Combined serum CTRP7 and CTRP15 levels as a novel biomarker for insulin resistance and type 2 diabetes mellitus.

Aims: This study aimed to examine the alterations in the serum CTRP7 and CTRP15 concentrations in patients newly diagnosed with type 2 diabetes mellitus (T2DM) and to assess the diagnostic potential of the log10 (CTRP7+CTRP15) for insulin resistance (IR) and T2DM. Methods: Serum CTRP7, CTRP15, and adiponectin levels were measured using an enzyme-linked immunosorbent assay (ELISA). Bioinformatics analysis was conducted to investigate CTRP7 and CTRP15-related genes and metabolic signaling pathways. Results: Log10 (CTRP7+CTRP15) levels were notably elevated in the impaired glucose tolerance (IGT) and T2DM cohorts compared with those in the normal control (NGT) cohort. Log10(CTRP7+CTRP15) exhibited positive correlations with HOMA-IR, area under the glucose curve (AUCg), HbA1c%, triglyceride (TG), visceral adiposity index (VAI), body mass index (BMI), and free fatty acid (FFA), levels but negative correlations with adiponectin. Multivariate stepwise regression analysis revealed that HOMA-IR, BMI, HbA1c and FFA levels were independent factors affecting the log10 (CTRP7+CTRP15). Logistic regression analysis revealed that log10 (CTRP7+CTRP15) was independently associated with T2DM and significantly associated with increased risk. Receiver operating characteristic (ROC) curve analysis indicated that the predictive value of log10 (CTRP7+CTRP15) for T2DM and IR was superior to that of CTRP7 or CTRP15 alone. Intervention studies demonstrated that insulin, FFAs and acute exercise contribute to the elevation of serum CTRP7 levels, while hyperglycemia inhibited CTRP7 secretion. Short-term changes in blood glucose, insulin, FFA and acute exercise had minimal effects on serum CTRP15 levels. Bioinformatics analysis revealed that CTRP7 and CTRP15 interact with multiple metabolism-related genes and are enriched in glucose and lipid metabolism-related pathways. Conclusion: Log10 (CTRP7+CTRP15) may serve as a valuable diagnostic marker for the management of metabolic-related diseases, particularly T2DM and IR.

Metabolic dysfunction, rather than obesity, is a risk factor for chronic kidney disease in Chinese population.

BACKGROUND: Metabolic dysfunction and obesity are closely related to chronic kidney disease (CKD). However, studies on the relationship between various metabolic syndrome-body mass index (MetS-BMI) phenotypes and the risk of CKD in the Chinese population have not yet been explored. MATERIALS AND METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) 2015 were analyzed in this study. This study enrolled 12,054 participants. Participants were divided into six distinct groups according to their MetS-BMI status. Across the different MetS-BMI groups, the odd ratios (ORs) for CKD were determined using multivariable logistic regression models. RESULTS: The prevalence of CKD was higher in metabolically unhealthy groups than in the corresponding healthy groups. Moreover, the fully adjusted model showed that all metabolically unhealthy individuals had an increased risk of developing CKD compared to the metabolically healthy normal weight group (OR = 1.62, p = 0.002 for the metabolically unhealthy normal weight group; OR = 1.55, p < 0.001 for the metabolically unhealthy overweight group; and OR = 1.77, p < 0.001 for the metabolically unhealthy obesity group. CONCLUSIONS: This study is the first to evaluate the relationship between the MetS-BMI phenotype and renal prognosis in the Chinese population. Individuals with normal weights are at different risk of developing CKD depending on their different metabolic phenotypes.