A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study

Active longevity and aging: dissecting the impacts of physical and sedentary behaviors on longevity and age acceleration

BACKGROUND

To examine the associations of physical activity (PA) and sedentary behavior (SB) with longevity and age acceleration (AA) using observational and Mendelian randomization (MR) studies, and quantify the mediating effects of lipids.

METHODS

In Guangzhou Biobank Cohort Study (GBCS), PA and SB were assessed by the Chinese Version of the International Physical Activity Questionnaire. Longevity was defined as participants whose age at follow-up or at death was at or above the 90th age percentile. AA was defined as the residual resulting from a linear model that regressed phenotypic age against chronological age. Linear regression and Poisson regression with robust error variance were used to assess the associations of total and specific PA in different intensities, and SB with AA and longevity, yielding βs or relative risks (RRs) and 95% confidence intervals (CIs). Two-sample MR was conducted to examine the causal effects. Mediation analysis was used to assess the mediating effects of lipids.

RESULTS

Of 20,924 participants aged 50 + years in GBCS, during an average follow-up of 15.0 years, compared with low PA, moderate and high PA were associated with higher likelihood of longevity (RR (95% CI): 1.56 (1.16, 2.11), 1.66 (1.24, 2.21), respectively), and also cross-sectionally associated with lower AA (β (95% CI): -1.43 (-2.41, -0.45), -2.09 (-3.06, -1.11) years, respectively). Higher levels of moderate PA (MPA) were associated with higher likelihood of longevity and lower AA, whereas vigorous PA (VPA) showed opposite effects. The association of PA with longevity observed in GBCS was mediated by low-density lipoprotein cholesterol (LDL-C) by 8.23% (95% CI: 3.58-39.61%), while the association with AA was mediated through LDL-C, triglycerides and total cholesterol by 5.13% (3.94-7.30%), 7.81% (5.98-11.17%), and 3.37% (2.59-4.80%), respectively. Additionally, in two-sample MR, SB was positively associated with AA (β (95% CI): 1.02 (0.67, 1.36) years).

CONCLUSIONS

PA showed protective effects on longevity and AA, with the effects being partly mediated through lipids. Conversely, SB had a detrimental impact on AA. MPA was associated with higher likelihood of longevity and reduced AA, whereas VPA showed adverse effects. Our findings reinforce the recommendation of "sit less and move more" to promote healthy longevity, and highlight the potential risks associated with VPA in the elderly.

Impact of accelerated biological aging and genetic variation on esophageal adenocarcinoma: Joint and interaction effect in a prospective cohort

Accelerated biological aging may be associated with increased risk of esophageal adenocarcinoma (EAC). However, its relationship with genetic variation, and its effect on improving risk population stratification, remains unknown. We performed an exposome association study to determine potential associated factors associated with EAC. To quantify biological age and its difference from chronological age, we calculated the BioAge10 and Biological Age Acceleration (BioAgeAccel) based on chronological age and nine biomarkers. Multivariable Cox regression models for 362,310 participants from the UK Biobank with a median follow-up of 13.70 years were performed. We established a weighted polygenic risk score (wPRS) associated with EAC, to assess joint and interaction effects with BioAgeAccel. Four indicators were used to evaluate their interaction effects, and we fitted curves to evaluate the risk stratification ability of BioAgeAccel. Compared with biologically younger participants, those older had higher risk of EAC, with adjusted HR of 1.79 (95%CI: 1.52-2.10). Compared with low wPRS and biologically younger group, the high wPRS and biologically older group had a 4.30-fold increase in HR (95% CI: 2.78-6.66), at meanwhile, 1.15-fold relative excess risk was detected (95% CI: 0.30-2.75), and 22% of the overall EAC risk was attributable to the interactive effects (95% CI: 12%-31%). The 10-year absolute incidence risk indicates that biologically older individuals should begin screening procedures 4.18 years in advance, while youngers can postpone screening by 4.96 years, compared with general population. BioAgeAccel interacted positively with genetic variation and increased risk of EAC, it could serve as a novel indicator for predicting incidence.

Biological aging mediates the associations of metabolic score for insulin resistance with all-cause and cardiovascular disease mortality among US adults: A nationwide cohort study

AIM

To investigate the associations of metabolic score for insulin resistance (METS-IR) with all-cause and cardiovascular disease (CVD)-specific mortality and the potential mediating role of biological ageing.

METHODS

A cohort of 19 204 participants from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 was recruited for this study. Cox regression models, restricted cubic splines, and Kaplan-Meier survival curves were used to determine the relationships of METS-IR with all-cause and CVD-specific mortality. Mediation analyses were performed to explore the possible intermediary role of biological ageing markers, including phenotypic age (PhenoAge) and biological age (BioAge).

RESULTS

During a median follow-up of 9.17 years, we observed 2818 deaths, of which 875 were CVD-specific. Multivariable Cox regression showed that the highest METS-IR level (Q4) was associated with increased all-cause (hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.14-1.67) and CVD mortality (HR 1.52, 95% CI 1.10-2.12) compared with the Q1 level. Restricted cubic splines showed a nonlinear relationship between METS-IR and all-cause mortality. Only METS-IR above the threshold (41.02 μg/L) was positively correlated with all-cause death. METS-IR had a linear positive relationship with CVD mortality. In mediation analyses, we found that PhenoAge mediated 51.32% (p < 0.001) and 41.77% (p < 0.001) of the association between METS-IR and all-cause and CVD-specific mortality, respectively. For BioAge, the mediating proportions of PhenoAge were 21.33% (p < 0.001) and 15.88% (p < 0.001), respectively.

CONCLUSIONS

This study highlights the detrimental effects of insulin resistance, as measured by METS-IR, on all-cause and CVD mortality. Moreover, it underscores the role of biological ageing in mediating these associations, emphasizing the need for interventions targeting both insulin resistance and ageing processes to mitigate mortality risks in metabolic disorders.

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.

Association between biological aging and the risk of mortality in individuals with non-alcoholic fatty liver disease: A prospective cohort study

BACKGROUND

The biological process of aging plays an important role in nonalcoholic fatty liver disease (NAFLD) development. However, epidemiological evidence about the association of biological aging with mortality risk among people with NAFLD is limited.

METHODS

A total of 2199 participants with NAFLD from the National Health and Nutrition Examination Surveys (NHANES) III were included. The outcomes were all-cause and cause-specific (cardiovascular disease [CVD], cancer, and diabetes) mortality. We computed three BA measures, the Klemera-Doubal method (KDM) age, Phenotypic age, and homeostatic dysregulation (HD), by using 18 age-associated clinical biomarkers, and assessed their associations with mortality risk using Cox proportional hazards (CPH) models.

RESULTS

After a median follow-up of 16 years, a total of 1077 deaths occurred. People with NAFLD who died during follow-up period exhibited higher baseline biological age (BA) and biological age accelerations (BAAs). The multivariate-adjusted CPH suggested that a one-standard deviation (SD) increase in KDM age acceleration, Phenotypic age acceleration, or HD was associated with a 3 %, 7 %, or 39 % elevated risk of all-cause mortality, respectively. The results of age-varying HRs showed that the associations of KDM age accelerations (AAs) and Phenotypic AAs with all-cause mortality appeared to be stronger in people with NAFLD younger than 45 years.

CONCLUSIONS

Biological aging was positively associated with both all-cause and cause-specific mortality among people with NAFLD, particularly among younger individuals.

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.

Association of biological age acceleration with cardiac morphology, function, and incident heart failure: insights from UK Biobank participants

AIMS

Advanced age is associated with an increased risk of adverse cardiovascular events. The relationship between biological age acceleration (BAA), cardiac size, cardiac function, and heart failure (HF) is not well-defined.

METHODS AND RESULTS

Utilizing the UK Biobank cohort, we assessed biological age using the Klemera-Doubal and PhenoAge methods. BAA was quantified by residual analysis compared with chronological age. Cardiovascular magnetic resonance (CMR) imaging provided detailed insights into cardiac structure and function. We employed multivariate regression to examine links between BAA and CMR-derived cardiac phenotypes. Cox proportional hazard regression models analysis was applied to explore the causative relationship between BAA and HF. Additionally, Mendelian randomization was used to investigate the genetic underpinnings of these associations. A significant correlation was found between increased BAA and deleterious changes in cardiac structure, such as diminished left ventricular mass, lower overall ventricular volume, and reduced stroke volumes across ventricles and atria. Throughout a median follow-up of 13.8 years, participants with greater biological aging showed a heightened risk of HF [26% per standard deviation (SD) increase in KDM-BA acceleration, 95% confidence intervals (CI): 23-28%; 33% per SD increase in PhenoAge acceleration, 95% CI: 32-35%]. Mendelian randomization analysis suggests a likely causal link between BAA, vital cardiac metrics, and HF risk.

CONCLUSION

In this cohort, accelerated biological aging may serve as a risk indicator for altered cardiac dimensions, functionality, and the onset of heart failure among middle-aged and elderly adults. It holds promise as a focal point for evaluating risk and developing targeted interventions.

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.

Exploring the impact of women-specific reproductive factors on phenotypic aging and the role of life's essential 8

BACKGROUND

Aging is an inevitable biological process. Accelerated aging renders adults more susceptible to chronic diseases and increases their mortality rates. Previous studies have reported the relationship between lifestyle factors and phenotypic aging. However, the relationship between intrinsic factors, such as reproductive factors, and phenotypic aging remains unclear.

METHODS

This study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 1999 to 2010 and 2015-2018, with 14,736 adult women. Random forest imputation was used to handle missing covariate values in the final cohort. Weighted linear regression was utilized to analyze the relationship between women-specific reproductive factors and PhenoAgeAccel. Considering the potential impact of menopausal status on the results, additional analyses were conducted on premenopausal and postmenopausal participants. Additionally, the Life's Essential 8 (LE8) was used to investigate the impact of healthy lifestyle and other factors on the relationship between women-specific reproductive factors and PhenoAgeAccel. Stratified analyses were conducted based on significant interaction p-values.

RESULTS

In the fully adjusted models, delayed menarche and gynecological surgery were associated with increased PhenoAgeAccel, whereas pregnancy history were associated with a decrease. Additionally, early or late ages of menopause, first live birth, and last live birth can all negatively impact PhenoAgeAccel. The relationship between women-specific reproductive factors and PhenoAgeAccel differs between premenopausal and postmenopausal women. High LE8 scores positively impacted the relationship between certain reproductive factors (age at menarche, age at menopause, age at first live birth, and age at last live birth) and phenotypic age acceleration. Stratified analysis showed significant interactions for the following variables: BMI with age at menarche, pregnancy history, and age at menopause; ethnicity with age at menopause, age at first live birth, and parity; smoking status with use of contraceptive pills and gynecologic surgery; hypertension with use of contraceptive pills, pregnancy history, and age at menopause.

CONCLUSION

Delayed menarche, gynecological surgery, and early or late ages of menopause, first live birth, and last live birth are associated with accelerated phenotypic aging. High LE8 score may alleviate the adverse effects of reproductive factors on phenotypic aging.

Association between Accelerated Biological Aging, Diet, and Gut Microbiome

Factors driving accelerated biological age (BA), an important predictor of chronic diseases, remain poorly understood. This study focuses on the impact of diet and gut microbiome on accelerated BA. Accelerated Klemera-Doubal biological age (KDM-BA) was estimated as the difference between KDM-BA and chronological age. We assessed the cross-sectional association between accelerated KDM-BA and diet/gut microbiome in 117 adult participants from the 10,000 Families Study. 16S rRNA sequencing was used to estimate the abundances of gut bacterial genera. Multivariable linear mixed models evaluated the associations between accelerated KDM-BA and diet/gut microbiome after adjusting for family relatedness, diet, age, sex, smoking status, alcohol intake, and BMI. One standard deviation (SD) increase in processed meat was associated with a 1.91-year increase in accelerated KDM-BA (p = 0.04), while one SD increase in fiber intake was associated with a 0.70-year decrease in accelerated KDM-BA (p = 0.01). Accelerated KDM-BA was positively associated with Streptococcus and negatively associated with Subdoligranulum, unclassified Bacteroidetes, and Burkholderiales. Adjustment for gut microbiome did not change the association between dietary fiber and accelerated KDM-BA, but the association with processed meat intake became nonsignificant. These cross-sectional associations between higher meat intake, lower fiber intake, and accelerated BA need validation in longitudinal studies.

The association between weekly exercise patterns and acceleration of aging: Evidence from a population-based study

BACKGROUND

Acceleration of aging is a major challenge in public health. Previous studies have focused on the associations between specific types of exercise or overall levels of physical activity with accelerated aging, with less attention given to the weekly exercise patterns.

OBJECTIVE

To explore the relationship between weekly exercise patterns and acceleration of aging among American adults.

METHODS

We extracted data from the 2015-2018 National Health and Nutrition Examination Survey (NHANES), involving 9850 participants aged ≥20 with comprehensive records on exercise and phenotypic age. Hierarchical clustering categorized participants into three groups based on weekly exercise time and days: cluster 1 (Rare or No Exercise), cluster 2 (Moderate Frequency, Moderate Duration) and cluster 3 (Moderate Frequency, Long Duration). Acceleration of aging was defined as the phenotypic age advance >0.

RESULTS

After full adjustment, weekly exercise time and days showed the significant non-linear negative correlation with accelerated aging. The risk of accelerated aging was lowest when weekly exercise days reached five and the weekly exercise time reached three hours. Both cluster 2 and cluster 3 were significantly negatively correlated with acceleration of aging. No significant differences were observed in the association with accelerated aging between cluster 2 and cluster 3.

CONCLUSIONS

These findings highlight the importance of targeted exercise programs for healthy aging. They also emphasize the need for public health initiatives to integrate regular physical activity into daily routines to improve the longevity and well-being of American adults.

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.

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.

Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies

Purpose

Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates environmental phenols' exposure with eGFR.

Methods

Our datasets for constructing the associations between environmental phenols' and eGFR were collected from the National Health and Nutrition Examination Survey (NHANES, 2013-2016). Five ML models were contained and fine-tuned to eGFR regression by phenols' exposure. Regression evaluation metrics were used to extract the limitation of the models. The most effective model was then utilized for regression, with interpretation of its features carried out using shapley additive explanations (SHAP) and the game theory python package to represent the model's regression capacity.

Results

The study identified the top-performing random forest (RF) regressor with a mean absolute error of 0.621 and a coefficient of determination of 0.998 among 3,371 participants. Six environmental phenols with eGFR in linear regression models revealed that the concentrations of triclosan (TCS) and bisphenol S (BPS) in urine were positively correlated with eGFR, and the correlation coefficients were β = 0.010 (p = 0.026) and β = 0.007 (p = 0.004) respectively. SHAP values indicate that BPS (1.38), bisphenol F (BPF) (0.97), 2,5-dichlorophenol (0.87), TCS (0.78), BP3 (0.60), bisphenol A (BPA) (0.59) and 2,4-dichlorophenol (0.47) in urinary contributed to the model.

Conclusion

The RF model was efficient in identifying a correlation between phenols' exposure and eGFR among United States NHANES 2013-2016 participants. The findings indicate that BPA, BPF, and BPS are inversely associated with eGFR.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

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.

Hormone Therapy and Biological Aging in Postmenopausal Women

Importance

Menopause is associated with biological aging, and hormone therapy (HT) is associated with health outcomes in postmenopausal women.

Objective

To evaluate the association between HT use and discrepancies between chronological and biological age in postmenopausal women as well as the potential modifying role of socioeconomic status (SES).

Design, Setting, and Participants

This population-based, retrospective cohort study included postmenopausal women registered in the UK Biobank. A baseline survey on HT use and biological aging biomarkers was conducted from March 2006 to October 2010. Data analyses were conducted in December 2023.

Exposures

Information regarding HT use, the age at starting HT, and HT duration was collected via a touchscreen questionnaire. SES was evaluated by education, family income, occupation, and the Townsend Deprivation Index.

Main Outcomes and Measures

Biological aging discrepancy was evaluated using validated phenotypic age, which was calculated using chronological age and 9 biomarkers measured at baseline. All-cause and cause-specific mortality were also assessed.

Results

Among the 117 763 postmenopausal women (mean [SD] age, 60.2 [5.4] years), 47 461 (40.3%) ever used HT. The mean phenotypic age was 52.1 (7.9) years. Ever use of HT was associated with a smaller biological aging discrepancy than never use of HT (β, -0.17 years; 95% CI, -0.23 to -0.10 years). This smaller aging discrepancy was more evident in those who started HT at age 55 years or older (β, -0.32 years; 95% CI, -0.48 to -0.15 years) and in those who used HT for 4 to 8 years (β, -0.25 years; 95% CI, -0.35 to -0.15 years). The association between HT and a smaller aging discrepancy was more evident in women with low SES, with a significant interaction observed for education (higher education: β, -0.08 years [95% CI, -0.17 to 0.01]; other education: β, -0.23 [95% CI, -0.32 to -0.14] years; P for interaction = .02). Phenotypic aging discrepancy mediated 12.7% (95% CI, 6.3% to 23.9%) of the association between HT and all-cause mortality and cause-specific mortality.

Conclusions and Relevance

In this study, postmenopausal women with historical HT use were biologically younger than those not receiving HT, with a more evident association observed in those with low SES. The biological aging discrepancy mediated the association between HT and decreased mortality. Promoting HT in postmenopausal women could be important for healthy aging.

Multi-discrimination exposure and biological aging: Results from the midlife in the United States study

Discrimination is a social determinant of health and health disparities for which the biological mechanisms remain poorly understood. This study investigated the hypothesis that discrimination contributes to poor health outcomes by accelerating biological processes of aging. We analyzed survey and blood DNA methylation data from the Midlife in the United States (MIDUS) study (N = 1967). We used linear regression analysis to test associations of everyday, major, and workplace discrimination with biological aging measured by the DunedinPACE, PhenoAge, and GrimAge2 epigenetic clocks. MIDUS participants who reported more discrimination tended to exhibit a faster pace of aging and older biological age as compared to peers who reported less discrimination. Effect-sizes for associations tended to be larger for the DunedinPACE pace-of-aging clock (effect-size range r = 0.1-0.2) as compared with the PhenoAge and GrimAge2 biological-age clocks (effect-sizes r < 0.1) and for experiences of everyday and major discrimination as compared with workplace discrimination. Smoking status and body-mass index accounted for roughly half of observed association between discrimination and biological aging. Reports of discrimination were more strongly associated with accelerated biological aging among White as compared with Black participants, although Black participants reported more discrimination overall and tended to exhibit older biological age and faster biological aging. Findings support the hypothesis that experiences of interpersonal discrimination contribute to accelerated biological aging and suggest that structural and individual-level interventions to reduce discrimination and promote adaptive coping have potential to support healthy aging and build health equity.

Does BioAge identify accelerated aging in individuals with bipolar disorder? An exploratory study in the FACE-BD cohort

BACKGROUND

Individuals with bipolar disorders (BD) have an estimated loss of life expectancy around 10-15 years. Several laboratory-measured biomarkers of accelerated aging exist (e.g., telomere length), however with a questionable transferability to bedside. There is a need for easily and inexpensively measurable markers of aging, usable in routine practice, such as BioAge.

METHODS

We calculated BioAge that estimates biological age based on routine blood tests and a physical exam, in a sample of 2220 outpatients with BD. We investigated associations between BioAge Acceleration (BioAgeAccel), which is an indicator of accelerated aging, and sociodemographic variables, clinical variables, and current psychotropic medication use.

RESULTS

Mean chronological age was 40.2 (±12.9). Mean BioAge was 39.1 (±12.4). Mean BioAgeAccel was 0.08 (±1.8). A minority of individuals (15%) had a BioAgeAccel above 2 years. Multivariable analyses suggested strong associations between a higher BioAgeAccel and younger age, male sex, overweight and sleep disturbances. Regarding current psychotropic medication use, discrepancies between univariate and multivariate analyses were observed.

CONCLUSIONS

A minority of individuals with BD had an accelerated aging as measured by BioAge. We identified associations with potentially modifiable factors, such as higher body mass index and sleep disturbances, that are however nonspecific to BD. These results require replications in independent samples of individuals with BD, and comparisons with a control group matched for age and gender. Longitudinal studies are also required to test whether any change in metabolic health, or sleep might decrease BioAgeAccel.

Factors influencing accelerated aging in patients with type 2 diabetes mellitus and coronary heart disease

Objective

To investigate the factors influencing accelerated aging in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD).

Methods

A total of 216 patients diagnosed with T2DM and CHD between August 2019 and August 2023 at Xuzhou Central Hospital were selected. Patients were divided into an aging group and a non-aging group, based on the positive or negative values of phenotypic age acceleration (PhenoAgeAccel). Logistic regression analysis was conducted. Variables that had a univariate analysis P< 0.05 were included in the multivariate analysis to identify factors influencing aging in patients with T2DM and CHD, and the area under the curve of the model was reported.

Results

This study included 216 patients, with 89 in the accelerated aging group, and 127 in the non-accelerated aging group. The average age of patients was 70.40 (95% CI: 69.10-71.69) years, with 137 males (63.4%). Compared with the non-accelerated aging group, patients in the accelerated aging group were older, with a higher proportion of males, and a higher prevalence of hypertension, stable angina pectoris, and unstable angina pectoris. Multivariate Logistic regression analysis indicated that the absolute value of neutrophils (NEUT#), urea (UREA), adenosine deaminase (ADA), and the triglyceride-glucose index (TyG) were risk factors for accelerated aging, while cholinesterase (CHE) was a protective factor. For each unit increase in NEUT#, UREA, ADA, and TyG, the risk of aging increased by 64%, 48%, 10%, and 789%, respectively. The overall area under the receiver operating characteristic (ROC) curve of the model in the training set was 0.894, with a 95% confidence interval (CI) of 0.851-0.938.

Conclusion

NEUT#, CHE, UREA, ADA, and TyG are predictors of accelerated aging in patients with T2DM and CHD, with the model showing favorable overall predictive performance.

Association between dietary quality and accelerated aging: a cross-sectional study of two cohorts

Background: Diet quality significantly influences aging processes and age-related health outcomes. This study aims to explore the association between dietary quality and accelerated aging in two large cohorts. Methods: This study collected data from the Kailuan and National Health and Nutrition Examination Survey (NHANES) cohorts; participants' dietary quality was evaluated using the American Heart Association (AHA) dietary score and Healthy Eating Index-2015 (HEI-2015), respectively. Accelerated aging in participants was determined by calculating the difference between phenotypic age and chronological age. Logistic regression models were used to explore the association between dietary quality scores and accelerated aging. Additionally, variations in this association across different subgroups were investigated. To minimize the influence of excessive aging, individuals aged 75 and above were excluded in sensitivity analyses. Results: In this study, we included 33 701 participants (27.3% female, mean age 57.29 ± 11.88) from the Kailuan study and 9285 participants (50.6% female, mean age 49.83 ± 17.62) from NHANES. In the Kailuan cohort, individuals with dietary scores ranging from 3 to 5 exhibited a 22% lower risk of accelerated aging compared to those scoring between 0 and 2 (OR = 0.78, 95% CI = 0.72-0.85). Similarly, in the NHANES cohort, participants in the highest quartile of HEI-2015 experienced a 34% reduction in the risk of accelerated aging compared to those in the lowest quartile (OR = 0.66, 95% CI = 0.52-0.84). Subgroup analyses underscored a more pronounced association between dietary quality and accelerated aging among males and individuals with unhealthy lifestyles. Sensitivity analyses confirmed the robustness of the association between dietary quality and accelerated aging. Conclusion: In summary, our study found a significant association between dietary quality and accelerated aging. Better dietary quality was associated with a reduced risk of accelerated aging, particularly among males, smokers, and participants with unhealthy lifestyles.

Association of chrononutrition patterns with biological aging: evidence from a nationally representative cross-sectional study

Previous studies mostly focused on the benefits of caloric restriction and fasting on longevity. However, whether the timing and frequency of eating affect aging remains unclear. Here, we investigated the associations between chrononutrition patterns and biological aging, and explored whether and to what extent dietary inflammation mediated this association. 16 531 adults aged 20 to 84 years from the National Health and Nutrition Examination Survey were collected. Chrononutrition patterns were determined with two 24-hour dietary recalls. Phenotypic age was calculated to reflect the biological aging status. The dietary inflammatory index (DII) was used to assess the dietary inflammation. After adjustment of the survey weight and multiple covariates including total energy intake, participants in the third tertile of the time of the first meal (mean 10 : 26) exhibited more advanced biological age (β 0.64; 95% CI, 0.26-1.00) and a higher incidence of accelerated aging (odds ratio (OR) 1.25; 95% CI, 1.06-1.47) compared to those of the first tertile (mean 6 : 14). Higher eating frequency was associated with delayed biological aging in both multivariable linear (β -0.31; 95% CI, -0.44 to -0.19) and logistic regression model (OR 0.90; 95% CI, 0.85-0.95). Furthermore, we found that DII rather than metabolic factors mediated the inverse association between eating frequency and biological aging (mediation proportion 24.67%; 95% CI, 19.83%-32.00%). Our findings demonstrated the association between chrononutrition patterns and biological aging among the US general population and the potential role of dietary inflammation in this association, suggesting that modifying chrononutrition patterns may be a practical and cost-effective strategy for combating aging.

Association of visceral adiposity index with phenotypic age acceleration: insight from NHANES 1999-2010

BACKGROUND

Obesity correlates with accelerated aging. This study aims to investigate the association between the visceral adiposity index (VAI) and accelerated aging.

METHODS

Biological aging was evaluated by phenotypic age acceleration (PhenoAgeAccel). Utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2010, we employed weighted multivariable logistic regression models, along with subgroup analysis, to examine the association between VAI and PhenoAgeAccel. Moreover, smooth curve fitting was utilized to identify potential nonlinear association, complemented by a two-piece linear regression model to investigate threshold effects.

RESULTS

Of the included 11,340 participants aged 20 years and older, the mean (95% CI) age was 46.569 (45.946, 47.191) years, and 49.189% were male. The mean (95% CI) VAI for all participants was 2.176 (2.114, 2.238), and the mean (95% CI) PhenoAgeAccel was -6.306 (-6.618, -5.994) years. In the fully adjusted model, each incremental unit increase of VAI was associated with a 0.312-year increase in PhenoAgeAccel (β = 0.312, 95% CI: 0.217, 0.408). This positive association was more statistically significant among individuals with cancer. Furthermore, a segmented association was observed between VAI and PhenoAgeAccel, with a turning point identified at 10.543. Below this threshold, VAI exhibited a positive correlation with PhenoAgeAccel (β = 0.617, 95% CI: 0.499, 0.735), while beyond it, the association became nonsignificant.

CONCLUSION

This study demonstrated a positive association between VAI and accelerated aging within a nationally representative population. The findings suggest that controlling adiposity may exert anti-aging effects and help prevent aging-related diseases.

Multinational evaluation of anthropometric age (AnthropoAge) as a measure of biological age in the USA, England, Mexico, Costa Rica, and China: a population-based longitudinal study

OBJECTIVE

To validate AnthropoAge, a new metric of biological age (BA), for prediction of all-cause mortality and age-related outcomes and characterize population-specific aging patterns using multinational longitudinal cohorts.

METHODS

We analyzed harmonized multinational data from the Gateway to Global Aging, including studies from the US, England, Mexico, Costa Rica, and China. We used body mass index and waist-to-height ratio to estimate AnthropoAge and AnthropoAgeAccel in participants aged 50-90 years old as proxies of BA and age acceleration, respectively. We compared the predictive capacity for all-cause mortality of AnthropoAge and chronological age (CA) using Cox models, described aging trends in all countries and explored the utility of longitudinal assessments of AnthropoAgeAccel to predict new-onset functional decline and age-related diseases using generalized estimating equations (GEE).

FINDINGS

Using data from 55,628 participants, we found AnthropoAge (c-statistic 0.772) outperformed CA (0.76) for prediction of mortality independently of comorbidities, sex, race/ethnicity, education, and lifestyle; this result was replicated in most countries individually except for Mexico. Individuals with accelerated aging had a ~39% higher risk of death, and AnthropoAge also identified trends of faster biological aging per year. In longitudinal analyses, higher AnthropoAgeAccel values were independently predictive of self-reported health deterioration and new-onset deficits in basic/instrumental activities of daily living (ADL/IADL), diabetes, hypertension, cancer, chronic lung disease, myocardial infarction, and stroke.

CONCLUSIONS

AnthropoAge is a robust and reproducible BA metric associated with age-related outcomes. Its implementation could facilitate modeling trends of biological aging acceleration in different populations, although recalibration may enhance its utility in underrepresented populations such as individuals from Latin America.

Inflammation, nutrition, and biological aging: The prognostic role of Naples prognostic score in nonalcoholic fatty liver disease outcomes

AIM

This study aimed to evaluate the prognostic value of the Naples Prognostic Score (NPS) for predicting mortality in patients with nonalcoholic fatty liver disease (NAFLD) and compare its performance with established non-invasive fibrosis scores, including the fibrosis-4 index (FIB-4) and NAFLD fibrosis score (NFS).

METHODS

Data from 10,035 NAFLD patients identified within the 1999-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed. Cox regression models assessed the association between NPS and all-cause mortality, while time-dependent ROC analysis compared its predictive accuracy with FIB-4 and NFS. Mediation analysis explored the role of phenotypic age acceleration (PhenoAgeAccel).

RESULTS

NPS was significantly associated with all-cause mortality, with each point increase corresponding to a 26 % increased risk (HR = 1.26, 95 % CI: 1.19-1.34). NPS demonstrated comparable predictive performance to FIB-4 and NFS, with further improvement when combined with either score (HRs of 2.03 and 2.11 for NPS + FIB-4 and NPS + NFS, respectively). PhenoAgeAccel mediated 31.5 % of the effect of NPS on mortality.

CONCLUSIONS

This study found that NPS has the potential to be an independent, cost-effective, and reliable novel prognostic indicator for NAFLD that may complement existing tools and help improve risk stratification and management strategies for NAFLD, thereby preventing adverse outcomes.

Synergistic impact of 25-hydroxyvitamin D concentrations and physical activity on delaying aging

OBJECTIVE

Our study aims to examine the independent and combined associations of serum 25-hydroxyvitamin D [25(OH)D] concentrations and physical activity (PA) status with phenotypic age (PhenoAge).

METHOD

The analysis included 18,738 participants from the NHANES 2007-2010 & 2015-2018. Phenotypic Age Acceleration (PhenoAgeAccel) was calculated as the residuals from regressing PhenoAge on chronological age. Weighted multivariable logistic regression models were used to analysis the relationship between 25(OH)D and PA with PhenoAgeAccel. Population attributable fraction (PAF) was used to estimate the proportion of PhenoAgeAccel which could be avoided if exposure were eliminated.

RESULTS

The multivariate-adjusted OR (95%CI) for PhenoAgeAccel with high 25(OH)D and adequate PA were 0.657 (0.549,0.787) (p < 0.001) for all, 0.663 (0.538,0.818) (p < 0.001) for participants whose age ≤65years old. Furthermore, there was multiplicative interaction between 25(OH)D and PA in age ≤65 years old group (0.729 (0.542,0.979), p = 0.036). High 25(OH)D level and adequate PA reduced the risk of PhenoAgeAccel by 14.3 % and 14.2 %, respectively. Notably, 30.7 % decrease was attributable to both high 25(OH)D level and engaging in adequate PA concurrently. Combining 25(OH)D above 80.4 nmol/l with PA decreased PhenoAge by 1.291 years (p < 0.001).

CONCLUSION

Higher 25(OH)D level was associated with lower risk of biological ageing. Combining 25(OH)D and PA demonstrated enhanced protective effects, especially in middle or young adults. These findings underscore the importance of outdoor PA in slowing down the aging process.

Decreased cobalamin sensitivity and biological aging acceleration in the general population

BACKGROUND

The evidence on the association between cobalamin (Cbl) and aging or relevant outcomes is limited and controversial. We aimed to investigate the relationships between cobalamin intake- and function-related biomarkers and biological aging.

METHODS

The study encompassed 22,812 participants aged 20 years and older from the National Health and Nutrition Examination Survey. A panel of biomarkers or algorithms was used to assess biological aging, including Klemera-Doubal Age Acceleration (KDMAccel), Phenotypic age acceleration (PhenoAgeAccel), telomere length, α-Klotho, and PhenoAge advancement. Weighted generalized linear regression analysis was used to assess the associations between cobalamin-intake biomarkers (serum cobalamin, cobalamin intake from food, cobalamin supplement use, serum methylmalonic acid [MMA], and homocysteine [Hcy]) and function-related biomarkers (functional cobalamin deficiency and cobalamin insensitivity index).

RESULTS

Among the 22,812 individuals, the weighted mean (SE) age was 48.3 (0.2) years and 48.0% were males. Unexpectedly, serum and dietary cobalamin as well as serum MMA and Hcy levels were positively associated with most indicators of biological aging. Cobalamin sensitivity was assessed by the combination of binary Cbllow/high and MMAlow/high or Hcylow/high (cutoff values: 400 pg/mL for cobalamin, 250 nmol/L for MMA, and 12.1 μmol/l for Hcy) and a newly constructed cobalamin insensitivity index (based on the multiplicative term of serum cobalamin and serum MMA or Hcy). The multivariable-adjusted β (95%CIs) of KDMAccel in the MMAlowCbllow, MMAlowCblhigh, MMAhighCbllow, and MMAhighCblhigh groups were reference, 0.27 (0.03 to 0.51), 0.85 (0.41 to 1.29), and 7.97 years (5.77 to 10.17) respectively, which were consistent for the combination of serum Hcy and cobalamin. Both cobalamin insensitivity indices were robustly associated with biological aging acceleration in a dose-response pattern (each p < 0.001).

CONCLUSIONS

Decreased cobalamin sensitivity but not cobalamin insufficiency might be associated with biological aging acceleration. Further studies would improve understanding of the underlying mechanisms between decreased cobalamin sensitivity and biological aging acceleration.

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

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

The association between SII and aging: evidence from NHANES 1999-2018

Background

The study aimed to examine the association between the systemic immune-inflammation index (SII), a contemporary metric of systemic inflammatory response, and biological aging, which are closely interconnected processes.

Methods

This cross-sectional study utilized 10 cycles of data from the NHANES database spanning from 1990 to 2018. The study examined the relationship between the SII index, calculated as P * N/L, where P represents preoperative peripheral platelet count, N represents neutrophil count, and L represents lymphocyte count, and biological aging. Biological aging was assessed through various methods, such as phenotypic age, phenotypic age acceleration (PhenoAgeAccel), biological age, and biological age acceleration (BioAgeAccel). Correlations were analyzed using weighted linear regression and subgroup analysis.

Results

Among the 7,491 participants analyzed, the average age was 45.26 ± 0.34 years, with 52.16% being female. The average phenotypic and biological ages were 40.06 ± 0.36 and 45.89 ± 0.32 years, respectively. Following adjustment for potential confounders, elevated SII scores were linked to increased phenotypic age, biological age, Phenotypic age acceleration, and Biological age acceleration. Positive correlations were observed between health behavior and health factor scores and biological aging, with stronger associations seen for health factors. In health factor-specific analyses, the β coefficient was notably higher for high BMI. The robust positive associations between SII scores and both phenotypic age and biological age in the stratified analyses were consistently observed across all strata.

Conclusion

The evidence from the NHANES data indicate that SII may serve as a valuable marker for assessing different facets of aging and health outcomes, such as mortality and the aging process. Additional research is warranted to comprehensively elucidate the implications of SII in the aging process and its utility as a clinical instrument for evaluating and addressing age-related ailments.

A proteomic signature of healthspan

The focus of aging research has shifted from increasing lifespan to enhancing healthspan to reduce the time spent living with disability. Despite significant efforts to develop biomarkers of aging, few studies have focused on biomarkers of healthspan. We developed a proteomics-based signature of healthspan (healthspan proteomic score (HPS)) using data from the UK Biobank Pharma Proteomics Project (53,018 individuals and 2920 proteins). A lower HPS was associated with higher mortality risk and several age-related conditions, such as COPD, diabetes, heart failure, cancer, myocardial infarction, dementia, and stroke. HPS showed superior predictive accuracy for these outcomes compared to chronological age and biological age measures. Proteins associated with HPS were enriched in hallmark pathways such as immune response, inflammation, cellular signaling, and metabolic regulation. Our findings demonstrate the validity of HPS, making it a valuable tool for assessing healthspan and as a potential surrogate marker in geroscience-guided studies.

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.

Associations of phthalates with accelerated aging and the mitigating role of physical activity

Phthalates are positioned as potential risk factors for health-related diseases. However, the effects of exposure to phthalates on accelerated aging and the potential modifications of physical activity remain unclear. A total of 2317 participants containing complete study-related information from the National Health and Nutrition Examination Survey 2007-2010 were included in the current study. We used two indicators, the Klemera-Doubal method biological age acceleration (BioAgeAccel) and phenotypic age acceleration (PhenoAgeAccel), to assess the accelerated aging status of the subjects. Multiple linear regression (single pollutant models), weighted quantile sum (WQS) regression, Quantile g-computation, and Bayesian kernel machine regression (BKMR) models were utilized to explore the associations between urinary phthalate metabolites and accelerated aging. Three groups of physical activity with different intensities were used to evaluate the modifying effects on the above associations. Results indicated that most phthalate metabolites were significantly associated with BioAgeAccel and PhenoAgeAccel, with effect values (β) ranging from 0.16 to 0.21 and 0.16-0.37, respectively. The WQS indices were positively associated with BioAgeAccel (0.33, 95% CI: 0.11, 0.54) and PhenoAgeAccel (0.50, 95% CI: 0.19, 0.82). Quantile g-computation indicated that phthalate mixtures were associated with accelerated aging, with effect values of 0.15 (95% CI: 0.02, 0.28) for BioAgeAccel and 0.39 (95% CI: 0.12, 0.67) for PhenoAgeAccel respectively. The BKMR models indicated a significant positive association between the concentrations of urinary phthalate mixtures with the two indicators. In addition, we found that most phthalate metabolites showed the strongest effects on accelerated aging in the no physical activity group and that the effects decreased gradually with increasing levels of physical activity (P < 0.05 for trend). Similar results were also observed in the mixed exposure models (WQS and Quantile g-computation). This study indicates that phthalates exposure is associated with accelerated aging, while physical activity may be a crucial barrier against phthalates exposure-related aging.

Accelerated biological aging mediated associations of ammonium, sulfate in fine particulate matter with liver cirrhosis

BACKGROUND

Although both air pollution and aging are related to the development of liver cirrhosis, the role of biological aging in association of the mixture of fine particulate matter (PM2.5) and its constituents with liver cirrhosis was unknown.

METHODS

This case-control retrospective study included 100 liver cirrhosis patients and 100 control subjects matched by age and sex. The concentrations of PM2.5 and its constituents were estimated for patients using machine-learning methods. The clinical biomarkers were used to calculate biological age using the Klemera-Doubalmethod (KDM) algorithms. Individual associations of PM2.5 and its constituents or biological age with liver cirrhosis were analyzed by generalized linear models. WQS and BKMR were applied to analyze association of mixture of PM2.5 and its constituents with liver cirrhosis. The mediation effect of biological age on associations of PM2.5 and its constituents with liver cirrhosis was further explored.

RESULTS

we found that each 1-unit increment in NH4+, NO3-, SO42- and biological age were related to 3.618-fold (95%CI: 1.896, 6.904), 1.880-fold (95%CI: 1.319, 2.680), 2.955-fold (95%CI: 1.656, 5.272) and 1.244-fold (95%CI: 1.093, 1.414) increased liver cirrhosis. Both WQS and BKMR models showed that the mixture of PM2.5 and its constituents was related to increased liver cirrhosis. Furthermore, the mediated proportion of biological age on associations of NH4+ and SO42- with liver cirrhosis were 14.7 % and 14.6 %, respectively.

CONCLUSIONS

Biological aging may partly explain the exposure to PM2.5 and its constituents in association with increased risk for liver cirrhosis, implying that delaying the aging process may be a key step for preventing PM2.5-related liver cirrhosis risk.

Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics

Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.

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.

Causal Association of Arterial Stiffness With the Risk of Chronic Kidney Disease

Background

Previous studies on the direction of the association between arterial stiffness (AS) and chronic kidney disease (CKD) were inconsistent, leaving a knowledge gap in understanding the temporal sequence of the association.

Objectives

This study sought to assess the temporal and longitudinal relationship between AS and CKD.

Methods

The temporal relationship between AS measured by brachial ankle pulse wave velocity and CKD measured by estimated glomerular filtration rate (eGFR) was analyzed among 7,753 participants with repeated examinations in the Kailuan study using cross-lagged panel analysis. The longitudinal associations of AS status and vascular aging (VA) phenotype with incident CKD were analyzed among 10,535 participants.

Results

The adjusted cross-lagged path coefficient (β 1 = -0.03; 95% CI: -0.06 to -0.01; P < 0.0001) from baseline brachial ankle pulse wave velocity to follow-up eGFR was significantly greater than the path coefficient (β 2 = -0.01; 95% CI: -0.02 to 0.01; P = 0.6202) from baseline eGFR to follow-up brachial ankle pulse wave velocity (P < 0.0001 for the difference). During a median follow-up of 8.48 years, 953 cases of incident CKD (9.05%) occurred. After adjustment for confounders, borderline (HR: 1.17; 95% CI: 1.08-1.38) and elevated AS (HR: 1.39; 95% CI: 1.12-1.72) was associated a higher risk of CKD, compared with normal AS. Consistently, supernormal VA (HR: 0.76; 95% CI: 0.66-0.86) was associated with a decreased and early VA (HR: 1.36; 95% CI: 1.29-1.43) was associated with an increased risk of CKD, compared with normal VA.

Conclusions

AS appeared to precede the decrease in eGFR. Additionally, increased AS and early VA were associated with an increased risk of incident CKD.

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.

Threshold and combined effects of heavy metals on the risk of phenotypic age acceleration among U.S. adults

Accumulation of heavy metals in the body has been shown to affect the phenotypic age (PhenoAge). However, the combined and threshold effects of blood heavy metals on the risk of PhenoAge acceleration (PhenoAgeAccel) are not well understood. A cross-sectional study was conducted using blood heavy metal data (N = 7763, age ≥18 years) from the 2015-2018 National Health and Nutrition Examination Survey. PhenoAgeAccel was calculated from actual age and nine biomarkers. Multiple regression equations were used to describe the relationship between heavy metals and PhenoAgeAccel. Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling was used to explore the relationship between the combined effects of heavy metals and PhenoAgeAccel. Threshold effect and multiple regression analyses were performed to explore the linear and nonlinear relationships between heavy metals and PhenoAgeAccel. Threshold effect analysis showed that blood mercury (Hg) concentration was linearly associated with PhenoAgeAccel. In contrast, lead (Pb), cadmium (Cd), manganese (Mn), and combined exposure were nonlinearly associated with PhenoAgeAccel. In addition, the combination of Pb, Cd, Hg, and Mn significantly affected PhenoAgeAccel. The risk of PhenoAgeAccel was increased by 207% (P < 0.0001). Meanwhile, a threshold relationship was found between blood Pb, Cd, Mn, and the occurrence of PhenoAgeAccel. Overall, our results indicate that combined exposure to heavy metals may increase the risk of PhenoAgeAccel. This study underscores the need to reduce heavy metal pollution in the environment and provides a reference threshold for future studies.

The Association between Dietary Nutrient Intake and Acceleration of Aging: Evidence from NHANES

The acceleration of aging is a risk factor for numerous diseases, and diet has been identified as an especially effective anti-aging method. Currently, research on the relationship between dietary nutrient intake and accelerated aging remains limited, with existing studies focusing on the intake of a small number of individual dietary nutrients. Comprehensive research on the single and mixed anti-aging effects of dietary nutrients has not been conducted. This study aimed to comprehensively explore the effects of numerous dietary nutrient intakes, both singly and in combination, on the acceleration of aging. Data for this study were extracted from the 2015-2018 National Health and Nutrition Examination Surveys (NHANES). The acceleration of aging was measured by phenotypic age acceleration. Linear regression (linear), restricted cubic spline (RCS) (nonlinear), and weighted quantile sum (WQS) (mixed effect) models were used to explore the association between dietary nutrient intake and accelerated aging. A total of 4692 participants aged ≥ 20 were included in this study. In fully adjusted models, intakes of 16 nutrients were negatively associated with accelerated aging (protein, vitamin E, vitamin A, beta-carotene, vitamin B1, vitamin B2, vitamin B6, vitamin K, phosphorus, magnesium, iron, zinc, copper, potassium, dietary fiber, and alcohol). Intakes of total sugars, vitamin C, vitamin K, caffeine, and alcohol showed significant nonlinear associations with accelerated aging. Additionally, mixed dietary nutrient intakes were negatively associated with accelerated aging. Single dietary nutrients as well as mixed nutrient intake may mitigate accelerated aging. Moderately increasing the intake of specific dietary nutrients and maintaining dietary balance may be key strategies to prevent accelerated aging.

Air pollution exposure, accelerated biological aging, and increased thyroid dysfunction risk: Evidence from a nationwide prospective study

BACKGROUND

Long-term air pollution exposure is a major health concern, yet its associations with thyroid dysfunction (hyperthyroidism and hypothyroidism) and biological aging remain unclear. We aimed to determine the association of long-term air pollution exposure with thyroid dysfunction and to investigate the potential roles of biological aging.

METHODS

A prospective cohort study was conducted on 432,340 participants with available data on air pollutants including particulate matter (PM2.5, PM10, and PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) from the UK Biobank. An air pollution score was calculated using principal component analysis to reflect joint exposure to these pollutants. Biological aging was assessed using the Klemera-Doubal method biological age and the phenotypic age algorithms. The associations of individual and joint air pollutants with thyroid dysfunction were estimated using the Cox proportional hazards regression model. The roles of biological aging were explored using interaction and mediation analyses.

RESULTS

During a median follow-up of 12.41 years, 1,721 (0.40 %) and 9,296 (2.15 %) participants developed hyperthyroidism and hypothyroidism, respectively. All air pollutants were observed to be significantly associated with an increased risk of incident hypothyroidism, while PM2.5, PM10, and NO2 were observed to be significantly associated with an increased risk of incident hyperthyroidism. The hazard ratios (HRs) for hyperthyroidism and hypothyroidism were 1.15 (95 % confidence interval: 1.00-1.32) and 1.15 (1.08-1.22) for individuals in the highest quartile compared with those in the lowest quartile of air pollution score, respectively. Additionally, we noticed that individuals with higher pollutant levels and biologically older generally had a higher risk of incident thyroid dysfunction. Moreover, accelerated biological aging partially mediated 1.9 %-9.4 % of air pollution-associated thyroid dysfunction.

CONCLUSIONS

Despite the possible underestimation of incident thyroid dysfunction, long-term air pollution exposure may increase the risk of incident thyroid dysfunction, particularly in biologically older participants, with biological aging potentially involved in the mechanisms.

From cup to clock: exploring coffee's role in slowing down biological aging

Background: Previous research has proposed that coffee consumption may have potential health benefits, yet the effect of coffee on one's biological age has not been determined to date. The purpose of this study is to investigate the influence of coffee drinking on biological aging. Methods: Participants were chosen from the National Health and Nutrition Examination Survey (NHANES) and had to meet the selection criteria. Coffee consumption was evaluated through two 24-hour dietary questionnaires. Biological age was measured using both the PhenoAge and KDM-BA algorithms. Multiple linear and logistic regression models were adopted to analyze the association of coffee consumption with biological aging. Results: A total of 13 384 participants with an average daily coffee consumption of 1.73 cups were included. Participants with higher coffee consumption tended to be older, male, non-Hispanic white; had a higher educational level beyond high school; were more likely to be married; had better financial status; and were less likely to smoke or engage in excessive drinking. These individuals with higher coffee consumption exhibited a younger biological age in relation to their chronological age, as indicated by lower mean advancements in PhenoAge and KDM-BA scores. Furthermore, coffee intake was found to be inversely related to PhenoAge and KDM-BA progressions, as well as to the chances of accelerated biological aging, both in unadjusted and adjusted models. These associations remained consistent across all age and gender groups. Additionally, some heterogeneity was also observed among body mass index and physical activity categories. Conclusions: Coffee drinking was inversely related to biological age advancements and the likelihood of accelerated biological aging. Moderate coffee consumption may offer substantial benefits in reducing biological aging.

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

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

Association between biological aging and diabetic retinopathy

The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06-1.18) and PA (OR = 1.11, 95% CI, 1.07-1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99-1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Associations between body mass index, biological age and frailty in the critically ill

BACKGROUND

The relationship between body mass index (BMI) and outcomes in the acute care setting is controversial, with evidence suggesting that obesity is either protective - which is also called obesity paradox - or associated with worse outcomes. The purpose of this study was to assess whether BMI was related to frailty and biological age, and whether BMI remained predictive of mortality after adjusting for frailty and biological age.

SUBJECTS

Of the 2950 patients who had a biological age estimated on admission to the intensive care unit, 877 (30 %) also had BMI and frailty data available for further analysis in this retrospective cohort study.

METHODS

Biological age of each patient was estimated using the Levine PhenoAge model based on results of nine blood tests that were reflective of DNA methylation. Biological age in excess of chronological age was then indexed to the local study context by a linear regression to generate the residuals. The associations between BMI, clinical frailty scale, and the residuals were first analyzed using univariable analyses. Their associations with mortality were then assessed by multivariable analysis, including the use of a 3-knot restricted cubic spline function to allow non-linearity.

RESULTS

Both frailty (p = 0.003) and the residuals of the biological age (p = 0.001) were related to BMI in a U-shaped fashion. BMI was not related to hospital mortality, but both frailty (p = 0.015) and the residuals of biological age (OR per decade older than chronological age 1.50, 95 % confidence interval [CI] 1.04-2.18; p = 0.031) were predictive of mortality after adjusting for chronological age, diabetes mellitus and severity of acute illness.

CONCLUSIONS

BMI was significantly associated with both frailty and biological age in a U-shaped fashion but only the latter two were related to mortality. These results may, in part, explain why obesity paradox could be observed in some studies.

Associations of seven measures of biological age acceleration with frailty and all-cause mortality among adult survivors of childhood cancer in the St. Jude Lifetime Cohort

Survivors of childhood cancer may experience accelerated biological aging, resulting in premature frailty and death. We used seven measures of biological age in the St. Jude Lifetime (SJLIFE) Cohort to compare biological age acceleration between the SJLIFE Cohort and the third United States National Health and Nutrition Examination Survey controls, explore trajectories of biological age according to cancer treatment and type, and test associations of biological age acceleration with frailty and death (mean follow-up of 26.5 years) among survivors. Survivors of cancer aged 5% faster per year and measured, on average, 0.6-6.44 years biologically older compared to controls and 5-16 years biologically older compared to age-matched individuals at the population level. Survivors treated with hematopoietic cell transplant and vinca alkaloid chemotherapy evidenced the fastest trajectories of biological aging. Biologically, older and faster-aging survivors consistently and robustly had a higher risk of frailty and died earlier than those with slower biological aging, suggesting a potential opportunity to intervene on excess aging.

Heterogeneity of aging and mortality risk among individuals with hypertension: Insights from phenotypic age and phenotypic age acceleration

OBJECTIVES

Hypertension, a key contributor to mortality, is impacted by biological aging. We investigated the relationship between novel biological aging metrics - Phenotypic Age (PA) and Phenotypic Age Acceleration (PAA) - and mortality in individuals with hypertension, exploring the mediating effects of arterial stiffness (estimated Pulse Wave Velocity, ePWV), and Heart/Vascular Age (HVA).

METHODS

Using data from 62,160 National Health and Nutrition Examination Survey (NHANES) participants (1999-2010), we selected 4,228 individuals with hypertension and computed PA, PAA, HVA, and ePWV. Weighted, multivariable Cox regression analysis yielded Hazard Ratios (HRs) relating PA, PAA to mortality, and mediation roles of ePWV, PAA, HVA were evaluated. Mendelian randomization (MR) analysis was employed to investigate causality between genetically inferred PAA and hypertension.

RESULTS

Over a 12-year median follow-up, PA and PAA were tied to increased mortality risks in individuals with hypertension. All-cause mortality hazard ratios per 10-year PA and PAA increments were 1.96 (95% CI, 1.81-2.11) and 1.67 (95% CI, 1.52-1.85), respectively. Cardiovascular mortality HRs were 2.32 (95% CI, 1.97-2.73) and 1.93 (95% CI, 1.65-2.26) for PA and PAA, respectively. ePWV, PAA, and HVA mediated 42%, 30.3%, and 6.9% of PA's impact on mortality, respectively. Mendelian randomization highlighted a causal link between PAA genetics and hypertension (OR = 1.002; 95% CI, 1.000-1.003).

CONCLUSION

PA and PAA, enhancing cardiovascular risk scores by integrating diverse biomarkers, offer vital insights for aging and mortality evaluation in individuals with hypertension, suggesting avenues for intensified aging mitigation and cardiovascular issue prevention. Validations in varied populations and explorations of underlying mechanisms are warranted.

Ratio of Red Blood Cell Distribution Width to Albumin Level and Risk of Mortality

Importance

The ratio of red blood cell distribution width (RDW) to albumin concentration (RAR) has emerged as a reliable prognostic marker for mortality in patients with various diseases. However, whether RAR is associated with mortality in the general population remains unknown.

Objectives

To explore whether RAR is associated with all-cause and cause-specific mortality and to elucidate their dose-response association.

Design, Setting, and Participants

This population-based prospective cohort study used data from participants in the 1998-2018 US National Health and Nutrition Examination Survey (NHANES) and from the UK Biobank with baseline information provided from 2006 to 2010. Included participants had complete data on serum albumin concentration, RDW, and cause of death. The NHANES data were linked to the National Death Index records through December 31, 2019. For the UK Biobank, dates and causes of death were obtained from the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland) to November 30, 2022.

Main Outcomes and Measures

Potential associations between RAR and the risk of all-cause and cause-specific mortality were evaluated using Cox proportional hazards regression models. Restricted cubic spline regressions were applied to estimate possible nonlinear associations.

Results

In NHANES, 50 622 participants 18 years of age or older years were included (mean [SD] age, 48.6 [18.7] years; 26 136 [51.6%] female), and their mean (SD) RAR was 3.15 (0.51). In the UK Biobank, 418 950 participants 37 years of age or older (mean [SD], 56.6 [8.1] years; 225 038 [53.7%] female) were included, and their mean RAR (SD) was 2.99 (0.31). The NHANES documented 7590 deaths over a median (IQR) follow-up of 9.4 (5.1-14.2) years, and the UK Biobank documented 36 793 deaths over a median (IQR) follow-up of 13.8 (13.0-14.5) years. According to the multivariate analysis, elevated RAR was significantly associated with greater risk of all-cause mortality (NHANES: hazard ratio [HR], 1.83 [95% CI, 1.76-1.90]; UK Biobank: HR, 2.08 [95% CI, 2.03-2.13]), as well as mortality due to malignant neoplasm (NHANES: HR, 1.89 [95% CI, 1.73-2.07]; UK Biobank: HR, 1.93 [95% CI, 1.86-2.00]), heart disease (NHANES: HR, 1.88 [95% CI, 1.74-2.03]; UK Biobank: HR, 2.42 [95% CI, 2.29-2.57]), cerebrovascular disease (NHANES: HR, 1.35 [95% CI, 1.07-1.69]; UK Biobank: HR, 2.15 [95% CI, 1.91-2.42]), respiratory disease (NHANES: HR, 1.99 [95% CI, 1.68-2.35]; UK Biobank: HR, 2.96 [95% CI, 2.78-3.15]), diabetes (NHANES: HR, 1.55 [95% CI, 1.27-1.90]; UK Biobank: HR, 2.83 [95% CI, 2.35-3.40]), and other causes of mortality (NHANES: HR, 1.97 [95% CI, 1.86-2.08]; UK Biobank: HR, 2.40 [95% CI, 2.30-2.50]) in both cohorts. Additionally, a nonlinear association was observed between RAR levels and all-cause mortality in both cohorts.

Conclusions and Relevance

In this cohort study, a higher baseline RAR was associated with an increased risk of all-cause and cause-specific mortality in the general population. These findings suggest that RAR may be a simple, reliable, and inexpensive indicator for identifying individuals at high risk of mortality in clinical practice.