Kazakh adults in Xinjiang have a prevalent obesity problem but a low prevalence of diabetes.

BACKGROUND: The prevalence of diabetes and obesity has been continuously rising worldwide over the last three decades, particularly in China. The prevalence varies widely among different ethnicities. In this study, we investigated the prevalence of diabetes and obesity, as well as the associated factors for diabetes in Kazakh adults in Xinjiang to improve diabetes screening. METHODS: We collected data from the Xinjiang physical examination in 2018, including a total sample of 118,505 Kazakh adults in Altay District. Data on demographic characteristics, medical history, physical examination, fasting plasma glucose (FPG) and serum lipid profiles were collected. The chi-square test was used to examine the differences between multiple variables. Multivariate logistic regression was performed to identify the factors associated with diabetes. RESULTS: The mean age was 43. 66 years (SD 14.14). 49.3% of the population were women and 75.5% were rural residents. The mean FPG was 5.33 mmol/L (SD 1.22). The prevalence of diabetes was 6.3% and 4.1% received a new diagnosis by FPG. 26.6% were diagnosed with impaired fasting glucose (IFG). The mean body mass index (BMI) was 26.29 kg/m2 (SD 14.14) and the mean waist circumference was 87.69 cm (SD 12.74). 33.2% of the population were overweight, and 33.0% were obese. The prevalence of central obesity was 51.4%. Diabetes was mostly positively associated with hypertension (OR = 3.821, P<0.001), hypertriglyceridemia (OR = 2.757, P<0.001), and hyper-LDL-cholesterolemia (OR = 2.331, P<0.001) in the Kazakh population. The ORs for overweight, obesity and central obesity predictive of diabetes were 1.265, 1.453 and 1.222 ( all P<0.001), respectively. CONCLUSIONS: Despite having a high prevalence of obesity and central obesity, the Kazakh population had a considerably low prevalence of diabetes. Obesity was not the most important risk factor for diabetes in Kazakh individuals. The awareness of diabetes was low. When screening for diabetes in Kazakhs, those with hypertension or dyslipidemia should receive more attention.

Nomogram Model for Screening the Risk of Type II Diabetes in Western Xinjiang, China.

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.