Objective To investigate the prevalence and risk factors of diabetes and prediabetes in Jingyuan County in Ningxia. Methods A cross-sectional survey including 10 639 participants (18-88 years of age) with a multistage sampling was conducted in Jingyuan County between January, 2014 and April, 2015. Questionnaires, physical examinations, and laboratory tests were included in the survey. Results Among all the subjects, 10 491 participants (men: 4 826, women: 5 665) with complete data were included in the analysis. The standardized prevalence of diabetes and prediabetes was 4.2% (men: 3.9%, women: 4.5%) and 8.8% (men: 7.6%, women 10.3%), respectively, in which the standardized prevalence of diabetes was higher in Hui (4.5%) than that in Han (3.5%) (P<0.05). Logistic regression analyses showed that age, family history of diabetes, overweight/obesity, hypertriglyceridemia and hypertension were positively associated with prediabetes and diabetes with the odds ratios being 1.60 and 2.14 (age, P<0.001), 1.40 and 3.32 (family history, P< 0.05), 1.47 and 1.57 (overweight/obesity, P< 0.001), 1.88 and 2.55 (hypertriglyceridemia, P<0.001), 1.44 and 1.89 (hypertension, P<0.001), respectively. Conclusions The prevalence of diabetes was relatively low in the rural area in Ningxia. However, it is still essential to take active interventions in people at high risk of diabetes in order to prevent the incident diabetes.

Objective To investigate the prevalence and associated risk factors of diabetes and prediabetes in Blang ethnic adults in Menghai county. Methods A cross-sectional survey including 3 365 Blang ethnic adults (aged 18 and above from 5 administrative villages) was conducted from February 2017 to March 2017 in Menghai county. A questionnaire, physical examination, and blood assays were included in the survey. Finally,a total of 3 237 adults with complete data were selected into this analysis. Results The standardized prevalence of diabetes and prediabetes in Blang ethnic adults were estimated based on the sixth national census in 2010. According to the 1999 WHO criteria, the overall standardized prevalence of diabetes and prediabetes were 8.5% (men: 10.2%, women: 6.8%) and 16.1% (men: 18.0%, women: 14.1%), in which the standardized prevalence of newly diagnosed diabetes among the total population was 7.3% (men: 8.7%, women: 5.8%). Multivariable multinominal logistic regression analyses showed that age, hypertension, hypertriglyceridemia, and central obesity were significantly positively associated with both diabetes and prediabetes, with the corresponding odds ratios of 1.74 and 1.37, 2.39 and 2.02, 2.30 and 1.34, 2.55 and 1.73, respectively. Conclusion The prevalence of diabetes is relatively high in Blang ethnic adults in Menghai county. Improving knowledge of diabetes among the local population is one of key steps in the prevention of diabetes.

Objective To establish simple screening models for nonalcoholic fatty liver disease (NAFLD) in the adult Blang population. Methods Based on the survey data of metabolic diseases in the Blang people aged 18 years or above in 2017, 2993 respondents were stratified by sex and age (at an interval of 5 years) and then randomly divided into modeling group with 1497 respondents and validation group with 1496 respondents. Related information was collected, including demographic data, smoking, drinking, family history of diseases and personal medical history, body height, body weight, waist circumference, and blood pressure, and related markers were measured, including fasting plasma glucose, 2-hour postprandial plasma glucose or blood glucose at 2 hours after glucose loading, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transpeptidase. The chi-square test was used for comparison of categorical data between two groups. Logistic regression analysis was used to establish the screening model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value were used to evaluate the screening performance of established models versus existing models in the study population, and the DeLong method was used for comparison of AUC. Results Three screening models for NAFLD were established based on physical and biochemical measurements, i.e., simple noninvasive model 1 (age, body mass index, and waist circumference), noninvasive model 2 with the addition of blood pressure, and model 3 with the combination of hematological parameters (diabetes and ALT/AST). In the modeling group, the three models had an AUC of 0.881 (95% confidence interval [ CI ]: 0.864-0.897), 0.892 (95% CI : 0.875-0.907), and 0.894 (95% CI : 0.877-0.909), respectively, and there was a significant difference between model 1 and models 2/3 ( P =0.004 0 and P < 0.001); in the validation group, the three models had an AUC of 0.891 (95% CI : 0.874-0.906), 0.892 (95% CI : 0.875-0.907), and 0.893 (95% CI : 0.876-0.908), respectively, and there was no significant difference between the three groups ( P > 0.05). Based on the overall consideration of screening performance, invasiveness, and cost, the simple noninvasive model 1 was considered the optimal screening model for NAFLD in this population. Model 1 had the highest Youden index at the cut-off value of 5 points, and when the score of ≥5 points was selected as the criteria for NAFLD, the model had a sensitivity of 86.5%, a specificity of 79.7%, a positive predictive value of 50.3%, and a negative predictive value of 96.1% in the modeling group and a sensitivity of 85.6%, a specificity of 80.6%, a positive predictive value of 51.7%, and a negative predictive value of 95.8% in the validation group. Conclusion The NAFLD screening models established for the adult Blang population based on age and obesity indicators have relatively higher sensitivity, specificity, and negative predictive value, and this tool is of important practical significance for the intervention of NAFLD and its closely related metabolic diseases in this population.