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Cardiovascular disease (CVD) has increasing challenges for human health with an increasingly aging population worldwide, imposing a significant obstacle to the goal of healthy aging. Rapid advancements in our understanding of biological aging process have shed new light on some important insights to aging-related diseases. Although numerous reviews delved into the mechanisms through which biological aging affects CVD and age-related diseases, most of these reviews relied heavily on research related to cellular and molecular processes often observed from animal experiments. Few reviews have provided insights that connect hypotheses regarding the biological aging process with the observed patterns of chronological aging-related impacts on CVD in human populations. The purpose of this review is to highlight some of the major questions in studies of aging-related CVD and provide our perspectives in the context of real-world patterns of CVD with multidimensional information and potential biological insights.

The American Heart Association's (AHA) Life's Essential 8 (LE8) metrics provide a framework for assessing cardiovascular health (CVH). This study evaluates the relationship between CVH levels from LE8 and mortality risk, considering biological aging's role. Using data from the NHANES non-CVD adult population, CVH scores were categorized as low (< 50), moderate (50–79), and high (≥ 80) per AHA guidelines. Cox regression model assessed the impact of CVH levels on all-cause and cardiovascular mortality, while examining four aging indicators as mediators. RCS explored the relationships between CVH scores and mortality risk. The model's performance was evaluated using nine machine learning algorithms, with SHAP analysis on the best model to determine CVH score components' importance. Cox regression showed that all-cause mortality rates decreased by 35% for moderate and 54% for high CVH groups compared to low CVH. The high CVH group had a 59% lower cardiovascular mortality rate. Each unit increase in CVH score reduced all-cause and cardiovascular mortality to 0.98 times. RCS analysis revealed a nonlinear trend between CVH scores and mortality risk. Biological aging indicators significantly mediated the CVH–mortality relationship, with PhenoAge (21.57%) and KDM-Age (20.33%) showing the largest effects. The XGBoost model outperformed others, with SHAP analysis ranking CVH components: physical activity, nicotine, blood pressure, BMI, lipids, healthy eating index, blood glucose, and sleep. Higher CVH levels correlate with reduced all-cause and cardiovascular mortality risk, with biological aging mediating these effects. Adhering to AHA's LE8 metrics is recommended to enhance life expectancy in the non-CVD population. [ABSTRACT FROM AUTHOR]

Growth differentiation factor 15 (GDF-15) has emerged as a significant biomarker of aging, linked to various physiological and pathological processes. This study investigates circulating GDF-15 levels in a cohort of healthy individuals from the Balearic Islands, exploring its associations with biological age markers, including multiple DNA methylation (DNAm) clocks, physical performance, and other age-related biomarkers. Seventy-two participants were assessed for general health, body composition, and physical function, with GDF-15 levels quantified using ELISA. Our results indicate that GDF-15 levels significantly increase with age, particularly in individuals over 60. Strong positive correlations were observed between GDF-15 levels and DNAm GrimAge, DNAm PhenoAge, Hannum, and Zhang clocks, suggesting that GDF-15 could serve as a proxy for epigenetic aging. Additionally, GDF-15 levels were linked to markers of impaired glycemic control, systemic inflammation, and physical decline, including decreased lung function and grip strength, especially in men. These findings highlight the use of GDF-15 as a biomarker for aging and age-related functional decline. Given that GDF-15 is easier to measure than DNA methylation, it has the potential to be more readily implemented in clinical settings for broader health assessment and management. [ABSTRACT FROM AUTHOR]

Background: Telomere length is an important indicator of biological age and a complex multi-factor trait. To date, the telomere interactome for comprehending the high-dimensional biological aspects linked to telomere regulation during childhood remains unexplored. Here we describe the multi-omics signatures associated with childhood telomere length. Methods: This study included 1001 children aged 6 to 11 years from the Human Early-life Exposome (HELIX) project. Telomere length was quantified via qPCR in peripheral blood of the children. Blood DNA methylation, gene expression, miRNA expression, plasma proteins and serum and urinary metabolites were measured through microarrays or (semi-) targeted assays. The association between each individual omics feature and telomere length was assessed in omics-wide association analyses. In addition, a literature-guided, sparse supervised integration method was applied to multiple omics, and latent components were extracted as predictors of child telomere length. The association of these latent components with early-life aging risk factors (child lifestyle, body mass index (BMI), exposure to smoking, etc.), were interrogated. Results: After multiple-testing correction, only two CpGs (cg23686403 and cg16238918 at PARD6G gene) out of all the omics features were significantly associated with child telomere length. The supervised multi-omics integration approach revealed robust associations between latent components and child BMI, with metabolites and proteins emerging as the primary contributing features. In these latent components, the contributing molecular features were known as involved in metabolism and immune regulation-related pathways. Conclusions: Findings of this multi-omics study suggested an intricate interplay between telomere length, metabolism and immune responses, providing valuable insights into the molecular underpinnings of the early-life biological aging. [ABSTRACT FROM AUTHOR]

Background: The present study explored the association between biological aging (BA), healthy eating index-2015 (HEI-2015) and non-alcoholic fatty liver disease (NAFLD) in the general population of the United States. Methods: We used data from the NHANES database between 2017–2018 years to conduct the study. Weighted multivariable logistic regression analysis, restricted cubic spline (RCS), and subgroup analysis were performed to analyze the association of BA and HEI-2015 with prevalence of NAFLD and the mediation effect of HEI-2015 was also discussed. Additionally, generalized additive model was conducted to investigate the association of BA and HEI-2015 with ZJU index, BARD score, and NAFLD fibrosis score. Results: There was a total of 2,421 individuals. RCS shown that BA was positively correlated with prevalence of NAFLD, while HEI-2015 was negative correlated with NAFLD risk. After adjusting for interfering factors, compared with the lowest quartiles of BA and HEI-2015, the odds ratios with 95% confidence intervals for NAFLD across the quartiles were (1.24 (0.84, 1.84), 2.07 (1.15, 3.73) and 2.49 (1.16, 5.38)) and (0.89 (0.66, 1.18), 0.87 (0.65, 1.16) and 0.64 (0.46, 0.87)), respectively. The BA was linear positive with ZJU index, BARD score and NAFLD fibrosis score. However, the linear negative correlation existed between HEI-2015 and ZJU index, BARD score and NAFLD fibrosis score. Mediation analysis showed that the positive correlation between BA and NAFLD could be mediated and weakened by HEI-2015. Conclusions: The prevalence of NAFLD gradually increases with BA, but this positive association can be weakened by the healthy diet. [ABSTRACT FROM AUTHOR]

Telomere length is a cellular aging marker implicated in various health outcomes. A growing body of evidence suggests a link between leukocyte telomere length (LTL) and mental health outcomes. However, there have been no studies focused on the relationship between LTL and the future risk of depression and anxiety. This study aimed to investigate the associations between LTL and depression/anxiety, examining both cross-sectional prevalence and prospective incidence. Data from 364,331 UK Biobank participants were analyzed. LTL was measured at baseline, and mental health status was assessed through hospital records and online surveys. Logistic regression and Cox proportional hazards models were employed for cross-sectional and prospective analyses with appropriate adjustment, respectively. The mean (SD) age of the subjects was 57.03 (13.34) years and follow-up duration was 8.80 (5.39) years. Cross-sectionally, shorter LTL was associated with increased odds of depression (OR: 1.401, 95 % CI: 1.291–1.521) and anxiety (1.347 (1.198–1.515)) at baseline, which remained significant after adjustment. Among those free of depression/anxiety at baseline, baseline shorter LTL was associated with a higher risk of incident depression (HR: 1.615, 95 % CI: 1.447–1.803) and anxiety (1.430 (1.293–1.581)) during follow-up period. These associations remained robust after adjusting for various covariates. Our findings indicated an association between shorter telomeres and an increased risk of prevalent depression/anxiety and shorter telomeres precede the onset of these mental health conditions. Considering the potential clinical implications, our study underscores the relevance of LTL as a predictive tool for identifying individuals at risk of developing depression and anxiety. • Comprehensive analysis of UK Biobank data with cross-sectional and longitudinal mental health assessments. • Shorter leukocyte telomere length is associated with increased risk of depression and anxiety disorders. • Findings suggest that leukocyte telomere length may serve as a biological marker for mental health interventions. • Results enhance understanding of cellular aging's role in mental health, offering insights for future research. [ABSTRACT FROM AUTHOR]

Humans are widely exposed to phenols, parabens, and phthalates with health risks, while the effects of these chemicals on biological aging remain unclear. Among 3,441 adults in the National Health and Nutrition Examination Survey 2005–2010, phenol, paraben, and phthalate concentrations were measured and phenotypic age acceleration (PhenoAgeAccel) was calculated. Linear regression and weighted quantile sum (WQS) regression were used to evaluate the associations of single and mixed chemicals with PhenoAgeAccel. Stratified analyses by chronological age and lifestyle were also performed. Individual phthalates were positively associated with PhenoAgeAccel. The WQS model found the positive relationship between mixed chemicals with PhenoAgeAccel (β = 0.175, 95%CI: 0.001, 0.349). The adverse impacts of phenols and phthalates on biological aging were stronger in older participants with significant interactions. Adherence to healthier lifestyle might partly reduce the positive relationships of phenols and phthalates with biological aging, especially among older adults, which is expected to be a viable intervention in the future. [ABSTRACT FROM AUTHOR]

Background/Objectives: Antiseizure drugs (ASDs) are the primary therapy for epilepsy, and the choice varies according to seizure type. Epilepsy patients experience chronic mitochondrial oxidative stress and increased levels of pro-inflammatory mediators, recognizable hallmarks of biological aging; however, few studies have explored aging markers in epilepsy. Herein, we addressed for the first time the impact of ASDs on molecular aging by measuring the telomere length (TL) and mtDNA copy number (mtDNA-CN). Methods: We used real-time quantitative PCR (QPCR) in epilepsy patients compared to matched healthy controls (CTs) and assessed the association with plasma levels of ASDs and other clinical variables. The sample comprised 64 epilepsy patients and 64 CTs. Patients were grouped based on monotherapy with lamotrigine (LTG) or valproic acid (VPA), and those treated with a combination therapy (LTG + VPA). Multivariable logistic regression was applied to analyze the obtained data. Results: mtDNA-CN was similar between patients and controls, and none of the comparisons were significant for this marker. TL was shorter in not seizure-free patients than in CTs (1.50 ± 0.35 vs. 1.68 ± 0.34; p < 0.05), regardless of the ASD therapy. These patients exhibited the highest proportion of adverse drug reactions. TL was longer in patients on VPA monotherapy, followed by patients on LTG monotherapy and patients on an LTG + VPA combined scheme (1.77 ± 0.24; 1.50 ± 0.32; 1.36 ± 0.37, respectively; p < 0.05), suggesting that ASD treatment differentially modulates TL. Conclusions: Our findings suggest that clinicians could consider TL measurements to decide the best ASD treatment option (VPA and/or LTG) to help predict ASD responses in epilepsy patients. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Are reproductive factors associated with biological aging, and does biological aging mediate the associations of reproductive factors with premature mortality? SUMMARY ANSWER Multiple reproductive factors are related to phenotypic age acceleration (PhenoAge-Accel), while adherence to a healthy lifestyle mitigates these harmful effects; PhenoAge-Accel mediated the associations between reproductive factors and premature mortality. WHAT IS KNOWN ALREADY Accelerated aging is a key contributor to mortality, but knowledge about the effect of reproductive factors on aging is limited. STUDY DESIGN, SIZE, DURATION This prospective cohort study included 223 729 women aged 40–69 years from the UK biobank in 2006–2010 and followed up until 12 November 2021. PARTICIPANTS/MATERIALS, SETTING, METHODS Reproductive factors were collected through a touchscreen questionnaire. Biological aging was assessed through PhenoAge-Accel. Multiple linear regression models were used to examine the relationships of reproductive factors with PhenoAge-Accel and estimate the modified effect of a healthy lifestyle. Furthermore, we applied mediation analysis to explore the mediating role of PhenoAge-Accel in the associations between reproductive factors and premature mortality. MAIN RESULTS AND THE ROLE OF CHANCE Early menarche (<12 years vs 13 years, β : 0.37, 95% CI: 0.30, 0.44), late menarche (≥15 years vs 13 years, β : 0.18, 95% CI: 0.11, 0.25), early menopause (<45 years vs 50–51 years, β : 0.62, 95% CI: 0.51, 0.72), short reproductive lifespan (<30 years vs 35–39 years, β : 0.81, 95% CI: 0.70, 0.92), nulliparity (vs two live births, β : 0.36, 95% CI: 0.30, 0.43), high parity (≥4 vs 2 live births, β : 0.49, 95% CI: 0.40, 0.59), early age at first live birth (<20 years vs 25–29 years, β : 0.66, 95% CI: 0.56, 0.75), and stillbirth (β : 0.51, 95% CI: 0.36, 0.65) were associated with increased PhenoAge-Accel. Furthermore, PhenoAge-Accel mediated 6.0%–29.7% of the associations between reproductive factors and premature mortality. Women with an unfavorable lifestyle and reproductive risk factors had the highest PhenoAge-Accel compared to those with a favorable lifestyle and without reproductive risk factors. LIMITATIONS, REASONS FOR CAUTION The participants in the UK Biobank were predominantly of White ethnicity; thus, caution is warranted when generalizing these findings to other ethnic groups. WIDER IMPLICATIONS OF THE FINDINGS Our findings reveal the harmful effects of multiple reproductive factors on biological aging and the mediating role of biological aging in the associations between reproductive factors and premature mortality. They highlight the significance of adhering to a healthy lifestyle to slow biological aging as a potential way to reduce premature mortality among women with reproductive risk factors. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by the National Natural Science Foundation of China (82003479, 82073660, 72204215), Hubei Provincial Natural Science Foundation of China (2023AFB663), Zhejiang Province Public Welfare Technology Application Research Project (GF22H269155), and China Postdoctoral Science Foundation (2019M662646, 2020T130220). The authors have no competing interests to disclose. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

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Objective: This study aims to investigate the association between central obesity and the risk of osteoarthritis, and the mediating role of biological age and biological aging advance in this relationship. Methods: The study is based on data from the National Health and Nutrition Examination Survey (NHANES) for the years 2005–2018. Thirteen commonly used clinical traits were used to calculate the Klemera-Doubal method age (KDM-Age) and phenotypic age (Pheno-Age) as two measures of biological aging. Additionally, KDM-Age advance and Pheno-Age advance were calculated as two measures of biological aging advance. Weighted multivariable logistic regression was used to analyze the association between central obesity and the risk of osteoarthritis (OA). Mediation analysis was then applied to elucidate the role of biological aging and biological aging advance in this relationship. Results: A total of 31,162 subjects aged ≥20 years were included in this study, of which 3,964 subjects reported having OA (14%). Compared to the Non-OA group, the OA group showed significantly higher proportions of central obesity, KDM-Age, KDM-Age advance, PhenoAge, and PhenoAge advance. Compared to the Non-central obesity group, the central obesity group had higher KDM-Age, KDM-Age advance, PhenoAge, PhenoAge advance, and a higher risk of OA (p < 0.05). Additionally, higher KDM-Age, KDM-Age advance, PhenoAge, and PhenoAge advance were positively correlated with the risk of OA (p < 0.05). Mediation analysis revealed that part of the association between central obesity and the risk of OA was mediated by KDM-Age, KDM-Age advance, PhenoAge, and PhenoAge advance (p < 0.05). Conclusion: Central obesity increases the risk of OA, with part of this association being mediated by biological aging and biological aging advance. [ABSTRACT FROM AUTHOR]

Although venous thromboembolism (VTE) is the third most common cardiovascular disease, and the risk of VTE increases sharply with advancing age, approximately 40% of VTE cases are currently classified as unprovoked, highlighting the importance of risk factor research. While chronological aging is associated with the risk of VTE, the association with biological aging remains unclear. Biological aging is highly complex, influenced by several dysregulated cellular and biochemical mechanisms. In the last decade, advancements in omics methodologies provided insights into the molecular complexity of biological aging. Techniques such as high-throughput genomics, epigenomics, transcriptomics, proteomics, and metabolomics analyses identified and quantified numerous epigenetic markers, transcripts, proteins, and metabolites. These methods have also revealed the molecular alterations organisms undergo as they age. Despite the progress, there is still a lack of consensus regarding the methods for assessing and validating these biomarkers, and their application lacks standardization. This review gives an overview of biomarkers of biological aging, including telomere length, and their potential role for VTE. Furthermore, we critically examine the advantages and disadvantages of the proposed methods and discuss possible future directions for investigating biological aging in VTE. [ABSTRACT FROM AUTHOR]

Little evidence exists regarding the associations between clinical parameter‐based biological aging and the incidence and outcome of chronic kidney disease (CKD). Thus, we aimed to assess the associations between biological aging, genetic risk, and the risk of CKD, as well as investigate the impact of accelerated biological aging on life expectancy. 281,363 participants free of kidney diseases from the UK Biobank were included in this prospective study. Biological age was measured from clinical traits using the KDM‐BA and PhenoAge algorithms, and the discrepancies from chronological age were defined as biological age accelerations. A polygenic score was calculated to indicate the genetic predisposition of the estimated glomerular filtration rate (eGFR). A cause‐specific competing risk model was used to estimate hazard ratios (HRs) and the corresponding confidence intervals (CIs) of incident CKD. We found that individuals with more pronounced accelerations in biological age exhibited an elevated risk of developing CKD (HRQuartile 4 vs. Quartile 1, 1.90; 95% CI, 1.77–2.05 for KDM‐BA acceleration and HRQuartile 4 vs. Quartile 1, 2.79; 95% CI, 2.58–3.01 for PhenoAge acceleration), with nonlinear relationships. Notably, there were positive additive interactions between biological aging and genetic risk on CKD risk. Among the CKD population, accelerated biological aging was associated with a further decline in life expectancy. Advanced biological aging may potentially increase the vulnerability to developing CKD in individuals aged midlife and beyond, particularly among those with high genetic risk for abnormal kidney function, and could reduce the life expectancy of CKD patients. [ABSTRACT FROM AUTHOR]

Calorie restriction (CR) and physical exercise (EX) are well‐established interventions known to extend health span and lifespan in animal models. However, their impact on human biological aging remains unclear. With recent advances in omics technologies and biological age (BioAge) metrics, it is now possible to assess the impact of these lifestyle interventions without the need for long‐term follow‐up. This study compared BioAge biomarkers in 41 middle‐aged and older adult long‐term CR practitioners, 41 age‐ and sex‐matched endurance athletes (EX), and 35 sedentary controls consuming Western diets (WD), through PhenoAge: a composite score derived from nine blood‐biomarkers. Additionally, a subset of participants (12 CR, 11 EX, and 12 WD) underwent multi‐omic profiling, including DNA methylation and RNAseq of colon mucosa, blood metabolomics, and stool metagenomics. A group of six young WD subjects (yWD) served as a reference for BioAge calculation using Mahalanobis distance across six omic layers. The results demonstrated consistently lower BioAge biomarkers in both CR and EX groups compared to WD controls across all layers. CR participants exhibited lower BioAge in gut microbiome and blood‐derived omics, while EX participants had lower BioAge in colon mucosa‐derived epigenetic and transcriptomic markers, suggesting potential tissue‐specific effects. Multi‐omic pathway enrichment analyses suggested both shared and intervention‐specific mechanisms, including oxidative stress and basal transcription as common pathways, with ether lipid metabolism uniquely enriched in CR. Despite limitations due to sample size, these findings contribute to the broader understanding of the potential anti‐aging effects of CR and EX, offering promising directions for further research. [ABSTRACT FROM AUTHOR]

Purpose: It is crucial to identify biomarkers that influence the aging process and associated health risks, given the growing severity of the global population aging issue. The objectives of our research were to evaluate cardiac metabolic index (CMI) as a novel biomarker for identifying individuals at increased risk of accelerated biological aging and to assess its use in guiding preventive strategies for aging-related health risks. Methods: The National Health and Nutrition Examination Survey (NHANES) provided cross-sectional data on participants with complete information on CMI, phenotypic age (PA), and other variables. Analyses of variance and weighted χ2 tests were conducted to assess differences between groups. The relationship between CMI and biological aging was investigated using a weighted multivariate logistic regression model, restricted cubic spline (RCS) regression analysis, subgroup analysis, and interaction testing. Results: A positive correlation between CMI and biological aging was observed in 6,272 participants. RCS regression analysis confirmed the non-linear relationship, identifying significant inflection point at 1.10. In the crude or adjusted models, the OR (95% CI), for the highest group versus the reference were 3.608 (3.108, 4.188), 3.397 (2.920, 3.952), and 1.550 (1.299, 1.850), respectively, when categorizing CMI into different groups. Subgroup analyses and interaction tests indicate that the association between CMI and biological aging remained consistent across different subgroups. Gender, race, education level, marital status, poverty income ratio (PIR), drinking status and diabetes had an interaction with CMI in relation to biological aging. Conclusion: An elevated CMI is linked to increased risk for biological aging. This relationship may inform more effective prevention and treatment strategies for biological aging in the future. CMI be integrated into routine health screenings or aging assessments by healthcare professionals. [ABSTRACT FROM AUTHOR]

Major depressive disorder accelerates DNA methylation age, a biological aging marker. Subclinical depressive symptoms are common, but their link to DNA methylation aging in older adults remains unexplored. This study analyzed the cross-sectional relationship between depressive symptoms and accelerated DNA methylation aging, considering gender and race/ethnicity in U.S. adults aged over 50. We used data from 3,882 diverse participants in the 2016 Health and Retirement Study wave, measuring blood DNA methylation age against chronologic age for acceleration. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale. Multiple linear regression evaluated the association between depressive symptoms and DNA methylation age acceleration, adjusting for sociodemographic factors, blood cell proportions, and health behaviors (physical activity, alcohol use, smoking, and chronic conditions). Gender and race/ethnicity modifications were also tested. Depressive symptoms, measured by continuous CES-D score, high depressive symptoms (CES-D ≥ 4), or any symptoms (CES-D ≥ 1), significantly correlated with increased GrimAge DNA methylation age acceleration (all p ≤.001) in unadjusted and sociodemographic-adjusted models but were nonsignificant in fully adjusted models. No significant gender or race/ethnicity effect modifications were found in fully adjusted models. Health behaviors significantly influence DNA methylation age acceleration and depressive phenotypes, underscoring the need to understand their roles in assessing psychological factors related to DNA methylation age acceleration. Public Significance Statement: This study suggests a correlation between depressive symptoms and accelerated biological aging in older adults that is potentially mediated by health behaviors. As this is a cross-sectional analysis, this correlation may be an indication of a directional relationship from depressive symptoms to accelerated biological aging, accelerated aging leading to depressive symptoms, or a potential third variable that is inducing this relationship. Future studies should assess directional and temporal relationships between depressive symptoms and accelerated biological aging and explore potential effect modification by gender and race/ethnicity, in the context of health behaviors. [ABSTRACT FROM AUTHOR]

Simple Summary: By analyzing inflammatory scores (INFLA-score, Systemic Inflammation Response Index—SIRI) and the Aging-Related Immune Phenotype (ARIP) in 249 participants aged 19–111 years, this study investigates the role of immune-inflammatory responses in semi- and supercentenarians who have survived significant challenges like pandemics. Statistical analyses indicated that the INFLA-score and SIRI increase with age, but no significant differences were observed between semi- and supercentenarians and other age groups. Moreover, ARIP values, specifically CD8 Naïve/Effector scores, calculated from a subcohort of 54 individuals, showed no notable differences across groups. These findings suggest that effective management of immune-inflammatory responses may play a role in achieving extreme longevity. Studying models of healthy aging and exceptional longevity is crucial to understanding a possible longevity signature, as most show resistance to age-related diseases. In particular, semi- and supercentenarians are a highly selected group, having survived significant adversities, including the Spanish flu and COVID-19 pandemics, indicating distinctive immune system characteristics. This paper analyzes the inflammatory scores (INFLA-score, Systemic Inflammation Response Index (SIRI)) and Aging-Related Immune Phenotype (ARIP) indicators calculated from the dataset of the DESIGN project, including 249 participants aged 19–111 years, aiming to understand the immune-inflammatory (IMFLAM) role in achieving longevity. Statistical analyses, including linear regression and one-way ANOVA, were performed to explore the correlations between these parameters and age. Both INFLA-score and SIRI showed a significant increase with age. However, no statistical differences were found when comparing the values of semi- and supercentenarians to other age groups, which are similar to adults and lower than younger centenarians. Regarding ARIP values, it is noteworthy that when comparing the CD8+ Naïve/Effector scores between groups, no significant differences were observed between the semi- and supercentenarian group and the other groups. These results support the idea that the control of IMFLAM response can promote extreme longevity. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the relationship between plant protein, animal protein and biological aging through different dimensions of biological aging indices. Then explore the effects of substitution of plant protein, animal protein, and their food sources on biological aging. Methods: The data came from 79,294 participants in the UK Biobank who completed at least two 24-h dietary assessments. Higher Klemera-Doubal Method Biological Age (HKDM-BA), higher PhenoAge (HPA), higher allostatic load (HAL), and longer telomere length (LTL) were estimated to assess biological aging. Logistic regression was used to estimate protein-biological aging associations. Substitution model was performed to assess the effect of dietary protein substitutions. Results: Plant protein intake was inversely associated with HKDM-BA, HPA, HAL, and positively associated with LTL (odds ratios after fully adjusting and comparing the highest to the lowest quartile: 0.83 (0.79–0.88) for HKDM-BA, 0.86 (0.72–0.94) for HPA, 0.90 (0.85–0.95) for HAL, 1.06 (1.01–1.12) for LTL), while animal protein was not correlated with the four indices. Substituting 5% of energy intake from animal protein with plant protein, replacing red meat or poultry with whole grains, and replacing red or processed meat with nuts, were negatively associated with HKDM-BA, HPA, HAL and positively associated with LTL. However, an inverse association was found when legumes were substituted for yogurt. Gamma glutamyltransferase, alanine aminotransferase, and aspartate aminotransferase mediated the relationship between plant protein and HKDM-BA, HPA, HAL, and LTL (mediation proportion 11.5–24.5%; 1.9–6.7%; 2.8–4.5%, respectively). Conclusion: Higher plant protein intake is inversely associated with biological aging. Although there is no association with animal protein, food with animal proteins displayed a varied correlation. [ABSTRACT FROM AUTHOR]

Objectives Exposure to stressful neighborhood environments is a well-established risk factor for health deterioration and premature death. However, the biological underpinnings are not fully understood. Epigenetic aging may function as a key molecular pathway to adverse health outcomes among residents of high-stress neighborhoods. This study examines the associations between neighborhood social stressors (socioeconomic deprivation, observed and perceived disorder, and low social cohesion) and epigenetic age (DunedinPACE and Principal component adjusted [PC] PCHorvath, PCHannum, PCPhenoAge, PCGrimAge). Further, we identify subpopulations most vulnerable to neighborhood stressors. Methods Respondent data are from the 2016 Health and Retirement Study (HRS) DNA methylation subsample. Neighborhood data come from respondent reports (2014/2016) and the census (2012–2016 ACS). The analytic sample included 3,146 adults ages 56 and older (mean age = 68.8), of whom 54.9% were women and 19.3% were non-White. Results In multilevel regression models adjusting for sociodemographic covariates, all neighborhood stressors were associated with faster DunedinPACE (B  = 0.008 to 0.017). Neighborhood deprivation, perceived disorder, and low cohesion were associated with PCPhenoAge (B  = 0.27 to 0.40) or PCGrimAge acceleration (B  = 0.23). Health behaviors explained these associations to some degree. However, no significant associations were found with PCHorvath and PCHannum. In interaction analyses, adverse associations with deprivation, observed disorder, and low cohesion were more pronounced for women. No consistent interactions were found for race/ethnic and education groups. Discussion Our findings indicate that neighborhood stressors can accelerate epigenetic aging, with older women particularly vulnerable to their effects. These findings provide insights into the biological foundations of health disparities rooted in neighborhood environments. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the associations between biological aging, metabolic heterogeneity of obesity, and rheumatoid arthritis (RA). Methods: This prospective cohort study analyzed 268,184 individuals from the UK Biobank. Biological age was estimated using phenotypic age (PhenoAge), Klemera-Doubal methods (KDM-BA), and telomere length. We calculated KDM-BA acceleration and PhenoAge acceleration after subtracting the effect of chronological age by regression residual. The metabolic heterogeneity of obesity can be evaluated by four BMI metabolic phenotypes, namely metabolically unhealthy normal weight (MUNW), metabolically healthy normal weight (MHNW), metabolically unhealthy overweight/obesity (MUOO), and metabolically healthy overweight/obesity (MHOO). Cox models were employed to estimate the associations between biological aging, metabolic heterogeneity of obesity, and RA risk. Results: A total of 2842 patients experienced RA during a mean follow-up time of 12.21 years. A standard deviation (SD) increase in KDM-BA acceleration and PhenoAge acceleration was associated with an increased risk of RA by 13% (hazard ratio = 1.13; 95% CI, 1.09–1.17) and 39% (HR = 1.39; 95% CI, 1.34–1.44), respectively. A SD increase in telomere length was associated with a reduced risk of RA by 5% (HR = 0.95; 95% CI, 0.91–0.98). Compared to the MHNW group, the MUOO group was associated with a 51% increase in the risk of incident RA. In the joint effect analysis, compared to the MHNW + KDM-BA younger subgroup, the HR (95% CI) for RA was 1.68 (1.48, 1.90) in the MUOO + KDM-BA older subgroup. Conclusion: Accelerated biological aging may heighten the susceptibility to RA, particularly in individuals with obesity or metabolic dysfunction. Key Points •Accelerated biological aging increases the risk of developing RA. •Overweight/obese people with a healthy metabolism have a higher risk of RA than those with normal weight and healthy metabolism. •The BMI metabolic phenotype has a strong modifying effect on the association between KDM-BA/PhenoAge and RA risk. [ABSTRACT FROM AUTHOR]

• Higher overall plant-based diets index (PDI) was associated with slower progression of phenotypic age (PA) and biological age (BA). • Higher healthy PDI was significantly associated with slower progression of PA and BA. • Higher unhealthy PDI appeared to accelerate the progression of PA and BA. Plant-based diets are recognized for their health benefits. However, evidence on the association between plant-based diet quality and aging in the US population is limited. This study aimed to investigate the association between different plant-based diet indices, phenotypic age acceleration (PhenoAgeAccel), and biological age acceleration (BioAgeAccel). We hypothesized that healthful plant-based diets would negatively affect PhenoAgeAccel and BioAgeAccel in US adults. The cross-sectional analysis included 22,363 participants, and information was obtained from the National Health and Nutrition Examination Survey database. The quality of plant-based diet was assessed using 3 indices: overall plant-based diet index (PDI), healthful PDI (hPDI), and unhealthful PDI (uPDI). Phenotypic age (PA) and biological age (BA) was calculated based on a linear combination of chronological age and 12 multi-system clinical chemistry biomarkers in accordance with the previously established method. PhenoAgeAccel and BioAgeAccel are the residuals of the PA and BA. Weighted linear regression analyses were performed to evaluate the relationships between PDI, hPDI and uPDI, and PhenoAgeAccel and BioAgeAccel. After adjusting for all covariates, we observed that a 10-unit higher PDI score was associated with 0.80 years lower PhenoAgeAccel (β: -0.80, 95% confidence interval [CI]: -0.94, -0.67), and 1.91 years lower BioAgeAccel (β: -1.91, 95% CI: -2.42,-1.40). A 10-unit higher hPDI score was associated with 0.83 years lower PhenoAgeAccel (β: -0.83, 95% CI: -0.96, -0.70), and 1.76 years lower BioAgeAccel (β: -1.76, 95% CI: -2.18, -1.34). Conversely, a 10-unit higher uPDI score was associated with 0.77 years higher PhenoAgeAccel (β: 0.77, 95% CI: 0.66, 0.89) and 1.21 years higher BioAgeAccel (β: 1.21, 95% CI: 0.80, 1.62). These findings suggest that US adults may be able to slow the aging process by increasing adherence to a healthy plant-based diet. Plant-based diets are thought to be beneficial to bring a variety of health outcomes. While evidence of the effects of plant-based diets on the rate of aging is limited. The analysis included 22,363 participants, and information was obtained from the National Health and Nutrition Examination Survey database. We found that overall plant-based diet index (PDI) and healthy PDI scores were significantly associated with slower progression of phenotypic age (PA) and biological age (BA), whereas unhealthy PDI seemed to accelerate the progression of PA and BA. [Display omitted] [ABSTRACT FROM AUTHOR]

Physical fitness has been extensively shown to strongly associate with general health status and major health risks. Here we tested the ability of a novel estimate of fitness age (FitAge) to differentiate aging trajectories. This study aimed at (1) testing the ability of FitAge to differentiate aging patterns among decelerated, normal, and accelerated agers in selected health domains, (2) estimating the risk for developing major health issues depending on the aging trajectory, and (3) comparing FitAge to body mass index (BMI) categorization in differentiating healthy from unhealthy aging patterns. A total of 176 volunteers participated in this cross-sectional study. Participants underwent clinical screening and a comprehensive assessment of body composition, nutritional and health-related status, cognitive functioning, and haematochemical analyses with routine tests, oxidative stress, and inflammation markers. Scores for major health risks were also computed. FitAge outperformed BMI in estimating major health risk scores and was able to differentiate decelerated from normal and accelerated agers for health risk profile and several physiological domains. Body composition, immune system activation, and inflammation markers emerged as those variables flagging the largest differences between decelerated and accelerated aging patterns. The novel estimate of biological aging can accurately differentiate both in women and men decelerated from accelerated agers in almost all the domains scrutinized. Overall, decelerated aging is linked to positively oriented features which associate with reduced risk of developing major health issues. The present findings have potential relevance and practical implications to identify individuals at higher risk of accelerated aging according to their FitAge estimated via simple and cost-effective motor tests. [ABSTRACT FROM AUTHOR]

Susan B. Levin argues that the human confidence that an ageless body would be better is irrational. She offers a Kantian-inspired argument to show that human understanding cannot rationally access the experiences of a post-human and ageless existence. We challenge this rationale with a three-step argument: first, an ageless body does not have to be post-human. One should distinguish between the transhumanist projects of life extension and accounts focused on enhancing well-being and quality of life. An existence without aging does not require a radical change in one's temporal intuitions, which makes rational discussion possible. Second, we defend that biological aging does not entail any valuable goods. These goods refer to the chronological dimension of aging. Finally, we argue that biological aging is indeed negative and one may need biotechnological interventions in aging to achieve internal transcendence. Thus, we rationally argue that an ageless body would be better. [ABSTRACT FROM AUTHOR]

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

Abstract Background The process of biological aging in patients diagnosed with chronic liver disease remains unclear. Aim The current study aims to investigate if there is an accelerated biological aging process in participants with advanced fibrosis (AF) and metabolic dysfunction-associated steatotic liver disease (MASLD). Methods Data from the 2017–2018 NHANES cycle were analyzed. AF was determined based on the values of liver stiffness measurement (LSM) and MASLD was defined according to new consensus nomenclature. Klemera-Doubal method biological age (KDM bioage) and Phenotypic age (Phenoage) were adopted to quantify biological age. Phenoage advancement (Phenoage_advance) and KDM advancement (KDM_advance) were generated as the difference between the calculated biological age and chronological age, and a positive residual was regarded as an indicator of accelerated biological aging. Results A total of 3974 participants was enrolled. The weight mean KDM_advance and phenoage_advance in AF group was 4.22 years (95%CI: 2.96–5.49 years) and 2.61 years (95%CI: 1.80–3.41 years), while in MASLD group was 0.37 years (95%CI: -0.28–1.03 years) and 0.04 years (95%CI: -0.64–0.72 years), respectively. Multivariate linear regression analysis showed that participants with AF had older KDM_advance and phenoage_advance compared with those without AF (1.50 years (95%CI: 0.23–2.77 years), P = 0.02; 1.00 years (95%CI: 0.18–1.82 years), P = 0.02; respectively), in models adjusting demographic characteristics, socioeconomic status, lifestyle factors, and comorbidities. No significant association was found between MASLD and KDM_advance and phenoage_advance. Conclusions AF, not MASLD, was independently associated with accelerated biological aging in adults from a US representative sample.

Abstract To investigate the relationship between Leisure time physical activity (LTPA) patterns and PhenoAgeAccel in patients with Type 2 diabetes (T2D), emphasizing the role of regular LTPA in mitigating biological aging. This study utilized data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, including 4,134 adults with T2D. Multivariable linear regression models and restricted cubic spline (RCS) methods were employed to assess the relationship between LTPA and Phenotypic age acceleration (PhenoAgeAccel), with segmented likelihood ratio tests to detect nonlinear thresholds. Stratified regression and interaction tests were conducted for robust analysis. Compared to individuals with no LTPA patterns, those with regular LTPA patterns had significantly lower PhenoAgeAccel scores (β = -1.164, 95% CI: -1.651 to -0.677, P

Abstract Background Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging. Methods PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors. Results Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel. Conclusions Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors.

Abstract Background With the rapid aging of the global population, identifying lifestyle patterns that effectively delay aging and reduce mortality risk is of paramount importance. This study utilizes the UK Biobank to analyze the associations of the Dietary Inflammatory Index, physical activity, and sleep on biological aging and all-cause mortality. Methods A prospective cohort study was conducted using data from over half a million UK Biobank participants. Two datasets were created by subjective and objective measurements of physical activity: the Subjective Physical Activity (SPA) and Objective Physical Activity (OPA) datasets. Lifestyle patterns, including diet habits, exercise levels, and sleep quality, were assessed within these datasets. Biological aging was quantified using validated methods, including Homeostatic Dysregulation, Klemera-Doubal Method Biological Age, Phenotypic Age, and Telomere Length. All-cause mortality data were obtained from the National Health Service. Statistical analyses included weighted linear regression and Cox proportional hazard models, adjusted for a range of covariates. Results The findings indicate that, in most cases, maintaining an anti-inflammatory diet, engaging in at least moderate physical activity, and ensuring healthy sleep conditions are associated with delayed physiological aging (Cohen’s d ranging from 0.274 to 0.633) and significantly reduced risk of all-cause mortality (HR-SPA: 0.690, 95% CI: 0.538, 0.884; HR-OPA: 0.493, 95% CI: 0.293, 0.828). These effects are particularly pronounced in individuals under 60 years of age and in women. However, it was observed that the level of physical activity recommended by the World Health Organization (600 MET-minutes/week) does not achieve the optimal effect in delaying biological aging. The best effect in decelerating biological aging was seen in the high-level physical activity group (≥ 3000 MET-minutes/week). The study also highlights the potential of biological age acceleration and telomere length as biomarkers for predicting the risk of mortality. Conclusions Choosing healthy lifestyle patterns, especially an anti-inflammatory diet, at least moderate physical activity, and healthy sleep patterns, is crucial for delaying aging and reducing mortality risk. These findings support the development of targeted interventions to improve public health outcomes. Future research should focus on objective assessments of lifestyle to further validate these associations.

Abstract Background Evidence for the relationship between individual and combined volatile organic compounds (VOCs) and cardiovascular disease (CVD) is limited. Besides, the mediating role of biological aging (BA) has not been studied. Therefore, this study aimed to examine the association between VOCs and CVD risk and to explore the mediating effects of BA. Methods Logistic regression models were used to investigate the relationships of metabolites of volatile organic compounds (mVOCs) and BA with CVD. In addition, weighted quantile sum (WQS) regression, adaptive elastic networks, and Environmental Risk Score (AENET-ERS) were utilized to assess overall associations of mixed VOCs co-exposure with CVD. Mediation analyses were used to identify potential mediating effects of BA. Results In the single-pollutant model, CYMA was shown to be associated with an increased risk of CVD. Additionally, we identified significantly positive associations between the WQS index and CVD (odds ratio (OR) = 1.292, 95% confidence interval (CI): 1.006, 1.660), and DHBMA had the greatest contribution for CVD (0.246). Furthermore, the AENET-ERS results showed that 8 mVOCs were significantly associated with CVD, and ERS was related to an elevated risk of CVD (OR = 1.538, 95%CI: 1.255, 1.884). Three BA indicators mediated the association of the mVOCs mixture with CVD, with mediating effect proportions of 11.32%, 34.34%, and 7.92%, respectively. Conclusion The risk of CVD was found to increase with both individual and combined exposure to VOCs. BA mediates the positive effects of VOCs on CVD, suggesting that this pathway may be one of the mechanisms of CVD.

Abstract Background Information on the influences of daily eating frequency (DEF) and nighttime fasting duration (NFD) on biological aging is minimal. Our study investigated the potential associations of DEF and NFD with accelerated aging. Methods Out of 24212 participants in NHANES 2003–2010 and 2015–2018, 4 predicted age metrics [homeostatic dysregulation (HD), Klemera–Doubal method (KDM), phenoAge (PA), and allostatic load (AL)] were computed based on 12 blood chemistry parameters. Utilizing 24-h dietary recall, DEF was measured by the frequency of eating occurrences, while NFD was determined by assessing the timing of the initial and final meals throughout the day. Weighted multivariate linear regression models and restricted cubic spline (RCS) were utilized to examine the associations. Results Compared to DEF of ≤ 3.0 times, subjects with DEF ≥ 4.6 times demonstrated lower KDM residual [β: -0.57, 95% confidence-interval (CI): (-0.97, -0.17)] and PA residual [β: -0.47, 95% CI: (-0.69, -0.25)]. In comparison to NFD between 10.1 and 12.0 h, individuals with NFD ≤ 10.0 h were at higher HD [β: 0.03, 95% CI: (0.01, 0.04)], KDM residual [β: 0.34, 95% CI: (0.05, 0.63)], and PA residual [β: 0.38, 95% CI: (0.18, 0.57)]. Likewise, those with NFD ≥ 14.1 h also had higher HD [β: 0.02, 95% CI: (0.01, 0.04)] and KDM residual [β: 0.33, 95% CI: (0.03, 0.62)]. The results were confirmed by the dose–response relationships of DEF and NFD with predicted age metrics. Lactate dehydrogenase (LDH) and globulin (Glo) were acknowledged as implicated in and mediating the relationships. Conclusions DEF below 3.0 times and NFD less than 10.0 or more than 14.1 h were independently associated with higher predicted age metrics.

Abstract Background Healthy dietary patterns have been negatively associated with methylation-based measures of biological age, yet previous investigations have been unable to establish the relationship between them and biological aging assessed through blood chemistry-based clinical biomarkers. We sought to assess the associations of 4 dietary metrics with 4 measures of biological age. Methods Among 16,666 participants in NHANES 1999–2018, 4 dietary metrics [Dietary inflammatory index (DII), Dietary approaches to stop hypertension index (DASH), Alternate mediterranean diet score (aMED), and Healthy eating index-2015 (HEI-2015)] were calculated through the ‘dietaryindex’ R package. Twelve blood chemistry parameters were utilized to compute 4 indicators of biological age [homeostatic dysregulation (HD), allostatic load (AL), Klemera–Doubal method (KDM), and phenotypic age (PA)]. Binomial logistic regression models and restricted cubic spline (RCS) regression were employed to evaluate the associations. Results All 4 dietary metrics were significantly associated with biological age acceleration or deceleration. In comparison to the lowest DII, the odds ratios (ORs) for accelerated HD, AL, KDM, and PA were 1.25 (1.08,1.45), 1.29 (1.11,1.50), 1.34 (1.08,1.65), and 1.61 (1.39,1.87) for the highest. The multivariable-adjusted ORs of the highest quartile of DASH, aMED, and HEI-2015 were 0.85 (0.73,0.97), 0.88 (0.74,1.04), and 0.84 (0.74,0.96) for HD, 0.64 (0.54,0.75), 0.61 (0.52,0.72), and 0.70 (0.59,0.82) for AL, 0.68 (0.54,0.85), 0.62 (0.50,0.76), and 0.71 (0.58,0.87) for KDM, and 0.50 (0.42,0.59), 0.64 (0.54,0.76), and 0.51 (0.44,0.58) for PA when compared with the lowest level. The findings were validated by the best-fitting dose-response curves for the associations. Among participants consuming dietary supplements (P interaction < 0.05), the positive effects of a healthy dietary pattern on biological aging were more pronounced. Systemic immune inflammation index (SII) and atherogenic index of plasma (AIP) were identified as being involved in and mediating the associations. Conclusions Biological aging assessed through blood chemistry-based clinical biomarkers is negatively associated with diet quality. The anti-aging benefits of improving the diet may be due to its ability to reduce inflammation and lower blood lipids.

Background: The process of biological aging in patients diagnosed with chronic liver disease remains unclear. Aim: The current study aims to investigate if there is an accelerated biological aging process in participants with advanced fibrosis (AF) and metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: Data from the 2017–2018 NHANES cycle were analyzed. AF was determined based on the values of liver stiffness measurement (LSM) and MASLD was defined according to new consensus nomenclature. Klemera-Doubal method biological age (KDM bioage) and Phenotypic age (Phenoage) were adopted to quantify biological age. Phenoage advancement (Phenoage_advance) and KDM advancement (KDM_advance) were generated as the difference between the calculated biological age and chronological age, and a positive residual was regarded as an indicator of accelerated biological aging. Results: A total of 3974 participants was enrolled. The weight mean KDM_advance and phenoage_advance in AF group was 4.22 years (95%CI: 2.96–5.49 years) and 2.61 years (95%CI: 1.80–3.41 years), while in MASLD group was 0.37 years (95%CI: -0.28–1.03 years) and 0.04 years (95%CI: -0.64–0.72 years), respectively. Multivariate linear regression analysis showed that participants with AF had older KDM_advance and phenoage_advance compared with those without AF (1.50 years (95%CI: 0.23–2.77 years), P = 0.02; 1.00 years (95%CI: 0.18–1.82 years), P = 0.02; respectively), in models adjusting demographic characteristics, socioeconomic status, lifestyle factors, and comorbidities. No significant association was found between MASLD and KDM_advance and phenoage_advance. Conclusions: AF, not MASLD, was independently associated with accelerated biological aging in adults from a US representative sample. [ABSTRACT FROM AUTHOR]

Background: Phenotypic age (PhenoAge), a widely used marker of biological aging, has been shown to be a robust predictor of all-cause mortality and morbidity in different populations. Existing studies on biological aging have primarily focused on individual domains, resulting in a lack of a comprehensive understanding of the multi-systemic dysregulation that occurs in aging. Methods: PhenoAge was evaluated based on a linear combination of chronological age (CA) and 9 clinical biomarkers in 952 multi-ethnic Asian women of reproductive age. Phenotypic age acceleration (PhenoAgeAccel), an aging biomarker, represents PhenoAge after adjusting for CA. This study conducts an in-depth association analysis of PhenoAgeAccel with clinical, nutritional, lipidomic, gut microbiome, and genetic factors. Results: Higher adiposity, glycaemia, plasma saturated fatty acids, kynurenine pathway metabolites, GlycA, riboflavin, nicotinamide, and insulin-like growth factor binding proteins were positively associated with PhenoAgeAccel. Conversely, a healthier diet and higher levels of pyridoxal phosphate, all-trans retinol, betaine, tryptophan, glutamine, histidine, apolipoprotein B, and insulin-like growth factors were inversely associated with PhenoAgeAccel. Lipidomic analysis found 132 lipid species linked to PhenoAgeAccel, with PC(O-36:0) showing the strongest positive association and CE(24:5) demonstrating the strongest inverse association. A genome-wide association study identified rs9864994 as the top genetic variant (P = 5.69E-07) from the ZDHHC19 gene. Gut microbiome analysis revealed that Erysipelotrichaceae UCG-003 and Bacteroides vulgatus were inversely associated with PhenoAgeAccel. Integrative network analysis of aging-related factors underscored the intricate links among clinical, nutritional and lipidomic variables, such as positive associations between kynurenine pathway metabolites, amino acids, adiposity, and insulin resistance. Furthermore, potential mediation effects of blood biomarkers related to inflammation, immune response, and nutritional and energy metabolism were observed in the associations of diet, adiposity, genetic variants, and gut microbial species with PhenoAgeAccel. Conclusions: Our findings provide a comprehensive analysis of aging-related factors across multiple platforms, delineating their complex interconnections. This study is the first to report novel signatures in lipidomics, gut microbiome and blood biomarkers specifically associated with PhenoAgeAccel. These insights are invaluable in understanding the molecular and metabolic mechanisms underlying biological aging and shed light on potential interventions to mitigate accelerated biological aging by targeting modifiable factors. [ABSTRACT FROM AUTHOR]

Simple Summary: Telomeres cap the ends of chromosomes. The length of telomeres is highly related to chronological age. As people age, their telomeres become shorter. Shorter telomeres put people at a greater risk of premature disease and death. A healthy lifestyle tends to preserve telomeres, whereas unhealthy practices cause increased biological aging and shorter telomeres. This study investigated the extent to which regular strength training is related to the length of telomeres in 4814 US men and women who were representative of the US adult population. Participants gave blood, and the length of telomeres in their blood cells was precisely measured. Participants also reported how often they engaged in exercises to strengthen their muscles. The findings showed that adults who strength trained regularly had significantly longer telomeres and therefore less biological aging than adults who did not strength train, even after taking into account many factors, including their age, sex, race, income, household size, smoking, body size, and participation in physical activities other than strength training. Telomere length is an index of cellular aging. Healthy lifestyles are associated with reduced oxidative stress and longer telomeres, whereas unhealthy behaviors are related to shorter telomeres and greater biological aging. This investigation was designed to determine if strength training accounted for differences in telomere length in a random sample of 4814 US adults. Data from the National Health and Nutrition Examination Survey (NHANES) were employed to answer the research questions using a cross-sectional design. Time spent strength training was calculated by multiplying days of strength training per week by minutes per session. Participation in other forms of physical activity was also calculated based on reported involvement in 47 other activities. Weighted multiple regression and partial correlation were used to calculate the mean differences in telomere length across levels of strength training, adjusting for differences in potential confounders. With the demographic covariates controlled, strength training and telomere length were linearly related (F = 14.7, p = 0.0006). Likewise, after adjusting for all the covariates, the linear association remained strong and significant (F = 14.7, p = 0.0006). In this national sample, 90 min per week of strength training was associated with 3.9 years less biological aging, on average. Regular strength training was strongly related to longer telomeres and less biological aging in 4814 US adults. [ABSTRACT FROM AUTHOR]

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

Background: Evidence for the relationship between individual and combined volatile organic compounds (VOCs) and cardiovascular disease (CVD) is limited. Besides, the mediating role of biological aging (BA) has not been studied. Therefore, this study aimed to examine the association between VOCs and CVD risk and to explore the mediating effects of BA. Methods: Logistic regression models were used to investigate the relationships of metabolites of volatile organic compounds (mVOCs) and BA with CVD. In addition, weighted quantile sum (WQS) regression, adaptive elastic networks, and Environmental Risk Score (AENET-ERS) were utilized to assess overall associations of mixed VOCs co-exposure with CVD. Mediation analyses were used to identify potential mediating effects of BA. Results: In the single-pollutant model, CYMA was shown to be associated with an increased risk of CVD. Additionally, we identified significantly positive associations between the WQS index and CVD (odds ratio (OR) = 1.292, 95% confidence interval (CI): 1.006, 1.660), and DHBMA had the greatest contribution for CVD (0.246). Furthermore, the AENET-ERS results showed that 8 mVOCs were significantly associated with CVD, and ERS was related to an elevated risk of CVD (OR = 1.538, 95%CI: 1.255, 1.884). Three BA indicators mediated the association of the mVOCs mixture with CVD, with mediating effect proportions of 11.32%, 34.34%, and 7.92%, respectively. Conclusion: The risk of CVD was found to increase with both individual and combined exposure to VOCs. BA mediates the positive effects of VOCs on CVD, suggesting that this pathway may be one of the mechanisms of CVD. Highlights: Individual and mixed exposure to VOCs were associated with elevated CVD risk. DHBMA dominates mixtures of mVOCs for increased CVD risk. BA mediates the effect of VOCs mixture exposure on CVD. [ABSTRACT FROM AUTHOR]

Background: With the rapid aging of the global population, identifying lifestyle patterns that effectively delay aging and reduce mortality risk is of paramount importance. This study utilizes the UK Biobank to analyze the associations of the Dietary Inflammatory Index, physical activity, and sleep on biological aging and all-cause mortality. Methods: A prospective cohort study was conducted using data from over half a million UK Biobank participants. Two datasets were created by subjective and objective measurements of physical activity: the Subjective Physical Activity (SPA) and Objective Physical Activity (OPA) datasets. Lifestyle patterns, including diet habits, exercise levels, and sleep quality, were assessed within these datasets. Biological aging was quantified using validated methods, including Homeostatic Dysregulation, Klemera-Doubal Method Biological Age, Phenotypic Age, and Telomere Length. All-cause mortality data were obtained from the National Health Service. Statistical analyses included weighted linear regression and Cox proportional hazard models, adjusted for a range of covariates. Results: The findings indicate that, in most cases, maintaining an anti-inflammatory diet, engaging in at least moderate physical activity, and ensuring healthy sleep conditions are associated with delayed physiological aging (Cohen's d ranging from 0.274 to 0.633) and significantly reduced risk of all-cause mortality (HR-SPA: 0.690, 95% CI: 0.538, 0.884; HR-OPA: 0.493, 95% CI: 0.293, 0.828). These effects are particularly pronounced in individuals under 60 years of age and in women. However, it was observed that the level of physical activity recommended by the World Health Organization (600 MET-minutes/week) does not achieve the optimal effect in delaying biological aging. The best effect in decelerating biological aging was seen in the high-level physical activity group (≥ 3000 MET-minutes/week). The study also highlights the potential of biological age acceleration and telomere length as biomarkers for predicting the risk of mortality. Conclusions: Choosing healthy lifestyle patterns, especially an anti-inflammatory diet, at least moderate physical activity, and healthy sleep patterns, is crucial for delaying aging and reducing mortality risk. These findings support the development of targeted interventions to improve public health outcomes. Future research should focus on objective assessments of lifestyle to further validate these associations. [ABSTRACT FROM AUTHOR]

Background and Objectives: Telomere length (TL) has been acknowledged as biomarker of biological aging. Numerous investigations have examined associations between individual early life factors and leukocyte TL; however, the findings were far from consistent. Research Design and Methods: We evaluated the relationship between individual and combined early life factors and leukocytes TL in middle and late life using data from the UK Biobank. The early life factors (eg, maternal smoking, breastfeeding, birth weight, and comparative body size and height to peers at age 10) were measured. The regression coefficients (β) and 95% confidence interval (CI) were applied to assess the link of the early life factors and TL in adulthood. Flexible parametric survival models incorporated age to calculate the relationship between early life factors and life expectancy. Results: Exposure to maternal smoking, lack of breastfeeding, low birth weight, and shorter height compared to peers at age 10 were identified to be associated with shorter TL in middle and older age according to the large population-based study with 197 504 participants. Individuals who experienced more than 3 adverse early life factors had the shortest TL in middle and late life (β = −0.053; 95% CI = −0.069 to −0.038; p < .0001), as well as an average of 0.54 years of life loss at the age of 45 and 0.49 years of life loss at the age of 60, compared to those who were not exposed to any early life risk factors. Discussion and Implications: Early life factors including maternal smoking, non-breastfed, low birth weight, and shorter height compared to peers at age 10 were associated with shorter TL in later life. In addition, an increased number of the aforementioned factors was associated with a greater likelihood of shorter TL in adulthood, as well as a reduced life expectancy. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The geroscience hypothesis proposes systemic biological aging is a root cause of cognitive decline. METHODS: We analyzed Framingham Heart Study Offspring Cohort data (n = 2296; 46% male; baseline age M = 62, SD = 9, range = 25–101 y). We measured cognitive decline across two decades of neuropsychological‐testing follow‐up. We measured pace of aging using the DunedinPACE epigenetic clock. Analysis tested if participants with faster DunedinPACE values experienced more rapid cognitive decline compared with those with slower DunedinPACE values. RESULTS: Participants with faster DunedinPACE had poorer cognitive functioning at baseline and experienced more rapid cognitive decline over follow‐up. Results were robust to confounders and consistent across population strata. Findings were similar for the PhenoAge and GrimAge epigenetic clocks. DISCUSSION: Faster pace of aging is a risk factor for preclinical cognitive decline. Metrics of biological aging may inform risk stratification in clinical trials and prognosis in patient care. Highlights: Faster DunedinPACE is associated with preclinical cognitive aging. Higher baseline cognition was protective of DunedinPACE‐associated cognitive decline. The DunedinPACE association with cognitive decline explained a fourth of dementia risk. [ABSTRACT FROM AUTHOR]

Background/Objectives: Studies investigating the associations between life-course socioeconomic status (SES) and biological aging (the difference between biological and chronological age, Δage) have mostly been focused on epigenetic clocks and on a limited number of mediators. The aim of this study was to investigate this relationship using a blood-based aging clock, as well as the potential mediation of different factors including lifestyles or their proxies and physical and mental wellbeing. Methods: A deep-learning aging clock based on 36 blood markers was deployed, in a large Italian population cohort: the Moli-sani study (N = 4772; ≥35 years; 48% men). SES was defined as an eight-level trajectory over the life course, which was tested with Δage in linear models incrementally adjusted for age, sex, and prevalent health conditions. Moreover, the proportion of associations explained by diverse potential mediators, including diet, smoking, physical activity, alcohol, body mass index (BMI), and physical and mental quality of life (QoL) was estimated. Results: Compared to participants with a stably high SES, those showing an educational and financial downward trajectory were older than their CA (β (95%CI) = 1.28 (0.73–1.83) years), as were those with a stably low SES (0.75 (0.25–01.25) years). These associations were largely explained by the tested mediators (overall proportion: 36.2% and 66.3%, respectively), prominently by physical QoL (20.7% and 41.0%), BMI (16.8% and 34.3%), lifestyle (10.6% and 24.6%), and dietary inflammatory score (5.3% and 9.2%). Conclusions: These findings indicate that life-course socioeconomic inequalities are associated with accelerated biological aging, suggesting physical wellbeing and pro-inflammatory lifestyles as potential public health targets to slow down this process in susceptible socioeconomic strata of the population. [ABSTRACT FROM AUTHOR]

Background: Healthy dietary patterns have been negatively associated with methylation-based measures of biological age, yet previous investigations have been unable to establish the relationship between them and biological aging assessed through blood chemistry-based clinical biomarkers. We sought to assess the associations of 4 dietary metrics with 4 measures of biological age. Methods: Among 16,666 participants in NHANES 1999–2018, 4 dietary metrics [Dietary inflammatory index (DII), Dietary approaches to stop hypertension index (DASH), Alternate mediterranean diet score (aMED), and Healthy eating index-2015 (HEI-2015)] were calculated through the 'dietaryindex' R package. Twelve blood chemistry parameters were utilized to compute 4 indicators of biological age [homeostatic dysregulation (HD), allostatic load (AL), Klemera–Doubal method (KDM), and phenotypic age (PA)]. Binomial logistic regression models and restricted cubic spline (RCS) regression were employed to evaluate the associations. Results: All 4 dietary metrics were significantly associated with biological age acceleration or deceleration. In comparison to the lowest DII, the odds ratios (ORs) for accelerated HD, AL, KDM, and PA were 1.25 (1.08,1.45), 1.29 (1.11,1.50), 1.34 (1.08,1.65), and 1.61 (1.39,1.87) for the highest. The multivariable-adjusted ORs of the highest quartile of DASH, aMED, and HEI-2015 were 0.85 (0.73,0.97), 0.88 (0.74,1.04), and 0.84 (0.74,0.96) for HD, 0.64 (0.54,0.75), 0.61 (0.52,0.72), and 0.70 (0.59,0.82) for AL, 0.68 (0.54,0.85), 0.62 (0.50,0.76), and 0.71 (0.58,0.87) for KDM, and 0.50 (0.42,0.59), 0.64 (0.54,0.76), and 0.51 (0.44,0.58) for PA when compared with the lowest level. The findings were validated by the best-fitting dose-response curves for the associations. Among participants consuming dietary supplements (Pinteraction < 0.05), the positive effects of a healthy dietary pattern on biological aging were more pronounced. Systemic immune inflammation index (SII) and atherogenic index of plasma (AIP) were identified as being involved in and mediating the associations. Conclusions: Biological aging assessed through blood chemistry-based clinical biomarkers is negatively associated with diet quality. The anti-aging benefits of improving the diet may be due to its ability to reduce inflammation and lower blood lipids. [ABSTRACT FROM AUTHOR]

Background: Information on the influences of daily eating frequency (DEF) and nighttime fasting duration (NFD) on biological aging is minimal. Our study investigated the potential associations of DEF and NFD with accelerated aging. Methods: Out of 24212 participants in NHANES 2003–2010 and 2015–2018, 4 predicted age metrics [homeostatic dysregulation (HD), Klemera–Doubal method (KDM), phenoAge (PA), and allostatic load (AL)] were computed based on 12 blood chemistry parameters. Utilizing 24-h dietary recall, DEF was measured by the frequency of eating occurrences, while NFD was determined by assessing the timing of the initial and final meals throughout the day. Weighted multivariate linear regression models and restricted cubic spline (RCS) were utilized to examine the associations. Results: Compared to DEF of ≤ 3.0 times, subjects with DEF ≥ 4.6 times demonstrated lower KDM residual [β: -0.57, 95% confidence-interval (CI): (-0.97, -0.17)] and PA residual [β: -0.47, 95% CI: (-0.69, -0.25)]. In comparison to NFD between 10.1 and 12.0 h, individuals with NFD ≤ 10.0 h were at higher HD [β: 0.03, 95% CI: (0.01, 0.04)], KDM residual [β: 0.34, 95% CI: (0.05, 0.63)], and PA residual [β: 0.38, 95% CI: (0.18, 0.57)]. Likewise, those with NFD ≥ 14.1 h also had higher HD [β: 0.02, 95% CI: (0.01, 0.04)] and KDM residual [β: 0.33, 95% CI: (0.03, 0.62)]. The results were confirmed by the dose–response relationships of DEF and NFD with predicted age metrics. Lactate dehydrogenase (LDH) and globulin (Glo) were acknowledged as implicated in and mediating the relationships. Conclusions: DEF below 3.0 times and NFD less than 10.0 or more than 14.1 h were independently associated with higher predicted age metrics. [ABSTRACT FROM AUTHOR]