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Background Chronic kidney disease (CKD) is the eleventh leading cause of death globally, and the burden of disease and economic impact caused by it is increasing rapidly. Its disability and mortality rates have exhibited the highest increase among all chronic diseases. Insulin resistance (IR) and obesity are closely associated with the onset and progression, and triglyceride-glucose (TyG) index can serve as a substitute indicator for IR. Nevertheless, the exact relationship between the TyG index and the development of CKD remains to be fully elucidated. Objective Through a cohort study, we aim to investigate the relationship between triglyceride-glucose (TyG) index and its combination with obesity indices in relation to the occurrence of CKD. Methods This retrospective cohort study selected 4 921 adult participants who underwent annual physical examinations at the Sichuan Provincial People's Hospital Health Management & Physical Examination from January 2015 to November 2022, according to specific inclusion and exclusion criteria. The study cohort was categorized into four groups based on quartiles of the baseline triglyceride-glucose (TyG) index: Q1 (5.43-6.66) , Q2 (6.67-7.04) , Q3 (7.05-7.43) , and Q4 (7.43-9.97) , with sample sizes of 1 230, 1 231, 1 230 and 1 230, respeciyvely Obesity-related indices including waist circumference (WC) , BMI, and waist-to-hip ratio (WHR) , were combined with the TyG index to form TyG-WC, TyG-BMI, and TyG-WHR indices. Based on the quartiles of the baseline TyG-WC index, the study subjects were divided into 4 groups, Q1 (204.49-523.14) , Q2 (523.15-593.21) , Q3 (593.22-657.16) , and Q4 (657.17-992.75) , with sample sizes of 1 230, 1 232, 1 229 and 1 230, respectively. Based on the quartiles of the baseline TyG-BMI index, the study subjects were divided into 4 groups, Q1 (92.43-149.16) , Q2 (149.17-168.43) , Q3 (168.49-188.92) , and Q4 (88.93-306.64) , with sample sizes of 1 228, 1 231, 1 232 and 1 230, respectively. Based on the quartiles of the baseline TyG-WHR index, the study subjects were divided into 4 groups, Q1 (2.76-5.66) , Q2 (5.67-6.26) , Q3 (6.27-6.83) , and Q4 (6.84-9.67) , with sample sizes of 1 230, 1 230, 1 231 and 1 230, respectively. The relationship between the TyG index and its combination with obesity indices and the incidence risk of CKD was examined by a Cox proportional hazards model, while a restricted cubic spline regression (RCS) was used to assess dose-response relationships. Results At the end of follow-up, there were 139 new cases of CKD in the study cohort, with an incidence rate of 2.8%. After accounting for potential confounding factors, the results showed that compared to the TyG index Q1 group, the TyG index Q4 group exhibited a significantly elevated risk of CKD incidence (HR=1.756, 95%CI=1.010-3.054) . Compared to the TyG-WC index Q1 group, the TyG-WC index Q4 group demonstrated a significantly higher risk of CKD incidence (HR=2.532, 95%CI=1.210-5.296) , with P6.93) associated with a greater risk of CKD incidence. The TyG-WC index had a linear dose-response relationship with the risk of CKD incidence (Pnonlinearity=0.078) , with an increasing trend of CKD incidence risk with increasing TyG-WC index values. Conclusion Both TyG and TyG-WC are risk factors for CKD, controlling for TyG and WC can effectively prevent and manage CKD, this finding holds great importance for the prevention and treatment of CKD.

Based on the National Census data of 2000, 2010, and 2020, with districts and counties as spatial units, the current situation of population aging is described using a time-series comparison method, and its distribution position is identified using a standard deviation ellipse. Moran's I index was used to investigate the spatial correlation and evolution characteristics of population aging, and the Dagum Gini coefficient and decomposition were used to evaluate its regional differences and sources. Then, a spatial econometric regression model was used to test the factors influencing the spatial differentiation of population aging. This study revealed that the population aging of counties in the Chengdu-Chongqing economic circle has been accelerating as a unit from 2000 to 2020. However, different regions before and after 2010 have shown relatively different changes. The center of population aging in the counties gradually shifted to Chengdu over time. Population aging in counties of the Chengdu-Chongqing economic circle mainly shows the spatial distribution characteristics of similar types of agglomeration; counties with significant spatial correlation are mainly characterized by high-high or low-low type clustering, of which the latter has gradually concentrated in Chengdu and Chongqing during the survey period. After 2010, the high-high type clustering areas rapidly concentrated on the two wings of the central axis of Chengdu and Chongqing. The overall difference in population aging of the counties in the Chengdu-Chongqing economic circle accelerated during the investigation period. The increase and decrease in intra-regional differences varied among different periods, with increased inter-regional differences occurring after 2010. Hypervariable density contributed the most to the overall difference in population aging. Compared with 2010, factors such as aging inertia, fertility inertia, population inflow, and education level had a greater impact on the spatial differentiation of population aging in the Chengdu-Chongqing economic circle in 2020, and an increasing number of elderly people have been observed to gather in more densely populated districts and counties since 2010. Overall, this study provides a detailed presentation of the spatial imbalance and dynamic evolution characteristics of population aging at the county level in the Chengdu-Chongqing economic circle over the past two decades and reports that, compared to 2000-2010, the population aging in the counties of the Chengdu-Chongqing economic circle during 2010-2020 underwent rapid deepening with an overall increase in differences and showed new changes in deepening speed, regional relative aging degree, and distribution of similar types of agglomeration counties, that could be considered, there are also certain differences in the causes of population aging in the counties.