCR7+, CD45RA+ cells; CD3+, CD8+, CD4-, CD57+ cells; and CD3+, CD8+, CD4-, PD1+ cells.

45. The kit of claim 44, comprising antibodies against: a. CD3, CD8, CD4, CD28, CCR7, and CD45RA; b. CD3, CD8, CD4, CD28, CCR7, CD45RA, CD25 and CD127; or c. CD3, CD8, CD4, CCR7, CD45RA, CD57 and PD1.

46. The kit of claim 45, wherein each antibody comprises a distinct fluorophore label enabling separate detection of each antibody.

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