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Yunxing Jiang,1,* Xianghui Zhang,1,* Rulin Ma,1 Xinping Wang,1 Jiaming Liu,1 Mulatibieke Keerman,1 Yizhong Yan,1 Jiaolong Ma,1 Yanpeng Song,1,2 Jingyu Zhang,1 Jia He,1 Shuxia Guo,1,3 Heng Guo1 1Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 2The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China; 3Department of Pathology and Key Laboratory of Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuxia Guo; Heng GuoDepartment of Public Health, Shihezi University School of Medicine, North 2th Road, Shihezi, Xinjiang, People’s Republic of ChinaTel +8618009932625Fax +8609932057153Email gsxshzu@sina.com; guoheng@shzu.edu.cnBackground: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks.Methods: The final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of

Wenqiang Wang,1,* Jia He,1,* Yunhua Hu,1 Yanpeng Song,2 Xianghui Zhang,1 Heng Guo,1 Xinping Wang,1 Mulatibieke Keerman,1 Jiaolong Ma,1 Yizhong Yan,1 Jingyu Zhang,1 Rulin Ma,1 Shuxia Guo1,3 1Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 2Department of Social Work, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China; 3Department of NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuxia GuoDepartment of Public Health, The Key Laboratory of Preventive Medicine, Building No. 1, Shihezi University School of Medicine, Suite 721, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86-1800-9932-625Fax +86-993-2057-153Email gsxshzu@sina.comRulin MaDepartment of Public Health, The Key Laboratory of Preventive Medicine, Building No. 1, Shihezi University School of Medicine, Suite 816, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86-1330-9930-561Fax +86-993-2057-153Email marulin@shzu.edu.cnBackground: We aimed to identify the relationship between metabolically healthy obesity (MHO), a special subtype of obesity, and the incidence of cardiovascular disease (CVD) in rural Xinjiang.Methods: Body mass index (BMI) and the Joint Interim Statement criteria were utilized to define obesity and metabolic status, respectively. A baseline survey was conducted between 2010 and 2012. The cohort was followed-up until 2017, including 5059 participants (2953 Uyghurs and 2106 Kazakhs) in the analysis.Results: During 6.78 years of follow-up, 471 individuals developed CVD, 10.8% (n=545) of whom were obese, and the prevalence of MHO and MHNW was 5.2% and 54.5%, respectively. Compar

Xianghui Zhang,1,* Yusong Ding,1,* Yinbao Shao,1 Jia He,1 Jiaolong Ma,1 Heng Guo,1 Mulatibieke Keerman,1 Jiaming Liu,1 Huili Si,2 Shuxia Guo,1,3 Rulin Ma1 1Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 2Department of Neurology, Shihezi People’s Hospital, Shihezi, Xinjiang, People’s Republic of China; 3Department of Pathology and Key Laboratory of Xinjiang Endemic and Ethnic Diseases (Ministry of Education), Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuxia GuoDepartment of Public Health, Shihezi University School of Medicine, Suite 721, Building No. 1, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86 1800-9932-625Fax +86 993-2057-153Email gsxshzu@sina.comRulin MaDepartment of Public Health, Shihezi University School of Medicine, Suite 816, Building No. 1, Beier Road, Shihezi, 832000, Xinjiang, People’s Republic of ChinaTel +86 1330-9930-561Fax +86 993-2057-153Email marulin@shzu.edu.cnBackground: Few studies have focused on the predictive ability of visceral obesity-related indices for metabolic syndrome (MetS), especially in different ethnic groups. This study aimed to evaluate the applicability of visceral obesity-related indices for MetS screening among three major ethnic groups living in remote rural areas of Xinjiang.Methods: Based on multistage stratified cluster random sampling method, 3,192 Uyghurs, 3,054 Kazakhs, and 3,658 Hans were recruited from Xinjiang, China. The Joint Interim Statement (JIS) criteria were used to define MetS. The receiver operating characteristic curve (ROC), area under the ROC curve (AUC), and predictive value of each visceral obesity-related index were used to evaluate the predictive ability of MetS.Results: After adjusting for potential confounding factors, the lipid accumulation

Yu Ren,1,* Bin Wei,2,* Yanpeng Song,2 Heng Guo,1 Xianghui Zhang,1 Xinping Wang,1 Yizhong Yan,1 Jiaolong Ma,1 Kui Wang,1 Mulatibieke Keerman,1 Jingyu Zhang,1 Rulin Ma,1 Jia He,1,3 Shuxia Guo1,3 1Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, People’s Republic of China; 2Department of Social Work, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, Xinjiang, People’s Republic of China; 3NHC Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (First Affiliated Hospital, School of Medicine, Shihezi University), Shihezi, Xinjiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shuxia Guo; Jia HeDepartment of Public Health, Shihezi University School of Medicine, North 2th Road, Shihezi, Xinjiang, People’s Republic of ChinaTel +8618009932625Fax +8609932057153Email gsxshzu@sina.com; hejia123.shihezi@163.comBackground: 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 wi