Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes.

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.

Fasting-mimicking diet causes hepatic and blood markers changes indicating reduced biological age and disease risk.

In mice, periodic cycles of a fasting mimicking diet (FMD) protect normal cells while killing damaged cells including cancer and autoimmune cells, reduce inflammation, promote multi-system regeneration, and extend longevity. Here, we performed secondary and exploratory analysis of blood samples from a randomized clinical trial (NCT02158897) and show that 3 FMD cycles in adult study participants are associated with reduced insulin resistance and other pre-diabetes markers, lower hepatic fat (as determined by magnetic resonance imaging) and increased lymphoid to myeloid ratio: an indicator of immune system age. Based on a validated measure of biological age predictive of morbidity and mortality, 3 FMD cycles were associated with a decrease of 2.5 years in median biological age, independent of weight loss. Nearly identical findings resulted from  a second clinical study (NCT04150159). Together these results provide initial support for beneficial effects of the FMD on multiple cardiometabolic risk factors and biomarkers of biological age.

More than bad luck: Cancer and aging are linked to replication-driven changes to the epigenome.

Aging is a leading risk factor for cancer. While it is proposed that age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. We show that aging and cancer share a common epigenetic replication signature, which we modeled using DNA methylation from extensively passaged immortalized human cells in vitro and tested on clinical tissues. This signature, termed CellDRIFT, increased with age across multiple tissues, distinguished tumor from normal tissue, was escalated in normal breast tissue from cancer patients, and was transiently reset upon reprogramming. In addition, within-person tissue differences were correlated with predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Our findings suggest that age-related replication may drive epigenetic changes in cells and could push them toward a more tumorigenic state.

Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality.

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

A multi-omics longitudinal aging dataset in primary human fibroblasts with mitochondrial perturbations.

Aging is a process of progressive change. To develop biological models of aging, longitudinal datasets with high temporal resolution are needed. Here we report a multi-omics longitudinal dataset for cultured primary human fibroblasts measured across their replicative lifespans. Fibroblasts were sourced from both healthy donors (n = 6) and individuals with lifespan-shortening mitochondrial disease (n = 3). The dataset includes cytological, bioenergetic, DNA methylation, gene expression, secreted proteins, mitochondrial DNA copy number and mutations, cell-free DNA, telomere length, and whole-genome sequencing data. This dataset enables the bridging of mechanistic processes of aging as outlined by the "hallmarks of aging", with the descriptive characterization of aging such as epigenetic age clocks. Here we focus on bridging the gap for the hallmark mitochondrial metabolism. Our dataset includes measurement of healthy cells, and cells subjected to over a dozen experimental manipulations targeting oxidative phosphorylation (OxPhos), glycolysis, and glucocorticoid signaling, among others. These experiments provide opportunities to test how cellular energetics affect the biology of cellular aging. All data are publicly available at our webtool: https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/.

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.

Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer's disease.

Alzheimer's disease (AD) risk increases exponentially with age and is associated with multiple molecular hallmarks of aging, one of which is epigenetic alterations. Epigenetic age predictors based on 5' cytosine methylation (DNAm), or epigenetic clocks, have previously suggested that epigenetic age acceleration may occur in AD brain tissue. Epigenetic clocks are promising tools for the quantification of biological aging, yet we hypothesize that investigation of brain aging in AD will be assisted by the development of brain-specific epigenetic clocks. Therefore, we generated a novel age predictor termed PCBrainAge that was trained solely in cortical samples. This predictor utilizes a combination of principal components analysis and regularized regression, which reduces technical noise and greatly improves test-retest reliability. To characterize the scope of PCBrainAge's utility, we generated DNAm data from multiple brain regions in a sample from the Religious Orders Study and Rush Memory and Aging Project. PCBrainAge captures meaningful heterogeneity of aging: Its acceleration demonstrates stronger associations with clinical AD dementia, pathologic AD, and APOE ε4 carrier status compared to extant epigenetic age predictors. It further does so across multiple cortical and subcortical regions. Overall, PCBrainAge's increased reliability and specificity makes it a particularly promising tool for investigating heterogeneity in brain aging, as well as epigenetic alterations underlying AD risk and resilience.

Contribution of life course circumstances to the acceleration of phenotypic and functional aging: A retrospective study.

Background: Accelerated aging leads to increasing burdens of chronic diseases in late life, posing a huge challenge to the society. With two well-developed aging measures (i.e., physiological dysregulation [PD] and frailty index [FI]), this study aimed to evaluate the relative contributions of life course circumstances (e.g., childhood and adulthood socioeconomic status) to variance in aging. Methods: We assembled data for 6224 middle-aged and older adults in China from the 2014 life course survey (June to December 2014), the 2015 biomarker collection (July 2015 to January 2016), and the 2015 main survey (July 2015 to January 2016) of the China Health and Retirement Longitudinal Study. Two aging measures (PD and FI) were calculated, with a higher value indicating more accelerated aging. Life course circumstances included childhood (i.e., socioeconomic status, war, health, trauma, relationship, and parents' health) and adulthood circumstances (i.e., socioeconomic status, adversity, and social support), demographics, and behaviours. The Shapley value decomposition, hierarchical clustering, and general linear regression models were performed. Findings: The Shapley value decomposition revealed that all included life course circumstances accounted for about 6·3% and 29·7% of variance in PD and FI, respectively. We identified six subpopulations who shared similar patterns in terms of childhood and adulthood circumstances. The most disadvantaged subpopulation (i.e., subpopulation 6 [more childhood trauma and adulthood adversity]) consistently exhibited accelerated aging indicated by the two aging measures. Relative to the most advantaged subpopulation (i.e., subpopulation 1 [less childhood trauma and adulthood adversity]), PD and FI in the most disadvantaged subpopulation were increased by an average of 0·14 (i.e., coefficient, by one-standard deviation, 95% confidence interval [CI] 0·06-0·21; p < 0·0001) and 0·10 (by one-point, 95% CI 0·09-0·11; p < 0·0001), respectively. Interpretation: Our findings highlight the different contributions of life course circumstances to phenotypic and functional aging. Special attention should be given to promoting health for the disadvantaged subpopulation and narrowing their health gap with advantaged counterparts. Funding: National Natural Science Foundation of China, Milstein Medical Asian American Partnership Foundation, Natural Science Foundation of Zhejiang Province, Fundamental Research Funds for the Central Universities, National Institute on Aging, National Centre for Advancing Translational Sciences, and Yale Alzheimer's Disease Research Centre.

Longitudinal Study of DNA Methylation and Epigenetic Clocks Prior to and Following Test-Confirmed COVID-19 and mRNA Vaccination.

The host epigenetic landscape rapidly changes during SARS-CoV-2 infection, and evidence suggest that severe COVID-19 is associated with durable scars to the epigenome. Specifically, aberrant DNA methylation changes in immune cells and alterations to epigenetic clocks in blood relate to severe COVID-19. However, a longitudinal assessment of DNA methylation states and epigenetic clocks in blood from healthy individuals prior to and following test-confirmed non-hospitalized COVID-19 has not been performed. Moreover, the impact of mRNA COVID-19 vaccines upon the host epigenome remains understudied. Here, we first examined DNA methylation states in the blood of 21 participants prior to and following test-confirmed COVID-19 diagnosis at a median time frame of 8.35 weeks; 756 CpGs were identified as differentially methylated following COVID-19 diagnosis in blood at an FDR adjusted p-value < 0.05. These CpGs were enriched in the gene body, and the northern and southern shelf regions of genes involved in metabolic pathways. Integrative analysis revealed overlap among genes identified in transcriptional SARS-CoV-2 infection datasets. Principal component-based epigenetic clock estimates of PhenoAge and GrimAge significantly increased in people over 50 following infection by an average of 2.1 and 0.84 years. In contrast, PCPhenoAge significantly decreased in people fewer than 50 following infection by an average of 2.06 years. This observed divergence in epigenetic clocks following COVID-19 was related to age and immune cell-type compositional changes in CD4+ T cells, B cells, granulocytes, plasmablasts, exhausted T cells, and naïve T cells. Complementary longitudinal epigenetic clock analyses of 36 participants prior to and following Pfizer and Moderna mRNA-based COVID-19 vaccination revealed that vaccination significantly reduced principal component-based Horvath epigenetic clock estimates in people over 50 by an average of 3.91 years for those who received Moderna. This reduction in epigenetic clock estimates was significantly related to chronological age and immune cell-type compositional changes in B cells and plasmablasts pre- and post-vaccination. These findings suggest the potential utility of epigenetic clocks as a biomarker of COVID-19 vaccine responses. Future research will need to unravel the significance and durability of short-term changes in epigenetic age related to COVID-19 exposure and mRNA vaccination.

Life course traumas and cardiovascular disease-the mediating role of accelerated aging.

The complex relationship between life course traumas and cardiovascular disease (CVD) and the underpinning pathways are poorly understood. We aimed to (1) examine the associations of three separate assessments including childhood, adulthood (after 16 years of age), and lifetime traumas (childhood or adulthood) with CVD; (2) examine the associations between diverse life course traumatic profiles and CVD; and (3) examine the extent to which PhenoAge, a well-developed phenotypic aging measure, mediated these associations. Using data from 104,939 participants from the UK Biobank, we demonstrate that subgroups of childhood, adulthood, and lifetime traumas were associated with CVD. Furthermore, life course traumatic profiles were significantly associated with CVD. For instance, compared with the subgroup experiencing nonsevere traumas across life course, those who experienced nonsevere childhood and severe adulthood traumas, severe childhood and nonsevere adulthood traumas, or severe traumas across life course had significantly higher odds of CVD (odds ratios: 1.07-1.33). Formal mediation analyses suggested that phenotypic aging partially mediated the above associations. These findings suggest a potential pathway from life course traumas to CVD through phenotypic aging, and underscore the importance of policy programs targeting traumas over the life course in ameliorating inequalities in cardiovascular health.

Resilience integrates concepts in aging research.

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

Tick tock, tick tock: Mouse culture and tissue aging captured by an epigenetic clock.

Aging is associated with dramatic changes to DNA methylation (DNAm), although the causes and consequences of such alterations are unknown. Our ability to experimentally uncover mechanisms of epigenetic aging will be greatly enhanced by our ability to study and manipulate these changes using in vitro models. However, it remains unclear whether the changes elicited by cells in culture can serve as a model of what is observed in aging tissues in vivo. To test this, we serially passaged mouse embryonic fibroblasts (MEFs) and assessed changes in DNAm at each time point via reduced representation bisulfite sequencing. By developing a measure that tracked cellular aging in vitro, we tested whether it tracked physiological aging in various mouse tissues and whether anti-aging interventions modulate this measure. Our measure, termed CultureAGE, was shown to strongly increase with age when examined in multiple tissues (liver, lung, kidney, blood, and adipose). As a control, we confirmed that the measure was not a marker of cellular senescence, suggesting that it reflects a distinct yet progressive cellular aging phenomena that can be induced in vitro. Furthermore, we demonstrated slower epigenetic aging in animals undergoing caloric restriction and a resetting of our measure in lung and kidney fibroblasts when re-programmed to iPSCs. Enrichment and clustering analysis implicated EED and Polycomb group (PcG) factors as potentially important chromatin regulators in translational culture aging phenotypes. Overall, this study supports the concept that physiologically relevant aging changes can be induced in vitro and used to uncover mechanistic insights into epigenetic aging.

Epigenetic aging of the demographically non-aging naked mole-rat.

The naked mole-rat (NMR) is an exceptionally long-lived rodent that shows no increase of mortality with age, defining it as a demographically non-aging mammal. Here, we perform bisulfite sequencing of the blood of > 100 NMRs, assessing > 3 million common CpG sites. Unsupervised clustering based on sites whose methylation correlates with age reveals an age-related methylome remodeling, and we also observe a methylome information loss, suggesting that NMRs age. We develop an epigenetic aging clock that accurately predicts the NMR age. We show that these animals age much slower than mice and much faster than humans, consistent with their known maximum lifespans. Interestingly, patterns of age-related changes of clock sites in Tert and Prpf19 differ between NMRs and mice, but there are also sites conserved between the two species. Together, the data indicate that NMRs, like other mammals, epigenetically age even in the absence of demographic aging of this species.

Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging.

To define metrics of phenotypic aging, it is essential to identify biological and environmental factors that influence the pace of aging. Previous attempts to develop aging metrics were hampered by cross-sectional designs and/or focused on younger populations. In the Baltimore Longitudinal Study of Aging (BLSA), we collected longitudinally across the adult age range a comprehensive list of phenotypes within four domains (body composition, energetics, homeostatic mechanisms and neurodegeneration/neuroplasticity) and functional outcomes. We integrated individual deviations from population trajectories into a global longitudinal phenotypic metric of aging and demonstrate that accelerated longitudinal phenotypic aging is associated with faster physical and cognitive decline, faster accumulation of multimorbidity and shorter survival. These associations are more robust compared with the use of phenotypic and epigenetic measurements at a single time point. Estimation of these metrics required repeated measures of multiple phenotypes over time but may uniquely facilitate the identification of mechanisms driving phenotypic aging and subsequent age-related functional decline.

A complex systems approach to aging biology.

Having made substantial progress understanding molecules, cells, genes and pathways, aging biology research is now moving toward integration of these parts, attempting to understand how their joint dynamics may contribute to aging. Such a shift of perspective requires the adoption of a formal complex systems framework, a transition being facilitated by large-scale data collection and new analytical tools. Here, we provide a theoretical framework to orient researchers around key concepts for this transition, notably emergence, interaction networks and resilience. Drawing on evolutionary theory, network theory and principles of homeostasis, we propose that organismal function is accomplished by the integration of regulatory mechanisms at multiple hierarchical scales, and that the disruption of this ensemble causes the phenotypic and functional manifestations of aging. We present key examples at scales ranging from sub-organismal biology to clinical geriatrics, outlining how this approach can potentially enrich our understanding of aging.

Genetic associations for two biological age measures point to distinct aging phenotypes.

Biological age measures outperform chronological age in predicting various aging outcomes, yet little is known regarding genetic predisposition. We performed genome-wide association scans of two age-adjusted biological age measures (PhenoAgeAcceleration and BioAgeAcceleration), estimated from clinical biochemistry markers (Levine et al., 2018; Levine, 2013) in European-descent participants from UK Biobank. The strongest signals were found in the APOE gene, tagged by the two major protein-coding SNPs, PhenoAgeAccel-rs429358 (APOE e4 determinant) (p = 1.50 × 10-72 ); BioAgeAccel-rs7412 (APOE e2 determinant) (p = 3.16 × 10-60 ). Interestingly, we observed inverse APOE e2 and e4 associations and unique pathway enrichments when comparing the two biological age measures. Genes associated with BioAgeAccel were enriched in lipid related pathways, while genes associated with PhenoAgeAccel showed enrichment for immune system, cell function, and carbohydrate homeostasis pathways, suggesting the two measures capture different aging domains. Our study reaffirms that aging patterns are heterogeneous across individuals, and the manner in which a person ages may be partly attributed to genetic predisposition.

Epigenetic age acceleration, fatigue, and inflammation in patients undergoing radiation therapy for head and neck cancer: A longitudinal study.

BACKGROUND: The authors measured epigenetic age acceleration (EAA) during and after cancer treatment and its association with inflammation and fatigue, which is a debilitating symptom in patients with cancer. METHODS: Patients who had head and neck cancer without distant metastases were assessed before, immediately after, and at 6 months and 12 months postradiotherapy. Blood DNA methylation was assessed using a proprietary bead chip (the Illumina MethylationEPIC BeadChip). EAA was calculated using the Levine epigenetic clock (DNAmPhenoAge), adjusted for chronological age. Fatigue was assessed using the Multidimensional Fatigue Inventory-20. Inflammatory markers were measured using standard techniques. RESULTS: Most patients (N = 133) were men, White, had advanced disease, and received concurrent chemoradiation. EAA changes over time were significant, with the largest increase (4.9 years) observed immediately after radiotherapy (P < .001). Increased EAA was associated with elevated fatigue (P = .003) over time, and patients who had severe fatigue experienced 3.1 years higher EAA than those who had low fatigue (P < .001), which was more prominent (5.6 years; P = .018) for patients who had human papillomavirus-unrelated disease at 12 months posttreatment. EAA was also positively associated with inflammatory markers, including C-reactive protein (CRP) and interleukin-6 (IL-6), over time (P < .001), and patients who had high CRP and IL-6 levels exhibited increases of 4.6 and 5.9 years, respectively, in EAA compared with those who had low CRP and IL-6 levels (P < .001). CRP and IL-6 mediated the association between EAA and fatigue (CRP: 95% CI, 0.060-0.279; IL-6: 95% CI, 0.024-0.220). CONCLUSIONS: Patients with head and neck cancer experienced increased EAA, especially immediately after treatment completion. EAA was associated with greater fatigue and inflammation, including 1 year after treatment. Inflammation may be a target to reduce the impact of age acceleration on poor functional outcomes.

Association of Epigenetic Age Acceleration With Risk Factors, Survival, and Quality of Life in Patients With Head and Neck Cancer.

PURPOSE: Epigenetic age acceleration (EAA) is robustly linked with mortality and morbidity. This study examined risk factors of EAA and its association with overall survival (OS), progression-free survival (PFS), and quality of life (QOL) in patients with head and neck cancer (HNC) receiving radiation therapy. METHODS AND MATERIALS: Patients without distant metastasis were enrolled and followed before and at the end of radiation therapy and at 6 and 12 months after radiation therapy. EAA was calculated with DNAmPhenoAge at all 4 time points. Risk factors included demographic characteristics, lifestyle, clinical characteristics, treatment-related symptoms, and blood biomarkers. Survival data were collected until August 2020, and QOL was measured using Functional Assessment of Cancer Therapy-HNC. RESULTS: Increased comorbidity, symptoms unrelated to human papilloma virus, and more severe treatment-related symptoms were associated with higher EAA (P = .03 to P < .001). A nonlinear association (quadratic) between body mass index (BMI) and EAA was observed: decreased BMI (<35 kg/m2; P = .04) and increased BMI (≥35 kg/m2; P = .01) were linked to higher EAA. Increased EAA (per year) was associated with worse OS (hazard ratio [HR], 1.11 [95% confidence interval {CI}, 1.03-1.18; P = .004]; HR, 1.10 [95% CI, 1.01-1.19; P = .02] for EAA at 6 and 12 months after treatment, respectively) and PFS (HR, 1.10 [95% CI, 1.02-1.19; P = .02]; HR, 1.14 [95% CI, 1.06-1.23; P < .001]; and HR, 1.08 [95% CI, 1.02-1.14; P = .01]) for EAA before, immediately after, and 6 months after radiation therapy, respectively) and QOL over time (ß = -0.61; P = .001). An average of 3.25 to 3.33 years of age acceleration across time, which was responsible for 33% to 44% higher HRs of OS and PFS, was observed in those who died or developed recurrence compared with those who did not (all P < .001). CONCLUSIONS: Compared with demographic and lifestyle factors, clinical characteristics were more likely to contribute to faster biological aging in patients with HNC. Acceleration in epigenetic age resulted in more aggressive adverse events, including OS and PFS. EAA could be considered as a marker for cancer outcomes, and decelerating aging could improve survival and QOL.

A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration.

Aging involves a diverse set of biological changes accumulating over time that leads to increased risk of morbidity and mortality. Epigenetic clocks are now widely used to quantify biological aging, in order to investigate determinants that modify the rate of aging and to predict age-related outcomes. Numerous biological, social and environmental factors have been investigated for their relationship to epigenetic clock acceleration and deceleration. The aim of this review was to synthesize general trends concerning the associations between human epigenetic clocks and these investigated factors. We conducted a systematic review of all available literature and included 156 publications across 4 resource databases. We compiled a list of all presently existing blood-based epigenetic clocks. Subsequently, we created an extensive dataset of over 1300 study findings in which epigenetic clocks were utilized in blood tissue of human subjects to assess the relationship between these clocks and numeral environmental exposures and human traits. Statistical analysis was possible on 57 such relationships, measured across 4 different epigenetic clocks (Hannum, Horvath, Levine and GrimAge). We found that the Horvath, Hannum, Levine and GrimAge epigenetic clocks tend to agree in direction of effects, but vary in size. Body mass index, HIV infection, and male sex were significantly associated with acceleration of one or more epigenetic clocks. Acceleration of epigenetic clocks was also significantly related to mortality, cardiovascular disease, cancer and diabetes. Our findings provide a graphical and numerical synopsis of the past decade of epigenetic age estimation research and indicate areas where further attention could be focused in the coming years.

Biological Aging Predicts Vulnerability to COVID-19 Severity in UK Biobank Participants.

BACKGROUND: Age and disease prevalence are the 2 biggest risk factors for Coronavirus disease 2019 (COVID-19) symptom severity and death. We therefore hypothesized that increased biological age, beyond chronological age, may be driving disease-related trends in COVID-19 severity. METHODS: Using the UK Biobank England data, we tested whether a biological age estimate (PhenoAge) measured more than a decade prior to the COVID-19 pandemic was predictive of 2 COVID-19 severity outcomes (inpatient test positivity and COVID-19-related mortality with inpatient test-confirmed COVID-19). Logistic regression models were used with adjustment for age at the pandemic, sex, ethnicity, baseline assessment centers, and preexisting diseases/conditions. RESULTS: Six hundred and thirteen participants tested positive at inpatient settings between March 16 and April 27, 2020, 154 of whom succumbed to COVID-19. PhenoAge was associated with increased risks of inpatient test positivity and COVID-19-related mortality (ORMortality = 1.63 per 5 years, 95% CI: 1.43-1.86, p = 4.7 × 10-13) adjusting for demographics including age at the pandemic. Further adjustment for preexisting diseases/conditions at baseline (ORM = 1.50, 95% CI: 1.30-1.73 per 5 years, p = 3.1 × 10-8) and at the early pandemic (ORM = 1.21, 95% CI: 1.04-1.40 per 5 years, p = .011) decreased the association. CONCLUSIONS: PhenoAge measured in 2006-2010 was associated with COVID-19 severity outcomes more than 10 years later. These associations were partly accounted for by prevalent chronic diseases proximate to COVID-19 infection. Overall, our results suggest that aging biomarkers, like PhenoAge may capture long-term vulnerability to diseases like COVID-19, even before the accumulation of age-related comorbid conditions.