Chongjian Wang

Evaluating the risk of type 2 diabetes mellitus using artificial neural network: An effective classification approach

AIM To develop and evaluate an effective classification approach without biochemical parameters to identify those at high risk of T2DM in rural adults. METHODS A cross-sectional survey was conducted. Of 8640 subjects who met inclusion criteria, 75% (N1=6480) were randomly selected to provide training set for constructing artificial neural network (ANN) and multivariate logistic regression (MLR) models. The remaining 25% (N2=2160) were assigned to validation set for performance comparisons of the ANN and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the validation set. RESULTS The prevalence rates of T2DM were 8.66% (n=561) and 9.21% (n=199) in training and validation sets, respectively. For ANN model, the sensitivity, specificity, positive and negative predictive value for identifying T2DM were 86.93%, 79.14%, 31.86%, and 98.18%, respectively, while MLR model were only 60.80%, 75.48%, 21.78%, and 94.52%, respectively. Area under the ROC curve (AUC) value for identifying T2DM when using the ANN model was 0.891, showing more accurate predictive performance than the MLR model (AUC=0.744) (P=0.0001). CONCLUSION The ANN model is an effective classification approach for identifying those at high risk of T2DM based on demographic, lifestyle and anthropometric data.

Prevalence, awareness, treatment, control of type 2 diabetes mellitus and risk factors in Chinese rural population: the RuralDiab study.

The study aimed to investigate prevalence, awareness, treatment and control of type 2 diabetes mellitus (T2DM), and to explore potential risk factors in rural areas of China. A total of 16413 individuals aged 18–74 years in rural districts were recruited from the Rural Diabetes, Obesity and Lifestyle (RuralDiab) study for the epidemiological research. Meanwhile, a meta-analysis including 7 published studies was conducted to validate the result of the cross-sectional study. The rates of crude and age-standardized prevalence, awareness, treatment and control of T2DM were 12.19%, 67.00%, 62.35%, 22.20% and 6.98%, 60.11%, 54.85%, 18.77%, respectively. The prevalence, awareness, treatment and control of T2DM displayed increased trends with age (Ptrend < 0.01) and were strongly associated with education, drinking, more vegetable and fruit intake, physical activity, family history of diabetes, body mass index (BMI). The results of this meta-analysis showed that the pooled prevalence, awareness, treatment and control of T2DM in China countryside were 7.3% (5.3–9.4%), 57.3% (36.9–77.6%), 48.4% (32.4–64.5%) and 21.0% (9.9–32.1%), respectively. The prevalence of T2DM was high with inadequate awareness, treatment and control of T2DM in China rural areas. Healthy lifestyles should be advocated to reduce prevalence and improve awareness, treatment, and control of T2DM in Chinese rural residents.

Development and Evaluation of a Simple and Effective Prediction Approach for Identifying Those at High Risk of Dyslipidemia in Rural Adult Residents

Background Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population. Methods Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35–78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve. Results Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001). Conclusion The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.

Association of the vitamin D binding protein polymorphisms with the risk of type 2 diabetes mellitus: a meta-analysis

Objective Previous studies on the association between vitamin D binding protein (DBP) polymorphisms and the risk of type 2 diabetes mellitus (T2DM) have produced conflicting results. The purpose of this meta-analysis was to examine whether DBP polymorphisms are associated with the risk of T2DM. Design Systematic review and meta-analysis. Methods All eligible studies were searched and acquired from the Cochrane, Pubmed, ISI, CNKI (Chinese) and Wanfang (Chinese) databases. ORs with corresponding 95% CIs were computed to estimate the association between DBP polymorphisms and T2DM. In addition, heterogeneity test, meta-regression and sensitivity analysis were also conducted. Results Six studies, which included 1191 cases and 882 controls, met the inclusion criteria and were included in the meta-analysis. The results showed that no significant associations were found between codon 416 and codon 420 polymorphisms in the DBP and the risk of T2DM in the overall analyses. In stratified analysis, significant associations between the codon 420 polymorphism and T2DM were found in Asians (allele Lys vs Thr: OR (95% CI) 1.49 (1.19 to 1.85), genotype Lys/Thr versus Thr/Thr: OR (95% CI) 1.80 (1.36 to 2.38), and Lys/Thr+Lys/Lys versus Thr/Thr: OR (95% CI) 1.81 (1.37 to 2.39), respectively) but not in Caucasians. For the codon 416, the significant association with T2DM was also detected in Asians (genotype Glu/Asp+Glu/Glu vs Asp/Asp: OR (95% CI) 1.36 (1.04 to 1.78)) but not in Caucasians. Conclusions This meta-analysis demonstrated that the DBP polymorphism was moderately associated with increased susceptibility to T2DM in Asians, but a similar association was not found in Caucasians. It suggested that ethnicity might be the potential factor associated with heterogeneity.

Development of a reverse transcription‐loop‐mediated isothermal amplification (RT‐LAMP) system for rapid detection of HDV genotype 1

The object of this study was to develop a reverse transcription‐loop‐mediated isothermal amplification (RT‐LAMP) assay for detecting hepatitis D virus (HDV) genotype 1. With an alignment analysis, a highly conserved sequence (nt 820–1020) was chosen as a suitable target to design LAMP primers. The optimal condition of RT‐LAMP was a 25‐μl reaction volume, which consists of the following components: 1·6 μmol l−1 each of FIP and BIP, 0·2 μmol l−1 each of F3 and B3, 1·5 μmol l−1 dNTPs, 4 mmol l−1 MgSO4, 8 U Bst DNA polymerase, 2U M‐MLV and 2 μl extracted RNA sample. The amplification reaction was carried out at 65°C for 50 min. Compared with conventional qualitative or quantitative real‐time reverse transcription polymerase chain reaction, the results of RT‐LAMP indicated a 1000‐fold increase in sensitivity for detecting HDV. There was no cross‐reaction for the RT‐LAMP method between HDV 1 and HIV, HAV, HBV, HCV and HEV.

Association of obesity categories and high blood pressure in a rural adult Chinese population.

Limited information is available on the prevalence of obesity and high blood pressure (HBP) in rural China. We conducted a cross-sectional survey in a rural adult Chinese population during July to August of 2007 and 2008. The relationship between various obesity categories and HBP was analysed by gender for 20 194 participants. Obesity categories were classified as general and central obesity in terms of body mass index (BMI) and waist circumference (WC), respectively; cross-classification of BMI and WC created another four groups: both BMI and WC normal (BNWN), BMI obesity and WC normal (BOWN), BMI normal and WC obesity (BNWO), and both BMI and WC obesity (BOWO). The rates of HBP for BNWN, BOWN, BNWO and BOWO groups were 20.8, 63.3, 39.8 and 48.7%, respectively, for men and 20.1, 28.0, 34.7 and 54.2%, respectively, for women. As compared with BNWN group, the adjusted odds ratio (OR) and 95% confidence interval (CI) of BOWN and BOWO for having HBP in men were 6.227 (2.712–14.300) and 4.842 (4.036–5.808), respectively. As compared with BNWN women, BNWO and BOWO women showed increased risk of HBP (adjusted OR=1.342, 95%CI=1.139–1.581 and adjusted OR=4.530, 95%CI=4.004–5.124, respectively). The prevalence of general and central obesity was strongly related to HBP. Men with obese BMI but normal WC may be at increased risk of HBP. Women should pay more attention to changes in visceral adipose distribution and keep both BMI and WC values within normal ranges to reduce obesity-related health problems.

Development and Validation of a Risk-Score Model for Type 2 Diabetes: A Cohort Study of a Rural Adult Chinese Population

Some global models to predict the risk of diabetes may not be applicable to local populations. We aimed to develop and validate a score to predict type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Data for a cohort of 12,849 participants were randomly divided into derivation (n = 11,564) and validation (n = 1285) datasets. A questionnaire interview and physical and blood biochemical examinations were performed at baseline (July to August 2007 and July to August 2008) and follow-up (July to August 2013 and July to October 2014). A Cox regression model was used to weigh each variable in the derivation dataset. For each significant variable, a score was calculated by multiplying β by 100 and rounding to the nearest integer. Age, body mass index, triglycerides and fasting plasma glucose (scores 3, 12, 24 and 76, respectively) were predictors of incident T2DM. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC), with optimal cut-off value 936. With the derivation dataset, sensitivity, specificity and AUC of the model were 66.7%, 74.0% and 0.768 (95% CI 0.760–0.776), respectively. With the validation dataset, the performance of the model was superior to the Chinese (simple), FINDRISC, Oman and IDRS models of T2DM risk but equivalent to the Framingham model, which is widely applicable in a variety of populations. Our model for predicting 6-year risk of T2DM could be used in a rural adult Chinese population.

The genetic polymorphisms in vitamin D receptor and the risk of type 2 diabetes mellitus: An updated meta-analysis

BACKGROUND AND OBJECTIVES Vitamin D receptor (VDR) genetic polymorphisms are considered to be associated with type 2 diabetes mellitus (T2DM), but this is inconclusive. The aim of this study is to quantify the association between polymorphisms of BsmI and FokI in the VDR gene and T2DM risk through literature review. METHODS AND STUDY DESIGN Original articles published from 1999 to June 2014 were discovered through PubMed, ISI Web of Science, China National Knowledge Infrastructure, Chinese Wanfang Database, and the Chinese Biomedical Literature Database. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated with software STATA version 12.0. RESULTS Twenty-three articles containing 30 case-control studies were included. The association between the BsmI polymorphism and T2DM was weak in two genetic models (Bb vs bb and BB+Bb vs bb). The subgroup analysis showed that this association was only found in the studies with a small sample size (<200). A strong association between FokI polymorphism and T2DM indicated that this gene polymorphism was possibly a risk factor for T2DM (ff vs FF: OR=1.57, 95% CI: 1.28-1.93, p<0.001; Ff vs FF: OR=1.54, 95% CI: 1.31-1.81, p<0.001; ff+Ff vs FF: OR=1.57, 95% CI: 1.35-1.83, p<0.001), especially in Chinese populations. CONCLUSION More reliable conclusions about associations between VDR genetic polymorphisms and T2DM will depend on studies with larger sample size and by ethnicity.

Glucagon gene polymorphism modifies the effects of smoking and physical activity on risk of type 2 diabetes mellitus in Han Chinese.

Few genome-wide association studies have considered interactions between multiple genetic variants and environmental factors associated with disease. The interaction was examined between a glucagon gene (GCG) polymorphism and smoking, alcohol consumption and physical activity and the association with risk of type 2 diabetes mellitus (T2DM) in a case-control study of Chinese Han subjects. The rs12104705 polymorphism of GCG and interactions with environmental variables were analyzed for 9619 participants by binary multiple logistic regression. Smoking with the C-C haplotype of rs12104705 was associated with increased risk of T2DM (OR=1.174, 95% CI=1.013-1.361). Moderate and high physical activity with the C-C genotype was associated with decreased risk of T2DM as compared with low physical activity with the genotype (OR=0.251, 95% CI=0.206-0.306 and OR=0.190, 95% CI=0.164-0.220). However, the interaction of drinking and genotype was not associated with risk of T2DM. Genetic polymorphism in rs12104705 of GCG may interact with smoking and physical activity to modify the risk of T2DM.

Resting heart rate as a marker for identifying the risk of undiagnosed type 2 diabetes mellitus: a cross-sectional survey

BackgroundFast resting heart rate might increase the risk of developing type 2 diabetes mellitus (T2DM). However, it is unclear whether resting heart rate could be used to predict the risk of undiagnosed T2DM. Therefore, the purposes of this study were to examine the association between resting heart rate and undiagnosed T2DM, and evaluate the feasibility of using resting heart rate as a marker for identifying the risk of undiagnosed T2DM.MethodsA cross-sectional survey was conducted. Resting heart rate and relevant covariates were collected and measured. Fasting blood samples were obtained to measure blood glucose using the modified hexokinase enzymatic method. Predictive performance was analyzed by Receiver Operating Characteristic (ROC) curve.ResultsThis study included 16, 636 subjects from rural communities aged 35–78 years. Resting heart rate was significantly associated with undiagnosed T2DM in both genders. For resting heart rate categories of <60, 60–69, 70–79, and ≥80 beats/min, adjusted odds ratios for undiagnosed T2DM were 1.04, 2.32, 3.66 and 1.05, 1.57, 2.98 in male and female subjects, respectively. For male subjects, resting heart rate ≥70 beats/min could predict undiagnosed T2DM with 76.56% sensitivity and 48.64% specificity. For female subjects, the optimum cut-off point was ≥79 beats/min with 49.72% sensitivity and 67.53% specificity. The area under the ROC curve for predicting undiagnosed T2DM was 0.65 (95% CI: 0.64-0.66) and 0.61(95% CI: 0.60-0.62) in male and female subjects, respectively.ConclusionsFast resting heart rate is associated with an increased risk of undiagnosed T2DM in male and female subjects. However, resting heart rate as a marker has limited potential for screening those at high risk of undiagnosed T2DM in adults living in rural areas.