Background. An estimated 425 million people globally have diabetes, accounting for 12% of the world’s health expenditures, and the number continues to grow, placing a huge burden on the healthcare system, especially in those remote, underserved areas. Methods. A total of 584,168 adult subjects who have participated in the national physical examination were enrolled in this study. The risk factors for type II diabetes mellitus (T2DM) were identified by p values and odds ratio, using logistic regression (LR) based on variables of physical measurement and a questionnaire. Combined with the risk factors selected by LR, we used a decision tree, a random forest, AdaBoost with a decision tree (AdaBoost), and an extreme gradient boosting decision tree (XGBoost) to identify individuals with T2DM, compared the performance of the four machine learning classifiers, and used the best-performing classifier to output the degree of variables’ importance scores of T2DM. Results. The results indicated that XGBoost had the best performance (accuracy=0.906, precision=0.910, recall=0.902, F‐1=0.906, and AUC=0.968). The degree of variables’ importance scores in XGBoost showed that BMI was the most significant feature, followed by age, waist circumference, systolic pressure, ethnicity, smoking amount, fatty liver, hypertension, physical activity, drinking status, dietary ratio (meat to vegetables), drink amount, smoking status, and diet habit (oil loving). Conclusions. We proposed a classifier based on LR-XGBoost which used fourteen variables of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of T2DM. The classifier can accurately screen the risk of diabetes in the early phrase, and the degree of variables’ importance scores gives a clue to prevent diabetes occurrence.

Objective: A simple type 2 diabetes mellitus (T2DM) screening model was established preciously based on easily available variables for identifying high-risk individuals in western Xinjiang, China. Methods: A total of 458,153 cases participating in the national health examination were recruited. Logistic regression and the least absolute shrinkage and selection operator (LASSO) models were used for univariate analysis, factors selection, and the establishment of prediction model. Receiver operating characteristic (ROC) curve, Hosmer–Lemeshow test and clinical decision curve (CDA) were applied for evaluating the discrimination, calibration and clinical validity, respectively. The optimal threshold for predicting risk factors for T2DM has been estimated as well. Results: The nomogram depicted the risk of T2DM based on different genders, the factors mainly consisted of age, family history of T2DM (FHOT), waist circumference (WC), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDLc), body mass index (BMI), high-density lipoprotein cholesterol (HDLc), etc. The area under ROC of men and women was 0.864 and 0.816 in the development group, similarly in the validation group, which was 0.865 and 0.815, respectively. The calibration curve showed that the nomogram was accurate for predicting the risk of T2DM, and the CDA proved great clinical application value of the nomogram. Threshold values of the age, WC, TC, TG, HDLc, BMI in different genders were 52.5 years old (men) and 48.5 years old (women), 85.50 cm (men) and 89.9 cm (women), 4.94 mmol/L (men) and 4.94mmol/L (women), 1.26mmol/L (men) and 1.67mmol/L (women), 1.40mmol/L (men) and 1.40mmol/L (women), 24.70kg/m² (men) and 24.95kg/m² (women), respectively. Conclusion: Our results give a clue that the nomogram may be useful for identifying adults who have high risk for diabetes, which is simple, affordable, with high credibility and can be widely implemented. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

Purpose: This study aimed to examine the prevalence and correlates of metabolic syndrome (MetS) in multi-ethnic populations of Northwest China based on Large-scale provincial health checking data. Patients and Methods: A total of 9,745,640 Chinese aged ≥ 18 years in Xinjiang, the largest autonomous region of multi-ethnic in China, were enrolled from Feb. to Sep. 2019. MetS was defined by modified Adult Treatment Panel (ATP III) criteria. Results: The overall prevalence of MetS was 20.85% [Sex: 20.06% female, 21.56% male; Age: 39.22% 60-years and above, 26.32% 40- to 59-years and 9.56% 18- to 39-years; Ethnicity: 28.19% Hui followed by Han (26.39%), Uyghur (18.56%), Other (18.61%), Kazak (17.98%), Mongolian (17.87%), Kyrgyz (14.44%)]. People living in cities and towns (23.03%) or North (24.78%) had higher prevalence of MetS than rural (19.94%) and South (17.66%). Although the prevalence of hypertension, smoking, drinking and lack of physical activity were higher in Kyrgyz, Mongolian and Kazak than those of other ethnic groups, the risk of MetS was lower. Conclusion: The results indicated that a high prevalence of MetS in Hui and Han groups and people living in cities and towns. Living in North Xinjiang and elder population tend to have higher prevalence of MetS. The prevention and management of MetS in these populations should be prioritized.