Metabolic syndrome in Xinjiang Kazakhs and construction of a risk prediction model for cardiovascular disease risk

• Published in: PloS one 2018-09, Vol.13 (9), p.e0202665
• Main Authors: Mao, Lei, He, Jia, Gao, Xiang, Guo, Heng, Wang, Kui, Zhang, Xianghui, Yang, Wenwen, Zhang, Jingyu, Li, Shugang, Hu, Yunhua, Mu, Lati, Yan, Yizhong, Ma, Jiaolong, Ding, Yusong, Zhang, Mei, Liu, Jiaming, Ma, Rulin, Guo, Shuxia
• Format: Article
• Language: English
• Subjects: Analysis; Attorneys; Bioindicators; Biology and Life Sciences; Biomarkers; Cardiovascular disease; Cardiovascular diseases; Diabetes; Diagnostic systems; Epidemiology; Factor analysis; Gastroenterology; Health aspects; Health risk assessment; Health risks; Hypertension; Kazakhs; Mathematical models; Medical records; Medicine; Medicine and Health Sciences; Men; Metabolic syndrome; Metabolic syndrome X; Mortality; Neural networks; Obesity; People and Places; Physical Sciences; Population; Prediction models; Public health; Regression analysis; Research and Analysis Methods; Risk assessment; Risk factors; Statistical analysis; Studies; Womens health
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0202665

The high prevalence of metabolic syndrome (MetS) and cardiovascular diseases (CVD) is observed among Kazakhs in Xinjiang. Because MetS may significantly predict the occurrence of CVD, the inclusion of CVD-related indicators in metabolic network may improve the predictive ability for a CVD-risk model for Kazakhs in Xinjiang. The study included 2,644 subjects who were followed for 5 years or longer. CVD cases were identified via medical records of the local hospitals from April 2016 to August 2017. Factor analysis was performed in 706 subjects (267 men and 439 women) with MetS to extract CVD-related potential factors from 18 biomarkers tested in a routine health check-up, served as a synthetic predictor (SP). We evaluated the predictive ability of the CVD-risk model using age and SP, logistic regression discrimination for internal validation (n = 384; men = 164, women = 220) and external validation (n = 219; men = 89, women = 130), calculated the probability of CVD for each participant, and receiver operating characteristic curves. According to the diagnostic criteria of JIS, the prevalence of MetS in Kazakh was 30.9%. Seven potential factors with a similar pattern were obtained from men and women and comprised the CVD predictors. When predicting CVD in the internal validation, the area under the curve (AUC) were 0.857 (95%CI 0.807-0.898) for men and 0.852 (95%CI 0.809-0.889) for women, respectively. In the external validation, the AUC to predict CVD were 0.914 (95%CI 0.832-0.963) for men and 0.848 (95%CI 0.774-0.905) for women. It is suggested that SP might serve as a useful tool in identifying CVD with in Kazakhs, especially for Kazakhs men. Among 7 potential factors were extracted from 18 biomarkrs in Kazakhs with MetS, and SP may be used for CVD risk assessment.