Analysis of the associated factors and cumulative effects of cardiometabolic multimorbidity among residents in southern Xinjiang
10.3760/cma.j.cn112150-20240613-00465
- VernacularTitle:新疆南疆居民心血管代谢性共病的关联因素及累计效应分析
- Author:
Silin CHEN
1
;
Dilimulati MUHETAER
;
Rulin MA
;
Bo YANG
;
Xuelian WU
;
Leyao JIAN
;
Jiahang LI
;
Jing CHENG
;
Shuxia GUO
;
Heng GUO
Author Information
1. 石河子大学医学院预防医学系，石河子 832000
- Publication Type:Journal Article
- Keywords:
Multimorbidity;
Cardiometabolic risk factors;
Network analysis;
Cross-sectional studies
- From:
Chinese Journal of Preventive Medicine
2025;59(3):292-301
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the associated factors and cumulative effects of cardiometabolic multimorbidity (CMM) among residents in southern Xinjiang.Methods:A stratified random cluster sampling method was used to conduct questionnaire surveys, physical examinations and laboratory tests among the personnel of the 51st Brigade, 3rd Division, Xinjiang, in 2016. The multivariate logistic regression, multivariate linear regression, restricted cubic spline, and network analysis methods were used to study the association of lifestyle (smoking, alcohol consumption and physical activity), socioeconomic (occupation, education and marital status) and clinical factors (waist circumference, body mass index and family history) with CMM.Results:A total of 12 773 study subjects were included. The prevalence of cardiovascular metabolic diseases among residents in southern Xinjiang was 52.49%. Specifically, the prevalence rates of dyslipidemia, hypertension, coronary heart disease, diabetes, and stroke were 31.14%, 29.95%, 6.78%, 6.26%, and 2.47%, respectively, and the prevalence of CMM was 19.06%. Multivariate logistic regression analysis revealed that the associations between clinical and socioeconomic factors and CMM significantly increased with higher scores. Specifically, the OR rose from 1.75 (clinical factors) and 1.07 (socioeconomic factors) on a score of 1 to 4.41 and 1.93 on a score of 3, respectively. The association between lifestyle factors and CMM was only observed at higher scores ( OR=1.26, 95% CI:1.07~1.62). The trend test using the scores of each group as continuous variables in the model showed that the risk of disease increased with the accumulation of clinical, socioeconomic and lifestyle factors (all P<0.05). Restricted cubic spline analysis demonstrated a non-linear relationship between the total number of associated factors and CMM ( Poverall<0.05 and Pnon-linear<0.05). Network analysis identified hypertension (strength=0.42) as the “core node” among the five diseases. When analyzing the three types of influencing factors, hypertension (strength=0.68), dyslipidemia (strength=0.47), coronary heart disease (strength=0.37), and clinical factors (strength=0.53) emerged as “core nodes”. In the network of nine associated factors, abnormal waist circumference and BMI (strength=0.90 and 0.84) were identified as “key factors”, while hypertension (strength=0.68) and dyslipidemia (strength=0.52) were identified as “key diseases”. Conclusion:The prevalence of CMM among residents in southern Xinjiang is high, and there is a cumulative effect of multiple factors. Hypertension and dyslipidemia are key diseases in the multimorbidity network, while abnormal BMI and waist circumference are key associated factors.
