Developments in molecular epidemiology of aging

Social Vulnerability and Biological Aging in New York City: An Electronic Health Records-Based Study

Chronological age is not an accurate predictor of morbidity and mortality risk, as individuals' aging processes are diverse. Phenotypic age acceleration (PhenoAgeAccel) is a validated biological age measure incorporating chronological age and biomarkers from blood samples commonly used in clinical practice that can better reflect aging-related morbidity and mortality risk. The heterogeneity of age-related decline is not random, as environmental exposures can promote or impede healthy aging. Social Vulnerability Index (SVI) is a composite index accounting for different facets of the social, economic, and demographic environment grouped into four themes: socioeconomic status, household composition and disability, minority status and language, and housing and transportation. We aim to assess the concurrent and combined associations of the four SVI themes on PhenoAgeAccel and the differential effects on disadvantaged groups. We use electronic health records data from 31,913 patients from the Mount Sinai Health System (116,952 person-years) and calculate PhenoAge for years with available laboratory results (2011-2022). PhenoAge is calculated as a weighted linear combination of lab results, and PhenoAgeAccel is the differential between PhenoAge and chronological age. A decile increase in the mixture of SVI dimensions was associated with an increase of 0.23 years (95% CI 0.21, 0.25) in PhenoAgeAccel. The socioeconomic status dimension was the main driver of the association, accounting for 61% of the weight. Interaction models revealed a more substantial detrimental association for women and racial and ethnic minorities with differences in leading SVI themes. These findings suggest that neighborhood-level social vulnerability increases the biological age of its residents, increasing morbidity and mortality risks. Socioeconomic status has the larger detrimental role among the different facets of social environment.

The mediating role of accelerated biological aging in the association between household air pollution from solid cooking fuels and neuropsychiatric disorders

BACKGROUND

The role of biological aging in the relationship between household air pollution (HAP) from polluting cooking fuels and neuropsychiatric diseases remains unclear.

METHODS

This nationwide cohort study enrolled 8550 participants aged 45-80 from 2011 to 2020. Biological age based on Klemera-Doubal method (KDM-BA) was derived from 11 clinical biomarkers. The associations of switching cooking fuels with KDM-BA acceleration (KDM-BAA) and neuropsychiatric diseases were elucidated by generalized linear models and Cox proportional hazard models, respectively. The mediating effects of KDM-BAA on associations between polluting cooking fuel and neuropsychiatric diseases were further examined. We also evaluated the potential of leisure engagement in attenuating KDM-BAA associated with polluting cooking fuels.

RESULTS

Compared to consistent use of cleaner fuels, consistent solid fuel use was associated with a 0.12 (95 % CI: 0.05, 0.18) year increase in KDM-BAA, a 25 % higher risk of neurological disease (HR = 1.25, 95 % CI: 1.03, 1.54), and a 34 % increased risk of psychiatric disorders (HR = 1.34, 95 % CI: 1.02, 1.78). Mediation analysis revealed that KDM-BAA significantly mediated the association between solid cooking fuel and neuropsychiatric diseases, with mediated proportions of 5.96 % (P = 0.006) for neurological disease and 5.71 % (P = 0.024) for psychiatric disease. Leisure engagement demonstrated stable benefits in attenuating KDM-BAA independently of cooking fuel use.

CONCLUSION

Our findings illuminate the pathways connecting HAP from solid fuel consumption with biological aging and neuropsychiatric disorders, and highlight the role of leisure engagement and expanding access to cleaner fuels in mitigating these adverse effects.

Accelerated biological aging increases the risk of short- and long-term stroke prognosis in patients with ischemic stroke or TIA

BACKGROUND

Biological age (BA), an integrated measure of physiological aging, has a clear link to stroke. There is a paucity of long-term longitudinal studies about the association between accelerated biological age and stroke prognosis in patients with previous strokes, and the differences in the predictive ability of various BA indicators calculated from clinical biochemistry biomarkers for future stroke outcomes are still unknown. To evaluate the role of three accelerated BA indicators for short- and long-term prognosis of patients with ischemic stroke or transient ischemic attack (TIA), and to identify the most appropriate predictor.

METHODS

This study included 7396 patients from the Third China National Stroke Registry (CNSR-III), a prospective national registry of patients with acute ischemic stroke or TIA between August 2015 and March 2018 in China. We constructed accelerated BA using three widely recognized algorithms: PhenoAge, Klemera-Doubal, and HD method. To ascertain the association of accelerated BA with the risk of short- and long-term stroke outcomes, a Cox or logistic regression model was conducted for the analysis. The net reclassification index and integrated discrimination improvement were used to evaluate the added model improvement ability of BA acceleration.

FINDINGS

Compared to those with the lowest of PhenoAge acceleration, patients with the highest were more likely to have a higher risk of stroke (HR 1.98, 95% CI 1.49-2.63, P < 0.001), ischemic stroke (HR 1.88, 95% CI 1.41-2.53, P < 0.001), composite vascular events (HR 2.03, 95% CI 1.53-2.68, P < 0.001), all-cause death (HR 7.02, 95% CI 3.41-14.47, P < 0.001) and the modified Rankin scale of 3-6 (OR 2.55, 95% CI 2.05-3.16, P < 0.001) at three months, and the association observed within one year and five years was similar to that within three months. The risk of all stroke outcomes for HDAge was consistent with PhenoAge acceleration, but KDMAge acceleration was the same, except for stroke within one year (HR 1.24, 95% CI 1.00-1.53, P = 0.053). PhenoAge acceleration provided a better improvement in the model's predictive ability for stroke prognosis, compared to BA determined by other algorithms.

INTERPRETATION

In this prospective cohort study, BA acceleration, particularly PhenoAge, may help identify stroke patients with risks of short- and long-term poor outcomes, potentially enabling subclinical prevention and early intervention.

FUNDING

This work was supported by grants from Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2019-I2M-5-029), the National Natural Science Foundation of China (U20A20358), Beijing Hospitals Authority Clinical Medicine Development of special funding support (ZLRK202312), the National Key R&D Program of China (No. 2022YFC3602500, 2022YFC3602505), Outstanding Young Talents Project of Capital Medical University (A2105), and Beijing High-Level Public Health Technical Personnel Construction Project (Discipline leader -03-12).

Evaluating the role of biological age in osteoporosis risk among middle-aged and older adults: A nationwide perspective

OBJECTIVES

This study aimed to investigate the association between biological age acceleration and osteoporosis (OP) risk in middle-aged and older adults using data from the National Health and Nutrition Examination Survey (NHANES). The research focused on analyzing the relationship between two biological aging metrics, Klemera-Doubal Method Age (KDMAge) and Phenotypic Age (PhenoAge), and OP risk.

METHODS

The study analyzed data from NHANES, which included 6550 participants aged 50 and above from survey cycles 2005-2010 and 2017-2018. Linear and logistic regression were used to investigate the relationship between biological age acceleration (KDMAgeAccel and PhenoAgeAccel) and OP. Subgroup analysis was performed by age, gender and other factors. Multivariable Cox regression analysis yielded Hazard Ratios (HRs) relating biological age acceleration to mortality were evaluated. The study also considered the mediating roles of body mass index (BMI).

RESULTS

KDMAgeAccel (odds ratio [OR] = 2.34, 95 % CI, 1.72-3.18) and PhenoAgeAccel (OR = 2.03, 95 % CI, 1.48-2.78) were significantly associated with increased OP risk and reduced bone mineral density (BMD). Specifically, higher KDMAgeAccel and PhenoAgeAccel were linked to higher OP prevalence and lower BMD at multiple sites. Subgroup analyses indicated that the association between accelerated biological age acceleration and OP risk was consistent across different demographics. Mediation analysis revealed that BMI partially mediated the relationship between accelerated biological age and OP, although other mechanisms are likely involved. Statistical analysis indicated that individuals with higher biological age metrics had increased mortality risk related to OP.

CONCLUSION

The findings suggest that accelerated biological age is a robust predictor of OP risk and related mortality. KDMAgeAccel and PhenoAgeAccel could serve as valuable biomarkers for identifying individuals at high risk for OP, guiding preventive strategies.

Association between plant and animal protein and biological aging: findings from the UK Biobank

PURPOSE

This study aimed to evaluate the relationship between plant protein, animal protein and biological aging through different dimensions of biological aging indices. Then explore the effects of substitution of plant protein, animal protein, and their food sources on biological aging.

METHODS

The data came from 79,294 participants in the UK Biobank who completed at least two 24-h dietary assessments. Higher Klemera-Doubal Method Biological Age (HKDM-BA), higher PhenoAge (HPA), higher allostatic load (HAL), and longer telomere length (LTL) were estimated to assess biological aging. Logistic regression was used to estimate protein-biological aging associations. Substitution model was performed to assess the effect of dietary protein substitutions.

RESULTS

Plant protein intake was inversely associated with HKDM-BA, HPA, HAL, and positively associated with LTL (odds ratios after fully adjusting and comparing the highest to the lowest quartile: 0.83 (0.79-0.88) for HKDM-BA, 0.86 (0.72-0.94) for HPA, 0.90 (0.85-0.95) for HAL, 1.06 (1.01-1.12) for LTL), while animal protein was not correlated with the four indices. Substituting 5% of energy intake from animal protein with plant protein, replacing red meat or poultry with whole grains, and replacing red or processed meat with nuts, were negatively associated with HKDM-BA, HPA, HAL and positively associated with LTL. However, an inverse association was found when legumes were substituted for yogurt. Gamma glutamyltransferase, alanine aminotransferase, and aspartate aminotransferase mediated the relationship between plant protein and HKDM-BA, HPA, HAL, and LTL (mediation proportion 11.5-24.5%; 1.9-6.7%; 2.8-4.5%, respectively).

CONCLUSION

Higher plant protein intake is inversely associated with biological aging. Although there is no association with animal protein, food with animal proteins displayed a varied correlation.

Accelerated biological aging: unveiling the path to cardiometabolic multimorbidity, dementia, and mortality

Background

Cardiometabolic multimorbidity (CMM) and aging are increasing public health concerns. This prospective study used UK Biobank cohort to investigate the relationship between biological aging and the trajectory of CMM to dementia and mortality.

Methods

CMM is the coexistence of at least two cardiometabolic diseases (CMD), including stroke, ischemic heart disease, and diabetes. Biological age was calculated using the KDM-BA and PhenoAge algorithms. Accelerated aging indicated biological age advances more rapidly than chronological age.

Results

The study included 415,147 individuals with an average age of 56.5 years. During the average 11-year follow-up period, CMD-free individuals with accelerated aging had a significantly greater risk of CMD (KDM-BA, HR 1.456; PhenoAge, HR 1.404), CMM (KDM-BA, HR 1.952; PhenoAge, HR 1.738), dementia (KDM-BA, HR 1.243; PhenoAge, HR 1.212), and mortality (KDM-BA, HR 1.821; PhenoAge, HR 2.047) in fully-adjusted Cox regression models (p < 0.05 for all). Accelerated aging had adjusted HRs of 1.489 (KDM-BA) and 1.488 (PhenoAge) for CMM, 1.434 (KDM-BA) and 1.514 (PhenoAge) for dementia, and 1.943 (KDM-BA) and 2.239 (PhenoAge) for mortality in participants with CMD at baseline (p < 0.05 for all). CMM significantly mediated accelerated aging's indirect effects on dementia by 13.7% (KDM-BA, HR) and 21.6% (PhenoAge); those on mortality were 4.7% (KDM-BA) and 5.2% (PhenoAge). The population attributable-risk of Life's Essential 8 score (≥80 vs. <80) were 0.79 and 0.43 for KDM-BA and PhenoAge accelerated aging, respectively.

Conclusion

Biological aging involves the entire trajectory of CMM from a CMD-free state to CMD, to CMM, and ultimately to dementia and death. Life's Essential 8 may be a potential target to counter age acceleration.

Association of biological aging with prostate cancer: insights from the National Health and Nutrition Examination Survey

The link between biological aging and prostate cancer (PCa) risk, particularly as indicated by elevated prostate-specific antigen (PSA) levels, remains uncertain. This study utilized data from the National Health and Nutrition Examination Survey (2001-2010) to explore this association. Biological age was assessed using Klemera-Doubal method age (KDMAge) and phenotypic age (PhenoAge). PCa was identified through self-reported diagnoses, and highly probable PCa was determined by PSA levels. We analyzed the prevalence of PCa and PSA-defined highly probable PCa across quartiles of biological age measures using weighted chi-square and linear trend tests. Associations were evaluated using weighted multiple logistic regression models. Among 7,209 and 6,682 males analyzed, the overall weighted prevalence of PCa was 2.86%, increasing to 9.60% in those aged 65 and above. A significant rise in PCa prevalence was observed with higher quartiles of KDMAge or PhenoAge (P for trend < 0.001), particularly in those under 65. In this younger group, higher PhenoAge acceleration quartiles were linked to increased PCa prevalence and higher risk of PCa (OR = 1.50, P = 0.015) as well as highly probable PCa in those without a diagnosis (OR = 1.28, P = 0.031). These findings suggest that accelerated biological aging is associated with an increased risk of PCa and may indicate early risk as signaled by PSA levels, even in those without a PCa diagnosis.

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.

Associations of daily eating frequency and nighttime fasting duration with biological aging in National Health and Nutrition Examination Survey (NHANES) 2003-2010 and 2015-2018

BACKGROUND

Information on the influences of daily eating frequency (DEF) and nighttime fasting duration (NFD) on biological aging is minimal. Our study investigated the potential associations of DEF and NFD with accelerated aging.

METHODS

Out of 24212 participants in NHANES 2003-2010 and 2015-2018, 4 predicted age metrics [homeostatic dysregulation (HD), Klemera-Doubal method (KDM), phenoAge (PA), and allostatic load (AL)] were computed based on 12 blood chemistry parameters. Utilizing 24-h dietary recall, DEF was measured by the frequency of eating occurrences, while NFD was determined by assessing the timing of the initial and final meals throughout the day. Weighted multivariate linear regression models and restricted cubic spline (RCS) were utilized to examine the associations.

RESULTS

Compared to DEF of ≤ 3.0 times, subjects with DEF ≥ 4.6 times demonstrated lower KDM residual [β: -0.57, 95% confidence-interval (CI): (-0.97, -0.17)] and PA residual [β: -0.47, 95% CI: (-0.69, -0.25)]. In comparison to NFD between 10.1 and 12.0 h, individuals with NFD ≤ 10.0 h were at higher HD [β: 0.03, 95% CI: (0.01, 0.04)], KDM residual [β: 0.34, 95% CI: (0.05, 0.63)], and PA residual [β: 0.38, 95% CI: (0.18, 0.57)]. Likewise, those with NFD ≥ 14.1 h also had higher HD [β: 0.02, 95% CI: (0.01, 0.04)] and KDM residual [β: 0.33, 95% CI: (0.03, 0.62)]. The results were confirmed by the dose-response relationships of DEF and NFD with predicted age metrics. Lactate dehydrogenase (LDH) and globulin (Glo) were acknowledged as implicated in and mediating the relationships.

CONCLUSIONS

DEF below 3.0 times and NFD less than 10.0 or more than 14.1 h were independently associated with higher predicted age metrics.

The association between accelerated biological aging and the risk of osteoarthritis: a cross-sectional study

Background

Biological age (BA) offers an effective assessment of true aging state. The progression of Osteoarthritis (OA) is closely associated with an increase in chronological age, the correlation between BA and OA has not been fully elucidated.

Methods

This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Thirteen commonly used clinical traits were employed to calculate two measures of BA: the Klemera-Doubal method age (KDM-Age) and phenotypic age (Pheno-Age). The residuals of the regression of these ages based on chronological age were calculated as KDM-Age or Pheno-Age acceleration, respectively. OA was determined through self-reported prior diagnoses. The prevalence of OA across different quartiles of BA was compared using weighted chi-square tests and linear trend tests. The association between BA and OA was assessed using weighted multivariate logistic regression models.

Results

A total of 30,547 participants aged ≥20 years were included in this study, 3,922 (14%) were diagnosed with OA. Participants with OA exhibited higher chronological age, KDM-Age, Pheno-Age, KDM-Age advance, and Pheno-Age advance compared to those without OA (p < 0.001). The prevalence of OA significantly increased with higher quartiles of KDM-Age advance and Pheno-Age advance (P for trend < 0.001). In the fully adjusted model, compared to the lowest quartile (Q1) of KDM-Age advance, the highest quartile (Q4) was associated with a 36.3% increased risk of OA (OR = 1.363; 95% CI = 1.213 to 1.532, p < 0.001). The highest quartile of Pheno-Age advance (Q4) was associated with a 24.3% increased risk of OA compared to Q1 (OR = 1.243; 95% CI = 1.113 to 1.389, p < 0.001). In males and young people, no statistical differences were found in OA risk between the highest and the lowest quartiles of KDM-Age advance (p = 0.151) and Pheno-Age advance (p = 0.057), respectively.

Conclusion

Adults with accelerated biological aging have an increased risk of OA, particularly among females and older adults.

Periodontitis prevalence and acceleration of biological aging: Insights from NHANES 2009-2014 and Mendelian randomization study

AIM

This study aimed to investigate the association of periodontitis with biological aging and to assess potential causality using Mendelian randomization (MR).

METHODS

A cross-sectional study with 9558 participants from the National Health and Nutrition Examination Survey (2009-2014) was conducted. Age acceleration (BioAgeAccel and PhenoAgeAccel) was calculated from clinical biomarkers and their discrepancies with chronological age. Two-sample MR analysis was performed using data from a large-scale genome-wide association study and UK Biobank.

RESULTS

Periodontitis was associated with increased biological aging, with 0.57-year (95% CI: 0.28-0.86, p < .001) increases in BioAgeAccel and 0.41-year (95% CI: 0.04-0.78, p = .034) increases in PhenoAgeAccel. Subgroup analysis found significantly stronger associations in males for BioAgeAccele (PINTERACTION = .006), and pronounced associations in young adults (pinteraction = .023), individuals with normal body mass index (pinteraction = .015), and current smokers (pinteraction = .016) for PehonAgeAccel. MR analysis did not provide strong evidence for a causal effect of periodontitis on biological aging (BioAgeAccel: IVW β = 0.008, 95% CI: -0.018 to 0.034, p = .553 and PhenoAgeAccel: IVW β = 0.016, 95% CI: -0.042 to 0.074, p = .585).

CONCLUSION

This study identified the association of periodontitis and its severity with accelerated aging, suggesting periodontal health could be a possible method in personalized preventive and therapeutic strategies of biological aging.

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.

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.

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.