Diabetes mellitus has a world death rate of 1.6 million (2016) of which Type 2 diabetes mellitus (T2DM) accounts for ~90% of all cases. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since lower socio-demographics layers are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate prediction models based on non-laboratory parameters. Here, we analysed data of 44,879 non-diabetic, UK-Biobank participants at the ages 40-65 within a time frame of 7.3±2.3 years. We devise a non-laboratory prediction model for T2DM onset probability using sex, age, weight, height, waist, hips-circumferences, Waist-Hips Ratio (WHR) and Body-Mass Index (BMI). This model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.84 95% CI) and an odds ratio (OR) between the top and lowest prevalence deciles of x42 (33-49). The logistic regression top predictive parameters are WHR with OR of 0.67 (0.49-0.88 95%CI) followed by BMI with OR of 0.53 (0.26-0.79). We further analyse the contribution of laboratory-based parameters and devise a blood-test model based on only five blood tests. In this model, we are using age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset more accurately. This model achieves an auROC of 0.89 (0.87-0.92) and a deciles' OR of x59 (27-75). We also analysed a model that included genotyping data and other environmental factors and found that it did not provide further benefit over the five-blood-tests model. Our models outperform the current state of the art, non-laboratory, Finnish Diabetes Risk Score and the German Diabetes Risk Score, trained on our data, achieving auROC of 0.74 (0.7-0.77) and 0.63 (0.59-0.67), respectively.
Evaluation of Risk of Type 2 Diabetes Mellitus in Medical Students Using Indian Diabetes Risk Score (IDRS)
