Comparability of biological aging measures in the National Health and Nutrition Examination Study, 1999-2002

Lifestyle factors and metabolomic aging biomarkers: Meta-analysis of cross-sectional and longitudinal associations in three prospective cohorts

Biological age uses biophysiological information to capture a person's age-related risk of adverse outcomes. MetaboAge and MetaboHealth are metabolomics-based biomarkers of biological age trained on chronological age and mortality risk, respectively. Lifestyle factors contribute to the extent chronological and biological age differ. The association of lifestyle factors with MetaboAge and MetaboHealth, potential sex differences in these associations, and MetaboAge's and MetaboHealth's sensitivity to lifestyle changes have not been studied yet. Linear regression analyses and mixed-effect models were used to examine the cross-sectional and longitudinal associations of scaled lifestyle factors with scaled MetaboAge and MetaboHealth in 24,332 middle-aged participants from the Doetinchem Cohort Study, Rotterdam Study, and UK Biobank. Random-effect meta-analyses were performed across cohorts. Repeated metabolomics measurements had a ten-year interval in the Doetinchem Cohort Study and a five-year interval in the UK Biobank. In the first study incorporating longitudinal information on MetaboAge and MetaboHealth, we demonstrate associations between current smoking, sleeping ≥8 hours/day, higher BMI, and larger waist circumference were associated with higher MetaboHealth, the latter two also with higher MetaboAge. Furthermore, adhering to the dietary and physical activity guidelines were inversely associated with MetaboHealth. Lastly, we observed sex differences in the associations between alcohol use and MetaboHealth.

When in the lifecourse? Socioeconomic position across the lifecourse and biological health score

PURPOSE

Educational attainment is associated with multiphysiological wear and tear. However, associations with measures of socioeconomic position (SEP) across different life-stages are not established.

METHODS

Using regression models and data from 8105 participants from the UK Household Longitudinal Study (Understanding Society), we examined associations of lifecourse SEP with an overall biological health score (BHS). BHS is broader than usual measures of biological 'wear and tear' and is based on six physiological subsystems (endocrine, metabolic, cardiovascular, inflammatory/immune, liver, and kidney), with higher scores indicating worse health. Lifecourse SEP was based on respondents' parental, first, and most recent occupations.

RESULTS

Associations with SEP at all life-stages demonstrated higher BHS with increasing disadvantage (e.g. slope index of inequality (SII) (95 % CI) for most recent SEP: 0.04 (0.02, 0.06)). There was little difference in the magnitude of associations for SEP measured at each life-stage. Cumulative disadvantage across the lifecourse showed a stepped association with increasing BHS (SII (95 % CI): 0.05 (0.04, 0.07)). Associations were largely driven by metabolic, cardiovascular, and inflammatory systems.

CONCLUSION

Our results suggest that disadvantaged SEP across the lifecourse contributes cumulatively to poorer biological health, highlighting that every life-stage should be a target for public health policies and intervention.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

Association between single and mixed exposure to polycyclic aromatic hydrocarbons and biological aging

Background

Aging is one of the most important public health issues. Previous studies on the factors affecting aging focused on genetics and lifestyle, but the association between polycyclic aromatic hydrocarbons (PAHs) and aging is still unclear.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2003-2010. A total of 8,100 participants was used to construct the biological age predictors by using recent advanced algorithms Klemera-Doubal method (KDM) and Mahalanobis distance. Two biological aging indexes, recorded as KDM-BA acceleration and PhenoAge acceleration, were used to investigate the relationship between single PAHs and biological age using a multiple linear regression analysis, and a weighted quantile sum (WQS) model was constructed to explore the mixed effects of PAHs on biological age. Finally, we constructed the restricted cubic spline (RCS) model to assess the non-linear relationship between PAHs and biological age.

Results

Exposure to PAHs was associated with PhenoAge acceleration. Each unit increase in the log10-transformed level of 1-naphthol, 2-naphthol, and 2-fluorene was associated with a 0.173 (95% CI: 0.085, 0.261), 0.310 (95% CI: 0.182, 0.438), and 0.454 (95% CI: 0.309, 0.598) -year increase in PhenoAge acceleration, respectively (all corrected P < 0.05). The urinary PAH mixture was relevant to KDM-BA acceleration (β = 0.13, 95% CI: 0, 0.26, P = 0.048) and PhenoAge acceleration (β = 0.59, 95% CI: 0.47, 0.70, P < 0.001), and 2-naphthol had the highest weight in the weighted quantile sum (WQS) regression. The RCS analyses showed a non-linear association between 2-naphthol and 2-fluorene with KDM-BA acceleration (all P < 0.05) in addition to a non-linear association between 1-naphthol, 2-naphthol, 3-fluorene, 2-fluorene, and 1-pyrene with PhenoAge acceleration (all P < 0.05).

Conclusion

Exposure to mixed PAHs is associated with increased aging, with 2-naphthol being a key component of PAHs associated with aging. This study has identified risk factors in terms of PAH components for aging.

Dietary diversity score and the acceleration of biological aging: a population-based study of 88,039 participants

OBJECTIVES

Our study aimed to investigate the association of dietary diversity score (DDS), as reflected by five dietary categories, with biological age acceleration.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

This study included 88,039 individuals from the UK Biobank.

METHODS

Biological age (BA) was assessed using Klemerae-Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age represents the age acceleration (AgeAccel), termed as "KDMAccel" and "PhenoAgeAccel". AgeAccel > 0 indicates faster aging. Generalized linear regression models were performed to assess the associations of DDS with AgeAccel. Similar analyses were performed for the five dietary categories.

RESULTS

After adjusting for multiple variables, DDS was inversely associated with KDMAccel (βHigh vs Low= -0.403, 95%CI: -0.492 to -0.314, P < 0.001) and PhenoAgeAccel (βHigh vs Low= -0.545, 95%CI: -0.641 to -0.450, P < 0.001). Each 1-point increment in the DDS was associated with a 4.4% lower risk of KDMAccel and a 5.6% lower risk of PhenoAgeAccel. The restricted cubic spline plots demonstrated a non-linear dose-response association between DDS and the risk of AgeAccel. The consumption of grains (βKDMAccel = -0.252, βPhenoAgeAccel = -0.197), vegetables (βKDMAccel = -0.044, βPhenoAgeAccel = -0.077) and fruits (βKDMAccel = -0.179, βPhenoAgeAccel = -0.219) was inversely associated with the two AgeAccel, while meat and protein alternatives (βKDMAccel = 0.091, βPhenoAgeAccel = 0.054) had a positive association (All P < 0.001). Stratified analysis revealed stronger accelerated aging effects in males, smokers, and drinkers. A strengthening trend in the association between DDS and AgeAccel as TDI quartiles increased was noted.

CONCLUSIONS

This study suggested that food consumption plays a role in aging process, and adherence to a higher diversity dietary is associated with the slowing down of the aging process.

Effect of long-term caloric restriction on telomere length in healthy adults: CALERIE™ 2 trial analysis

Caloric restriction (CR) modifies lifespan and aging biology in animal models. The Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE™) 2 trial tested translation of these findings to humans. CALERIE™ randomized healthy, nonobese men and premenopausal women (age 21-50y; BMI 22.0-27.9 kg/m2), to 25% CR or ad-libitum (AL) control (2:1) for 2 years. Prior analyses of CALERIE™ participants' blood chemistries, immunology, and epigenetic data suggest the 2-year CR intervention slowed biological aging. Here, we extend these analyses to test effects of CR on telomere length (TL) attrition. TL was quantified in blood samples collected at baseline, 12-, and 24-months by quantitative PCR (absolute TL; aTL) and a published DNA-methylation algorithm (DNAmTL). Intent-to-treat analysis found no significant differences in TL attrition across the first year, although there were trends toward increased attrition in the CR group for both aTL and DNAmTL measurements. When accounting for adherence heterogeneity with an Effect-of-Treatment-on-the-Treated analysis, greater CR dose was associated with increased DNAmTL attrition during the baseline to 12-month weight-loss period. By contrast, both CR group status and increased CR were associated with reduced aTL attrition over the month 12 to month 24 weight maintenance period. No differences were observed when considering TL change across the study duration from baseline to 24-months, leaving it unclear whether CR-related effects reflect long-term detriments to telomere fidelity, a hormesis-like adaptation to decreased energy availability, or measurement error and insufficient statistical power. Unraveling these trends will be a focus of future CALERIE™ analyses and trials.

Retirement Makes You Old? Causal Effect of Retirement on Biological Age

Retirement is a critical life event for older people. Health scholars have scrutinized the health effects of retirement, but its consequences on age-related diseases and mortality are unclear. We extend this body of research by integrating measurements of biological age, representing the physiological decline preceding disease onset. Using data from the UK Biobank and a fuzzy regression discontinuity design, we estimated the effects of retirement on two biomarker-based biological age measures. Results showed that retirement significantly increases biological age for those induced to retire by the State Pension eligibility by 0.871-2.503 years, depending on sex and specific biological age measurement. Given the emerging scientific discussion about direct interventions to biological age to achieve additional improvements in population health, the positive effect of retirement on biological age has important implications for an increase in the State Pension eligibility age and its potential consequences on population health, public health care policy, and older people's labor force participation. Overall, this study provides novel empirical evidence contributing to the question of what social factors make people old.

Association between biological aging and periodontitis using NHANES 2009-2014 and mendelian randomization

Aging is a recognized risk factor for periodontitis, while biological aging could provide more accurate insights into an individual's functional status. This study aimed to investigate the potential association between biological aging and periodontitis. Epidemiological data from 9803 participants in the 2009-2014 National Health and Nutrition Examination Survey were analyzed at a cross-sectional level to assess this link. Three biological ages [Klemera-Doubal method (KDM), PhenoAge, and homeostatic dysregulation (HD)] and two measures of accelerated biological aging (BioAgeAccel and PhenoAgeAccel) were set as primary exposure and were calculated. Logistic regression and restricted cubic spline regression were employed to examine the relationship between biological aging and periodontitis. Additionally, Mendelian randomization analysis was conducted to explore the causal connection between accelerated biological aging and periodontitis. After adjusting for age, gender, race, educational level, marital status, ratio of family income, and disease conditions, this study, found a significant association between subjects with older higher biological ages, accelerated biological aging, and periodontitis. Specifically, for a per year increase in the three biological ages (HD, KDM, and PhenoAge), the risk of periodontitis increases by 15%, 3%, and 4% respectively. Individuals who had positive BioAgeAccel or PhenoAgeAccel were 20% or 37% more likely to develop periodontitis compared with those who had negative BioAgeAccel or PhenoAgeAccel. Furthermore, a significant non-linear positive relationship was observed between the three biological ages, accelerated biological aging, and periodontitis. However, the Mendelian randomization analysis indicated no causal effect of accelerated biological aging on periodontitis. Our findings suggest that biological aging may contribute to the risk of periodontitis, highlighting the potential utility of preventive strategies targeting aging-related pathways in reducing periodontitis risk among older adults.

A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study

BACKGROUND

Accelerated biological aging is an increasingly popular way to track the acceleration of biology over time that may not be captured by calendar time. Biological aging has been linked to external and internal chronic stressors and has the potential to be used clinically to understand a person's personalized functioning and predict future disease. We compared the association of different measures of biological aging and incident cardiovascular disease (CVD) overall and by race.

METHODS AND RESULTS

We used multiple informants models to compare the strength of clinical marker-derived age acceleration, 5 measures of epigenetic age acceleration (intrinsic and extrinsic epigenetic age acceleration, GrimAge acceleration, and PhenoAge acceleration), and 1 established clinical predictor of future CVD, Framingham 10-year risk score, with incident CVD over an 11-year period (2007-2018). Participants were 913 self-identified Black or White (41% and 59%, respectively) female or male (51% and 49%, respectively) individuals enrolled in the US-based CARDIA (Coronary Artery Risk Development in Young Adults) cohort study. The analytic baseline for this study was the 20-year follow-up examination (2005-2006; median age 45 years). We also included race-specific analysis. We found that all measures were modestly correlated with one another. However, clinical marker-derived age acceleration and Framingham 10-year risk score were more strongly associated with incident CVD than all the epigenetic measures. Clinical marker-derived age acceleration and Framingham 10-year risk score were not significantly different than one another in their association with incident CVD.

CONCLUSIONS

The type of accelerated aging measure should be taken into consideration when comparing their association with clinical outcomes. A multisystem clinical composite shows associations with incident CVD equally to a well-known clinical predictor.

Early-life risk factors, accelerated biological aging and the late-life risk of mortality and morbidity

BACKGROUND

Early-life exposure increases health risks throughout an individual's lifetime. Biological aging is influenced by early-life risks as a key process of disease development, but whether early-life risks could accelerate biological aging and elevate late-life mortality and morbidity risks remains unknown. Knowledge is also limited on the potential moderating role of healthy lifestyle.

METHODS

We investigate associations of three early-life risks around birth, breastfeeding, maternal smoking and birth weight, with biological aging of 202 580 UK Biobank participants (54.9 ± 8.1 years old). Biological aging was quantified as KDM-BA, PhenoAge and frailty. Moderate alcohol intake, no current smoking, healthy diet, BMI <30 kg/m2 and regular physical activity were considered as healthy lifestyles. Mortality and morbidity data were retrieved from health records.

RESULTS

Individual early-life risk factors were robustly associated with accelerated biological aging. A one-unit increase in the 'early-life risk score' integrating the three factors was associated with 0.060 (SE=0.0019) and 0.036-unit (SE = 0.0027) increase in z-scored KDM-BA acceleration and PhenoAge acceleration, respectively, and with 22.3% higher odds (95% CI: 1.185-1.262) of frailty. Increased chronological age and healthy lifestyles could mitigate the accelerations of KDM-BA and PhenoAge, respectively. Associations of early-life risk score with late-life mortality and morbidity were mediated by biological aging (proportions: 5.66-43.12%). KDM-BA and PhenoAge accelerations could significantly mediate the impact on most outcomes except anxiety, and frailty could not mediate the impact on T2D.

CONCLUSION

Biological aging could capture and mediate the late-life health risks stemming from the early-life risks, and could be potentially targeted for healthy longevity promotion.

Living in poverty and accelerated biological aging: evidence from population-representative sample of U.S. adults

BACKGROUND

Biological aging reflects a decline in the functions and integrity of the human body that is closely related to chronological aging. A variety of biomarkers have been found to predict biological age. Biological age higher than chronological age (biological age acceleration) indicates an accelerated state of biological aging and a higher risk of premature morbidity and mortality. This study investigated how socioeconomic disadvantages influence biological aging.

METHODS

The data from the National Health and Nutrition Examination Survey (NHANES) IV, including 10 nationally representative cross-sectional surveys between 1999-2018, were utilized. The analytic sample consisted of N = 48,348 individuals (20-84 years). We used a total of 11 biomarkers for estimating the biological age. Our main outcome was biological age acceleration, indexed by PhenoAge acceleration (PAA) and Klemera-Doubal biological age acceleration (KDM-A). Poverty was measured as a ratio of family income to the poverty thresholds defined by the U.S. Census Bureau, adjusted annually for inflation and family size (5 categories). The PAA and KDM-A were regressed on poverty levels, age, their interaction, education, sex, race, and a data collection wave. Sample weights were used to make the estimates representative of the U.S. adult population.

RESULTS

The results showed that higher poverty was associated with accelerated biological aging (PAA: unstandardized coefficient B = 1.38 p <.001, KDM: B = 0.96, p = .026 when comparing the highest and the lowest poverty level categories), above and beyond other covariates. The association between PAA and KDM-A and age was U-shaped. Importantly, there was an interaction between poverty levels and age (p <.001), as the effect of poverty was most pronounced in middle-aged categories while it was modest in younger and elderly groups.

CONCLUSION

In a nationally representative US adult population, we found that higher poverty was positively associated with the acceleration of biological age, particularly among middle-aged persons.

Association of sleep duration, chronotype, social jetlag, and sleep disturbance with phenotypic age acceleration: A cross-sectional analysis

OBJECTIVE

Sleep is a critical health-related behavior; research evidence has shown that sleep duration, poor sleep quality and insomnia are associated with aging and relevant age-related diseases. However, the associations between sleep duration, chronotype, sleep disturbance, and biological age have not been comprehensively assessed. This study aimed to examine sleep characteristics with biological age.

METHODS

The study included 6534 participants aged 20 years and older from the National Health and Nutrition Examination Survey between 2017 and March 2020. Sleep questionnaires were used to collect information on sleep duration and wake behavior on workdays and workfree days and sleep disturbance. Phenotypic age acceleration (PhenoAgeAccel) was estimated as a biological age measure using 9 blood chemistry biomarkers.

RESULTS

Long sleep (>9 hours) and extremely short sleep (≤4 hours) on workdays were positively associated with PhenoAgeAccel, compared with optimal sleep duration (7-8 hours). Similar positive associations with PhenoAgeAccel were observed for sleep duration on workfree days and across the whole week. Both slightly evening and evening chronotypes were associated with faster PhenoAgeAccel compared to morning chronotype. Social jetlag and sleep disturbance were not associated with PhenoAgeAccel, while long corrected social jetlag was associated with faster PhenoAgeAccel. The associations of sleep duration, chronotype, and corrected social jetlag with PhenoAgeAccel appeared stronger among females than among males.

CONCLUSIONS

Findings suggest a U-shape relationship between sleep duration and biological aging; slightly evening and evening chronotypes may be risk factors for aging. Further studies are needed to confirm these findings.

Relationship between dietary macronutrients intake and biological aging: a cross-sectional analysis of NHANES data

PURPOSE

This study aimed to investigate the association between macronutrient intake and biological age.

METHODS

Data were collected from 26,381 adults who participated in the United States National Health and Nutrition Examination Survey (NHANES). Two biological ages were estimated using the Klemera-Doubal method (KDM) and PhenoAge algorithms. Biological age acceleration (AA) was computed as the difference between biological age and chronological age. The associations between macronutrient intakes and AA were investigated.

RESULTS

After fully adjusting for confounding factors, negative associations were observed between AA and fiber intake (KDM-AA: β - 0.53, 95% CI - 0.62, - 0.43, P < 0.05; PhenoAge acceleration: β - 0.30, 95% CI - 0.35, - 0.25, P < 0.05). High-quality carbohydrate intake was associated with decreased AA (KDM-AA: β - 0.57, 95% CI - 0.67, - 0.47, P < 0.05; PhenoAge acceleration: β - 0.32, 95% CI - 0.37, - 0.26, P < 0.05), while low-quality carbohydrate was associated with increased AA (KDM-AA: β 0.30, 95% CI 0.21, 0.38, P < 0.05; PhenoAge acceleration: β 0.16, 95% CI 0.11, 0.21, P < 0.05). Plant protein was associated with decreased AA (KDM-AA: β - 0.39, 95% CI - 0.51, - 0.27, P < 0.05; PhenoAge acceleration: β - 0.21, 95% CI - 0.26, - 0.15, P < 0.05). Long-chain SFA intake increased AA (KDM-AA: β 0.16, 95% CI 0.08, 0.24, P < 0.05; PhenoAge acceleration: β 0.11, 95% CI 0.07, 0.15, P < 0.05). ω-3 PUFA was associated with decreased KDM-AA (β - 0.18, 95% CI - 0.27, - 0.08, P < 0.05) and PhenoAge acceleration (β - 0.09, 95% CI - 0.13, - 0.04, P < 0.05).

CONCLUSION

Our findings suggest that dietary fiber, high-quality carbohydrate, plant protein, and ω-3 PUFA intake may have a protective effect against AA, while low-quality carbohydrate and long-chain SFA intake may increase AA. Therefore, dietary interventions aimed at modifying macronutrient intakes may be useful in preventing or delaying age-related disease and improving overall health.

The mediating role of accelerated biological aging in the association between blood metals and cognitive function

Aging is a key risk factor in cognitive diseases. Recently, metal exposures were found associated with both biological aging and cognitive function. Here, we aim to evaluate the associations of blood metals with cognitive function and the mediated effect of biological aging. Fourteen metals were detected and biological age was calculated through Klemera and Doubal method among 514 adults in Beijing, China. The generalized linear models indicated that the copper (Cu), molybdenum (Mo), and strontium (Sr) were positively associated with biological aging [βCu (95% CI): 12.76 (9.26, 16.27); βMo (95% CI): 1.50 (0.15, 2.85)], and βSr (95% CI): 1.86 (0.68, 3.03)], while vanadium (V) was inversely related to biological aging [βV (95% CI): -0.76 (-1.48, -0.05)]. Subsequently, Cu, lead (Pb), selenium (Se), and biological aging were associated with cognitive function and further mediation analyses confirmed that biological aging partially mediated (33.98%, P = 0.019) the association of Cu and cognitive function. Additionally, we constructed a lifestyle index that implied the modifiable healthy lifestyle could slow aging to attenuate the detrimental effect of metals on cognition. Our findings provide insights into the potential pathways linking multiple metals exposure to aging and cognition and underscore the importance of adopting healthy lifestyles.

Patterns and Life Course Determinants of Black-White Disparities in Biological Age Acceleration: A Decomposition Analysis

Despite the prominence of the weathering hypothesis as a mechanism underlying racialized inequities in morbidity and mortality, the life course social and economic determinants of Black-White disparities in biological aging remain inadequately understood. This study uses data from the Health and Retirement Study (n = 6,782), multivariable regression, and Kitagawa-Blinder-Oaxaca decomposition to assess Black-White disparities across three measures of biological aging: PhenoAge, Klemera-Doubal biological age, and homeostatic dysregulation. It also examines the contributions of racial differences in life course socioeconomic and stress exposures and vulnerability to those exposures to Black-White disparities in biological aging. Across the outcomes, Black individuals exhibited accelerated biological aging relative to White individuals. Decomposition analyses showed that racial differences in life course socioeconomic exposures accounted for roughly 27% to 55% of the racial disparities across the biological aging measures, and racial disparities in psychosocial stress exposure explained 7% to 11%. We found less evidence that heterogeneity in the associations between social exposures and biological aging by race contributed substantially to Black-White disparities in biological aging. Our findings offer new evidence of the role of life course social exposures in generating disparities in biological aging, with implications for understanding age patterns of morbidity and mortality risks.

Relationship between an ageing measure and chronic obstructive pulmonary disease, lung function: a cross-sectional study of NHANES, 2007-2010

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is a disease associated with ageing. However, actual age does not accurately reflect the degree of biological ageing. Phenotypic age (PhenoAge) is a new indicator of biological ageing, and phenotypic age minus actual age is known as phenotypic age acceleration (PhenoAgeAccel). This research aimed to analyse the relationship between PhenoAgeAccel and lung function and COPD.

DESIGN

A cross-sectional study.

PARTICIPANTS

Data for the study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007-2010. We defined people with forced expiratory volume in 1 s/forced vital capacity <0.70 after inhaled bronchodilators as COPD and the rest of the population as non-COPD. Adults aged 40 years or older were enrolled in the study.

PRIMARY AND SECONDARY OUTCOME MEASURES

Linear and logistic regression were used to investigate the relationship between PhenoAgeAccel, lung function and COPD. Subgroup analysis was performed by gender, age, ethnicity and smoking index COPD. In addition, we analysed the relationship between the smoking index, respiratory symptoms and PhenoAgeAccel. Multiple models were used to reduce confounding bias.

RESULTS

5397 participants were included in our study, of which 1042 had COPD. Compared with PhenoAgeAccel Quartile1, Quartile 4 had a 52% higher probability of COPD; elevated PhenoAgeAccel was also significantly associated with reduced lung function. Further subgroup analysis showed that high levels of PhenoAgeAccel had a more significant effect on lung function in COPD, older adults and whites (P for interaction <0.05). Respiratory symptoms and a high smoking index were related to higher indicators of ageing.

CONCLUSIONS

Our study found that accelerated ageing is associated with the development of COPD and impaired lung function. Smoking cessation and anti-ageing therapy have potential significance in COPD.

Discordant Biological and Chronological Age: Implications for Cognitive Decline and Frailty

BACKGROUND

Older adults with discordant biological and chronological ages (BA and CA) may vary in cognitive and physical function from those with concordant BA and CA.

METHODS

To make our approach clinically accessible, we created easy-to-interpret participant groups in the Health, Aging, and Body Composition Study (N = 2 458, 52% female participants, 65% White participants, age: 73.5 ± 2.8) based on medians of CA, and a previously validated BA index comprised of readily available clinical tests. Joint models estimated associations of BA-CA group with cognition (Modified Mini-Mental State Examination [3MS] and Digit Symbol Substitution Test [DSST]) and frailty over 10 years.

RESULTS

The sample included the following: 32%, Young group (BA and CA < median); 21%, Prematurely Aging group (BA ≥ median, CA < median), 27%, Old group (BA and CA ≥ median), and 20%, Resilient group (BA < median, CA ≥ median). In education-adjusted models of cognition, among those with CA < median, the Prematurely Aging group performed worse than the Young at baseline (3MS and DSST p < .0001), but among those with CA ≥ median, the Resilient group did not outperform the Old group (3MS p = .31; DSST p = .25). For frailty, the Prematurely Aging group performed worse than the Young group at baseline (p = .0001), and the Resilient group outperformed the Old group (p = .003). For all outcomes, groups did not differ on change over time based on the same pairwise comparisons (p ≥ .40).

CONCLUSIONS

Discordant BA and CA identify groups who have greater cognitive and physical functional decline or are more protected than their CA would suggest. This information can be used for risk stratification.

Food insecurity as a cause of adiposity: evolutionary and mechanistic hypotheses

Food insecurity (FI) is associated with obesity among women in high-income countries. This seemingly paradoxical association can be explained by the insurance hypothesis, which states that humans possess evolved mechanisms that increase fat storage to buffer against energy shortfall when access to food is unpredictable. The evolutionary logic underlying the insurance hypothesis is well established and experiments on animals confirm that exposure to unpredictable food causes weight gain, but the mechanisms involved are less clear. Drawing on data from humans and other vertebrates, we review a suite of behavioural and physiological mechanisms that could increase fat storage under FI. FI causes short-term hyperphagia, but evidence that it is associated with increased total energy intake is lacking. Experiments on animals suggest that unpredictable food causes increases in retained metabolizable energy and reductions in energy expenditure sufficient to fuel weight gain in the absence of increased food intake. Reducing energy expenditure by diverting energy from somatic maintenance into fat stores should improve short-term survival under FI, but the trade-offs potentially include increased disease risk and accelerated ageing. We conclude that exposure to FI plausibly causes increased adiposity, poor health and shorter lifespan. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

IgG glycans in health and disease: Prediction, intervention, prognosis, and therapy

Immunoglobulin (IgG) glycosylation is a complex enzymatically controlled process, essential for the structure and function of IgG. IgG glycome is relatively stable in the state of homeostasis, yet its alterations have been associated with aging, pollution and toxic exposure, as well as various diseases, including autoimmune and inflammatory diseases, cardiometabolic diseases, infectious diseases and cancer. IgG is also an effector molecule directly involved in the inflammation processes included in the pathogenesis of many diseases. Numerous recently published studies support the idea that IgG N-glycosylation fine-tunes the immune response and plays a significant role in chronic inflammation. This makes it a promising novel biomarker of biological age, and a prognostic, diagnostic and treatment evaluation tool. Here we provide an overview of the current state of knowledge regarding the IgG glycosylation in health and disease, and its potential applications in pro-active prevention and monitoring of various health interventions.

Healthy Lifestyle Behaviors and Biological Aging in the U.S. National Health and Nutrition Examination Surveys 1999-2018

People who have a balanced diet and engage in more physical activity live longer, healthier lives. This study aimed to test the hypothesis that these associations reflect a slowing of biological processes of aging. We analyzed data from 42 625 participants (aged 20-84 years, 51% female participants) from the National Health and Nutrition Examination Surveys (NHANES), 1999-2018. We calculated adherence to a Mediterranean diet (MeDi) and level of leisure time physical activity (LTPA) using standard methods. We measured biological aging by applying the PhenoAge algorithm, developed using clinical and mortality data from NHANES-III (1988-94), to clinical chemistries measured from a blood draw at the time of the survey. We tested the associations of diet and physical activity measures with biological aging, explored synergies between these health behaviors, and tested heterogeneity in their associations across strata of age, sex, and body mass index. Participants who adhered to the MeDi and who did more LTPA had younger biological ages compared with those who had less-healthy lifestyles (high vs low MeDi tertiles: β = 0.14 standard deviation [SD] [95% confidence interval {CI}: -0.18, -0.11]; high vs sedentary LTPA, β = 0.12 SD [-0.15, -0.09]), in models controlled for demographic and socioeconomic characteristics. Healthy diet and regular physical activity were independently associated with lower clinically defined biological aging, regardless of age, sex, and BMI category.

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.

Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset

BACKGROUND

Diet may influence biological aging and the discrepancy (∆age) between a subject's biological age (BA) and chronological age (CA). We aimed to investigate the correlation of dietary flavonoids with the ∆age of organs (heart, kidney, liver) and the whole body.

METHOD

A total of 3193 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2007-2008 and 2017-2018. Dietary flavonoids intake was assessed using 24-h dietary recall method. Multiple linear regression analysis was performed to evaluate the association of dietary flavonoids intake with the ∆age of organs (heart, kidney, liver) and the whole body. BA was computed based on circulating biomarkers, and the resulting ∆age was tested as an outcome in linear regression analysis.

RESULTS

The ∆age of the whole body, heart, and liver was inversely associated with higher flavonoids intake (the whole body ∆age β = - 0.58, cardiovascular ∆age β = - 0.96, liver ∆age β = - 3.19) after adjustment for variables. However, higher flavonoids intake positively related to renal ∆age (β = 0.40) in participants with chronic kidney disease (CKD). Associations were influenced by population characteristics, such as age, health behavior, or chronic diseases. Anthocyanidins, isoflavones and flavones had the strongest inverse associations between the whole body ∆age and cardiovascular ∆age among all the flavonoids subclasses.

CONCLUSION

Flavonoids intake positively contributes to delaying the biological aging process, especially in the heart, and liver organ, which may be beneficial for reducing the long-term risk of cardiovascular or liver disease.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

The variable associations between PFASs and biological aging by sex and reproductive stage in NHANES 1999-2018

BACKGROUND

Per- and polyfluoroalkyl substances (PFASs) are endocrine disrupting chemicals that have myriad effects on human physiology. Estrogenic PFASs may influence biological aging by mimicking the activity of endogenous estrogens, which can decrease inflammation and oxidative stress and enhance telomerase activity. We hypothesized that PFAS exposure would be differentially associated with measures of biological aging based on biological sex and reproductive stage.

METHODS

We analyzed associations between serum PFAS levels and measures of biological aging for pre- and postmenopausal women and men (n = 3193) using data from the 2003 to 2018 waves of the National Health and Nutrition Examination Survey. Examining PFASs both individually and in mixture models, we investigated four measures of clinical aging (Homeostatic Dysregulation, the Klemera-Doubal Method, Phenotypic Age Acceleration, and Allostatic Load), oxidative stress, and telomere length.

RESULTS

PFOA and PFOS were negatively associated with Phenotypic Age Acceleration (e.g. decelerated aging) for men B = -0.22, 95% CI: -0.32, -0.12; B = -0.04, 95% CI: -0.06, -0.03) , premenopausal women (B = -0.58, 95% CI: -0.83, -0.32; B = -0.15, 95% CI: -0.20, -0.09), and postmenopausal women (B= -0.22, 95% CI: -0.43, -0.01; B = -0.05, 95% CI: -0.08, -0.02). In mixture models, we found net negative effects for Phenotypic Age Acceleration and Allostatic Load for men, premenopausal women, and postmenopausal women. We also found significant mixture effects for the antioxidants bilirubin and albumin among the three sample groups. We found no evidence to support effects on telomere length.

DISCUSSION

Our findings suggest that PFAS exposure may be inversely associated with some measures of biological aging at the relatively low levels of exposure in this sample, regardless of reproductive stage and sex, which does not support our hypothesis. This research provides insights into how PFAS exposure may variably influence aging measures depending on the physiological process investigated.

Comparison of healthspan-related indicators between adults with and without HIV infection aged 18-59 in the United States: a secondary analysis of NAHNES 1999-March 2020

BACKGROUND

As persons with HIV (PWH) live longer they may experience a heightened burden of poor health. However, few studies have characterized the multi-dimentional health of PWH. Thus, we aimed to identify the extent and pattern of health disparities, both within HIV infection status and across age (or sex) specific groups.

METHODS

We used cross-sectional data from the US National Health and Nutrition Examination Survey, 1999-March 2020. The adjusted prevalence of six healthspan-related indicators-physical frailty, activities of daily living (ADL) disability, mobility disability, depression, multimorbidity, and all-cause death-was evaluated. Logistic regression and Cox proportional hazards analyses were used to investigate associations between HIV status and healthspan-related indicators, with adjustment for individual-level demographic characteristics and risk behaviors.

RESULTS

The analytic sample consisted of 33 200 adults (170 (0.51%) were PWH) aged 18-59 years in the United States. The mean (interquartile range) age was 35.1 (25.0-44.0) years, and 49.4% were male. PWH had higher adjusted prevalences for all of the 6 healthspan-related indicators, as compared to those without HIV, ranged from 17.4% (95% CI: 17.4%, 17.5%) vs. 2.7% (95%CI: 2.7%, 2.7%) for all-cause mortality, to 84.3% (95% CI: 84.0%, 84.5%) vs. 69.8% (95%CI: 69.7%, 69.8%) for mobility disability. While the prevalence difference was largest in ADL disability (23.4% (95% CI: 23.2%, 23.7%); P < 0.001), and least in multimorbidity (6.9% (95% CI: 6.8%, 7.0%); P < 0.001). Generally, the differences in prevalence by HIV status were greater in 50-59 years group than those in 18-29 group. Males with HIV suffered higher prevalence of depression and multimorbidity, while females with HIV were more vulnerable to functional limitation and disabilities. HIV infection was associated with higher odds for 3 of the 6 healthspan-related indicators after fully adjusted, such as physical frailty and depression. Sensitivity analyses did not change the health differences between adults with and without HIV infection.

CONCLUSIONS

In a large sample of U.S. community-dwelling adults, by identifying the extent and pattern of health disparities, we characterized the multi-dimentional health of PWHs, providing important public health implications for public policy that aims to improve health of persons with HIV and further reduce these disparities.

Blunted Rest-Activity Circadian Rhythm Is Associated With Increased Rate of Biological Aging: An Analysis of NHANES 2011-2014

Impaired rest-activity circadian rhythm has been associated with increased risk for morbidity and mortality. Animals with mutations in clock genes display accelerated aging and shortened life span. Whether impaired rest-activity circadian rhythm is also associated with processes of aging in humans has not been explored. We analyzed accelerometry and physiological data from 7 539 adults participating in the 2011-2014 waves of the U.S. National Health and Nutrition Examination Surveys. We used accelerometry data to compute rest-activity rhythm measurements. We used physiological data to compute measurements of biological aging according to 3 published algorithms: Klemera-Doubal method (KDM) Biological Age, PhenoAge, and homeostatic dysregulation (HD). In the models adjusting multiple covariates, participants with higher relative amplitude (RA) and interdaily stability (IS) and lower intradaily variability (IV) exhibited less advanced biological aging indexed by KDM and PhenoAge (effect sizes for 1-quantile increase in these rest-activity measurements ranged from 0.54 to 0.57 "years" for RA, 0.24 to 0.28 "years" for IS, and 0.24 to 0.35 "years" for IV, ps < .001). Similar finding was observed for biological aging indexed by HD, but the significance was limited to RA with 1-quantile increase in RA associated with 0.09 log units decrease in HD (p < .001). The results indicate that blunted rest-activity circadian rhythm is associated with accelerated aging in the general population, suggesting that interventions aiming at enhancing circadian rhythm may be a novel approach for the extension of a healthy life span.

Associations between multiple metals exposure and biological aging: Evidence from the Dongfeng-Tongji cohort

Aging is related to a progressive decline in physiological functions and is affected by environmental factors. Metal exposures are linked with many health effects, but have poorly understood associations with aging. In this study, a total of 33,916 participants from the Dongfeng-Tongji cohort were included to establish biological age (BA) predictors by using recent advanced algorithms, Klemera and Doubal method (KDM) and Mahalanobis distance. Two biological aging indexes (BAIs), recorded as KDM-accel [the residual from regressing KDM-BA on chronological age] and physiological dysregulation (PD), were separately defined and tested on their associations with mortality by using Cox proportional hazard models. Among 3320 subjects with laboratory determinations of 23 metals in plasma, the individual and overall associations between these metals and BAIs were evaluated by using multiple-linear regression and weighted quantile sum (WQS) models. Both BAIs were prospectively associated with all-cause mortality among the whole participants [KDM-accel: HR(95%CI) = 1.23(1.18, 1.29); PD: HR(95%CI) = 1.37(1.31, 1.42)]. Each 1-unit increment in ln-transformed strontium and molybdenum were cross-sectionally associated with a separate 0.71- and 0.34-year increase in KDM-accel, and each 1 % increment in copper, rubidium, strontium, cobalt was cross-sectionally associated with a separate 0.10 %, 0.10 %, 0.09 %, 0.02 % increase in PD (all FDR < 0.05). The WQS models observed mixture effects of multi-metals on aging, with a 0.20-year increase in KDM-accel and a 0.04 % increase in PD for each quartile increase in ln-transformed concentrations of all metals [KDM-accel: β(95%CI) = 0.20(0.08, 0.32); PD: β(95%CI) = 0.04(0.02, 0.06)]. Our findings revealed that plasma strontium, molybdenum, copper, rubidium and cobalt were associated with accelerated aging. Multi-metals exposure showed mixture effects on the aging process, which highlights potential preventative interventions.

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.

Weight change across adulthood and accelerated biological aging in middle-aged and older adults

BACKGROUND

Little is known regarding the association between weight change and accelerated aging.

OBJECTIVES

This study aimed to estimate the influence of weight change across adulthood on biological aging acceleration in middle-aged and older adults in the United States.

METHODS

We used data of 5553 adults (40-84 y) from the National Health and Nutrition Examination Survey 1999-2010. Weight change patterns (i.e., stable normal, maximal overweight, obese to nonobese, nonobese to obese, and stable obese) and absolute weight change groups across adulthood (i.e., from young to middle adulthood, young to late adulthood, and middle to late adulthood) were defined. A biological aging measure (i.e., phenotypic age acceleration [PhenoAgeAccel]) at late adulthood was calculated. Survey analysis procedures with the survey weights were performed.

RESULTS

Across adulthood, maximal overweight, nonobese to obese, and stable obesity were consistently associated with higher PhenoAgeAccel. For instance, from young to middle adulthood, compared with participants who had stable normal weight, participants experiencing maximal overweight, moving from the nonobese to obese, and maintaining obesity had 1.71 (standard error [SE], 0.21; P < 0.001), 3.62 (SE, 0.28; P < 0.001), and 6.61 (SE, 0.58; P < 0.001) higher PhenoAgeAccel values, respectively. From young to middle adulthood, relative to absolute weight loss or gain of <2.5 kg, weight loss of ≥2.5 kg was marginally associated with lower PhenoAgeAccel (P = 0.054), whereas an obese to nonobese pattern from middle to late adulthood was associated with higher PhenoAgeAccel (P < 0.001).

CONCLUSIONS

Maximal overweight, nonobese to obese, and stable obesity across adulthood, as well as an obese to nonobese pattern from middle to late adulthood, were associated with accelerated biological aging. In contrast, weight loss from young to middle adulthood was associated with decelerated biological aging. The findings highlight the potential role of weight management across adulthood for aging. Monitoring weight fluctuation may help identify the population at high risk of accelerated aging.

Effect of dietary inflammatory potential on the aging acceleration for cardiometabolic disease: A population-based study

BACKGROUND/AIM

Optimized dietary patterns have been considered an important determinant of delaying aging in cardiometabolic disease (CMD). Dietary pattern with high-level dietary inflammatory potential is a key risk factor for cardiometabolic disease, and has drawn increasing attention. The aim of this study was to investigate whether dietary pattern with high dietary inflammatory potential was associated with aging acceleration in cardiometabolic disease.

MATERIALS AND METHODS

We analyzed the cross-sectional data from six survey cycles (1999-2000, 2001-2002, 2003-2004, 2005-2006, 2007-2008, and 2009-2010) of the National Health and Nutritional Examination Surveys (NHANES). A total of 16,681 non-institutionalized adults and non-pregnant females with CMD were included in this study. Dietary inflammatory index (DII) was used to assess the dietary inflammatory potential. The two age acceleration biomarkers were calculated by the residuals from regressing chronologic age on Klemera-Doubal method biological age (KDM BioAge) or Phenotypic Age (PhenoAge), termed "KDMAccel" and "PhenoAgeAccel." A multivariable linear regression accounting for multistage survey design and sampling weights was used in different models to investigate the association between DII and aging acceleration. Four sensitivity analyses were used to ensure the robustness of our results. Besides, we also analyzed the anti-aging effects of DASH-type dietary pattern and "Life's Simple 7".

RESULTS

For 16,681 participants with CMD, compared with the first tertile of DII after adjusting for all potential confounders, the patients with second tertile of DII showed a 1.02-years increase in KDMAccel and 0.63-years increase in PhenoAgeAccel (KDMAccel, β = 1.02, 95% CI = 0.64 to 1.41, P < 0.001; PhenoAgeAccel, β = 0.63, 95% CI = 0.44 to 0.82, P < 0.001), while the patients with the third tertile of DII showed a 1.48-years increase in KDMAccel and 1.22-years increase in PhenoAgeAccel (KDMAccel, β = 1.48, 95% CI = 1.02 to 1.94, P < 0.001; PhenoAgeAccel, β = 1.22, 95% CI = 1.01 to 1.43, P < 0.001). In addition, DASH-type dietary pattern was associated with a 0.57-years reduction in KDMAccel (β = -0.57, 95% CI = -1.08 to -0.06, P = 0.031) and a 0.54-years reduction in PhenoAgeAccel (β = -0.54, 95% CI = -0.80 to -0.28, P < 0.001). The each one-unit increase in CVH score was associated with a 1.58-years decrease in KDMAccel (β = -1.58, 95% CI = -1.68 to -1.49, P < 0.001) and a 0.36-years in PhenoAgeAccel (β = -0.36, 95% CI = -0.41 to -0.31, P < 0.001).

CONCLUSION

Among CMD, the dietary pattern with high dietary inflammatory potential was association with aging acceleration, and the anti-aging potential of DASH-type dietary pattern and "Life's Simple 7" should also be given attention, but these observations require future prospective validation.

Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality

Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (P_interaction = 0.04) and for daughters as compared to sons (P_interaction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.

Biological aging in maltreated children followed up into middle adulthood

BACKGROUND

Childhood adversity has been linked to many indicators of shorter healthy lifespan, including earlier onset of disease and disability as well as early mortality. These observations suggest the hypothesis that childhood maltreatment may accelerate aging.

OBJECTIVE

To characterize the relationship between childhood maltreatment and accelerated biological aging in a prospective cohort of 357 individuals with documented cases of childhood maltreatment and 250 controls matched on demographic and socioeconomic factors.

METHODS

Cases were drawn from juvenile and adult court records from the years 1967 through 1971 in a large Midwest metropolitan geographic area. Cases were defined as having court-substantiated cases of childhood physical or sexual abuse, or neglect occurring at age 11 or younger. Controls were selected from the same schools and hospitals of birth and matched on age, sex, race, and approximate socioeconomic status. We compared biological aging in these two groups using two blood-chemistry algorithms, the Klemera-Doubal method Biological Age (KDM BA) and the PhenoAge. Algorithms were developed and validated in data from the National Health and Nutrition Examination Surveys (NHANES) using published methods and publicly available software.

RESULTS

Participants (55% women, 49% non-White) had mean age of 41 years (SD=4). Those with court substantiated childhood maltreatment history exhibited more advanced biological aging as compared with matched controls, although this difference was statistically different for only the KDM BA measure (KDM BA Cohen's D=0.20, 95% CI=[0.03,0.36], p = 0.02; PhenoAge Cohen's D=0.09 95% CI=[-0.08,0.25], p = 0.296). In subgroup analyses, maltreatment effect sizes were larger for women as compared to men and for White participants as compared to non-White participants, although these differences were not statistically significant at the α= 0.05 level.

CONCLUSIONS AND RELEVANCE

As of midlife, effects of childhood maltreatment on biological aging are small in magnitude but discernible. Interventions to treat psychological and behavioral sequelae of exposure to childhood maltreatment, including in midlife adults, have potential to protect survivors from excess burden of disease, disability, and mortality in later life.

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.

Predictive Utility of Mortality by Aging Measures at Different Hierarchical Levels and the Response to Modifiable Life Style Factors: Implications for Geroprotective Programs

Background

Aging, as a multi-dimensional process, can be measured at different hierarchical levels including biological, phenotypic, and functional levels. The aims of this study were to: (1) compare the predictive utility of mortality by three aging measures at three hierarchical levels; (2) develop a composite aging measure that integrated aging measures at different hierarchical levels; and (3) evaluate the response of these aging measures to modifiable life style factors.

Methods

Data from National Health and Nutrition Examination Survey 1999–2002 were used. Three aging measures included telomere length (TL, biological level), Phenotypic Age (PA, phenotypic level), and frailty index (FI, functional level). Mortality information was collected until December 2015. Cox proportional hazards regression and multiple linear regression models were performed.

Results

A total of 3,249 participants (20–84 years) were included. Both accelerations (accounting for chronological age) of PA and FI were significantly associated with mortality, with HRs of 1.67 [95% confidence interval (CI) = 1.41–1.98] and 1.59 (95% CI = 1.35–1.87), respectively, while that of TL showed non-significant associations. We thus developed a new composite aging measure (named PC1) integrating the accelerations of PA and FI, and demonstrated its better predictive utility relative to each single aging measure. PC1, as well as the accelerations of PA and FI, were responsive to several life style factors including smoking status, body mass index, alcohol consumption, and leisure-time physical activity.

Conclusion

This study demonstrates that both phenotypic (i.e., PA) and functional (i.e., FI) aging measures can capture mortality risk and respond to modifiable life style factors, despite their inherent differences. Furthermore, the PC1 that integrated phenotypic and functional aging measures outperforms in predicting mortality risk in comparison with each single aging measure, and strongly responds to modifiable life style factors. The findings suggest the complementary of aging measures at different hierarchical levels and highlight the potential of life style-targeted interventions as geroprotective programs.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Social mobility and biological aging among older adults in the United States

Lower socioeconomic status is associated with faster biological aging, the gradual and progressive decline in system integrity that accumulates with advancing age. Efforts to promote upward social mobility may, therefore, extend healthy lifespan. However, recent studies suggest that upward mobility may also have biological costs related to the stresses of crossing social boundaries. We tested associations of life-course social mobility with biological aging using data from participants in the 2016 Health and Retirement Study (HRS) Venous Blood Study who provided blood-chemistry (n = 9,255) and/or DNA methylation (DNAm) data (n = 3,976). We quantified social mobility from childhood to later-life using data on childhood family characteristics, educational attainment, and wealth accumulation. We quantified biological aging using 3 DNAm "clocks" and 3 blood-chemistry algorithms. We observed substantial social mobility among study participants. Those who achieved upward mobility exhibited less-advanced and slower biological aging. Associations of upward mobility with less-advanced and slower aging were consistent for blood-chemistry and DNAm measures of biological aging, and were similar for men and women and for Black and White Americans (Pearson-r effect-sizes ∼0.2 for blood-chemistry measures and the DNAm GrimAge clock and DunedinPoAm pace-of-aging measures; effect-sizes were smaller for the DNAm PhenoAge clock). Analysis restricted to educational mobility suggested differential effects by racial identity; mediating links between educational mobility and healthy aging may be disrupted by structural racism. In contrast, mobility producing accumulation of wealth appeared to benefit White and Black Americans equally, suggesting economic intervention to reduce wealth inequality may have potential to heal disparities in healthy aging.

Does Modulation of an Epigenetic Clock Define a Geroprotector?

There is growing interest in the development of interventions (e.g., drugs, diets, dietary supplements, behavioral therapies, etc.) that can enhance health during the aging process, prevent or delay multiple age-related diseases, and ultimately extend lifespan. However, proving that such 'geroprotectors' do what they are hypothesized to do in relevant clinical trials is not trivial. We briefly discuss some of the more salient issues surrounding the design and interpretation of clinical trials of geroprotectors, including, importantly, how one defines a geroprotector. We also discuss whether emerging surrogate endpoints, such as epigenetic clocks, should be treated as primary or secondary endpoints in such trials. Simply put, geroprotectors should provide overt health and disease prevention benefits but the time-dependent relationships between epigenetic clocks and health-related phenomena are complex and in need of further scrutiny. Therefore, studies that enable understanding of the relationships between epigenetic clocks and disease processes while simultaneously testing the efficacy of a candidate geroprotector are crucial to move the field forward.

Distinct biological ages of organs and systems identified from a multi-omics study

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research

PURPOSE OF REVIEW

Acceleration of biological processes of aging is hypothesized to drive excess morbidity and mortality in socially disadvantaged populations. DNA methylation measures of biological aging provide tools for testing this hypothesis.

RECENT FINDINGS

Next-generation DNA methylation measures of biological aging developed to predict mortality risk and physiological decline are more predictive of morbidity and mortality than the original epigenetic clocks developed to predict chronological age. These new measures show consistent evidence of more advanced and faster biological aging in people exposed to socioeconomic disadvantage and may be able to record the emergence of socially determined health inequalities as early as childhood. Next-generation DNA methylation measures of biological aging also indicate race/ethnic disparities in biological aging. More research is needed on these measures in samples of non-Western and non-White populations. New DNA methylation measures of biological aging open opportunities for refining inference about the causes of social disparities in health and devising policies to eliminate them. Further refining measures of biological aging by including more diversity in samples used for measurement development is a critical priority for the field.

Comparing qPCR and DNA methylation-based measurements of telomere length in a high-risk pediatric cohort

Various approaches exist to assess population differences in biological aging. Telomere length (TL) is one such measure, and is associated with disease, disability and early mortality. Yet, issues surrounding precision and reproducibility are a concern for TL measurement. An alternative method to estimate TL using DNA methylation (DNAmTL) was recently developed. Although DNAmTL has been characterized in adult and elderly cohorts, its utility in pediatric populations remains unknown. We examined the comparability of leukocyte TL measurements generated using qPCR (absolute TL; aTL) to those estimated using DNAmTL in a high-risk pediatric cohort (N = 269; age: 8–13 years, 83% investigated for maltreatment). aTL and DNAmTL measurements were correlated with one another (r = 0.20, p = 0.001), but exhibited poor measurement agreement and were significantly different in paired-sample t-tests (Cohen’s d = 0.77, p < 0.001). Shorter DNAmTL was associated with older age (r = −0.25, p < 0.001), male sex (β = −0.27, p = 0.029), and White race (β = −0.74, p = 0.008). By contrast, aTL was less strongly associated with age (r = −0.13, p = 0.040), was longer in males (β = 0.31, p = 0.012), and was not associated with race (p = 0.820). These findings highlight strengths and limitations of high-throughput measures of TL; although DNAmTL replicated hypothesized associations, aTL measurements were positively skewed and did not replicate associations with external validity measures. These results also extend previous research in adults and suggest that DNAmTL is a sensitive TL measure for use in pediatric populations.

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.

Effect of Diet, Exercise, or Both on Biological Age and Healthy Aging in Older Adults with Obesity: Secondary Analysis of a Randomized Controlled Trial

OBJECTIVES

To determine the effect of diet, exercise, and diet-exercise in combination on measures of biological age.

DESIGN

Secondary analysis of a 1-year randomized, controlled trial.

SETTING

University-based Medical Center.

PARTICIPANTS

One-hundred-seven older (age≥65 yrs.) adults with obesity (BMI≥30 kg/m2) were randomized and 93 completed the study. Analyses used intention-to-treat.

INTERVENTIONS

Participants were randomized to a control group, a weight-management (diet) group, an exercise group, or a weight-management-plus-exercise (diet-exercise) group.

MAIN OUTCOME MEASURES

We calculated Klemera-Doubal Method (KDM) biological age, Homeostatic Dysregulation (HD) score, and Health Aging Index (HAI) score at baseline, and changes at 6- and 12-months.

RESULTS

Diet and diet-exercise decreased KDM biological age more than exercise and control (-2.4±0.4, -2.2±0.3, -0.2±0.4, and 0.2±0.5, respectively, P<0.05 for the between group-differences). Diet and diet-exercise also decreased HD score more than exercise and control (-1.0±0.3, -1.1±0.3, 0.1±0.3, and 0.3±0.3 respectively, P<0.05). Moreover, diet-exercise decreased HAI score more than exercise, diet, or control (-1.1±0.2, -0.5±0.2, -0.5±0.2, and 0.0±0.2, respectively, P<0.05).

CONCLUSIONS

These findings suggest that diet and diet-exercise are both effective methods of improving biological age, and that biological age may be a valuable method of assessing geroprotective interventions in older humans.

Associations between life course socioeconomic conditions and the Pace of Aging

BACKGROUND

Socioeconomic disadvantage is a well-established predictor of morbidity and mortality, and is thought to accelerate the aging process. This study examined associations between life course socioeconomic conditions and the Pace of Aging, a longitudinal measure of age-related physiological decline.

METHODS

Data were drawn from a Swiss population-based cohort of individuals originally recruited between 2003 and 2006, and followed up for 11 years (2834 women, 2475 men aged 35 - 75 years (mean 52)). Pace of Aging was measured using three repeated assessments of 12 biomarkers reflecting multiple body systems. Analysis tested associations of socioeconomic conditions with physiological status at baseline and with the Pace of Aging.

RESULTS

Participants with more life course socioeconomic disadvantage were physiologically older at baseline and experienced faster Pace of Aging. Effect-sizes (β) for associations of childhood socioeconomic disadvantage with baseline physiological status ranged from 0.1-0.2; for adulthood socioeconomic disadvantage, effect-sizes ranged from 0.2-0.3. Effect-sizes were smaller for associations with the Pace of Aging (< 0.05 for childhood disadvantage, 0.05-0.1 for adulthood disadvantage). Those who experienced disadvantaged socioeconomic conditions from childhood to adulthood aged 10% faster over the 11 years of follow-up as compared with those who experienced consistently advantaged socioeconomic conditions. Covariate adjustment for health behaviors attenuated associations, but most remained statistically significant.

CONCLUSIONS

Socioeconomic inequalities contribute to a faster Pace of Aging, partly through differences in health behaviors. Intervention to slow aging in at risk individuals is needed by midlife, before aetiology of aging-related diseases become established.

Effect of Physical Activity, Smoking, and Sleep on Telomere Length: A Systematic Review of Observational and Intervention Studies

Aging is a risk factor for several pathologies, restricting one’s health span, and promoting chronic diseases (e.g., cardiovascular and neurodegenerative diseases), as well as cancer. Telomeres are regions of repetitive DNA located at chromosomal ends. Telomere length has been inversely associated with chronological age and has been considered, for a long time, a good biomarker of aging. Several lifestyle factors have been linked with telomere shortening or maintenance. However, the consistency of results is hampered by some methodological issues, including study design, sample size, measurement approaches, and population characteristics, among others. Therefore, we aimed to systematically review the current literature on the effects of three relevant lifestyle factors on telomere length in human adults: physical activity, smoking, and sleep. We conducted a qualitative systematic review of observational and intervention studies using the Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The systematic literature search covered articles published in MEDLINE and EMBASE databases (from 2010 to 2020). A total of 1400 studies were identified; 83 were included after quality control. Although fewer sedentary activities, optimal sleep habits, and non- or ex-smoker status have been associated with less telomere shortening, several methodological issues were detected, including the need for more targeted interventions and standardized protocols to better understand how physical activity and sleep can impact telomere length and aging. We discuss the main findings and current limitations to gain more insights into the influence of these lifestyle factors on the healthy aging process.

A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub ( http://github.com/dayoonkwon/BioAge ), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

Conceptual and analytical overlap between allostatic load and systemic biological aging measures: Analyses from the National Survey of Midlife Development in the United States

BACKGROUND

Indices quantifying allostatic load (AL) and biological aging (BA) have independently received widespread use in epidemiological literature. However, little attention has been paid to their conceptual and quantitative overlap. By reviewing literature utilizing measures of AL and BA, and conducting comparative analysis, we highlight similarities and differences in biological markers employed and approach toward scale construction. Further, we outline opportunities where both types of indices might be improved by adopting methodological features of the other.

METHODS

Using data from the National Survey of Midlife Development in the United States (N=2,055, age=26-86), we constructed three AL indices: one common literature standard, and two alternative formulations informed by previous work with measures of BA. The performance of AL indices was juxtaposed against two commonly employed BA indices: Klemera-Doubal Method Biological Age and Homeostatic Dysregulation.

RESULTS

All indices correlated with chronological age. Participants with higher AL and older BA performed worse on tests of physical and subjective functioning. Further, participants with increased life-course risk exposure exhibited higher AL and BA. Notably, alternative AL formulations tended to exhibit effect-sizes equivalent to or larger than those observed for BA measures, and displayed superior mortality prediction.

CONCLUSIONS

In addition to their conceptual similarity, AL and BA indices also exhibit significant analytical similarity. Further, BA measures are robust to construction using a panel of biomarkers not observed in previous iterations, including carotenoids indexing antioxidant capacity. In turn, AL indices could benefit by adopting the methodological rigor formalized within BA composites, such as applying biomarker down-selection criteria.

Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring

Aging affects most living organisms and includes the processes that reduce health and survival. The chronological and the biological age of individuals can differ remarkably, and there is a lack of reliable biomarkers to monitor the consequences of aging. In this review we give an overview of commonly mentioned and frequently used potential aging-related biomarkers. We were interested in biomarkers of aging in general and in biomarkers related to cellular senescence in particular. To answer the question whether a biological feature is relevant as a potential biomarker of aging or senescence in the scientific community we used the PICO strategy known from evidence-based medicine. We introduced two scoring systems, aimed at reflecting biomarker relevance and measurement effort, which can be used to support study designs in both clinical and research settings.