OBJECTIVE:Loneliness undergoes changes over time, but its cumulative effect on frailty is unknown. We aimed to investigate the association between cumulative loneliness and frailty. METHODS:3781 participants from English Longitudinal Study of Ageing (ELSA) waves 2 to 9 were involved. Cumulative loneliness was evaluated by Cumulative Loneliness Index-Sum (CLI-Sum); Cumulative Loneliness Index-AUC (CLI-AUC) and Time-Weighted Average Loneliness Index (TWA-LI). Thirty-items frailty index (FI) was used to assess frailty status. Multivariate linear mixed models and cox proportional hazards models were utilized. RESULTS:Participants in the highest quartile (Quartile 4) of cumulative loneliness exhibited worse frailty compared to Quartile 1: Quartile 4 groups showed faster annual increases in FI, the β (95%CI) were as follows: 0.28 (0.12-0.45) for CLI-Sum, 0.20 (0.04-0.37) for CLI-AUC, and 0.28 (0.12-0.44) for TWA-LI. In addition, Quartile 4 groups associated with higher frailty risk, the hazard ratios (95%CI) were as follows: 1.53 (1.24-1.89) for CLI-Sum; 1.65 (1.34-2.02) for CLI-AUC and 1.61 (1.31-1.99) for TWA-LI and all P trend <0.05. DISCUSSION:Cumulative loneliness can significantly accelerate the frailty progression. Our study emphasizes the significance of improving duration of loneliness.

OBJECTIVES:The C-reactive protein-triglyceride glucose index (CTI) is a novel biomarker integrating insulin resistance and systemic inflammation, both of which are implicated in the pathogenesis of chronic kidney disease (CKD). This study aimed to investigate the association of CTI with CKD and the reduction of renal function. METHODS:This study used China Health and Retirement Longitudinal Study data from 2011 to 2015. The 2011 and 2015 cross-sectional studies included 5,242 and 8,056 participants aged ≥45 years, respectively. A total of 3,435 eligible subjects participated in the longitudinal cohort study. Logistic regression, linear regression, and restricted cubic splines were used to assess the association of CTI with CKD, estimated glomerular filtration rate, and reduction of renal function, with subgroup and interaction analyses performed. RESULTS:The study demonstrated a positive association between CTI and CKD in both 2011 (odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.21, 1.41; P < .001) and 2015 (OR = 1.44, 95% CI: 1.34, 1.56; P < .001) cross-sectional studies, with inverse associations observed with estimated glomerular filtration rate. In the longitudinal cohort study, there was a positive association of CTI with the incidence of new-onset CKD (OR = 1.26, 95% CI: 1.08, 1.47; P = .003) and reduction of renal function with CKD (OR = 1.71, 95% CI: 1.31, 2.25; P < .001). CONCLUSION:CTI levels are significantly associated with CKD and the reduction of renal function, indicating its potential as a clinical biomarker for CKD risk and renal changes in middle-aged and older Chinese.

Depression is a leading cause of mental and physical disability globally, with its onset and progression influenced by a complex interplay of dietary, psychological, and biological factors. Recent research suggests a link between sugar-sweetened beverage (SSB) consumption and depression risk, although the potential biological pathways underlying this association remain poorly understood. This study utilized data from 192,045 participants in the UK Biobank to examine the prospective association between SSB consumption and incident depression using Cox proportional hazards models. SSBs were defined as the sum of five beverage categories assessed via the Oxford WebQ 24-hour dietary recall. Directional consistency of the association was further examined across three external supporting datasets encompassing diverse populations: NHANES, YRBSS, and the Jiangsu Provincial Database. We further investigated whether proteins, metabolites, inflammatory markers, and brain imaging phenotypes may serve as candidate mediators statistically consistent with mediation of the SSB-depression association. High SSB consumption was associated with an 18

Large-scale metabolomics enables systematic profiling of circulating small-molecule metabolites and provides an integrative view of genetic, environmental, lifestyle, and microbial influences on human health, yet comprehensive evidence linking metabolites to diverse traits, diseases, comorbidity, prediction, and causality remains limited. Here, we analyze 251 Nuclear magnetic resonance (NMR)-derived plasma metabolites in 212,751 UK Biobank participants, with independent validation in 177,013 individuals from a newly released data wave. Testing across 884 health-related traits, 722 prevalent diseases, and 1137 incident diseases using multivariable regression and stringent Bonferroni correction, we identified 67,505 metabolite-trait, 21,982 metabolite-prevalent disease, and 41,214 metabolite-incident disease associations, with replication rates of 76.1%, 83.3%, and 74.0%, respectively. Associations were most concentrated in endocrine, metabolic, and circulatory diseases. Unsupervised clustering of disease-metabolite profiles revealed 10 and 12 disease clusters (for prevalent and incident diseases, respectively) that span International Classification of Diseases (ICD)-10 chapters yet share convergent metabolomic signatures, providing a metabolic basis for clinical comorbidity. Metabolite-based predictive models showed stronger performance for short-term than long-term outcomes, with creatinine as the most frequently selected predictor across prevalent disease models. Bidirectional Mendelian randomisation (MR) identified 61 putative metabolite-to-disease causal effects and 558 disease-driven metabolic alterations. This study provides a systematically validated plasma metabolomic atlas (https://net-matebolic.vercel.app/) linking metabolites with human traits and diseases, offering insights into comorbidity, risk prediction, and potential causal pathways.

Cardiovascular–kidney–metabolic (CKM) syndrome represents a complex interplay of cardiovascular, kidney, and metabolic disorders. Dietary factors, particularly those with pro-inflammatory and pro-oxidant properties, may influence disease progression and mortality, yet their role in the CKM syndrome population remains underexplored. To evaluate potential associations of the dietary inflammatory index (DII), the dietary oxidative balance score (DOBS), and mortality in individuals with CKM syndrome. A total of 17,317 individuals with CKM syndrome were enrolled and stratified into three groups: stage 0, stages 1–3, and stage 4. DII and DOBS were derived from 26 and 16 preselected dietary components, respectively. Kaplan–Meier analysis, Cox proportional-hazards models, and forest plots were used to examine the associations between dietary scores and mortality across CKM stages. No significant associations were found in stage 0. In stages 1–3 and 4, pro-inflammatory/pro-oxidant diets were associated with increased mortality, with HRs for all-cause, cardiovascular, and non-cardiovascular mortality of 1.46 (1.10–1.94), 1.42 (0.70–2.87), and 1.47 (1.09–1.98) in stages 1–3, and 2.07 (1.33–3.20), 2.20 (1.06–4.59), and 2.01 (1.25–3.24) in stage 4, respectively. Pro-inflammatory and pro-oxidant dietary patterns, as measured by DII and DOBS, are associated with an increased risk of all-cause and non-cardiovascular mortality in both CKM stages 1–3 and stage 4, and with cardiovascular mortality specifically in stage 4. No significant association with cardiovascular mortality was observed in stages 1–3.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its metabolic underpinnings remain poorly understood. Here we integrated multimodal neuroimaging (MRI), plasma metabolomics, and genomic data from the UK Biobank to identify metabolic markers of brain aging and evaluate their causal relevance. Using 1079 imaging-derived phenotypes (IDPs) from 4333 healthy participants, we trained and validated machine learning models for brain age prediction, with a least absolute shrinkage and selection operator (LASSO) regression model achieving the best performance (mean absolute error = 3.26 years, R² = 0.68). Brain age gap (BAG) was then estimated in 37,458 participants. Association analyses in 21,780 individuals identified nine plasma metabolites significantly linked to BAG after Bonferroni correction, with glucose showing the strongest effect (β = 0.32, P = 9.90 × 10⁻¹²). Genome-wide association studies (GWAS) identified 392 BAG-associated single-nucleotide polymorphisms (SNPs) (P < 5 × 10⁻⁸), and two-sample Mendelian randomization (MR) provided evidence supporting a potential causal role of glucose in accelerating brain aging. Clinically, elevated plasma glucose was positively associated with seven brain disorders, including all-cause dementia, Alzheimer's disease, vascular dementia, Parkinson's disease, stroke, depression, and anxiety, and negatively associated with cognitive performance, movement function, and mental health outcomes. Higher glucose concentrations were also associated with reduced regional brain volumes across 80 cortical, subcortical, and cerebellar regions. These findings implicate glucose metabolism as a modifiable pathway in brain aging, with implications for early intervention strategies aimed at preserving brain health across the lifespan.

ABSTRACT Background Little is known about the joint associations between trajectories of frailty and depression with cognitive function. This study aims to explore the multitrajectories of frailty and depression and their joint impact on cognition. Methods A total of 8600 participants from the Health and Retirement Study (HRS) (1996–2018) were analysed using a group‐based trajectory model for 10‐year multitrajectories. Participants were classified into five groups based on their trajectories. Multivariable linear mixed models and Cox proportional hazards models were utilized. Results Compared with Group 1 (stable robust and nondepressed), Groups 2 (‘worsening prefrailty without depression,’ β = −0.022 SD/year), 3 (‘stable prefrailty with escalating depressive symptoms,’ β = −0.016 SD/year), 4 (‘increasing frailty alongside worsening depressive symptoms,’ β = −0.034 SD/year) and 5 (‘high and escalating frailty with persistent depression,’ β = −0.055 SD/year) exhibited accelerated cognitive decline. Dementia risk was significantly higher in G2 (HR = 1.26, 95% CI: 1.08–1.48), G3 (HR = 1.54, 95% CI: 1.31–1.80), G4 (HR = 1.81, 95% CI: 1.54–2.14) and G5 (HR = 1.86, 95% CI: 1.48–2.33) compared with G1. Conclusions Worsening frailty and depression accelerate cognitive decline and risk of dementia, underscoring the need to address both conditions to mitigate cognition.

This study was conducted to evaluate the relationship between 17 urinary metabolites of volatile organic compounds (mVOCs) in adolescents and renal function parameters (estimated glomerular filtration rate (eGFR), albumin-to-creatinine ratio (ACR), urinary albumin, serum uric acid (SUA), and blood urea nitrogen (BUN)). In adjusted generalised linear models (GLM), mVOCs were positively correlated with eGFR, urinary albumin, and BUN, and mVOCs were negatively correlated with ACR and SUA. Weighted Quartile Sum (WQS) index correlated with eGFR [β(95%CI): 0.040 (0.028, 0.052)], urine albumin [β(95%CI): 0.275 (0.203, 0.622)], SUA [β(95%CI): 0.040 (0.025, 0.055)] and BUN [β(95%CI): 0.102 (0.082, 0.122)]. In Bayesian Kernel Machine Regression (BKMR) model, total compound effect was positively correlated with eGFR, positive associations were observed in high concentration of the mixture with urine albumin and ACR. Findings suggest that single and mixed exposures to mVOCs may affect renal parameters in adolescents.

This study is intended to examine the connection between residential environmental quality and lung function as well as its deterioration. To this end, we used data from 2011 to 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). A total of 7,129 subjects aged ≥45 years participated in the cross-sectional study, with 5,009 subjects completing four-year follow-up. Linear and logistic regression were applied to explore associations of living environmental quality with PEF, PEF% pred, and severe airflow limitation. Additionally, linear regression was also employed to investigate the relationship between living environmental quality and the annual decline in PEF and PEF% pred, complemented by subgroup analyses. Additionally, sensitivity analyses were conducted. The results showed that, compared to high-risk living environments, both middle- and low-risk living environments showed positive associations with PEF and PEF% pred, and effectively relieve severe airflow limitation. Furthermore, the longitudinal analysis indicated that the low-risk living environment is significantly associated with a slow decline in PEF (β = -3.07, 95%CI: -5.13 ~ -1.01, P = 0.003) and PEF% pred (β = -0.009, 95%CI: -0.015 ~ -0.004, P = 0.001), particularly among urban residents. In summary, a higher quality of life environment score is associated with preserved lung function and a slower decline.

OBJECTIVES:A physical activity paradox is suggested by recent evidence that leisure-time physical activity (LTPA) is beneficial, whereas it may be detrimental to those with high occupational physical activity (OPA) levels. We aimed to investigate the association of domain-specific PA on the age of the whole body and organs (heart, kidneys, and liver). STUDY DESIGN:The analysis utilized data from NHANES, a cross-sectional study with 14,168 enrolled adult participants. METHODS:Domain-specific PA (i.e., Leisure, Transportation, and Occupation) was assessed using the Global Physical Activity Questionnaire (GPAQ). Biological age was calculated from circulating biomarkers. To quantify differences in physiological age between participants, biological age acceleration (BAA) was assessed. Linear regression analysis was used to assess the association of domain-specific PA with BAA of the whole body and organs (heart, kidney, and liver). RESULTS:The multivariate linear model showed that total PA was negatively associated with whole-body BAA (β -0.47, 95 % CI (-0.71, -0.22)). In addition, LTPA was negatively associated with whole-body BAA (β -0.41, 95 % CI (-0.60, -0.21)) and liver BBA (β -1.20, 95 % CI (-2.05, -0.35)). Whereas OPA was positively associated with heart BAA (β 1.69, 95 % CI (1.05, 2.33)), and we found that only participants who performed OPA≥300 min/week were positively associated with heart BAA (β 1.97, 95 % CI (1.27, 2.66)). CONCLUSIONS:LTPA was associated with slower whole-body and live BAA, while high OPA is associated with higher heart BAA. Associations between domain-specific physical activity and biological aging may differ across activity domains.

Phthalates have raised concerns on health outcomes including depression, due to its ubiquity. Knowledge is lacking on the role of modifiable lifestyle in attenuating phthalates' adverse effects. We aimed to evaluate the interaction effects of lifestyle with urinary phthalate metabolites (UPMs) on depression. A total of 3588 participants aged ≥ 20 from the National Health and Nutrition Examination Survey 2011-2018 were involved. We used multivariate logistic regression models and Bayesian Kernel Machine Regression models to evaluate the associations of UPMs (individual or mixture) and lifestyle with depression. Positive associations of individual UPMs and its mixture with depression were observed in total population and participants maintaining an unfavorable lifestyle. No such association was found in participants with a healthy lifestyle. Interactions between lifestyle category with MECPP (P for interaction = 0.028), and ΣDEHP (P for interaction = 0.087) on depression were observed. Additionally, smoking, alcohol consumption and physical activity in healthy levels showed the greatest effect against depression among the common lifestyle combinations. In conclusion, positive associations of UPMs with depression risk, and interaction effects of lifestyle and UPMs on depression were observed. Our findings indicate that healthy lifestyle might weaken the adverse effects of phthalate exposure on depression risk.

BACKGROUND:Overactive bladder (OAB) is a syndrome marked by urinary urgency. Given the crucial role of metabolic anomalies in the pathogenesis of OAB, the aim of this study was to investigate the associations between different triglyceride glucose index (TyG)-related indicators and OAB. METHODS:9024 participants aged ≥ 20 years from NHANES 2005-2018 were involved. Weighted multivariate logistic regression was employed to assess the relationship between three TyG-related indicators and OAB with subgroup and interaction analyses. In addition, ROC, DeLong's test and confusion matrix were further utilized to assess the predictive power of different indicators for OAB in the total population versus different subgroups of the population. RESULTS:TyG-related indicators were positively associated with OAB. The associations were statistically different in age and physical activity subgroups (all p for interaction < 0.1). In the whole population, TyG-WHtR demonstrated the highest predictive ability, with the largest AUC of 0.625 (95 %CI: 0.609, 0.641), and was relatively more predictive in the < 60 years and moderate-to-vigorous physical activity subgroups. CONCLUSIONS:Positive associations of TyG-related indicators with OAB were observed. TyG-WHtR has the strongest predictive performance for OAB in the total population.

BACKGROUND:The burden of high high body mass index (BMI) in adolescent and young adults (AYA) is largely unknown. Therefore, we aim to assess this burden. METHODS:Data were extracted from GBD 2021. Age-standardized mortality, DALY rates (ASMR, ASDR) and estimated annual percentage change (EAPC) were used to describe the burden. Pearson's correlation coefficient was used to evaluate the correlation between the sociodemographic index (SDI) and ASMR/ASDR. RESULTS:From 1990 to 2021, the death and DALY number attributable to high BMI in AYA had increased by 109 % and 141 % respectively. Low-middle SDI regions showed the most significant upward trend (EAPC = 1.37 for ASMR and 1.97 for ASDR). All diseases caused by high BMI showed a upward trend except for asthma and leukemia. ASMR of this burden was negatively correlated with the SDI (r = -0.13, p < 0.001), while the relationship between ASDR and SDI was opposite (r = 0.23, p < 0.001). The burden of osteoarthritis caused by high BMI (r = 0.68, p < 0.001), low back pain (r = 0.67, p < 0.001), gout (r = 0.62, p < 0.001) due to high BMI rose with SDI, which contributed to the severe DALY burden in high SDI regions. CONCLUSIONS:The burden of high BMI is still rising in AYA. Targeted measures need to be taken in different regions.

Evidence regarding the cumulative impact of exposure to diverse volatile organic compounds (VOCs) on frailty remains limited. We aimed to explore the association between VOCs and frailty, and to investigate the mediating effects of oxidative stress and inflammation. Our study involved 4,677 participants aged ≥20 from the National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression, weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were employed. We further conducted mediation analysis to explore the mediating role of oxidative stress and inflammation. Multivariable logistic regression revealed several VOCs were significantly correlated with frailty, particularly for N-Acetyl-S-(2-carboxyethyl)-L-cysteine (CEMA), N-Acetyl-S-(2-cyanoethyl)-L-cysteine (CYMA), N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine (DHBMA), mandelic acid (MA), N-Acetyl-S-(4-hydroxy-2-buten-1-yl)-l-cysteine (MHBMA3) and N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine (HPMMA). WQS regression confirmed synergistic effects of combined VOCs exposure on frailty (OR = 1.53, 95%CI: (1.28, 1.83)), with four mVOCs (DHBMA, CEMA, HPMMA and CYMA) contributing most substantially. BKMR analysis further identified this positive correlation, and the association was more significant in the older group. Mediation analysis showed these relationships were partially mediated by oxidative stress and inflammatory pathways. This study offers novel evidence for a positive correlation between VOCs and frailty, with DHBMA being the most significant contributor. Oxidative stress and inflammation potentially act as mediators.

BACKGROUND:Circadian syndrome (CircS) is a complex syndrome involving disorders of biological rhythms and multidimensional health risks. The objective of this research is to explore the association between CircS and functional disability. METHODS:This research utilized data from CHARLS, spanning the years 2011-2015. In the cross-sectional study, data from 12,758 Chinese adults aged 45 years and above in 2011 were used, and logistic regression models were employed to explore the association of CircS and its components with functional disability. A total of 8538 eligible subjects participated in the longitudinal study, and Cox regression models were used to analyze the longitudinal association and subgroup analysis. RESULTS:The cross-sectional analysis revealed positive associations of CircS with both ADL disability (OR = 1.68, 95% CI: 1.41, 2.00, p < 0.001) and IADL disability (OR = 1.74, 95% CI: 1.49, 2.04, p < 0.001). Longitudinal analysis also indicated that CircS was significantly associated with an elevated risk of developing both ADL disability (HR = 1.57, 95% CI: 1.33, 1.86, p < 0.001) and IADL disability (HR = 1.22, 95% CI: 1.05, 1.43, p = 0.012). Subgroup analysis found that the association between CircS and ADL disability had significant interactions with gender (p for interaction = 0.046) and smoking status (p for interaction < 0.001). CONCLUSION:Our findings find a positive association between CircS and functional disability, and the importance of gender and smoking status in these associations are emphasized.

BACKGROUND: Kidney dysfunction is an important modifiable risk factor for stroke, yet its attributable global burden remains understudied. This analysis quantifies its impact across demographics and projects future trends. METHODS AND RESULTS: Using Global Burden of Disease 2021 data, we analyzed stroke-related deaths and disability-adjusted life years (DALYs) attributable to kidney dysfunction globally, regionally, and nationally, stratified by age, sex, and socio-demographic index (SDI). Trends (1992?2021) were assessed via age-period-cohort (APC) modeling and estimated annual percentage change (EAPC). Contributions of aging, population growth, and epidemiological shifts were quantified through decomposition analysis. Bayesian models projected trends to 2040. Globally, age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life year rate (ASDR) declined (EAPC: -1.85% (95% CI -1.95 to -1.74) and -1.73% (95% CI -1.82 to -1.63)), yet absolute deaths and DALYs rose to 676,000 and 15.009 million in 2021. The burden surged after age 80, disproportionately affecting males and low-SDI regions. Middle-SDI regions showed the steepest declines, while Southern Sub-Saharan Africa experienced rising ASMR and ASDR. Projections suggest continued declines, particularly in females, though disparities persist. CONCLUSIONS: Despite global declines, stroke burden attributable to kidney dysfunction remains elevated in older males and low-SDI regions. Targeted interventions addressing kidney health and equitable healthcare access are critical to mitigating disparities and reducing future burden. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by Department of Finance of Jilin Province (grant number 2023SCI32?JLSWSRCZX2023-30),Jilin Provincial Natural Science Foundation(grant number YDZJ202501ZYTS315) and Jilin Province Tianhua Health Public Welfare Foundation(grant number J2023JKJ031). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: this study was exempt from ethics board review and did not require informed consent from individuals. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable The original data supporting this study were derived from the following publicly available sources: Global Burden of Disease (GBD) 2021 data: \[GBD Compare/Viz Hub\](https://vizhub.healthdata.org/gbd-results/). All raw data from these sources are de-identified and freely accessible without restrictions. Processed datasets generated during the current study (including age-period-cohort model parameters and regional stratification results) are available from the corresponding author upon reasonable request, subject to compliance with institutional data governance policies. Requests will be responded to within 4 weeks.

OBJECTIVE:The overwhelming majority of prediction models have not been applied. An evidence-based review is needed to show that the new research is justified. This study aimed to develop an assessment tool for researchers and peer reviewers to conduct a rapid and comprehensive evaluation on the necessity and feasibility of planning clinical prediction model before its startup. METHODS:The framework for developing quality assessment tools was followed to develop the necessity and Feasibility Assessment Tool of CLInical Prediction models for individual prognosis (FATCLIP). Firstly, the scope, framework, and item pool of the FATCLIP was identified by a steering group comprising 15 experts through a web-based meeting. Then, an iterative Delphi process was conducted to refine the FATCLIP, in which the Delphi group enrolled 34 experts from multidiscipline, including epidemiologists, statisticians, clinicians, evidence-based medicine specialists, health care administrators and academic journal editors. RESULTS:Through twice steering group meetings and 2 rounds of the Delphi process, the framework of FATCLIP was determined based on expert consensus, including 6 domains and 31 signaling questions. The six domains were as follows: prediction outcome, review of existing models, candidate predictors, data, development and validation, and application and extension. At the same time, the usage manual of FATCLIP was also presented. CONCLUSIONS:The FATCILP aims to assist researchers and peer reviewers to detect potential challenges during the development and application of the clinical prediction model for individual prognosis before its start-up, so that the research of clinical prediction models could be efficient and avoid research waste.

BACKGROUND:Accelerometer-derived physical activity is associated with reduced stroke risk. The biological pathways underpinning this relationship, however, are not yet understood. Herein, we aim to identify metabolic signatures associated with accelerometer-measured PA and investigate their relationships with reduced stroke incidence. METHOD:Utilizing UK Biobank accelerometer data, we derived physical activity into total physical activity (TPA), moderate-to-vigorous physical activity (MVPA), and light physical activity (LPA) and linked them to 249 NMR-quantified plasma metabolites. The metabolomic signatures (TPA-/MVPA-/LPA-metabolomic signatures) were developed through internal validation followed by elastic-net regression modeling. Cox proportional hazards models evaluated activity-stroke associations (adjusted for sociodemographic/genetic factors), followed by mediation analysis to quantify metabolomic signature effects. RESULTS:Through UK Biobank study (N = 29445; 14.1-year follow-up with 513 stroke events), we identified 195 TPA, 173 MVPA, and 164 LPA metabolite associations (FDR < 0.05), with 107, 92, and 15 validated, respectively. Elastic net-derived physical activity-metabolomic signatures (TPA-/MVPA-metabolomic signatures) correlated with physical activity intensities (r = 0.20-0.30, P < 0.001) and were associated with reduced stroke risk: TPA-metabolomic signatures (HR = 0.61, 95% CI: 0.44-0.87); MVPA-metabolomic signatures (HR = 0.50, 95%CI: 0.29-0.88). Mediation analyses showed TPA-metabolomic signatures and MVPA-metabolomic signatures explained 12.2% and 8.5% of physical activity-stroke associations (P < 0.001), implicating specific lipoprotein subclasses and lipids as key mediators. CONCLUSION:TPA-metabolomic signatures and MVPA-metabolomic signatures, particularly the 11 key metabolites included, significantly mediate the association between accelerometer-derived physical activity and stroke risk.

BACKGROUND:The association between the quality of low-carbohydrate diets (LCDs) and depression symptoms remains underexplored. This study investigates the effects of LCDs on depression symptoms, with a specific focus on distinguishing the quality and quantity of macronutrients. METHODS:In this cross-sectional study, 28,791 participants aged ≥20 were involved. Three LCD scores were constructed based the quality and quantity of macronutrients: overall LCDs (OLCDs), healthy LCDs (HLCDs: characterized by reduced intake of low-quality carbohydrates [e.g., refined sugars], higher plant-based proteins, and unsaturated fats), and unhealthy LCDs (ULCDs: characterized by reduced intake of high-quality carbohydrates [e.g., whole grains], higher animal proteins, and saturated fats). The associations between LCD patterns and depression symptoms were evaluated using multivariable logistic regression. A restricted cubic spline (RCS) regression model was used to estimate the dose-response relationship. RESULTS:Comparing extreme quartiles of HLCDs, the adjusted odds ratio (OR) (95 % CI) for depression was 0.70 (0.57, 0.86) (P-trend <0.001). No statistical significance was observed in ULCDs and depression symptoms. Non-linear relationships were identified for OLCDs (P-non-linear = 0.017), HLCDs (P-non-linear <0.001), with depression symptoms. CONCLUSION:Macronutrient quality modifies LCD-depression associations, with healthier patterns showing inverse correlations. Our finding indicates inverse association between HLCDs and depression risk. Further longitudinal or interventional studies are required to validate these findings and explore mechanistic pathways.

Purpose: This study aimed to examine independent association between inflammatory biomarkers and all-cause mortality as well as cardio-cerebrovascular disease (CCD) mortality among U.S. adults with diabetes. Methods: A cohort of 6412 U.S. adults aged 20 or older was followed from the start until December 31, 2019. Statistical models such as Cox proportional hazards model (Cox) and Kaplan-Meier (K-M) survival curves were employed to investigate the associations between the inflammatory biomarkers and all-cause mortality and CCD mortality. Results: After adjusting for confounding factors, the highest quartile of inflammatory biomarkers (NLR HR = 1.99; 95 % CI:1.54-2.57, MLR HR = 1.93; 95 % CI:1.46-2.54, SII HR = 1.49; 95 % CI:1.18-1.87, SIRI HR = 2.32; 95 % CI:1.81-2.96, nLPR HR = 2.05; 95 % CI:1.61-2.60, dNLR HR = 1.94; 95 % CI:1.51-2.49, AISI HR = 1.73; 95 % CI:1.4 1-2.12)) were positively associated with all-cause mortality compared to those in the lowest quartile. K-M survival curves indicated that participants with an inflammatory biomarker above a certain threshold had a higher risk of both all-cause mortality and CCD mortality (Log rank P < 0.05). Conclusion: Some biomarkers such as NLR, MLR, SII, AISI, SIRI, and dNLR, are significantly associated with all-cause mortality and CCD mortality among U.S. adults with diabetes. The risk of both outcomes increased when the biomarkers surpassed a specific threshold.