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.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

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.

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.

Abstract PR009: Revisiting the bad luck hypothesis: Cancer risk and aging are linked to replication-driven changes to the epigenome

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.

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.

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.

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.

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.

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.

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.

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.

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.

Aging biomarkers and the brain

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.

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 (OR_Mortality = 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 (OR_M = 1.50, 95% CI: 1.30–1.73 per 5 years, p = 3.1 × 10⁻⁸) and at the early pandemic (OR_M = 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.

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.

Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study

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.

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 (β = -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.

New Computational Approaches to Aging Research

Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require systems biology approaches. This talk will illustrate how epigenetic data can be integrated with cellular, physiological, proteomic, and clinical data to model age-related changes that propagate up the levels—finally manifesting as age-related disease or death. I will also describe how network modeling and machine learning approaches (linear and non-linear) can be used to identify causal features in aging from which to generate novel biomarkers. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.

Underlying features of epigenetic aging clocks in vivo and in vitro

Epigenetic clocks, developed using DNA methylation data, have been widely used to quantify biological aging in multiple tissues/cells. However, many existing epigenetic clocks are weakly correlated with each other, suggesting they may capture different biological processes. We utilize multi-omics data from diverse human tissue/cells to identify shared features across eleven existing epigenetic clocks. Despite the striking lack of overlap in CpGs, multi-omics analysis suggested five clocks (Horvath1, Horvath2, Levine, Hannum, and Lin) share transcriptional associations conserved across purified CD14+ monocytes and dorsolateral prefrontal cortex. The pathways enriched in the shared transcriptional association suggested links between epigenetic aging and metabolism, immunity, and autophagy. Results from in vitro experiments showed that two clocks (Levine and Lin) were accelerated in accordance with two hallmarks of aging-cellular senescence and mitochondrial dysfunction. Finally, using multi-tissue data to deconstruct the epigenetic clock signals, we developed a meta-clock that demonstrated improved prediction for mortality and robustly related to hallmarks of aging in vitro than single clocks.

Schizophrenia and Epigenetic Aging Biomarkers: Increased Mortality, Reduced Cancer Risk, and Unique Clozapine Effects

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.

COVID-19 severity is predicted by earlier evidence of accelerated 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.

Changing Disease Prevalence, Incidence, and Mortality Among Older Cohorts: The Health and Retirement Study

BACKGROUND

This article investigates changes in disease prevalence, incidence, and mortality among four cohorts of older persons in the Health and Retirement Study.

METHODS

We examine two cohorts initially aged 51 to 61, whom we call younger cohorts, and two older cohorts aged 70 to 80 at the start of observation. Each of the paired cohorts was born about 10 years apart. We follow the cohorts for approximately 10 years.

RESULTS

The prevalence of cancer, stroke, and diabetes increased in later-born cohorts; while the prevalence of myocardial infarction decreased markedly in both later-born cohorts. The incidence of heart disease, myocardial infarction, and stroke decreased among those in the later-born older cohort; while only the incidence of myocardial infarction decreased in the later-born younger cohort. On the other hand, diabetes incidence increased among those in both later-born cohorts. Death rates among those with heart disease, cancer, and diabetes decreased in the later-born cohorts. The declining incidence of three cardiovascular conditions among those who are over age 70 reflects improving population health and has resulted in stemming the increase in prevalence of people with heart disease and stroke.

DISCUSSION

While these results provide some important signs of improving population health, especially among those over 70; trends for those less than 70 in the United States are not as positive.

Mouse brain transcriptome responses to inhaled nanoparticulate matter differed by sex and APOE in Nrf2-Nfkb interactions

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.

A FUNCTIONAL EPIGENETIC CLOCK FOR RATS

Evidence from humans suggests that incorporation of phenotypic aging measures in the development of epigenetic clocks leads to more functionally relevant biomarkers. As a result, the aim of this study was to utilize a deeply phenotyped rat cohort—that included data from rotarod, open field, frailty index, and FACS—to generate a novel epigenetic clock. DNA methylation was assessed via reduced representation bisulfite sequencing (RRBS) for n=142 male Fischer rats from NIA aging colony, ranging in age from 1 to 27 months. Phenotypic traits were combined to generate an multi-system aging measure that was then used to train the epigenetic clock. Using an independent validation sample, age-adjusted epigenetic clock measures were associated with numerous traits, including: open field time resting (p=0.005), open field time climbing (p=0.001), body weight (p=0.02), and rotarod max (p=0.04). In moving forward, it will be important to examine cross-species comparisons, longitudinal change, and functional enrichment.

NOVEL BIOMARKERS OF BIOLOGICAL AGE IN THE HEALTH AND RETIREMENT STUDY

This symposium presents early results on epigenetic, transcriptomic and other aging biomarkers such as telomere length and mitochondrial DNA copy number from the Health and Retirement Study that allows a detailed examination of the biological pathways through which socioeconomic conditions influence the human aging process. In 2016 HRS collected 6 tubes of blood from people who completed the 2016 interview, had been in the sample at the prior wave and were not in a nursing home (n=9,973) to maintain a nationally-representative sample. These blood samples were analyzed for novel biomarkers of aging that included global methylation arrays, whole transcriptome sequencing, telomere length and mitochondrial DNA copy number among other biomarkers that were shown to be related to both social and economic circumstances and health outcomes at older ages. This level of integration of biological data to address social disparities hasn’t been accomplished before on a large nationally-representative sample of Americans and will provide a unique opportunity to understand the biological mechanisms through which social disparities affect human health. The symposium will describe the utility of measuring novel age related biomarkers in a nationally representative population study such as HRS and the potential research opportunities that can be pursued using this publicly available resource. It will provide an overview of the measurement and distribution of epigenetic, transcriptomic and telomere length and mitochondrial DNA copy number as novel aging biomarkers. It will also describe the utility of these biomarkers in further understanding the biological underpinnings of socioeconomic differences in health and mortality.

GENETIC PREDISPOSITION TO ACCELERATED BIOLOGICAL AGES PREDICTED BY BIOCHEMICAL MARKERS

Biological ages predicted by biochemical markers (biomarkers) outperform other measures in predicting a variety of aging outcomes. Several have been developed in recent studies, and there is evidence that each may independently predict mortality. While the included biomarkers are disease-associated, it is unclear what aspects of aging are captured. We aimed to understand and quantify genetic predisposition to accelerated biological ages, determined based on two measures, PhenoAge (9 biomarkers plus chronological age, Levine et al. 2018) and BioAge (7 biomarkers plus chronological age, Levine 2013). We performed genome-wide scans using the UK Biobank data (n=107,460 for PhenoAge, n=98,446 for BioAge). The SNP-based (single nucleotide polymorphism) heritability estimates were 14.45% and 12.39% for PhenoAge and BioAge, respectively. Both shared the strongest signal in the APOE region, with opposite associations with e2 and e4 alleles. e2 was associated with younger BioAge but older PhenoAge. e4 was associated with older BioAge but younger PhenoAge. BioAge was highly genetically correlated with its element of systolic blood pressure (rg=0.84) and the genetic correlation between PhenoAge and red blood cell distribution width was 0.65. Previous genome-wide association study findings of the top hits suggest that BioAge mostly captures cardiac aging but PhenoAge has more to do with inflammatory aging. The results are consistent with SNP clusters by associations with a broad range of aging traits, including an independent cluster with SNPs near the APOE. Genetic risk scores will be created to quantify the genetic predisposition and will be tested for associations with numerous aging traits.

ASSOCIATIONS OF GENETICS AND LIFE COURSE CIRCUMSTANCES WITH A NOVEL AGING MEASURE THAT CAPTURES MORTALITY RISK

We aimed to evaluate associations between a comprehensive set of factors, including genetics and childhood and adulthood circumstances, and a novel aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the U.S. population. Using data from 2339 adults (aged 51+) from the U.S. Health and Retirement Study, we found that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic scores [PGSs] domains, and 1 demographics, and 1 behaviors domains) accounted for about 30% of variance in PhenoAge after accounting for chronological age. Among the 4 circumstances domains, adulthood adversity was the largest contributor (9%), while adulthood socioeconomic status (SES), childhood adversity, and childhood SES accounted for 2.8%, 2.1%, 0.7%, respectively. All PGSs contributed 3.8% of variance in PhenoAge (after accounting for chronological age). Further, using Hierarchical Clustering, we identified 6 distinct subpopulations/clusters based on the 4 circumstances domains, and 3 subpopulations/clusters of them that appear to represent disadvantaged circumstances were associated with higher PhenoAge. Finally, there was a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the most apparently disadvantaged subpopulation/cluster, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. We concluded that socioenvironmental circumstances during childhood and adulthood account for a sizable proportion of differences in phenotypic aging among U.S. older adults. The disadvantaged subpopulations exhibited accelerated aging and the detrimental effects may be further exacerbated among persons with genetic predisposition to coronary artery disease.

DNA METHYLATION: CAUSE OR CONSEQUENCE OF AGING?

Epigenetic changes are one of the Hallmarks of Aging. DNA methylation is a key epigenetic mark that has been shown to change during aging. Several "clocks" have been developed whereby changes in DNA methylation can be used to predict chronological, and perhaps, biological age. This symposium will focus on recent advances in understanding how and why changes in DNA methylation occur during aging and whether these changes play a causal role in age-related functional declines and disease.

EPIGENETIC PROFILES OF ALZHEIMER’S DISEASE

Although age is highly correlated with incidence of Alzheimer’s Dementia (AD), the field continues to lack a clear understanding of how either normal and/or pathological aging processes drive neurodegeneration. As such, there remains a clear lack of valid and reliable clinical biomarkers to predict that disease’s future development and severity. Epigenetic age based on DNA methylation (DNAm) in brain have been shown to relate to AD neuropathology and cognitive decline. However, they were not initially designed as AD biomarkers. We hypothesized that supervised and unsupervised machine learning techniques (e.g. network analysis, clustering, and regressed-based techniques) could be used to build composite scoring variables from DNAm data that are predictive of AD progression. This work analyzes the methylation of 3 brain regions (cerebellum (CBM), prefrontal cortex (PFC), striatum (ST))—totaling 1,047 brain methylation samples. The samples contain neuropathologically confirmed AD cases and controls, and is enriched for APOE4+ carriers. Detailed subject-level information concerning cognitive measures, lifestyle choices, medications, and neuropathology at death were also considered. Based on epigenome-wide association study (EWAS), we identified a CpG in AIMP2 that is a robust predictor of AD-related phenotypes. Using network analysis, we have also identified co-methylation modules that relate to multifactorial AD phenotypes. Following validation, we intend to follow-up on the biological processes and molecular pathways associated with these epigenetic signatures. In moving forward, predictors of AD diagnosis and prognostication have major implications for early detection and treatment of this major age-related disease.

SCHIZOPHRENIA EPIGENETIC AGING PATTERNS REFLECT ALTERED MORTALITY AND CANCER RISKS

Schizophrenia (SZ) is associated with large increases in all-cause mortality, high smoking rates, and elevated levels of age-associated proteins—suggesting individuals with SZ may experience accelerated rates of biological aging. Yet surprisingly, multiple previous studies found no association between SZ and biological age using Horvath’s epigenetic clock, a well-recognized and validated biomarker of aging based on DNA methylation (DNAm) levels. However, numerous epigenetic clocks have been developed to date, many of which are better indicators of differential lifespan and healthspan than the original Horvath clock. Thus, we hypothesize that these epigenetic clocks may be better proxies for the presumed accelerated aging rate in SZ. Here we investigate 14 epigenetic clocks using three publicly available DNAm datasets from whole blood, comparing SZ to non-psychiatric controls (NPC). In all data sets, we find SZ age acceleration in three clocks previously shown to be most predictive of age-related morbidity and mortality risk. In contrast, two clocks developed to capture mitotic rate are decelerated in SZ, consistent with low cancer rates despite smoking observed in epidemiological studies of SZ. We use these clocks to investigate the determinants of altered aging in SZ, such as smoking, alcohol, BMI, age-associated proteins, blood cell composition, and psychotropic medications. Principal component analysis suggests mortality clock acceleration, mitotic clock deceleration, and medication effects are independent phenomena in SZ. Our study demonstrates the importance of studying the various epigenetic clocks in tandem and highlights their potential utility for understanding how mental illness influences long-term outcomes including cancer and early mortality.

SYSTEMS-LEVEL MODELING OF BIOLOGICAL AND MOLECULAR AGING CHANGES OVER TIME

Aging is associated with numerous changes at all levels of biological organization. Harnessing this information to develop measures that accurately and reliably quantify the biological aging process will require longitudinal modeling and incorporation of systems level approaches. We will describe applications of network modeling for longitudinal multi-system biomarker data. Using data from the Baltimore Longitudinal Study of Aging (BLSA) we are able to generate systems level models of biological and physiological function, and then demonstrate how these networks change with age. We will also link systems-level aging changes to hallmarks of aging, including epigenetic alterations, senescence, mitochondrial dysfunction, and proteostasis. Given the complexity of the biological aging process, modeling of systems dynamics over time will both lead to the development of better biomarkers of aging, and also inform our conceptualization of how alterations at the molecular level propagate up levels of organization to eventually influence morbidity and mortality risk.

DEVELOPMENT OF EPIGENETIC MEASURES FOR GEROSCIENCE CLINICAL TRIALS

One of the major goals of the NIA is to oversee development of biomarkers of aging. In recent years, DNA methylation has emerged as a promising avenue from which to quantify biological age. We and others have shown that these measures track age across various tissues and cells, and further deviations between chronological and “epigenetic age” have been shown to confer risk for various aging outcomes. However, the usefulness of these measures will depend on both their modifiability and ability to capture known targetable hallmarks of aging. Using DNA methylation data from cell line experiments, we have recently generated epigenetic predictors of cellular senescence for both human and mouse that when assessed in vivo from bulk samples, show age-related increases and are associated with aging outcomes. In moving forward, measures such as these may serve as promising surrogate endpoints for assessing efficacy of senolytic drugs and/or other anti-aging therapeutics.

EPIGENETIC PROFILES OF BIOLOGICAL AGING HALLMARKS

Investigation into the hallmarks of aging point to the existence of shared mechanisms that underlie the biological aging process. While there is a general consensus that hallmarks of aging rarely occur in isolation, little is known in regards to their overlapping networks or how interactions contribute to manifestations at the clinical level. Here, we examine whether shared epigenetic alterations—one of the proposed hallmark of aging—underlies diverse conditions characterized by other hallmarks, including cellular senescence, loss of proteostasis, genomic instability, mitochondrial dysfunction, and inflammation. Using weighted network analysis, we identified consistent overlaps in the methylation profiles across the different traits. For instance, epigenetic modules that were distinct in senescence were also affected in progeroid syndromes (Hutchinson-Gilford Progeria Syndrome and Werner’s Syndrome) and smokers. These CpGs tended to be located in CpG islands, which are notable for their strong association with transcriptional regulation. Overall, our results suggest that epigenetic alterations intersect with various hallmarks of aging. In moving forward, incorporation of this understanding may lead to the development of biomarkers that better capture the biological (rather than chronological) aging process.

ANALYTICAL CONSIDERATIONS OF DEVELOPING A PHENOTYPIC AGING MEASURE: THE CONCEPTUAL FRAMEWORK MUST COME FIRST!

We propose a latent structural model framework where phenotypic aging is a latent variable influenced by chronological age, genes and environment. Within this framework, phenotypic age influences aging-related outcomes and is reflected by latent domains of aging (body composition, energetics, homeostasis, and neural functioning) reflected by biomarkers. First, we validate the framework by selecting age-associated domain-specific biomarkers and assessing internal consistency and convergent construct validity (Cronbach’s alpha). Using data from the Baltimore Longitudinal Study of Aging, within-domain Cronbach’s alphas ranged from 0.80 to 0.92, supporting convergent construct validity. Second, we evaluate two broad methods for combining biomarkers into one phenotypic age measure customized to different objectives: 1) confirmatory factor analysis of chronological age-adjusted biomarkers to create a measure to identify pleiotropic genetic and environmental mechanisms, and 2) machine-learning methods to create a measure optimizing predictive and concurrent criterion validity. This framework will enable evaluation of candidate biological mechanisms of aging.

Education and Psychosocial Functioning Among Older Adults: 4-Year Change in Sense of Control and Hopelessness

Objectives

This study investigates education differences in levels and change in sense of control and hopelessness among older adults.

Method

We used data from the Health and Retirement Study, an ongoing biennial survey of a nationally representative sample of older Americans, to examine education differences in sense of control (e.g., mastery and perceived constraints) and hopelessness. Our sample included 8,495 adults aged 52 and older who were interviewed in 2006/2008 and 2010/2012. We assessed separate models for change in sense of control and hopelessness, accounting for recent changes in social circumstances and health status.

Results

Low mastery, perceived constraints, and hopelessness were highest among individuals with less than a high school education. Over a 4-year period, this group experienced the greatest declines in psychosocial functioning, as indicated by greater increases in low mastery, perceived constraints, and hopelessness. Education differences existed net of recent negative experiences, specifically the loss of intimate social relationships and social support and increases in disease and disability.

Discussion

These findings highlight the importance of education for sense of control and hopelessness in older adulthood and demonstrate the cumulative advantage of higher levels of education for psychosocial functioning.

Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: Evidence from the Health and Retirement Study

BACKGROUND

An individual's rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure.

METHODS AND FINDINGS

Based on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches-Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2%), closely followed by adulthood adversity, which was suggested to contribute 9.0% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of the population.

CONCLUSIONS

In a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.

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

BACKGROUND

Higher mortality experienced by socially disadvantaged groups and/or racial/ethnic minorities is hypothesized to be, at least in part, due to an acceleration of the aging process. Using a new epigenetic aging measure, Levine DNAmAge, this study aimed to investigate whether epigenetic aging accounts for mortality disparities by race/ethnicity and education in a sample of U.S. postmenopausal women.

METHODS

1834 participants from an ancillary study (BA23) in the Women's Health Initiative, a national study that recruited postmenopausal women (50-79 years) were included. Over the 22 years of follow-up, 551 women died, and 31,946 person-years were observed. Levine DNAmAge (unit in years) was calculated based on an equation that we previously developed in an independent sample, which incorporates methylation levels at 513 CpG sites.

RESULTS

As previously reported, non-Hispanic blacks and Hispanics were epigenetically older than non-Hispanic whites of the same chronological age. Similarly, those with less education had older epigenetic ages than expected in the full sample, as well as among non-Hispanic whites and Hispanics, but not among non-Hispanic blacks. Non-Hispanic blacks and those with low education exhibited the greatest risk of mortality. However, this association was partially attenuated when accounting for differences in DNAmAge. Furthermore, formal mediation analysis suggested that DNAmAge partially mediated the mortality increase among non-Hispanic blacks, compared to non-Hispanic whites (proportion mediated, 15.8%, P = 0.002), as well as the mortality increase for those with less than high school education, compared to college educated (proportion mediated, 11.6%, P < 2E-16).

CONCLUSIONS

Among a group of postmenopausal women, non-Hispanic blacks and those with less education exhibit higher epigenetic aging, which partially accounts for their shorter life expectancies.

Predictors and Implications of Accelerated Cognitive Aging

Aging is a major risk factor for both normal and pathological cognitive decline. However, individuals vary in their rate of age-related decline. We developed an easily interpretable composite measure of cognitive age, and related both the level of cognitive age and cognitive slope to sociodemographic, genetic, and disease indicators and examine its prediction of dementia transition. Using a sample of 19,594 participants from the Health and Retirement Study, cognitive age was derived from a set of performance tests administered at each wave. Our findings reveal different conclusions as they relate to levels versus slopes of cognitive age, with more pronounced differences by sex and race/ethnicity for absolute levels of cognitive decline rather than for rates of declines. We also find that both level and slope of cognitive age are inversely related to education, as well as increased for persons with APOE ε4 and/or diabetes. Finally, results show that the slope in cognitive age predicts subsequent dementia among non-demented older adults. Overall, our study suggests that this measure is applicable to cross-sectional and longitudinal studies on cognitive aging, decline, and dementia with the goal of better understanding individual differences in cognitive decline.

Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades

Increasing life expectancy has been interpreted as improving health of a population. However, mortality is not always a reliable proxy for the pace of aging and could instead reflect achievement in keeping ailing people alive. Using data from NHANES III (1988-1994) and NHANES IV (2007-2010), we examined how biological age, relative to chronological age, changed in the United States between 1988 and 2010, while estimating the contribution of changes in modifiable health behaviors. Results suggest that biological age is lower for more recent periods; however, the degree of improvement varied across age and sex groups. Overall, older adults experienced the greatest improvement or decreases in biological age. Males, especially those in the youngest and oldest groups, experienced greater declines in biological age than females. These differences were partially explained by age- and sex-specific changes in behaviors, such as smoking, obesity, and medication use. Slowing the pace of aging, along with increasing life expectancy, has important social and economic implications; thus, identifying modifiable risk factors that contribute to cohort differences in health and aging is essential.

Humanin Prevents Age-Related Cognitive Decline in Mice and is Associated with Improved Cognitive Age in Humans

Advanced age is associated with a decline in cognitive function, likely caused by a combination of modifiable and non-modifiable factors such as genetics and lifestyle choices. Mounting evidence suggests that humanin and other mitochondrial derived peptides play a role in several age-related conditions including neurodegenerative disease. Here we demonstrate that humanin administration has neuroprotective effects in vitro in human cell culture models and is sufficient to improve cognition in vivo in aged mice. Furthermore, in a human cohort, using mitochondrial GWAS, we identified a specific SNP (rs2854128) in the humanin-coding region of the mitochondrial genome that is associated with a decrease in circulating humanin levels. In a large, independent cohort, consisting of a nationally-representative sample of older adults, we find that this SNP is associated with accelerated cognitive aging, supporting the concept that humanin is an important factor in cognitive aging.

Time and the Metrics of Aging

The study of aging relates to changes in physical and functional dimensions that occur over time in living organisms. Yet, a model that establishes the hierarchical relationship and interlaced time courses of molecular, phenotypic, and functional hierarchical domains of aging in humans has not been established. We propose that studying the mechanisms and consequences of aging through the lens of these hierarchical domains and their connections will provide clarity in semantics and enhance a translational perspective. The study of human aging would be most informative from a life course, longitudinal perspective, given that manifestations of aging are already detectable early in life at the molecular level, yet the phenotypic responses remain masked by compensatory/resiliency mechanisms. Understanding the nature of these mechanisms is paramount for developing interventions that reduce the burden of disease and disability in older persons.

Biological Age, Not Chronological Age, Is Associated with Late-Life Depression

Background

The pathophysiology of late-life depression (LLD) is complex and heterogeneous, with age-related processes implicated in its pathogenesis. This study examined the cross-sectional and longitudinal association between depressive symptoms and a baseline multibiomarker algorithm of biological age (BA) that aggregates indicators of inflammatory, metabolic, cardiovascular, lung, liver, and kidney functioning.

Method

Data were analyzed from 2,776 men and women from the prospective observational Health Aging and Body Composition Study, who had both evaluable chronological age (CA) and BA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale.

Results

A covariate-adjusted regression model showed that BA (B = 0.03, p = .0471) but not CA (B = -0.01, p = .7185) is associated with baseline CES-D scores. The mean baseline BA for individuals with a CES-D ≥ 10 was 1.28 years greater than in those with a CES-D < 10. Comparatively, there is only a 0.05-year difference in mean CA between the two depression groups. A covariate-adjusted longitudinal model found that baseline BA predicts CES-D score at follow-up (B = 0.04, p = .0058), whereas CA does not (B = 0.03, p = .4125). Additionally, an older BA significantly predicted a CES-D ≥ 10 (B = 0.02, p = .032) over a 10-year period.

Conclusions

A multibiomarker index of an older adult's BA outperformed their CA in predicting subsequent increased and clinically significant depressive symptoms. This result supports the evolving view of LLD as a brain disorder resulting from deleterious age-associated changes across numerous physiological systems.

Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?

The geroscience hypothesis posits that therapies to slow biological processes of aging can prevent disease and extend healthy years of life. To test such "geroprotective" therapies in humans, outcome measures are needed that can assess extension of disease-free life span. This need has spurred development of different methods to quantify biological aging. But different methods have not been systematically compared in the same humans. We implemented 7 methods to quantify biological aging using repeated-measures physiological and genomic data in 964 middle-aged humans in the Dunedin Study (New Zealand; persons born 1972-1973). We studied 11 measures in total: telomere-length and erosion, 3 epigenetic-clocks and their ticking rates, and 3 biomarker-composites. Contrary to expectation, we found low agreement between different measures of biological aging. We next compared associations between biological aging measures and outcomes that geroprotective therapies seek to modify: physical functioning, cognitive decline, and subjective signs of aging, including aged facial appearance. The 71-cytosine-phosphate-guanine epigenetic clock and biomarker composites were consistently related to these aging-related outcomes. However, effect sizes were modest. Results suggested that various proposed approaches to quantifying biological aging may not measure the same aspects of the aging process. Further systematic evaluation and refinement of measures of biological aging is needed to furnish outcomes for geroprotector trials.

An epigenetic biomarker of aging for lifespan and healthspan

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

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

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

Contemporaneous Social Environment and the Architecture of Late-Life Gene Expression Profiles

Environmental or social challenges can stimulate a cascade of coordinated physiological changes in stress response systems. Unfortunately, chronic activation of these adaptations under conditions such as low socioeconomic status (SES) can have negative consequences for long-term health. While there is substantial evidence tying low SES to increased disease risk and reduced life expectancy, the underlying biology remains poorly understood. Using pilot data on 120 older adults from the Health and Retirement Study (United States, 2002-2010), we examined the associations between SES and gene expression levels in adulthood, with particular focus on a gene expression program known as the conserved transcriptional response to adversity. We also used a bioinformatics-based approach to assess the activity of specific gene regulation pathways involved in inflammation, antiviral responses, and stress-related neuroendocrine signaling. We found that low SES was related to increased expression of conserved transcriptional response to adversity genes and distinct patterns of proinflammatory, antiviral, and stress signaling (e.g., sympathetic nervous system and hypothalamic-pituitary-adrenal axis) transcription factor activation.