Retroelement-age: biological aging captured by locus-specific human endogenous retroviruses and line-1 DNA methylation states

Abstract

The present disclosure relates to use of DNA methylation states of retroelements as an epigenetic biomarker of aging. Disclosed herein are retroelement-based epigenetic clocks, and methods for determining age (epigenetic, biological or chronological) of a subject by measuring DNA methylation status at CpG loci located in at least one retroelement. Also, disclosed are methods of treatment, prevention, assessing impact or efficacy, and prediction of outcome based on epigenetic age determinations made in accordance with the described methods and using retroelement-based epigenetic clocks. Also disclosed is use of an antiretroviral therapy for treatment of an age-associated disease or condition, rejuvenation and / or reducing epigenetic age.

Description

RETROELEMENT-AGE: BIOLOGICAL AGING CAPTURED BY LOCUS-SPECIFIC HUMAN ENDOGENOUS RETROVIRUSES AND LINE-1 DNA METHYLATION STATES CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Application Nos.63 / 534,450, filed August 24, 2023, and 63 / 547,605, filed November 7, 2023, each of which is hereby incorporated by reference herein in its entirety. TECHNICAL FELD Embodiments of the disclosure generally relate to the field of aging, determining age, as well as prevention and treatment of age-related diseases and conditions. BACKGROUND Retroelements such as human endogenous retroviruses (HERVs) and long interspersed nuclear elements (LINEs) are remnants of ancient viral infections that constitute a significant portion of the human genome (Lander et al.2001; Nurk et al.2022). While the majority of retroelements embedded within the human genome are typically repressed by epigenetic mechanisms that include DNA methylation and chromatin modifications, the activity of specific HERVs and LINEs in the human genome has been shown to impact gene regulation, gene expression, genomic stability, development, and the pathogenesis of various human diseases (Dopkins & Nixon 2023; Fueyo et al. 2022; Beck et al.2011). The aging process involves a multitude of changes at the cellular and molecular levels, which include senescence, telomere shortening, and changes in gene expression. Notably, epigenetic patterns also change over one’s lifespan, suggesting that epigenetic changes may constitute an important component of the aging process. The epigenetic marker perhaps most studied is DNA methylation, the presence of methyl groups at CpG dinucleotides. These dinucleotides are often located near gene promoters and associate with gene expression levels. The loss of DNA methylation, or particular changes in methylation patterns, has been shown to occur with age at repetitive DNA sequences, introns, and intergenic regions of the genome16. Early studies suggest that global levels of DNA methylation increase over the first few years of life and then decrease beginning in late adulthood. One of the most consistent findings across several species is a decreasein global DNA methylation levels with age affecting repetitive DNA sequences, introns, and intergenic regions. Previous research has shown that consistent changes in specific DNA methylation patterns in the human genome can be utilized to develop highly accurate predictors of age and health span in cells and tissues termed an ‘epigenetic clock’. However, despite DNA methylation playing a key role in silencing retroelements, the potential of using the DNA methylation (DNAm) states of specific HERVs and LINE elements for estimating chronological or biological age remains unclear. BRIEF SUMMARY In some aspects, the present disclosure provides a method for determining an age (e.g., biological / epigenetic age or chronological age) of a biological sample, comprising selectively measuring the methylation status or levels of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of the biological sample, and comparing the measured methylation marker status or levels to reference levels for the methylation markers to determine the age of the sample based on said methylation levels. In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element (LINE). In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element-1 (LINE-1). In some embodiments, the retroelement is a Human Endogenous RetroVirus (HERV) element. In some embodiments, the retroelement is a Long Interspersed Nuclear Element (LINE), e.g., LINE-1. In some aspects, the present disclosure provides a computer-readable medium comprising computer-readable instructions that, when executed by a computer, causes the computer to perform operations comprising: a) receiving information corresponding to methylation status or levels of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of the biological sample; and b) determining the age of the biological sample by comparing the measured methylation marker levels to reference marker levels and by applying a statistical prediction algorithm to the measured methylation marker levels. In some embodiments, the pretrained mode comprises an elastic net. In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV)element and / or at least one Long Interspersed Nuclear Element (LINE). In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element-1 (LINE-1). In some embodiments, the age that is being predicted in accordance with the methods described herein is epigenetic or biological age. In some embodiments, the reference levels are from an age-correlated reference biological sample, such as a chronological age-correlated reference biological sample. In some embodiments, the reference levels are from a biological sample or samples of subjects (e.g., population of subjects) of certain chronological age or ages. In certain embodiments, the reference levels are average levels from a population of subjects of a certain chronological age. In some embodiments, the biological sample(s) used as a reference is from the same cell or tissue as the biological sample the age of which is being determined in accordance with the methods provided herein. In some embodiments, the biological sample(s) used as a reference is not from the same cell or tissue, or do not necessarily have to be from the same cell or tissue, as the biological sample the age of which is being determined in accordance with the methods provided herein. In some embodiments, the DNA is isolated from a biological sample before measuring the methylation status or levels of a set of CpG methylation markers. In some embodiments, the biological sample comprises a cell, a tissue, and / or a bodily fluid. In some embodiments, the biological sample is blood (e.g., whole blood) or peripheral blood mononuclear cells (PBMCs). In some embodiments, the biological sample is a tissue (e.g., tissue biopsy). The tissue can be any tissue known in the art or described herein. In some embodiments, the tissue is dermal tissue. In some embodiments, the biological sample is a cell. In some embodiments, the cell is any cell known in the art or described herein. In some embodiments, the cell is an immune cell, a stem cell or a somatic cell. In some embodiment, the biological sample comprises immune cells, stem cells, somatic cells, or any combination thereof. In some embodiments, the cell is a stem cell, e.g., an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC). In some embodiments, the cell is a somatic cell, e.g., a fibroblast, a keratinocyte, a dermal cell, or an epithelial cell. In some embodiments, the cell is a fibroblast. In some embodiments, the cell is not an endothelial cell. In other embodiments, the cell is an endothelial cell.In some aspects, the present disclosure provides a method of determining a biological or epigenetic age of a cell, the method comprising: 1) selectively measuring the methylation status or levels of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of the cell, said selective measuring comprising receiving a sequence of inputs corresponding to methylation values at a plurality of CpG sites; and 2) applying a pretrained model to the sequence of inputs to predict the epigenetic or biological age of the cell. In some aspects, the present disclosure provides a method of determining biological or epigenetic age of a mammalian biological sample (e.g., a mammalian cell), comprising: 1) comprising selectively measuring the methylation status or levels of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of the biological sample, said selective measuring comprising receiving a sequence of inputs corresponding to methylation values at a plurality of CpG sites; and 2) applying a pretrained model on the sequence of inputs to predict the biological or epigenetic age of the mammalian biological sample (e.g., a mammalian cell). In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element (LINE). In some embodiments, said set of CpG methylation markers are located in at least one retroelement which is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element-1 (LINE-1). In some embodiments, the age that is being predicted in accordance with the methods described herein is epigenetic or biological age. In some embodiments, the biological sample is a cell or tissue that has been subjected to rejuvenation. In some embodiments, the rejuvenation is by transient expression of one or more reprogramming factors. In some embodiments, the cell or tissue is a fibroblast or population of fibroblasts (e.g., dermal fibroblasts). In some embodiments, the reprogramming factors are any one, two, three or more (or all) selected from: Oct4, Sox2, Klf4, and c-Myc. In some embodiments, the biological sample is obtained from a subject before the DNA is isolated. In some embodiments, the subject is a mammal. In some embodiments, the subject is an experimental animal or an animal model (e.g., an animal model of disease). In some embodiments, the subject is a human. In some embodiments, the subject is a human having, is suspected to have, or is at risk of, an age-related disease or condition. In some embodiments, the subject has, is at risk of having, or exhibits symptoms of premature aging.In some embodiments, the CpG methylation markers are located in at least or more than one, five, ten, fifteen, twenty or each of the HERV elements selected from Table 1 or Table 8. In some embodiments, the CpG methylation markers are located in at least or more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or each of the HERV elements selected from Table 1 or Table 8. In some embodiments, the method further comprises selectively measuring the methylation levels at, at least or more than, 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 8. In some embodiments, the method further comprises selectively measuring the methylation levels at, at least or more than, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 8. In some embodiments, the CpG methylation markers comprise at least or more than one, two, three, four, five, ten, fifteen, twenty, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. In some embodiments, the CpG methylation markers are located in at least or more than one, five, ten, fifteen, twenty or each of the LINE-1 elements selected from Table 1 or Table 9. In some embodiments, the CpG methylation markers are located in, at least or more than, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or each of the LINE-1 elements selected from Table 1 or Table 9. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 9. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 9. In some embodiments, the CpG methylation markers comprise at least or more than one, two, three, four, five, ten, fifteen, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766,cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of any one of Tables 2 to 7. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 of the CG loci designations, or at each of the CG loci designations of any one of Tables 2 to 7. In some embodiments, the CpG methylation markers are located in at least or more than one, five, ten, fifteen, or twenty retroelements selected from any one of Tables 2 to 7. In some embodiments, the CpG methylation markers are located in at least or more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 retroelements selected from any one of Tables 2 to 7. In some embodiments, the method of comprises selectively measuring the methylation levels at each of the CG loci designations of any one of Tables 2 to 7. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of Table 10. In some embodiments, the method comprises selectively measuring the methylation levels at, at least or more than, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 of the CG loci designations, or at each of the CG loci designations of Table 10. In some embodiments, the CpG methylation markers are located in at least or more than one, five, ten, fifteen, or twenty retroelements selected from any one of Tables 2 to 7. In some embodiments, the CpG methylation markers are located in at least or more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 retroelements selected from Table 10. In some embodiments, the method of comprises selectively measuring the methylation levels at each of the CG loci designations of Table 10. In some embodiments, the CpG methylation markers are located in at least one, two, three, four, five, six, seven or each of the following retroelements: HML-2_17p13.1, HERVL18_6p22.1c, HUERSP3_6p21.32, MER41_22q12.3e, L1FLnl_10q26.3e, L1FLnl_1p31.3z, L1FLnI_7q22.2a, L1FLnI_18q23l, and / or L1FLnI_Xp22.2h, or any combination thereof. In some embodiments, the CpG methylation markers are located in HML-2_17p13.1, HERVL18_6p22.1c, HUERSP3_6p21.32, and / or MER41_22q12.3e. In some embodiments, the CpG methylation markers are located in L1FLnl_10q26.3e, L1FLnl_1p31.3z, L1FLnI_7q22.2a, L1FLnI_18q23l, and / or L1FLnI_Xp22.2h.In some embodiments, the CpG methylation markers are located within retroelements in one or more gene regulatory or gene enhancer regions. In some embodiments, the CpG methylation markers are located within one or more retroelements located in one or more of the following genes, regions and / or elements: long intergenic non-coding RNA (lincRNA) ENST0000577807, ZSCAN31 gene, ZKSCAN3 gene, an intergenic region of heterochromatin / ZNF genes and repeats, an enhancer region of the anti-inflammatory and metabolic regulator C1QTNF6 gene, downstream of the 3’UTR of the ADGRA1 gene, an enhancer region of the DEPDC1 gene, an enhancer region of the ORC5 gene, NFATC1 gene, and / or an enhancer and active transcriptional start region of the TLR7 gene, or any combination thereof. In some embodiments, the CpG methylation markers are located within one or more retroelements located in one or more of the following genes (or regulatory elements thereof): ZSCAN31 gene, ZKSCAN3 gene, ZNF genes and repeats, C1QTNF6 gene, the ADGRA1 gene, DEPDC1 gene, ORC5 gene, NFATC1 gene, and / or TLR7 gene, or any combination thereof. In some embodiments, the CpG methylation markers comprise at least or more than one, two, three, four, five, or each, of the CG loci designations: cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, and cg23987336, or any combination thereof (e.g., located at the HML-2_17p13.1 locus). In some embodiments, the CpG methylation markers comprise at least or more than one, two, three, four, five, ten, fifteen, or each, of the CG loci designations: HERVL18_6p22.1c loci (cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, and / or cg23702226), HUERSP3_6p21.32 loci (cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, and / or cg23917817), and / or MER41_22q12.3e loci (cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, and / or cg26538224), or any combination thereof. In some embodiments, the CpG methylation markers comprise at least or more than one, two, three, four, five, ten, fifteen, or each, of the CG loci designations: L1FLnl_10q26.3e loci (cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, and / or cg25120459) and / or L1FLnl_1p31.3z loci (cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, and / or cg26064634), or any combination thereof. In some aspects, provided herein is any one or more of the epigenetic clocks described herein, such as an epigenetic clock comprising a set of CpG methylation markers from a retroelement (e.g., HERV and / or LINE retroelement), and optionally their coefficients (e.g., asprovided herein), for measuring the CpG methylation status or level in DNA (such as genomic or nuclear DNA) of a biological sample. In some aspects, provided herein is a method for screening for an agent capable of reducing biological or epigenetic age of a subject or a biological sample from a subject, comprising: (i) determining original biological or epigenetic age of a subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer-readable medium described herein, (ii) administering an agent to the subject (e.g., an effective amount of an agent in one or more doses), (iii) determining post-treatment biological or epigenetic age of the subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer- readable medium described herein; wherein if the post-treatment biological or epigenetic age is lower than the original age, the method further comprises selecting the agent as capable of reducing biological or epigenetic age. In some aspects method for reducing biological or epigenetic age of a subject or a biological sample from a subject, comprising: (i) determining original biological or epigenetic age of a subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer-readable medium described herein, (ii) administering an agent to the subject (e.g., an effective amount of an agent in one or more doses), (iii) determining post-treatment biological or epigenetic age of the subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer- readable medium described herein; wherein if the post-treatment biological or epigenetic age is lower than the original biological or epigenetic age, the method further comprises administering the agent to the subject for reducing biological or epigenetic age. In some aspects, provided herein is a method for reducing biological or epigenetic age of a subject or a biological sample from a subject, or a method for preventing or treating premature aging, comprising: (i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer-readable medium described herein, and if the biological or epigenetic age is higher than the actual or chronological age of the subject,(ii) administering an agent to the subject for reducing biological or epigenetic age (e.g., an effective amount of an agent in one or more doses). In some aspects, provided herein is a method for treating or preventing an age-related disease or condition in a subject, comprising: (i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer-readable medium described herein, and if the biological or epigenetic age is higher than the actual or chronological age of the subject, (ii) administering an agent to the subject for reducing biological or epigenetic age and / or an agent for treating or preventing the age-related disease or condition (e.g., an effective amount of an agent in one or more doses). In some aspects, provided here is a method for treating or preventing inflammaging in a subject, comprising: (i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with any of the methods described herein and / or using any of the computer-readable medium described herein, and if the biological or epigenetic age is higher than the actual or chronological age of the subject, (ii) administering to an agent to the subject for treating or preventing inflammaging (e.g., an effective amount of an agent in one or more doses). In some aspects of the methods described herein, the agent is an antiretroviral agent. In some aspects of the methods described herein, the agent is a DNA methyltransferase inhibitor. In some aspects, provided herein is a method for reducing biological or epigenetic age of a subject or a cell or tissue from a subject, or a method for preventing or treating premature aging, comprising: administering an antiretroviral agent to the subject (e.g., an effective amount of an agent in one or more doses). In some aspects, provided herein is a method for treating or preventing an age-related disease or condition in a subject, comprising administering an antiretroviral agent to the subject (e.g., an effective amount of an agent in one or more doses). In some embodiments, the antiretroviral agent is a reverse transcriptase inhibitor, such as, any of nucleoside reverse transcriptase inhibitors or non-nucleoside reverse transcriptase inhibitors known in the art or described herein.BRIEF DESCRIPTION OF THE DRAWINGS FIGS.1A-1F depict the construction of Composite Retroelement-Age and Retroelement-Age V2 Epigenetic Clocks. FIG.1A depicts a diagram of workflow utilized to construct Retroelement- Age and Retroelement-Age V2. FIG.1B shows age estimation 10-fold cross validation in training and FIG.1C shows test datasets for Retroelement-Age. FIGS.1D-1E show Retroelement-Age V2 clocks. Panels report the sample size (n), the median absolute error (MAE), and Pearson’s correlation coefficient (r). FIG. 1F shows intersection plot of existing first, second, and third- generation epigenetic clocks CpGs, Retroelement-Age, and Retroelement-Age V2. FIGS.2A-F depict the enrichment plot of retroelement-based epigenetic clocks in consensus transcription factor binding sites (TFBS), ENCODE ChromHMM chromatin states, and histone modifications. FIGS.2A-C respectively show the enrichment plot of retroelement-based epigenetic clocks in consensus TFBS, ENCODE ChromHMM chromatin states, and histone modifications for Retroelement-Age V1. FIGS.2D-F respectively show the enrichment plot of retroelement-based epigenetic clocks in consensus TFBS, ENCODE ChromHMM chromatin states, and histone modifications for Retroelement-Age V2. The SeSAMEe R package tool KnowYourCG was utilized (Zhou et al. 2018). Fisher’s exact test with false discovery rate (FDR), estimate represents fold enrichment, and overlap the number of CpGs. FIGS.3A-C depict correlograms of chronological age, retroelement-based epigenetic clocks, first-generation epigenetic clocks, second-generation epigenetic clocks, and PC-based epigenetic clocks in two external DNA methylation datasets (GSE184269 and GSE213478). Pearson correlation coefficient displayed. FIGS.4A-C depict the application of retroelement-based epigenetic clocks to accelerated aging in HIV and antiretroviral therapy. FIG. 4A shows median absolute error in chronological age prediction for Retroelement-Age V2, first-generation epigenetic clocks (Horvath1 and Hannum), second-generation epigenetic clock (PhenoAge), and PC-based epigenetic clocks for control, HIV+, and HIV+ART+ participants. FIG. 4B shows age acceleration detected utilizing Retroelement-Age V2 in external data from people living with HIV compared to healthy controls. FIG.4C shows Retroelement-Age V2 responsive to antiretroviral therapy (ART) in people living with HIV followed longitudinally before and after 96 weeks of ART. Student’s T tests and paired T tests, **** P<0.001.FIGS.5A-F depict the application of retroelement-based epigenetic clocks to transient reprogramming-induced rejuvenation. FIG. 5A shows Retroelement-Age V1 estimates of fibroblasts transiently reprogrammed at Day 0-51. FIG. 5B shows Retroelement-Age V2 estimates of fibroblasts transiently reprogrammed at Day 0-51. FIGS.5C-D respectively show Retroelement-Age V1 and V2 estimates of fibroblasts transiently reprogrammed. FIGS. 5E-F respectively show Retroelement-Age V1 and V2 estimates of endothelial cells transiently reprogrammed. Student’s T tests and paired T tests, * P<0.05. FIGS.6A-G depict the construction of Multi-Tissue and Pan-Mammalian Retroelement- based Epigenetic Clocks. FIG.6A depicts Multi-Tissue RetroAge GTEx project data GSE213478. FIG.6B shows age estimation 10 fold cross validation in training. FIG.6C depicts test datasets for Multi-Tissue RetroAge GTEx project data GSE213478. FIG. 6D depicts construction of a unique Pan-Mammalian Retroelement-based Epigenetic Clock based on data from the international Mammalian Methylation consortium(Lu et al.2023). FIG.6E depicts age estimation 10 fold cross validation in training, and FIG.6F shows test datasets. Panels report the sample size (n), the median absolute error (MAE), and Pearson’s correlation coefficient (r). FIG.6G shows intersection plot of published Pan-mammalian Universal epigenetic clocks CpGs, Universal-Retroelement Age, and Universal Transposable Element (TE) Age. FIGS.7A-B depict external validation of retroelement-based epigenetic clocks. FIG.7A shows a scatterplot of Retroelement-Age V2 versus chronological age in 790 people. FIG. 7B shows a scatterplot of Retroelement-Age versus chronological age in 790 people. Independent data generated by the Alzheimer's Disease Neuroimaging Initiative study. FIG.8 shows reliability of retroelement-based epigenetic clock. Intra-class correlation coefficient (ICC) of Retroelement-Age V2 on 30 replicate blood sample MethylationEPIC v1.0 samples from Independent data generated by the Alzheimer's Disease Neuroimaging Initiative study. FIGS.9A-C show integration of Age-related transposable element expression and retroelement-based DNA methylation epigenetic clocks. FIG.9A shows a diagram of workflow utilized to construct immune transcriptome Telescope-Age. FIG.9B shows age estimation 10-fold cross validation in training, and FIG.9C shows test datasets for Telescope-Age. FIG.10 depicts Application of Retroelement-Age V2 in tracking human cortical brain organoids culture age. FIG.11 is a schematic view of an exemplary computing node according to embodiments of the present disclosure.DETAILED DESCRIPTION In some aspects, provided herein is use of retroelement DNA methylation at CpG loci for measuring age (biological or epigenetic age, or chronological age) of a subject or a biological sample of a subject. In some embodiments, the retroelements referenced herein are endogenous retroelements. In some aspects, provided herein is an epigenetic clock described herein and its use for determining age of a subject or a biological sample of a subject. In some embodiments the subject is a mammal, e.g., a human. The mammal can be healthy, suffering from a disease or condition impacting aging (for example, an infectious disease, e.g., by HIV-1) or an age-related disease or condition (e.g., a chronic disease associated with aging), or subjected to a rejuvenation treatment (e.g., transient epigenetic reprogramming). In some embodiments, the measurement of the DNA methylation status is performed in a set of CpG loci (such as any of at least or more than 1, 3, 5, 10, 15, 20, 25, 30, 35, 40, 50, 60, 70, 80, 90, 95, 100, 125, 150, 175, or 200 of those specified herein), located within one more (e.g., at least or more than 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) retroelements specified herein (such as HERV-K and / or LITE, or others described herein). In some aspects, provided herein are methods of prevention or treatment of an age-related disease or condition (e.g., a chronic disease associated with aging), prevention or treatment of premature aging, reducing biological or epigenetic age, or rejuvenation in a subject in need thereof by administering to the subject an antiretroviral therapy or agent, as described herein. The invention described herein relates to DNA methylation of retroelements and its use in predicting chronological or biological age. DNA methylation plays a crucial role in the regulation of retroelements, primarily by suppressing their activity and maintaining genome stability. DNA methylation, particularly at the CpG dinucleotides, can inhibit the transcription of retroelements. Indeed, the reactivation of certain HERVs has been observed to increase with age17,18. The invention described herein is based on the investigation by the inventors of DNA methylation dynamics of retroelements as chronological or biological age predictors. The Telescope- annotation of Illumina®’s MethylationEPIC v1.0 platform was enhanced to identify CpGs located within manually curated locus-specific HERVs and LINEs (Bendall et al.2019), which were previously unaccounted for in standard and custom add on annotations. Using the improved Telescope annotation-based retroelement CpGs and a DNA methylation dataset from 12,670 individuals across the lifespan, a highly accurate chronological age composite Retroelement-Age clock was developed based on DNA methylation states of HERVs and LINEs. Also constructed wasa composite Retroelement-Age V2 clock compatible with MethylationEPIC v1.0 and 2.0 data platforms. These retroelement-based clocks showed significant correlation with chronological age and capture the unique dynamics of DNA methylation changes linked to aging compared to existing epigenetic clocks. These retroelement clocks also extend to diverse human tissues and across mammalian species using DNA methylation from GTEx (Oliva et al.2023) and from the Mammalian Methylation Consortium (Lu et al. 2023). The results described herein confirm that the dysregulation of retroelements play a role in the distinctive biological features associated with aging and demonstrate the utility of retroelement clocks in assessing long term human brain organoid cultures and cellular rejuvenation via transient reprogramming. Notably, HERV-Age, LINE-1-Age, Retroelement-Age, and Retroelement-Age V2 clocks capture facets of biological aging distinct from existing first and second-generation epigenetic clocks as greater than 99% of all the retroelement clocks’ CpGs were unique and not part of preexisting epigenetic clock algorithms. The retroelement clocks provided herein were reversed during transient epigenetic reprogramming, accelerated in people living with HIV, responsive to antiretroviral therapy, and accurate in estimating long-term culture ages of human brain organoids. HERV-Age, LINE-1-Age, Retroelement-Age, and Retroelement-Age V2 complement existing epigenetic clocks and confirm that dysregulation of transposable elements contributes to biological hallmarks of immune aging. Definitions Unless defined otherwise, all scientific and technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. Generally, nomenclatures utilized in connection with, and techniques of, chemistry, biochemistry, molecular biology, pharmacology and toxicology are those well-known and commonly used in the art. The articles “a” and “an,” as used herein, refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element. A “CpG locus” or “CpG site,” as used herein, is a dinucleotide DNA sequence with cytosine followed by guanine from 5’ to 3’ direction bound by a phosphodiester bond. In mammals, the number of CpG loci are less than would be expected due to high mutagenic tendency of such sites(with C undergoing deamination to form T nucleotide). In mammals, approximately 80% of CpG loci are methylated. “Methylation status of a CpG locus or loci,” as used herein, is methylation status at the C-5 position of cytosine of the CG dinucleotide. “Actual age” or “chronological age” is an age of a subject (e.g., age of a subject from whom a biological sample is obtained) from birth or conception. Biological or epigenetic age is the age determined using a set of CpG methylation markers of a biological sample obtained from a subject, as described herein. “A set of CpG methylation markers,” as used herein, is the status, level and / or pattern of DNA methylation at certain CpG loci. In particular, described herein is a set of CpG methylation markers located within one or more retroelements in genomic DNA of a biological sample obtained from a subject. In some embodiments, as described herein, the DNA methylation at such CpG loci is age-related. A “subject” is a human or a non-human animal. This term includes mammals, such as humans, primates, livestock animals, companion animals and rodents. “Administering” or “administration of” an agent or therapy to a subject can be carried out using one of a variety of methods known to those skilled in the art. For example, a compound or an agent can be administered, intravenously, arterially, intradermally, intramuscularly, intraperitoneally, subcutaneously, ocularly, sublingually, orally (by ingestion), intranasally (by inhalation), intraspinally, intracerebrally, and transdermally (by absorption, e.g., through a skin duct). In some embodiments, the administration is parenteral. In some embodiments, the administration is enteral or oral. Administering can also be performed, for example, once, a plurality of times, and / or over one or more extended periods. Appropriate methods of administering a substance, a compound or an agent to a subject will also depend, for example, on the age and / or the physical condition of the subject and the chemical and biological properties of the compound or agent (e.g., solubility, digestibility, bioavailability, stability and toxicity). An “effective amount” or a “therapeutically effective amount” of a drug or agent is an amount of a drug or an agent that, when administered to a subject will have or expected to have a therapeutic effect. The therapeutic effect does not necessarily occur by administration of one dose, and may occur only after administration of a series of doses. Thus, a therapeutically effective amount may be administered in one or more administrations. The precise effective amount neededfor a subject will depend upon, for example, the subject’s size, health and age, and the nature and extent of the condition being treated. The term “treating” a disease or condition in a subject, as used herein, refers to administering a therapy or agent to the subject having or suspected of having a disease or condition (i.e., after the onset of the disease or condition), such that at least one symptom of the disease or condition is decreased or prevented from worsening. Desirable effects of treatment include decreasing the rate of progression, ameliorating or palliating the pathological state, and remission or improved prognosis of a particular disease, disorder, or condition. An individual is successfully “treated,” for example, if one or more symptoms associated with a particular disease, disorder, or condition are mitigated or eliminated. The term “preventing” a disease or condition in a subject, as used herein, refers to administering a therapy or agent to the subject prior to the onset of the disease or condition, when administration of the medicament to a statistical sample prior to the onset of the disease or condition reduces the occurrence of the disease or condition in the treated sample relative to an untreated control sample, or delays the onset or reduces the occurrence or severity of one or more symptoms of the disease or condition relative to the untreated control sample. Retroelements In some embodiments, the retroelement is any mobile or transposable element, e.g., in genomic or nuclear DNA of a subject. In some embodiments, the retroelement is any endogenous mobile or transposable element, e.g., in genomic or nuclear DNA of a subject. In some embodiments, the retroelement is a Human Endogenous RetroVirus (HERV) element and / or Long Interspersed Nuclear Element (LINE). In some embodiments, the retroelement is a Human Endogenous RetroVirus (HERV) element and / or Long Interspersed Nuclear Element-1 (LINE-1). In some embodiments, only the DNA methylation states of transposable elements are measured in the methods and retroelement clocks provided herein. In some embodiments, only the DNA methylation states of CpG loci within transposable elements are measured in the methods and retroelement clocks provided herein. In some embodiments, only the DNA methylation states of one or more retroelement(s) provided in any one of Tables 1-9 are measured in the methods and retroelement clocks provided herein. In some embodiments, only the DNA methylation states of CpG loci within of one or moreretroelement(s) provided in any one of Tables 1-9 are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of HERV (e.g., HERV-K) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of CpG loci within HERV (e.g., HERV-K) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of LINEs (e.g., LINE-1) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of CpG loci within LINEs (e.g., LINE-1) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of HERV (e.g., HERV-K) and LINE (e.g., LINE-1) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of CpG loci within HERV (e.g., HERV-K) and LINE (e.g., LINE-1) are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of HERV-K and MER41 are measured in the methods and retroelement clocks provided herein. In some embodiments, only the DNA methylation states of CpG loci within HERV-K and MER41 are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation states of HML-2 are measured in the methods and retroelement clocks provided herein. In some embodiments, only the DNA methylation states of CpG loci within HML-2 are measured in the methods and retroelement clocks provided herein. In some embodiments, the DNA methylation change is measured at CCCTC-binding factor (CTCF) sites in one or more retroelements. In some embodiments, the DNA methylation change is measured at CpG loci in (e.g., proximal to) CCCTC-binding factor (CTCF) sites in one or more retroelements. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, or all) retroelements selected from Table 1. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, or all) of the following retroelements or retroelement families selected from Table 1; e.g., retroelement families L1FLI, L1FLnl, L1ORF2, ERV316A3, ERVL,ERVLB4, ERVLE, HARLEQUIN, HERV3, HERV30, HERV4, HERV9, HERVE, HERVEA, HERVFH19, HERVFH21, HERVFRD, HERVH, HERVH48, HERVI, HERVIP10F, HERVIP10FH, HERVK11, HERVK11D, HERVK14C, HERVKC4, HERVL, HERVL18, HERVL32, HERVL40, HERVL66, HERVL74, HERVP71A, HERVS71, HERVW, HML1, HML2, HML3, HML4, HML5, HML6, HUERSP1, HUERSP2, HUERSP3, HUERSP3B, LTR23, LTR25, LTR46, LTR57, MER101, MER34B, MER4, MER41, MER4B, MER61, PABLA, PABLB, PRIMA4, and PRIMA41. In certain embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more of retroelement families L1FLnl, HERVL18, HML2, HUERSP3, MER41, or any combination thereof. In some such embodiments, CpG methylation is measured in each of retroelements families L1FLnl, HERVL18, HML2, HUERSP3, and MER41. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (or all) retroelements selected from Table 2. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (or all) retroelements selected from Table 3. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or all) retroelements selected from Table 4. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or all) of the following retroelements or retroelement families selected from Table 4; e.g., retroelement families ERV316A3, ERVLB4, ERVLE, HARLEQUIN, HERV3, HERV4, HERV9, HERVEA, HERVFRD, HERVH, HERVIP10FH, HERVL, HERVS71, HML5, HML6, HUERSP2, L1FLI, L1FLnl, LTR25, MER1010, MER4, MER41, MER4B, MER61, PABLA, and PRIMA41. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (or all) retroelements selected from Table 5. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (or all) retroelements selected from Table 6. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (or all) retroelements selected from Table 7.In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, or all) retroelements selected from Table 8. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, or all) retroelements selected from Table 9. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 500, 750, 1000, or all) retroelements selected from the Excel spreadsheet (“acel14288-sup- 0001-DataS1__xlsx”) provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which is incorporated by reference herein in its entirety. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more regulators of retroelement activity (e.g., in an enhancer region). For example and without limitation, in some such embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more quiescent or heterochromatic regions, and / or with stabilizers of heterochromatin stability. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more active transcription start site (TSS)-proximal promoter and / or Polycomb-repressed regions. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more regions containing H3K9me2, H3K9me3, and / or H3K4me1 regions. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more quiescent, active, and / or poised enhancer regions. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or associated with, one or more zinc finger, including KRAB zinc finger, proteins. Without being bound by theory or technology, in some such embodiments, the DNA methylation change is measured at the CpG methylation markers located in, or in elements (e.g., enhancers and active transcriptional start regions) associated with, one or more of genes ZNF654, ATF7IP, ZBTB2, MAFG, NFE2L1, ZNF317, ZSCAN31, ZKSCAN3, C1QTNF6, ADGRA1, DEPDC1, ORC5, NFATC1, TLR7, or any combination thereof.In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in at least one, two, three, four, five, six, seven or each of the following retroelements: HML-2_17p13.1, HERVL18_6p22.1c, HUERSP3_6p21.32, MER41_22q12.3e, L1FLnl_10q26.3e, L1FLnl_1p31.3z, L1FLnI_7q22.2a, L1FLnI_18q23l, and / or L1FLnI_Xp22.2h, or any combination thereof. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in HML-2_17p13.1, HERVL18_6p22.1c, HUERSP3_6p21.32, and / or MER41_22q12.3e. In some embodiments, the DNA methylation change is measured at the CpG methylation markers located in L1FLnl_10q26.3e, L1FLnl_1p31.3z, L1FLnI_7q22.2a, L1FLnI_18q23l, and / or L1FLnI_Xp22.2h. In some embodiments, the DNA methylation change is measured in the set of the CpG loci within one or more retroelements located in one or more gene regulatory or gene enhancer regions. In some embodiments, the DNA methylation change is measured in the set of the CpG loci within one or more retroelements located in one or more of the following genes, regions and / or elements: long intergenic non-coding RNA (lincRNA) ENST0000577807, ZSCAN31 gene, ZKSCAN3 gene, an intergenic region of heterochromatin / ZNF genes and repeats, an enhancer region of the anti-inflammatory and metabolic regulator C1QTNF6 gene, downstream of the 3’UTR of the ADGRA1 gene, an enhancer region of the DEPDC1 gene, an enhancer region of the ORC5 gene, NFATC1 gene, and / or an enhancer and active transcriptional start region of the TLR7 gene, or any combination thereof. In some embodiments, the DNA methylation change is measured in the set of the CpG loci within one or more retroelements located in one or more of the following genes (or regulatory elements thereof): ZSCAN31 gene, ZKSCAN3 gene, ZNF genes and repeats, C1QTNF6 gene, the ADGRA1 gene, DEPDC1 gene, ORC5 gene, NFATC1 gene, and / or TLR7 gene, or any combination thereof. In some embodiments, the reference to the DNA methylation change here or elsewhere in the specification refers to the DNA methylation status and / or level. In some embodiments the reference to the DNA methylation change here or elsewhere in the specification refers to the DNA methylation status.Methylation markers: CpG Loci within Retroelements In some embodiments, the CpG loci provided herein (the methylation of which is assessed) are located within the retroelement sequence, such as within the endogenous retroelement sequence, e.g., within one or more HERVs and / or one or more LINEs. CG designations (e.g., a CpG loci identified by a cg#) are disclosed herein and are recognized and understood by those of skill in the relevant art. Specifically, the location on the chromosome and retroelement is known or can be determined for the CG designations provided herein based on the publicly available information. For instance, cg# information may be found using the University of California Santa Cruz Genome Browser Gatemay (genome.ucsc.edu / cgi- bin / hgGateway), which provides information regarding the location of CG sites in the human genome. In some embodiments, the CG designations provided herein are based on the Illumina CG database. See also, e.g., the Excel from the Supporting Information to Ndhlovu et al., specifically incorporated by reference herein in its entirety (see TelescopeAnnotation excel file for the chromosome and genomic location). In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 1. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 1. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 1. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 2. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 2. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 2. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 3. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 3. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 3. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 4. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of theCpG loci provided in Table 4. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 4. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 5. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 5. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 5. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 6. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 6. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 6. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 7. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 7. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 7. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 8. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 8. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 8. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in Table 9. In some embodiments, the DNA methylation change is measured in at least 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in Table 9. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in Table 9. In some embodiments, the DNA methylation change is measured in the set of the CpG loci comprising one or more (or all) of: cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013,cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. In some embodiments, the DNA methylation change is measured in the set of the CpG loci comprising one or more (or all) of: cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. In some embodiments, the DNA methylation change is measured in the set of the CpG loci provided in the Excel spreadsheet “acel14288-sup-0001-DataS1_xlsx” provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which is incorporated by reference herein in its entirety. In some embodiments, the DNA methylation change is measured in at least 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% or all of the CpG loci provided in the Excel spreadsheet “acel14288-sup-0001-DataS1_xlsx” provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which is incorporated by reference herein in its entirety. In some embodiments, the DNA methylation change is measured in at least one or more (e.g., at least or more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 500, 750, 1000, or all) of the CpG loci provided in the Excel spreadsheet “acel14288-sup-0001- DataS1_xlsx” provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which is incorporated by reference herein in its entirety. In some embodiments, the DNA methylation change is measured at each of the CG loci provided in the Excel spreadsheet “acel14288-sup-0001-DataS1_xlsx” provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which is incorporated by reference herein in its entirety. In some embodiments, the DNA methylation change is measured in the set of the CpG loci comprising one or more (or all) of: cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, and cg23987336, or any combination thereof (which CpGs are located at the HML-2_17p13.1 locus). In some embodiments, the CpGs are related to or located within a 517bp long intergenic non- coding RNA (lincRNA) ENST0000577807 and overlap with annotated enhancer regions based on ENCODE ChromHMM. In some embodiments, the DNA methylation change is measured in the set of the CpG loci comprising one or more (or all) of: HERVL18_6p22.1c loci (cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226), HUERSP3_6p21.32 loci (cg03978169,cg13904130, cg15737123, cg18927185, cg22136013, cg23917817), and / or MER41_22q12.3e loci (cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224), or any combination thereof. In some embodiments, the CpGs (e.g., within HERVL18_6p22.1c) are in the ZSCAN31 and ZKSCAN3 genes. In some embodiments, the CpGs (e.g., within HUERSP3_6p21.32) are in an intergenic region of heterochromatin / ZNF genes and repeats. In some embodiments, the CpGs (e.g., within MER41_22q12.3e) are near an enhancer region of the anti-inflammatory and metabolic regulator C1QTNF6 gene. In some embodiments, the DNA methylation change is measured in the set of the CpG loci comprising one or more (or all) of: L1FLnl_10q26.3e loci (cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, and cg25120459) and / or L1FLnl_1p31.3z loci (cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634), or any combination thereof. In some embodiments, the CpGs (e.g., within L1FLnl_10q26.3e) are downstream of the 3’UTR of the ADGRA1 gene. In some embodiments, the CpGs (e.g., within L1FLnl_1p31.3z) are located in an enhancer region of the DEPDC1 gene. In some embodiments, the CpGs (e.g., within L1FLnI_7q22.2a) are in an enhancer region of the ORC5 gene. In some embodiments, the CpGs (e.g., within L1FLnI_18q23l) are in the NFATC1 gene. In some embodiments, the CpGs (e.g., within L1FLnI_Xp22.2h) are contained in an enhancer and active transcriptional start region of the TLR7 gene. Retroelement-Age Clocks of the Invention and Determination of Biological Age In some embodiments, provided herein is a retroelement-based epigenetic clock. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 1317 CpG loci) provided in Table 1. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 1317 CpG loci) provided in Table 2. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 1378 CpG loci) provided in Table 3. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 95 CpG loci) provided in Table 4.In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 734 CpG loci) provided in Table 5. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 398 CpG loci) provided in Table 6. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all 201 CpG loci) provided in Table 7. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all CpG loci) provided in Table 8. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising the CpG loci (e.g., all CpG loci) provided in Table 9. The retroelement DNA methylation data utilized to construct retroelement-based epigenetic clocks described herein can be found on Zenodo at DOI: 10.5281 / zenodo.11099870, which is incorporated by reference herein in its entirety. The code, retroelement-based clock coefficients, and script to calculate retroelement epigenetic ages from the DNA methylation data can be found on Zenodo at DOI: 10.5281 / zenodo.11099870, which is incorporated by reference herein in its entirety. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 1. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 2. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 3. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 4. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 5.In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 6. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 7. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 8. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 9. In some embodiments, a Retroelement-Age epigenetic clock is provided herein comprising at least or more than 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, or 99% of the CpG loci provided in Table 10. In some embodiments, the CpGs used in a Retroelement-Age epigenetic clock provided herein do are not the same and do not overlap with previously known epigenetic clocks. In some embodiments, the CpGs used in a Retroelement-Age epigenetic clock provided herein do are not the same and do not overlap with epigenetic clocks described in any one or more or all of: Horvath, 2013; Horvath, Oshima, 2018; Levine, 2018; Hannum, 2013; Lu, Seeboth, 2019; Lu, Quach, 2019; Belsky, 2022; Teschendorff, 2020; Ying, 2024. In some embodiments, equal to or less than 45, 40, 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3 or 2 CpGs used in a Retroelement-Age epigenetic clock used herein are the same or overlap with previously known epigenetic clocks. In some embodiments, the only CpGs used in a Retroelement-Age epigenetic clock used herein that are the same or overlap with previously known epigenetic clocks are any one or more (or all) of: cg06672696, cg07286682, cg08822136, cg16936289, cg16810279, cg22277154, cg13261390, cg22277154, and cg24251135. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1100,1200, or 1300 of CpG loci, such as a combination of any of the retroelement CpG loci described herein (e.g., within any one of Tables 1-10). In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1100, 1200, or 1300 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 1 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1100, 1200, or 1300 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 2 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000, 1100, 1200, or 1300 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 3 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, or 95 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 4 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, or 734 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 5 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, or 398 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 6 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, or 200 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 7 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, or 1000 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 8 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50,60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, or 1000 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 9 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, or 1000 of CpG loci, such as a combination of any of the retroelement CpG loci described in Table 10 herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least or more than 50, 60, 65, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500, 600, 700, 800, 900, or 1000 of CpG loci, such as a combination of any of the retroelement CpG loci described in the Excel from the Supporting Information referenced and incorporated by reference herein. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises 95 CpG loci described in Table 4 herein, for predicting biological age of immune cells and / or for assessments and methods relating to treating or preventing inflammaging. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises 734 CpG loci described in Table 5 herein, for use with a multi-tissue biological sample from a single subject. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least 398 CpG loci described in Table 6 herein, for predicting biological age in pan-mammalian species. In some embodiments, a Retroelement-Age epigenetic clock used herein comprises at least 201 CpG loci described in Table 7 herein, for predicting biological age in pan-mammalian species. In some embodiments, a pretrained model is applied on a sequence of inputs comprising a retroelement-based epigenetic clock to predict the epigenetic age, optionally wherein the pretrained model comprises an elastic net. In some embodiments, a retroelement-based epigenetic clock is constructed, at least in part, by training an Elastic net linear regression model. In some embodiments, the “coefficients” located in each of the Tables disclosed herein refer to a regression coefficient for each CpG loci, and the larger the absolute value of a coefficient, the stronger the effect of the corresponding feature on the target variable. By way of example, approximately 345 of the CpG sites disclosed herein exhibit coefficients above the 75th percentile, and approximately 138 CpG sites are above the 90th percentile. Such sites may be considered the more important contributors to the models provided herein based on their coefficients. See, for example, the CpG sites provided in any of the Tables 1-9,the Excel spreadsheet “acel14288-sup-0001-DataS1_xlsx” (TelescopeAnnotationCpGEpicV1) in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, which information in the Excel is specifically incorporated by reference herein in its entirety, or Table 10 provided herein. In some embodiments, at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, or 100% of the CpG sites described herein that exhibit coefficients above 75th percentile are used in the methods or epigenetic clocks described herein (such as any of such CpG loci in any of Tables 1-10). In some embodiments, at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, or 100% of the CpG sites described herein that exhibit coefficients above 90th percentile are used in the methods or epigenetic clocks described herein (such as any of such CpG loci in any of Tables 1-10). In some embodiments, at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, or 100% of the CpG sites described herein that exhibit coefficients above 95th percentile are used in the methods or epigenetic clocks described herein (such as any of such CpG loci in any of Tables 1-10). In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are any of the CpG methylation markers having coefficients above the 50th or 60thpercentile described in any one or more, or each, of the Tables 1- 10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are at least or more than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175 or 200 CpG methylation markers having coefficients above the 50th or 60thpercentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are any of the CpG methylation markers having coefficients above the 75th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are at least or more than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175 or 200 CpG methylation markers having coefficients above the 75th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof.In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are any of the CpG methylation markers having coefficients above the 75th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are at least or more than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175 or 200 CpG methylation markers having coefficients above the 75th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are any of the CpG methylation markers having coefficients above the 90th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are at least or more than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175 or 200 CpG methylation markers having coefficients above the 90th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are any of the CpG methylation markers having coefficients above the 95th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the CpG methylation markers the methylation status or levels of which are assessed by the methods described herein are at least or more than 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 125, 150, 175 or 200 CpG methylation markers having coefficients above the 95th percentile described in any one or more, or each, of the Tables 1-10, e.g., in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9 and / or Table 10, and any combination thereof. In some embodiments, the epigenetic or biological age is determined based on comparing the methylation status or level of a set of CpG loci described herein in a biological sample of a subjectwith the methylation status or level of the same set of CpG loci from an age-correlated reference biological sample or samples from a population of subjects. In some embodiments, the chronological age is determined based on comparing the methylation status or level of a set of CpG loci described herein in a biological sample of a subject with the methylation status or level of the same set of CpG loci from an age-correlated reference biological sample or samples from a population of subjects. Without being bound by theory or methodology, an exemplary workflow comprises: (1) generating DNA methylation data, e.g., on Illumina MethylationEPIC V1 or V2 Array platforms or assess DNA methylation at RetroAge loci using another technology such as targeted bisulfite sequencing; and (2) utilizing the DNA methylation quantification at those CpG loci in a predetermined model, e.g., the models provided herein. An example of the code that can be used to generate a predicted age from such data is as follows: library(data.table) # Replace the file path with file path for your DNA methylation data file_path <- ' / Users / Data / ALL_CpGs_betamatrix.csv' # Read the CSV file using fread data <- fread(file_path, header = TRUE, data.table = FALSE) # Assuming `data` is your data frame of generated DNA methylation rownames(data) <- data[, 1] # Set row names to first column data <- data[,-1] # Remove the first column datMeth <- t(data) # Load necessary libraries and coefficients library(glmnet) coefficients_df <- read.csv(" / Users / RetroAgeModel / HERVLINE1predictorCpGss.csv") calculate_epigenetic_age <- function(methylation_data, coefficients_df) { # Exclude the intercept when checking for CpGs CpGs_to_check <- coefficients_df$name[coefficients_df$name != "(Intercept)"] # Ensure that the methylation data contains the required CpGs if (!all(CpGs_to_check %in% colnames(methylation_data))) {stop("The methylation data does not have all the required CpGs!") } # Calculate the epigenetic age using the dot product of the coefficients and the methylation data predictions <- as.matrix(methylation_data[, CpGs_to_check, drop = FALSE]) %*% coefficients_df$coefficient[coefficients_df$name != "(Intercept)"] # Add intercept, since it's present in the coefficients_df intercept <- coefficients_df$coefficient[coefficients_df$name == "(Intercept)"] predictions <- predictions + intercept return(predictions) } # Calculate and print the epigenetic age epi_age <- calculate_epigenetic_age(datMeth, coefficients_df) print(epi_age) write.csv(epi_age, file="PredictRetroAge.csv", quote=F) sessionInfo() Without being bound by theory or methodology, another exemplary workflow comprises the following steps: 1. Input Data Collection Methylation Data: For each individual, obtain the DNA methylation data at the specific CpG sites used in the model. Methylation levels are typically represented as beta values, which range from 0 (completely unmethylated) to 1 (fully methylated). 2. Preparation of the Formula Model Equation: The biological age is calculated using a linear equation of the form: Biological Age = β 0+ Σ i = 1 n ( β i × CpG i ) Biological Age=β 0 + i=1 Σ n (β i ×CpG i ) β 0 β 0is the intercept. β i β i are the coefficients associated with each CpG site. CpG i CpG i represents the methylation value at the i-th CpG site. n n is the number of CpG sites used in the model. 3. Implementation of the Calculation Apply the Coefficients: Multiply each CpG site’s methylation value by its corresponding coefficient β i β i . Summation: Sum all the resulting values from the previous step. Add Intercept: Add the intercept β 0 β 0 to the sum obtained. 4. Provided herein is an Example Calculation Suppose a model has the following parameters: Intercept (β 0 β 0 ): 35 Coefficients ( β 1 , β 2 , β 3 β 1 ,β 2 ,β 3 ): 1.2, -0.8, 0.5 Methylation values for an individual at CpG sites 1, 2, and 3: 0.75, 0.60, 0.85 One would calculate the biological age as follows: Biological Age = 35 + (1.2 × 0.75 ) + ( − 0.8 × 0.60 ) + ( 0.5 × 0.85 ) Biological Age=35+(1.2×0.75)+(−0.8×0.60)+(0.5×0.85) Biological Age = 35 + 0.9 − 0.48 + 0.425 Biological Age=35+0.9−0.48+0.425 Biological Age = 35 +0.845 Biological Age=35+0.845 Biological Age = 35.845 Biological Age=35.845 5. Interpretation The result, 35.845 in this case, represents the biological age of the individual based on the methylation levels at the specified CpG sites. 6. Software Implementation This calculation can be easily implemented using statistical software like R, Python. By following the steps provided herein a skilled artisan could translate methylation data from specific CpG sites into a calculated biological age, using the intercept and coefficients from the models described herein (or known in the art). Biological Samples and DNA from Biological Samples Biological sample used in accordance with the inventions described herein can be a cell or tissue, or a sample of any cell or tissue including, without limitation, samples from whole blood, blood peripheral mononuclear cells (PBMC), mammary tissue, muscle skeletal, lung, ovary, kidney, testis, prostate, colon etc. In some embodiments, biological sample is a blood sample. In some embodiments, biological sample is a PBMC sample. In some embodiments, biological sample is from or comprises immune cells or tissues, including, without limitation lymphocyte, lymphoid lineage cell, monocyte, basophil, neutrophil, eosinophil, and / or myeloid lineage cell. In some embodiments, biological sample is from or comprises an immune cell, a stem cell or a somatic cell. In some embodiments, biological sample is from or comprises a stem cell (e.g., an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC)). In some embodiments, biological sample is from or comprises a somatic cell. In some embodiments, the biological sample is from saliva, buccal swab or nasal swab. In some embodiment, the biological sample is from the bone marrow. In some embodiment, the biological sample is from urine or fecal matter. In some embodiment, the biological sample is from uterine, cervical, prostate, testicular, breast, or ovarian tissue. In some embodiment, the biological sample is from neurons, astrocytes or brain tissue. In some embodiment, the biological sample is from liver tissue. In some embodiment, the biologicalsample is from stomach or intestinal tissue. In some embodiment, the biological sample is from pancreatic tissue. In some embodiment, the biological sample is from skin, epithelial cells or tissue, dermal tissue or dermal fibroblasts. In some embodiment, the biological sample is fibroblasts (e.g., dermal fibroblasts) or keratinocytes. In some embodiment, the biological sample is epithelial cells or tissue. In some embodiment, the biological sample is from musculoskeletal tissue, muscle tissue or bone tissue. In some embodiment, the biological sample is from lung tissue. In some embodiments, a biological sample is from a cell or tissue of a healthy subject. In some embodiments, a biological sample is from a cell or tissue of a subject suffering from a disease or condition. In some embodiments, a biological sample is from a subject suffering from an age- related disease or condition. In some embodiments, a biological sample is a cancer or tumor sample. The cancer or tumor tissue can be a cancer or tumor of any of the above-mentioned tissues or origins. In some embodiments, provided herein is DNA isolated from a biological sample. In some embodiments, provided herein is DNA isolated from a biological sample obtained from a subject. In some embodiments, DNA is genomic DNA (e.g., isolated genomic DNA). In some embodiments, DNA is nuclear DNA (e.g., isolated nuclear DNA). In some embodiments, DNA is isolated genomic nuclear DNA. Subject In some embodiments, the subject is a mammal. In some embodiments, the subject is a human. In some embodiments, the subject is a non-human mammal, e.g., a primate, a domestic animal, a farm animal or a wild animal. In some embodiments, the subject has (e.g., has been diagnosed with) an age-related disease or condition. In some embodiments, the subject is at risk of developing an age-related disease or condition. In some embodiments, the subject has symptoms of or is at risk of premature aging. In some embodiments, the subject has or is at risk of a chronic disease (e.g., a chronic disease associated with premature aging). In some embodiments, the subject has (e.g., has been diagnosed with) a disease or condition impacting aging (for example, an infectious disease, such as a viral infection, e.g., by HIV-1). In some embodiments, the subject’s biological age is higher than the subject’s actual or chronological age (e.g., at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 years higher). In some embodiments, the subject is over 35, 40, 45, 50, 55, 60, 65, 70, or 75 years old in actual orchronological age. In some embodiments, the subject is over 35, 40, 45, 50, 55, 60, 65, 70, or 75 years old in biological age as determined by any of the methods described herein. In some embodiments, the subject does not have a retroviral infection. In some embodiments, the subject does not have an HIV. In other embodiments, the subject has a retroviral infection (e.g., HIV). In some embodiments, the subject has a disease or condition associated with reactivation of one or more retroelements, such as any one or more retroelements described herein (e.g., HERV and / or LINE). In some embodiments, the subject has a disease or condition associated with reactivation of HERV-K-derived retrovirus like particles or transcriptional derepression of HERV-K. In some embodiments, the subject has a disease or condition associated with reactivation of LINE- derived retrovirus like particles or transcriptional derepression of LINEs. In some embodiments, the HERV is any one or more of the HERV types described herein (for example, HERV-K, e.g., HML- 2). In some embodiments, the LINE is any one or more of the LYNE types described herein (e.g., MER41). In some embodiments, a biological sample of a subject is characterized by reactivation of one or more retroelements, such as any one or more retroelements described herein (e.g., HERV and / or LINE). In some embodiments, the HERV is any one or more of the HERV types described herein (for example, HERV-K, e.g., HML-2). In some embodiments, the LINE is any one or more of the LYNE types described herein (e.g., MER41). In some embodiments, the subject exhibits biomarkers of aging in the immune system or cells. In some embodiments, the subject exhibits inflammaging. In some embodiments, the subject exhibits cellular senescence and / or tissue aging. In some embodiments, the subject has renal fibroinflammation and / or diseased kidney. Methods of the Invention In some aspects, provided herein are methods for determining biological age of a subject or a biological sample of a subject. In some embodiments, the determining of the biological age is by determining methylation status or level of a set of CpG methylation markers located in a retroelement in the DNA of a biological sample of a subject. In some embodiments, the determining is by use of Retroelement-Age clock based on DNA methylation states of HERVs and LINEs as described herein (such as at the CpG loci within HERVs and LINEs, as described herein). In some embodiments, the determining of the biological age is by use of any of the retroelement-basedepigenetic clocks described herein (e.g., any one of epigenetic clocks of Tables 1-9, or any epigenetic clocks substantially similar thereto). In some embodiments, the determining is by use of a computer-readable medium described herein. In some embodiments, the determining is by receiving a sequence of inputs corresponding to methylation state or level at a plurality of CpG loci, and applying a pretrained model on the sequence of inputs to predict the biological age. In some embodiments, the pretrained model comprises an elastic net. Although the term “biological age” is referenced herein, this term can be replaced with the term “epigenetic age” in the methods and compositions described herein. In other embodiments, it is the chronological age that can be determined using the methods described herein. In some embodiments, the methods for determining biological age comprise measuring the methylation status or level of at least one, ten, twenty, thirty, forty, fifty, sixty, seventy, eighty, ninety or one hundred, or more, or a set of (such as any set described herein), of CpG methylation markers (CpG loci) described herein (located in at least one retroelement in genomic or nuclear DNA of a biological sample). In some embodiments, the methods comprise measuring the methylation of only at least one or more CpG methylation markers or CpG loci described herein (located in at least one retroelement in genomic or nuclear DNA of a biological sample), and does not comprise measuring the methylation of any CpG loci located within sequences that are not retroelement sequences. In some embodiments, the methods comprise measuring the methylation of only at least one or more CpG methylation markers or CpG loci described herein (located in at least one retroelement in genomic or nuclear DNA of a biological sample), and does not comprise measuring the methylation of any other CpG loci. In some embodiments, the methods comprise measuring the methylation of only at least one or more CpG methylation markers or CpG loci described herein (located in at least one retroelement in genomic or nuclear DNA of a biological sample), and does not comprise measuring the methylation of any CpG loci located within sequences that are not HERV or LINE sequences. In some aspects, provided herein are methods for predicting or determining whether a therapy, agent or a factor is capable of affecting biological age of a subject or a biological sample of a subject, as described herein. In some embodiments, provided herein are methods for predicting or determining whether a therapy or agent (e.g., a drug) is capable of reducing biological age of a subject or a biological sample of a subject, as described herein. In some embodiments, provided herein are methods for predicting or determining whether a factor or an agent (e.g., an environmental or nutritional factor or agent) is capable of accelerating aging of a subject or a biological sample of asubject, as described herein. In some embodiments, such determining or predicting comprises monitoring for effects or assessing an impact of a therapy. In some embodiments, such determining or predicting comprises screening for agents having an effect. In some embodiments, the methods for predicting or determining comprise assessing CpG methylation markers located in at least one retroelement as described herein before and / or after administration of a therapy, agent or factor as described herein. In some embodiments, the subject has an age-related disease or condition. In some embodiments, provided herein are methods that comprise: (i) determining pre- treatment biological age of a subject or biological sample of a subject as described herein, followed by (ii) administering a therapy, agent or a factor (e.g., for a period of time in an effective amount, an amount known to be effective when administered to the subject), and (iii) again determining post- treatment biological age of the subject or biological sample of a subject as described herein. In some embodiments, if the post-treatment biological age is lower than the pre-treatment biological age, the therapy, agent or a factor is capable of reducing biological age. In some embodiments, if the post- treatment biological age is higher than the pre-treatment biological age, the therapy, agent or a factor is capable of increasing biological age. In some embodiments, if the post-treatment biological age is lower than the pre-treatment biological age, the therapy, agent or factor can be continued to be administered to the subject for reducing biological age, or can be administered to subjects (mammals) for reducing biological age. In some embodiments, the therapy or agent is known in the art or predicted to be capable of rejuvenation or reduction of biological age of cells or tissues. In some embodiments, provided herein are screening methods for identification of therapies, factors or agents capable of reducing or accelerating aging. In some aspects, provided herein are methods for predicting or determining biological age- related health outcomes or responsiveness to therapy, as described herein. In some embodiments, the decision whether to treat a subject with an agent or therapy (e.g. an agent or therapy known to be effective in a certain age group) is made based on biological age determined in accordance with the methods described herein. In some aspects, provided herein are methods for determining methylation status or level of a set of CpG methylation markers located in a retroelement in DNA of a biological sample of a subject, as described herein. In some embodiments, the set of CpG methylation markers is one of the select sets described herein, including in Tables 1-9. In some aspects, provided herein are methods for preventing or treating an age-related disease or condition in a subject, comprising: (i) determining biological age of the subject or abiological sample of the subject, as described herein, and if the biological age is higher than the actual or chronological age of the subject, (ii) administering to the subject a therapy or agent for preventing or treating the age-related disease or condition. In some embodiments, the therapy or agent for treating the age-related disease or condition is a known in the art therapy or agent for the treatment of an age-related disease or condition. In some embodiments, the therapy or agent is an antiretroviral therapy or agent. In some embodiments, the therapy or agent is an anti-retroviral agent for the treatment of HIV. In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV-negative. In some embodiments, the subject being treated would not be otherwise receiving an antiretroviral therapy (e.g., for reasons unrelated to age or an age-related disease or condition). In some embodiments, the therapy or agent is an epigenetic modulator therapy or agent. In some embodiments, the therapy or agent is a DNA methyltransferase inhibitor, such as those known in the art or utilized for cancer treatment and research. In other embodiments, the therapy or agent employs a CRISPR / Cas9-based epigenetic editor, which may include, for example, a CRISPR / Cas9 system fused to an epigenetic modifier recruiting DNA methyltransferases or histone modifiers directly to retroelement loci to silence them. In some embodiments, the subject being treated is not afflicted with a cancer, has not been diagnosed with cancer, is in remission from cancer, or would not otherwise be receiving such a cancer treatment. In some aspects, provided herein are methods of preventing or treating an age-related disease or condition in a subject, comprising: (i) determining methylation status or level of a set of CpG methylation markers located in a retroelement in DNA of a biological sample of a subject, as described herein, and if the methylation status or level indicates that biological age of the subject is higher than the actual or chronological age of the subject, (ii) administering to the subject a therapy or agent for preventing or treating the age-related disease or condition. In some embodiments, the therapy or agent for treating the age-related disease or condition is a known in the art therapy or agent for the treatment of an age-related disease or condition. In some embodiments, the therapy or agent is an anti-retroviral therapy or agent. In some embodiments, the therapy or agent is an anti- retroviral agent for the treatment of HIV. In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV- negative. In some embodiments, the subject being treated would not be otherwise receiving an antiretroviral therapy (e.g., for reasons unrelated to age or an age-related disease or condition). In some embodiments, the therapy or agent is an epigenetic modulator therapy or agent. In someembodiments, the therapy or agent is a DNA methyltransferase inhibitor, such as those known in the art or utilized for cancer treatment and research. In other embodiments, the therapy or agent employs a CRISPR / Cas9-based epigenetic editor, which may include, for example, a CRISPR / Cas9 system fused to an epigenetic modifier recruiting DNA methyltransferases or histone modifiers directly to retroelement loci to silence them. In some embodiments, the subject being treated is not afflicted with a cancer, has not been diagnosed with cancer, is in remission from cancer, or would not otherwise be receiving such a cancer treatment. In some aspects, provided herein are methods for preventing or treating premature aging, reducing biological age or rejuvenation of a subject, comprising: (i) determining biological age of the subject or a biological sample of the subject, e.g., by determining methylation status or level of a set of CpG methylation markers located in a retroelement in DNA of a biological sample of a subject, as described herein; and if it is desirable to reduce the biological age of the subject, (ii) administering to the subject a therapy or agent for reducing biological age or rejuvenation. In some embodiments, the therapy or agent for reducing biological age or rejuvenation is a known in the art therapy or agent for such indication. In some embodiments, the therapy or agent is an anti-retroviral therapy or agent. In some embodiments, the therapy or agent is an anti-retroviral agent for the treatment of HIV. In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV-negative. In some embodiments, the subject being treated would not be otherwise receiving an antiretroviral therapy (e.g., for reasons unrelated to age or an age-related disease or condition). In some embodiments, the therapy or agent is an epigenetic modulator therapy or agent. In some embodiments, the therapy or agent is a DNA methyltransferase inhibitor, such as those known in the art or utilized for cancer treatment and research. In other embodiments, the therapy or agent employs a CRISPR / Cas9-based epigenetic editor, which may include, for example, a CRISPR / Cas9 system fused to an epigenetic modifier recruiting DNA methyltransferases or histone modifiers directly to retroelement loci to silence them. In some embodiments, the subject being treated is not afflicted with a cancer, has not been diagnosed with cancer, is in remission from cancer, or would not otherwise be receiving such a cancer treatment. In some aspects, provided herein are methods for preventing or treating inflammaging in a subject, comprising: (i) determining biological age of the subject or a biological sample of the subject, e.g., by determining methylation status or level of a set of CpG methylation markers located in a retroelement in DNA of a biological sample of a subject, as described herein; and if it isdesirable to reduce the biological age of the subject, (ii) administering to the subject a therapy or agent for inflammaging. As used herein, inflammaging is a chronic, low-grade inflammation which is associated with or increases with aging (see, e.g., Smith et al., 2024, GeroScience, 1-17, which is incorporated by reference herein in its entirety). In some embodiments, the therapy or agent for inflammaging is a known in the art therapy or agent for such indication. In some embodiments, the therapy or agent is an anti-retroviral therapy or agent. In some embodiments, the therapy or agent is an anti-retroviral agent for the treatment of HIV. In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV-negative. In some embodiments, the subject being treated would not be otherwise receiving an antiretroviral therapy (e.g., for reasons unrelated to age or an age-related disease or condition). In some embodiments, the therapy or agent is an epigenetic modulator therapy or agent. In some embodiments, the therapy or agent is a DNA methyltransferase inhibitor, such as those known in the art or utilized for cancer treatment and research. In other embodiments, the therapy or agent employs a CRISPR / Cas9-based epigenetic editor, which may include, for example, a CRISPR / Cas9 system fused to an epigenetic modifier recruiting DNA methyltransferases or histone modifiers directly to retroelement loci to silence them. In some embodiments, the subject being treated is not afflicted with a cancer, has not been diagnosed with cancer, is in remission from cancer, or would not otherwise be receiving such a cancer treatment. In some aspects, provided herein are methods for preventing or treating an age-related disease or condition in a subject, comprising administering to the subject an anti-retroviral therapy or agent. In some embodiments, the therapy or agent is an anti-retroviral agent for the treatment of HIV (e.g., an anti-HIV-1 drug). In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV-negative. In some embodiments, the subject being treated would not be otherwise receiving an antiretroviral therapy (e.g., for reasons unrelated to age or an age-related disease or condition). In some embodiments, the therapy or agent is an epigenetic modulator therapy or agent. In some embodiments, the therapy or agent is a DNA methyltransferase inhibitor, such as those known in the art or utilized for cancer treatment and research. In other embodiments, the therapy or agent employs a CRISPR / Cas9-based epigenetic editor, which may include, for example, a CRISPR / Cas9 system fused to an epigenetic modifier recruiting DNA methyltransferases or histone modifiers directly to retroelement loci to silence them. In some embodiments, the subject being treated is not afflictedwith a cancer, has not been diagnosed with cancer, is in remission from cancer, or would not otherwise be receiving such a cancer treatment. In some aspects, provided herein are methods for preventing or treating premature aging, reducing biological age or rejuvenation of a subject in a subject, comprising administering to the subject an anti-retroviral therapy or agent. In some embodiments, the therapy or agent is an anti- retroviral agent for the treatment of HIV (e.g., an anti-HIV-1 drug). In some embodiments, the subject being treated does not have a retroviral (e.g., HIV) infection. In some embodiments, the subject being treated is HIV-negative. Anti-retroviral therapy or agent can be any known in the art or described herein. Anti- retroviral therapy or agent is a state of the art FDA approved antiretroviral therapy (e.g., administered in accordance with the FDA label). In some embodiments, an anti-retroviral therapy or agent is a therapy or agent that inhibits activity, production or infection of a retrovirus (such as HERV, e.g., HERV-K). In some embodiments, an anti-retroviral therapy or agent is an anti- retroviral therapy or agent effective to inhibit HERV (e.g., HERV-K) activity (e.g., reverse transcriptase activity), infection or viral production (e.g., as shown by any assay known in the art or described herein). In some embodiments, an anti-retroviral therapy or agent is a reverse transcriptase (RT) inhibitor or an integrase inhibitor of a retrovirus (such as HERV, e.g., HERV-K). In some embodiments, an anti-retroviral therapy or agent is a nucleoside or non-nucleotide reverse transcriptase (RT) inhibitor. In some embodiments, an anti-retroviral therapy or agent is an integrase inhibitor. In some embodiments, the antiretroviral therapy or agent is a reverse transcriptase inhibitor, such as, any of nucleoside reverse transcriptase inhibitors or non-nucleoside reverse transcriptase inhibitors known in the art or described herein. In some embodiments, an anti-retroviral therapy or agent is Abacavir, Zidovudine or Raltegravir. In some embodiments, an anti-retroviral therapy or agent is not a protease inhibitor. In some embodiments, an anti-retroviral therapy or agent is any one described in Tyagi et al., 2017, Retrovirology 14:21, which is incorporated by reference herein in its entirety and specifically for its description of anti-retroviral drugs. In some embodiments, a therapy or agent described herein (e.g., an antiretroviral agent) is administered for at least 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 1.5 years, 2 years or longer. In some embodiments, a therapy or agent described herein (e.g., an antiretroviral agent) results in significant reduction of biological age of a subject (e.g., after at least or more than 1, 1.5 or 2 years of therapy). In some embodiments, the methods described herein reduce biological age by at least or more than 1month, 2 months, 3 months, 6 months, 9 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 4 years, or 5 years. In some embodiments, it is desirable to reduce the determined biological age of the subject when the subject’s biological age is higher than the subject’s actual or chronological age. In some embodiments, the subject is treated in accordance with the methods described herein when the subject’s biological age is at least or more than 1 month, 2 months, 3 months, 6 months, 9 months, 1 year, 1.5 years, 2 years, 2.5 years, 3 years, 4 years, or 5 years higher than the subject’s actual or chronological age. In some aspects, provided herein are methods for determining biological age of a cell or tissue subjected to rejuvenation by transient reprogramming, as described herein (such as by determining methylation status of a set of CpG methylation markers located in a retroelement in DNA of a biological sample of a subject before and after it is subjected to rejuvenation by transient reprogramming). The rejuvenation by transient reprogramming can be as described in Gill et al., 2022, eLife 11:e71624, which is incorporated by reference herein in its entirety and specifically for its description of rejuvenation by transient reprogramming (including reprogramming factors used for the same). In some aspects, provided herein are methods for determining biological age of a cell or tissue subjected to rejuvenation by transient expression of one or more reprogramming factors. In some embodiments, the cell or tissue is dermis (e.g., a population of dermal fibroblasts). In some embodiments, the cell or tissue is a fibroblast (e.g., a population of dermal fibroblasts). In some embodiments, the cell or tissue is epithelial cell or tissue (e.g., a population of epithelial cells or tissues). In some embodiments, the cell or tissue is not an endothelial cell or tissue. In some embodiments, the reprogramming factors are one or more Yamanaka factors. In some embodiments, the reprogramming factors are any one, two, three or more (or all) selected from: Oct4, Sox2, Klf4, and c-Myc. In some embodiments, biological age is assessed at day 0 (before the transient reprogramming), and day(s) 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or 21 after the transient reprogramming. In any of the methods provided herein, the step of determining biological age of a biological sample can be performed using any of the Retroelement-Age clocks provided herein. In any of the methods provided herein, the step of determining biological age of a biological sample can be performed using a set of or all retroelement CpG methylation markers identified in any one or more of Tables 1-7. In any of the methods provided herein, the step of determining biological age of a biological sample can be performed using the code, retroelement-based clock coefficients, and / orscript to calculate biological age from the DNA methylation found on Zenodo at DOI: 10.5281 / zenodo.11099870, which are incorporated by reference herein in their entirety. In some embodiments, the biological age is determined based on comparing the methylation status or level of a set of CpG loci described herein in a biological sample of a subject with the methylation status or level of the same set of CpG loci from an age-correlated reference biological sample or samples from a population of subjects. In some embodiments, the age-correlated reference biological sample is from the same cell or tissue as the biological sample for which the age is being determined. In some embodiments, the age-correlated reference biological sample is representative of a certain chronological age. In some embodiments, the age-correlated reference biological sample is from a healthy subject. The comparing may comprise any statistical method known in the art or described herein. Computer-readable medium and computer-based methods In some aspects, provided herein is computer-readable medium for determining biological age of a subject or a biological sample of a subject. In some embodiments, the computer medium processes information regarding methylation status or level of a set of CpG methylation markers located in a retroelement in the DNA of a biological sample of a subject. In some aspects, provided herein is computer-readable medium comprising computer- readable instructions that, when executed by a computer, causes the computer to perform operations comprising: a) receiving information about methylation status or level of a set of CpG methylation markers located in a retroelement (e.g., at least one HERV element and / or at least one LINE) in the DNA of a biological sample of a subject; b) determining the age of the biological sample by comparing said methylation status or level to methylation status or level of the same set of CpG methylation markers from an age-correlated reference biological sample, and by applying a statistical prediction algorithm to the methylation status or level. In some aspects, provided herein is a method of determining biological age of a biological sample, the method comprising: measuring the methylation status or levels of a set of CpG methylation markers in genomic DNA of the biological cell (e.g., a cell),said measuring comprising receiving a sequence of inputs corresponding to methylation values at a plurality of CpG loci; and applying a pretrained model on the sequence of inputs to predict the biological age, optionally wherein said set of methylation markers comprise CpG methylation markers located in at least one HERV element and / or at least one LINE (e.g., LINE-1). In some embodiments, the biological sample is a mammalian biological sample (e.g., human biological sample). In some embodiments, the biological sample is a tissue or bodily fluid, such as any tissue or bodily fluid described herein. In some embodiments, the biological sample is a cell (or population of cells), such as any cell described herein. In some embodiments, the biological sample is a mammalian cell (e.g., a human cell). In some embodiments, the cell is an immune cell, a stem cell or a somatic cell. In some embodiments, the stem cell is an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC). In some embodiments, the somatic cell is a fibroblast, a keratinocyte, an epithelial cell, or a cell derived from peripheral blood cells (PBMCs). In some embodiments, the CpG methylation markers are located in at least one HERV element described herein or selected from any one or more of Tables 1-9. In some embodiments, the CpG methylation markers are located in at least one LINE element described herein or selected from any one or more of Tables 1-10. In some embodiments, the methylation status or level information is collected at a set of CpG loci, or at each of the CpG loci, selected from any one of Tables 1-10. In some embodiments, the CpG methylation markers comprise one or more (or at least 5, 10, 15, or all) CpG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. In some embodiments, the CpG methylation markers comprise one or more (or at least 5, 10, 15, or all) CpG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. Referring now to Fig.11, a schematic of an example of a computing node is shown. Computing node 10 is only one example of a suitable computing node and is not intended to suggest any limitation as to the scope of use or functionality of embodiments described herein. Regardless,computing node 10 is capable of being implemented and / or performing any of the functionality set forth hereinabove. In computing node 10 there is a computer system / server 12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations that may be suitable for use with computer system / server 12 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed computing environments that include any of the above systems or devices, and the like. Computer system / server 12 may be described in the general context of computer system- executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system / server 12 may be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. As shown in Fig. 11, computer system / server 12 in computing node 10 is shown in the form of a general-purpose computing device. The components of computer system / server 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including system memory 28 to processor 16. Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, Peripheral Component Interconnect (PCI) bus, Peripheral Component Interconnect Express (PCIe), and Advanced Microcontroller Bus Architecture (AMBA). Computer system / server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system / server 12, and it includes both volatile and non-volatile media, removable and non-removable media.System memory 28 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer system / server 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a "hard drive"). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus 18 by one or more data media interfaces. As will be further depicted and described below, memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

[0001] Program / utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and / or methodologies of embodiments as described herein. Computer system / server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system / server 12; and / or any devices (e.g., network card, modem, etc.) that enable computer system / server 12 to communicate with one or more other computing devices. Such communication can occur via Input / Output (I / O) interfaces 22. Still yet, computer system / server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet) via network adapter 20. As depicted, network adapter 20 communicates with the other components of computer system / server 12 via bus 18. It should be understood that although not shown, other hardware and / or software components could be used in conjunction with computer system / server 12. Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. In various embodiments, a learning system is provided. In some embodiments, a feature vector is provided to a learning system. Based on the input features, the learning system generatesone or more outputs. In some embodiments, the output of the learning system is a feature vector. In some embodiments, the learning system comprises a SVM. In other embodiments, the learning system comprises an artificial neural network. In some embodiments, the learning system is pre- trained using training data. In some embodiments training data is retrospective data. In some embodiments, the retrospective data is stored in a data store. In some embodiments, the learning system may be additionally trained through manual curation of previously generated outputs. In some embodiments, the learning system, is a trained classifier. In some embodiments, the trained classifier is a random decision forest. However, it will be appreciated that a variety of other classifiers are suitable for use according to the present disclosure, including linear classifiers, support vector machines (SVM), or neural networks such as recurrent neural networks (RNN). Suitable artificial neural networks include but are not limited to a feedforward neural network, a radial basis function network, a self-organizing map, learning vector quantization, a recurrent neural network, a Hopfield network, a Boltzmann machine, an echo state network, long short term memory, a bi-directional recurrent neural network, a hierarchical recurrent neural network, a stochastic neural network, a modular neural network, an associative neural network, a deep neural network, a deep belief network, a convolutional neural networks, a convolutional deep belief network, a large memory storage and retrieval neural network, a deep Boltzmann machine, a deep stacking network, a tensor deep stacking network, a spike and slab restricted Boltzmann machine, a compound hierarchical-deep model, a deep coding network, a multilayer kernel machine, or a deep Q-network. The present disclosure may be embodied as a system, a method, and / or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure. The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes 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), a static random access memory (SRAM), a portable compactdisc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device. Computer readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-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 Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readableprogram instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure. Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (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 readable program instructions. These computer readable 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 data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks. The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks. The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchartillustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. Numbered Embodiments 1. A method for determining age (e.g., biological, epigenetic or chronological age) of a subject or a biological sample from a subject, comprising: measuring methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample from a subject, and comparing the measured methylation status or level to methylation status or level of the same set of CpG methylation markers from an age-correlated reference biological sample, or an age- correlated reference biological samples from a population of subjects, thereby determining the age based on said methylation status or level. 2. The method of embodiment 1, wherein the at least one retroelement is a Human Endogenous RetroVirus (HERV) element and / or Long Interspersed Nuclear Element (LINE). 3. The method of embodiment 1 or 2, wherein the at least one retroelement is a HERV element, wherein the HERV element is 1, 5, 10, 15, 20 or more, or each, of the HERV elements selected from the HERV elements described herein or listed in any one of Tables 1-10. 4. The method of any one of embodiments 1-3, wherein the at least one retroelement is HERV- K. 5. The method of any one of embodiments 1-4, wherein the at least one retroelement is a LINE, wherein the LINE is 1, 5, 10, 15, 20 or more, or each, of the LINE elements selected from the LINE elements described herein or listed in any one of Tables 1-10. 6. The method of any one of embodiments 1-5, wherein the at least one retroelement is LINE-1. 7. The method of any one of embodiments 1-6, wherein the at least one retroelement is any 1, 5, 10, 15, 20 or more of the retroelements described herein or listed in any one of Tables 1-10. 8. The method of any one of embodiments 1-7, comprising measuring the methylation status or level at any 25, 50, 75, 100 or more, or each, of the CG loci designations of any one of Tables 1-10. 9. The method of any one of embodiments 1-8, wherein the CpG methylation markers comprise at least 1, 5, 10, 15, 20 or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123,cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. 10. The method of any one of embodiments 1-9, wherein the CpG methylation markers comprise at least 1, 5, 10, 15 or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. 11. The method of any one of embodiments 1-10, wherein the determining of biological age is performed using a computer-readable medium and comprising applying a statistical prediction algorithm to the measured CpG methylation marker levels. 12. The method of any one of embodiments 1-11, wherein the measuring comprises receiving a sequence of inputs corresponding to methylation status or level at a plurality of CpG loci, and applying a pretrained model on the sequence of inputs to predict the biological age. 13. The method of any one of embodiments 1-12, wherein the biological sample is a tissue or bodily fluid. 14. The method of any one of embodiments 1-12, wherein the biological sample is blood or peripheral blood cells (PBMCs). 15. The method of any one of embodiments 1-12, wherein the biological sample is an immune cell or population of immune cells. 16. The method of any one of embodiments 1-12, wherein the biological sample is a stem cell or a population of stem cells, optionally wherein the stem cell is an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC). 17. The method of any one of embodiments 1-12, wherein the biological sample is a somatic cell or a population of somatic cells, optionally wherein the somatic cell is a fibroblast, a keratinocyte, a dermal cell, or an epithelial cell. 18. The method of any one of embodiments 1-17, wherein the subject is a mammal, optionally wherein the subject is a human. 19. The method of any one of embodiments 1-17, wherein the DNA (e.g., genomic or nuclear DNA) is isolated from the biological sample prior to the measuring step. 20. The method of any one of embodiments 1-17, wherein the biological sample is obtained from the subject prior to the measuring step (and optionally prior to the step of isolating the DNA from the biological sample).21. The method of any one of claims 1-20, wherein the measuring is performed using any of the epigenetic clocks described herein, optionally any one of the epigenetic clocks described in Tables 1-9. 22. A computer-readable medium comprising computer-readable instructions that, when executed by a computer, causes the computer to perform operations comprising: a) receiving information corresponding to methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample from a subject; and b) determining the age (e.g., biological, epigenetic, or chronological age) of the biological sample by comparing the received methylation status or level to methylation status or level of the same set of CpG methylation markers from an age-correlated reference biological sample, or an age- correlated reference biological samples from a population of subjects, and by applying a statistical prediction algorithm to the measured methylation status or level. 23. The computer-readable medium of embodiment 22, wherein the at least one retroelement is a Human Endogenous RetroVirus (HERV) element and / or Long Interspersed Nuclear Element (LINE). 24. The computer-readable medium of embodiment 22 or 23, wherein the at least one retroelement is a HERV element, wherein the HERV element is 1, 5, 10, 15, 20 or more, or each, of the HERV elements selected from the HERV elements described herein or listed in any one of Tables 1-10. 25. The computer-readable medium of any one of embodiments 22-24, wherein the at least one retroelement is HERV-K. 26. The computer-readable medium of any one of embodiments 22-25, wherein the at least one retroelement is a LINE, wherein the LINE is 1, 5, 10, 15, 20 or more, or each, of the LINE elements selected from the LINE elements described herein or listed in any one of Tables 1-10. 27. The computer-readable medium of any one of embodiments 22-26, wherein the at least one retroelement is LINE-1. 28. The computer-readable medium of embodiments 22-27, wherein the at least one retroelement is any 1, 5, 10, 15, 20 or more of the retroelements described herein or listed in any one of Tables 1- 10.29. The computer-readable medium of any one of embodiments 22-28, comprising receiving information corresponding to methylation status or level at any 25, 50, 75, 100 or more, or each, of the CG loci designations of any one of Tables 1-10. 30. The computer-readable medium of any one of embodiments 22-29, wherein the CpG methylation markers comprise at least 1, 5, 10, 15, 20 or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. 31. The computer-readable medium of any one of embodiments 22-30, wherein the CpG methylation markers comprise at least 1, 5, 10, 15 or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. 32. The computer-readable medium of any one of embodiments 22-31, wherein the biological sample is a tissue or bodily fluid. 33. The computer-readable medium of any one of embodiments 22-31, wherein the biological sample is blood or peripheral blood cells (PBMCs). 34. The computer-readable medium of any one of embodiments 22-31, wherein the biological sample is an immune cell or population of immune cells. 35. The computer-readable medium of any one of embodiments 22-31, wherein the biological sample is a stem cell or a population of stem cells, optionally wherein the stem cell is an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC). 36. The computer-readable medium of any one of embodiments 22-31, wherein the biological sample is a somatic cell or a population of somatic cells, optionally wherein the somatic cell is a fibroblast, a keratinocyte, a dermal cell, or an epithelial cell. 37. The computer-readable medium of any one of embodiments 22-31, wherein the subject is a mammal, optionally wherein the subject is a human. 38. The computer-readable medium of any one of embodiments 22-37, wherein the received information corresponds to any of the epigenetic clocks described herein, optionally any one of the epigenetic clocks described in Tables 1-10.39. The method of any one of embodiments 1-21 or the computer-readable medium of any one of embodiments 22-38, wherein the biological sample is a cell or tissue that has been subjected to rejuvenation by transient expression of one or more reprogramming factors, optionally wherein the cell or tissue is a fibroblast or population of fibroblasts (e.g., dermal fibroblasts), and optionally wherein the reprogramming factors are any one, two, three or more (or all) selected from: Oct4, Sox2, Klf4, and c-Myc. 40. A method for screening for an agent capable of reducing biological or epigenetic age of a subject or a biological sample from a subject, comprising: (i) determining original biological or epigenetic age of a subject or biological sample of a subject in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38, (ii) administering an agent to the subject (e.g., an effective amount of an agent in one or more doses), (iii) determining post-treatment biological or epigenetic age of the subject or biological sample of a subject as described herein in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38; wherein if the post-treatment biological or epigenetic age is lower than the original age, the method further comprises selecting the agent as capable of reducing biological or epigenetic age. 41. A method for reducing biological or epigenetic age of a subject or a biological sample from a subject, comprising: (i) determining original biological or epigenetic age of a subject or biological sample of a subject in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38, (ii) administering an agent to the subject (e.g., an effective amount of an agent in one or more doses), (iii) determining post-treatment biological or epigenetic age of the subject or biological sample of a subject as described herein in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38; wherein if the post-treatment biological or epigenetic age is lower than the original biological or epigenetic age, the method further comprises administering the agent to the subject for reducing biological or epigenetic age. 42. A method for reducing biological or epigenetic age of a subject or a biological sample from a subject, or a method for preventing or treating premature aging, comprising:(i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38, and if the biological or epigenetic age is higher than the actual or chronological age of the subject, (ii) administering an agent to the subject for reducing biological or epigenetic age (e.g., an effective amount of an agent in one or more doses). 43. A method for treating or preventing an age-related disease or condition in a subject, comprising: (i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38, and if the biological or epigenetic age is higher than the actual or chronological age of the subject, (ii) administering an agent to the subject for reducing biological or epigenetic age and / or an agent for treating or preventing the age-related disease or condition (e.g., an effective amount of an agent in one or more doses). 44. A method for treating or preventing inflammaging in a subject, comprising: (i) determining biological or epigenetic age of a subject or biological sample of a subject in accordance with the methods of any one of embodiments 1-21 or using the computer-readable medium of any one of embodiments 22-38, and if the biological or epigenetic age is higher than the actual or chronological age of the subject, (ii) administering to an agent to the subject for treating or preventing inflammaging (e.g., an effective amount of an agent in one or more doses). 45. The method of any one of embodiments 41-44, wherein the agent is a DNA methyltransferase inhibitor. 46. The method of any one of embodiments 41-44, wherein the agent is an anti-retroviral agent. 47. A method for reducing biological or epigenetic age of a subject or a cell (e.g., population of cells) or tissue from a subject, or a method for preventing or treating premature aging, comprising: administering an antiretroviral agent to the subject (e.g., an effective amount of an agent in one or more doses). 48. A method for treating or preventing an age-related disease or condition in a subject, comprising administering an antiretroviral agent to the subject (e.g., an effective amount of an agent in one or more doses).49. The method of any one of embodiments 46- 48, wherein the antiretroviral agent is an anti- HIV agent (e.g., an anti-HIV-1 agent). 50. The method of any one of embodiments 46-48, wherein the antiretroviral agent inhibits activity, production or infection of a retrovirus, optionally the retrovirus is HERV (e.g., HERV-K). 51. The method of any one of embodiments 46-50, wherein the antiretroviral agent is a reverse transcriptase (RT) inhibitor or an integrase inhibitor of a retrovirus, optionally the retrovirus is HERV (e.g., HERV-K). 52. The method of any one of embodiments 46-51, wherein the antiretroviral agent is Abacavir, Zidovudine or Raltegravir. 53. The method of any one of embodiments 46-52, wherein the subject does not have a retroviral (e.g., HIV) infection. 54. The method of any one of embodiments 41-53, wherein the agent is administered for at least or more than 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months, 1 year, 1.5 years, 2 years or longer. 55. A method for determining methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample from a subject, isolating DNA (e.g., genomic or nuclear) from the biological sample. measuring methylation status or level of a set of CpG methylation markers located in at least one retroelement in the isolated DNA, and comparing said methylation status or level to methylation status or level of the same set of CpG methylation markers from an age-correlated reference biological sample, or an age-correlated reference biological samples from a population of subjects, thereby determining the biological age of the biological sample form the subject. 56. The method of embodiment 55, wherein the at least one retroelement is a Human Endogenous RetroVirus (HERV) element and / or Long Interspersed Nuclear Element (LINE). 57. The method of embodiment 55 or 56, wherein the at least one retroelement is a HERV element, wherein the HERV element is 1, 5, 10, 15, 20 or more, or each, of the HERV elements selected from the HERV elements described herein or listed in any one of Tables 1-9. 58. The method of any one of embodiments 55-57, wherein the at least one retroelement is HERV-K.59. The method of any one of embodiments 55-58, wherein the at least one retroelement is a LINE, wherein the LINE is 1, 5, 10, 15, 20 or more, or each, of the LINE elements selected from the LINE elements described herein or listed in any one of Tables 1-9. 60. The method of any one of embodiments 55-59, wherein the at least one retroelement is LINE-1. 61. The method of any one of embodiments 55-60, wherein the at least one retroelement is any 1, 5, 10, 15, 20 or more of the retroelements described herein or listed in any one of Tables 1-9. 62. The method of any one of embodiments 55-61, comprising measuring the methylation status or level at any 25, 50, 75, 100 or more, or each, of the CG loci designations of any one of Tables 1-9. 63. The method of any one of embodiments 55-62, wherein the CpG methylation markers comprise at least 1, 5, 10, 15, 20 or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof. 64. The method of any one of embodiments 55-63, wherein the CpG methylation markers comprise at least 1, 5, 10, 15 or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof. 65. The method of any one of embodiments 55-64, wherein the determining of biological age is performed using a computer-readable medium and comprising applying a statistical prediction algorithm to the measured CpG methylation marker levels. 66. The method of any one of embodiments 55-65, wherein the measuring comprises receiving a sequence of inputs corresponding to methylation status or level at a plurality of CpG loci, and applying a pretrained model on the sequence of inputs to predict the biological age. 67. The method of any one of embodiments 55-66, wherein the biological sample is a tissue or bodily fluid. 68. The method of any one of embodiments 55-66, wherein the biological sample is blood or peripheral blood cells (PBMCs). 69. The method of any one of embodiments 55-66, wherein the biological sample is an immune cell or population of immune cells.70. The method of any one of embodiments 55-66, wherein the biological sample is a stem cell or a population of stem cells, optionally wherein the stem cell is an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC). 71. The method of any one of embodiments 55-66, wherein the biological sample is a somatic cell or a population of somatic cells, optionally wherein the somatic cell is a fibroblast, a keratinocyte, a dermal cell, or an epithelial cell. 72. The method of any one of embodiments 55-71, wherein the subject is a mammal, optionally wherein the subject is a human. 73. The method of any one of embodiments 55-72, wherein the biological sample is obtained from the subject prior to the step of isolating the DNA from the biological sample. 74. The method of any one of claims 55-73, wherein the measuring is performed using any of the epigenetic clocks described herein, optionally any one of the epigenetic clocks described in Tables 1-9. 75. An epigenetic clock of Table 1. 76. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 1. 77. An epigenetic clock of Table 2. 78. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 2. 79. An epigenetic clock of Table 3. 80. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 3. 81. An epigenetic clock of Table 4. 82. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 4. 83. An epigenetic clock of Table 5.84. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 5. 85. An epigenetic clock of Table 6. 86. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 6. 87. An epigenetic clock of Table 7. 88. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 7. 89. An epigenetic clock of Table 8. 90. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 8. 91. An epigenetic clock of Table 9. 92. An epigenetic clock comprising measured methylation status or level of at least or more than 50%, 55%, 60%, 65%, 70%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% of the CpG loci provided in Table 9. 93. A means for determining age (biological, epigenetic, or chronological) of a biological sample based on measured methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample. 94. The means of claim 93, wherein the age is biological age or epigenetic age. 95. The means of claim 93, wherein the age is chronological age. 96. An epigenetic clock comprising means for measuring methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample. 97. An epigenetic clock measuring methylation status or level of a set of CpG methylation markers located in at least one retroelement in DNA (e.g., genomic or nuclear DNA) of a biological sample for determining age (biological, epigenetic, or chronological) of the biological sample. 98. An epigenetic clock comprising a set of CpG methylation markers located in at least one retroelement, optionally wherein the retroelement is at least one HERV element and / or at least oneLINE-1, and optionally wherein the epigenetic clock provides a regression coefficient for each CpG marker in the set as described herein. EXAMPLES Epigenetic clocks are highly accurate biological markers of aging based on patterns of DNA methylation at specific regions of the human genome. They offer a way to measure biological age, which can be distinct from chronological age (Bell et al., 2019; Bocklandt et al., 2011; Hannum et al., 2013; Horvath, 2013; Horvath & Raj, 2018). However, current first-, second-, and third- generation epigenetic clocks have been constructed from underlying DNA methylation features that have not focused on retroelements (Belsky et al., 2022; Hannum et al., 2013; Horvath, 2013; Levine et al., 2018; Lu, Quach, et al., 2019; Ying et al., 2024). Yet, context-dependent DNA methylation changes have been shown to occur with age at repetitive DNA sequences, introns, and intergenic regions of the genome, which are enriched in retroelements (Jones et al., 2015). The reactivation of certain HERVs and LINEs have been observed to increase with age (De Cecco et al., 2019; Liu et al., 2023; Zhang et al., 2023); however, the potential of using the DNA methylation states of specific HERVs and LINEs in epigenetic clocks for estimating age remained unclear. The examples presented below show that the DNA methylation states of specific HERVs and LINEs in epigenetic clocks can be used for estimating age. Example 1: Development of epigenetic clocks based on HERVs and LINEs: Retroelement-Age. Analysis of MethylationEPIC data relies on a reference annotation of CpGs typically utilizing Illumina®’s default annotation that does not identify whether a CpG is located within a locus-specific HERV or LINE-1 element23. Moreover, manually curated locus-specific HERV CpGs are not available in enhanced annotations of the MethylationEPIC24,25data. Hence, a manually curated locus-specific HERV and LINE annotation including 60 HERV families22and 13,545 loci derived from L1Base (Bendall et al.2019) was leveraged to annotate probes included on the Illumina®Infinium®MethylationEPIC (EPIC) v1.0 array. Telescope annotates HERVs based on a prototypical transcriptional unit containing an internal protein-coding region flanked by LTR regulatory regions. Utilizing the Telescope database, this annotation approach identified that 10,917 probes (1.26% of EPIC v.1.0) assessed DNA methylation at a CpG within a HERV transcriptional unit or within an active LINE element. A composite retroelement epigenetic clock (Retroelement- Age) was developed by considering both the DNA methylation states of HERVs and LINEs thatpredicted chronological age. A dataset of blood DNAm EPICv.1.0 array data from 12,670 people (40.89% female) with chronological ages ranging from 12 to 100 years old was leveraged. The database was quality controlled, normalized, and filtered to 5,436 CpGs based on the HERV annotation described herein. The dataset was preprocessed by randomly splitting it into an 80% training and a 20% test data set. A generalized linear elastic net model was fit with 10-fold cross- validation using regression tool Glmnet on the 80% training dataset of 10,138 samples (FIG.1A). Retroelement-Age V1 consisted of 1,317 CpG sites (Table 1) and was highly predictive of chronological age in both training (Pearson's r=0.95, median absolute error (MAE) = 2.57) and test datasets (Pearson's r=0.89, MAE = 3.81). Since the majority of existing DNA methylation data is on the older 450K array, a compatible Retroelement-Age-450 clock was also constructed from retroelement CpGs common to the 450K and EPIC v.1.0 arrays consisting of 1,317 CpG sites predictive of chronological age in both training (Pearson's r=0.90, MAE = 3.67) and test datasets (Pearson's r=0.88, MAE = 4.02) (Table 2). Given the development of a new HumanMethylation EPIC v.2.0 DNA methylation array likely to be utilized in future geroscience research (Noguera- Castells et al.2023), enhancement of the utility of a composite Retroelement-Clock was sought for compatibility with both EPIC v.1.0 and v.2.0 data. Hence, the manually curated annotation of Telescope-based EPIC v.1.0 CpGs was extended to overlapping probes on the EPIC v2.0 platform. The retroelement CpGs for EPIC v2.0 were also expanded to include LTR and LINE elements identified by RepeatMasker. Utilizing this expanded annotation of compatible retroelement CpGs for MethylationEPIC v2.0, an EPIC v.1.0 and v.2.0 compatible retroelement-based epigenetic clock (Retroelement-Age V2) was constructed based on a generalized linear elastic net model fit with 10- fold cross-validation on an 80% training dataset of 10,138 samples (FIG.1A). The composite Retroelement-Age V2 epigenetic clock consisted of 1,378 CpG sites (Table 3) and was the most predictive of chronological age (FIGS.1D-E) compared to prior retroelement-based clocks in both training (Pearson's r=0.97, MAE = 1.87) and test datasets (Pearson's r=0.96, MAE = 2.08). This was likely due to MethylationEPIC v2.0 containing more reliable probes(Noguera-Castells et al.2023; Kaur et al.2023). Example 2: Retroelement-Age clocks are constructed from unique CpGs when compared to existing epigenetic clocks. When budling an epigenetic clock utilizing machine learning algorithms of large DNA methylation datasets, it is not typically known a priori which CpG sites are most informative forpredicting age or other outcomes of interest13,42. Hence, deconstructing epigenetic clocks and understanding the underlying biological mechanisms of these clocks has emerged as a major focus43. The approach disclosed herein relied on only utilizing manually annotated HERV and LINE CpGs to construct an epigenetic clock based on the DNA methylation states of retroelements in the human genome. Hence, the retroelement epigenetic clocks provide insight into the role of changing DNA methylation patterns and potential HERV transcriptional activation and active LINE-1 elements in the biology of aging. To compare whether any of the CpGs utilized in the construction of the disclosed Retroelement-Age clocks predicting chronological age overlapped with CpGs utilized in the construction of 12 existing first, second, and third generation epigenetic clocks that were not constructed based on retroelement CpGs. First, the intersection of the Retroelement-Age V1 and Retroelement-Age V2 clocks provided herein were examined with each of the Horvath multi-tissue predictor DNAmAge based on 353 CpG sites11, the Horvath skin-and-blood clock based on 391 CpG sites44, Levine’s DNAmPhenoAge clock based on 513 CpG sites45, Hannum’s clock based on 71 CpG sites12, Lu’s telomere length predictor based on 140 CpGs46, DNA methylation based mortality risk assessment GrimAge based on 1,030 CpGs47, DunedinPace of Aging based on 173 CpGs48, EpiTOC / EpiTOC2 mitotic clocks based on 354 and 151 CpGs49, and causality-enriched epigenetic clocks AdaptAge, DamAge, and CausAge (Ying et al. 2024) were examined. All the CpGs utilized in the Retroelement-Age V1 clock were unique and did not overlap with any existing epigenetic clocks suggesting that retroelement-based epigenetic clocks may capture novel biological DNA methylation features of aging not previously recognized (FIG.1F). As shown by prior studies (Liu et al.2020) a subset of shared CpGs were observed among Horvath’s first-generation, second- generation clocks, and DunedInPace of aging, suggesting the construction of these clocks captured some similar DNA methylation features of aging (FIG.1F). Notably, it was observed that the Retroelement-Age V2 clock overlapped with 9 CpGs (cg06672696, cg07286682, cg08822136, cg16936289, cg16810279, cg22277154, cg13261390, cg22277154, and cg24251135) used in AdaptAge CausAge, and DamAge causality-enriched epigenetic clocks recently developed using Mendelian randomization (Ying et al.2024). This observation suggests some of the signal from the Retroelement-Age V2 clock may include sites that contribute and / or protect against aging. Example 3: Retroelement-age clocks are enriched in quiescent, active, and poised enhancer regions. The overlap of CpGs in the retroelement clocks in specific regions of the epigenome based on consensus transcription factor bindings sites (TFBSs), chromatin states, and consensus histonemodifications was examined. Consensus ChromHMM derived from 833 ENCODE ChromHMM calls from ENCODE version 2 were used23. Retroelement-Age was enriched in KRAB zinc finger protein ZNF654, ATF7IP, ZBTB2, MAFG, quiescent / heterochromatin states, and regions containing H3K9me2 and H3K9me3 (FIGS.2A-C). H3K9me3 deposition has been shown to be mediated by ATF7IP and SETDB1, which also are required for silencing retroelements (Hu et al. 2021). KRAB-containing zinc finger proteins are known as major regulators of retroelement activity53. ZBTB2-binding dynamics in vivo are sensitive to differential DNA methylation and has been shown to repress the retrovirus HIV-154,55, and the bidirectional regulator of transcription MAFG56. Retroelement-Age V2 CpGs were enriched in MAFG and interaction partner NFE2L157,58, ZNF317, and ZNF654 (FIG.2D). ZNF317 has been recently suggested to primarily target eutheria- specific ERVs59. In addition to being enriched in quiescent chromatin states, Retroelement-Age V2 was enriched in active transcription start site (TSS)-proximal promoter states, repressed Polycomb states, and enhancer states (FIG.2E). Polycomb group proteins are hypothesized as an evolutionarily conserved mechanism to silence transposable elements (Déléris et al.2021). Retroelement-Age V2 CpGs were enriched in H3K4me1 regions, which is both repressive and marker of poised and active enhancers60,61(FIG.2F). These findings suggest that the retroelement- age clocks provided herein contain CpGs involved in the regulation and silencing of retroelements. Example 4: Independent validation and reliability of blood-based retroelement-based epigenetic clocks. Evaluation of the performance and validation of the retroelement-based epigenetic clocks was sought in completely independent and external DNA methylation datasets. First, a DNA methylation EPIC v.1.0 dataset (GSE184269) of blood and peripheral blood mononuclear cells (PBMC) from healthy individuals interspersed over a wide chronological age range (22-83 years) from the NIH Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing (GESTALT) study (Roy et al.2023) was utilized to examine the relationships between chronological age, predicted epigenetic age based on the new retroelement clocks (Retroelement-Age V1, Retroelement-Age V2), and predicted epigenetic ages from existing epigenetic clocks. Despite being constructed from largely unique CpGs compared to previous epigenetic clocks, it was found that Retroelement-Age V1 (Pearson’s r=0.99), and Retroelement-Age V2 (Pearson’s r=0.99) significantly associated with chronological age in blood to a similar or better degree than existing epigenetic clock algorithms (e.g., Horvath1 (r=0.96), Hannum (r=0.98), and PhenoAge (r=0.98), seeFIG.3A. It was also observed that this relationship was similar or better than new principal component-based versions of epigenetic clocks62including PCHorvath1 (r=0.97), PCHorvath2 (r=0.95), PCHannum (r=0.97), PCPhenoAge (r=0.96), and PCGrimAge (r=0.98) (FIG.3A). Next, the analysis was extended to DNA methylation data of peripheral blood mononuclear cells (PBMC) from the GESTALT study dataset and similar robust relationships were observed between Retroelement-Age V1 (Pearson's r=0.99) and Retroelement-Age V2 (Pearson's r=0.98) with chronological age (FIG. 3B). Second, Retroelement-Age V1 and V2 were evaluated in another external DNA methylation EPIC v1.0 dataset generated from whole blood from Genotype-Tissue Expression (GTEx) for participants ranging in age from 22-70 years old (Oliva et al.2023) and observed that Retroelement-Age V1 (Pearson's r=0.96) and Retroelement-Age V2 (Pearson's r=0.92) significantly associated with chronological age (FIG.3C). Additionally, Retroelement-Age V1 and V2 were assessed in a blood DNA methylation EPIC v1.0 dataset of 790 people generated by the Alzheimer's Disease Neuroimaging Initiative study and found Retroelement-Age V1 (Pearson's r=0.72) and Retroelement-Age V2 (Pearson's r=0.83) significantly associated with chronological age (FIGS. 7A-B). Next, the test-retest reliability of RepeatAge V2 using 30 replicate blood-sample MethylationEPIC v1.0 samples was evaluated based on the intraclass correlation coefficient (ICC) using a linear mixed-effects model. It was found that reliability was high with an ICC of 0.996 (FIG. 8). Example 5: Effect of HIV-1 infection and antiretroviral therapy on retroelement-based epigenetic clocks Prior studies have reported accelerated epigenetic aging in people living with infectious diseases, such as HIV-165–68. Thus, it was tested whether the retroelement clocks of the invention would capture accelerated epigenetic aging in a DNA methylation dataset from a cohort of 185 people living with HIV-1 (with pre-ART and post-ART longitudinal samples) and 44 demographically matched healthy controls (GSE217633)67. Evidence of epigenetic age acceleration in people living with HIV-1 pre-ART was replicated (FIGS. 4A-B). A significant epigenetic age acceleration of mean average 2.8 years in people living with HIV-1 compared to epigenetic age difference from chronological age of -0.19 years in healthy controls was found (FIG.4B). Prior work has shown several FDA approved antiretroviral drugs can effectively inhibit HERV-K (Tyagi et al.2017). To investigate whether antiretroviral therapy utilized to treat HIV-1 would reduce retroelement-based epigenetic age, a significant reduction in epigenetic age was found following 96weeks of antiretroviral therapy after comparing longitudinal samples of people living with HIV-1 at pre- and post-ART timepoints (FIG.4C). Example 6: Application to human cortical organoids during long term maturation. Prior work has shown the utility in epigenetic clocks in predicting culture age of human cortical organoids maintained in long-term cultures up to 694 days69. Retroelement-Age V2 was applied to MethylationEPIC DNA methylation data (GSE150122) across 13 timepoints generated for six human induced pluripotent stem cell (hiPSC) lines obtained from five individuals (four male and one female) that were differentiated to human cortical spheroids (hCS)70. It was observed that there was a significant relationship (r= 0.80, P<0.0001) between the in vitro culture age of hCS and predicted Retroelement-Age V2 (FIG.10). These findings are consistent with application of Horvath’s epigenetic clock69and suggest a dynamic relationship between human endogenous retroviruses and induced pluripotent stem cells71. Example 7: Cell-type specific transient reprogramming-induced rejuvenation strategies utilizing Yamanaka factors reverse Retroelement-based epigenetic clocks Transient reprogramming has emerged as a strategy to epigenetically rejuvenate cells as an anti-aging strategy43,72. A key outcome measure utilized to assess epigenetic rejuvenation through transient reprogramming has been epigenetic clocks. However, findings suggests some clocks may not have utility in assessing epigenetic rejuvenation achieved by transient reprogramming43,72. Based on prior work showing HERVs are a key mechanism involved in human iPSC generation and re- establishment of differentiation potential of induced pluripotent stem cells (iPSCs)71, examination of whether HERV-Age would inform transient reprogramming-induced rejuvenation strategies was pursued. A published epigenetic rejuvenation DNA methylation dataset GSE16517972was leveraged and epigenetic ages calculated for control and transiently reprogrammed fibroblasts at Day 0, 10, 13, 15, and 17. Retroelement-Age was significantly reversed in reprogrammed cells compared to control cells at Day 10 and Day 17 of the dataset (FIG.5A). Retroelement-Age V2 was significantly reversed in reprogrammed cells compared to control cells at Day 10, 13, 15, and 17 (FIG.5B). Next, the retroelement-age clocks were applied to GSE142439 that included transient expression of reprogramming factors in fibroblasts and endothelial cells from aged and young human participants (Sarkar et al.2020). It was found that Retroelement-Age and Retroelement-Age V2 was significantly reversed in fibroblasts (FIGS.5C-D). However, according to the retroelement-based epigenetic clocks a rejuvenation effect in endothelial cells was not observed suggesting the importance ofconsidering cell-type specific responses in transient reprogramming-induced rejuvenation strategies (FIGS. 5E-F). These findings suggest retroelement-based epigenetic clocks as an additional outcome measure to consider in ongoing transient reprogramming efforts and anti-aging strategies. Whether a new transient-native-treatment (TNT) reprogramming strategy shown to correct aberrant transposable element expression impacts retroelement-based epigenetic clocks remains unclear73. Example 8: Immune Transcriptome Telescope-Age Evaluation of whether age-related RNA expression of locus specific retroelements in the immune system overlapped with DNA methylation loci identified in the retroelement clocks was sought. Transcriptomic age signatures have been shown to offer a complementary predictor of biological aging74,75. Evidence suggest that age-related repetitive element transcript accumulation and changes in DNA methylation states may play a role in inflammaging (Smith et al. 2024) and transcriptomic age signatures have been shown to offer a complementary predictor of biological aging (Bulteau & Francesconi 2022). Thus, the DNA methylation retroelement-based epigenetic clocks were employed to evaluate whether age-related RNA expression of locus-specific retroelements in the immune system could be utilized to develop a transcriptome retroelement-based predictor of chronological age. The Telescope computational pipeline22was utilized to estimate HERV and LINE-1 element expression resolved to specific genomic locations in an independent RNA-Seq dataset of blood from 157 human donors spanning chronological ages 20-74 year (GEO: GSE193141) (FIG. 9A). Using a generalized linear elastic net model fit with 10-fold cross- validation on an 80% training and 20% test dataset using both HERV and LINEs expression in blood, it was found that the expression levels of 95 retroelements (Table 4) were predictive of chronological age in both training (Pearson's r=0.98, MAE = 4.22) and test datasets (Pearson's, MAE = 9.99)r=0.62 testing) (FIGS.9B-C). Among the elements was observed 41 LINEs, supporting the hypothesis regarding the resurrection of particular LINEs in the aging process9,10. Integrating loci from the retroelement-based epigenetic clocks, overlap of age-associated DNA methylation and RNA changes in ERVLE_1q25.3d, L1FLnI_11p13w, and L1FLnI_6q12d were found (FIGS. 9A- C). These findings suggest that distinct retroelement transcriptome profiles can be utilized as a predictor of chronological age and that a subset of the loci identified in retroelement-based epigenetic DNA methylation clocks may relate to transcription of specific retroelements.Example 9: Evidence of Human Multi-Tissue Retroelement clock To examine whether DNA methylation states of retroelements could be utilized beyond the immune system to capture human aging, a publicly available Genotype-Tissue Expression (GTEx) DNAm data for 987 human samples from nine tissue types spanning breast mammary tissue, muscle skeletal, lung, ovary, kidney, testis, prostate, colon, and whole blood were used76. To test whether there was evidence of a human multi-tissue retroelement clock, an expanded list of 74,577 retroelement CpGs based on the HERV and LINE-1 annotations was utilized and a new composite retroelement-based epigenetic clock (Retroelement-TissueAge) was constructed based on a generalized linear elastic net model fit with 10-fold cross-validation on an 80% training dataset and 20% validation dataset. (FIG.6A). Since the GTEx DNA methylation dataset consisted of multiple tissues from the same donor, steps were taken to minimize data leakage and ensure each donor was only in one of the sets (either training or validation). The Multi-Tissue Retroelement epigenetic clock consisted of 717 CpG sites (Table 5) and was highly predictive of chronological age in both training (r=0.99, MAE = 0.76) and test datasets (r=0.75, MAE = 5.07) (FIGS.6B-C). Example 10: Evidence of Pan-Mammalian Species Multi-Tissue Retroelement clock Evidence of an evolutionarily conserved universal retroelement-based epigenetic clock predicting chronological age was sought. Recent data from the international Mammalian Methylation consortium demonstrated the development of universal pan-mammalian clocks using DNA methylation data from over 11,000 samples spanning 59 tissue types and across 185 mammalian species using the HorvathMammalMethylChip40 (Mammal40) array, a mammalian array that targets over 36,000 conserved CpGs across mammalian species77,78. However, the specific CpGs utilized to construct these pan-mammalian clocks did not focus on CpGs located within retroelements or other transposable elements77. Hence, to evaluate whether the Mammal40 platform contained probes overlapping with retrotransposable elements, RepeatMasker annotated probes in the Mammal40 platform were used and 563 loci were identified in the class of DNA, LINE, LTR, SINE, and Unknown. Next, the pan-mammalian DNA methylation dataset consisting of 12,146 samples was utilized, and relative ages calculated as log transformed chronological ages with an offset of 2 years was added as utilized in the construction of the original development of universal pan-mammalian clocks (Lu et al.2023). Utilizing all 563 DNA, LINE, LTR, SINE, and unknown CpGs identified by RepeatMasker annotated probes, a pan-mammalian species retroelement clock was developed consisting of 398 CpG sites (Table 6) that was highly predictive of chronological agein both training (r=0.94) and test datasets (r=0.94). Next, only the 211 CpGs in LINEs and LTR were used and it was found that a highly predictive pan-mammalian species clock could be constructed based on only 201 CpGs in HERVs and LINEs (Table 7) covered in the dataset (FIGS.6D-F). The intersection of the CpGs utilized in the pan-mammalian TransAge and RetroAge clocks were examined with the published 3 universal pan-mammalian clocks (Lu et al.2023). Notably, there was no overlap with CpGs utilized to construct the currently available pan-mammalian epigenetic clocks (FIG.6G), suggesting additional insights into the evolutionary components of aging-associated processes not previously detected. Discussion based on the data in the Examples 1-10: The development of a myriad of epigenetic clocks based on distinct DNA methylation patterns have emerged as promising candidate biomarkers warranting further validation to quantify chronological and biological age for the geroscience field (Moqri et al. 2024). A subset of epigenetic clocks have also been shown to have utility in predicting aging-related health outcomes and proposed as complementary outcome measures for geroscience clinical trials (Moqri et al.2023). While epigenetic clock-based measures have been linked to specific hallmarks of aging79, the role of epigenetic clocks in mechanisms of aging linked to retroelements remains unclear. Disclosed herein is the potential of DNA methylation states in HERVs and LINEs as highly accurate epigenetic clocks predicting chronological age, supporting the hypothesis that a subset of specific retroelements in the genome may be involved in aging. Using only HERV and LINE-1 DNA methylation states, highly accurate retroelement-based epigenetic clocks that minimally overlapped with preexisting first and second-generation epigenetic clocks were developed by the inventors. Evidence of clock CpGs located in HERV elements previously linked to aging and immune function, such as HERV-K and MER41 was found. Clock CpGs were also found by the inventors in novel retroelements not previously linked to aging, suggesting a broader role of locus-specific retroelements in biological aging. Applying these retroelement-based epigenetic clocks to DNA methylation datasets, it was found by the inventors that retroelement clocks are reversed during transient epigenetic reprogramming, accelerated in people living with HIV, and responsive to antiretroviral therapy. Finally, it was demonstrated by the inventors that retroelement clocks extend to diverse human tissues and across mammalian species using DNA methylation from GTEx (Oliva et al. 2023) and from the Mammalian Methylation Consortium (Lu et al.2023). Together, these findings support a hypothesis of dysregulation of endogenous retroelements as a contributor to the biological hallmarksof aging and suggest that therapeutic interventions modifying the epigenetic states of specific retroelements in the human genome could have beneficial effects against a root cause of aging and disease. Additionally, these studies suggest that retroelement-based epigenetic clocks are evolutionarily conserved throughout mammals, and that further study of which throughout post- speciation events could provide valuable insight into the evolutionary components of aging- associated processes and the impact of paleovirology on each species’ lifespan. Various studies have provided evidence suggesting a correlation between aging, chronic diseases, and the reactivation of specific retroelements, primarily LINEs and HERV-K -derived retrovirus like particles (RVLPs). Prior work has shown that the transcriptional de-repression of LINEs in immune cells triggers interferon production to contribute to inflammaging10,76. Recent findings have suggested a key role of the endogenous retrovirus HERV-K (HML-2) activation in cellular senescence and tissue aging9. Higher expression of transposable elements and HERVs has been shown to trigger renal fibro-inflammation and found to be associated with diseased human kidneys (Dhillon et al.2023). Data from Drosophila models of aging have revealed that stimulating retrotransposon activity increases mortality and accelerates a subset of aging phenotypes77. The data provided herein extends these findings and show that DNA methylation changes of locus-specific retroelements may play a key role in immune aging and potentially eliciting biological hallmarks of aging in the immune system. As described herein, it is notable that the set of CpGs that were utilized to construct the retroelement-based epigenetic clocks described herein did not show a significant overlap with pre- existing first and second-generation epigenetic clock algorithms (Horvath 2013; Levine et al.2018; Belsky et al. 2022; Lu, Quach, et al. 2019). Differences between clocks have arisen from the focus on DNA methylation data compatibility, considerations of probe reliability, datasets utilized, and being trained to predict different aging-related variables, such as chronological age, composite biomarkers of aging, mortality risk, or mitotic divisions. The observation of minimal overlap between the retroelement-based clocks of the disclosed invention and prior epigenetic clocks may be due to the retroelement-based clocks being developed on CpGs found on the EPIC platform. However, it was observed that examination of all 4,466 retroelement related CpGs common to the EPIC and 450K arrays did not show high overlap with prior first-, second-, and third-generation epigenetic clocks developed largely on 450K CpGs, suggesting that retroelement-based epigenetic clocks capture previously undetected facets of biological aging to complement current epigenetic clocks. Of particular note was the observation that 7 CpGs (cg16951108, cg19786602, cg11630939,cg08960549, cg08883146, cg24004238, and cg23987336) that were part of the HERV-Age were located at the HML-2_17p13.1 locus. These CpGs related to a 517bp long intergenic non-coding RNA (lincRNA) ENST0000577807 and overlapped with annotated enhancer regions based on ENCODE ChromHMM. The HERV-K (HML-2) family are the most recently integrated HERV family and recently the activation of HERV-K has been linked to aging and cellular senescence79. Additionally, loci including HERVL18_6p22.1c (cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226), HUERSP3_6p21.32 (cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817), and MER41_22q12.3e (cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224) contained 6 CpGs each that were part of HERV-Age. CpGs in HERVL18_6p22.1c were within the ZSCAN31 and ZKSCAN3 genes. ZKCAN3 is a master transcriptional repressor of autophagy80and has been shown to mitigate cellular senescence by stabilizing heterochromatin81. CpGs in HUERSP3_6p21.32 were in an intergenic region of heterochromatin / ZNF genes and repeats. CpGs in MER41_22q12.3e were near an enhancer region of the anti-inflammatory and metabolic regulator C1QTNF6 gene82–86. The MER41 family is an endogenized gammaretrovirus whose LTR has been coopted as an IFNG- inducible binding sites bound by STAT1 and / or IRF1 affecting to promote inflammasome production87. In LINE-1-Age, it was observed that 8 CpGs were each located at the L1FLnl_10q26.3e (cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, and cg25120459) and L1FLnl_1p31.3z (cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634). CpGs in L1FLnl_10q26.3e were downstream of the 3’UTR of the ADGRA1 gene that encodes a G-protein-coupled receptor that has been shown to play an essential role for human pluripotent stem cells and reprogramming towards pluripotency88. CpGs in L1FLnl_1p31.3z were located in an enhancer region of the DEPDC1 gene that has been shown as a novel cell cycle related gene that regulates mitotic progression and has been shown to inhibit aging-related transcription factor FOXO389, 90. L1FLnI_7q22.2a contained 5 CpGs in an enhancer region of the ORC5 gene that is essential for the initiation of DNA replication and belongs to a family of origin recognition complex subunits that when depleted delay aging in budding yeast models91. L1FLnI_18q23l contained 4 CpGs in the NFATC1 gene that regulates activation, proliferation, differentiation, and programmed death of T cells92. L1FLnI_Xp22.2h contained 4 CpGs that were in a enhancer and active transcriptional start region of the TLR7 gene, part of a family of Toll-like receptors that show age-associateddysregulation of toll-like receptor signaling likely contributing to increased morbidity and mortality from infectious diseases in older people93. Epigenetic clocks should be applied to the proper context and appear to capture distinct aspects of aging and associate with different biological hallmarks of aging, environmental exposures, traits, and disease patterns (Oblak et al.2021; Higgins-Chen et al.2021; Horvath & Raj 2018; Liu et al. 2020). It was found that using a limited set of CpGs from the HorvathMammalMethylChip40 (Mammal40) array that only contained 643 loci based on RepeatMasker in the class of DNA, LINE, LTR, SINE, and Unknown, a highly accurate universal mammalian retroelement methylation clock could be constructed based on 220 CpGs. Less than 3% (6 CpGs) overlapped with CpGs utilized to construct the 3 Universal Pan-Mammalian methylation clocks (Lu et al.2023). The additional findings to those reported by Lu et al. suggest an evolutionarily conserved dysregulation of DNA methylation states within retroelements may play a role beyond mammalian development into aging (Garcia-Perez et al.2016). Interestingly, beyond the universal retroelement-based epigenetic clocks, an accurate retroelement methylation clock could be constructed for naked mole-rats, which have long lifespans and reduced transposon-derived sequences (Kim et al.2011). Due to the likely increase in EPIC v.2.0 data for the geroscience field, an EPIC v.1.0 and v.2.0 compatible retroelement-based epigenetic clock (Retroelement-Age V2) was developed by the inventors that contains CpGs covered on Illumina’s MethylationEPIC v2.0 kit. An interesting feature of Retroelement-Age V2 is that none of the CpGs utilized to construct this clock overlapped with 9 existing first and second-generation epigenetic clocks. A significant enrichment of 80 CpGs was found in this clock at CCCTC-binding factor (CTCF) sites of the genome. These findings suggest that DNA methylation changes in retroelements, particularly at CTCF (CCCTC-binding factor) binding sites, may play a role in aging. CTCF is a critical protein involved in the organization of chromatin structure and the regulation of gene expression. It acts as an insulator, helping to define boundaries between chromatin domains and regulate the accessibility of genes to the transcriptional machinery. The disclosure supports prior DNA methylation work showing changes with aging observed in CTCF binding sites (Reynolds et al.2014) and a pan-tissue DNA methylation epigenetic clock based on deep learning that found that the most important CpG sites were proximal to CTCF binding sites (de Lima Camillo et al.2022). Prior epigenetic clocks detect accelerated aging effects related to infection with the exogenous retrovirus HIV-1 (Horvath & Levine 2015; Gross et al. 2016; Shiau et al.2021; Horvath, Stein, et al.2018; Leung et al.2017). People living with HIV-1 are a population who exhibitincreased features of biological aging (Cole et al. 2017; Guaraldi et al.2009; Justice 2010), likely due to infection from the exogenous retrovirus HIV-1, chronic inflammation, antiretroviral therapy, and lifestyle effects (Goulet et al. 2007; Deeks et al. 2013; High et al.2012). Prior work has shown that HIV-1 infection activates certain HERVs including HERV-K. This activation may be an additional factor contributing to accelerated / attenuated aging in people living with HIV related to immune dysfunction, inflammation, and immunosenescence. Prior epigenetic clocks did not provide insights into whether altered DNA methylation states of HERVs or LINEs related to HIV-1 infection. By applying the retroelement-based epigenetic clocks, evidence that suggests an increase in the epigenetic age compared to chronological age related to HIV can be found (using the epigenetic clocks and methodologies described herein), supporting the hypothesis that retroviruses may accelerate biological aging. Additionally, examining longitudinal data from people living with HIV-1 (PLWH) receiving antiretroviral therapy (Esteban-Cantos et al. 2021), it was found by the inventors that antiretroviral therapy treatment significantly reverses HIV-1-related increased retroelement-based epigenetic age. A portion of this effect due to antiretroviral therapy may reflect CD4 T cell immune restoration in people living with HIV. In summary, the findings disclosed herein highlight the use of DNA methylation states of specific retroelements as reliable predictors of chronical and potentially biological aging, complementing existing epigenetic clocks and offering an additional mechanism to consider in epigenetic clock signals. Materials and Methods for Examples 1-10: Discovery cohort The TruDiagnostic Biobank cohort, previously described in (Chen et al. 2023), included 13,109 individuals who took the commercial TruDiagnostic TruAge test and had their DNA methylation data generated from whole blood. The participants were recruited between October 2020 and April 2023 and were predominantly from the United States. MethylationEPIC v1.0 DNA methylation pre-processing and analysis: Peripheral blood samples were collected using a lancet and capillary method and placed in a lysis buffer for DNA extraction. Then, 500 ng of DNA was treated with bisulfite using the EZ DNA Methylation kit from Zymo Research following the manufacturer's instructions. The bisulfite-treated DNA samples were randomly assigned to a well on the Infinium HumanMethylationEPIC BeadChip, which was then amplified, hybridized, stained, washed, and imaged with the IlluminaiScan SQ instrument to obtain raw image intensities. To pre-process the TruDiagnostic methylation data, we used the minfi pipeline (Aryee et al.2014), and low quality samples were identified using the qcfilter() function from the ENmix package(Xu et al. 2016), using default parameters. A total of 12,670 individuals, representing 96.7% of the original samples, passed the QA / QC (p < 0.05) and were deemed to be high quality samples. HERV, LINE-1, and Composite Retroelement Clocks Construction: CpGs covered on the Illumina®Infininium®MethylationEPIC (EPIC) V1 array were filtered using a manually curated locus-specific HERV annotation of 60 HERV families22. Using the Telescope database, we identified 10,917 probes, accounting for 1.26% of the EPIC v1.0 array, that assess DNA methylation at CpG sites located within HERV transcriptional units or active LINEs (Bendall et al.2019). The locus specific HERV and LINE-1 annotations were then used to filter two beta matrixes of CpGs for all 12,670 samples. The caret R package was used to load metadata for chronological age of all 12,670 samples. The composite HERV and LINE-1 filtered beta matrix was then split into 80% training and 20% validation datasets. The glmnet R package was then used to train models for Retroelement-Age V1 clock with a 10-fold validation utilizing an elastic net. Retroelement-Age V2 extended the manually curated annotation of Telescope-based EPIC v.1.0 CpGs to overlapping probes on the EPIC v2.0 platform. The retroelement CpGs for EPIC v2.0 were also expanded to include LTR and LINE elements identified by RepeatMasker. Retroelement CpG datasets utilized for Retroelement-Age V1 and V2 are available at Zenodo at DOI: 10.5281 / zenodo.11099870. Multi-Tissue and Pan-Mammalian Species Retroelement Clocks Construction: Retroelement filtered beta matrixes were for a GTEx dataset of 987 samples (GSE213478) and Pan-Mammalian Species dataset of 12,146 samples (GSE223748) were split into 80% training and 20% validation datasets. Chronological ages and log transformed chronological ages with an offset of 2 years added were used. The glmnet R package was then used to train models for clocks with a 10- fold validation utilizing an elastic net. CpG Feature Enrichment The knowYourCG tool was utilized for examining CpG feature enrichment using Illumina probe IDs and databases associated with certain CpGs available at github.com / zhou- lab / KYCG_knowledgebase_EPIC / tree / main / Studies. Fisher’s exact test was utilized to test CpG enrichments. Enrichment results were visualized using the KYCG_plotDot function in SeSAMe.Public datasets: Data used for validation and testing of retroelement clocks was publicly available via Gene Expression Omnibus (GEO). MethylationEPIC v1.0 array IDATs were downloaded from GEO using the following accession numbers: GSE165179, GSE184269, GSE213478, GSE223748, GSE142439. All GTEx protected data was accessed via the GTEx Portal. Epigenetic Clock and Cell Type Deconvolution: Published epigenetic clocks were calculated according to published methods from processed DNA methylation data. To calculate the principal component-based epigenetic clock for the Horvath multi-tissue clock, Hannum clock, DNAmPhenoAge clock, GrimAge clock, and telomere length we used the custom R script available via GitHub (github.com / MorganLevineLab / PC-Clocks). Non- principal component-based (non-PC) Horvath, Hannum, and DNAmPhenoAge epigenetic metrics were calculated using the methyAge function in the ENMix R package. The pace of aging clock, DunedinPACE, was calculated using the PACEProjector function from the DunedinPACE package available via GitHub (github.com / danbelsky / DunedinPACE). We used a 12 cell immune deconvolution method to estimate cell type proportions(Zheng et al. 2018) Data availability The retroelement DNA methylation data utilized to construct retroelement-based epigenetic clocks can be found on Zenodo at DOI: 10.5281 / zenodo.11099870. Code Availability The code, retroelement-based clock coefficients, and script to calculate retroelement epigenetic ages from any DNA methylation EPIC v.1.0 or 2.0 dataset can be found on Zenodo at DOI: 10.5281 / zenodo.11099870. RETROELEMENT-AGE V1 CODE: # Set file paths to DNA methylation dataset for RetroelementV1 CpGs from 12,670 people age 12- 100 years old file_path <- 'RetroAgeV1MethylationDataset.csv' metadata_path <- 'RetroMetadata.csv' # Read the CSV files using fread data <- fread(file_path, header = TRUE, data.table = FALSE) metadata <- read.csv(metadata_path, header = TRUE)# Set row names to first column and remove it rownames(data) <- data[, 1] data <- data[, -1] # Transpose DNA methylation dataset methylation_data <- t(data) # Extract chronological ages from metadata ages <- metadata$Decimal.Chronological.Age # Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the DNA methylation retroelement dataset into training and validation sets beta_noob <- data beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) #Build Retroelement Clock utilizing glmnet library(doParallel) registerDoParallel(8) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) #Plot the training and test chronological age and retroelement predictions plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min))#Save coefficents tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) write.csv(predictors, file="Retroelement_AgeV1_coefficents.csv", quote=F) sessionInfo() RETROELEMENT-AGE V2 CODE: library(data.table) library(caret) library(glmnet) # Set file paths to DNA methylation dataset for RetroelementV2 CpGs from 12,670 people age 12- 100 years old file_path <- 'RetroAgeV2MethylationDataset.csv' metadata_path <- 'RetroMetadata.csv' # Read the CSV files using fread data <- fread(file_path, header = TRUE, data.table = FALSE) metadata <- read.csv(metadata_path, header = TRUE) # Set row names to first column and remove it rownames(data) <- data[, 1] data <- data[, -1] # Transpose DNA methylation dataset methylation_data <- t(data) # Extract chronological ages from metadata ages <- metadata$Decimal.Chronological.Age # Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the DNA methylation retroelement dataset into training and validation sets beta_noob <- data beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices]train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) #Build Retroelement Clock utilizing glmnet library(doParallel) registerDoParallel(8) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) #Plot the training and test chronological age and retroelement predictions plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) #Save coefficents tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) write.csv(predictors, file="Retroelement_AgeV2_coefficents.csv", quote=F) sessionInfo() HERV AGE CODE: library(data.table) data <- read.csv(' / Users / mcorley / Downloads / HERVs_CpGs.csv', header = T, row.names = 1) head(data, n=10) methylation_data <- t(data) metadata <- read.csv(' / Users / mcorley / Downloads / Metadata.csv', header = T)library("caret") ages <- metadata$Decimal.Chronological.Age # Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the data into training and validation sets beta_noob <- data beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) rm(beta_noob_train) rm(beta_noob_validation) library(glmnet) #Train PC clock. Can test different models using different alpha and lambda parameters (see glmnet documentation) library(doParallel) registerDoParallel(2) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) #Examine full model plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) #Examine sparse modelplot(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit, datMeth_validation, s = cv$lambda.1se)) (best.lambda <- cv$lambda.min) coef(cv, s = "lambda.min") #Save CpG predictors for model tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) #Save reduced CpG predictors for model tmp_coeffs2 <- coef(cv, s = "lambda.1se") predictors2 <- data.frame(name = tmp_coeffs2@Dimnames[[1]][tmp_coeffs2@i + 1], coefficient = tmp_coeffs2@x) write.csv(predictors, file="HERVpredictorCpGs.csv", quote=F) sessionInfo() write.csv(predictors2, file=" HERVpredictorCpGssreduced.csv", quote=F) sessionInfo() png("HERVagetrain.png", # create PNG for the heat map width = 8*600, # 5 x 300 pixels height = 8*600, res = 600, # 300 pixels per inch pointsize = 10) # smaller font size #Examine full model par(cex.axis = 2.0, mar = c(5, 4, 4, 2) + 0.5) # Increase the size of the axis labels par(cex.lab = 2.0) plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) # Calculate the correlation and the median absolute errorcor_value <- cor(train_targets, predict(fit, datMeth_train, s = cv$lambda.min)) # Define a function to calculate the median absolute error medAE <- function(actual, predicted) { return(median(abs(actual - predicted))) } medAE_value <- medAE(train_targets, predict(fit, datMeth_train, s = cv$lambda.min)) # Increased text size x <- 30 y <- 90 labels <- paste("n=10,138") text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 85 labels <- paste("r: ", round(cor_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 80 labels <- paste("MAE: ", round(medAE_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 dev.off() png("HERVagetest.png", # create PNG for the heat map width = 8*600, # 5 x 300 pixels height = 8*600, res = 600, # 300 pixels per inch pointsize = 10) # smaller font size #Examine full model par(cex.axis = 2.0, mar = c(5, 4, 4, 2) + 0.5) # Increase the size of the axis labels par(cex.lab = 2.0) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing", xlim = c(0, 100), ylim = c(0, 100)) cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) # Calculate the correlation and the median absolute errorcor_value <- cor(validation_targets, predict(fit, datMeth_validation, s = cv$lambda.min)) # Define a function to calculate the median absolute error medAE <- function(actual, predicted) { return(median(abs(actual - predicted))) } medAE_value <- medAE(validation_targets, predict(fit, datMeth_validation, s = cv$lambda.min)) # Increased text size x <- 30 y <- 90 labels <- paste("n=2,532") text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 85 labels <- paste("r: ", round(cor_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 80 labels <- paste("MAE: ", round(medAE_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 dev.off() LINE-1 CLOCK: library(data.table) data <- read.csv(' / Users / mcorley / Downloads / LINE1_CpGs_betas.csv',header = T, row.names = 1) head(data, n=10) methylation_data <- t(data) metadata <- read.csv(' / Users / mcorley / Downloads / metadata.csv', header = T) library("caret") ages <- metadata$Decimal.Chronological.Age # Create a data partition set.seed(42) # Set seed for reproducibilitytrain_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the data into training and validation sets beta_noob <- data beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) rm(beta_noob_train) rm(beta_noob_validation) library(glmnet) #Train clock library(doParallel) registerDoParallel(2) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) plot(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit, datMeth_validation, s = cv$lambda.1se)) (best.lambda <- cv$lambda.min)coef(cv, s = "lambda.min") #Save CpG predictors for model tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) #Save reduced CpG predictors for model tmp_coeffs2 <- coef(cv, s = "lambda.1se") predictors2 <- data.frame(name = tmp_coeffs2@Dimnames[[1]][tmp_coeffs2@i + 1], coefficient = tmp_coeffs2@x) write.csv(predictors, file="Line1predictorCpGssreduced.csv", quote=F) sessionInfo() write.csv(predictors2, file="LINE1dpredictorCpGsreduced.csv", quote=F) sessionInfo() png("HERVagetrain.png", # create PNG for the heat map width = 8*600, # 5 x 300 pixels height = 8*600, res = 600, # 300 pixels per inch pointsize = 10) # smaller font size par(cex.axis = 2.0, mar = c(5, 4, 4, 2) + 0.5) # Increase the size of the axis labels par(cex.lab = 2.0) plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.1se)) # Calculate the correlation and the median absolute error cor_value <- cor(train_targets, predict(fit, datMeth_train, s = cv$lambda.1se)) # Define a function to calculate the median absolute error medAE <- function(actual, predicted) { return(median(abs(actual - predicted))) } medAE_value <- medAE(train_targets, predict(fit, datMeth_train, s = cv$lambda.1se)) # Increased text sizex <- 30 y <- 90 labels <- paste("n=10,138") text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 85 labels <- paste("r: ", round(cor_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 80 labels <- paste("MAE: ", round(medAE_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 dev.off() png("LINE1agetest.png", # create PNG for the heat map width = 8*600, # 5 x 300 pixels height = 8*600, res = 600, # 300 pixels per inch pointsize = 10) # smaller font size #Examine full model par(cex.axis = 2.0, mar = c(5, 4, 4, 2) + 0.5) # Increase the size of the axis labels par(cex.lab = 2.0) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Testing", xlim = c(0, 100), ylim = c(0, 100)) cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.1se)) # Calculate the correlation and the median absolute error cor_value <- cor(validation_targets, predict(fit, datMeth_validation, s = cv$lambda.1se)) # Define a function to calculate the median absolute error medAE <- function(actual, predicted) { return(median(abs(actual - predicted))) } medAE_value <- medAE(validation_targets, predict(fit, datMeth_validation, s = cv$lambda.1se))# Increased text size x <- 30 y <- 90 labels <- paste("n=2,532") text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 85 labels <- paste("r: ", round(cor_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 x <- 30 y <- 80 labels <- paste("MAE: ", round(medAE_value, 2)) text(x = x, y = y, labels = labels, cex = 2.0) # Increase the text size by specifying cex = 1.5 dev.off() RETROAGE CLOCK: library(data.table) data <- read.csv(' / Users / mcorley / Downloads / ALL_CpGs_betamatrix.csv', header = T, row.names = 1) head(data, n=10) methylation_data <- t(data) metadata <- read.csv(' / Users / mcorley / Downloads / metadata.csv', header = T) library("caret") ages <- metadata$Decimal.Chronological.Age # Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the data into training and validation sets beta_noob <- data beta_noob_train <- beta_noob[, train_indices]beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) rm(beta_noob_train) rm(beta_noob_validation) library(glmnet) #Train clock library(doParallel) registerDoParallel(4) cv = cv.glmnet(datMeth_train, train_targets, nfolds=20,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) plot(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit, datMeth_train, s = cv$lambda.1se)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.1se),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit, datMeth_validation, s = cv$lambda.1se)) (best.lambda <- cv$lambda.min) coef(cv, s = "lambda.min") #Save CpG predictors for model tmp_coeffs <- coef(cv, s = "lambda.min")predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) #Save reduced CpG predictors for model tmp_coeffs2 <- coef(cv, s = "lambda.1se") predictors2 <- data.frame(name = tmp_coeffs2@Dimnames[[1]][tmp_coeffs2@i + 1], coefficient = tmp_coeffs2@x) write.csv(predictors, file="retropredictorCpGss.csv", quote=F) sessionInfo() MULTI-TISSUE RETROAGE: library(data.table) # Replace the file path with your actual file path file_path <- ' / Users / mcorley / Downloads / GSE213478_methylation_DNAm_noob_final_BMIQ_all_tissues_987.t xt' # Read the CSV file using fread data <- fread(file_path, header = TRUE) rownames(data) <- data$V1 # Read the filter list from the uploaded CSV filter_row_names <- fread(' / Users / mcorley / Desktop / meth_data / CpGsHERVsEPICV2allfiltered.csv', header = TRUE, data.table = FALSE) # Filter your data based on this list filtered_data <- data[rownames(data) %in% filter_row_names$Probe_ID, ] my_row_names <- filtered_data$V1 beta_noob <- data.frame(value_column = filtered_data) # Adjust as needed rownames(beta_noob) <- my_row_names beta_noob <- beta_noob[,-1] # Remove the first column metadata <- read.csv(' / Users / mcorley / Downloads / GTEXmerge.csv', header = T) library("caret") ages <- metadata$AGE# Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE) # Split the data into training and validation sets beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) rm(beta_noob_train) rm(beta_noob_validation) library(glmnet) library(doParallel) registerDoParallel(4) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) #Examine full model plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) (best.lambda <- cv$lambda.min) coef(cv, s = "lambda.min") #Save CpG predictors for model tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x)write.csv(predictors, file="Retroage2predictorCpGsGTEX2.csv", quote=F) sessionInfo() PAN-MAMMALIAN SPECIES RETROAGE library(data.table) library(dplyr) # Replace the file path with your actual file path file_path <- ' / Users / mcorley / Downloads / GSE223748_datBetaNormalized.csv' # Read the CSV file using fread data <- fread(file_path, header = TRUE) rownames(data) <- data$V1 filter_row_names <- fread(' / Users / mcorley / Desktop / meth_data / overlapRepeatMaskerMammal40.csv', header = TRUE, data.table = FALSE) # Filter your data based on this list filtered_data <- data[rownames(data) %in% filter_row_names$external_gr.values, ] write.csv(filtered_data, file = "RepeatMaskerMammal40data.csv", row.names = FALSE) filter_col_names <- fread(' / Users / mcorley / Downloads / mammal40datameta1.csv', header = TRUE, data.table = FALSE) filtered_data2 <- filtered_data %>% select(filter_col_names$Sample_ID) my_row_names <- filtered_data$V1 beta_noob <- data.frame(value_column = filtered_data2) # Adjust as needed rownames(beta_noob) <- my_row_names beta_noob <- beta_noob[,-1] # Remove the first column metadata <- read.csv(' / Users / mcorley / Downloads / mammal40datameta1.csv', header = T) library("caret") ages <- metadata$AGE2 # Create a data partition set.seed(42) # Set seed for reproducibility train_indices <- createDataPartition(ages, p = 0.8, list = FALSE)# Split the data into training and validation sets beta_noob_train <- beta_noob[, train_indices] beta_noob_validation <- beta_noob[, -train_indices] train_targets <- ages[train_indices] validation_targets <- ages[-train_indices] datMeth_train <- t(beta_noob_train) datMeth_validation <- t(beta_noob_validation) library(glmnet) #Train clock. library(doParallel) registerDoParallel(4) cv = cv.glmnet(datMeth_train, train_targets, nfolds=10,alpha=0.5, family="gaussian", parallel=TRUE) fit = glmnet(datMeth_train, train_targets, family="gaussian", alpha=0.5, nlambda=100) plot(cv) #Examine full model plot(train_targets,predict(fit,datMeth_train,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Training") cor(train_targets,predict(fit,datMeth_train,s = cv$lambda.min)) plot(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min),xlab = "Age",ylab = "Predicted Age", main = "Testing") cor(validation_targets,predict(fit,datMeth_validation,s = cv$lambda.min)) (best.lambda <- cv$lambda.min) coef(cv, s = "lambda.min") #Save CpG predictors for model tmp_coeffs <- coef(cv, s = "lambda.min") predictors <- data.frame(name = tmp_coeffs@Dimnames[[1]][tmp_coeffs@i + 1], coefficient = tmp_coeffs@x) write.csv(predictors, file="MammalAge40predictors2.csv", quote=F) sessionInfo()CODE TO RUN CLOCKS ON INDEPENDENT DATA USING WEIGHTS IN CSV FILE # Load necessary libraries and coefficients library(glmnet) coefficients_df <- read.csv(" / Users / mcorley / Downloads / LTRretropredictorCpGss.csv") calculate_epigenetic_age <- function(methylation_data, coefficients_df) { # Ensure that the methylation data contains the required CpGs if (!all(coefficients_df$name %in% colnames(methylation_data))) { stop("The methylation data does not have all the required CpGs!") } # Calculate the epigenetic age using the dot product of the coefficients and the methylation data predictions <- as.matrix(methylation_data[, coefficients_df$name, drop = FALSE]) %*% coefficients_df$coefficient # Add intercept, assuming it's named '(Intercept)' in the coefficients_df if ('(Intercept)' %in% coefficients_df$name) { intercept <- coefficients_df$coefficient[coefficients_df$name == '(Intercept)'] predictions <- predictions + intercept } return(predictions) } # Replace with the path to your new methylation data new_data <-read.csv(" / Users / mcorley / Downloads / EXTERNALDATA.csv") datMeth <- t(new_data) # 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Krasnova, K. A. Kulakova, J. V. Sopova, E. Y. Smirnov, S. A. Silonov, E. V. Lomert, O. A. Bystrova, M. G. Martynova, I. E. Neganova, Essential Role of Adhesion GPCR, GPR123, for Human Pluripotent Stem Cells and Reprogramming towards Pluripotency. Cells 12 (2023). 89. Y. Mi, C. Zhang, Y. Bu, Y. Zhang, L. He, H. Li, H. Zhu, Y. Li, Y. Lei, J. Zhu, DEPDC1 is a novel cell cycle related gene that regulates mitotic progression. BMB Rep. 48, 413–418 (2015). 90. L. Shen, H. Li, A. Zhang, R. Liu, C. Zhou, Y. Zhang, K. Zhao, M. Bretches, L. Lu, S.-Y. Yang, B. Ning, DEPDC1 / EEF1A1 complex promotes the progression of human osteosarcoma via downregulation of FOXO3a, bioRxiv (2021)p.2021.04.14.439766. 91. K. StĊpieĔ, A. Skoneczna, M. Kula-Maximenko, Ł. Jurczyk, M. MołoĔ, Depletion of the Origin Recognition Complex Subunits Delays Aging in Budding Yeast. Cells 11 (2022). 92. S. Chuvpilo, A. Avots, F. Berberich-Siebelt, J. Glöckner, C. Fischer, A. Kerstan, C. Escher, I. Inashkina, F. Hlubek, E. Jankevics, T. Brabletz, E. Serfling, Multiple NF-ATc isoforms with individual transcriptional properties are synthesized in T lymphocytes. J. Immunol. 162, 7294–7301 (1999). 93. A. C. Shaw, A. Panda, S. R. Joshi, F. Qian, H. G. Allore, R. R. Montgomery, Dysregulation of human Toll-like receptor function in aging. Ageing Res. Rev.10, 346–353 (2011).Tables Table 1: Composite Retroelement-Age CpGsTable 2: Composite Retroelement-Age-450 ClockTable 3: Composite Retroelement-Age V2 CpGsTable 4: Immune Transcriptome Retroelement-AgeTable 5: Human Multi-Tissue Retroelement clock CpGsTable 6: Pan-Mammalian Species Multi-Tissue Transposable Element Clock CpGsTable 7: Pan-Mammalian Species Multi-Tissue Retroelement Clock CpGsTable 8: HERV-Age CpGsTable 9: LINE-1-Age CpGsTable 10. Notable CpGs from the Excel spreadsheet “acel14288-sup-0001-DataS1.xlsx” provided in the Supporting Information for Ndhlovu et al., 2024, Aging Cell, 00:e14288, the information from which is incorporated by reference herein in its entirety (the CpG provided herein are from: HML2_17p13.1, HERVL18_6p22.1c, MER41_22q12.3e, HERVL18_6p22.1c, HUERSP3_6p21.32, MER41_22q12.3e, L1FLnl_10q26.3e, L1FLnl_1p31.3z L1FLnI_7q22.2a, L1FLnI_18q23l, and L1FLnI_Xp22.2h)The disclosure is not limited to the exemplary embodiments and applications presented herein or to the manner in which the exemplary embodiments and applications operate or are described herein. It should be understood that any use of subheadings herein are for organizational purposes, and should not be read to limit the application of those subheaded features to the various embodiments herein. Each and every feature described herein is applicable and usable in all the various embodiments discussed herein and that all features described herein can be used in any contemplated combination, regardless of the specific example embodiments that are described herein. It should further be noted that exemplary description of specific features are used, largely for informational purposes, and not in any way to limit the design, subfeature, and functionality of the specifically described feature. Incorporation by Reference All publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. Specifically, publication Ndhlovu et al., 2024, Aging Cell, 00:e14288 is incorporated by reference herein in its entirety including, without limitation, the Supporting Information provided therein and the datasets provided on Zenodo referenced therein. Also incorporated by reference in their entirety are any polynucleotide and polypeptide sequences which reference an accession number correlating to an entry in a public database, such as those maintained by The Institute for Genomic Research (TIGR) on the world wide web at tigr.org and / or the National Center for Biotechnology Information (NCBI) on the World Wide Web at ncbi.nlm.nih.gov. Equivalents Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

What is claimed is:

1. A method for determining an age of a biological sample, comprising: selectively measuring the methylation levels of a set of methylation markers in genomic DNA of the biological sample, said set of methylation markers comprising CpG methylation markers located in at least one retroelement, wherein the at least one retroelement is a Human Endogenous RetroVirus (HERV) element and / or at least one Long Interspersed Nuclear Element-1 (LINE-1); and comparing the measured methylation marker levels to reference levels for the methylation markers to determine the age of the sample based on said methylation levels.

2. The method of claim 1, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the HERV elements selected from Table 1 or Table 8.

3. The method of claim 1 or 2, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 8.

4. The method of any one of claims 1 to 3, wherein the CpG methylation markers comprise one, five, ten, fifteen, twenty or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof.

5. The method of any one of claims 1 to 4, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the LINE-1 elements selected from Table 1 or Table 9.

6. The method of any one of claims 1 to 5, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 9.

7. The method of any one of claims 1 to 6, wherein the CpG methylation markers comprise one, five, tent, fifteen or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof.

8. The method of any one of claims 1 to 7, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of any one of Tables 2 to 7.

9. The method of any one of claims 1 to 8, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, or twenty retroelements selected from any one of Tables 2 to 7.

10. The method of claim 8, comprising selectively measuring the methylation levels at each of the CG loci designations of any one of Tables 2 to 7.

11. A computer-readable medium comprising computer-readable instructions that, when executed by a computer, causes the computer to perform operations comprising: a) receiving information corresponding to methylation levels of a set of methylation markers in a biological sample, said markers comprising CpG methylation markers located in at least one HERV element and / or at least one LINE-1; and b) determining the age of the biological sample by comparing the measured methylation marker levels to reference marker levels and by applying a statistical prediction algorithm to the measured methylation marker levels.

12. The computer-readable medium of claim 11, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the HERV elements selected from Table 1 or Table 8.

13. The computer-readable medium of claim 11 or 12, wherein the information corresponds to the methylation levels at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 8.

14. The computer-readable medium of any one of claims 11 to 13, wherein the CpG methylation markers comprise one, five, ten, fifteen, twenty or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof.

15. The computer-readable medium of any one of claims 11 to 14, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the LINE-1 elements selected from Table 1 or Table 9.

16. The computer-readable medium of any one of claims 11 to 15, wherein the information corresponds the methylation levels at at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 9.

17. The computer-readable medium of any one of claims 11 to 16, wherein the CpG methylation markers comprise one, five, ten, fifreen or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof.

18. The computer-readable medium of any one of claims 11 to 17, wherein the information corresponds to the methylation levels at at least 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of any one of Tables 2-7.

19. The computer-readable medium of any one of claims 11 to 18, wherein the biological sample comprises tissue, bodily fluid, or combinations thereof.

20. The computer-readable medium of any one of claims 11 to 19, wherein the information corresponds to the methylation levels at each of the CG loci designations of any one of Tables 2 to 7.

21. A method of determining a biological age of a cell, the method comprising: selectively measuring the methylation levels of a set of methylation markers in genomic DNA of the cell, said selective measuring comprising receiving a sequence of inputs corresponding to methylation values at a plurality of CpG sites; and applying a pretrained model to the sequence of inputs to predict the epigenetic age, wherein said set of methylation markers comprise CpG methylation markers located in at least one HERV element and / or at least one LINE-1.

22. The method of claim 21, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the HERV elements selected from Table 1 or Table 8.

23. The method of claim 21 or 22, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 8.

24. The method of any one of claims 21 to 23, wherein the CpG methylation markers comprise one, five, ten, fifteen, twenty or more, or each, of the CG loci designations cg16951108, cg19786602, cg11630939, cg08960549, cg08883146, cg24004238, cg23987336, cg11341932, cg12212060, cg19203575, cg20850981, cg21177257, cg23702226, cg03978169, cg13904130, cg15737123, cg18927185, cg22136013, cg23917817, cg05209515, cg06637550, cg09686907, cg20866592, cg26440042, cg26538224, or any combination thereof.

25. The method of any one of claims 21-24, wherein the CpG methylation markers are located in at least one, five, ten, fifteen, twenty or each of the LINE-1 elements selected from Table 1 or 9.

26. The method of any one of claims 21-25, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 100 of the CG loci designations, or at each of the CG loci designations of Table 1 or Table 9.

27. The method of any one of claims 21 to 26, wherein the CpG methylation markers comprise one, five, ten, fifteen or more, or each, of the CG loci designations cg03230038, cg09292354, cg09699225, cg13754569, cg17005927, cg18635896, cg20654619, cg25120459, cg00318766, cg08413469, cg08825216, cg16166651, cg18167921, cg19634693, cg20322613, cg26064634, or any combination thereof.

28. The method of any one of claims 21-27, comprising selectively measuring the methylation levels at each of the CG loci designations of Table 2.

29. The method of any one of claims 21 to 28, comprising selectively measuring the methylation levels at at least 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of any one of Tables 3 to 7.

30. The method of any one of claims 21 to 29, wherein the cell is an immune cell, a stem cell or a somatic cell.

31. The method of claim 30, wherein the stem cell is an induced pluripotent stem cell (iPSC) or embryonic stem cell (ESC).

32. The method of any one of claim 30, wherein the somatic cell is a fibroblast, a keratinocyte, an epithelial cell, or a cell derived from peripheral blood cells (PBMCs).

33. The method of any one of claims 21 to 32, wherein the pretrained model comprises an elastic net.

34. A method of determining a biological age of a mammalian cell, the method comprising: obtaining at least one biological sample from the mammal and selectively measuring the methylation levels of a set of methylation markers in the genomic DNA of the at least one biological sample, said selective measuring comprising receiving a sequence of inputs corresponding to methylation values at a plurality of CpG sites; andapplying a pretrained model on the sequence of inputs to predict the epigenetic age, wherein said set of methylation markers comprise CpG methylation markers located in at least one HERV element and / or at least one LINE-1.

35. The method of claim 34, wherein the biological sample comprises immune cells, stem cells, somatic cells, or any combination thereof.

36. The method of claim 35, wherein the stem cells are iPSCs or ESCs.

37. The method of claim 35, wherein the somatic cells are fibroblasts, keratinocytes, epithelial cells, PBMCs, or any combination thereof.

38. The method of any one of claims 34 to 37, comprising selectively measuring the methylation levels at least 25, 50, 75 or 95 of the CG loci designations, or at each of the CG loci designations of any one of Tables 1 to 9.

39. The method of any one of claims 34 to 38, wherein the pretrained model comprises an elastic net.

40. An epigenetic clock comprising a set of CpG methylation markers located in at least one retroelement, wherein the retroelement is at least one HERV element and / or at least one LINE-1.

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