Quantification of biological aging in young adults

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood-brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer's disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM's ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals' rates of adverse cognitive change with age.

Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study

Background

Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.

Methods

This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera-Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.

Results

About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of -0.19 (95% CI -0.34, -0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.

Conclusions

Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.

Funding

This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.

Association between cardiometabolic index and biological ageing among adults: a population-based study

BACKGROUND

Cardiovascular health (CVH) is closely associated with ageing. This study aimed to investigate the association between cardiometabolic index (CMI), a novel indicator of cardiometabolic status, and biological ageing.

METHODS

Cross-sectional data were obtained from participants with comprehensive CMI and biological age data in the National Health and Nutrition Examination Survey from 2011 to 2018. Biological age acceleration (BioAgeAccel) is calculated as the differences between biological age and chronological age, and that biological age is derived from a model incorporating eight biomarkers. Weighted multivariable regression, sensitivity analysis, and smoothing curve fitting were performed to explore the independent association between CMI and the acceleration of biological age. Subgroup and interaction analyses were performed to investigate whether this association was consistent across populations.

RESULTS

In 4282 subjects ≥ 20 years of age, there was a positive relationship between CMI and biological age. The BioAgeAccel increased 1.16 years for each unit CMI increase [1.16 (1.02, 1.31)], and increased 0.99 years for per SD increase in CMI [0.99 (0.87, 1.11)]. Participants in the highest CMI quartile had a BioAgeAccel that was 2.49 years higher than participants in the lowest CMI quartile [2.49 (2.15, 2.83)]. In stratified studies, the positive correlation between CMI and biological age acceleration was not consistent across strata. This positive correlation was stronger in female, diabetes, and non-hypertension populations.

CONCLUSIONS

CMI is positively correlated with biological ageing in adults in the United States. Prospective studies with larger sample sizes are required to validate our findings.

Metabolic Status Modulates Global and Local Brain Age Estimates in Overweight and Obese Adults

BACKGROUND

As people live longer, maintaining brain health becomes essential for extending health span and preserving independence. Brain degeneration and cognitive decline are major contributors to disability. In this study, we investigated how metabolic health influences the brain age gap estimate (brainAGE), which measures the difference between neuroimaging-predicted brain age and chronological age.

METHODS

K-means clustering was applied to fasting metabolic markers including insulin, glucose, leptin, cortisol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, steady-state plasma glucose, and body mass index of 114 physically and cognitively healthy adults. The homeostatic model assessment for insulin resistance served as a reference. T1-weighted brain magnetic resonance imaging was used to calculate voxel-level and global brainAGE. Longitudinal data were available for 53 participants over a 3-year interval.

RESULTS

K-means clustering divided the sample into 2 groups, those with favorable (n = 58) and those with suboptimal (n = 56) metabolic health. The suboptimal group showed signs of insulin resistance and dyslipidemia (false discovery rate-corrected p < .05) and had older global brainAGE and local brainAGE, with deviations most prominent in cerebellar, ventromedial prefrontal, and medial temporal regions (familywise error-corrected p < .05). Longitudinal analysis revealed group differences but no significant time or interaction effects on brainAGE measures.

CONCLUSIONS

Suboptimal metabolic status is linked to accelerated brain aging, particularly in brain regions rich in insulin receptors. These findings highlight the importance of metabolic health in maintaining brain function and suggest that promoting metabolic well-being may help extend health span.

Gerotherapeutics: aging mechanism-based pharmaceutical and behavioral interventions to reduce cancer racial and ethnic disparities

The central premise of this article is that a portion of the established relationships between social determinants of health and racial and ethnic disparities in cancer morbidity and mortality is mediated through differences in rates of biological aging processes. We further posit that using knowledge about aging could enable discovery and testing of new mechanism-based pharmaceutical and behavioral interventions ("gerotherapeutics") to differentially improve the health of cancer survivors from minority populations and reduce cancer disparities. These hypotheses are based on evidence that lifelong differences in adverse social determinants of health contribute to disparities in rates of biological aging ("social determinants of aging"), with individuals from minoritized groups experiencing accelerated aging (ie, a steeper slope or trajectory of biological aging over time relative to chronological age) more often than individuals from nonminoritized groups. Acceleration of biological aging can increase the risk, age of onset, aggressiveness, and stage of many adult cancers. There are also documented negative feedback loops whereby the cellular damage caused by cancer and its therapies act as drivers of additional biological aging. Together, these dynamic intersectional forces can contribute to differences in cancer outcomes between survivors from minoritized vs nonminoritized populations. We highlight key targetable biological aging mechanisms with potential applications to reducing cancer disparities and discuss methodological considerations for preclinical and clinical testing of the impact of gerotherapeutics on cancer outcomes in minoritized populations. Ultimately, the promise of reducing cancer disparities will require broad societal policy changes that address the structural causes of accelerated biological aging and ensure equitable access to all new cancer control paradigms.

Integrating the environmental and genetic architectures of aging and mortality

Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality (n = 492,567) and then assessed the associations of these exposures with a proteomic age clock (n = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3-26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5-49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk.

Five hypothesized biological mechanisms linking adverse childhood experiences with anxiety, depression, and PTSD: A scoping review

Adults with symptoms of anxiety, depression, or PTSD and a history of adverse childhood experiences (ACEs) may experience more severe symptoms than those without ACEs. The identification of mechanisms linking ACEs to later mental health problems may provide salient treatment targets to improve outcomes. Several biological markers (cortisol, inflammation, allostatic load, DNA methylation, and telomere length) that are indicative of functional variation in stress response systems, have been hypothesized as potential mechanisms linking ACEs to later mental health outcomes. Much of the evidence supporting this hypothesis examines isolated pairwise associations between variables and it is unclear whether statistical tests of mediation support these conclusions. It is also unclear how much of the extant research has used theory to guide mediation analyses, which may be a salient factor in the recognition of a mechanism. This scoping review surveyed research conducting mediation analysis examining the indirect effect of any of these five biological markers on the relationship between ACEs and anxiety, depression, or PTSD. It further surveyed the use of theory in these analyses. Pubmed and seven electronic databases were searched: (1) APA PsychInfo (2) CINAHL Plus (3) Health Source: Nursing/Academic Edition (4) MEDLINE (5) Psychology and Behavioral Sciences Collection (6) Science and Technology Collection, and (7) SocINDEX. A total of 16 articles were identified. The majority of studies examined depression as an outcome and the statistical significance of indirect effects were mixed across mediators. Common theoretical models and frameworks were consistent with life course theory and evolutionary or developmental perspectives.

Methods for the assessment of biological age - A systematic review

Biological age has long been proposed to complement chronological age because it has the potential to provide a more accurate assessment of someone's ageing process and functional status. At present, there are several methods to determine an individual's biological age through the measurement of biomarkers of ageing. This review compares methods for assessing biological age in adults, analyses biomarkers of ageing, and determines the goals for which biological age can be calculated, in order to help determine a gold standard for measuring biological age. Articles were eligible if studies included a test battery and statistical method to calculate biological age. Literature research included the databases Medline, Embase, Cochrane Library, Web of Science and ClinicalTrials.gov. In total, 56 studies were included and the risk of bias in each of them was assessed. The most commonly used methods to assess biological age are Klemera and Doubal's method, principal component analysis, multiple linear regression, PhenoAge and Hochschild's method. Klemera and Doubal's method has proved the most reliable. Apart from using different statistical methods, the difference between the biological ageing scores lies in the choice of biomarkers of ageing, especially the inclusion of chronological age as a biomarker of ageing. Most of the included studies aimed to establish a new biological ageing score or compare biological age to different measurements of functionality of the human body. In conclusion, there is still no consensus on a gold standard and more research on this topic is necessary. Study protocol PROSPERO ID: CRD42021287548.

Unequal but widespread despairs: Social inequalities and self-rated health trends in the United States in 1972-2018

SIGNIFICANCE

Past studies show rising mortality and morbidity among middle-aged white Americans since the 21st century. This research analyses trends in declining self-rated health (SRH) across demographic groups, focusing on shifts in SRH inequalities by gender, race, and socioeconomic status (SES). It sheds light on declining health trends in the United States and deepens our understanding of health inequalities and their dynamics in high-income countries.

METHOD

We analyse 29 waves of cross-sectional data from the General Social Survey (1972-2018, N = 46,133) using Bayesian Hierarchical Age-Period-Cohort Cross-Classified Random Effect models (BHAPC-CCRM) to estimate age, period, and cohort effects, and changes in health gaps over time as interactions between period and race, gender, or SES.

RESULTS

SRH improved until the 21st century but then declined across all gender, race, income, education, and employment groups after controlling for age and cohort effects. The racial health gap has continued since 2000, with a slight erosion of white health privilege. Nonwhite, low-income, non-college-educated, unemployed, and unmarried individuals have seen further declines in SRH. Baby Boomers' health advantage was wiped out after 2000.

INTERPRETATION

In line with the health reversal literature in the U.S. and the U.K., SRH has deteriorated in the 21st century for all racial, gender, and SES groups in the U.S. The diminishing SRH advantage for whites results from a faster decline compared to Blacks and other non-white groups. However, significant racial and SES disparities in SRH persist, with disadvantaged groups experiencing poorer SRH. We discuss the policy implications.

Whole-body networks: a holistic approach for studying aging

Aging is a multi-organ disease, yet the traditional approach has been to study each organ in isolation. Such organ-specific studies have provided invaluable information regarding its pathomechanisms. However, an overall picture of the whole-body network (WBN) during aging is still incomplete. In this study, we analyzed the functional magnetic resonance imaging blood-oxygen level-dependent, respiratory rate and heart rate time series of a young and an elderly group during eyes-open resting-state. We constructed WBNs by exploring the time-lagged coupling between the different organs. First, we showed that our analytical pipeline could identify regional differences in the networks of both cohorts, allowing us to proceed with the remaining analyses. The comparison of the WBNs revealed a complex relationship where some connections were stronger and some weaker in the elderly. Finally, the interconnectivity and segregation of the WBNs were negatively correlated with the short-term memory and verbal learning of the young participants. This study: i) validated our methodology, ii) identified differences in the WBNs of the two groups and iii) showed correlations of WBNs with behavioral measures. In conclusion, the concept of WBN shows great potential for the understanding of aging and age-related diseases.

Lifetime age-related changes in clinical laboratory results, aging clocks and mortality predictors in 2412 Golden Retrievers

In this study, we investigated age-related changes in clinical laboratory data and their association with mortality in dogs from the Golden Retriever Lifetime Study. By analyzing complete blood count (CBC) and biochemistry data from 2'412 Golden Retrievers over 16,678 visits, we observed significant changes during the first 2 years of life and throughout aging. Based on these observations, we developed a biological aging clock using a LASSO model to predict age based on blood markers, achieving an accuracy of R = 0.78. Although the biological age clock and pace of aging did not significantly improve mortality prediction, a model incorporating all blood biomarkers showed better predictive power for lifetime (C-index = 0.763) and 1-year mortality (AUC = 0.817). Our findings underscore the importance of comprehensive blood analysis for aging and mortality prediction in dogs and open the door for the development of novel methods to investigate aging in companion animals.

Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease

Introduction

DNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models are effective, their interpretability is limited by their dependence on small and potentially stochastic sets of CpG sites. Here, we propose that the reliability of DNAm age clocks should stem from their capacity to detect comprehensive and targeted aging signatures.

Methods

We compiled publicly available DNAm whole-blood samples (n = 17,726) comprising the entire human lifespan (0-112 years). We used a pre-trained network-coherent autoencoder (NCAE) to compress DNAm data into embeddings, with which we trained interpretable neural network epigenetic clocks. We then retrieved their age-specific epigenetic signatures of aging and examined their functional enrichments in age-associated biological processes.

Results

We introduce NCAE-CombClock, a novel highly precise (R2 = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers. Additionally, we developed a suite of interpretable NCAE-Age neural network classifiers tailored for adolescence and young adulthood. These clocks can accurately classify individuals at critical developmental ages in youth (AUROC = 0.953, 0.972, and 0.927, for 15, 18, and 21 years) and capture fine-grained, single-year DNAm signatures of aging that are enriched in biological processes associated with anatomic and neuronal development, immunoregulation, and metabolism. We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn's disease.

Discussion

In this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.

Mitochondrial DNA variants and their impact on epigenetic and biological aging in young adulthood

The pace of biological aging varies between people independently of chronological age and mitochondria dysfunction is a key hallmark of biological aging. We hypothesized that higher functional impact (FI) score of mitochondrial DNA (mtDNA) variants might contribute to premature aging and tested the relationships between a novel FI score of mtDNA variants and epigenetic and biological aging in young adulthood. A total of 81 participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort had good quality genetic data as well as blood-based markers to estimate biological aging in the late 20. A subset of these participants (n = 69) also had epigenetic data to estimate epigenetic aging in the early 20s using Horvath's epigenetic clock. The novel FI score was calculated based on 7 potentially pathogenic mtDNA variants. Greater FI score of mtDNA variants was associated with older epigenetic age in the early 20s and older biological age in the late 20s. These medium to large effects were independent of sex, current BMI, cigarette smoking, cannabis, and alcohol use. These findings suggest that elevated FI score of mtDNA variants might contribute to premature aging in young adulthood.

Adherence to Life's Essential 8 is associated with delayed biological aging: a population-based cross-sectional study

INTRODUCTION AND OBJECTIVES

The aim of this study was to explore the potential of adhering to the American Heart Association's updated Life's Essential 8 (LE8) scores in delaying biological aging amid growing concerns about aging populations and related diseases.

METHODS

A total of 18 261 adults (≥ 20 years old) were examined using National Health and Nutrition Examination Survey data from 2005-2010 and 2015-2018. The LE8 includes 8 components, covering health behaviors and factors. Acceleration of biological aging was defined as an excess of biological/phenotypic age over chronological age, assessed by using clinical biomarkers. The association between LE8 score and biological aging was explored through regression analyses.

RESULTS

Each 10-point increase in LE8 scores was associated with a 1.19-year decrease in biological age and a 1.63-year decrease in phenotypic age. Individuals with high cardiovascular health (CVH) had a 90% reduction in their risk of accelerated aging based on biological age and an 81% reduction based on phenotypic age compared with individuals with low CVH. Bootstrap-based model estimates and weighted quantile sum regression suggested that health factors, particularly blood glucose, had strong impact on delaying aging. The association between smoking and biological aging seemed to differ depending on the definition of aging used. Among all subgroups, LE8 consistently correlated negatively with biological aging, despite observed interactions. Three sensitivity analyses confirmed the robustness of our conclusions.

CONCLUSIONS

A higher CVH is associated with a lower risk of biological aging. Maintaining elevated LE8 levels across demographics, regardless of cardiovascular history, is recommended to delay aging and promote healthy aging, with significant implications for primary health care.

Social and environmental predictors of gut microbiome age in wild baboons

Mammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age. Here we test this idea by creating a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting "microbiome clock" predicts host chronological age. Deviations from the clock's predictions are linked to some demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and have high social status (both sexes). However, an individual's "microbiome age" does not predict the attainment of developmental milestones or lifespan. Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.

Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future

Predicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape. Yet, over the past decade, groundbreaking advancements have emerged. Epigenetic clocks, derived from DNA methylation patterns, have established themselves as powerful aging biomarkers, capable of estimating biological age and assessing aging rates across diverse tissues with remarkable precision. These clocks provide predictive insights into mortality and age-related disease risks, effectively distinguishing biological age from chronological age and illuminating enduring questions in gerontology. Despite significant progress in epigenetic clock development, substantial challenges remain, underscoring the need for continued investigation to fully unlock their potential in the science of aging.

Wild capuchin monkeys as a model system for investigating the social and ecological determinants of ageing

Studying biological ageing in animal models can circumvent some of the confounds exhibited by studies of human ageing. Ageing research in non-human primates has provided invaluable insights into human lifespan and healthspan. Yet data on patterns of ageing from wild primates remain relatively scarce, centred around a few populations of catarrhine species. Here, we introduce the white-faced capuchin, a long-lived platyrrhine primate, as a promising new model system for ageing research. Like humans, capuchins are highly social, omnivorous generalists, whose healthspan and lifespan relative to body size exceed that of other non-human primate model species. We review recent insights from capuchin ageing biology and outline our expanding, integrative research programme that combines metrics of the social and physical environments with physical, physiological and molecular hallmarks of ageing across the natural life courses of multiple longitudinally tracked individuals. By increasing the taxonomic breadth of well-studied primate ageing models, we generate new insights, increase the comparative value of existing datasets to geroscience and work towards the collective goal of developing accurate, non-invasive and reliable biomarkers with high potential for standardization across field sites and species, enhancing the translatability of primate studies.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.

DNA-methylation age and accelerated epigenetic aging in blood as a tumor marker for predicting breast cancer susceptibility

BACKGROUND

DNA methylation (DNAm)-based marker of aging, referred to as 'epigenetic age' or 'DNAm age' is a highly accurate multi-tissue biomarker for aging, associated with age-related disease risk, including cancer. Breast cancer (BC), an age-associated disease, is associated with older DNAm age and epigenetic age acceleration (age accel) at tissue levels. But this raises a question on the predictability of DNAm age/age accel in BC development, emphasizing the importance of studying DNAm age in pre-diagnostic peripheral blood (PB) in BC etiology and prevention.

METHODS

We included postmenopausal women from the largest study cohort and prospectively investigated BC development with their pre-diagnostic DNAm in PB leukocytes (PBLs). We estimated Horvath's pan-tissue DNAm age and investigated whether DNAm age/age accel highly correlates with risk for developing subtype-specific BC and to what degree the risk is modified by hormones and lifestyle factors.

RESULTS

DNAm age in PBLs was tightly correlated with age in this age range, and older DNAm age and epigenetic age accel were significantly associated with risk for developing overall BC and luminal subtypes. Of note, in women with bilateral oophorectomy before natural menopause experiencing shorter lifetime estrogen exposure than those with natural menopause, epigenetic age accel substantially influenced BC development, independent of obesity status and exogeneous estrogen use.

CONCLUSIONS

Our findings contribute to better understanding of biologic aging processes that mediate BC carcinogenesis, detecting a non-invasive epigenetic aging marker that better reflects BC development, and ultimately identifying the elderly with high risk who can benefit from epigenetically targeted preventive interventions.

Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults

Biological age is an indicator of whether an individual is experiencing rapid, slowing, or normal aging. Perceived age is highly correlated with biological age, which reflects health appraisal and is often used as a clinical marker of aging. Perceived age has been reported as an important indicator of biological age and general health status, not only in older adult populations but also in young and middle-aged adults. However, there is a lack of objective methods for quantifying perceived age in these younger age groups. Thus, this study aimed to propose a novel perceived age estimation algorithm to meet the need for an objective method to predict perceived age. This cross-sectional study included 609 healthy men and 1388 healthy women (29.02-57.91 years, average 44.4 years) from 2017 to 2019 using data from the Korean Medicine Daejeon Citizen Cohort Study. The proposed algorithm comprised two steps. First, the initial predicted perceived age was estimated from facial images using a convolutional neural network (CNN) ensemble model. Then, the final perceived age was estimated using regression from the chronological age, sex, BMI, and initial predicted perceived age obtained in the first step. Better performance results were obtained by model averaging and model stacking generated from various basic regression models. The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.

Identification and functional characterisation of DNA methylation differences between East- and West-originating Finns

Eastern and Western Finns show a striking difference in coronary heart disease-related mortality; genetics is a known contributor for this discrepancy. Here, we discuss the potential role of DNA methylation in mediating the discrepancy in cardiometabolic disease-risk phenotypes between the sub-populations. We used data from the Young Finns Study (n = 969) to compare the genome-wide DNA methylation levels of East- and West-originating Finns. We identified 21 differentially methylated loci (FDR < 0.05; Δβ >2.5%) and 7 regions (smoothed FDR < 0.05; CpGs ≥ 5). Methylation at all loci and regions associates with genetic variants (p < 5 × 10-8). Independently of genetics, methylation at 11 loci and 4 regions associates with transcript expression, including genes encoding zinc finger proteins. Similarly, methylation at 5 loci and 4 regions associates with cardiometabolic disease-risk phenotypes including triglycerides, glucose, cholesterol, as well as insulin treatment. This analysis was also performed in LURIC (n = 2371), a German cardiovascular patient cohort, and results replicated for the association of methylation at cg26740318 and DMR_11p15 with diabetes-related phenotypes and methylation at DMR_22q13 with triglyceride levels. Our results indicate that DNA methylation differences between East and West Finns may have a functional role in mediating the cardiometabolic disease discrepancy between the sub-populations.

Deep learning predicted perceived age is a reliable approach for analysis of facial ageing: A proof of principle study

BACKGROUND

Perceived age (PA) has been associated with mortality, genetic variants linked to ageing and several age-related morbidities. However, estimating PA in large datasets is laborious and costly to generate, limiting its practical applicability.

OBJECTIVES

To determine if estimating PA using deep learning-based algorithms results in the same associations with morbidities and genetic variants as human-estimated perceived age.

METHODS

Self-supervised learning (SSL) and deep feature transfer (DFT) deep learning (DL) approaches were trained and tested on human-estimated PAs and their corresponding frontal face images of middle-aged to elderly Dutch participants (n = 2679) from a population-based study in the Netherlands. We compared the DL-estimated PAs with morbidities previously associated with human-estimated PA as well as genetic variants in the gene MC1R; we additionally tested the PA associations with MC1R in a new validation cohort (n = 1158).

RESULTS

The DL approaches predicted PA in this population with a mean absolute error of 2.84 years (DFT) and 2.39 years (SSL). In the training-test dataset, we found the same significant (p < 0.05) associations for DL PA with osteoporosis, ARHL, cognition, COPD and cataracts and MC1R, as with human PA. We also found a similar but less significant association for SSL and DFT PAs (0.69 and 0.71 years per allele, p = 0.008 and 0.011, respectively) with MC1R variants in the validation dataset as that found with human, SSL and DFT PAs in the training-test dataset (0.79, 0.78 and 0.71 years per allele respectively; all p < 0.0001).

CONCLUSIONS

Deep learning methods can automatically estimate PA from facial images with enough accuracy to replicate known links between human-estimated perceived age and several age-related morbidities. Furthermore, DL predicted perceived age associated with MC1R gene variants in a validation cohort. Hence, such DL PA techniques may be used instead of human estimations in perceived age studies thereby reducing time and costs.

A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age

Biological clocks and other molecular biomarkers of aging are difficult to implement widely in a clinical setting. In this study, we used routinely collected hematological markers to develop an aging clock to predict blood age and determine whether the difference between predicted age and chronologic age (aging gap) is associated with advanced aging in mice. Data from 2,562 mice of both sexes and three strains were drawn from two longitudinal studies of aging. Eight hematological variables and two metabolic indices were collected longitudinally (12,010 observations). Blood age was predicted using a deep neural network. Blood age was significantly correlated with chronological age, and aging gap was positively associated with mortality risk and frailty. Platelets were identified as the strongest age predictor by the deep neural network. An aging clock based on routinely collected blood measures has the potential to provide a practical clinical tool to better understand individual variability in the aging process.

Evaluation of Brain Age as a Specific Marker of Brain Health

Brain age is a powerful marker of general brain health. Furthermore, brain age models are trained on large datasets, thus giving them a potential advantage in predicting specific outcomes - much like the success of finetuning large language models for specific applications. However, it is also well-accepted in machine learning that models trained to directly predict specific outcomes (i.e., direct models) often perform better than those trained on surrogate outcomes. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models and direct models for predicting specific health outcomes in the context of Alzheimer's Disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that direct models outperform brain age models without finetuning. Finetuned brain age models yielded similar performance as direct models, but importantly, did not outperform direct models although the brain age models were pretrained on 1000 times more data than the direct models: N = 53,542 vs N = 50. Overall, our results do not discount brain age as a useful marker of general brain health. However, in this era of large-scale brain age models, our results suggest that small-scale, targeted approaches for extracting specific brain health markers still hold significant value.

Maternal smoking during pregnancy could accelerate aging in the adulthood: evidence from a perspective study in UK Biobank

BACKGROUND

Maternal smoking during pregnancy (MSDP) is significantly linked to the short- or long-term health of offspring. However, little research has examined whether MSDP affect the aging rate of offspring.

METHODS

This study used questionnaires to determine out whether the participants' mothers smoked when they were pregnant. For evaluating aging rate, we used the following several outcome measures: telomere length, frailty index, cognitive function, homeostatic dysregulation score, KDM-age, age-related hospitalization rate, premature death, and life expectancy.

RESULT

After adjusting for covariates, we found that the offspring of the MSDP group had significantly shorter telomere length in adulthood by 0.8 % (β = -0.008,95%CI:-0.009 to -0.006) compared with non-MSDP group. Compared to the non-MSDP group, participants in MSDP group showed higher levels of homeostatic dysregulation (β = 0.015,95%CI: 0.007-0.024) and were frailer (β = 0.008,95%CI:0.007-0.009). The KDM age increased by 0.100 due to MSDP (β = 0.100,95 % CI:0.018-0.181), and the age acceleration of KDM algorithm also increases significantly (β = 0.101, 95%CI:0.020-0.183). Additionally, we found that the risk of aging-related hospitalizations was significantly higher than the non-MSDP group by 10.4 %(HR = 1.104,95%CI:1.066-1.144). Moreover, MSDP group had a 12.2 % increased risk of all-cause premature mortality (HR = 1.122,95%CI:1.064-1.182) and a significant risk of lung cancer-specific premature mortality increased by 55.4 %(HR = 1.554,95%CI:1.346-1.793). In addition, participants in the MSDP group had significantly decreased cognitive function and shorter life expectancies than those in non-MSDP group.

CONCLUSION

Our findings indicated a significant association between MSPD and accelerated aging, elevated hospitalization rates, increased premature mortality rates, and reduced life expectancies in offspring.

Pace of Aging in older adults matters for healthspan and lifespan

As societies age, policy makers need tools to understand how demographic aging will affect population health and to develop programs to increase healthspan. The current metrics used for policy analysis do not distinguish differences caused by early-life factors, such as prenatal care and nutrition, from those caused by ongoing changes in people's bodies due to aging. Here we introduce an adapted Pace of Aging method designed to quantify differences between individuals and populations in the speed of aging-related health declines. The adapted Pace of Aging method, implemented in data from the US Health and Retirement Study and English Longitudinal Study of Aging (N=21,463), integrates longitudinal data on blood biomarkers, physical measurements, and functional tests. It reveals stark differences in rates of aging between population subgroups and demonstrates strong and consistent prospective associations with incident morbidity, disability, and mortality. Pace of Aging can advance the population science of healthy longevity.

Exploring multi-omics and clinical characteristics linked to accelerated biological aging in Asian women of reproductive age: insights from the S-PRESTO study

BACKGROUND

Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging.

METHODS

PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors.

RESULTS

Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel.

CONCLUSIONS

Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Limited role of biological aging in unhealthy aging: A cross-sectional analysis of global life expectancy and disability data

BACKGROUND

Biological aging is known to impact quality of life, but its precise role is debated.

OBJECTIVE

This study explores how biological aging, measured by life expectancy at birth (e0), affects unhealthy aging as indicated by years lost due to disability (YLD).

METHODS

Data from international organizations, including e0, YLD, and confounding factors like income, obesity, and urbanization, were analyzed. Correlations were visualized with scatter plots, and associations were assessed using Pearson's and nonparametric methods. Partial, multilevel modelling and multiple regression analyses were conducted to determine e0's impact on YLD.

RESULTS

e0 strongly correlated with YLD, explaining about 50 % of its variance. After adjusting for confounders, e0's contribution fell to 12.18 %. Multiple regression identified e0 and urbanization as significant predictors.

CONCLUSIONS

Biological aging influences YLD, but its effect diminishes when accounting for other factors, highlighting the need for a comprehensive approach to healthy aging.

Hypoxia and aging: molecular mechanisms, diseases, and therapeutic targets

Aging is a complex biological process characterized by the gradual decline of cellular functions, increased susceptibility to diseases, and impaired stress responses. Hypoxia, defined as reduced oxygen availability, is a critical factor that influences aging through molecular pathways involving hypoxia-inducible factors (HIFs), oxidative stress, inflammation, and epigenetic modifications. This review explores the interconnected roles of hypoxia in aging, highlighting how hypoxic conditions exacerbate cellular damage, promote senescence, and contribute to age-related pathologies, including cardiovascular diseases, neurodegenerative disorders, cancer, metabolic dysfunctions, and pulmonary conditions. By examining the molecular mechanisms linking hypoxia to aging, we identify key pathways that serve as potential therapeutic targets. Emerging interventions such as HIF modulators, antioxidants, senolytics, and lifestyle modifications hold promise in mitigating the adverse effects of hypoxia on aging tissues. However, challenges such as the heterogeneity of aging, lack of reliable biomarkers, and safety concerns regarding hypoxia-targeted therapies remain. This review emphasizes the need for personalized approaches and advanced technologies to develop effective antiaging interventions. By integrating current knowledge, this review provides a comprehensive framework that underscores the importance of targeting hypoxia-induced pathways to enhance healthy aging and reduce the burden of age-related diseases.

Moderate BMI accumulation modified associations between blood benzene, toluene, ethylbenzene and xylene (BTEX) and phenotypic aging: mediating roles of inflammation and oxidative stress

The associations between blood benzene, toluene, ethylbenzene, and xylenes (BTEX) and biological aging among general adults remain elusive. The present study comprised 5780 participants from the National Health and Nutrition Examination Survey 1999-2010. A novel measure of biological aging, phenotypic age acceleration (PhenoAge.Accel), derived from biochemical markers was calculated. Weighted generalized linear regression and weighted quantile sum regression (WQS) were utilized to assess the associations between BTEX components and mixed exposure, and PhenoAge.Accel. The mediating roles of systemic immune-inflammation index (SII) and oxidative stress indicators (serum bilirubin and gamma-glutamyl transferase), along with the modifying effects of body mass index (BMI) were also examined. In the single-exposure model, the highest quantile of blood benzene (b = 0.89, 95%CI: 0.58 to 1.20), toluene (b = 0.87, 95%CI: 0.52 to 1.20), and ethylbenzene (b = 0.80, 95%CI: 0.46 to 1.10) was positively associated with PhenoAge.Accel compared to quantile 1. Mixed-exposure analyses revealed a consistent positive association between BTEX mixed exposure and PhenoAge.Accel (b = 0.88, 95%CI: 0.56 to 1.20), primarily driven by benzene (92.78%). The association between BTEX and PhenoAge.Accel was found to be partially mediated by inflammation and oxidative stress indicators (ranging from 3.2% to 13.7%). Additionally, BMI negatively modified the association between BTEX mixed exposure and PhenoAge.Accel, with a threshold identified at 36.2 kg/m^2. Furthermore, BMI negatively moderated the direct effect of BTEX mixed exposure on PhenoAge.Accel in moderated mediation models, while positively modified the link between SII and PhenoAge.Accel in the indirect path (binteraction = 0.04, 95%CI: 0.01 to 0.06). Overall, BTEX mixed exposure was associated with PhenoAge.Accel among US adults, with benzene may have reported most contribution, and inflammation and oxidative damage processes may partially explain this underlying mechanism. The study also highlighted the potential benefits of appropriate BMI increased. Additional large-scale cohort studies and experiments were necessary to substantiate these findings.

Trajectories of Hearing From Childhood to Adulthood

OBJECTIVES

The Dunedin Multidisciplinary Health and Development Study provides a unique opportunity to document the progression of ear health and hearing ability within the same cohort of individuals from birth. This investigation draws on hearing data from 5 to 13 years and again at 45 years of age, to explore the associations between childhood hearing variables and hearing and listening ability at age 45.

DESIGN

Multiple linear regression analyses were used to assess associations between childhood hearing (otological status and mid-frequency pure-tone average) and (a) age 45 peripheral hearing ability (mid-frequency pure-tone average and high-frequency pure-tone average), and (b) age 45 listening ability (listening in spatialized noise and subjective questionnaire on listening experiences). Sex, childhood socioeconomic status, and adult IQ were included in the model as covariates.

RESULTS

Peripheral hearing and listening abilities at age 45 were consistently associated with childhood hearing acuity at mid-frequencies. Otological status was a moderate predicting factor for high-frequency hearing and utilization of spatial listening cues in adulthood.

CONCLUSIONS

We aim to use these findings to develop a foundational model of hearing trajectories. This will form the basis for identifying precursors, to be investigated in a subsequent series of analyses, that may protect against or exacerbate hearing-associated cognitive decline in the Dunedin Study cohort as they progress from mid-life to older age.

Now and then in eukaryotic DNA methylation

Since the mid-1970s, increasingly innovative methods to detect DNA methylation provided detailed information about its distribution, functions, and dynamics. As a result, new concepts were formulated and older ones were revised, transforming our understanding of the associated biology and catalyzing unprecedented advances in biomedical research, drug development, anthropology, and evolutionary biology. In this review, we discuss a few of the most notable advances, which are intimately intertwined with the study of DNA methylation, with a particular emphasis on the past three decades. Examples of these strides include elucidating the intricacies of 5-methylcytosine (5-mC) oxidation, which are at the core of the reversibility of this epigenetic modification; the three-dimensional structural characterization of eukaryotic DNA methyltransferases, which offered insights into the mechanisms that explain several disease-associated mutations; a more in-depth understanding of DNA methylation in development and disease; the possibility to learn about the biology of extinct species; the development of epigenetic clocks and their use to interrogate aging and disease; and the emergence of epigenetic biomarkers and therapies.

Is age more than a number? Accounting for adult development and aging in the study of psychoneuroimmunology, stress, and health

Traditional stress-and-health models link stressors to their health consequences through a well-characterized cascade. Most of the research assumes that the stress-health sequence unfolds in the same way across adulthood, whether a person is 25 years old or 80. Taking a "developmental" or "lifespan" approach has been synonymous with studying the lasting health impacts of early life experiences. However, theories and evidence from adult development and geroscience suggest that stress-health dynamics evolve in important ways over the adult lifespan-from the stressors that we encounter, to the emotion regulation strategies that we use to confront challenges, to the psychosocial resources at our disposal, to the cellular milieu, and thus to the magnitude of stressors' biological and functional consequences. This critical review synthesizes theoretical perspectives and selected empirical literature on the social-emotional and biological dimensions of aging to promote an Integrative Model of Aging, Stress, and Health. Through this integration, the model illustrates how an interdisciplinary, developmental perspective can enrich our understanding of stress's consequences for health across adulthood. It also seeks to guide a new generation of research questions that confront aging with a multidimensional approach. The piece concludes with personal reflections on the foundational legacy of the author's mentor, Dr. Janice Kiecolt-Glaser.

HbA1c and leukocyte mtDNA levels as major factors associated with post-COVID-19 syndrome in type 2 diabetes patients

Post-COVID-19 syndrome (PCS) is an emerging health problem in people recovering from COVID-19 infection within the past 3-6 months. The current study aimed to define the predictive factors of PCS development by assessing the mitochondrial DNA (mtDNA) levels in blood leukocytes, inflammatory markers and HbA1c in type 2 diabetes patients (T2D) with regard to clinical phenotype, gender, and biological age. In this case-control study, 65 T2D patients were selected. Patients were divided into 2 groups depending on PCS presence: the PCS group (n = 44) and patients who did not develop PCS (n = 21) for up to 6 months after COVID-19 infection. HbA1c and mtDNA levels were the primary factors linked to PCS in different models. We observed significantly lower mtDNA content in T2D patients with PCS compared to those without PCS (1.26 ± 0.25 vs. 1.44 ± 0.24; p = 0.011). In gender-specific and age-related analyses, the mt-DNA amount did not differ significantly between the subgroups. According to the stepwise multivariate logistic regression analysis, low mtDNA content and HbA1c were independent variables associated with PCS development, regardless of oxygen, glucocorticoid therapy and COVID-19 severity. The top-performing model for PCS prediction was the gradient boosting machine (GBM). HbA1c and mtDNA had a notably greater influence than the other variables, indicating their potential as prognostic biomarkers.

Integrating aging biomarkers and immune function to predict kidney health: insights from the future of families and child wellbeing study

Biomarkers of biological aging predict health outcomes more accurately than chronological age. This study examines the relationship between aging biomarkers, immune function, and kidney health using the Future of Families and Child Wellbeing Study Biomarker Dataset. Using Wave 5 (year 9) and Wave 6 (year 15), we examined biomarker data from a total of 4898 individuals. The panel of aging biomarkers, comprised of epigenetic clocks (GrimAge, Horvath), immune function markers (CD8 + T cells, plasmablasts), and metabolic indicators (GDF-15, leptin), was evaluated in depth. We used principal component analysis (PCA) and K-means clustering for subtype identification. A random forest regressor was employed to predict kidney function using Cystatin C levels, and the importance of features was assessed. Three clusters with unique biological age and immune function profiles were identified. Cluster 1 had younger biological age markers. In Cluster 2, both GrimAge and GDF-15 levels were significantly increased, indicating an elevated risk for age-related diseases. According to predictive modeling, GrimAge, Pack Years, and immune function markers had the strongest influence on Cystatin C levels (R2 = 0.856). The incorporation of immune aging markers enhanced the predictive power, emphasizing the importance of immunosenescence in renal health. Aging biomarkers and immune function significantly impact kidney health prediction. The study results call for the utilization of extensive biomarker tests for individualized elderly care and early recognition of kidney deterioration. Clinical practice can be improved by incorporating biological age assessments for the prevention and management of age-related diseases.

Sex-Differential Markers of Psychiatric Risk and Treatment Response Based on Premature Aging of Functional Brain Network Dynamics and Peripheral Physiology

BACKGROUND

Aging is a multilevel process of gradual decline that predicts morbidity and mortality. Independent investigations have implicated senescence of brain and peripheral physiology in psychiatric risk, but it is unclear whether these effects stem from unique or shared mechanisms.

METHODS

To address this question, we analyzed clinical, blood chemistry, and resting-state functional neuroimaging data in a healthy aging cohort (n = 427; ages 36-100 years) and 2 disorder-specific samples including patients with early psychosis (100 patients, 16-35 years) and major depressive disorder (MDD) (104 patients, 20-76 years).

RESULTS

We identified sex-dependent coupling between blood chemistry markers of metabolic senescence (i.e., homeostatic dysregulation), functional brain network aging, and psychiatric risk. In females, premature aging of frontoparietal and somatomotor networks was linked to greater homeostatic dysregulation. It also predicted the severity and treatment resistance of mood symptoms (depression/anxiety [all 3 samples], anhedonia [MDD]) and social withdrawal/behavioral inhibition (avoidant personality disorder [healthy aging], negative symptoms [early psychosis]). In males, premature aging of the default mode, cingulo-opercular, and visual networks was linked to reduced homeostatic dysregulation and predicted the severity and treatment resistance of symptoms relevant to hostility/aggression (antisocial personality disorder [healthy aging], mania/positive symptoms [early psychosis]), impaired thought processes (early psychosis, MDD), and somatic problems (healthy aging, MDD).

CONCLUSIONS

Our findings identify sexually dimorphic relationships between brain dynamics, peripheral physiology, and risk for psychiatric illness, suggesting that the specificity of putative risk biomarkers and precision therapeutics may be improved by considering sex and other relevant personal characteristics.

Aging Skeletal Muscles: What Are the Mechanisms of Age-Related Loss of Strength and Muscle Mass, and Can We Impede Its Development and Progression?

As we age, we lose muscle strength and power, a condition commonly referred to as sarcopenia (ICD-10-CM code (M62.84)). The prevalence of sarcopenia is about 5-10% of the elderly population, resulting in varying degrees of disability. In this review we emphasise that sarcopenia does not occur suddenly. It is an aging-induced deterioration that occurs over time and is only recognised as a disease when it manifests clinically in the 6th-7th decade of life. Evidence from animal studies, elite athletes and longitudinal population studies all confirms that the underlying process has been ongoing for decades once sarcopenia has manifested. We present hypotheses about the mechanism(s) underlying this process and their supporting evidence. We briefly review various proposals to impede sarcopenia, including cell therapy, reducing senescent cells and their secretome, utilising targets revealed by the skeletal muscle secretome, and muscle innervation. We conclude that although there are potential candidates and ongoing preclinical and clinical trials with drug treatments, the only evidence-based intervention today for humans is exercise. We present different exercise programmes and discuss to what extent the interindividual susceptibility to developing sarcopenia is due to our genetic predisposition or lifestyle factors.

Association of a pace of aging epigenetic clock with rate of cognitive decline in the Framingham Heart Study Offspring Cohort

INTRODUCTION

The geroscience hypothesis proposes systemic biological aging is a root cause of cognitive decline.

METHODS

We analyzed Framingham Heart Study Offspring Cohort data (n = 2296; 46% male; baseline age M = 62, SD = 9, range = 25-101 y). We measured cognitive decline across two decades of neuropsychological-testing follow-up. We measured pace of aging using the DunedinPACE epigenetic clock. Analysis tested if participants with faster DunedinPACE values experienced more rapid cognitive decline compared with those with slower DunedinPACE values.

RESULTS

Participants with faster DunedinPACE had poorer cognitive functioning at baseline and experienced more rapid cognitive decline over follow-up. Results were robust to confounders and consistent across population strata. Findings were similar for the PhenoAge and GrimAge epigenetic clocks.

DISCUSSION

Faster pace of aging is a risk factor for preclinical cognitive decline. Metrics of biological aging may inform risk stratification in clinical trials and prognosis in patient care.

Highlights

Faster DunedinPACE is associated with preclinical cognitive aging.Higher baseline cognition was protective of DunedinPACE-associated cognitive decline.The DunedinPACE association with cognitive decline explained a fourth of dementia risk.

The relative effects of parental alcohol use disorder and maltreatment on offspring alcohol use: Unique pathways of risk

Childhood adversity represents a robust risk factor for the development of harmful substance use. Although a range of empirical studies have examined the consequences of multiple forms of adversity (i.e., childhood maltreatment, parental alcohol use disorder [AUD]), there is a dearth of information on the relative effects of each form of adversity when considered simultaneously. The current study utilizes structural equation modeling to investigate three unique and amplifying pathways from parental AUD and maltreatment exposure to offspring alcohol use as emerging adults: (1) childhood externalizing symptomatology, (2) internalizing symptomatology, and (3) affiliation with substance-using peers and siblings. Participants (N = 422) were drawn from a longitudinal follow-up study of emerging adults who participated in a research summer camp program as children. Wave 1 of the study included 674 school-aged children with and without maltreatment histories. Results indicated that chronic maltreatment, over and above the effect of parent AUD, was uniquely associated with greater childhood conduct problems and depressive symptomatology. Mother alcohol dependence was uniquely associated with greater affiliation with substance-using peers and siblings, which in turn predicted greater alcohol use as emerging adults. Results support peer and sibling affiliation as a key mechanism in the intergenerational transmission of substance use between mothers and offspring.

Allostatic load in rat model: An efficient tool for evaluating and understanding aging

AIM

Aging and age-related diseases are an ever-increasing social and public health problem. Allostatic load (AL) shows great potential as an interdisciplinary tool for assessing the aging of human beings but as yet lacks investigation in animal models which is our study focus at.

METHODS

Here a continuous study of AL was conducted on naturally aging rats. Blood samples were collected from the rats at ages of 5, 8, 14, 18, and 21 months. Dozens of blood biochemical indicators, including serum corticosterone, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, C-reactive protein, interleukin-6, 25-hydroxyvitamin D, free fatty acid, CD3+ T cell count, CD4+/CD3+ T cell ratio, CD8+/CD3+ T cell ratio, and CD3/4/8+ T cell apoptosis, were determined.

RESULTS

AL was scored from those indicators, and we found that AL score gradually increased with age.

CONCLUSIONS

AL can reliably reveal the cumulative and systemic changes in aging. Geriatr Gerontol Int 2024; 24: 1077-1084.

Using non-invasive behavioral and physiological data to measure biological age in wild baboons

Biological aging is near-ubiquitous in the animal kingdom, but its timing and pace vary between individuals and over lifespans. Prospective, individual-based studies of wild animals-especially non-human primates-help identify the social and environmental drivers of this variation by indicating the conditions and exposure windows that affect aging processes. However, measuring individual biological age in wild primates is challenging because several of the most promising methods require invasive sampling. Here, we leverage observational data on behavior and physiology, collected non-invasively from 319 wild female baboons across 2402 female-years of study, to develop a composite predictor of age: the non-invasive physiology and behavior (NPB) clock. We found that age predictions from the NPB clock explained 51% of the variation in females' known ages. Further, deviations from the clock's age predictions predicted female survival: females predicted to be older than their known ages had higher adult mortality. Finally, females who experienced harsh early-life conditions were predicted to be about 6 months older than those who grew up in more benign conditions. While the relationship between early adversity and NPB age is noisy, this estimate translates to a predicted 2-3 year reduction in mean adult lifespan in our model. A constraint of our clock is that it is tailored to data collection approaches implemented in our study population. However, many of the clock's components have analogs in other populations, suggesting that non-invasive data can provide broadly applicable insight into heterogeneity in biological age in natural populations.

Clonal Hematopoiesis in Patients With Human Immunodeficiency Virus and Cancer

BACKGROUND

Cancer-related deaths for people with human immunodeficiency virus (PWH) are increasing due to longer life expectancies and disparately poor cancer-related outcomes. We hypothesize that advanced biological aging contributes to cancer-related morbidity and mortality for PWH and cancer. We sought to determine the impact of clonal hematopoiesis (CH) on cancer disparities in PWH.

METHODS

We conducted a retrospective study to compare the prevalence and clinical outcomes of CH in PWH and people without HIV (PWoH) and cancer. Included in the study were PWH and similar PWoH based on tumor site, age, tumor sequence, and cancer treatment status. Biological aging was also measured using epigenetic methylation clocks.

RESULTS

In 136 patients with cancer, PWH had twice the prevalence of CH compared to similar PWoH (23% vs 11%, P = .07). After adjusting for patient characteristics, PWH were 4 times more likely than PWoH to have CH (odds ratio, 4.1 [95% confidence interval, 1.3-13.9]; P = .02). The effect of CH on survival was most pronounced in PWH, who had a 5-year survival rate of 38% if they had CH (vs 59% if no CH), compared to PWoH who had a 5-year survival rate of 75% if they had CH (vs 83% if no CH).

CONCLUSIONS

This study provides the first evidence that PWH may have a higher prevalence of CH than PWoH with the same cancers. CH may be an independent biological aging risk factor contributing to inferior survival for PWH and cancer.

The ESCAPE Trial for Older People With Chronic Low Back Pain: A Feasibility Study of a Clinical Trial of Group-Based Exercise in Primary Health Care

Low back pain is a highly disabling health condition that generates high costs for patients and healthcare systems. For this reason, it is considered a serious public health problem worldwide. This pilot study aimed to assess the feasibility of a future randomized controlled trial (RCT) by evaluating adherence to treatment, contamination between groups, satisfaction with treatment, and understanding of the exercise instructions provided by the physiotherapist. Additionally, we sought to identify and implement necessary modifications to the exercise protocol for better suitability in older people. We conducted a prospective, registered pilot RCT comparing an 8-week group-based exercise program with a waiting list in older people (≥60 years old) with chronic low back pain. Sixty participants were recruited through social media, pamphlets, and invitations at community referral centers. The study demonstrated the feasibility of a full RCT. Participants reported high satisfaction with the treatment (i.e., 100% indicated willingness to return for future services) and a high understanding of the exercise instructions (i.e., 81.8% reported "very easy" comprehension). Adherence to the exercise program exceeded the average reported for group exercise interventions in older adults (i.e., 82.58%). Dropout was associated solely with preexisting physical activity levels. The exercise protocol was successfully adapted to better suit the needs of the older adult population. This pilot RCT demonstrates the feasibility of a full-scale RCT to evaluate the effectiveness of group exercise in improving pain intensity and disability in older adults with chronic low back pain. The implemented adjustments to the exercise protocol and overall study approach strengthen the methodological foundation and expected accuracy of the future RCT.

Childhood Maltreatment and Biological Aging in Middle Adulthood: The Role of Psychiatric Symptoms

Background

Childhood maltreatment and psychiatric morbidity have each been associated with accelerated biological aging primarily through cross-sectional studies. Using data from a prospective longitudinal study of individuals with histories of childhood maltreatment and control participants followed into midlife, we tested 2 hypotheses examining whether 1) psychiatric symptoms mediate the relationship between childhood maltreatment and biological aging and 2) psychiatric symptoms of anxiety, depression, or posttraumatic stress disorder (PTSD) act in conjunction with childhood maltreatment to exacerbate the association of child maltreatment to aging.

Methods

Children (ages 0-11 years) with documented histories of maltreatment and demographically matched control children were followed into adulthood (N = 607) and interviewed over several waves of the study. Depression, anxiety, and PTSD symptoms were assessed at mean ages of 29 (interview 1) and 40 (interview 2) years. Biological age was measured from blood chemistries collected later (mean age = 41 years) using the Klemera-Doubal method. Hypotheses were tested using linear regressions and path analyses.

Results

Adults with documented histories of childhood maltreatment showed more symptoms of depression, PTSD, and anxiety at both interviews and more advanced biological aging, compared with control participants. PTSD symptoms at both interviews and depression and anxiety symptoms only at interview 2 predicted accelerated biological aging. There was no evidence of mediation; however, anxiety and depression moderated the relationship between childhood maltreatment and biological aging.

Conclusions

These new findings reveal the shorter- and longer-term longitudinal impact of PTSD on biological aging and the amplifying effect of anxiety and depression on the relationship between child maltreatment and biological aging.

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

Telomere Length, HLA, and Longevity-Results from a Multicenter Study

Aging is an exceptionally complex process that depends on genetic, environmental, and lifestyle factors. Previous studies within the International HLA and Immunogenetics Workshop (IHIWS) component "Immunogenetics of Ageing" showed that longevity is associated with positive selection of HLA-DRB1*11- and DRB1*16-associated haplotypes, shown to be protective against diseases. Within the 18th IHIWS, we aimed to investigate the relevance of telomere length for successful aging and its association with classical HLAs. In total 957 individuals from Bulgaria, Turkey, Romania, and Poland in two age groups, elderly individuals (age 65-99 years) and ethnically matched young group (age 18-64 years), were investigated. The obtained results confirmed interpopulation differences in the distribution of HLA alleles, documented the lengths of telomeres in analyzed populations, and demonstrated significant associations of telomere length with aging as well as with the presence of some HLA class I or class II alleles. They suggest that telomere length assessment combined with HLA genotyping may help identify immunogenetic profiles associated with longevity. The associations between HLA and telomeres support the theory that HLA genes influence the aging process. However, further research is needed to clarify the biological basis of the observed relationships.

Lifestyle Changes in Aging and their Potential Impact on POAG

Primary open angle glaucoma is a primary mitochondrial disease with oxidative stress triggering neuroinflammation, eventually resulting in neurodegeneration. This affects many other areas of the brain in addition to the visual system. Aging also leads to inflammaging - a low-grade chronic inflammatory reaction in mitochondrial dysfunction, so these inflammatory processes overlap in the aging process and intensify pathophysiological processes associated with glaucoma. Actively counteracting these inflammatory events involves optimising treatment for any manifest systemic diseases while maintaining chronobiology and improving the microbiome. Physical and mental activity also provides support. This requires a holistic approach towards optimising neurodegeneration treatment in primary open angle glaucoma in addition to reducing intraocular pressure according personalised patient targets.

An estimate of the longitudinal pace of aging from a single brain scan predicts dementia conversion, morbidity, and mortality

To understand how aging affects functional decline and increases disease risk, it is necessary to develop accurate and reliable measures of how fast a person is aging. Epigenetic clocks measure aging but require DNA methylation data, which many studies lack. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain MRI: the Dunedin Pace of Aging Calculated from NeuroImaging or DunedinPACNI. Exporting this measure to the Alzheimer's Disease Neuroimaging Initiative and UK Biobank neuroimaging datasets revealed that faster DunedinPACNI predicted participants' cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed the expected socioeconomic health gradient. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.

Disentangling the relationship between biological age and frailty in community-dwelling older Mexican adults

OBJECTIVE

Older adults have heterogeneous aging rates. Here, we explored the impact of biological age (BA) and accelerated aging on frailty in community-dwelling older adults.

METHODS

We assessed 735 community-dwelling older adults from the Coyocan Cohort. BA was measured using AnthropoAge, accelerated aging with AnthropoAgeAccel, and frailty using both Fried's phenotype and the frailty index. We explored the association of BA and accelerated aging (AnthropoAgeAccel ≥0) with frailty at baseline and characterized the impact of both on body composition and physical function. We also explored accelerated aging as a risk factor for frailty progression after 3-years of follow-up.

RESULTS

Older adults with accelerated aging have higher frailty prevalence and indices, lower handgrip strength and gait speed. AnthropoAgeAccel was associated with higher frailty indices (β=0.0053, 95%CI 0.0027-0.0079), and increased odds of frailty at baseline (OR 1.16, 95%CI 1.09-1.25). We observed a sexual dimorphism in body composition and physical function linked to accelerated aging in non-frail participants; however, this dimorphism was absent in pre-frail/frail participants. Accelerated aging at baseline was associated with higher risk of frailty progression over time (OR 1.74, 95%CI 1.11-2.75).

CONCLUSIONS

Despite being intertwined, biological accelerated aging is largely independent of frailty in community-dwelling older adults.

Association Between Phenotypic Age and the Risk of Mortality in Patients With Heart Failure: A Retrospective Cohort Study

BACKGROUND

Chronological age (CA) is an imperfect proxy for the true biological aging state of the body. As novel measures of biological aging, Phenotypic age (PhenoAge) and Phenotypic age acceleration (PhenoAgeAccel), have been shown to identify morbidity and mortality risks in the general population.

HYPOTHESIS

PhenoAge and PhenoAgeAccel might be associated with mortality in heart failure (HF) patients.

METHODS

This cohort study extracted adult data from the National Health and Nutrition Examination Survey (NHANES) databases. Weighted univariable and multivariable Cox models were performed to analyze the effect of PhenoAge and PhenoAgeAccel on all-cause mortality in HF patients, and hazard ratio (HR) with 95% confidence intervals (CI) was calculated.

RESULTS

In total, 845 HF patients were identified, with 626 all-cause mortality patients. The findings suggested that (1) each 1- and 10-year increase in PhenoAge were associated with a 3% (HR = 1.03, 95% CI: 1.03-1.04) and 41% (HR = 1.41, 95% CI: 1.29-1.54) increased risk of all-cause mortality, respectively; (2) when the PhenoAgeAccel < 0 as reference, the ≥ 0 group was associated with higher risk of all-cause mortality (HR = 1.91, 95% CI = 1.49-2.45). Subgroup analyses showed that (1) older PhenoAge was associated with an increased risk of all-cause mortality in all subgroups; (2) when the PhenoAgeAccel < 0 as a reference, PhenoAgeAccel ≥ 0 was associated with a higher risk of all-cause mortality in all subgroups.

CONCLUSION

Older PhenoAge was associated with an increased risk of all-cause mortality in HF patients. PhenoAge and PhenoAgeAccel can be used as convenient tools to facilitate the identification of at-risk individuals with HF and the evaluation of intervention efficacy.