Developments in molecular epidemiology of aging

Comparative Analysis of Brain Age Prediction Using Structural and Diffusion MRIs in Neonates

Using machine learning techniques to predict brain age from multimodal data has become a crucial biomarker for assessing brain development. Among various types of brain imaging data, structural magnetic resonance imaging (sMRI) and diffusion magnetic resonance imaging (dMRI) are the most commonly used modalities. sMRI focuses on depicting macrostructural features of the brain, while dMRI reveals the orientation of major white matter fibers and changes in tissue microstructure. However, their differential capabilities in reflecting newborn age and clinical implications have not been systematically studied. This study aims to explore the impact of sMRI and dMRI on brain age prediction. Comparing predictions based on T2-weighted(T2w) and fractional anisotropy (FA) images, we found their mean absolute errors (MAE) in predicting infant age to be similar. Exploratory analysis revealed for T2w images, areas such as the cerebral cortex and ventricles contribute most significantly to age prediction, whereas FA images highlight the cerebral cortex and regions of the main white matter tracts. Despite both modalities focusing on the cerebral cortex, they exhibit significant region-wise differences, reflecting developmental disparities in macro- and microstructural aspects of the cortex. Additionally, we examined the effects of prematurity, gender, and hemispherical asymmetry of the brain on age prediction for both modalities. Results showed significant differences (p<0.05) in age prediction biases based on FA images across gender and hemispherical asymmetry, whereas no significant differences were observed with T2w images. This study underscores the differences between T2w and FA images in predicting infant brain age, offering new perspectives for studying infant brain development and aiding more effective assessment and tracking of infant development.

U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study

Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.

Early-life exposure to tobacco, genetic susceptibility, and accelerated biological aging in adulthood

Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high-polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.

Joint association of biological aging and lifestyle with risks of cancer incidence and mortality: A cohort study in the UK Biobank

BACKGROUND

Aging is a risk factor for cancer incidence and mortality. Biological aging can reflect the aging degree of the body better than chronological age and can be aggravated by unhealthy lifestyle factors. We aimed to assess the joint effect of biological aging and lifestyle with risks of cancer incidence and mortality.

METHODS

This study included a total of 281,889 participants aged 37 to 73 from the UK Biobank database. Biological age was derived from chronological age and 9 clinical blood indicators, and lifestyle score was constructed by body mass index, smoking status, alcohol consumption, physical activity, and diet. Multivariate Cox hazard proportional regression model was used to analyze the independent and joint association of biological aging and lifestyle with risks of cancer incidence and mortality, respectively.

RESULTS

Over a median follow-up period of 12.3 years, we found that older biological age was associated with increased risks of overall cancer, digestive system cancers, lung, breast and renal cancers incidence and mortality (HRs: 1.12-2.25). In the joint analysis of biological aging and lifestyle with risks of cancer incidence and mortality, compared with unhealthy lifestyle and younger biological age, individuals with healthy lifestyle and older biological age had decreased risks of incidence (8% ∼ 60%) and mortality (20% ∼ 63%) for overall, esophageal, colorectal, pancreatic and lung cancers.

CONCLUSIONS

Biological aging may be an important risk factor for cancer morbidity and mortality. A healthier lifestyle is more likely to mitigate the adverse effects of biological aging on overall cancer and some site-specific cancers.

Influencing Factors of Healthy Aging Risk Assessed Using Biomarkers: A Life Course Perspective

Assessing individual risks of healthy aging using biomarkers and identifying associated factors have become important areas of research. In this study, we conducted a literature review of relevant publications between 2018 and 2023 in both Chinese and English databases. Previous studies have predominantly used single biomarkers, such as C-reactive protein, or focused on specific life course stages and factors such as socioeconomic status, mental health, educational levels, and unhealthy lifestyles. By summarizing the progress in this field, our study provides valuable insights and future directions for promoting healthy aging from a life course perspective.

Enabling translational geroscience by broadening the scope of geriatric care

Geroscience poses that core biological mechanisms of aging contribute to chronic diseases and disabilities in late life and that health span and longevity can be modulated by pharmacological and behavioral interventions. Despite strong evidence from studies in model organisms and great potentials for translation, most geriatricians remain skeptical that geroscience will help them in the day-by-day battle with the consequences of aging in their patients. We believe that a closer collaboration between gerontologists and geriatricians is the key to overcome this impasse. There is evidence that trajectories of health with aging are rooted in intrinsic and extrinsic exposures that occur early in life and affect the pace of molecular and cellular damage accumulation with aging, also referred to as the "pace" of biological aging. Tools that measure the pace of aging currently allow for the identification of individuals experiencing accelerated aging and at higher risk of multimorbidity and disability. What we term "Translational Geroscience", i.e., the merger of fundamental and translational science with clinical practice, is thus poised to extend the action of geriatric care to a life course perspective. By targeting core mechanisms of aging, gerotherapeutics should be effective in treating patients with multimorbidity and disability, phenotypes that are all too common among geriatric patients nowadays. We call for initiatives that enhance the flow of ideas between gerontologists and geriatricians to facilitate the growth of translational geroscience. This approach can widen the scope of geriatric care, including a new role for geroscience in the promotion and operationalization of healthy longevity.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Measuring healthy ageing: current and future tools

Human ageing is a complex, multifactorial process characterised by physiological damage, increased risk of age-related diseases and inevitable functional deterioration. As the population of the world grows older, placing significant strain on social and healthcare resources, there is a growing need to identify reliable and easy-to-employ markers of healthy ageing for early detection of ageing trajectories and disease risk. Such markers would allow for the targeted implementation of strategies or treatments that can lessen suffering, disability, and dependence in old age. In this review, we summarise the healthy ageing scores reported in the literature, with a focus on the past 5 years, and compare and contrast the variables employed. The use of approaches to determine biological age, molecular biomarkers, ageing trajectories, and multi-omics ageing scores are reviewed. We conclude that the ideal healthy ageing score is multisystemic and able to encompass all of the potential alterations associated with ageing. It should also be longitudinal and able to accurately predict ageing complications at an early stage in order to maximize the chances of successful early intervention.

Biomarkers of aging through the life course: A Recent Literature Update

Purpose of review

The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course.

Recent findings

The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches.

Summary

Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.

Adolescent peer struggles predict accelerated epigenetic aging in midlife

This study examined struggles to establish autonomy and relatedness with peers in adolescence and early adulthood as predictors of advanced epigenetic aging assessed at age 30. Participants (N = 154; 67 male and 87 female) were observed repeatedly, along with close friends and romantic partners, from ages 13 through 29. Observed difficulty establishing close friendships characterized by mutual autonomy and relatedness from ages 13 to 18, an interview-assessed attachment state of mind lacking autonomy and valuing of attachment at 24, and self-reported difficulties in social integration across adolescence and adulthood were all linked to greater epigenetic age at 30, after accounting for chronological age, gender, race, and income. Analyses assessing the unique and combined effects of these factors, along with lifetime history of cigarette smoking, indicated that each of these factors, except for adult social integration, contributed uniquely to explaining epigenetic age acceleration. Results are interpreted as evidence that the adolescent preoccupation with peer relationships may be highly functional given the relevance of such relationships to long-term physical outcomes.

Accelerated biological aging and risk of depression and anxiety: evidence from 424,299 UK Biobank participants

Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%-8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%-13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Short-Term Exposure of PM2.5 and Epigenetic Aging: A Quasi-Experimental Study

Epigenetic age (EA) is an emerging DNA methylation-based biomarker of biological aging, but whether EA is causally associated with short-term PM_2.5 exposure remains unknown. We conducted a quasi-experimental study of 26 healthy adults to test whether short-term PM_2.5 exposure accelerates seven EAs with three health examinations performed before, during, and after multiple PM_2.5 pollution waves. Seven EAs were derived from the DNA methylation profiles of the Illumina HumanMethylationEPIC BeadChip from CD4+ T-helper cells. We found that an increase of 10 μg/m³ in the 0-24 h personal PM_2.5 exposure prior to health examinations was associated with a 0.035, 0.035, 0.050, 0.055, 0.052, and 0.037-unit increase in the changes of z-scored DNA methylation age acceleration (AA,Horvath), AA (Hannum), AA (GrimAge), DunedinPoAm, mortality risk score (MS), and epiTOC, respectively (p-values < 0.05). The same increase in the 24-48 h average personal PM_2.5 exposure yielded smaller effects but was still robustly associated with the changes in AA (GrimAge), DunedinPoAm, and MS. Such acute aging effects of PM_2.5 were mediated by the changes in several circulating biomarkers, including EC-SOD and sCD40L, with up to ∼28% mediated proportions. Our findings demonstrated that short-term PM_2.5 exposure could accelerate aging reflected by DNA methylation profiles via blood coagulation, oxidative stress, and systematic inflammation.

Role of sleep quality in the acceleration of biological aging and its potential for preventive interaction on air pollution insults: Findings from the UK Biobank cohort

Sleep has been associated with aging and relevant health outcomes, but the causal relationship remains inconclusive. In this study, we investigated the associations of sleep behaviors with biological ages (BAs) among 363,886 middle and elderly adults from UK Biobank. Sleep index (0 [worst]-6 [best]) of each participant was retrieved from the following six sleep behaviors: snoring, chronotype, daytime sleepiness, sleep duration, insomnia, and difficulties in getting up. Two BAs, the KDM-biological age and PhenoAge, were estimated by corresponding algorithms based on clinical traits, and their residual discrepancies with chronological age were defined as the age accelerations (AAs). We first observed negative associations between the sleep index and the two AAs, and demonstrated that the change of AAs could be the consequence of sleep quality using Mendelian randomization with genetic risk scores of sleep index and BAs. Particularly, a one-unit increase in sleep index was associated with 0.104- and 0.119-year decreases in KDM-biological AA and PhenoAge acceleration, respectively. Air pollution is another key driver of aging. We further observed significant independent and joint effects of sleep and air pollution (PM_2.5 and NO₂ ) on AAs. Sleep quality also showed a modifying effect on the associations of elevated PM_2.5 and NO₂  levels with accelerated AAs. For instance, an interquartile range increase in PM_2.5  level was associated with 0.009-, 0.044-, and 0.074-year increase in PhenoAge acceleration among people with high (5-6), medium (3-4), and low (0-2) sleep index, respectively. Our findings elucidate that better sleep quality could lessen accelerated biological aging resulting from air pollution.

An exposomic framework to uncover environmental drivers of aging

The exposome, the environmental complement of the genome, is an omics level characterization of an individual's exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.

DunedinPACE, a DNA methylation biomarker of the pace of aging

Background

Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously, we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al., 2020). Here, we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).

Methods

We used data from the Dunedin Study 1972-1973 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.

Results

DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.

Conclusions

DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.

Funding

This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.

GrimAge Outperforms Other Epigenetic Clocks in the Prediction of Age-Related Clinical Phenotypes and All-Cause Mortality

The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesized to represent a measure of biological aging with a positive/negative residual signifying age acceleration (AA)/deceleration, respectively. The present study examines the associations of 4 epigenetic clocks-Horvath, Hannum, PhenoAge, GrimAge-with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). HorvathAA and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of walking speed, .polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.

Contributing factors to advanced brain aging in depression and anxiety disorders

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d = 0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d = 0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.