A Cross-sectional Study of the Association of cMetS and Other Obesity Indicators with Cardiometabolic Co-morbidities in People over 35 Years of Age in Anhui Province

Abstract

Background With the gradual aging of China's population and the gradual rise of chronic disease co-morbidities, cardiometabolic co-morbidities (CMM) have become one of the most damaging co-morbidities. Current studies on prediction and intervention methods for CMM have focused on individual cardiovascular diseases and lifestyle, while studies on CMM as a whole are lacking. Objective To explore the association of Continuous Metabolic Syndrome Score (cMetS) and other obesity indicators with CMM, and to further confirm whether these indicators can be used as a simple indicator for screening CMM, as well as to estimate the threshold for prediction of CMM in the middle-aged and elderly population in Anhui Province. Methods The study included 131 390 participants from the Anhui Province Cardiovascular Disease High-Risk Population Early Screening and Comprehensive Intervention Project from 2017 to 2021, divided into CMM (779 males, 866 females) and non-CMM groups (53 020 males, 76 725 females). General patient information and biochemical markers were collected, and the waist-to-height ratio (WHtR), WHT.5R, body roundness index (BRI), and cMetS were calculated. Differences in CMM prevalence by gender and age group were compared using the Bonferroni method. Multivariate Logistic regression analysis was employed to investigate the factors influencing CMM. Receiver operating characteristic (ROC) curves for predicting CMM using cMetS and obesity indices were plotted, and the area under the ROC curve (AUC) was calculated. The value of different indices in predicting CMM status was assessed using paired sample tests. Results In the male cohort, the CMM group showed higher values for age, BMI, waist circumference (WC), mean arterial pressure (MAP), fasting plasma glucose (FPG), triglycerides (TG), diabetes, ischemic heart disease, stroke, WHtR, WHT.5R, BRI, and cMetS than the non-CMM group. Smoking and alcohol consumption, as well as total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C), were higher in the non-CMM group (P<0.05). In females, similar trends were observed, with lower levels of TC and HDL-C in the CMM group (P<0.05). The prevalence of CMM varied across different age groups in both male and female patients (P<0.05). Multivariate Logistic regression analysis indicated that increases in cMetS, WHtR, WHT.5R, BRI, and BMI are risk factors for CMM in both genders (P<0.05). ROC curve analysis showed that in males, the AUC for cMetS was higher than that for WHtR (Z=6.16, P<0.001), BRI (Z=6.16, P<0.001), WHT.5R (Z=7.21, P<0.001), and BMI (Z=9.36, P<0.001). Similar findings were observed for females, with cMetS outperforming the other indices. Conclusion In both genders, cMetS and other obesity indices are closely associated with CMM, with cMetS being a superior identifier. cMetS serves as a novel marker for diagnosing CMM, highlighting its significance in the prevention of this condition.

Keywords

• metabolic syndrome|cardiometabolic multimorbidity|continuous metabolic syndrome score|anhui province|cross-sectional studies