Zhongguo quanke yixue (Jan 2025)
Trend of Onset Time of Diabetes Mellitus and Its Correlation with Chinese Visceral Adiposity Index: a Prospective Cohort Study
Abstract
Background Diabetes mellitus is a global public health issue. Cross sectional studies have found that visceral fat is closely related to the prevalence of diabetes mellites, while prospective cohort studies on the trend of onset time of diabetes mellitus and its correlation with Chinese visceral adiposity index (CVAI) are scant. Objective To analyze the trend of onset time of diabetes mellitus and its correlation with CVAI in Pudong New Area, Shanghai, residents by the prospective cohort study, thus providing evidence for the scientific prevention and treatment of diabetes mellitus. Methods This was a prospective cohort study involving 5 236 residents from 12 townships and 35 village committees who participated in the chronic disease risk factor monitoring project in Pudong New Area, from January to July, 2013. Baseline data were collected, including CVAI, visceral adiposity index (VAI), body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), a body shape index (ABSI), and body adiposity index (BAI). Follow-up was conducted in 2016 and 2019. By the end of follow-up in October 2019, the incidence of new onset of diabetes mellitus in this cohort was calculated through questionnaire survey, laboratory testing, medical system visit information and vital statistics information system. According to the baseline quartile, the CVAI, VAI, BMI, WC, WHR, WHtR, ABSI, and BAI of the included population were divided into Q1 to Q4 quartiles. The number of cases in CVAI Q1-Q4 groups was 1 306, 1 307, 1 307, and 1 307, respectively. The number of cases in VAI Q1-Q4 groups was 1 300, 1 316, 1 306, and 1 306, respectively. The number of cases in BMI Q1-Q4 groups was 1 305, 1 302, 1 312, and 1 311, respectively. The number of cases in WC Q1-Q4 groups was 1 302, 1 273, 1 287, and 1 367, respectively. The number of cases in WHR Q1-Q4 groups was 1 180, 1 203, 1 332, and 1 514, respectively. The number of cases in WHtR Q1-Q4 groups was 1 199, 1 393, 1 400, and 1 237, respectively. The number of cases in ABSI Q1-Q4 groups was 1 316, 1 302, 1 302, and 1 308 respectively. The number of cases in BAI Q1-Q4 groups was 1 310, 1 304, 1 308, and 1 307, respectively. The multivariable Cox regression analysis was used to analyze the correlation of CVAI and other obesity indicators with the onset of diabetes mellitus. The predictive potential of CVAI and other obesity indicators in diabetes mellitus was assessed using receiver operator characteristic (ROC) curves. Results The incidence density of diabetes mellitus in Pudong New Area was 33.55/1 000 person-years from 2013 to 2016, and 23.25/ 1 000 person-years from 2017 to 2019. With aging, the total incidence density of diabetes mellitus showed an increasing trend (2013-2016: χ2=28.503, Ptrend<0.001; 2017-2019: χ2=25.600, Ptrend<0.001). By 2016, the baseline CVAI quartile was positively correlated with the cumulative incidence of diabetes mellitus (CVAI: χ2=131.865, Ptrend<0.001) and the incidence density (CVAI: χ2=100.105, Ptrend<0.001). Mutivariable Cox regression analysis after adjusting for relevant confounders showed that compared with CVAI in Q1, the risk of diabetes mellitus in men with CVAI in Q4 increased by 79.4% (HR=1.794, 95%CI=1.044-3.083, P<0.05). Women had a 371.2% increased risk of diabetes mellitus (HR=4.712, 95%CI=2.601-8.538, P<0.05). ROC curve results showed that in predicting the incidence of male diabetes, the area under the ROC curve (AUC) of CVAI for male diabetes was 0.600 (95%CI=0.561-0.640), with the Youden index of 0.181, and the cutoff value of 104.118. Delong test showed that CVAI had the highest accuracy in predicting female diabetes mellitus (AUC=0.699), with the Youden index of 0.317, and the optimal cutoff value of 104.609. Conclusion From 2013 to 2019, the incidence density of diabetes mellitus increased with the increased age in Pudong New Area, Shanghai. Compared with other obesity indicators, CVAI can be used as an indicator to predict the risk of diabetes mellitus.
Keywords
