Animal Models Relevant for Geroscience: Current Trends and Future Perspectives in Biomarkers, and Measures of Biological Aging.

For centuries, aging was considered inevitable and immutable. Geroscience provides the conceptual framework to shift this focus toward a new view that regards aging as an active biological process, and the biological age of an individual as a modifiable entity. Significant steps forward have been made toward the identification of biomarkers for and measures of biological age, yet knowledge gaps in geroscience are still numerous. Animal models of aging are the focus of this perspective, which discusses how experimental design can be optimized to inform and refine the development of translationally relevant measures and biomarkers of biological age. We provide recommendations to the field, including: the design of longitudinal studies in which subjects are deeply phenotyped via repeated multilevel behavioral/social/molecular assays; the need to consider sociobehavioral variables relevant for the species studied; and finally, the importance of assessing age of onset, severity of pathologies, and age-at-death. We highlight approaches to integrate biomarkers and measures of functional impairment using machine learning approaches designed to estimate biological age as well as to predict future health declines and mortality. We expect that advances in animal models of aging will be crucial for the future of translational geroscience but also for the next chapter of medicine.

mLiftOver: harmonizing data across Infinium DNA methylation platforms.

MOTIVATION: Infinium DNA methylation BeadChips are widely used for genome-wide DNA methylation profiling at the population scale. Recent updates to probe content and naming conventions in the EPIC version 2 (EPICv2) arrays have complicated integrating new data with previous Infinium array platforms, such as the MethylationEPIC (EPIC) and the HumanMethylation450 (HM450) BeadChip. RESULTS: We present mLiftOver, a user-friendly tool that harmonizes probe ID, methylation level, and signal intensity data across different Infinium platforms. It manages probe replicates, missing data imputation, and platform-specific bias for accurate data conversion. We validated the tool by applying HM450-based cancer classifiers to EPICv2 cancer data, achieving high accuracy. Additionally, we successfully integrated EPICv2 healthy tissue data with legacy HM450 data for tissue identity analysis and produced consistent copy number profiles in cancer cells. AVAILABILITY AND IMPLEMENTATION: mLiftOver is implemented R and available in the Bioconductor package SeSAMe (version 1.21.13+): https://bioconductor.org/packages/release/bioc/html/sesame.html. Analysis of EPIC and EPICv2 platform-specific bias and high-confidence mapping is available at https://github.com/zhou-lab/InfiniumAnnotationV1/raw/main/Anno/EPICv2/EPICv2ToEPIC_conversion.tsv.gz. The source code is available at https://github.com/zwdzwd/sesame/blob/devel/R/mLiftOver.R under the MIT license.

Validation of biomarkers of aging.

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Contextual modifiers of healthspan, lifespan, and epigenome in mice under chronic social stress.

Sustained life stress and low socioeconomic status are among the major causes of aging-related diseases and decreased life expectancy. Experimental rodent models can help to identify the underlying mechanisms, yet very few studies address the long-term consequences of social stress on aging. We conducted a randomized study involving more than 300 male mice of commonly used laboratory strains (C57BL/6J, CD1, and Sv129Ev) chosen for the spontaneous aggression gradient and stress-vulnerability. Mice were exposed to a lifelong chronic psychosocial stress protocol to model social gradients in aging and disease vulnerability. Low social rank, inferred based on a discretized aggression index, was found to negatively impact lifespan in our study population. However, social rank interacted with genetic background in that low-ranking C57BL/6J, high-ranking Sv129Ev, and middle-ranking CD1 mice had lower survival, respectively, implying a cost of maintaining a given social rank that varies across strains. Machine learning linear discriminant analysis identified baseline fat-free mass as the most important predictor of mouse genetic background and social rank in the present dataset. Finally, strain and social rank differences were significantly associated with epigenetic changes, most significantly in Sv129Ev mice and in high-ranking compared to lower ranking subjects. Overall, we identified genetic background and social rank as critical contextual modifiers of aging and lifespan in an ethologically relevant rodent model of social stress, thereby providing a preclinical experimental paradigm to study the impact of social determinants of health disparities and accelerated aging.

Minimum standards for evaluating machine-learned models of high-dimensional data.

The maturation of machine learning and technologies that generate high dimensional data have led to the growth in the number of predictive models, such as the "epigenetic clock". While powerful, machine learning algorithms run a high risk of overfitting, particularly when training data is limited, as is often the case with high-dimensional data ("large p, small n"). Making independent validation a requirement of "algorithmic biomarker" development would bring greater clarity to the field by more efficiently identifying prediction or classification models to prioritize for further validation and characterization. Reproducibility has been a mainstay in science, but only recently received attention in defining its various aspects and how to apply these principles to machine learning models. The goal of this paper is merely to serve as a call-to-arms for greater rigor and attention paid to newly developed models for prediction or classification.

DNA methylation dynamics and dysregulation delineated by high-throughput profiling in the mouse.

We have developed a mouse DNA methylation array that contains 296,070 probes representing the diversity of mouse DNA methylation biology. We present a mouse methylation atlas as a rich reference resource of 1,239 DNA samples encompassing distinct tissues, strains, ages, sexes, and pathologies. We describe applications for comparative epigenomics, genomic imprinting, epigenetic inhibitors, patient-derived xenograft assessment, backcross tracing, and epigenetic clocks. We dissect DNA methylation processes associated with differentiation, aging, and tumorigenesis. Notably, we find that tissue-specific methylation signatures localize to binding sites for transcription factors controlling the corresponding tissue development. Age-associated hypermethylation is enriched at regions of Polycomb repression, while hypomethylation is enhanced at regions bound by cohesin complex members. Apc Min/+ polyp-associated hypermethylation affects enhancers regulating intestinal differentiation, while hypomethylation targets AP-1 binding sites. This Infinium Mouse Methylation BeadChip (version MM285) is widely accessible to the research community and will accelerate high-sample-throughput studies in this important model organism.

Epigenetic moderators of naltrexone efficacy in reducing heavy drinking in Alcohol Use Disorder: a randomized trial.

Polymorphisms in genes associated with opioid signaling and dopamine reuptake and inactivation may moderate naltrexone efficacy in Alcohol Use Disorder (AUD), but the effects of epigenetic modification of these genes on naltrexone response are largely unexplored. This study tested interactions between methylation in the µ-opioid receptor (OPRM1), dopamine transporter (SLC6A3), and catechol-O-methyltransferase (COMT) genes as predictors of naltrexone effects on heavy drinking in a 16-week randomized, placebo-controlled trial among 145 treatment-seeking AUD patients. OPRM1 methylation interacted with both SLC6A3 and COMT methylation to moderate naltrexone efficacy, such that naltrexone-treated individuals with lower methylation of the OPRM1 promoter and the SLC6A3 promoter (p = 0.006), COMT promoter (p = 0.005), or SLC6A3 3' untranslated region (p = 0.004), relative to placebo and to those with higher OPRM1 and SLC6A3 or COMT methylation, had significantly fewer heavy drinking days. Epigenetic modification of opioid- and dopamine-related genes may represent a novel pharmacoepigenetic predictor of naltrexone efficacy in AUD.

Association of Epigenetic Age Acceleration With Incident Mild Cognitive Impairment and Dementia Among Older Women.

BACKGROUND: Epigenetic age acceleration (AgeAccel), which indicates faster biological aging relative to chronological age, has been associated with lower cognitive function. However, the association of AgeAccel with mild cognitive impairment (MCI) or dementia is not well-understood. We examined associations of 4 AgeAccel measures with incident MCI and dementia. METHODS: This prospective analysis included 578 older women from the Women's Health Initiative Memory Study selected for a case-cohort study of coronary heart disease (CHD). Women were free of CHD and cognitive impairment at baseline. Associations of AgeAccel measures (intrinsic AgeAccel [IEAA], extrinsic AgeAccel [EEAA], AgeAccelPheno, and AgeAccelGrim) with risks for incident adjudicated diagnoses of MCI and dementia overall and stratified by incident CHD status were evaluated. RESULTS: IEAA was not significantly associated with MCI (HR, 1.23; 95% CI, 0.99-1.53), dementia (HR, 1.10; 95% CI, 0.88-1.38), or cognitive impairment (HR, 1.18; 95% CI, 0.99-1.40). In stratified analysis by incident CHD status, there was a 39% (HR, 1.39; 95% CI, 1.07-1.81) significantly higher risk of MCI for every 5-year increase in IEAA among women who developed CHD during follow-up. Other AgeAccel measures were not significantly associated with MCI or dementia. CONCLUSIONS: IEAA was not significantly associated with cognitive impairment overall but was associated with impairment among women who developed CHD. Larger studies designed to examine associations of AgeAccel with cognitive impairment are needed, including exploration of whether associations are stronger in the setting of underlying vascular pathologies.

Predicting physiological aging rates from a range of quantitative traits using machine learning.

It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual's physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.

Novel age-associated DNA methylation changes and epigenetic age acceleration in middle-aged African Americans and whites.

BACKGROUND: African Americans (AAs) experience premature chronic health outcomes and longevity disparities consistent with an accelerated aging phenotype. DNA methylation (DNAm) levels at specific CpG positions are hallmarks of aging evidenced by the presence of age-associated differentially methylated CpG positions (aDMPs) that are the basis for the epigenetic clock for measuring biological age acceleration. Since DNAm has not been widely studied among non-European populations, we examined the association between DNAm and chronological age in AAs and whites, and the association between race, poverty, sex, and epigenetic age acceleration. RESULTS: We measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N = 244) and white (N = 243), men (N = 248), and women (N = 239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p = 0.48). We identified 4930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic, and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+ 2.04 and + 1.24 years, respectively, p < 0.05). CONCLUSIONS: AAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities.

Blood Leukocyte DNA Methylation Predicts Risk of Future Myocardial Infarction and Coronary Heart Disease.

BACKGROUND: DNA methylation is implicated in coronary heart disease (CHD), but current evidence is based on small, cross-sectional studies. We examined blood DNA methylation in relation to incident CHD across multiple prospective cohorts. METHODS: Nine population-based cohorts from the United States and Europe profiled epigenome-wide blood leukocyte DNA methylation using the Illumina Infinium 450k microarray, and prospectively ascertained CHD events including coronary insufficiency/unstable angina, recognized myocardial infarction, coronary revascularization, and coronary death. Cohorts conducted race-specific analyses adjusted for age, sex, smoking, education, body mass index, blood cell type proportions, and technical variables. We conducted fixed-effect meta-analyses across cohorts. RESULTS: Among 11 461 individuals (mean age 64 years, 67% women, 35% African American) free of CHD at baseline, 1895 developed CHD during a mean follow-up of 11.2 years. Methylation levels at 52 CpG (cytosine-phosphate-guanine) sites were associated with incident CHD or myocardial infarction (false discovery rate<0.05). These CpGs map to genes with key roles in calcium regulation (ATP2B2, CASR, GUCA1B, HPCAL1), and genes identified in genome- and epigenome-wide studies of serum calcium (CASR), serum calcium-related risk of CHD (CASR), coronary artery calcified plaque (PTPRN2), and kidney function (CDH23, HPCAL1), among others. Mendelian randomization analyses supported a causal effect of DNA methylation on incident CHD; these CpGs map to active regulatory regions proximal to long non-coding RNA transcripts. CONCLUSION: Methylation of blood-derived DNA is associated with risk of future CHD across diverse populations and may serve as an informative tool for gaining further insight on the development of CHD.

The role of epigenetic aging in education and racial/ethnic mortality disparities among older U.S. Women.

BACKGROUND: Higher mortality experienced by socially disadvantaged groups and/or racial/ethnic minorities is hypothesized to be, at least in part, due to an acceleration of the aging process. Using a new epigenetic aging measure, Levine DNAmAge, this study aimed to investigate whether epigenetic aging accounts for mortality disparities by race/ethnicity and education in a sample of U.S. postmenopausal women. METHODS: 1834 participants from an ancillary study (BA23) in the Women's Health Initiative, a national study that recruited postmenopausal women (50-79 years) were included. Over the 22 years of follow-up, 551 women died, and 31,946 person-years were observed. Levine DNAmAge (unit in years) was calculated based on an equation that we previously developed in an independent sample, which incorporates methylation levels at 513 CpG sites. RESULTS: As previously reported, non-Hispanic blacks and Hispanics were epigenetically older than non-Hispanic whites of the same chronological age. Similarly, those with less education had older epigenetic ages than expected in the full sample, as well as among non-Hispanic whites and Hispanics, but not among non-Hispanic blacks. Non-Hispanic blacks and those with low education exhibited the greatest risk of mortality. However, this association was partially attenuated when accounting for differences in DNAmAge. Furthermore, formal mediation analysis suggested that DNAmAge partially mediated the mortality increase among non-Hispanic blacks, compared to non-Hispanic whites (proportion mediated, 15.8%, P = 0.002), as well as the mortality increase for those with less than high school education, compared to college educated (proportion mediated, 11.6%, P < 2E-16). CONCLUSIONS: Among a group of postmenopausal women, non-Hispanic blacks and those with less education exhibit higher epigenetic aging, which partially accounts for their shorter life expectancies.

Tracking the Epigenetic Clock Across the Human Life Course: A Meta-analysis of Longitudinal Cohort Data.

Background: Epigenetic clocks based on DNA methylation yield high correlations with chronological age in cross-sectional data. Due to a paucity of longitudinal data, it is not known how Δage (epigenetic age - chronological age) changes over time or if it remains constant from childhood to old age. Here, we investigate this using longitudinal DNA methylation data from five datasets, covering most of the human life course. Methods: Two measures of the epigenetic clock (Hannum and Horvath) are used to calculate Δage in the following cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) offspring (n = 986, total age-range 7-19 years, 2 waves), ALSPAC mothers (n = 982, 16-60 years, 2 waves), InCHIANTI (n = 460, 21-100 years, 2 waves), SATSA (n = 373, 48-99 years, 5 waves), Lothian Birth Cohort 1936 (n = 1,054, 70-76 years, 3 waves), and Lothian Birth Cohort 1921 (n = 476, 79-90 years, 3 waves). Linear mixed models were used to track longitudinal change in Δage within each cohort. Results: For both epigenetic age measures, Δage showed a declining trend in almost all of the cohorts. The correlation between Δage across waves ranged from 0.22 to 0.82 for Horvath and 0.25 to 0.71 for Hannum, with stronger associations in samples collected closer in time. Conclusions: Epigenetic age increases at a slower rate than chronological age across the life course, especially in the oldest population. Some of the effect is likely driven by survival bias, where healthy individuals are those maintained within a longitudinal study, although other factors like the age distribution of the underlying training population may also have influenced this trend.

Analysis of repeated leukocyte DNA methylation assessments reveals persistent epigenetic alterations after an incident myocardial infarction.

BACKGROUND: Most research into myocardial infarctions (MIs) have focused on preventative efforts. For survivors, the occurrence of an MI represents a major clinical event that can have long-lasting consequences. There has been little to no research into the molecular changes that can occur as a result of an incident MI. Here, we use three cohorts to identify epigenetic changes that are indicative of an incident MI and their association with gene expression and metabolomics. RESULTS: Using paired samples from the KORA cohort, we screened for DNA methylation loci (CpGs) whose change in methylation is potentially indicative of the occurrence of an incident MI between the baseline and follow-up exams. We used paired samples from the NAS cohort to identify 11 CpGs which were predictive in an independent cohort. After removing two CpGs associated with medication usage, we were left with an "epigenetic fingerprint" of MI composed of nine CpGs. We tested this fingerprint in the InCHIANTI cohort where it moderately discriminated incident MI occurrence (AUC = 0.61, P = 6.5 × 10-3). Returning to KORA, we associated the epigenetic fingerprint loci with cis-gene expression and integrated it into a gene expression-metabolomic network, which revealed links between the epigenetic fingerprint CpGs and branched chain amino acid (BCAA) metabolism. CONCLUSIONS: There are significant changes in DNA methylation after an incident MI. Nine of these CpGs show consistent changes in multiple cohorts, significantly discriminate MI in independent cohorts, and were independent of medication usage. Integration with gene expression and metabolomics data indicates a link between MI-associated epigenetic changes and BCAA metabolism.

An epigenetic biomarker of aging for lifespan and healthspan.

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

Genetic variants in sex hormone pathways and the risk of type 2 diabetes among African American, Hispanic American, and European American postmenopausal women in the US.

BACKGROUND: Sex hormones are implicated in the development of diabetes. However, whether genetic variations in sex hormone pathways (SHPs) contribute to the risk of type 2 diabetes mellitus (T2DM) remains to be determined. This study investigated associations between genetic variations in all candidate genes in SHPs and T2DM risk among a cohort of women participating in the Women's Health Initiative (WHI). METHODS: Single nucleotide polymorphisms (SNPs) located within 30 kb upstream and downstream of SHP genes were comprehensively examined in 8180 African American, 3498 Hispanic American, and 3147 European American women in the WHI. In addition, whether significant SNPs would be replicated in independent populations was examined. RESULTS: After adjusting for age, region, and ancestry estimates and correcting for multiple testing, seven SNPs were significantly associated with the risk of T2DM among Hispanic American women were identified in the progesterone receptor (PGR) gene, with rs948516 showing the greatest significance (odds ratio 0.67; 95% confidence interval 0.57-0.78; P = 8.8 × 10-7 ; false discovery rate, Q = 7.8 × 10-4 ). These findings were not replicated in other ethnic groups in the WHI or in sex-combined analyses in replication studies. CONCLUSION: Significant signals were identified implicating the PGR gene in T2DM development in Hispanic American women in the WHI, which are consistent with genome-wide association studies findings linking PGR to glucose homeostasis. Nevertheless, the PGR SNPs-T2DM association was not statistically significant in other ethnic populations. Further studies, especially sex-specific analyses, are needed to confirm the findings and clarify the role of SHPs in T2DM.

GWAS of epigenetic aging rates in blood reveals a critical role for TERT.

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) paradoxically confer higher IEAA (P < 2.7 × 10-11). Causal modeling indicates TERT-specific and independent effects on LTL and IEAA. Experimental hTERT-expression in primary human fibroblasts engenders a linear increase in DNA methylation age with cell population doubling number. Together, these findings indicate a critical role for hTERT in regulating the epigenetic clock, in addition to its established role of compensating for cell replication-dependent telomere shortening.

Relationships of sex hormone levels with leukocyte telomere length in Black, Hispanic, and Asian/Pacific Islander postmenopausal women.

BACKGROUND: Sex hormones may play important roles in sex-specific biological aging. In the study, we specifically examined associations between circulating sex hormone concentrations and leukocyte telomere length (TL). METHODS: A cross-sectional study was conducted among 1124 Black, 444 Hispanic, and 289 Asian/Pacific Islander women in the Women's Health Initiative Observational Cohort. Estradiol and testosterone concentrations were measured using electrochemiluminescence immunoassays; TL was measured using quantitative polymerase chain reaction. RESULTS: Women in the study were aged 50-79 years. Estradiol concentrations were not significantly associated with TL in this sample. The associations between total and free testosterone and TL differed by race/ethnicity (Pinteraction = 0.03 and 0.05 for total and free testosterone, respectively). Total and free testosterone concentrations were not associated with TL in Black and Hispanic women, whereas in Asian/Pacific Islander women their concentrations were inversely associated with TL (Ptrend = 0.003 for both). These associations appeared robust in multiple subgroup analyses and multivariable models adjusted for potential confounding factors. In Asian/Pacific Islander women, a doubling of serum free and total testosterone concentrations was associated with a 202-bp shorter TL (95% confidence interval [CI] 51-353 bp) and 203-bp shorter TL (95% CI 50-355 bp), respectively. CONCLUSIONS: Serum estradiol concentrations were not associated with leukocyte TL in this large sample of postmenopausal women. Total and free testosterone concentrations were inversely associated with TL in Asian/Pacific Islander women, but not in Black and Hispanic women, although future studies to replicate our observations are warranted particularly to address potential ethnicity-specific relationships.

Age-associated microRNA expression in human peripheral blood is associated with all-cause mortality and age-related traits.

Recent studies provide evidence of correlations of DNA methylation and expression of protein-coding genes with human aging. The relations of microRNA expression with age and age-related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole-blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10-4 (Bonferroni-corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age-associated mRNA expression possibly driven by age-associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict 'microRNA age' that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all-cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10-5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole-blood microRNA expression profiling. Age-associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age-related diseases.