Methods of guiding treatment using a network of biomarkers

Abstract

The disclosure relates to methods, kits, and systems that can be used to inform and guide clinical treatments and aging interventions.

Description

METHODS OF GUIDING TREATMENT USING A NETWORK OF BIOMARKERSSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

[0001] The inventions herein were made with Government support under grant number R21 AG070356 awarded by the National Institutes of Health. The Government has certain rights in the inventions disclosed herein.BACKGROUND

[0002] Aging is a complex process that begins decades before the onset of common age-related conditions such as frailty and overt chronic disease (See, e.g., Liu Y et al. Expression of pl6(INK4a) in peripheral blood T-cells is a biomarker of human aging. Aging Cell (2009); Elliott ML, Caspi A, Houts RM, et al. Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy. Nat. Aging (2021); and Belsky DW, Caspi A, Houts R, et al. Quantification of biological aging in young adults. Proceedings of the National Academy of Sciences of the United States of America (2015)). As such, many aspects of aging remain poorly understood. However, the immune system is a central mediator of organismal aging with well-described early changes beginning in the second or third decade of life. Therefore, there exists a need for biomarkers of immune system aging to identify early vulnerabilities and allow targeted interventions to both repair immune system function and improve an individual’s aging trajectory.

[0003] Immune system aging is comprised of a process commonly referred to as immunosenescence, characterized by changes in the proportion and function of immune cell types, as well as replicative or cellular senescence. Cellular senescence is an umbrella term describing a cellular response to a wide variety of exposures such as stress, DNA damaging agents, common medical procedures and drugs, physical activity, nutrients, and can be associated with other molecular changes including genomic instability, telomere attrition, epigenetic changes, autophagy deregulation, and impaired proteostasis (See, e.g., He S, Sharpless NE. Senescence in Health and Disease. Cell (2017)). Senescent cells are characterized by stable growth arrest, resistance to apoptosis, and a complex, pro-inflammatory secretome. Cellular senescence promotes aging, and removal of senescent cells reverses aging pathology (See, e.g., Baker DJ etal. Clearance of pl6Ink4a-positive senescent cells delays ageing-associated disorders. Nature (2011); Baker DJ et al. Naturally occurring pl6INK4A -positive cells shorten healthy lifespan. Nature 2016); and Xu M, et al. Senolytics improve physical function and increase lifespan in old age. Nature Medicine (2018)). Induction of cellular senescence solely in immune cells can induce cellular’ senescence in diverse cell and tissue types throughout the body, impairing their function (See, e.g., Yousefzadeh MJ et al. An aged immune system drives senescence and ageing of solid organs. Nature (2021)).

[0004] Cells that have undergone cellular senescence accumulate with age. The rate of accumulation depends on both the rate of induction by the stressors described above, as well as the capacity of the immune system, particularly cytotoxic CD8+ T cells, to surveil and clear’ these altered cells (See, e.g., Marin I, Serrano M, Pietrocola F. Recent insights into the crosstalk between senescent cells and CD8+ T lymphocytes. Aging (2023); and Kang T-W et al. Senescence surveillance of prc-malignant hepatocytes limits liver cancer development. Nature (2011)). In biologically young, healthy organisms, cellular senescence is maintained in an adaptive balance. As the organism ages or the immune system is impaired due to other challenges, cellular senescence accumulates and causes organismal dysfunction. While multiple lines of evidence from independent labs suggest a role for the adaptive immune system in the maintenance of cellular senescence, the relationship is complex and is just beginning to be understood.

[0005] Previous studies showed little association between cellular senescence and traditional measures of immunosenescence. In certain embodiments described herein, biomarkers were interrogated and those results were compared to the expression of pl 6, a biomarker most uniquely associated with cellular senescence, and longitudinal changes in pl6. Biomarkers that correlated with pl6 individually were retained for further analysis as potential biomarkers of immuno surveillance of senescent cells. As described herein, further interrogation and characterization of this biomarker system established a broader relationship between cellular senescence and adaptive immune system function. The disclosure herein further describes evidence of clinical relevance in early aging and metabolic phenotypes in a cohort of healthy adults.SUMMARY

[0006] In certain embodiments, a method of guiding treatment of a subject by measuring markers of immune system function is provided. In certain embodiments, a method of guiding treatment of a subject comprises: a) generating an immune longevity score for the subject; b) comparing the immune longevity score for the subject with a pre-determined threshold for the immune longevity score; and c) guiding treatment of the subject based on the comparison of the immune longevity score for the subject with a pre-determined threshold for the immune longevity score.

[0007] In certain embodiments, a method of guiding treatment of a subject further comprises guiding treatment of a subject by measuring markers of cellular senescence comprising: a) generating a cellular senescence score for the subject; b) comparing the cellular senescence score for the subject with a pre-determined range for the cellular senescence score; and c) guiding treatment of the subject based on both (1) the comparison of the cellular senescence score for the subject with the pre-determined range for the cellular senescence score; and (2) the comparison of the immune longevity score for the subject with the pre-determined threshold for the immune longevity score.

[0008] In certain embodiments, the generating a cellular senescence score for the subject comprises: a) detecting a level of gene expression of pl6 in a blood sample from the subject; and b) comparing the level of gene expression of pl6 in the blood sample from the subject with a predetermined range for the cellular senescence score in a sample.

[0009] In certain embodiments, a method of guiding treatment of a subject comprises generating an immune longevity score for the subject. In certain such embodiments, generating an immune longevity score for the subject comprises: a) detecting a level of gene expression of pl6, CD244, LAG3, and CD28 in a blood sample from the subject; b) generating an immune longevity score for the subject based on the gene expression levels of pl6, CD244, LAG3, and CD28 in the blood sample from the subject and the gender and chronological age of the subject.

[0010] In certain embodiments, a method of guiding treatment of a subject comprises generating an immune longevity score for the subject. In certain such embodiments, generating an immune longevity score for the subject comprises: a) detecting a level of gene expression of CD244, LAG3, and CD28 in a blood sample from the subject; b) generating an immune longevity score for the subject based on the gene expression levels of CD244, LAG3, and CD28 in the blood sample from the subject and the gender and chronological age of the subject.

[0011] In certain embodiments, a method of guiding treatment of a subject comprises generating an immune longevity score for the subject. In certain such embodiments, generating an immune longevity score for the subject comprises: a) counting lymphocytes, neutrophils, CD3+, CD56+ / CD16+, CD3+CD4+, CD3+CD8+, CD3+ CD8+CD28-, and CD3+CD8+CD95- cells from the subject; b) generating an immune longevity score for the subject based on lymphocytes, neutrophils, CD3+, CD56+ / CD16+, CD3+CD4+, CD3+CD8+, CD3+ CD8+CD28-, and CD3+CD8+CD95- cell counts in the sample and chronological age of the subject.

[0012] In certain embodiments, wherein two or more immune longevity scores are calculated for a subject; the first immune longevity score is calculated at a first time point and the one or more additional immune longevity scores are calculated at later time points, and treatments and their outcomes are evaluated by comparing any changes between the two or more immune longevity scores collected at different time points. In certain embodiments, the two or more longevity scores do not substantially change over time. In certain embodiments, a first immune longevity score guides the subject to receive a treatment, the subject receives that treatment, and a subsequent immune longevity score suggests that the treatment was effective. In certain embodiments, a first immune longevity score guides the subject to receive a first treatment; the subject receives the first treatment; a second immune longevity score suggests that the treatment was ineffective; guided by one or more of the first and second immune longevity scores, the subject receives a second treatment; and a third immune longevity score suggests that the treatment was effective.

[0013] In certain embodiments, wherein two or more cellular senescence scores are calculated for a subject; the first cellular senescence score is calculated at a first time point and the one or more additional cellular senescence scores are calculated at later time points; and treatments and their outcomes are evaluated by comparing any changes between the two or more cellular senescence scores collected at different time points. In certain embodiments, the two or more cellular senescence scores do not substantially change over time. In certain embodiments, a first cellular senescence score guides the subject to receive a treatment, the subject receives that treatment, and a subsequent cellular’ senescence score suggests that the treatment was effective, hr certain embodiments, a first cellular senescence score guides the subject to receive a first treatment; the subject receives the first treatment; a second cellular senescence score suggests that the treatment was ineffective; guided by one or more of the first and second cellular senescence scores, thesubject receives a second treatment; and a third cellular senescence score suggests that the treatment was effective.

[0014] In certain embodiments, the immune longevity score from the subject indicates that the immune longevity score is low, and the subject is treated with at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus. In certain embodiments, the subject is treated with a 5-10 mg dose of at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus, and a second immune longevity score is calculated between 3 to 8 weeks after treatment. In certain embodiments, the second immune longevity score from the subject indicates that the immune longevity score is low, and the subject is treated with a reduced dose of at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus, and a third immune longevity score is calculated between 3 to 8 weeks after treatment with the reduced dose.

[0015] In certain embodiments, if the subject’s cellular senescence score is low and the subject’s immune longevity score is optimal, the subject is evaluated for at least one of: heart disease and cancer.

[0016] In certain embodiments, if the subject’s cellular senescence score is low and the subject’s immune longevity score is low, the subject is evaluated for at least one of: heart disease, cancer, acute infection, chronic infection, and chronic inflammation.

[0017] In certain embodiments, if the subject’s cellular senescence score is optimal and the subject’s immune longevity score is low, the subject is evaluated for at least one of: acute infection, chronic infection, and chronic inflammation.

[0018] In certain embodiments, if the subject’s cellular' senescence score is high and the subject’s immune longevity score is optimal, the subject is evaluated for at least one of: a sedentary lifestyle, psychological stress, smoking, past chemotherapy, and past radiation treatment.

[0019] In certain embodiments, if the subject’s cellular' senescence score is high and the subject’s immune longevity score is low, the subject is evaluated for at least one of: acute infection, chronic infection, chronic inflammation, a sedentary lifestyle, psychological stress, smoking, past chemotherapy treatment, and past radiation treatment.

[0020] Other features and advantages of the concepts disclosed herein will be apparent from the following detailed description, the examples, and the claims included herein.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1. Relationship between biomarkers within the biomarker network. Scatterplot correlation matrix of expression levels (log2) of pl6, pl4, LAG3, CD28, CD244 as well as chronological age. Scatterplot correlation matrices of expression levels (log2) of p 16, p!4, LAG3, CD28, CD244 as well as chronological age are shown. Each correlation circle represents the correlation between each pair of variables on a scale from (+1) (boxes marked with a “P” to indicate positive) to (-1) (boxes marked with an “N” to indicate negative). The size of each circle represents the significance test between the variables. A larger circle indicates a more significant relationship, and the Pearson correlation coefficient is shown as a number and a line of linear fit on the corresponding scatterplot. The histograms (diagonal across the matrix) show the distribution of each biomarker in the entire cohort.

[0022] Figure 2. Building an immune model of immune health, Immune Longevity Score (“ILS”). Model components and pairwise relationships between components are shown. In this case, the ILS was built with gender, age, CD28, CD244, pl6, and LAG3. For continuous variables, black lines indicate the highest value for each variable in a population, and gray lines indicate the lowest value for each variable in a population. For the categorical variable gender, categories are labeled with text label. Each square demonstrates a relationship between the two variables with respect to immune longevity score (0-100; with 0 being the worst and 100 being the best).

[0023] Figure 3. Building an immune model of immune health, Immune Longevity Score (“ILS”). Model components and pairwise relationships between components are shown. In this case, the ILS was built with gender, age, CD28, CD244, and LAG3. For continuous variables, black lines indicate the highest value for each variable in a population, and gray lines indicate the lowest value for each variable in a population. For the categorical variable gender, categories are labeled with text label. Each square demonstrates a relationship between the two variables with respect to immune longevity score (0-100; with 0 being the worst and 100 being the best).

[0024] Figure 4. Building an immune model of immune health, Immune Longevity Score (“ILS”). Components of an ILS and pairwise relationships between components are shown. In this case, the ILS was built with age, lymphocyte counts, neutrophil counts, CD3+ cell counts, CD56+ / CD16+ cell counts, CD3+CD4+ cell counts, CD3+CD8+ cell counts, CD3+ CD8+CD28- cell counts, and CD3+CD8+CD95- cell counts. All counts were calculated as percent of all white blood cells. Black lines indicate the highest value for each variable in a population, and gray lines indicate thelowest value for each variable in a population. Each square demonstrates a relationship between the two variables with respect to immune longevity score (0-100; with 0 being the worst and 100 being the best).

[0025] Figure 5. Immune longevity score distribution across lifespan in healthy subjects. Immune longevity score values are plotted against chronological age. Trendline for males and females is shown.

[0026] Figure 6. Shows a decision tree. Subjects can be split into groups la, lb, 2a, 2b, 3a, and 3b depending on their ILS and their cellular- senescence levels. In certain embodiments, subsequent investigations and interventions can be guided based on which group a subject is placed into.DETAILED DESCRIPTION

[0027] Section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control.

[0028] Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0029] As used herein, a “subject” can be an individual that is a human or other animal. A “patient” refers to a class of subjects who is under the care of a treating physician (e.g., a medical doctor or veterinarian). The subject can be male or female of any age. Exemplary and non-limiting subjects include, humans, non-human primates, rabbits, mice, rats, horses, dogs, and cats.

[0030] The term “sample,” as used herein, refers to a composition that is obtained or derived from a subject. The sample can be whole blood or a blood sample that has been fractionated. The sample may be peripheral blood leukocytes including neutrophils, eosinophils, basophils, lymphocytes, and monocytes. In some embodiments, the sample is a peripheral blood lymphocyteselected from B cells, T cells, monocytes, and NK cells. In some embodiments, the sample is a peripheral blood T lymphocyte (e.g., a T cell) or a subset of T cells (e.g., CD3+, CD8+ cells). In some embodiments, the sample is a tissue biopsy. In certain embodiments, the sample comprises genetic information. In certain embodiments, the sample comprises at least one of proteins, metabolites, steroids, hormones, sugars, salts, or other physiological components.[00311 As used herein, the term “gene” refers to a nucleic acid that encodes an RNA, for example, nucleic acid sequences including, but not limited to, structural genes encoding a polypeptide. The term “gene” also refers broadly to any segment of DNA associated with a biological function. As such, the term “gene” encompasses sequences including but not limited to a coding sequence, a promoter region, a transcriptional regulatory sequence, a non-expressed DNA segment that is a specific recognition sequence for regulatory proteins, a non-expressed DNA segment that contributes to gene expression, a DNA segment designed to have desired parameters, or combinations thereof. A gene can be obtained by a variety of methods, including cloning from a biological sample, synthesis based on known or predicted sequence information, and recombinant derivation from one or more existing sequences.

[0032] The term “gene expression” generally refers to the cellular processes by which a biologically active polypeptide is produced from a DNA sequence and exhibits a biological activity in a cell. As such, gene expression involves the processes of transcription and translation, but also involves post-transcriptional and post-translational processes that can influence a biological activity of a gene or gene product. These processes include, but are not limited to RNA synthesis, processing, and transport, as well as polypeptide synthesis, transport, and post-translational modification of polypeptides. Additionally, processes that affect protein-protein interactions within the cell can also affect gene expression as defined herein. In some embodiments, the phrase “gene expression” refers to a subset of these processes. As such, “gene expression” refers in some embodiments to transcription of a gene in a cell type or tissue. Thus, the phrase “expression level” can refer to a steady state level of an RNA molecule in a cell, the RNA molecule being a transcription product of a gene. Expression levels can be expressed in whatever terms are convenient, and include, but are not limited to absolute and relative measures. For example, an expression level can be expressed as the number of molecules of mRNA transcripts per cell or per microgram of total RNA isolated from cell. Alternatively or in addition, an expression level in a first cell can be stated as a relative amount versus a second cell (e.g., a fold enhancement or foldreduction), wherein the first cell and the second cell are the same cell type from different subjects, different cell types in the same subject, or the same cell type in the same subject but assayed at different times (e.g., before and after a given treatment, at different chronological time points, etc.).

[0033] The term “gene product” generally refers to the product of a transcribed gene, such as a protein, peptide, or enzyme. The term “gene product” may also refer to non-coding RNA, such as a functional RNA (fRNA), for example, micro RNAs (miRNA), piRNAs, ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and the like.

[0034] The term “sensitivity” refers to a measurement of the proportion of actual positively identified results in a binary test (e.g., the proportion of individuals identified as having a disease or condition who are correctly identified as having the disease or condition in a diagnostic test).

[0035] The term “specificity” refers to a measurement of the proportion of actual negatively identified results in a binary test (e.g., the proportion of individuals identified as not having a disease or condition that are correctly identified as not having the disease or condition in a diagnostic test).

[0036] The term “negative predictive value” refers to the proportion of identified negative results that are actually negative for a disease or condition in a diagnostic test.

[0037] The term “positive predictive value” refers to the proportion of identified positive results that are actually positive for a disease or condition in a diagnostic test.

[0038] The term “threshold” refers to a specific level at which a measured parameter has been established. In certain embodiments, the exact threshold values vary depending on the method of measuring gene expression used and can be determined empirically by comparison to reference samples. In certain embodiments, expression levels above this threshold and below this threshold are indicative of a positive or negative diagnostic outcome, respectively. A specific cutoff for the threshold may be set depending on the desired sensitivity and specificity for a subject population.

[0039] In certain embodiments, a range may be used to identify an optimal score. In certain embodiments, a range has both an upper limit and a lower limit.

[0040] The terms “predicting” and “likelihood” as used herein does not mean that the outcome is occurring with 100% certainty. Instead, it is intended to mean that the outcome is more likely occurring than not. Acts taken to “predict” or “make a prediction” can include the determination of the likelihood that an outcome is more likely occurring than not.

[0041] The term “composite score” or “composite result” refers to a score that is generated through analyzing two or more variables. In certain embodiments, variables represent individual scores, and in certain embodiments, represent scores from individual biomarkers. Examples of variables used to calculate a composite score include, but are not limited to, demographic metrics such as age and gender, measurements of gene expression, measurements of protein levels, measurements of organ and systems function such as cognition, or ability to walk as ascertained by physical or written testing, genotyping, other measurements of health or senescence based on testing, measurements of molecules in bodily fluids, such as urine or blood, measurements of molecules in the lungs, such as oxygen levels, and measurements of other biomarkers. In certain embodiments, a variable is a measure of chronic disease of one or more specific organs or systems in an organism diagnosed by standard clinical testing. In certain embodiments, a variable is a measure of the function of one or more specific organs or systems in an organism. In certain embodiments, a variable is a measure of the overall function of an organism and is not organ or system specific. In certain embodiments, two or more variables are used to calculate a first composite score, which is itself a variable that is then combined with other variables to calculate a second composite score. In certain embodiments, a threshold is established using a composite score. In certain embodiments, a composite score is generated for a subject. In certain such embodiments, the composite score generated for a subject is compared to the threshold established for that composite score.

[0042] In certain embodiments, a composite score is generated using one or more algorithms. In certain embodiments, algorithms for generating a composite score can include variables that are given identical or different weights, depending on how the algorithm is constructed. For example, and not limitation, a variable that represents a certain biomarker might be given a weight equivalent to 50% of the score even if there are three other different variables used to generate the composite score. In certain other embodiments with the same four biomarkers, each biomarker might be given an equivalent weight (25%) when generating a composite score. In certain embodiments, variables can be added together to create a composite score. In certain such embodiments, variables can have either a positive or negative value when used to calculate the composite score. For example, and not limitation, a composite score might be calculated by adding together the weighted variables A and B, and then subtracting the weighted variable C. In certain embodiments, a variable can be excluded from a composite score if the value associated with that specific variablefalls outside of a given range. For example, and not limitation, a variable may only be part of a composite score if it falls between 0.3 and 0.7 units. If that variable exceeds 0.7 units or is less than 0.3 units, it is excluded from the composite score. In certain embodiments, the value of a variable can function as a gateway to one or more different algorithms. For example, and not limitation, if a subject is homozygous wild-type or heterozygous at a given locus, a composite score is calculated using algorithm A. If a subject is homozygous mutant at that locus, a composite score is calculated using algorithm B. In certain embodiments, gateway variables can be used that result in three or more aims, for example, and not limitation, if a variable is scored between 0 and 0.3 units, a composite score is calculated using algorithm A, if a variable is scored greater than 0.3 but less than 0.9 units, a composite score is calculated using algorithm B, if a variable is scored at or above 0.9 units, a composite score is calculated using algorithm C. In certain embodiments, a gateway variable can also function as a way to exclude a subject. For example, and not limitation, if a subject is homozygous wild-type or heterozygous at a given locus, a composite score is calculated using algorithm A. If that subject is homozygous mutant at that locus, no composite score is calculated.

[0043] In certain embodiments, algorithms for generating a composite score can include statistical methods for determining values. For example, and not limitation, algorithms can include linear regression analysis, non-linear regression modeling, machine learning methods, tree analysis, probability theory methods, and other methods known to those of skill in the art.

[0044] The terms pl6, pl6(INK4a), and p 16I K4‘ are used interchangeably. The terms pl4, pl4(ARF), and p 14ARIare used interchangeably.

[0045] The methods described herein can be used to detect gene expression in a biological sample, and more particularly in a blood sample in a subject (e.g., a human patient). Gene expression levels can be determined in whole blood samples or, more typically, the whole blood sample can be manipulated or fractionated prior to determining gene expression level. Manipulation of blood samples is well known in the art and can include separation of red blood cells from white blood cells and plasma, or separation of various cell types from each other, including isolating specific white blood cells, or more specifically isolating T-lymphocytes, and measuring gene expression levels in the isolated cell type(s). In some embodiments, gene expression levels of plb^43are measured from a sample of isolated peripheral blood T-lymphocytes.

[0046] The level of gene expression can be determined using a variety of molecular biology techniques that are well known in the art. For example, if the expression level is to be determined by analyzing RNA isolated from the biological sample, techniques for determining the RNA expression level include, but are not limited to, Northern blotting, nuclease protection assays, quantitative PCR (e.g., digital RT-PCR and / or real time quantitative RT-PCR), branched DNA assay, direct sequencing of RNA by RNA seq, nCounter gene expression technology (NanoString Technologies), single cell sequencing, reserve transcription loop-mediated isothermal amplification (RT-LAMP), and droplet digital PCR technology.

[0047] Alternatively, expression levels can be determined by analyzing protein levels in a biological sample using antibodies. Methods for quantifying specific proteins in biological samples are known in the art. Representative antibody-based techniques include, but are not limited to, immunodetection methods such as ELISA, Western blotting, in-cell Western, beadbased immunoaffinity, immunoaffinity columns, and 2-D gel separation.

[0048] Methods for nucleic acid isolation can comprise simultaneous isolation of total nucleic acid, or separate and / or sequential isolation of individual nucleic acid types (e.g., genomic DNA, cell-free RNA, organelle DNA, total cellular' RNA, mRNA, polyA+ RNA, rRNA, tRNA) followed by optional combination of multiple nucleic acid types into a single sample. Such isolation techniques are known to those skilled in the art. Nucleic acids that are to be used for subsequent amplification and labeling can be analytically pure as determined by spectrophotometric measurements or by analysis following electrophoretic resolution (Bio Analyzer, Agilent). The nucleic acid sample can be free of contaminants such as polysaccharides, proteins, and inhibitors of enzyme reactions. When an RNA sample is intended for use as probe, it can be free of nuclease contamination. Contaminants and inhibitors can be removed or substantially reduced using resins for DNA extraction (e.g., CHELEX™ 100 from BioRad Laboratories, Hercules, Calif., United States of America) or by standard phenol extraction and ethanol precipitation. Isolated nucleic acids can optionally be fragmented by restriction enzyme digestion or shearing prior to amplification.

[0049] Various methods for designing primers for specific nucleic acid sequences of interest are well known in the art. For example, primers for amplifying p 14ARFand p 16IXK4ilseparately can be designed based upon the specific sequences chosen. For example, p I4ARIand p I 6IXK4LItranscripts have a unique exon 1 but share exon 2. Therefore, to design primers specific for pl4ARForp l6IXK4a.aforward primer can be selected for each unique exon 1 and a reverse primer can be selected for the common exon 2. Conversely, suitable primers may be designed to amplify the shared portion of exon 2 of p I4ARIand pl6™K4ato determine the expression level of both genes together. In addition, it can be beneficial to design primers that flank the exon / intron junction, for example, to eliminate amplification signal from genomic DNA contamination in RT-PCR reaction. Non-limiting exemplary primers for detecting p 14AR[and p!6LNK4aare described in U.S. Patent Application No. 16 / 078,476.

[0050] In some embodiments of the present invention, the abundance of specific mRNA species present in a biological sample (for example, mRNA extracted from peripheral blood T lymphocytes) is assessed by real-time quantitative RT-PCR. Standard molecular biological techniques are used in conjunction with specific PCR primers to quantitatively amplify those mRNA molecules corresponding to the gene or genes of interest. Methods for designing specific PCR primers and for performing quantitative amplification of nucleic acids including mRNA arc well known in the art. See e.g., Heid et al., 1996; Sambrook & Russell, 2001; Joyce, 2002; Vandesompele et al., 2002. In some embodiments, a technique for determining expression level includes the use of the TAQMAN® Real-time Quantitative PCR System (ThermoFisher Scientific, United States of America).

[0051] Specific primers for genes of interest (e.g., plb1^^) are employed for determining expression levels of these genes. In some embodiments, the expression level of one or more housekeeping genes (e.g., YWHAZ) are also determined in order to normalize a determined expression level. In one aspect, the level of expression of pl 6™^ from a sample may be normalized to a housekeeping gene from a batch of combined samples. In another aspect, the level of expression of plb™^ from a sample may be normalized to a housekeeping gene from the same sample.

[0052] The primers and probes used for amplification and detection may include a detectable label, such as a radiolabel, fluorescent label, or enzymatic label. See, U.S. Patent No US 5,869,717, hereby incorporated by reference. In certain embodiments, the probe is fluorescently labeled. Fluorescently labeled nucleotides may be produced by various techniques, such as those described in Kambara et al., Bio / Technol., 6:816-21, (1988); Smith et al., Nucl. Acid Res., 13:2399-2412, (1985); and Smith et al., Nature, 321: 674-679, (1986), the contents of each of which are herein incorporated by reference herein for their teachings thereof. The fluorescent dye may be linked tothe deoxyribose by a linker arm that is easily cleaved by chemical or enzymatic means. There are numerous linkers and methods for attaching labels to nucleotides, as shown in Oligonucleotides and Analogues: A Practical Approach, IRL Press, Oxford, (1991); Zuckerman et al., Polynucleotides Res., 15: 5305-5321, (1987); Sharma et al., Polynucleotides Res., 19:3019, (1991); Giusti et al., PCR Methods and Applications, 2:223-227, (1993); Fung et al. (U.S. Patent Number 4,757,141); Stabinsky (U.S. Patent Number 4,739,044); Agrawal et al., Tetrahedron Letters, 31:1543-1546, (1990); Sproat et al., Polynucleotides Res., 15:4837, (1987); and Nelson et al., Polynucleotides Res., 17:7187-7194, (1989), the contents of each of which are herein incorporated by reference herein for their teachings thereof. Extensive guidance exists in the literature for derivatizing fluorophore and quencher molecules for covalent attachment via common reactive groups that may be added to a nucleotide. Many linking moieties and methods for attaching fluorophore moieties to nucleotides also exist, as described in Oligonucleotides and Analogues, supra; Guisti ct al., supra; Agrawal ct al., supra; and Sproat et al., supra.

[0053] The products of the Quantitative PCR employed in the TAQMAN® Real-time Quantitative PCR System can be detected using a probe oligonucleotide that specifically hybridizes to the PCR product. Typically, this probe oligonucleotide is labeled at the 5' and / or 3' ends with one or more detectable labels described herein. In some embodiments, the 5' end is labeled with a fluorescent label and the 3' end is labeled with a fluorescence quencher. In some embodiments, the 5' end is labeled with tetrachloro-6-carboxyfluorescein (TET™; Applera Corp., Norwalk, Conn., United States of America) and / or 6-FAM™ (Applera Corp.) and the 3' end includes a tetramethylrhodamine (TAMRA™; Applera Corp.), NFQ, BHQ, and / or MGB quencher.

[0054] Additional exemplary and non-limiting detectable labels may be attached to the primer or probe and may be directly or indirectly detectable. The exact label may be selected based, at least in part, on the particular type of detection method used. Exemplary detection methods include radioactive detection, optical absorbance detection, e.g., UV-visible absorbance detection, optical emission detection, e.g., fluorescence; phosphorescence or chemiluminescence; Raman scattering. Preferred labels include optically-detectable labels, such as fluorescent labels. Examples of fluorescent labels include, but are not limited to, 4-acetamido-4 -isothiocyanatostilbene- 2,2 'disulfonic acid; acridine and derivatives: acridine, acridine isothiocyanate; 5-(2'- aminoethyl)aminonaphthalene-l-sulfonic acid (EDANS); 4-amino-N-[3- vinylsulfonyl)phenyllnaphthalimide-3,5 disulfonate; N-(4-anilino- 1 -naphthyl)maleimide;anthranilamide; BODIPY; alexa; fluorescin; conjugated multi-dyes; Brilliant Yellow; coumarin and derivatives; coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumaran 151); cyanine dyes; cyanosine; 4',6-diaminidino-2- phenylindole (DAPI); 5'5''-dibromopyrogallol-sulfonaphthalein (Bromopyrogallol Red); 7- diethylamino-3-(4'-isothiocyanatopheny 1 )-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4 '-diisothiocyanatodihydro-stilbene-2, 2 '-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulfonic acid; 5-[dimethylamino]naphthalene-l-sulfonyl chloride (DNS, dansylchloride); 4- dimethylaminophenylazophenyl-4'-isothiocyanate (DABITC); eosin and derivatives; eosin, eosin isothiocyanate, erythrosin and derivatives; erythrosin B, erythrosin, isothiocyanate; ethidium; fluorescein and derivatives; 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazin-2- yl)aminofluorescein (DTAF), 2',7'-dimethoxy-4'5'-dichloro-6-carboxyfluorescein, fluorescein, fluorescein isothiocyanate, QFITC, (XRTTC); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4-mcthylumbcllifcroncortho crcsolphthalcin; nitrotyrosinc; pararosaniline; Phenol Red; B-phycoerythrin; o-phthaldialdehyde; pyrene and derivatives: pyrene, pyrene butyrate, succinimidyl 1 -pyrene; butyrate quantum dots; Reactive Red 4 (Cibacron™ Brilliant Red 3B-A) rhodamine and derivatives: 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid; terbium chelate derivatives; Atto dyes, Cy3; Cy5; Cy5.5; Cy7; IRD 700; IRD 800; La Jolta Blue; phthalo cyanine; and naphthalo cyanine. Labels other than fluorescent labels are contemplated by the methods described herein, including other optically-detectable labels.

[0055] Other methodologies for determining gene expression levels can also be employed, including but not limited to Amplified Antisense RNA (aaRNA) and Global RNA Amplification (Van Gelder et al., 1990; Wang et al., 2000; U.S. Pat. No. 6,066,457 to Hampson et al.). In accordance with the methods of the presently disclosed subject matter, any one of the above- mentioned PCR techniques or related techniques can be employed to perform the step of amplifying the nucleic acid sample and / or quantitating the expression of a particular target nucleic acid. In addition, such methods can be optimized for amplification of a particular subset of nucleic acid (e.g., specific mRNA molecules versus total mRNA), and representative optimization criteriaand related guidance can be found in the art. See Williams, 1989; Linz et al., 1990; Cha & Thilly, 1993; McPherson et al., 1995; Roux, 1995; Robertson & Walsh-Weller, 1998.

[0056] For any particular biomarker, graphical distributions of gene expression levels for subjects that do or do not develop a particular disease or condition are not completely distinct but instead will overlap. Therefore, any diagnostic test that measures a biomarker does not absolutely distinguish low-risk patients from patients that are at high-risk for developing a particular disease or condition with 100% accuracy. The graphical area of overlap correlates to a range of gene expression levels wherein the test cannot distinguish low-risk or normal from high risk. Thus, the developer of the test must select a threshold level of expression from the area of overlap and conclude that levels above the threshold are considered at risk for developing the disease or condition and expression levels below the threshold are considered to be normal or not at risk. The smaller the area of overlap, the more accurate the diagnostic test will be.

[0057] Determining the exact threshold value to determine those at risk and those not at risk of developing a particular disease or condition will depend upon the assay format being developed. In certain embodiments, threshold values may be determined empirically using techniques well known by those skilled in the art.

[0058] One exemplary and non-limiting way to determine the ability of a particular test to distinguish two populations can be by using receiver operating characteristic (ROC) analysis. To draw a ROC curve, the true positive rate (TPR) and false positive rate (FPR) are determined as the decision threshold is varied continuously. Since TPR is directly correlated with sensitivity and FPR is inversely correlated with specificity (1 -specificity), the ROC graph is sometimes called the sensitivity vs (1 -specificity) plot. The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition. A perfect test will have an area under the ROC curve of 1.0 whereas a random test will have an area of 0.5. Therefore, any actual diagnostic test analyzed using ROC analysis will have an area under the ROC curve somewhere between 0.5 and 1.0. The closer to 1.0 the curve is, the more accurate the test is.

[0059] ROC analysis is often used to select a threshold that provides an acceptable level of specificity and sensitivity to distinguish a "diseased" subpopulation from a "non-diseased" subpopulation. In general, optimal threshold is the point on the ROC curve closest to the upper left comer (100% sensitivity; 100% specificity). However, depending on the disease or patient population a more detailed description of ROC analysis and its use for evaluating diagnostic testsand predictive models can be found in the art, for example, in Zou et al., Circulation. 2007;115:654-657.

[0060] In addition to the measurement of area under the curve (AUC), the effectiveness of a given biomarker to predict or diagnose a disease can be estimated through several additional measures of diagnostic test accuracy (described in Fischer et al., Intensive Care Med. 29: 1043-51, 2003). These measures include sensitivity and specificity, likelihood ratios (LR), and diagnostic odds ratios (OR).

[0061] In certain embodiments, the ROC curve area is an area ranging from about 0.5 to about 1, including each fractional integer within the specified range. In one aspect, the ROC curve area is greater than at least 0.5, at least 0.6, at least 0.7, at least 0.8, at least 0.9, or even at least 0.95.

[0062] In certain embodiments, the suitable positive likelihood ratio is a ratio (calculated as sensitivity / ( 1 -specificity)) of at least 1 , at least 2, at least 3, at least 5, at least 10; and a negative likelihood ratio (calculated as (l-scnsitivity) / spccificity) of less than 1, less than or equal to 0.5, less than or equal to 0.3, less than or equal to 0.1; an odds ratio different from 1, at least about 2 or more, at least about 3 or more, at least about 4 or more, at least about 5 or more, or even at least about 10 or more.

[0063] In certain embodiments, biomarkers can be coupled with other markers to generate a composite score. Methods for combining assay results can comprise, but are not limited to, the use of multivariate logistic regression, n-of-m analysis, decision tree analysis, calculating hazard ratios, and other methods known to those skilled in the art. In certain embodiments, a composite result which is determined by combining individual markers measured prior to intervention, maybe treated as if it itself is a marker; that is, a threshold determined for a composite result as described herein for individual markers, and the composite result can be used to calculate odds ratios for individual patients.

[0064] Cellular senescence refers to a cell losing the ability to proliferate. In many cases, cellular senescence represents a permanent cell cycle arrest in which cells remain metabolically active and adopt characteristic phenotypic changes. The onset of cellular senescence can occur in response to stress stimuli, such as, for example, cell stress caused by inflammation. Life experiences that can affect cellular senescence levels include, but are not limited to, consumption of alcohol, smoking, sedentary lifestyle, stress, chronic inflammation, environmental exposure, radiation, chemotherapy, exposure to poisons, and dietary decisions. Markers of cellular senescence include, 1but are not limited to, pl4, pl6, Klotho, pl5, MDM2, p21, p53, macroH2A, IL-6, IGFBP-2, PAL 1, HMGB1, p38 MAPK, SA- / TGal, markers of DNA methylation, and telomere length. In certain embodiments, a marker of cellular senescence is used to measure cellular senescence levels. In certain embodiments, a cellular senescence score is assigned to a subject based on the cellular senescence levels of that subject.[00651 When the art discusses cellular senescence, it is referring to the cellular senescent load, or the quantity of cells in an organism or tissue that are senescent. While the cellular senescent load increases with age and individual senescent cells expressing pl6 are permanently growth arrested and accumulate, the rate of an increase in senescence can be changed by preferentially removing the senescent cells. So pl6 expression levels of the organism or tissue as a whole can be decreased by preferentially removing senescent cells. Thus, where those skilled in the art refer to reducing cellular senescence, they are really referring to reducing the cellular senescent load in the organism or tissue.

[0066] In certain embodiments, cellular senescence is best measured by pl6 levels. For example, and not limitation, expression of pl6 is not detected in young cells, increases exponentially with chronological age (doubling approximately every 8 years in humans), and is potently activated by age-promoting stimuli, including, but not limited to, cigarette smoking, physical inactivity, radiation, cytotoxic chemotherapy administration, chronic HIV infection, and bone marrow transplantation. Exposure to these toxic stimuli can cause acceleration of aging phenotypes and can be monitored through expression of pl6 in various tissues, including T cells in peripheral blood. In certain embodiments, measuring pl6 levels in peripheral blood, and from T cells in particular, provides an overall view of organismal aging (See, e.g. US Patent No. 8,158,347). In contrast, measuring pl6 from a specific tissue, such as an organ, may provide insight into the health of that organ, but not necessarily the overall health of the organism. In addition, pl6 levels can increase in some organs in response to insult or injury. Thus, in certain embodiments, measuring l6 levels in peripheral blood provides a more comprehensive measure of organismal senescence state than measuring pl6 from one or more individual tissues.

[0067] Experts consider accumulation of cellular senescent cells to be irreversible without intervention with compounds described to have senolytic properties (see, e.g., Song et al., Cells (2020)). The exact mechanism(s) by which senolytics remove senescent cells and reduce the overall cellular senescence load is unknown and likely to be compound- specific but may involverestoring ability of senescent cells to undergo apoptosis rather than by reversing the cellular senescence in individual cells, causing them to regain the ability to divide.

[0068] Senescent cells are resistant to apoptosis and accumulate in blood and tissues. Recent evidence suggests that senescent cells can be cleared by the immune system. Therefore, accumulation and turnover of senescent cells exists in a balance. As the organism ages (or receives age-accelerating stimuli), the rate of accumulation of senescent cells can increase or the immune system ability to clear senescent cells declines (immunosenescence). In addition, interaction between senescent cells in tissues and immune cells affects immune system function. Senescent cells in tissues recruit and make immune cells exhausted and dysfunctional via Senescent- Associated Secretory Phenotype (often referred to as ‘SASP’). As a result of all the above scenarios, senescent cells accumulate at a higher rate, causing decline in physiological reserve and aging.

[0069] In certain embodiments, potential mechanisms of reducing senescent cell load include, but are not limited to, enhancement of the immune system to improve targeting and turnover of senescent cells; turnover of the senescent cells themselves by allowing apoptosis or clearance by Natural Killer (NK) cells and / or CD8+ T -lymphocytes; blocking SASP secretion from existing senescent cells and thus preventing formation of new senescent cells by paracrine stimulation; blocking SASP secretion from existing senescent cells and thus preventing deterioration of the immune system.

[0070] The senescence program is driven by a complex interplay of signaling pathways. To promote and support cell cycle arrest, pl6 and the p53 (TP53) target p21 (CDKN1A), inhibit cyclin-dependent kinases (CDKs), thereby preventing phosphorylation of the retinoblastoma protein (pRb) and thus in turn suppressing the expression of proliferation-associated genes (see, e.g., Narita et al, 2003, 2006; Collado et al, 2007). In addition, the nuclear factor kappa B protein complex (NF-kB) acts as a master regulator of SASP expression and therefore affects both the microenvironment of senescent cells and their immune surveillance (see, e.g., Acosta et al, 2008; Krizhanovsky et al, 2008; Xue et al, 2011; Lasorella et al, 2014). Clearance of senescent cells by the immune system helps limit their prolonged retention in tissues, a trait that might derive from their intrinsic resistance to apoptosis (see, e.g., Yosef et al, 2016). The anti- apop totic BCL-2 family members BCL-W, BCL-XL, and BCL-2 were shown to facilitate the resistance of senescent cells to apoptosis (see, e.g., Chang et al, 2016; Yosef et al, 2016). However, the contribution of pathwaysthat regulate the formation of senescent cells to the resistance of these cells to cell death has yet to be determined. On one hand, senescent cells cannot accumulate p53 protein to the levels required for apoptosis (Seluanov et al, 2001). On the other hand, the p53 target p21, via its ability to promote cell cycle inhibition, can protect some cells from apoptosis (Abbas & Dutta, 2009).

[0071] Immuno senescence refers to the gradual deterioration of the immune system due to increasing age and exposure to insults. In certain embodiments, immuno senescence renders the immune system slow to respond to stimuli (although it is still capable of being activated), increasing susceptibility to both infections and age-related diseases. In certain embodiments, immunosenescence can be reversible. In contrast, the cellular senescence state of individual cells, for example and not limitation, as measured by pl6 levels in T cells, is not reversible. Thus, in certain embodiments, increased expression of p 16 in T cells can indicate cellular senescence, but not necessarily indicate immunosenescence. Immunosenescence, also a factor in aging, is characterized by changes in T cell subsets (decrease in naive T cells, increase in memory CD8+ T cells), decrease of T cell activation (loss of CD28 expression), and changes in expression of certain genes that suggest T cell exhaustion, for example and not limitation, CD 140, CD244, CD 160, and LAG3. While T cells can simultaneously display features of cellular senescence and immunosenescence, these processes correlate only weakly. Thus, in certain embodiments, cellular senescence and immunosenescence represent distinct processes that both contribute to aging and inflammatory phenotypes across tissue types. In certain such embodiments, measuring biomarkers of both immunosenescence and cellular senescence and combining those measurements into a composite score provides more information than measuring cellular senescence and immunosenescence separately. In certain embodiments, because cellular senescence load (the quantity of senescent cells) is regulated by the immune system, considering both measurements of cellular senescence and immune health / immunosenescence provides a more complete picture of the overall senescence state and physiological reserve of a subject.

[0072] CD28 is expressed on the surface of T cells and CD28 signaling is involved in the initial activation of naive CD8+ and CD4+ T cells. CD28 in humans is expressed on approximately 80% of CD4+ T cells and 50% of CD8+ T cells. And the loss of CD28 expression from both CD8+cells and CD4+cells has been associated with immunosenescence and physical frailty (Ng. et al., 2015).

[0073] CD244 is a transmembrane cell surface receptor expressed on NK cells and some T cells. In humans, CD244 is alternatively spliced resulting in two different receptors that differ in theirextracellular domains. CD244 signaling is complex and involves both activating and inactivating effects (See, e.g., Agresta et al., Frontiers in Immunology (2018)). CD48 is a known ligand for CD244.

[0074] T cell cellular senescence, which can be measured by measuring pl6 levels, can be distinguished from T cell anergy and T cell exhaustion that occurs as immunosenescence progresses. T cell anergy is a hyporesponsive state in T cells which is triggered by excessive activation of the T cell receptor (TCR) and either strong co-inhibitory molecule signaling or limited presence of concomitant co- stimulation through CD28. In certain embodiments, T cell anergy can be measured by measuring CD28 expression levels. T cell exhaustion occurs after repeated activation of T cells during chronic infection.

[0075] In certain embodiments, T cell exhaustion manifests with several characteristic features, such as progressive and hierarchical loss of effector functions, sustained upregulation and co- cxprcssion of multiple inhibitory receptors, altered expression and use of key transcription factors, metabolic derangements, and a failure to transition to quiescence and acquire antigen-independent memory T cell homeostatic responsiveness. Although T cell exhaustion was first described in chronic viral infection in mice, it has also been observed in humans during infections such as HIV and hepatitis C virus (HCV), as well as in cancer. While T cell exhaustion prevents optimal control of infections and tumors, modulating pathways overexpressed in exhaustion — for example, and not limitation, by targeting programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte antigen 4 (CTLA4) — can reverse this dysfunctional state and reinvigorate immune responses.

[0076] T cell signaling is complex and involves many different factors and genes that work in parallel, contradictory, synergistic, or competing signaling pathways. Accordingly, in certain embodiments, a measurement of gene expression of a single gene may not be very informative as a marker for measuring immunosenescence. In certain embodiments, a measurement of immunosenescence is performed by measuring gene expression from two or more genes involved in immunosenescence and comparing the relative levels of those genes to produce a composite score that better represents the immunosenescence state of the subject than measuring any of those same genes separately. For example and not limitation, CD244 signaling is complex and is only partially understood and probably has effects on multiple different cellular processes, but by comparing CD244 expression levels with expression levels of other markers of immunosenescence, a composite score can be generated that better represents theimmunosenescence state of a subject than measuring CD244 alone. As another example and not limitation, CD28 expression is associated with T-cell anergy and CD244 expression is associated with T-cell exhaustion, therefore by looking at both CD28 expression and CD244 expression, one can capture different processes that are involved in immunosenescence and gain more insight into the immunosenescence state of a subject than could be achieved by measuring a single marker. In certain embodiments, generating a score by measuring both CD28 and CD244 provides a composite score that represents immunosenescence and, optionally, that composite score can be combined with other markers of cellular senescence to create a second composite score that can be used to guide treatment of a subject.

[0077] Both immunosenescence and cellular senescence involve the complex interplay of multiple signal transduction pathways, and can be thought of as progressive processes. For example, and not limitation, the immunosenescence of an organism’s immune system can become more or less dysfunctional over time, depending on which signal transduction pathways arc activated and how those signal transduction pathways interact with other active and inactive signal transduction pathways that affect immunosenescence.

[0078] In certain embodiments, understanding cellular senescence progression and / or immunosenescence progression comprises evaluating multiple different markers in a composite score. Similarly, composite scores can be used to evaluate the likelihood that a particular subject will respond negatively or positively to a proposed treatment or intervention. In certain embodiments, at least one marker in a composite score evaluates the general health of the individual, such as, for example, one or more markers for physiological reserve or senescence. In certain embodiments, at least one marker in a composite score comprises evaluating one or more specific markers specific to one or more particular organs or tissues. For example, and not limitation, when considering risk of developing a kidney related disease, one can include a marker for kidney function. In certain embodiments, a method of generating a composite score comprises generating a composite score from both markers of general health and markers for specific tissues and / or organs.

[0079] In certain embodiments, Immune Longevity Score (“ILS”) is an output of a model to define immune health and the ability of the immune system to withstand infection and suppress inflammation. In certain embodiments, an ILS model endpoint is a non-accidental mortality. In certain embodiments, an ILS model endpoint is the presence of two or more chronic diseases. Incertain embodiments, an ILS endpoint is a history of infection with clinical symptoms lasting more than three weeks or that required hospitalization for three or more days. In certain embodiments, an ILS endpoint is immune resilience, as defined by Ahuja SK et al. Immune resilience despite inflammatory stress promotes longevity and favorable health outcomes including resistance to infection. Nat Commun 2023.[00801 In certain embodiments, variables that can be used to calculate an ILS include counting naive T cells (CD8+CD45RA+CD45RO- or CD3+CD8+CD95-). In certain embodiments, variables that can be used to calculate an ILS include absolute counts, ratios, or percentages of all white blood cells. In certain embodiments, variables that can be used to calculate an ILS include counts of immunosenescent effector cells (CD8+CD28-). In certain embodiments, variables that can be used to calculate an ILS include counting CD3+CD4+ T-cells. In certain embodiments, variables that can be used to calculate an ILS include counting CD3+CD8+ T-cells. In certain embodiments, variables that can be used to calculate an ILS include calculating the ratio of CD3+CD4+ to CD3+CD8+ T-cells. In certain embodiments, variables that can be used to calculate an ILS include counting terminally differentiated effector cells (CD8+T-bet- Eomesodermin (Eomes)+). hi certain embodiments, variables that can be used to calculate an ILS include counting exhausted T lymphocytes including one or more of CD3+CD8+PD-L1+ cells, CD3+CD8+Tim3+ cells, CD3+CD8+CD244+ cells, CD3+CD8+CD160+ cells, CD3+CD8+CTLA-4+ cells, CD3+CD8+LAG3+ cells, and CD3+CD8+TIGIT+ cells.

[0081] In certain embodiments, where cells are counted, the value of the variable can be in the form of absolute counts, ratios relative to one or more other variables or standards (e.g., the ratio of naive T cells to some other cell), or percentages relative to one or more other variables or standards (e.g., the percentage of naive T cells to some other cell).

[0082] In certain embodiments, where a model that calculates an ILS can be expressed as a probability of immune health. This can be expressed as an arbitrary scale that reflects the mathematical probability of the score. In certain embodiments, a model that calculates an ILS can be expressed as a probability of immune health on a scale of 0-100, with 0 being the lowest and 100 being the highest. In certain embodiments, and in the calculations in the Examples included herein, a scale of 0-100 was used, with 0 being the lowest and 100 being the highest.

[0083] In certain embodiments, ILS is an algorithm that incorporates pl6, LAG3, CD244, CD28, age, gender and their mathematical interactions. In certain embodiments, ILS is an algorithm thatincorporates LAG3, CD244, CD28, age, and gender and their mathematical interactions. In certain embodiments, ILS is an algorithm that incorporates cell counts for lymphocytes, neutrophils, CD3+ cells, CD56+ / CD16+ cells, CD3+CD4+ cells, CD3+CD8+ cells, CD3+CD8+CD28- cells, CD3+CD8+CD95- cells, age and their mathematical interactions. In certain embodiments, interventions can improve levels of a single biomarker or multiple biomarkers to improve an overall ILS.

[0084] In certain embodiments, ILS and cellular senescence are measured and assessed for levels. In certain embodiments, cellular senescence in the bottom 25% of the population is considered low, cellular senescence in the range of 26-45% in the population is considered optimal, cellular senescence higher than 45% of the population is considered high. In certain embodiments, the range for low / optimal / high cellular senescence levels may vary depending on the reference population and the purposes of the test. In certain such embodiments, range for low / optimal / high cellular senescence can be determined experimentally.

[0085] In certain embodiments, an ILS comprises a threshold that separates subjects with low ILS from those with an optimal ILS. In certain embodiments, this threshold can be determined experimentally based on the subject population and the algorithms used to create the ILS. In certain embodiments, the output of ILS is converted to a scale between 0-100. In certain embodiments, using an arbitrary ILS scale of immune health between 0-100 (with 0 being the lowest, and 100 being the highest), an ILS score less than 40 is considered low, and an ILS equal to or above 40 is considered optimal.

[0086] In certain embodiments, if cellular senescence is low, subjects may have a genetic risk for heart disease or cancers. In certain embodiments, additional testing for early indicators of heart disease (ApoB, Lp(a), CAC score, CT angiogram) or cancer (liquid biopsy testing such as Galleri® or whole-body radiation-free MRI scans) are implemented.

[0087] In certain embodiments, if cellular senescence levels are high, subjects have excessive accumulation of senescent cells due to (a) too much input into induction of cellular senescence (Figure 6; group 3a), (b) due to induction and lack of clearance (Figure 6; group 3b) or due to lack of clearance (Figure 6, group 2b). A sedentary lifestyle, psychological stress, previous medical treatments (e.g., radiation and chemotherapy) can all lead to senescent cell accumulation even in the presence of optimal ILS. Possible treatments for high cellular senescence include, but are not limited to, dietary interventions, lifestyle interventions designed to reduce stress (both physicaland psychological stress), and the use of senolytics. Examples of molecules with senolytic effect that can be used in combination with the methods discussed herein include, but are not limited to, rapamycin and its analogs (“rapalogs”), fisetin, quercetin by itself or in combination with dasatinib (“D+Q”), metformin, SGLT2 inhibitors, including, but not limited to, canagliflozin, dapafliflozin, empagliflozin, and ertugliflozin, HIF inhibitors, including, but no limited to, roxadustat, molidustat, vadavustat, and daprodustat, glucagon-like peptide-1 (GLP-l) agonists, also known as GLP-1 analogs, including, but no limited to semaglutide, dulaglutide and GLP-l receptor agonist, tirzepatide, and beta blockers, including metoprolol. Examples of lifestyle interventions include, fasting, caloric restriction, use of probiotics and other dietary interventions, optimal exercise, and sleep hygiene. In certain embodiments, where a subject has high cellular senescence and low ILS, one can treat the low ILS prior and cellular senescence in either order or at the same time. In certain such embodiments, one may achieve better outcomes by treating the low ILS before treating the high cellular senescence.

[0088] In certain embodiments, a low ILS may signify vulnerabilities in immune system that need to be addressed for successful aging. Potential causes of low ILS include, but are not limited to, acute infection, chronic infection / viral reactivation, chronic inflammation. In certain embodiments, one can evaluate whether a subject has an acute infection by using known methods, for example, and not limitation, testing for elevated white blood counts, and testing for specific pathogens where such tests are available. In certain embodiments, one can evaluate whether a subject has a chronic infection / viral reactivation by using known methods, for example and not limitation, reviewing the patients medical history for previous infections, and by checking titers of CMV, HSV (herpes simplex virus) group of viruses, varicella zoster virus, EBV, and HIV. In certain embodiments, one can evaluate whether a subject has chronic inflammation using known methods, for example, and not limitation, reviewing the subject’ s medical history and investigating potential sources of chronic inflammation, including, but not limited to, high BMI, arterial disease, liver disease, kidney disease, or gut-related diseases including autoimmune diseases.

[0089] In many cases, determining how to treat a subject is more important than the treatment itself. A mistargeted treatment is unlikely to be efficacious and may be detrimental. As discussed in the Examples, each individual is a complex combination of accumulated experiences and genetics, each comprising its own mixture of insults and injuries. Understanding what those mixtures of characteristics are is almost impossible by trying to correlate single biomarkers withconditions that may be undetectable until later in life. Instead, by measuring both ILS and cellular senescence, one can track whether an individual is experiencing healthy aging, or whether there are issues with their ILS or cellular senescence levels. If there are issues with one or more of cellular senescence levels and ILS, one can attempt to tease out the cause of the issues and address them using the best-known interventions. In certain embodiments, where a subject has a low ILS, one can examine the malleable variables that compose the score. For example, and not limitation, if a subject has a low ILS, one can examine whether LAG3, CD244, or CD28 are significantly outside of their optimal range that they are driving the low ILS score. In certain such embodiments, where a subset of these markers is driving the low ILS score (e.g., very high expression of LAG3), one can tailor interventions to target that particular gene (e.g., use therapies known to reduce T cell exhaustion and monitor efficacy by rechecking LAG3 expression). In certain embodiments, where a subject has a low ILS, one can try to address the ILS (a composite score) directly, without targeting interventions at any subset of the malleable variables that compose an ILS.

[0090] In certain embodiments, detecting early aging through ILS and cellular senescence goes hand in hand with monitoring ILS and cellular senescence to determine whether a particular intervention is efficacious. Because each individual is their own mix of experiences and genetics, it is very difficult to predict whether any particular intervention will be efficacious, even using the best available science. But by continuing to follow a subject over time, through multiple interventions, if necessary, one can detect an unfavorable early aging trajectory, identify efficacious treatments, and potentially restore that subject to a favorable aging profile.

[0091] The present invention also provides diagnostic kits for calculating ILS, cellular senescence or both ILS and cellular senescence. In certain embodiments, the diagnostic kit comprises reagents for measuring the level of one or more genes indicative of cellular senescence, immunosenescence, T-cell exhaustion, and immune resilience. In certain embodiments, the kit further includes reagents for isolating a sample in which one or more genes or gene products may be measured. In certain embodiments, the kit further includes reagents for genotyping a subject.

[0092] In some embodiments, the kits include quantitative RT-PCR reagents (RT-PCR kits). In certain embodiments, a kit that includes quantitative RT-PCR reagents includes the following: (a) primers used to amplify each of a combination of biomarkers (e.g., pl 6) described herein; (b) buffers and enzymes including a reverse transcriptase; (c) one or more thermostable polymerases;and (d) SYBR® Green or a labelled probe, e.g., a TaqMan® probe. In another embodiment, the RT-PCR kits described herein also includes (a) a reference control RNA.

[0093] In certain embodiments, RT-PCR kits comprise pre-selected primers specific for amplifying a particular cDNA. The RT-PCR kits may also comprise enzymes suitable for reverse transcribing and / or amplifying nucleic acids (e.g., polymerases such as Ta ). and deoxynucleotides and buffers needed for the reaction mixture for reverse transcription and amplification. The RT- PCR kits may also comprise probes specific for a particular cDNA. The probes may or may not be labelled with a detectable label (e.g., a fluorescent label). In certain embodiments, each component of the RT-PCR kit is generally in its own suitable container. Thus, these kits generally comprise distinct containers suitable for each individual reagent, enzyme, buffer, primer and probe. The kit may comprise reagents and materials so that a suitable housekeeping gene can be used to normalize the results, such as, for example, tyrosine 3-monooxygenase / tryptophan 5- monooxygenase activation protein, zeta polypeptide (YWHAZ) or p-actin. Further, the RT-PCR kits may comprise instructions for performing the assay and methods for interpreting and analyzing the data resulting from the performance of the assay. In certain embodiments, the kits contain instructions for performing assays designed to interrogate one or more of the levels of cellular senescence, immunosenescence, and immune resilience of a subject.

[0094] The values from the assays described above, such as, but not limited to, expression data, statistical analyses, composite score, and / or threshold score can be calculated and stored manually. Alternatively, the above-described steps can be completely or partially performed by a computer program product. In some embodiments, the methods of the invention are computer-implemented methods. In some embodiments, at least one step of the described methods is performed using at least one processor. In certain embodiments, all of the steps of the described methods are performed using at least one processor. Further embodiments are directed to a system for carrying out the methods of the invention. The system can include, without limitation, at least one processor and / or memory device.

[0095] Accordingly, aspects of the present disclosure may be implemented entirely in hardware, entirely in software (including firmware, resident software, micro-cods, etc.) or by combining software and hardware implementation that may all generally be referred to herein as a "circuit," "module," "component," or "system." Furthermore, aspects of the present disclosure may take theT1form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

[0096] Any combination of one or more computer readable media may be utilized. The computer readable media may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following; a portable computer diskette, a hard disk, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an appropriate optical fiber with a repeater, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0097] A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0098] Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel JADE, Emerald, C++, C#, VB.NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Interact using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS).

[0099] Aspects of the present disclosure may be described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable instruction execution apparatus, create a mechanism for implementing the functions / acts specified m the flowchart and / or block diagram block or blocks.

[0100] These computer program instructions may also be stored in a computer readable medium that when executed can direct a computer, other programmable data processing apparatus, or other devices to function in a particular' manner, such that the instructions when stored in the computer readable medium produce an article of manufacture including instructions which when executed, cause a computer to implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable instruction execution apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatuses or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0101] The following examples, which are included herein for illustration purposes only, are not intended to be limiting.EXAMPLES

[0102] Two cohorts were enrolled. Community-dwelling adults were enrolled into a natural aging registry study (community cohort) and patients seeking physician-guided health interventions for longevity were enrolled into a long-term registry study (physician-guided cohort). Adults between the ages of 25-85 years old living in North Carolina were recruited into the community cohort. Exclusion criteria- diagnosis of an autoimmune disorder, history or current chemotherapy, immunotherapy, or radiation therapy, history of solid organ or bone marrow transplants, dialysis, or an active infection (acute or chronic) for which antibiotics and / or antivirals were prescribed within the last 14 days. To ensure representation across age ranges among the 250 participants, we enrolled 30 participants in the age group of 25-34 years, 45 in 35-44, 50 in 45-55, 50 in 55-64, 45 in 65-74, and 30 in 75-85.

[0103] Peripheral blood samples were collected at a lab facility (Any Lab Test Now, Durham, NC) or at the Raffaele Medical Group clinic (New York, New York) using sample collection kits that stabilize whole blood. 7.5ml of blood was used to isolate T cells as previously reported. T cell pellets were stored at -80°C until further analysis. Gene expression of pl4, pl6, LAG3, CD28, and CD244 was analyzed by real-time qPCR and normalized to housekeeping genes. Positive and negative controls were included in each run, and Cts over 37 were considered below the limit of detection. Expression of each interrogated gene was reported as log2 of arbitrary units, as is standard for qPCR reporting.

[0104] Peripheral blood samples were collected at the Raffaele Medical Group clinic into EDTA blood collection tubes and were analysed by immunotyping (Immunogenetics center, UCLA) to determine lymphocyte counts, neutrophil counts, CD3+ cell counts, CD56+ / CD16+ cell counts, CD3+CD4+ cell counts, CD3+CD8+ cell counts, CD3+ CD8+CD28- cell counts, and CD3+CD8+CD95- cell counts. All counts were calculated as percent of all white blood cells.

[0105] Descriptive statistics were reported as mean (standard deviation (sd)) for continuous variables, and as frequency (%) for categorical variables. For all analyses, missing data for any reason was not imputed. If a measure was missing, the subject was excluded from summary statistics and analyses. Pairwise associations were quantified using Pearson correlation coefficient and p value (two-tailed). A one-way ANOVA analyses was used where three or more groups were present. Two-tailed p values of less than 0.05 were considered statistically significant. Statistical analyses were performed in SAS version 9.4 and JMP 12.2.0 (SAS Institute, Cary, NC).Table 1.Participant characteristics* >140 mmHg, ** total cholesterol >200 md / dL

[0106] In this study, a total of 482 participants were enrolled in one of two cohorts - 250 community-dwelling adults (21-85 years) were enrolled into a community cohort and 232 adult patients (>21 years) seeking physician-guided health interventions for longevity were enrolled into a physician-guided cohort. As shown in Table 1, demographics and clinical characteristics were similar across cohorts with some exceptions. The community cohort had more female participants,higher BMI, a higher rate of prediabetes and diabetes. The physician-guided cohort had a higher rate of hyperlipidemia and pre-diabetes, as well as small statistically, but not clinically significant differences in other measures such as blood pressure, heart rate, hemoglobin and hematocrit. Notably, the physician-guided cohort had a higher neutrophil-to-lymphocyte ratio, a prognostic factor of morbidity and mortality in malignant, immunologic, infectious, and cardiovascular diseases as well as a marker of muscle weakness and stress.

[0107] A network of biomarkers that link cellular senescence and immune system function was identified. This network included gene expression of pl6, pl4, LAG3, CD28, and CD244 measured in peripheral blood T lymphocytes. pl6 is the biomarker most uniquely associated with cellular senescence and has been used as a marker of established senescent cells in studies linking cellular senescence, functional decline, and aging-related diseases. A decrease in CD28, and an increase in LAG3 and CD244 have all been implicated in the loss of immune function, particularly in CD8+ T cells. pl4, a transcript related to pl6, is a regulator of cell cycle arrest and apoptosis through p53 / p21. Pairwise associations between biomarkers within this network are shown in Figure 1. Overall, there was a large degree of association between all the components, with CD28 negatively correlating with the other markers. While pl6 and pl4 were associated with chronological age, other markers were weakly associated and had highly heterogeneous levels of expression in individuals within every age group. Therefore, there is a significant association between levels of biomarkers of cellular- senescence, pl 6, and biomarkers of adaptive immune function.

[0108] A recent study of ~ 48,500 participants developed a quantitative measure of immune resilience linked to longevity and defined immune resilience as the ability of the immune system to withstand antigen challenges and regulate inflammation (See Ahuja SK et al. Immune resilience despite inflammatory stress promotes longevity and favorable health outcomes including resistance to infection. Nat Commun 2023). That study weighted CD4+ and CD8+ absolute T cell counts to derive progressively worsening immune grades, with grade I defined as a primordial state that is optimal for immune health. The same algorithm was used herein to categorize immune resilience grades for 167 participants with known CD4+ and CD8+ T cell counts. Classification of each subject as belonging to a specific immune health grade was used as an endpoint to build a model of immune health.

[0109] A model of immune health was constructed (Figure 2) that included chronological age, and molecular measures of cellular senescence (pl6, pl4), T cell proliferation (CD28), and T cell exhaustion (LAG3, CD244). The model also included gender to account for gender-dependent changes in the immune system. Immune resilience (defined as belonging to one of the immune health grades (Ahuja SK, et al. Immune resilience despite inflammatory stress promotes longevity and favorable health outcomes including resistance to infection. Nat Commun 2023)) was used as a dependent variable. P16, pl4, LAG3, CD28, CD244, chronological age, gender as well as their second-degree interactions were tested as independent variables. Model fit was determined using step-wise backwards variable elimination.

[0110] In other examples (Figure 3), input variables in the immune health model included chronological age, gender, T cell proliferation (CD28), and T cell exhaustion (LAG3, CD244).

[0111] In other examples (Figure 4), input variables in the immune health model included chronological age, lymphocyte counts, neutrophil counts, CD3+ cell counts, CD56+ / CD16+ cell counts, CD3+CD4+ cell counts, CD3+CD8+ cell counts, CD3+ CD8+CD28- cell counts, and CD3+CD8+CD95- cell counts.

[0112] As shown in Figures 2-4, there is a complex interaction between the components. Each model contains two-way interactions between model variables in addition to single variables. Gray lines indicate the lowest value for each variable in a population, and black lines the highest. Then the dependency of change in one variable over the other in each pair with respect to immune resilience was determined. For example, in the pl6 vs Age pair (Figure 2), at younger ages, pl6 expression has no impact on immune resilience, however at older ages, patients with lower pl6 (grey line) are predicted to have higher immune resilience than patients with higher pl6 (black line). Note that that relationship is not linear and higher pl6 levels in patients less than 50 years of age indicate higher immune resilience, which then rapidly declines for the same pl6 value in patients past the age of 50. Another example is the complex relationship between pl6 and LAG3 (Figure 2). Lower levels of LAG3 (gray line) are associated with increasing immune resilience at pl6 levels <12 (see x axis), once pl6 expression reaches 12, the effect of LAG3 on immune resilience plateaus. Whereas high LAG3 expression is associated with low immune resilience regardless of pl6 expression levels.

[0113] The models developed in Figures 2-4 are examples of Immune Longevity Scores (“ILS”). In certain embodiments, ILS is calculated using a composite score weighing pl 6, LAG3, CD28,CD244, chronological age, the second-degree interactions between those biomarkers or chronological age, and gender. In these examples, ILS varies from 0-100 with 0 being the lowest / worst and 100 being the highest / best.

[0114] Immune function declines with age. ILS values were plotted against chronological age. Figure 5 shows that ILS declines with age in the whole population, but on average females tend to have higher ILS values than males in the same age group.

[0115] ILS also correlate with immune subsets known to change with aging (immunosenescence; Table 2).

[0116] As previously mentioned, decline in immune system function is linked to the earliest stages of organismal decline, well before presentation of chronic diseases. The association between this biomarker network and clinical markers of physiologic decline was examined. Both cellular senescence and ILS correlated with several aspects of early aging (Table 3) such as cardiovascular and pulmonary dysfunction and inflammation. Cellular senescence, but not ILS, correlated with metabolic dysfunction and ILS, and not pl6, correlated with hormone levels.Table 2.Correlation Between ILS and T Cell Immune Subsets. Pearson correlation (r), level of significance (p value), and sample size for each correlation are shown.Table 3.Correlation Between ILS or pl 6 with Early Clinical Signs of Aging.

[0117] Aging research has historically focused on the very old and on diseases of old age. However, biological aging begins early and, as such, investigations in subjects across a wide age range and outside the context of reactive disease care are foundational to geroscience. Described herein is a network of biomarkers that captures both cellular senescence and immune function and shows their association with early indicators of physiologic decline.

[0118] Selection criteria for the genes described herein included an independent association with pl6 and each marker’s capacity to capture distinct as well as potentially overlapping biological functions of the adaptive immune system. Multiple markers of presumably similar function (e.g., T cell exhaustion) were screened for association with pl6 as well as the ability to change with longitudinal changes in pl 6. LAG3 and CD244 were both described as markers of T cell exhaustion and LAG3 is a target of immuno-oncology therapies. CD244 appears to have a distinct role in T cell function, accumulating with age in CD8+ T cells where it is associated with increased proliferation and apoptosis, and terminal differentiation, hi addition, CD244 has been shown to be an inhibitor of autophagy by directly interacting with and suppressing activity of the Beclin- 1 / Vps34 complex. Autophagy is essential for the function of T cells including effector CD8 T cell survival and memory formation, and its activity declines with age. The finding herein that immune biomarkers (LAG3, CD244, CD28) and p 14 are strongly associated with immune resilience, i.e., the ability of the organism to withstand immune challenge and resist inflammation, reinforces the importance of the selected markers.

[0119] An ILS was calculated using qPCR (gene expression levels) of CD28, CD244, LAG3, pl6 combined with variables reflecting the chronological age and gender of each subject. qPCR data was collected for immune markers for 160 subjects. Evaluating these markers, and optimizing their interactions using machine learning, yielded the following Immune Longevity Score algorithm:100 / (1 + Exp( -"Lin[IHG-I]"n ) + Exp( "Lin[IHG-IIa]"n - "Lin[IHG-I]"n )+Exp( "Lin[IHG-IIb] "n - "Lin[IHG-I] "n ))“Exp” is exponential function, “n” is the nth term (designates the subject).Where Lin[IHG-I], Lin[IHG-IIa, and Lin[IHG-IIb] are calculated as followsLin[IHG-I](-15.4362781218295) + Match( Gender,"female", 0.963628556879925,"male", -0.963628556879925)+ 0.0584748474650751 * Age + -0.142903436177096 * pl6 + -2.14036581358955 *LAG3 + 2.98335829487238 * CD28 + -0.474364034712243 * CD244 + (Age-57.6934131736527) * (pl6 - 11.0979041916168) * 0.0339866437427015 + (Age-57.6934131736527) * (LAG3 - 13.5965269461078) * 0.0432564268223547 + (Age-57.6934131736527) * (CD244 - 14.8492074568862) * -0.0843811422608377 + (pl6-11.0979041916168) * (LAG3 - 13.5965269461078) * 0.293793040304397 + (p!6-11.0979041916168) * (CD244 - 14.8492074568862) * -0.300605219620014Lin[IHG-IIa](-15.8223423769403) + Match( Gender,"female", 0.788434601104814,"male", -0.788434601104814)+ 0.0721073996090148 * Age + -0.220859594709112 * pl6 + -1.54984225016664 * LAG3 + 2.46343314248198 * CD28 + -0.357628904389047 * CD244 + (Age -57.6934131736527) * (pl6 - 11.0979041916168) * 0.0678292795464829 + (Age -57.6934131736527) * (LAG3 - 13.5965269461078) * -0.0570418568078138 + (Age -57.6934131736527) * (CD244 - 14.8492074568862) * -0.0221743431015218 + (pl6 -11.0979041916168) * (LAG3 - 13.5965269461078) * 1.35252877734925 + (pl6 -11.0979041916168) * (CD244 - 14.8492074568862) * -1.01674753907716Lin[IHG-IIb](-21.0325115721259) + Match( Gender,"female", 0.438555345995713,"male", -0.438555345995713,)+ 0.0824524391848522 * Age + -0.0971858916806699 * pl6 + -0.881621854860166 * LAG3 + 1.84197489927559 * CD28 + -0.0980630695959634 * CD244 + (Age -57.6934131736527) * (p!6 - 11.0979041916168) * 0.0361223290107881 + (Age -57.6934131736527) * (LAG3 - 13.5965269461078) * -0.0460847451530455 + (Age -57.6934131736527) * (CD244 - 14.8492074568862) * -0.0264623709136402 + (pl6 -11.0979041916168) * (LAG3 - 13.5965269461078) * 1.44218150997861 + (pl6-11.0979041916168) * (CD244 - 14.8492074568862) * -1.67079791229116

[0120] An ILS was calculated using frequency of subsets of white blood cells and molecular markers of immune subsets measured by flow cytometry. Data from 86 subjects was used to construct this ILS model. Evaluating these markers, and optimizing their interactions using machine learning, yielded the following Immune Longevity Score algorithm:100 / (1 + Exp( -”Lin[IHG-I] 3"n ) + Exp( ”Lin[IHG-IIa] 3"n - ”Lin[IHG-I] 3"n )+Exp( "Lin[IHG-IIb] 3"n - "Lin[IHG-I] 3"n ))Where Lin[IHG-I], Lin[IHG-IIa, and Lin[IHG-IIb] are calculated as followsLin[IHG-I]129.831820545412 + -1.06658361880718 * Age + 1.62983475267518 * Lymph+1.57210464650793 * Neutroph + -4.61427660077586 * CD3+ +-1.65115832522229 * CD56+ / CD16++ 4.2697186225962 * CD3+CD4++-0.0321034424790533 * CD3+CD8+ + -0.147235075143763 *CD3+CD8+CD28- + -0.342820144157112 * CD3+CD8+CD95-+0.110681979912614 * (CD56+ / CD16+- 13.1341463414634) * (Age - 56.8048780487805)+0.047003974802727 * (CD3+CD8+CD95- - 27.7317073170732) * (Age-56.8048780487805) + 0.141992199411269 * (CD56+ / CD16+- 13.1341463414634) * (Lymph - 31.0292682926829) + -0.0411784326278148 * (CD56+ / CD16+-13.1341463414634) * (Neutroph - 56.9134146341463) + -0.146825563889514 * (CD56+ / CD16+- 13.1341463414634) * (CD3+ - 72.9756097560976)+0.131038321887343 * (CD3+CD8+ - 22.9634146341463) * (CD3+- 72.9756097560976) + 0.163178506551388 * (CD3+CD8+-22.9634146341463) * (CD56+ / CD16+- 13.1341463414634)Lin[IHG-IIa]173.141117117639 + -1.00258795265993 * Age + 0.909595215925754 * Lymph+1.09664374981996 * Neutroph + -4.71208369492271 * CD3+ +-1.28167989024484 * CD56+ / CD16++ 4.16954621653979 * CD3+CD4++0.527436596678701 * CD3+CD8++ -0.205984547545526 *CD3+CD8+CD28- + -0.291604639211886 * CD3+CD8+CD95-+0.125692258621041 * (CD56+ / CD16+- 13.1341463414634) * (Age - 56.8048780487805)+0.0459840880264339 * (CD3+CD8+CD95- - 27.7317073170732) * (Age -56.8048780487805) + 0.291493780985479 * (CD56+ / CD16+- 13.1341463414634) * (Lymph - 31.0292682926829) + 0.0812090036241397 * (CD56+ / CD16+ -13.1341463414634) * (Neutroph - 56.9134146341463) + -0.13939729630399 * (CD56+ / CD16+- 13.1341463414634) * (CD3+ - 72.9756097560976)+0.107612089527686 * (CD3+CD8+- 22.9634146341463) * (CD3+- 72.9756097560976) + 0.140040795135303 * (CD3+CD8+-22.9634146341463) * (CD56+ / CD16+- 13.1341463414634)Lin[IHG-IIb]172.401593776566 + -0.886056452885842 * Age + 0.49721780679655 * Lymph+0.719081609969359 * Neutroph + -3.56848228644321 * CD3+ + -1.05485546658965 * CD56+ / CD16++ 3.10837804584867 * CD3+CD4++-0.00473409736642065 * CD3+CD8++ -0.196082773838061 *CD3+CD8+CD28- + -0.280035062750336 * CD3+CD8+CD95-+0.100312430123854 * (CD56+ / CD16+- 13.1341463414634) * (Age - 56.8048780487805)+0.0373123778442115 * (CD3+CD8+CD95- - 27.7317073170732) * (Age -56.8048780487805) + 0.308741276940106 * (CD56+ / CD16+- 13.1341463414634) * (Lymph - 31.0292682926829) + 0.101667525220192 * (CD56+ / CD16+ -13.1341463414634) * (Neutroph - 56.9134146341463) + -0.16489749852773 * (CD56+ / CD16+- 13.1341463414634) * (CD3+ - 72.9756097560976)+0.00207044653259791 * (CD3+CD8+- 22.9634146341463) * (CD3+- 72.9756097560976) + 0.0462952076165853 * (CD3+CD8+-22.9634146341463) * (CD56+ / CD16+- 13.1341463414634)

[0121] With increasing attention on interventions that may extend human healthspan and ongoing clinical trials to alter organismal cellular senescence and other aging pathways, it is increasingly important to understand the dynamics of these processes in humans. In certain embodiments, a decline in immune function may precede accumulation of senescent cells. In some embodiments, addressing immune dysfunction in some patients may provide an indirect way of targeting cellular senescence and improving aging trajectory. In certain embodiments, understanding the interplay between the immune system and cellular senescence may improve patient selection for treatment and avoid harm. For example, and not limitation, therapies that target senescent cells directly may be inappropriate for patients with already low cellular senescence and normal immune biomarkers, while patients with low cellular senescence but dysregulated immune function may benefit from interventions targeting the immune system. And vice versa, patients with high cellular senescence but normal immune biomarkers may be better suited for a scnolytic approach absent an immune intervention.

[0122] Figure 6 shows a clinical decision tree based on the measurements of ILS and cellular senescence. So, for example and not limitation, if cellular senescence is low but ILS is optimal, the subject can undergo additional testing for heart disease or cancer. As another non-limiting example, if cellular senescence is optimal and ILS is low, then the subject should be evaluated for causes of low ILS, and, potentially receive treatment for low ILS. Other outcomes are apparent from the decision tree in Figure 6.

[0123] As demonstrated in the work described herein with the cohort of 482 participants, the interplay between health, cellular senescence, and ILS is incredibly complex (see, e.g., Figures 2- 4). And each subject has their own unique set of insults and injuries that may have affected one or more of their ILS or cellular’ senescence. For example, and not limitation, a first subject may be a cancer survivor, having undergone DNA damaging chemotherapy. A second subject, may have early-stage atherosclerosis. A third subject may have a low level chronic viral infection. Each of these experiences may leave the subject with sub-optimal cellular’ senescence and / or low ILS scores. But each subject may respond differently to different interventions targeting their specific clinical problems. And, almost all subjects, will have tens if not hundreds of accumulated incidents throughout their lifetimes such as these that affect both their cellular senescence levels and their ILS scores. However, subjects will have their individual resilience to these insults. Twosubjects receiving the same chemotherapy regimen, may have different levels of cellular senescence post treatment. Although trends can be identified, no two subjects will be exactly the same and treatment needs to be guided.

[0124] Further, because each subject is a complex mixture of different genetic backgrounds and experiences resulting in a unique combination of accumulated insults and injuries, treatments that help one individual maintain healthy ILS and cellular senescence levels may be ineffective in others, seemingly similar individuals. Interventions can be chosen based on the best available data regarding what interventions may be effective. But continual monitoring of ILS and cellular senescence levels both prior to treatment and after treatment provides important data and feedback on whether those interventions are working, or whether they are ineffective, or possibly even detrimental.

[0125] As a result, to provide the most effective treatment, each subject should be followed individually and tested multiple times in semi-regular intervals. Interventions that work for one subject may not work for some other subjects. By using the inventions described herein, one can follow a subject’s cellular senescence and ILS and guide interventions to find the treatments that are best tailored to that subject. By following a subject’s ILS and cellular’ senescence over time, a clinician has access to a feedback loop, informing future intervention decisions based on what has been tried in the past, what has worked, and what has not worked. In certain embodiments, a subject can be followed over long periods to confirm that the interventions are helping to maintain healthy ILS and cellular senescence. In certain embodiments, a subject may be followed without any accompanying interventions to confirm that their ILS and cellular senescence levels remain optimal, and to detect early signs of unhealthy aging, which might otherwise not be apparent. For example, and not limitation, a subject may be followed for 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, or 24 months. In certain embodiments, a subject may be followed for years, including, but not limited to, 1, 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, or 30 years. In certain embodiments, a subject may be followed for decades, including but not limited to, following a subject for their entire lifespan.

[0126] The following examples describe case studies that demonstrate how a patient’s ILS score and cellular senescence levels can be used to guide treatment.

[0127] In one case study, a subject was tested and found to have a low ILS. By examining the individual components of the ILS, it was found that the subject had high LAG3 expression levels. Based on that analysis, the subject was targeted with a rapamycin intervention to reduce the high LAG3 score, and attempt to bring the ILS to optimal levels. The subject was tested before and after treatment with rapamycin for expression of LAG3. Treatment with 5 mg of rapamycin weekly did not change LAG3 expression levels (when tested at 5 weeks of treatment). After an 8-week washout period, the patient began a new regimen of 3 mg rapamycin weekly. LAG3 expression decreased by 40% at 3 weeks into the treatment and these levels were sustained for at least 4 months after the end of treatment and increased ILS from 73 to 90.

[0128] In certain embodiments, if a subject has low ILS and T cell exhaustion in the top 60% or population, they can be treated with mTOR inhibitors such as rapamycin or rapologs (including, but not limited to, everolimus, temsirolimus, and ridaforolimus). Starting doses can vary and can be determined by the clinician. For example, but not limitation, the subject may receive a stalling does of 5-6 mg weekly. In certain embodiments, the clinician can test plasma sirolimus levels shortly after dosing (e.g., within an hour of dosing, or at 30 min after dosing). In certain embodiments, if plasma sirolimus levels are within a 2-10ng / ml window, treatment is continued and LAG3 and ILS is tested in 4-6 weeks. In certain embodiments, if the plasma sirolimus levels are higher than lOng / ml, the dose can be decreased accordingly to bring plasma sirolimus levels into that range. If ILS did not decrease, rapamycin dose can be decreased by 30-50% and LAG3 and ILS levels tested again in 4-6 weeks.

[0129] In a second case study, a subject exhibited greatly elevated cellular senescence (100% above average levels for their age group). After testing for common causes of elevated cellular senescence and interviews with the clinician, it was learned that this particular subject exercised frequently, strenuously, at high altitude (10-12mi bike ride daily). The elevated levels of cellular senescence were sustained through 3 tests. After the third test, a substantial adjustment to exercise intensity was prescribed and resulted in a 50% decrease in cellular senescence levels within 12 months.

[0130] In a third case study, a subject had an acute episode of diverticulitis, which was treated with levofloxacin (500mg / once a day) and metronidazole (500mg each 3 times a day). Following treatment and recovery from the acute infection, this subject had a low ILS. When looking at the individual components of the ILS, CD244 levels stood out as being increased by 740%. Thesubject’s ILS and CD244 levels remained elevated for about 7 months consistent with slow clinical recovery from the infection and antibiotic intervention. Continued monitoring showed the subject’s ILS increased and the CD244 levels decreased by 40% 4 months later, consistent with slow recovery of the immune system from the infection.

[0131] As another example, T cell exhaustion can be induced directly by stress hormones- dopamine, norepinephrine (also known as noradrenaline), and epinephrine (also known as adrenaline). In subjects with high LAG3 levels that report high levels of stress, ablation of 0i- adrenergic signaling with low dose beta-blockers such as metoprolol 2.5mg daily (vs lOmg standard of care) would be recommended.

[0132] There is a time-honored adage in medicine - ‘what gets measured, gets managed’. Aging, in its early stages, is rarely managed in part because clinically relevant molecular measures are lacking. This remains true despite growing recognition that progressive and dramatic loss of physiological resilience precedes frailty and chronic disease by many decades. The ability to measure and map changes in immune aging through cellular senescence and immune function creates the potential for improved safety, efficacy, and personalization of diverse interventions aimed at improving health span, longevity, and functional performance throughout life.

[0133] Using the inventions described herein, clinicians can use a network of cellular senescence and immune system biomarkers to deconstruct aging risks and contextualize vulnerability and resilience within individual patients over time.

Claims

CLAIMSWhat is claimed is:

1. A method of guiding treatment of a subject by measuring markers of immune system function comprising: a) generating an immune longevity score for the subject; b) comparing the immune longevity score for the subject with a pre-determined threshold for the immune longevity score; and c) guiding treatment of the subject based on the comparison of the immune longevity score for the subject with a pre-determined threshold for the immune longevity score.

2. The method of claim 1, further comprising guiding treatment of a subject by measuring markers of cellular senescence comprising: a) generating a cellular senescence score for the subject; b) comparing the cellular senescence score for the subject with a pre-determined range for the cellular senescence score; and c) guiding treatment of the subject based on both (1) the comparison of the cellular senescence score for the subject with the pre-determined range for the cellular senescence score; and (2) the comparison of the immune longevity score for the subject with the predetermined threshold for the immune longevity score.

3. The method of claim 2, wherein the generating a cellular senescence score for the subject comprises: a) detecting a level of gene expression of pl6 in a blood sample from the subject; and b) comparing the level of gene expression of pl6 in the blood sample from the subject with a pre-determined range for the cellular senescence score in a sample.

4. The method of claims 1 and 2, wherein the generating an immune longevity score for the subject further comprises:a) detecting a level of gene expression of pl 6, CD244, LAG3, and CD28 in a blood sample from the subject; b) generating an immune longevity score for the subject based on the gene expression levels of pl 6, CD244, LAG3, and CD28 in the blood sample from the subject and the gender and chronological age of the subject.

5. The method of claims 1 and 2, wherein the generating an immune longevity score for the subject further comprises: a) detecting a level of gene expression of CD244, LAG3, and CD28 in a blood sample from the subject; b) generating an immune longevity score for the subject based on the gene expression levels of CD244, LAG3, and CD28 in the blood sample from the subject and the gender and chronological age of the subject.

6. The method of claims 1 and 2, wherein the generating an immune longevity score for the subject further comprises: a) counting lymphocytes, neutrophils, CD3+, CD56+ / CD16+, CD3+CD4+, CD3+CD8+, CD3+ CD8+CD28-, and CD3+CD8+CD95- cells from the subject; b) generating an immune longevity score for the subject based on lymphocytes, neutrophils, CD3+, CD56+ / CD16+, CD3+CD4+, CD3+CD8+, CD3+ CD8+CD28-, and CD3+CD8+CD95- cell counts in the sample and chronological age of the subject.

7. The method of claims 1-6, wherein two or more immune longevity scores are calculated for a subject; wherein the first immune longevity score is calculated at a first time point and the one or more additional immune longevity scores are calculated at later time points, and wherein treatments and their outcomes arc evaluated by comparing any changes between the two or more immune longevity scores collected at different time points.

8. The method of claims 2-7 wherein two or more cellular senescence scores are calculated for a subject; wherein the first cellular senescence score is calculated at a first time point and the one or more additional cellular senescence scores are calculated at later time points; andwherein treatments and their outcomes are evaluated by comparing any changes between the two or more cellular’ senescence scores collected at different time points.

9. The method of claim 7, wherein the two or more immune longevity scores do not substantially change over time.

10. The method of claim 8, wherein the two or more cellular senescence scores do not substantially change over time.

11. The method of claim 7, wherein a first immune longevity score guides the subject to receive a treatment, the subject receives that treatment, and a subsequent immune longevity score suggests that the treatment was effective.

12. The method of claim 8, wherein a first cellular senescence score guides the subject to receive a treatment, the subject receives that treatment, and a subsequent cellular senescence score suggests that the treatment was effective.

13. The method of claim 7, wherein: a first immune longevity score guides the subject to receive a first treatment; the subject receives the first treatment; a second immune longevity score suggests that the treatment was ineffective; guided by one or more of the first and second immune longevity scores, the subject receives a second treatment; and a third immune longevity score suggests that the treatment was effective.

14. The method of claim 8, wherein: a first cellular senescence score guides the subject to receive a first treatment; the subject receives the first treatment; a second cellular senescence score suggests that the treatment was ineffective; guided by one or more of the first and second cellular senescence scores, the subject receives a second treatment; anda third cellular senescence score suggests that the treatment was effective.

15. The method of claim 1, wherein the immune longevity score from the subject indicates that the immune longevity score is low, and the subject is treated with at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus.

16. The method of claim 15, wherein the subject is treated with a 5-10 mg dose of at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus, and a second immune longevity score is calculated between 3 to 8 weeks after treatment.

17. The method of claim 16, wherein the second immune longevity score from the subject indicates that the immune longevity score is low, and the subject is treated with a reduced dose of at least one of sirolimus, everolimus, temsirolimus, and ridaforolimus, and a third immune longevity score is calculated between 3 to 8 weeks after treatment with the reduced dose.

18. The method of claim 2, wherein if the subject’s cellular senescence score is low and the subject’s immune longevity score is optimal, the subject is evaluated for at least one of: heart disease and cancer.

19. The method of claim 2, wherein if the subject’s cellular senescence score is low and the subject’s immune longevity score is low, the subject is evaluated for at least one of: heart disease, cancer, acute infection, chronic infection, and chronic inflammation.

20. The method of claim 2, wherein if the subject’s cellular senescence score is optimal and the subject’s immune longevity score is low, the subject is evaluated for at least one of: acute infection, chronic infection, and chronic inflammation.

21. The method of claim 2, wherein if the subject’s cellular senescence score is high and the subject’s immune longevity score is optimal, the subject is evaluated for at least one of: a sedentary lifestyle, psychological stress, smoking, past chemotherapy, and past radiation treatment.

22. The method of claim 2, wherein if the subject’s cellular senescence score is high and the subject’s immune longevity score is low, the subject is evaluated for at least one of: acuteinfection, chronic infection, chronic inflammation, a sedentary lifestyle, psychological stress, smoking, past chemotherapy treatment, and past radiation treatment.

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