Using metabolism related factors constructing a predictive model for the risk of cardiovascular diseases in Xinjiang Kazakh population

Shuxia Guo; Lei Mao; Peihua Liao; Rulin MA; Xianghui Zhang; Heng Guo; Jia HE; Yunhua HU; Xinping Wang; Jiaolong MA; Jiaming Liu; Lati MU; Yizhong Yan; Jingyu Zhang; Kui Wang; Yanpeng Song; Wenwen Yang; Wushoer Puerhati

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Using metabolism related factors constructing a predictive model for the risk of cardiovascular diseases in Xinjiang Kazakh population

VernacularTitle: 以代谢相关因子构建新疆哈萨克族人群心脑血管疾病发病风险预测模型
Author: Shuxia GUO ¹ ; Lei MAO ¹ ; Peihua LIAO ² ; Rulin MA ¹ ; Xianghui ZHANG ¹ ; Heng GUO ¹ ; Jia HE ¹ ; Yunhua HU ¹ ; Xinping WANG ¹ ; Jiaolong MA ¹ ; Jiaming LIU ¹ ; Lati MU ¹ ; Yizhong YAN ¹ ; Jingyu ZHANG ¹ ; Kui WANG ¹ ; Yanpeng SONG ¹ ; Wenwen YANG ¹ ; Wushoer PUERHATI ²
Author Information

1. Department of Preventive Medicine, School of Medicine, Shihezi University, Shihezi 832000, China
2. Department of Chronic Diseases, Xinjiang Uygur Autonomous Region Center for Disease Control, Urumqi 830000, China
Publication Type:Clinical Trail
Keywords: Metabolic syndrome; Factor analysis; Cardiovascular diseases; Risk predictive model; Kazakh
From: Chinese Journal of Endocrinology and Metabolism 2020;36(1):51-57
CountryChina
Language:Chinese
Abstract: Objective:To construct and confirm a predictive model for the risks of cardiovascular diseases (CVD) with metabolic syndrome (MS) and its factors in Xinjiang Kazakh population.
Methods:A total of 2 286 Kazakh individuals were followed for 5 years from 2010 to 2012 as baseline survey. They were recruited in Xinyuan county, Yili city, Xinjiang. CVD cases were identified via medical records of the local hospitals in 2013, 2016 and 2017, respectively. Factor analysis was performed on 706 MS patients at baseline, and main factors, age, and sex were extracted from 18 medical examination indexs to construct a predictive model of CVD risk. After excluding the subjects with CVD at baseline and incomplete data, 2007 were used as internal validation, and 219 Kazakhs in Halabra Township were used as external validation. Logistic regression discriminations were used for internal validation and external validation, as well as to calculate the probability of CVD for each participant and receiver operating characteristic curves.
Results:The prevalence of MS in Kazakh was 30.88%. Seven main factors were extracted from the Kazakh MS population, namely obesity factor, blood lipid and blood glucose factor, liver function factor, blood lipid factor, renal metabolic factor, blood pressure factor, and liver enzyme factor. The area under the curve (AUC) for predicting CVD in the internal validation was 0.773 (95%CI 0.754-0.792). In the external validation, the AUC for predicting CVD was 0.858 (95%CI 0.805-0.901).
Conclusions:The CVD risk prediction model constructed by 7 main factors extracted from Kazakh MS patients has high validation efficiency and can be used for risk assessment of CVD in Xinjiang Kazakh population.