ObjectiveTo investigate the association between cardiometabolic indicators and the risk of incident renal insufficiency using a longitudinal physical examination cohort. MethodsBased on a large-scale, multi-center, repeated-measurement physical examination cohort in China, this study evaluated the impact of baseline cardiovascular metabolic indicators on incident renal insufficiency using logistic regression, Cox proportional hazards models, Poisson regression, and generalized linear mixed-effects models. Furthermore, latent class mixed models (LCMM) were employed to identify the longitudinal trajectories of these indicators, and the associations between distinct trajectory patterns and the risk of renal insufficiency were analyzed using Cox and logistic regression models. ResultsDerived from a massive longitudinal cohort of 680 000 individuals across a multi-center healthcare network, a total of 264 379 participants were ultimately included, among whom 82 849 developed renal insufficiency during the follow-up period. Baseline analyses revealed that elevated levels of triglycerides (TG) and fasting glucose (FG) consistently increased the risk of renal insufficiency across multiple statistical models (all PFDR<0.001). Trajectory analyses demonstrated that high-level longitudinal trajectories of FG (HR=1.274, 95%CI 1.230 to 1.320) and diastolic blood pressure (DBP) (HR=1.042, 95%CI 1.023 to 1.061) were significant independent risk factors for the disease. Notably, the evolutionary trajectory of systolic blood pressure (SBP) exhibited opposite association directions between the Cox and logistic models, whereas the TG trajectory lost its statistical significance after fully adjusting for smoking and alcohol consumption. ConclusionBoth baseline characteristics and longitudinal variations of cardiovascular metabolic indicators are significantly associated with the incidence of renal insufficiency. Abnormal levels and high-level evolutionary trajectories of fasting glucose and triglycerides serve as robust risk factors for the disease. Regular monitoring and longitudinal tracking of these metabolic profiles are of profound clinical value for the prevention and early identification of renal insufficiency.