Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

Abstract

The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults. Patients and Methods: This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram. Results: After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.-878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful. Conclusion:Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.

Application of a Novel Prediction Model for Predicting 2-Year Risk of Non-Alcoholic Fatty Liver Disease in the Non-Obese Population with Normal Blood Lipid Levels: A Large Prospective Cohort Study from China

“…We followed the methods of Cai et al 2021. 20 To improve the robustness and reliability of our conclusions, 3659 NAFLD patients were randomly divided into a development cohort with 2744 participants and a validation cohort with 915 participants at a ratio of 7.5: 2.5 using R caret package, which met the theoretical ratio of 3: 1. The types of data for this study are divided into measured data, non-normally distributed measured data and count data.…”

Associations Between the Metabolic Score for Insulin Resistance Index and the Risk of Type 2 Diabetes Mellitus Among Non-Obese Adults: Insights from a Population-Based Cohort Study

“…Type 2 diabetes mellitus (T2DM) is a major concern in healthcare worldwide due to its high prevalence and severe complications. 1,2 In 2017, approximately 462 million people were affected by T2DM, which corresponds to a prevalence of 6059 cases per 100,000 people. It is predicted to increase to 7079 per 100,000 people by 2030, reflecting a continued rising and showing no signs of stabilization worldwide.…”

A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population

“…Obesity is an important risk factor for the onset of T2DM. 2 , 18 , 19 BMI is considered a marker for obesity and a predictor variable for most risk models of diabetes. However, studies have suggested that BMI does not reflect central obesity.…”

“… 1 It has rapidly become a serious public health problem in both developed and developing countries in recent decades due to the rise in obesity and sedentary lifestyle. 2 , 3 It has caused a tremendous burden on the global healthcare system. Studies have reported that the economic burden in the US alone was estimated at $327 billion in 2017.…”

“…A previous study reported that such models can estimate the risk score of diabetes and improve patient prognosis in obese patients. 2 In addition to complex mathematical formulations and population heterogeneity, simple and intuitive tools can facilitate the implementation of these risk-prediction models. The nomogram transforms a complex regression equation into a simple and intuitive graph, which has great clinical application value.…”