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.
ObjectiveTo investigate the association between kidney function indicators in routine health examinations and the incidence of urinary stone disease using longitudinal repeated-measurement data. MethodsThis study was based on the large-scale longitudinal data in the WHALE study. Cox proportional hazards models were first applied to examine the associations between baseline kidney function indicators and incident urolithiasis. Subsequently, Poisson regression and mixed-effects models were used to analyze repeated-measurement data to further explore the relationships between kidney function indicators and urolithiasis. Latent class trajectory modeling was then employed to characterize longitudinal patterns of kidney function indicators. Based on the optimal trajectory models, logistic regression analyses and Cox regression analyses were performed to assess the associations between trajectory classes and the risk of urolithiasis. ResultsAmong 313 280 health examination participants included in the study, 8 864 incident cases of urolithiasis were identified during follow-up. In the baseline Cox analyses, cystatin C showed the strongest association with urolithiasis (HR=1.230, 95%CI 1.083 to 1.396, FDR=0.001). In analyses incorporating repeated measurements, serum uric acid demonstrated a strong association in both Poisson regression and mixed-effects models (RR=1.123, 95%CI 1.094 to 1.152, FDR<0.001; RR=1.106, 95%CI 1.075 to 1.137, FDR<0.001). In trajectory analyses, U-shaped trajectories of blood urea (HR=0.548, 95%CI 0.438 to 0.685, FDR<0.001) and uric acid (HR=0.910, 95%CI 0.857 to 0.685, FDR=0.005) were associated with a lower risk of urolithiasis. ConclusionBoth baseline levels and longitudinal changes in kidney function indicators measured during routine health examinations are significantly associated with the incidence of urolithiasis. Regular assessment and longitudinal monitoring of kidney function may contribute to the prevention and early identification of urolithiasis development and progression.
ObjectiveThis longitudinal cohort study aimed to identify the association between baseline physiological characteristics and the risk of post-trauma depressive symptoms, characterize the heterogeneous developmental trajectories of these symptoms, and explore differences in baseline biomarkers across these trajectory groups to provide evidence for early clinical intervention. MethodsA total of 4 709 trauma patients were recruited from the China Severe Trauma Cohort (CSTC) between March 2020 and July 2025. 17 baseline biomarkers and 6 composite inflammatory index were measured, and depressive symptoms were assessed using the PHQ-9 at 1, 3, 6, and 12 months post-admission. Multivariate-adjusted Poisson regression was used to evaluate the predictive value of baseline indicators for post-trauma depressive symptoms, and Latent Class Mixed Models (LCMM) were employed to identify depression symptom developmental trajectories. ResultsElevated baseline systemic inflammation (higher leukocytes, NLR, SII, and CAR) and compromised nutritional/metabolic status (lower RBC count and albumin) significantly increased the risk of post-trauma depressive symptoms throughout the follow-up period. Trajectory analysis identified two distinct classes: "Consistently Low" and "Persistently Elevated" symptoms. Individuals in the "Persistently Elevated" group were significantly characterized by baseline profiles of low lymphocytes, albumin, red blood cells, and hematocrit. Additionally, biomarker effects exhibited some heterogeneity across gender. ConclusionSystemic inflammation activation (high white blood cell count, NLR, SII and CAR) and biological disorders driven by impaired red blood cell or nutritional status (low red blood cell count, MCHC and albumin) are significant predictors of the occurrence and persistent evolution of depressive symptoms. Baseline innate immune activation and insufficient nutritional reserves serve as early biological predictors driving the onset and chronicity of post-trauma depressive symptoms. Integrating routine hematological indicators facilitates clinical risk stratification and precision prediction of symptom trajectories.