Association of the newly proposed dietary index for gut microbiota and depression: the mediation effect of phenotypic age and body mass index.

BACKGROUND: Gut microbiota and depression have garnered attention. The dietary index for gut microbiota (DI-GM) is a newly proposed index that reflects the diversity of gut microbiota, yet its association with depression remains unstudied. METHODS: Data from the National Health and Nutrition Examination Survey were analyzed. Depression was assessed using Patient Health Questionnaire (PHQ-9). Dietary recall data were used to calculate the DI-GM (including components beneficial and unfavorable to gut microbiota). Multivariable weighted logistic and linear regression were employed to investigate the association of DI-GM with depression and total PHQ-9 score. The potential mediating role of phenotypic age and body mass index (BMI) was explored. Secondary analyses included subgroup analyses, restricted cubic spline (RCS), and multiple imputation. RESULTS: A higher DI-GM and beneficial gut microbiota score were associated with a lower prevalence of depression (DI-GM: OR = 0.94, 95% CI = 0.89, 0.99; beneficial gut microbiota score: OR = 0.88, 95% CI = 0.82, 0.94) and lower total PHQ-9 score (DI-GM: ß=-0.09, 95% CI=-0.14, -0.04; beneficial gut microbiota: ß=-0.15, 95% CI=-0.21, -0.08). RCS indicated a non-linear relationship between DI-GM and depression. A significant mediating effect of phenotypic age (proportion of mediation: 19.81%, 95% CI: 12.86-63.00%) and BMI (proportion of mediation: 16.49%, 95% CI: 12.87-62.00%) was observed. CONCLUSIONS: The newly proposed DI-GM was negatively associated with the prevalence of depression and total PHQ-9 score. Mediation analyses demonstrated a significant mediating effect of phenotypic age and BMI.

Identifying biological markers and sociodemographic factors that influence the gap between phenotypic and chronological ages.

INTRODUCTION: The world's population is aging rapidly, leading to increased public health and economic burdens due to age-related cardiovascular and neurodegenerative diseases. Early risk detection is essential for prevention and to improve the quality of life in elderly individuals. Plus, health risks associated with aging are not directly tied to chronological age, but are also influenced by a combination of environmental exposures. Past research has introduced the concept of "Phenotypic Age," which combines age with biomarkers to estimate an individual's health risk. METHODS: This study explores which factors contribute most to the gap between chronological and phenotypic ages. We combined ten machine learning regression techniques applied to the NHANES dataset, containing demographic, laboratory and socioeconomic data from 41,474 patients, to identify the most important features. We then used clustering analysis and a mixed-effects model to stratify by sex, ethnicity, and education. RESULTS: We identified 28 demographic, biological and environmental factors related to a significant gap between phenotypic and chronological ages. Stratifying for sex, education and ethnicity, we found statistically significant differences in the outcome distributions. CONCLUSION: By showing that health risk prevention should consider both biological and sociodemographic factors, we offer a new approach to predict aging rates and potentially improve targeted prevention strategies for age-related conditions.

Phenotypic age mediates effects of Life's Essential 8 on reduced mortality risk in US adults.

Objective: This study aimed to find out whether phenotypic age could mediate the protective effects of a healthy lifestyle on mortality. Methods: We included adult participants with available data for individual phenotypic age (PhenoAge) and Life's Essential 8 (LE8) scores from the National Health and Nutrition Examination Survey 2005-2010 (three cycles) and linked mortality records until 31 December 2019. Adjusted hazard ratios (HR) were estimated to evaluate the associations of PhenoAge and LE8 scores with all-cause and cardiovascular mortality risk. Mediation analyses were performed to estimate the proportional contribution of PhenoAge to the effect of LE8 on mortality risks. Results: A 1-year increment in PhenoAge was associated with a higher risk of all-cause (HR = 1.04 [95% confidence interval, 1.04-1.05]) and cardiovascular (HR = 1.04 [95% confidence interval, 1.04-1.05]) mortality, independent of chronological age, demographic characteristics, and disease history. High level of LE8 (score: 80-100) was associated with a 3.30-year younger PhenoAge. PhenoAge was estimated to mediate 36 and 22% of the effect of LE8 on all-cause and cardiovascular mortality, respectively (all P < 0.001). As for single-metric scores of LE8, PhenoAge mediated 30%, 11%, 9%, and 7% of the effects of the healthy diet, smoking status, blood pressure, and physical activity on all-cause mortality risk, respectively (all P < 0.05). Conclusion: Adherence to LE8 recommendations slows phenotypic aging. PhenoAge could mediate the effect of LE8 on mortality risk.

Dose-response relationship of dietary Omega-3 fatty acids on slowing phenotypic age acceleration: a cross-sectional study.

Purpose: This study investigates the association between dietary Omega-3 fatty acid intake and accelerated phenotypic aging, referred to as PhenoAgeAccel. PhenoAgeAccel is defined as the difference between phenotypic biological age, calculated using blood biochemical markers, and chronological age. This study assesses the potential of Omega-3 intake to slow biological aging and its implications for public health. Methods: Utilizing data from the NHANES from 1999 to 2018, this cross-sectional study included 20,337 adult participants. Through a nationally representative sample combined with comprehensive phenotypic age calculation methods, a cross-sectional analysis of Omega-3 fatty acid intake and accelerated phenotypic aging was conducted. Weighted generalized linear regression models and restricted cubic spline analyses were applied to explore the potential non-linear relationships between them. Threshold effects were further clarified through piecewise regression models, and the impact of different demographic and health characteristics was evaluated through interaction effect tests. Results: After adjusting for various potential confounding factors, a significant negative correlation was found between Omega-3 fatty acid intake and PhenoAgeAccel (ß = -0.071; 95% CI: -0.119, -0.024; p = 0.004), indicating that an increase in Omega-3 intake is associated with a slowdown in PhenoAgeAccel. Specifically, for each unit increase in Omega-3 intake, the accelerated phenotypic aging decreased by an average of 0.071 units, revealing a significant linear negative correlation between Omega-3 intake and PhenoAgeAccel. Moreover, threshold effect analysis identified an Omega-3 fatty acid intake threshold (1.103 grams/day), beyond which the impact of Omega-3 intake on accelerated phenotypic aging tends to stabilize. Additionally, factors such as gender, age, race, and hypertension may influence the relationship between Omega-3 intake and PhenoAgeAccel, suggesting individual dietary guidance needs in different populations. Conclusion: This study highlights the potential role of dietary Omega-3 fatty acids in regulating PhenoAgeAccel and supports the strategy of delaying the aging process through dietary interventions to increase Omega-3 intake. The findings of this study contributes to the development of precise nutritional intervention strategies for different populations to optimize healthy longevity.

α-Klotho: the hidden link between dietary inflammatory index and accelerated ageing.

Recent studies suggest an association between greater dietary inflammatory index (DII) and higher biological ageing. As α-Klotho has been considered as a longevity protein, we examined whether α-Klotho plays a role in the association between DII and ageing. We included 3054 participants from the National Health and Nutrition Examination Survey. The associations of DII with biological and phenotypic age were assessed by multivariable linear regression, and the mediating role of α-Klotho was evaluated by mediation analyses. Participants' mean age was 58·0 years (sd 11·0), with a median DII score of 1·85 and interquartile range from 0·44 to 2·79. After adjusting for age, sex, race/ethnicity, BMI, education, marital status, poverty income ratio, serum cotinine, alcohol, physical activity, a higher DII was associated with both older biological age and phenotypic age, with per DII score increment being associated with a 1·01-year increase in biological age (1·01 (95 % CI: 1·005, 1·02)) and 1·01-year increase in phenotypic age (1·01 (1·001, 1·02)). Negative associations of DII with α-Klotho (ß = -1·01 pg/ml, 95 % CI: -1·02, -1·006) and α-Klotho with biological age (ß= -1·07 years, 95 % CI: -1·13, -1·02) and phenotypic age (ß= -1·03 years, 95 % CI: -1·05, -1·01) were found. Furthermore, α-Klotho mediated 10·13 % (P < 0·001) and 9·61 % (P < 0·001) of the association of DII with biological and phenotypic age, respectively. Higher DII was associated with older biological and phenotypic age, and the potential detrimental effects could be partly mediated through α-Klotho.

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.

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.

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.

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 ß 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.

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 (ß = 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 (ß = 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.

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.

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 ß (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.

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.

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.

Better Life's Essential 8 contributes to slowing the biological aging process: a cross-sectional study based on NHANES 2007-2010 data.

Objective: To investigate the relationship between Life's Essential 8 (LE8) and Phenotypic Age Acceleration (PhenoAgeAccel) in United States adults and to explore the impact of LE8 on phenotypic biological aging, thereby providing references for public health policies and health education. Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2010, this cross-sectional study analyzed 7,339 adults aged 20 and above. Comprehensive assessments of LE8, PhenoAgeAccel, and research covariates were achieved through the integration of Demographics Data, Dietary Data, Laboratory Data, and Questionnaire Data derived from NHANES. Weighted generalized linear regression models and restricted cubic spline plots were employed to analyze the linear and non-linear associations between LE8 and PhenoAgeAccel, along with gender subgroup analysis and interaction effect testing. Results: (1) Dividing the 2007-2010 NHANES cohort into quartiles based on LE8 unveiled significant disparities in age, gender, race, body mass index, education level, marital status, poverty-income ratio, smoking and drinking statuses, diabetes, hypertension, hyperlipidemia, phenotypic age, PhenoAgeAccel, and various biological markers (p < 0.05). Mean cell volume demonstrated no intergroup differences (p > 0.05). (2) The generalized linear regression weighted models revealed a more pronounced negative correlation between higher quartiles of LE8 (Q2, Q3, and Q4) and PhenoAgeAccel compared to the lowest LE8 quartile in both crude and fully adjusted models (p < 0.05). This trend was statistically significant (p < 0.001) in the full adjustment model. Gender subgroup analysis within the fully adjusted models exhibited a significant negative relationship between LE8 and PhenoAgeAccel in both male and female participants, with trend tests demonstrating significant results (p < 0.001 for males and p = 0.001 for females). (3) Restricted cubic spline (RCS) plots elucidated no significant non-linear trends between LE8 and PhenoAgeAccel overall and in gender subgroups (p for non-linear > 0.05). (4) Interaction effect tests denoted no interaction effects between the studied stratified variables such as age, gender, race, education level, and marital status on the relationship between LE8 and PhenoAgeAccel (p for interaction > 0.05). However, body mass index and diabetes manifested interaction effects (p for interaction < 0.05), suggesting that the influence of LE8 on PhenoAgeAccel might vary depending on an individual's BMI and diabetes status. Conclusion: This study, based on NHANES data from 2007-2010, has revealed a significant negative correlation between LE8 and PhenoAgeAccel, emphasizing the importance of maintaining a healthy lifestyle in slowing down the biological aging process. Despite the limitations posed by the study's design and geographical constraints, these findings provide a scientific basis for the development of public health policies focused on healthy lifestyle practices. Future research should further investigate the causal mechanisms underlying the relationship between LE8 and PhenoAgeAccel and consider cross-cultural comparisons to enhance our understanding of healthy 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.

Association of Physical Activity with Phenotypic Age among Populations with Different Breakfast Habits.

BACKGROUND: The global aging situation has reached a serious stage, and healthy lifestyles, like regular physical activity and eating breakfast, could slow the process. Phenotypic age (PhenoAge) is regarded as a novel measure of aging. Therefore, our study aimed to quantify the impact of physical activity and eating breakfast on aging via PhenoAge and phenotypic age acceleration (PhenoAgeAccel). METHODS: A total of 3719 adults who participated in the National Health and Nutrition Examination Survey were involved in this study. Physical activity was divided into an active group and an inactive group. According to the number of reported breakfast recalls, eating breakfast was divided into the no recalls group, one recall group, and both recalls group. Sensitivity analysis was performed by stratified analysis. RESULTS: Active physical activity was a protective factor for PhenoAge and PhenoAgeAccel. Compared to the inactive group, the ß values of the active group were -8.36 (-10.09, -6.62) for PhenoAge and -1.67 (-2.21, -1.14) for PhenoAgeAccel. The stratified analysis results showed that in the groups reporting breakfast in both recalls, one recall, and no recalls, the ß values of the active group were -8.84 (-10.70, -6.98), -8.17 (-12.34, -4.00), and -3.46 (-7.74, 0.82), respectively, compared to the inactive group. CONCLUSIONS: Active physical activity was strongly correlated with lower values of PhenoAge and PhenoAgeAccel, but the association was no longer statistically significant when combined with not regularly eating breakfast.

Inverted U-shaped relationship between sleep duration and phenotypic age in US adults: a population-based study.

Sleep is a modifiable behavior that can be targeted in interventions aimed at promoting healthy aging. This study aims to (i) identify the sleep duration trend in US adults; (ii) investigate the relationship between sleep duration and phenotypic age; and (iii) explore the role of exercise in this relationship. Phenotypic age as a novel index was calculated according to biomarkers collected from US adults based on the National Health and Nutrition Examination Survey (NHANES). Sleep information was self-reported by participants and discerned through individual interviews. The principal analytical method employed was weighted multivariable linear regression modeling, which accommodated for the complex multi-stage sampling design. The potential non-linear relationship was explored using a restricted cubic spline (RCS) model. Furthermore, subgroup analyses evaluated the potential effects of sociodemographic and lifestyle factors on the primary study outcomes. A total of 13,569 participants were finally included in, thereby resulting in a weighted population of 78,880,615. An examination of the temporal trends in sleep duration revealed a declining proportion of individuals with insufficient and markedly deficient sleep time since the 2015-2016 cycle. Taken normal sleep group as a reference, participants with extreme short sleep [ß (95% CI) 0.582 (0.018, 1.146), p = 0.044] and long sleep [ß (95% CI) 0.694 (0.186, 1.203), p = 0.010] were both positively associated with phenotypic age using the fully adjusted model. According to the dose-response relationship between sleep duration and phenotypic age, long sleep duration can benefit from regular exercise activity, whereas short sleep duration with more exercise tended to have higher phenotypic age. There is an inverted U-shaped relationship between short and long sleep durations and phenotypic age. This study represents an important step forward in our understanding of the complex relationship between sleep and healthy aging. By shedding light on this topic and providing practical exercise recommendations for promoting healthy sleep habits, researchers can help individuals live longer, healthier, and more fulfilling lives.

Association between dietary and behavioral-based oxidative balance score and phenotypic age acceleration: a cross-sectional study of Americans.

OBJECTIVES: In light of the rise in the global aging population, this study investigated the potential of the oxidative balance score (OBS) as an indicator of phenotypic age acceleration (PhenoAgeAccel) to better understand and potentially slow down aging. METHODS: Utilizing data from the National Health and Nutrition Examination Survey collected between 2001 and 2010, including 13,142 United States adults (48.7% female and 51.2% male) aged 20 and above, OBS and PhenoAgeAccel were calculated. Weighted generalized linear regression models were employed to explore the associations between OBS and PhenoAgeAccel, including a sex-specific analysis. RESULTS: The OBS demonstrated significant variability across various demographic and health-related factors. There was a clear negative correlation observed between the higher OBS quartiles and PhenoAgeAccel, which presented sex-specific. RESULTS: the negative association between OBS and PhenoAgeAccel was more pronounced in male than in female. An analysis using restricted cubic splines revealed no significant non-linear relationships. Interaction effects were noted solely in the context of sex and hyperlipidemia. CONCLUSIONS: A higher OBS was significantly associated with a slower aging process, as measured by lower PhenoAgeAccel. These findings underscore the importance of OBS as a biomarker in the study of aging and point to sex and hyperlipidemia as variables that may affect this association. Additional research is required to confirm these results and to investigate the biological underpinnings of this relationship.