Sha Fu

Objective: To develop and validate cluster-specific diagnostic models for osteoporosis in patients with chronic kidney disease (CKD) using routine clinical parameters. Methods: Data were obtained from the National Health and Nutrition Examination Survey. A total of 3,806 CKD patients were enrolled and randomly allocated to training (70%, n = 2,667) and internal validation (30%, n = 1,139) cohorts, with an additional external validation cohort from the China Renal Data System or Chinese Renal Disease Data System. K-means clustering was performed to identify distinct subgroups. Weighted univariate logistic regression, Least Absolute Shrinkage and Selection Operator and Boruta algorithm were used for feature selection. Final diagnostic models were constructed using weighted multivariable logistic regression. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: Three distinct CKD phenotypic clusters were identified; two were selected for model development. Group A retained six variables (sex, age, body mass index, serum uric acid, serum creatinine, and fracture history) with AUCs of 0.880, 0.839, and 0.815 across training, internal, and external (CRDS) validation. Group B retained seven variables (sex, age, marital status, red blood cell count, body mass index, race, and fracture history) with AUCs of 0.824, 0.806, and 0.808. Both models demonstrated favorable calibration and clinical utility. NRI revealed divergent subgroup effects: gains in Group B (0.0059) versus losses in Group A (-0.0149). Conclusions: Cluster-specific diagnostic models based on weighted logistic regression exhibited robust discriminative performance for osteoporosis diagnosis in CKD patients, with heterogeneous reclassification gains across phenotypic subgroups.

Acute kidney injury (AKI) remains a frequent complication in intensive care unit (ICU) settings. Clopidogrel, a P2Y12 receptor inhibitor, has been reported

Recent studies show that C-reactive protein (CRP) is not only a biomarker but also a pathogenic mediator contributing to the development of inflammation and ageing-related diseases. However, serum levels of CRP in the healthy ageing population remained unclear, which was investigated in the present study. Serum levels of high sensitive C-reactive protein (hs-CRP), glucose (Glu), triglyceride (TG), cholesterol (CHOL), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), superoxide dismutase (SOD), serum creatinine (SCr), serum uric acid (SUA) were measured in 6060healthy subjects (3672 male and 2388 female, mean age:45.9 years) who received routine physical examination at Sun Yat-sen Memorial Hospital, Guangzhou, China. In total of 6060 healthy people, serum levels of hs-CRP were significantly increased with ageing (P < 0.05), particularly in those with age over 45-year-old (1.31[0.69–2.75] vs 1.05[0.53–2.16]mg/L, P < 0.001). Interestingly, levels of serum hs-CRP were significantly higher in male than female population (1.24[0.65–2.57] vs 1.07[0.53–2.29]mg/L, P < 0.001). Correlation analysis also revealed that serum levels of hs-CRP positively correlated with age and SUA, but inversely correlated with serum levels of HDL-c and SOD (all P < 0.05). Baseline levels of serum hs-CRP are increased with ageing and are significantly higher in male than female healthy population. In addition, elevated serum levels of hs-CRP are also associated with increased SUA but decreased HDL-c and SOD. Thus, serum levels of hs-CRP may be an indicator associated with ageing in healthy Chinese population.