Development and Validation of a Nomogram to Predict Type 2 Diabetes Mellitus in Overweight and Obese Adults: A Prospective Cohort Study from 82938 Adults in China

Abstract

Background. The twin epidemic of overweight/obesity and type 2 diabetes mellitus (T2DM) is a major public health problem globally, especially in China. Overweight/obese adults commonly coexist with T2DM, which is closely related to adverse health outcomes. Therefore, this study aimed to develop risk nomogram of T2DM in Chinese adults with overweight/obesity. Methods. We used prospective cohort study data for 82938 individuals aged ≥20 years free of T2DM collected between 2010 and 2016 and divided them into a training (n = 58056) and a validation set (n = 24882). Using the least absolute shrinkage and selection operator (LASSO) regression model in training set, we identified optimized risk factors of T2DM, followed by the establishment of T2DM prediction nomogram. The discriminative ability, calibration, and clinical usefulness of nomogram were assessed. The results were assessed by internal validation in validation set. Results. Six independent risk factors of T2DM were identified and entered into the nomogram including age, body mass index, fasting plasma glucose, total cholesterol, triglycerides, and family history. The nomogram incorporating these six risk factors showed good discrimination regarding the training set, with a Harrell’s concordance index (C-index) of 0.859 [95% confidence interval (CI): 0.850–0.868] and an area under the receiver operating characteristic curve of 0.862 (95% CI: 0.853–0.871). The calibration curves indicated well agreement between the probability as predicted by the nomogram and the actual probability. Decision curve analysis demonstrated that the prediction nomogram was clinically useful. The consistent of findings was confirmed using the validation set. Conclusions. The nomogram showed accurate prediction for T2DM among Chinese population with overweight and obese and might aid in assessment risk of T2DM.

The hypermethylation of FOXP3 gene as an epigenetic marker for the identification of arsenic poisoning risk

“…The combination of variables identified by these nomogram models has been recognized as an important reference for disease prevention and treatment strategies. [32][33][34] In this study, we identified that FOXP3 methylation and age as the powerful predictors of arsenic poisoning risk. Based on this, the nomogram model was performed to integrate the contribution of these two predictors to the probability of individual arsenic poisoning to obtain an accurate risk score.…”

Development and validation of a nomogram to estimate future risk of type 2 diabetes mellitus in adults with metabolic syndrome: prospective cohort study

“…Wang et al [30] constructed a logistic diagnostic model to evaluate probability of T2DM in western Xinjiang whose data came from a cross-sectional study. Liu et al [31] focused on the risk of T2DM in the overweight and obese adults. Regrettably, all these studies mentioned above did not perform external validation and did not create a web-based calculator.…”