Association of dietary index for gut microbiota with premature and all-cause mortality: A mediation analysis of biological age.

This study investigated the association between the C-reactive protein-albumin-lymphocyte (CALLY) index and all-cause mortality and cardiovascular disease (CVD) mortality in patients with chronic kidney disease(CKD) while exploring biological aging as a potential mediator. This study included 4,515 patients with CKD from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2010. The CALLY index was assessed only at baseline. The association between the CALLY index and all-cause and cardiovascular mortality was analyzed using Cox proportional hazards models. Additionally, Kaplan–Meier curves, smooth curve fitting, segmented linear regression, and various subgroup and sensitivity analyses were performed. Finally, the mediating role of biological age acceleration was explored. Among 4,515 participants, with a median follow-up of 127 months and 2,264 recorded deaths, a higher Ln-CALLY was associated with a lower risk of all-cause mortality (HR = 0.845, 95% CI: 0.817, 0.874) and cardiovascular disease (CVD) mortality (HR = 0.860, 95% CI: 0.809, 0.915). Smoothed curve fitting and threshold effect analyses indicated that the effect was more pronounced when Ln-CALLY was lower than 4.96. Mediation analyses further revealed that biological age acceleration (BioAgeAccel) mediated 14.168% of the association between CALLY and all-cause mortality and 27.442% associated with CVD mortality. In the CKD population, CALLY values were negatively associated with all-cause and cardiovascular mortality, with BioAgeAccel significantly mediating this relationship.

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

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

Joint association of frailty index and biological aging with all-cause and cause-specific mortality: a population-based longitudinal cohort study.

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. 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. 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. 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.

To investigate the associations of metabolic score for insulin resistance (METS-IR) with all-cause and cardiovascular disease (CVD)-specific mortality and the potential mediating role of biological ageing. A cohort of 19 204 participants from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 was recruited for this study. Cox regression models, restricted cubic splines, and Kaplan-Meier survival curves were used to determine the relationships of METS-IR with all-cause and CVD-specific mortality. Mediation analyses were performed to explore the possible intermediary role of biological ageing markers, including phenotypic age (PhenoAge) and biological age (BioAge). During a median follow-up of 9.17 years, we observed 2818 deaths, of which 875 were CVD-specific. Multivariable Cox regression showed that the highest METS-IR level (Q4) was associated with increased all-cause (hazard ratio [HR] 1.38, 95% confidence interval [CI] 1.14-1.67) and CVD mortality (HR 1.52, 95% CI 1.10-2.12) compared with the Q1 level. Restricted cubic splines showed a nonlinear relationship between METS-IR and all-cause mortality. Only METS-IR above the threshold (41.02 μg/L) was positively correlated with all-cause death. METS-IR had a linear positive relationship with CVD mortality. In mediation analyses, we found that PhenoAge mediated 51.32% (p < 0.001) and 41.77% (p < 0.001) of the association between METS-IR and all-cause and CVD-specific mortality, respectively. For BioAge, the mediating proportions of PhenoAge were 21.33% (p < 0.001) and 15.88% (p < 0.001), respectively. This study highlights the detrimental effects of insulin resistance, as measured by METS-IR, on all-cause and CVD mortality. Moreover, it underscores the role of biological ageing in mediating these associations, emphasizing the need for interventions targeting both insulin resistance and ageing processes to mitigate mortality risks in metabolic disorders.

Circadian Syndrome (CircS) is a potent risk factor for adverse health outcomes. Its impact on biological aging, particularly among vulnerable populations like cancer survivors, and the pathways through which it influences mortality remain poorly understood. We analyzed data from 10,191 adults in the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Mortality data was accessed from the NHANES-linked National Death Index database. CircS was defined as a score ≥ 4 based on seven components. Biological aging was quantified using Klemera-Doubal Method Biological Age, Phenotypic Age, and their age-acceleration derivatives. Nonlinear regression with restricted cubic splines was used to assess the association between CircS and biological aging, stratified by cancer history. Causal mediation analysis within a Cox proportional hazards framework was used to investigate the mediating role of biological aging in the association between CircS and all-cause mortality, stratified by cancer history. The population had a median age of 49years, and median follow-up period of 90months, with 48.5% were males. CircS was significantly associated with advanced biological age across all four metrics (P < 0.001). This association was non-linear in cancer-free individuals, with a steeper increase at higher scores, but was linear in cancer survivors. In cancer-free population, the association between CircS and mortality was largely mediated by biological aging (proportion mediated: 79.1-100%, (P < 0.001). In cancer survivors, biological aging played suppressor mediating effect, where a detrimental indirect effect through accelerated aging masked a slightly protective direct effect of CircS on mortality. CircS is associated with accelerated biological aging. Biological aging is a critical pathway linking CircS to mortality, especially among cancer survivors. These findings highlight the importance of circadian health in the aging process and suggest that biological age may be a potential therapeutic target for mitigating mortality risk, particularly in cancer survivors.

BackgroundCardiometabolic index (CMI) is a comprehensive clinical parameter which integrates overweight and abnormal lipid metabolism. However, its relationship with all-cause, cardiovascular disease (CVD), and cancer mortality is still obscure. Thus, a large-scale cohort study was conducted to illustrate the causal relation between CMI and CVD, cancer, and all-cause mortality among the common American population.MethodsOur research was performed on the basis of National Health and Nutrition Examination Survey (NHANES) database, involving 40,275 participants ranging from 1999 to 2018. The formula of CMI is [waist circumference (cm) / height (cm)] × [triglyceride (mg/dL) / high-density lipoprotein cholesterol (mg/dL)]. Outcome variables consisted of CVD, cancer, and all-cause mortality, which were identified by the International Classification of Diseases (ICD)-10. The correlation between CMI and mortality outcomes was analyzed utilizing the Kaplan–Meier survival modeling, univariate/multivariate Cox regression analysis, smooth curve fitting analysis, threshold effect analysis, and subgroup analysis. Stratification factors for subgroups included age, race/ethnicity, sex, smoking behavior, drinking behavior, BMI, hypertension, and diabetes.ResultsThe baseline characteristics table includes 4,569 all-cause-induced death cases, 1,113 CVD-induced death cases, and 1,066 cancer-induced death cases. Without adjustment for potential covariates, significantly positive causal correlation existed between CMI and all-cause mortality (HR = 1.03, 95% CI 1.02,1.04, P-value<0.05), CVD mortality (HR = 1.04, 95% CI 1.03, 1.05, P-value<0.05) and cancer mortality(HR = 1.03, 95% CI 1.02, 1.05, P-value<0.05); whereas, after confounding factors were completely adjusted, the relationship lost statistical significance in CMI subgroups (P for trend>0.05). Subgroup analysis found no specific subgroups. Under a fully adjusted model, a threshold effect analysis was performed combined with smooth curve fitting, and the findings suggested an L-shaped nonlinear association within CMI and all-cause mortality (the Inflection point was 0.98); in particular, when the baseline CMI was below 0.98, there existed a negative correlation with all-cause mortality with significance (HR 0.59, 95% CI 0.43, 0.82, P-value<0.05). A nonlinear relation was observed between CMI and CVD mortality. Whereas, the correlation between CMI and cancer mortality was linear.ConclusionsAmong the general American population, baseline CMI levels exhibited an L-shaped nonlinear relationship with all-cause mortality, and the threshold value was 0.98. What’s more, CMI may become an effective indicator for CVD, cancer, and all-cause mortality prediction. Further investigation is essential to confirm our findings.

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA. Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts. A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations. All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

The dietary index for gut microbiota (DI-GM) is a newly proposed metric for assessing diet quality, and its relationship with biological age is unclear. We hypothesize that consuming foods conducive to a healthy gut microbiota environment may decelerate aging. This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2007 to 2018. The DI-GM was calculated by averaging the intakes from two 24-h dietary recall interviews. The biological age indicators were assessed using the Klemera-Doubal Method (KDM), phenotypic age (PA), and homeostasis disorder (HD). Logistic regression, restricted cubic splines (RCS), and mediation analysis were employed to explore the association between DI-GM and KDM, PA, and HD. The study included 20,671 participants. According to the logistic regression model, adjusting for all covariates, a negative association was observed between the DI-GM score and biomarkers of biological aging. Compared to participants in the lowest quartile for DI-GM scores, those in the highest quartile exhibited reduced odds ratio (OR) for all of the biological age indicators, namely biological age assessed via KDM (OR: 0.69, 95% CI: 0.60-0.79), PA (OR: 0.84, 95% CI: 0.73-0.97), and HD (OR: 0.86, 95% CI: 0.76-0.98). Additionally, RCS analysis revealed a nonlinear association between DI-GM and biological age. Mediation analysis showed that the body mass index (BMI) partly mediated the association between DI-GM and biological age. Therefore, we concluded that a higher DI-GM score is associated with a lower risk of accelerated aging, with BMI mediating this association. Future research should validate these findings through the use of longitudinal studies.

BackgroundThis study aimed to explore the associations of cardiometabolic index (CMI) with all-cause and cause-specific mortality among the overweight and obese population.MethodsMortality data for 13,674 participants with overweight or obesity were sourced from the National Death Index (NDI) and linked to the National Health and Nutrition Examination Survey (NHANES) datasets. We specifically examined the correlations of CMI with all-cause, premature, and cancer mortality. To ensure a comprehensive analysis, various statistical techniques were employed, including the Cox regression model, subgroup and sensitivity analysis, and restricted cubic spline (RCS) regression analysis. We also explored the potential mediating effect of inflammation-related indicators within these associations.ResultsAfter adjusting for all covariates, CMI remained positively associated with all-cause, premature, and cancer mortality among overweight and obese adults. For all-cause mortality, the hazard ratio (HR) was 1.14 [95% confidence interval (CI): 1.01–1.28, P = 0.041]. For premature mortality, the HR was 1.24 (95% CI: 1.08–1.42, P = 0.003). For cancer mortality, the HR was 1.33 (95% CI: 1.08–1.63, P = 0.006). When continues CMI was stratified into quartiles, significant correlations were maintained with all-cause mortality (P for trend = 0.003), premature mortality (P for trend = 0.006), and cancer mortality (P for trend = 0.007). Subgroup and sensitivity analyses indicated the robustness of results. Mediation analysis revealed that neutrophils mediated 16.27% of the correlation between CMI and all-cause mortality, and 11.01% of the association between CMI and premature mortality.ConclusionsElevated CMI is positively associated with all-cause, premature, and cancer mortality among overweight and obese adults. The associations appeared to be partially mediated by inflammatory pathways, suggesting a mechanism linking CMI to adverse health outcomes. These findings may offer valuable insights for early risk stratification and the formulation of intervention strategies within overweight and obese populations.

BACKGROUND: Hyperlipidemia is a major global public health issue and a significant risk factor for various chronic diseases, including cardiovascular disease and diabetes. Insulin resistance (IR) is closely associated with hyperlipidemia. Estimated glucose disposal rate (eGDR), a non-invasive tool for assessing IR, may have clinical utility in identifying hyperlipidemia and predicting its prognosis. METHODS: This study is a secondary analysis of retrospective cohort data based on publicly available databases, specifically the U.S. National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS)—incorporating both cross-sectional and longitudinal follow-up data to systematically evaluate the relationship between eGDR and the risk of hyperlipidemia and mortality. Multivariable weighted logistic regression models were employed to analyze the risk of hyperlipidemia, while Cox proportional hazards models were used to assess all-cause and cardiovascular disease (CVD) mortality. Generalized additive models and smooth curve fitting were applied to identify potential nonlinear relationships, and subgroup as well as sensitivity analyses were conducted to verify the robustness of the findings. RESULTS: In the NHANES cohort, each standard deviation increase in eGDR was associated with a 11.5% reduction in the risk of hyperlipidemia (OR = 0.885 [0.867, 0.903]), an 8.6% reduction in all-cause mortality (HR = 0.914 [0.892, 0.936]), and a 10.4% reduction in CVD mortality (HR = 0.896 [0.859, 0.936]). In the CHARLS cohort, each SD increase in eGDR was associated with a 7.1% reduction in the risk of hyperlipidemia (OR = 0.929 [0.905, 0.954]) and an 9.2% reduction in all-cause mortality (HR = 0.908 [0.869,0.949]). A nonlinear inverse relationship was observed between eGDR and the risk of hyperlipidemia, with evidence of a significant threshold effect. Kaplan–Meier survival curves demonstrated significantly lower all-cause and CVD mortality among individuals with higher eGDR levels. Stratified analyses indicated that eGDR showed strong consistency and predictive value across different population subgroups, with particularly pronounced effects observed among younger individuals and those with diabetes. CONCLUSION: Our study demonstrates that eGDR, as an indicator of insulin sensitivity, is significantly associated with the risk of hyperlipidemia and mortality, including both all-cause and CVD mortality. Improving eGDR levels may help reduce the health burden associated with hyperlipidemia and supports its potential clinical application in hyperlipidemia management and metabolic disease risk assessment.

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

BackgroundCardiovascular-Kidney-Metabolic (CKM) syndrome typically commences with the interaction of insulin resistance (IR), excessive or dysfunctional obesity, and the consequent systemic inflammatory response and oxidative stress. The relationship between the triglyceride-glucose (TyG) index and TyG-related indices that may simply assess IR and obesity, as well as the mortality risk in the CKM syndrome population, remains ambiguous.MethodsThis study included 6,383 participants from the National Health and Nutrition Examination Survey (NHANES) 2009–2018. The TyG index, TyG-waist-to-height ratio (TyG-WHtR), TyG-waist circumference (TyG-WC), and TyG-body mass index (TyG-BMI) were developed. Cox proportional hazards models, smooth curve fitting, and two-stage Cox proportional hazards models were employed to examine the association of TyG and TyG-related indices with all-cause and cardiovascular mortality in the CKM syndrome population. Subgroup analyses and interaction tests were conducted to evaluate the risk within various demographics.ResultsIn survey-weighted multifactorial regression analyses, a significant positive association existed between TyG, TyG-related indices, and both all-cause mortality and cardiovascular mortality, except for the TyG index, which did not demonstrate a significant link with all-cause mortality. Of these indices, the TyG-WC index exhibited the strongest correlation with all-cause mortality, with a hazard ratio (HR) of 1.50 and a 95% confidence interval (CI) of 1.18–1.92, followed by the TyG-WHtR index (HR: 1.45, 95%CI 1.13–1.85). The TyG-WHtR index demonstrated the strongest correlation with cardiovascular mortality (HR: 1.85, 95% CI 1.19–2.86), followed by the TyG-WC index(HR: 1.83, 95%CI 1.21–2.78). An L-shaped association was identified between TyG-WHtR, TyG-BMI, and all-cause mortality in CKM syndrome during the examination of nonlinear relationships (both P for log-likelihood ratio < 0.05). The TyG-WHtR, TyG-WC, and TyG-BMI indices exhibited a more pronounced correlation with all-cause mortality in those with CKM syndrome stages 1 and 3 (P value < 0.05, P for interaction < 0.05).ConclusionOur study emphasizes the association between TyG and TyG-related indices and mortality in individuals with CKM syndrome stages 0–3. Individuals with CKM syndrome stages 1 and 3 should be more vigilant to abnormal alterations in TyG-related indices.

The associations of polybrominated diphenyl ethers (PBDEs) with biological aging are unclear. This study explores the possible relationship between PBDEs and accelerated aging. Cross‐sectional data from 6,091 subjects of the National Health and Nutrition Examination Survey (NHANES) 2005–2010 and 2015–2016 are analyzed. Serum PBDE concentrations are quantified via automated liquid‐liquid extraction and subsequent sample purification, with seven PBDEs displaying a capture rate higher than 70%, identified as the exposure. Homeostatic dysregulation (HD), Klemera–Doubal method (KDM), phenoAge (PA), and allostatic load (AL) are utilized to assess biological aging. The associations are assessed with weighted multivariate linear regression models, restricted cubic spline (RCS), weighted quantile sum regression, and Quantile G‐computation analysis. Regarding individual exposures, significant positive associations of PBDE47, PBDE99, PBDE100, PBDE154, and PBDE85 with HD, KDM residual, and PA residual, and PBDE100 with AL (β > 0, P < 0.050) are detected. The associations are further validated by RCS. Mixed PBDEs show a positive relationship with HD, KDM residual, PA residual, and AL (β > 0, P < 0.050), with PBDE99, PBDE47, and PBDE85 as the most significant contributing PBDEs. Exposure to the PBDE mixture exhibits a positive association with predicted age metrics, highlighting PBDE99, PBDE47, and PBDE85 as the significant chemicals.