Search

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.

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.

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.

Item does not contain fulltext

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.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

For many years, it was believed that the aging process was inevitable and that age-related diseases could not be prevented or reversed. The geroscience hypothesis, however, posits that aging is, in fact, malleable and, by targeting the hallmarks of biological aging, it is indeed possible to alleviate age-related diseases and dysfunction and extend longevity. This field of geroscience thus aims to prevent the development of multiple disorders with age, thereby extending healthspan, with the reduction of morbidity toward the end of life. Experts in the field have made remarkable advancements in understanding the mechanisms underlying biological aging and identified ways to target aging pathways using both novel agents and repurposed therapies. While geroscience researchers currently face significant barriers in bringing therapies through clinical development, proof-of-concept studies, as well as early-stage clinical trials, are underway to assess the feasibility of drug evaluation and lay a regulatory foundation for future FDA approvals in the future.

Aging is the leading risk factor for cancer. While it’s been proposed that the age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. Here, we show that both aging and cancer share a common epigenetic replication signature, which we modeled from DNA methylation data in extensively passaged immortalized human cells in vitro and tested on clinical tissues. This epigenetic signature of replication – termed CellDRIFT – increased with age across multiple tissues, distinguished tumor from normal tissue, and was escalated in normal breast tissue from cancer patients. Additionally, within-person tissue differences were correlated with both predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Overall, our findings suggest that age-related replication drives epigenetic changes in cells, pushing them towards a more tumorigenic state.One sentence summaryCellular replication leaves an epigenetic fingerprint that may partially underly the age-associated increase in cancer risk.

Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.

BackgroundEpigenetic clocks are promising tools for the study of aging in humans. The clocks quantify biological aging above and beyond chronological age, demonstrate systematic associations with risk factors that accelerate aging, and predict age-related morbidity and mortality. There is interest in using them as surrogate endpoints in intervention studies. However, the large number of clocks, decentralized publication and explosive popularity in the last decade has made for poor accessibility and standardization. This has hampered the abilities of new researchers to conduct truly hypothesis driven research—whether by not knowing about the best available clocks for a given question, or by systematically testing many or all as they become available.ResultsWe report a centralized R package which can be installed and run locally on the user’s machine, and provides a standardized syntax for epigenetic clock calculation. The package includes a set of helper functions to assist with navigating clock literature and selecting clocks for analysis, as well as affording the user with the details of clock calculation. We describe each clock’s resilience to missing CpG information, combined with functionality to assess the need for imputation in the user’s own data. Furthermore, we demonstrate that while CpGs may not be shared among clocks with similar outputs, many clocks have highly correlated outputs.ConclusionsDue to the previous decentralization of epigenetic clocks, gathering code and performing systematic analysis, particularly in protected datasets, has required significant information gathering effort. Here, we offer an R package with standardized implementation and potential for future growth and clock incorporation to assist with hypothesis driven investigation of aging as measured by epigenetic clocks. We show the potential of this package to drive the user to think globally about signals captured by epigenetic clocks, as well as to properly identify the potential and limitations of each clock in their current research.

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.

Background Many DNA methylation-based indicators have been developed as summary measures of epigenetic aging. We examine the associations between 13 epigenetic clocks, including 4 second generation clocks, as well as the links of the clocks to social, demographic, and behavioral factors known to be related to health outcomes: sex, race/ethnicity, socioeconomic status, obesity, and lifetime smoking pack-years. Methods The Health and Retirement Study is the data source which is a nationally representative sample of Americans over age 50. Assessment of DNA methylation was based on the EPIC chip and epigenetic clocks were developed based on existing literature. Results The clocks vary in the strength of their relationships with age, with each other and with independent variables. Second generation clocks trained on health-related characteristics tend to relate more strongly to the sociodemographic and health behaviors known to be associated with health outcomes in this age group. Conclusions Users of this publicly available data set should be aware that epigenetic clocks vary in their relationships to age and to variables known to be related to the process of health change with age.

Ageing is a degenerative process that leads to tissue dysfunction and death. A proposed cause of ageing is the accumulation of epigenetic noise that disrupts gene expression patterns, leading to decreases in tissue function and regenerative capacity1–3. Changes to DNA methylation patterns over time form the basis of ageing clocks4, but whether older individuals retain the information needed to restore these patterns—and, if so, whether this could improve tissue function—is not known. Over time, the central nervous system (CNS) loses function and regenerative capacity5–7. Using the eye as a model CNS tissue, here we show that ectopic expression of Oct4 (also known as Pou5f1), Sox2 and Klf4 genes (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice. The beneficial effects of OSK-induced reprogramming in axon regeneration and vision require the DNA demethylases TET1 and TET2. These data indicate that mammalian tissues retain a record of youthful epigenetic information—encoded in part by DNA methylation—that can be accessed to improve tissue function and promote regeneration in vivo. Expression of three Yamanaka transcription factors in mouse retinal ganglion cells restores youthful DNA methylation patterns, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice, suggesting that mammalian tissues retain a record of youthful epigenetic information that can be accessed to improve tissue function.

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.

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.

Background Schizophrenia (SZ) is associated with increased all-cause mortality, smoking, and age-associated proteins, yet multiple previous studies found no association between SZ and biological age using Horvath’s epigenetic clock, a well-established aging biomarker based on DNA methylation. However, numerous epigenetic clocks that may capture distinct aspects of aging have been developed. This study tested the hypothesis that altered aging in SZ manifests in these other clocks. Methods We performed a comprehensive analysis of 14 epigenetic clocks categorized according to what they were trained to predict: chronological age, mortality, mitotic divisions, or telomere length. To understand the etiology of biological age differences, we also examined DNA methylation predictors of smoking, alcohol, body mass index, serum proteins, and cell proportions. We independently analyzed 3 publicly available multiethnic DNA methylation data sets from whole blood, a total of 567 SZ cases and 594 nonpsychiatric controls. Results All data sets showed accelerations in SZ for the 3 mortality clocks up to 5 years, driven by smoking and elevated levels of 6 age-associated proteins. The 2 mitotic clocks were decelerated in SZ related to antitumor natural killer and CD8T cells, which may help explain conflicting reports about low cancer rates in epidemiological studies of SZ. One cohort with available medication data showed that clozapine is associated with male-specific decelerations up to 7 years in multiple chronological age clocks. Conclusions Our study demonstrates the utility of studying the various epigenetic clocks in tandem and highlights potential mechanisms by which mental illness influences long-term outcomes, including cancer and early mortality.

Purpose The hallmark menopausal symptom, vasomotor symptoms (VMS), has been linked to adverse health indicators. However, the relationship between VMS and biological aging has not been tested. We examined associations between menopausal VMS and biological aging as assessed by 2 DNA methylation-based epigenetic aging indicators previously linked to poor health outcomes. Methods Participants were members of the Women’s Health Initiative Observational Study integrative genomics substudy (N = 1206) who had both ovaries and were not taking hormone therapy. Relationships between VMS at enrollment (presence, severity) or VMS timing groups (no VMS: not at menopause onset nor at study enrollment; early VMS: at menopause onset but not at enrollment; persistent VMS: at menopause onset and study enrollment; and late VMS: at enrollment but not at menopause onset) and epigenetic clock indicators predictive of physical aging and early death (DNAm PhenoAge, DNAm GrimAge) were tested in linear regression models adjusting for age, race/ethnicity, hysterectomy, education, body mass index, smoking, and, in additional models, sleep disturbance. Results Women were on average 65 years of age at enrollment. Severe hot flashes at enrollment were associated with higher DNAm PhenoAge [relative to no hot flashes: B (SE) = 2.79 (1.27), P = 0.028, multivariable]. Further, late-occurring VMS were associated with both higher DNAm PhenoAge [B (SE) = 2.15 (0.84), P = 0.011] and DNAm GrimAge [B (SE) = 1.09 (0.42), P = 0.010, multivariable] relative to no VMS. Main Conclusions Among postmenopausal women, severe or late-occurring VMS were associated with accelerated epigenetic age, controlling for chronological age. Postmenopausal women with severe or late-occurring VMS may have greater underlying epigenetic aging.

Background: Age is the largest risk factor for breast cancer development. Faster biological aging, particularly in breast tissue, may predispose women to higher risk of developing breast cancer. Recently, a set of promising biomarkers of aging, called “epigenetic clocks” (also referred to as “epigenetic age”) have been developed to estimate aging rates in tissues and cells. Using one of the original epigenetic clocks based on DNA methylation patterns, we previously demonstrated that normal breast tissue from luminal breast cancer patients exhibited accelerated aging compared to patients without breast cancer. Here we test whether incorporation of nearly a dozen epigenetic clocks, each capturing slightly different biological aging phenomena, can consistently distinguish normal breast tissue from cancer patients versus healthy controls. We also examined concordance between epigenetic age assessment from paired breast tissue and peripheral blood mononuclear cells. Methods: We examined 282 specimens including DNA from 94 normal breast tissues (41 from patients with breast cancer, and 53 from non-cancer controls) and 188 blood samples (97 from breast cancer, and 91 from controls). DNA methylation was assessed via the Illumina HumanMethylation450k and HumanMethylationEPIC BeadChip for both tissue and blood samples, and 11 epigenetic clocks were calculated following published methods. Associations of these clocks with breast cancer were examined using logistic regression models and Receiver Operating Characteristics (ROC) curves to determine predictive power in distinguishing samples from cancer patients versus healthy controls. Results: After accounting for chronological age, we observed increases in most epigenetic clocks for cancer patients compared to normal samples from controls, which indicates accelerated epigenetic aging (e.g., Zhang clock in tissue, 0.55 vs. -0.42; in blood, 0.10 vs. -0.20, all P Conclusions: Epigenetic aging-based biomarkers distinguish non-tumor breast tissues of patients with breast cancer from breast tissues of healthy controls. These findings raise the possibility that epigenetic markers assessed on needle biopsies of the breast might identify individuals at risk for developing breast cancer. Citation Format: Erin Hofstatter, Morgan Levine, Zuyun Liu, Tess O'Meara, Disha Dalela, Lajos Pusztai. Evidence of accelerated epigenetic aging of breast tissues in patients with breast cancer compared to women without cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-09-02.

Epigenetic clocks have come to be regarded as powerful tools for estimating aging. However, a major drawback in their application is our lack of mechanistic understanding. We hypothesize that uncovering the underlying biology is difficult due to the fact that epigenetic clocks are multifactorial composites: They are comprised of disparate parts, each with their own causal mechanism and functional consequences. Thus, only by deconstructing epigenetic clock signals will it be possible to glean biological insight. Here we clustered 5,717 clock CpGs into twelve distinct modules using multi-tissue and in-vitro datasets. We show that epigenetic clocks are comprised of different proportions of modules, which may explain their discordance when simultaneously applied in a given study. We also observe that epigenetic reprogramming does not ‘reset’ the entire clock and instead the observed rejuvenation is driven by a subset of modules. Overall, two modules stand-out in terms of their unique features. The first is one of the most responsive to epigenetic reprogramming; is the strongest predictor of all-cause mortality; and shows increases with in vitro passaging up until senescence burden begins to emerge. The light-second module is moderately responsive to reprogramming; is very accelerated in tumor vs. normal tissues; and tracks with passaging in vitro even as population doublings decelerate. Overall, we show that clock deconstruction can identify unique DNAm alterations and facilitate our mechanistic understanding of epigenetic clocks.

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., The exceptionally long-lived naked mole-rat is characterized by the lack of increased mortality with aging. Here the authors perform epigenetic studies to show that naked mole-rats epigenetically age despite their non-increasing mortality rate.

Aging is the leading risk factor for cancer. While some have proposed that the age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. Here, we show that both aging and cancer share a common epigenetic replication signature, which we modeled from DNA methylation data in extensively passaged immortalized human cells in vitro and tested on clinical tissues. This epigenetic signature of replication – termed CellDRIFT – increased with age across multiple tissues, distinguished tumor from normal tissue, and was escalated in normal breast tissue from cancer patients. Additionally, within-person tissue differences were correlated with both predicted lifetime tissue-specific stem cell divisions and tissue-specific cancer risk. Overall, our findings suggest that age-related replication drives epigenetic changes in cells, potentially pushing them towards a more tumorigenic state. Citation Format: Christopher J. Minteer, Kyra Thrush, Peter Niimi, Joel Rozowsky, Jason Liu, Mor Frank, Thomas McCabe, Erin Hofstatter, Mariya Rozenblit, Lajos Pusztai, Kenneth Beckman, Mark Gerstein, Morgan E. Levine. Revisiting the bad luck hypothesis: Cancer risk and aging are linked to replication-driven changes to the epigenome [abstract]. In: Proceedings of the AACR Special Conference: Aging and Cancer; 2022 Nov 17-20; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2022;83(2 Suppl_1):Abstract nr PR009.

The Louisville Twin Study (LTS) began in 1958 and became a premier longitudinal twin study of cognitive development. The LTS continuously collected data from twins through 2000 after which the study closed indefinitely due to lack of funding. Now that the majority of the sample is age 40 or older (61.36%, N = 1770), the LTS childhood data can be linked to midlife cognitive functioning, among other physical, biological, social, and psychiatric outcomes. We report results from two pilot studies in anticipation of beginning the midlife phase of the LTS. The first pilot study was a participant tracking study, in which we showed that approximately 90% of the Louisville families randomly sampled (N = 203) for the study could be found. The second pilot study consisted of 40 in-person interviews in which twins completed cognitive, memory, biometric, and functional ability measures. The main purpose of the second study was to correlate midlife measures of cognitive functioning to a measure of biological age, which is an alternative index to chronological age that quantifies age as a function of the breakdown of structural and functional physiological systems, and then to relate both of these measures to twins' cognitive developmental trajectories. Midlife IQ was uncorrelated with biological age (- .01) while better scores on episodic memory more strongly correlated with lower biological age (- .19 to - .31). As expected, midlife IQ positively correlated with IQ measures collected throughout childhood and adolescence. Additionally, positive linear rates of change in FSIQ scores in childhood significantly correlated with biological age (- .68), physical functioning (.71), and functional ability (- .55), suggesting that cognitive development predicts lower biological age, better physical functioning, and better functional ability. In sum, the Louisville twins can be relocated to investigate whether and how early and midlife cognitive and physical health factors contribute to cognitive aging.

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.

BackgroundWhile childhood and adulthood traumatic experiences have been linked to subsequent cardiovascular disease (CVD), the relationship between life course traumas and CVD and the underpinning pathways are poorly understood. This study aimed to: (1) examine the associations of childhood, adulthood, and lifetime traumas with CVD; (2) examine the associations between diverse life course traumatic profiles and CVD; and (3) examine the extent to which Phenotypic Age (PhenoAge), a well-developed phenotypic aging measure, mediates these associations.MethodsWe included 104,939 participants from the UK Biobank who completed the 2016 online mental health questionnaire. CVD outcomes including ischemic heart disease, myocardial infarction, and stroke were ascertained. Childhood, adulthood, and lifetime traumas were categorized into three subgroups (mild, moderate, and severe), respectively. Four life course traumatic profiles were defined as non-severe traumas across life course, non-severe childhood and severe adulthood traumas, severe childhood and non-severe adulthood traumas, and severe traumas across life course based on both childhood and adulthood traumas. PhenoAge was measured using an equation previously developed. Multivariable logistic models and formal mediation analyses were performed.ResultsOf 104,939 participants, 7,398 (7.0%) were diagnosed with CVD. Subgroups of childhood, adulthood, and lifetime traumas were associated with CVD, respectively. Furthermore, life course traumatic profiles were significantly associated with CVD. For instance, compared with subgroups experiencing non-severe traumas across life course, those who experienced non-severe childhood and severe adulthood traumas, severe childhood and non-severe adulthood traumas, and severe traumas across life course had higher odd of CVD, with odds ratios of 1.07 (95% confidence interval [CI]: 1.00, 1.15), 1.17 (95% CI: 1.09, 1.25), and 1.33 (95% CI: 1.24, 1.43), respectively. Formal mediation analyses suggested that PhenoAge partially mediated the above associations. For instance, PhenoAge mediated 5.8% of increased CVD events in subgroups who experienced severe childhood traumas, relative to those experiencing mild childhood traumas.ConclusionsChildhood, adulthood, and lifetime traumas, as well as diverse life course traumatic profiles, were associated with CVD. Furthermore, phenotypic aging partially mediated these associations. These findings suggest a potential pathway from life course traumas to CVD through phenotypic aging, and underscore the importance of policy programs targeting traumatic events over the life course in ameliorating inequalities in cardiovascular health.

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.

Accelerated aging implies health inequalities in late life and consequently, poses a huge challenge to society. With two well-validated aging measures, this study aimed to evaluate the relative contributions of life course circumstances to variance in these aging measures with policy implications. We assembled data for 6224 middle-aged and older adults (≥45 years) who participated in the 2014 life course survey, the 2015 biomarker collection, and the 2015 main survey of the China Health and Retirement Longitudinal Study (CHARLS). Two aging measures, including physiological dysregulation (PD) and frailty index (FI), were calculated. Life course circumstances, i.e., 70 circumstances variables involving childhood and adulthood circumstances, demographics, and behaviors, were categorized into 11 study domains for simplicity. The Shapley value decomposition, hierarchical clustering, and general linear regression models were performed. The Shapley value decomposition revealed that all 11 study domains accounted for about 6.3% and 29.7% of the variance in PD and FI, respectively. We then identified six subpopulations who shared similar patterns in terms of childhood and adulthood circumstances. One subpopulation (i.e., disadvantaged) who reported experiencing more childhood and adulthood adversity consistently exhibited accelerated aging indicated by the two aging measures. We conclude that life course circumstances contribute differently to the acceleration of phenotypic and functional aging in Chinese middle-aged and older adults. Special attention should be given to promoting health for the disadvantaged subpopulation and narrowing their health gap with advantaged counterparts. Our findings highlight the role of life course circumstances in ameliorating health inequalities in late life.

Purpose: Age is one of the strongest risk factors for the development of breast cancer, however the underlying etiology linking age and breast cancer remains unclear. We have previously observed links between epigenetic aging signatures in breast/tumor tissue and breast cancer risk/prevalence. However, these DNA methylation-based aging biomarkers capture diverse epigenetic phenomena and it is not known to what degree they relate to breast cancer risk, and/or progression. Methods: Using six epigenetic clocks, we analyzed whether they distinguish normal breast tissue adjacent to tumor (cases) vs normal breast tissue from healthy controls (controls). Results: The Levine (p=0.0037) and Yang clocks (p=0.023) showed significant epigenetic age acceleration in cases vs controls in breast tissue. We observed that much of the difference between cases and controls is driven by CpGs associated with polycomb related genes. Thus, we developed a new score utilizing only CpGs associated with polycomb related genes and demonstrated that it robustly captured epigenetic age acceleration in cases vs controls (p=0.00012). Finally, we tested whether this same signal could be seen in peripheral blood. We observed no difference in cases vs. controls and no correlation between matched tissue/blood samples, suggesting that peripheral blood is not a good surrogate marker for epigenetic age acceleration. Conclusions: Moving forward, it will be critical for studies to elucidate whether epigenetic age acceleration in breast tissue precedes breast cancer diagnosis and whether methylation changes at CpGs associated with polycomb related genes can be used to assess the risk of developing breast cancer among unaffected individuals.

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.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.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;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.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.

Epigenetic clocks have been widely used to predict disease risk in multiple tissues or cells. Their success as a measure of biological aging has prompted research on the connection between epigenetic pathways of aging and the socioeconomic gradient in health and mortality. However, studies examining social correlates of epigenetic aging have yielded inconsistent results. We conducted a comprehensive, comparative analysis of associations between various dimensions of socioeconomic status (SES) (education, income, wealth, occupation, neighborhood environment, and childhood SES) and eight epigenetic clocks in two large U.S. aging studies: The Multi-Ethnic Study of Atherosclerosis (MESA) (n=1,211) and the Health and Retirement Study (HRS) (n=4,018). In both studies, we found robust associations between SES measures in adulthood and the GrimAge and DunedinPoAm clocks (Bonferroni corrected p-value

Educational inequalities in mortality have been observed for decades, however the underlying biological mechanisms are not well known. We assessed the mediating role of altered aging of immune cells functioning captured by DNA methylation changes in blood (known as epigenetic clocks) in educational associated all-cause mortality. Data were from eight prospective population-based cohort studies, representing 13,021 participants. We found educational inequalities in mortality were larger for men than for women, estimated by hazard differences and ratios. Epigenetic clocks explained approximately 50% of educational inequalities in mortality for men, while the proportion was small for women. Most of this mediation was explained by differential effects of unhealthy lifestyles and morbidities of the WHO risk factors for premature mortality. These results support DNA methylation-based epigenetic aging as a signature of educational inequalities in life expectancy emphasizing the need for policies to address the unequal social distribution of these WHO risk factors.

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 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.

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., A genome‐wide association study for differences in aging rates was performed in the UK Biobank sample using two previously established aging measures. Results suggest that one was associated with genes involved in lipid metabolism and blood pressure, while the other was associated with genes involved in immune functioning. APOE e4 was associated with faster aging in the measure reflecting lipid age, yet paradoxically, was associated with reduced aging in the measure reflecting immune functioning and inflammation.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for ∼25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

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 radiotherapy. METHODS AND MATERIALS: Patients without distant metastasis were enrolled and followed before and end of radiotherapy, and 6-months and 12-months post-radiotherapy. EAA was calculated with DNAmPhenoAge at all four times. Risk factors included demographics, lifestyle, clinical characteristics, treatment-related symptoms, and blood biomarkers. Survival data were collected until August 2020; QOL was measured using Functional Assessment of Cancer Therapy–HNC. RESULTS: Increased comorbidity, HPV-unrelated, and severer treatment-related symptoms were associated with higher EAA (p=0.03 to

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.

This chapter lays out the dimensions of morbidity and the processes linking morbidity and mortality. It provides evidence of recent trends in morbidity of the older American population: decline in some types of disability but not others, disease, and physiological dysregulation among the older American population. In addition, the chapter uses data from two recent cohorts to look at survival without disease and the age at onset of diseases. We have generally seen an increase in the prevalence and time with disease; but disease appears less disabling now than in the past. In addition, the onset of myocardial infarction appears to have been delayed by the recent control of biological risk. Most of the evidence does not support the idea that we have experienced a recent compression of morbidity; it does support some delay and retarding of progression of the morbidity process.

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.

ii.AbstractAging is known to elicit dramatic changes to DNA methylation (DNAm), although the causes and consequences of such alterations are not clear and require further exploration. 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 RRBS. 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 DNAmCULTURE, 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 SUZ12, 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 moving forward, used to uncover mechanistic insights into epigenetic aging.

With no known treatments or vaccine, COVID-19 presents a major threat, particularly to older adults, who account for the majority of severe illness and deaths. The age-related susceptibility is partly explained by increased comorbidities including dementia and type II diabetes [1]. While it is unclear why these diseases predispose risk, we hypothesize that increased biological age, rather than chronological age, may be driving disease-related trends in COVID-19 severity with age. To test this hypothesis, we applied our previously validated biological age measure (PhenoAge) [2] composed of chronological age and nine clinical chemistry biomarkers to data of 347,751 participants from a large community cohort in the United Kingdom (UK Biobank), recruited between 2006 and 2010. Other data included disease diagnoses (to 2017), mortality data (to 2020), and the UK national COVID-19 test results (to May 31, 2020) [3]. Accelerated aging 10-14 years prior to the start of the COVID-19 pandemic was associated with test positivity (OR=1.15 per 5-year acceleration, 95% CI: 1.08 to 1.21, p=3.2×10−6) and all-cause mortality with test-confirmed COVID-19 (OR=1.25, per 5-year acceleration, 95% CI: 1.09 to 1.44, p=0.002) after adjustment for demographics including current chronological age and pre-existing diseases or conditions. The corresponding areas under the curves were 0.669 and 0.803, respectively. Biological aging, as captured by PhenoAge, is a better predictor of COVID-19 severity than chronological age, and may inform risk stratification initiatives, while also elucidating possible underlying mechanisms, particularly those related to inflammaging.

The neurotoxicity of air pollution is undefined for sex and APOE alleles. These major risk factors of Alzheimer’s disease (AD) were examined in mice given chronic exposure to nPM, a nano-sized subfraction of urban air pollution. In the cerebral cortex, female mice had two-fold more genes responding to nPM than males. Transcriptomic responses to nPM had sex-APOE interactions in AD-relevant pathways. Only APOE3 mice responded to nPM in genes related to Abeta deposition and clearance (Vav2, Vav3, S1009a). Other responding genes included axonal guidance, inflammation (AMPK, NFKB, APK/JNK signaling), and antioxidant signaling (NRF2, HIF1A). Genes downstream of NFKB and NRF2 responded in opposite directions to nPM. Nrf2 knockdown in microglia augmented NFKB responses to nPM, suggesting a critical role of NRF2 in air pollution neurotoxicity. These findings give a rationale for epidemiologic studies of air pollution to consider sex interactions with APOE alleles and other AD-risk genes.

Deaths from coronavirus disease (COVID-19) have exceeded 300,000 persons globally, calling for rapid development of mobile diagnostics that can assay widespread prevalence and infection rates. Data provided in this study supports the utility of a newly-designed lateral flow immunoassay (LFA) for detecting SARS-CoV-2 IgM and IgG antibodies. We employed a clinical cohort of 1,892 SARS-CoV-2 patients and controls, including individuals diagnosed by RT-qPCR at Yale New Haven Hospital, The First Affiliated Hospital of Anhui Medical University, the Chinese Center for Disease Control and Prevention of Hefei City (Hefei CDC), Anhui Province (Anhui Province CDC), and Fuyang City (Fuyang CDC). The LFA studied here detects SARS-CoV-2 IgM and IgG antibodies with a specificity of 97.9–100% for IgM, 99.7–100% for IgG, and sensitivities ranging from 94.1–100% for patients >14-days post symptom onset. Sensitivity decreases in patients

Robust biomarkers of aging have been developed from DNA methylation in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages—by incorporating behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relates to phenotypic aging. Reduced representation bisulfite sequencing (RRBS) data was used to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. This measure correlated with age at (r = 0.93) in an independent sample, and related to physical functioning (p=5.9e-3), after adjusting for age and cell counts. DNAmAge was also found to correlate with age in male C57BL/6 mice (r = 0.79), and was decreased in response to caloric restriction. Our signatures driven by CpGs in intergenic regions that showed substantial overlap with H3K9me3, H3K27me3, and E2F1 transcriptional factor binding.

Aging has been shown to be a strong driver of DNA methylation changes, leading to the development of robust biomarkers in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages for studying mammalian aging. Additionally, we incorporated behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relate to phenotypic aging and reflect higher-order molecular aging changes. We used DNAm data from reduced representation bisulfite sequencing (RRBS) to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. In an independent sample of n=32 F344 rats, we found that this measure correlated with age at (r=0.93), and related to physical functioning (5.9e-3), after adjusting for age and differential cell counts. DNAmAge was also found to correlate with age in C57BL/6 mice (r=0.79), and was decreased in response to caloric restriction (CR), such that the longer the animal was on a CR diet, the greater the decrease in DNAm. We also observed resetting of DNAm when kidney and lung fibroblasts when converted to induced pluripotent stem cells (iPSCs). Using weighted gene correlation network analysis (WGCNA) we identified two modules that appeared to drive our DNAmAge measure. These two modules contained CpGs in intergenic regions that showed substantial overlap with histone marks H3K9me3, H3K27me3, and E2F1 transcriptional factor binding. In moving forward, our ability to unravel the complex signals linking DNA methylation changes to functional aging would require experimental studies in model systems in which longitudinal epigenetic changes can be related to other molecular and physiological hallmarks of aging.