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.

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.

Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical basis and data support for improving the health of residents in this region. Methods We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016.The normalized difference vegetation index(NDVI)was used to estimate residential greenness.The generalized linear mixed model(GLMM)was used to examine the association between residential greenness and cardiometabolic risk factors. Results Higher residential greenness was associated with lower cardiometabolic risk factor prevalence.After adjustments were made for age,sex,education,and marital status,for each interquartile range(IQR)increase of NDVI500-m,the risk of hypertension was reduced by 10.3%(OR=0.897,95%CI=0.836-0.962),the risk of obesity by 20.5%(OR=0.795,95%CI=0.695-0.910),the risk of type 2 diabetes by 15.1%(OR=0.849,95%CI=0.740-0.974),and the risk of dyslipidemia by 10.5%(OR=0.895,95%CI=0.825-0.971).Risk factor aggregation was reduced by 20.4%(OR=0.796,95%CI=0.716-0.885)for the same.Stratified analysis showed that NDVI500-m was associated more strongly with hypertension,dyslipidemia,and risk factor aggregation among male participants.The association of NDVI500-m with type 2 diabetes was stronger among participants with a higher education level.PM10 and physical activity mediated 1.9%-9.2%of the associations between NDVI500-m and obesity,dyslipidemia,and risk factor aggregation. Conclusion Higher residential greenness has a protective effect against cardiometabolic risk factors among rural residents in Xinjiang.Increasing the area of green space around residences is an effective measure to reduce the burden of cardiometabolic-related diseases among rural residents in Xinjiang.