Factor Analysis of Metabolic Syndrome and Its Relationship with the Risk of Cardiovascular Disease in Ethnic Populations in Rural Xinjiang, China.

Abstract

BACKGROUND:

This cohort study created a risk equation of CVD for the Uyghur and Kazakh ethnic groups with metabolic syndrome (MetS) in Xinjiang and its associated factors, evaluated the model's feasibility, and provided theoretical support for the prevention and early diagnosis of CVD.

METHODS:

A total of 5655 participants from Xinyuan and Jiashi counties in Xinjiang from 2010 to 2012 were selected, including 3770 and 1885 training and validation samples, respectively. A factor analysis was performed on 975 patients with MetS in the training sample, whereas potential factors related to CVD were extracted from 21 MetS biomarkers. Cox regression was used to create and verify a CVD-risk prediction model based on training samples. The receiver operating characteristic curve was drawn to evaluate the model's prediction efficiency.

RESULTS:

The cumulative incidence of CVD was 9.20% (training sample, 9.12%; validation sample, 9.36%). Nine potential factors were extracted from the training sample population with MetS to predict the CVD risk: lipid (hazard ratio [HR], 1.205), obesity (HR, 1.047), liver function (HR, 1.042), myocardial enzyme (HR, 1.008), protein (HR, 1.024), blood pressure (HR, 1.027), liver enzyme (HR, 1.012), renal metabolic (HR, 1.015), and blood glucose (HR, 1.010). The area under the curve of the training and validation samples was 0.841 (95% confidence interval [CI], 0.821-0.861) and 0.889 (95% CI, 0.870-0.909), respectively.

CONCLUSION:

The CVD prediction model created with nine potential factors in patients with MetS in Kazakh and Uyghur has a good predictive power.

• Authors: Yu Ren, Bin Wei, Yanpeng Song, Heng Guo, Xianghui Zhang, Xinping Wang, Yizhong Yan, Jiaolong Ma, Kui Wang, Mulatibieke Keerman, Jingyu Zhang, Rulin Ma, Jia He, Shuxia Guo
• Journal: International journal of general medicine (Int J Gen Med) Vol. 14 Pg. 4317-4325 ( 2021) ISSN: 1178-7074 [Print] New Zealand
• PMID: 34408474 (Publication Type: Journal Article)
• Copyright: © 2021 Ren et al.