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 network structure of comorbid depression and anxiety symptoms among medical staff and analyze differences across institutional types. MethodsA convenience sampling method was used to select medical staff from medical institutions at various levels in Guang'an City as participants between August 10 and 15, 2024. General demographic questionnaires, the Chinese version of the Patient Health Questionnaire (PHQ-9) for depression screening, and the Chinese version of the Generalized Anxiety Disorder Scale (GAD-7) were used to survey them. The study aimed to analyze the influencing factors of anxiety and depression and construct a network model. Predictability, bridging strength, and node strength were used to assess the network structure. The non-parametric bootstrap method was employed to evaluate the accuracy and stability of the network, and finally, a Network Comparison Test (NCT) was used to examine the impact of different levels of healthcare institutions on the network model. ResultsA total of 889 participants were included in the study. The analysis showed that the incidence of depressive symptoms (PHQ-9≥5) among healthcare workers was 44.88%, while the incidence of anxiety symptoms (GAD-7≥5) was 43.98%, with a comorbidity rate of 36.67%. Network analysis revealed that the top three symptoms with the highest node strength were difficulty relaxing (A4), excessive worry (A3), and fatigue (D4). The top three symptoms with the highest bridging strength were irritability/anger (A6), fatigue (D4), and worrying about terrible things happening (A7). The different levels of healthcare institutions did not have a significant impact on the network model. ConclusionThe central symptoms (such as difficulty relaxing, excessive worry, and fatigue) and key bridging symptoms (such as irritability/anger, fatigue, and worrying about terrible things happening) in the anxiety and depression symptom network can serve as potential intervention targets for healthcare workers at risk of depressive and anxiety symptoms.
