Physical activity reduces cardiovascular risk in type 2 diabetes mellitus (T2DM), yet the intensity-specific metabolic mechanisms underlying this protection remain poorly characterized. Using the UK Biobank prospective cohort, we included 6,887 T2DM patients with complete physical activity and plasma NMR metabolomics data. Three intensity-specific metabolic signature scores (walking, moderate-, and vigorous-intensity) were constructed via elastic net regression. Restricted cubic spline Cox regression was used to model dose–response relationships between total physical activity and major adverse cardiovascular events (MACE). Multi-level mediation analyses quantified the proportion of the exercise–MACE association statistically accounted for by each intensity-specific metabolic pathway. Over a median follow-up of 15.17 years, 974 MACE events occurred. Total physical activity was inversely associated with MACE risk in a dose–response manner (P overall = 0.007), plateauing at approximately 3,500–4,000 MET-min/week. Compared with the low activity group, moderately active and highly active groups showed 17
Body mass index (BMI) alone inadequately captures the heterogeneous cardiometabolic risk associated with obesity. In four Chinese cohorts(N = 175,642), BMI–biomarker phenotypic profiles were identified using residual-based clustering of seven routine biomarkers, revealing sex-specific metabolic patterns associated with type 2 diabetes(T2D), chronic kidney disease (CKD) and cardiovascular disease (CVD). The high triglycerides (HTG) profile increased T2D risk and the high blood pressure (HBP) profile increased CVD risk across sexes. Showing distinct sex differences, the high fasting glucose (HFG) profile elevated T2D risk in women (hazard ratio [HR] 2.38[2.03–2.78]) while increasing CVD risk in men (HR 1.12, 95
The obesity paradox, generally defined by body mass index (BMI), refers to the observation that individuals with overweight or obesity have lower mortality risk in certain conditions. However, inherent observational confounding and reverse causation may bias these findings, and whether genetic susceptibility shows the same pattern remains unclear. To provide an epidemiological and genetic context for the obesity paradox in cardiovascular-kidney-metabolic (CKM) syndrome, two-sample Mendelian randomization (MR) analyses were conducted to investigate the potential causal effects of higher BMI on susceptibility to six CKM conditions, and Global Burden of Disease (GBD) 2021 data were used to quantify the proportions of deaths from these conditions attributable to high BMI. Genome-wide association studies (GWAS) further characterized the genetic associations with CKM phenotypes. To investigate the obesity paradox among CKM stages, associations of measured BMI with all-cause and cardiovascular disease (CVD) mortality were evaluated in UK Biobank (n = 488,656) and NHANES (n = 13,114) using Cox regression, restricted cubic splines (RCS), and two-piecewise Cox models. In UK Biobank, a BMI genetic risk score (BMI-GRS) was conducted to examine whether genetic susceptibility to higher BMI exhibited mortality association patterns comparable to those observed for measured BMI. Exploratory proteomic mediation analyses were subsequently conducted among participants with advanced CKM stages. Genetically predicted higher BMI was associated with greater susceptibility to all six conditions, and GBD analyses indicated a corresponding high-BMI-attributable mortality burden. CKM GWAS showed enrichment of lipid and lipoprotein metabolism pathways. In both cohorts, measured BMI showed broadly U-shaped associations with mortality, with the fitted curve nadir generally shifting toward higher BMI as CKM stage advanced. For all-cause mortality, the estimated breakpoint increased from 25.88 to 27.06 kg/m2 across stages 2–4 in UK Biobank and from 22.10 kg/m2 in stage 0 to approximately 30 kg/m2 in stages 2–4 in NHANES, with similar trends for CVD mortality. Conversely, BMI-GRS showed approximately linear adverse associations with all-cause mortality (HR per 1-SD increment 1.05, 95
Cumulative exposure to metabolic abnormalities may provide a time-integrated measure of long-term metabolic burden as a complement to single baseline measurements. However, the comparative prognostic value of different cumulative metabolic indices for predicting incident cardiometabolic multimorbidity (CMM) in individuals with cardiovascular-kidney-metabolic (CKM) syndrome remains unclear. We conducted a prospective cohort study using data from the China Health and Retirement Longitudinal Study (CHARLS) including 3,788 non-diabetic adults with CKM stages 0–3 who were free of diabetes at both the 2011 baseline and the 2015 landmark. Six cumulative metabolic indices were calculated using repeated measurements obtained in 2012 and 2015. Associations with incident CMM were evaluated using Cox proportional hazards models and restricted cubic splines. Predictive performance was assessed using 5-year area under the receiver operating characteristic curve (AUC) derived from Cox model-predicted 5-year risks, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Joint analyses with renal function, subgroup analyses, and multiple sensitivity analyses were conducted to evaluate robustness. During a median follow-up of 5.0 years, 154 participants (4.1
The PREVENT equations represent a paradigm shift in cardiovascular risk assessment by incorporating cardiovascular-kidney-metabolic (CKM) health metrics. However, in individuals with CKM stages 0–3, the associations of TyG and TyG-obesity composite indices with all-cause mortality (ACM) and cardiovascular mortality (CVM), and whether they provide incremental predictive value beyond a PREVENT-variable-based model, remain unclear. We analyzed data from 12,947 adults with CKM stages 0–3 in the National Health and Nutrition Examination Survey (NHANES), with a median follow-up of 10.25 years. Seven indices were calculated: TyG alone and six TyG-obesity composite indices. Weighted Cox proportional hazards models assessed associations with ACM and CVM. Kaplan-Meier curves were used for ACM, and Aalen-Johansen cumulative incidence functions with cause-specific Cox and Fine-Gray models were used for CVM. Restricted cubic splines (RCS) examined nonlinearity, and Cox-based quantile g-computation (qgcomp) assessed the joint effect of all seven indices. Incremental predictive value beyond the PREVENT-variable-based model was evaluated using changes in C-statistic (ΔC), time-dependent AUC at 10 years, survival-based continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI), bootstrap resampling, calibration, and decision curve analysis. Subgroup, stage-specific and sensitivity analyses were also performed. During follow-up, 1,284 ACM and 342 CVM occurred. Kaplan-Meier and Aalen-Johansen curves showed that cumulative incidence increased across TyG quartiles for both ACM and CVM. After full adjustment, all seven indices were independently associated with ACM; TyG-BMI showed the strongest association (HR = 1.99, 95
