Prediction of the 10-year incidence of type 2 diabetes mellitus based on advanced anthropometric indices using machine learning methods in the Iranian population

Abstract

Background

Type 2 diabetes mellitus (T2DM) is a growing chronic disease that can lead to disability and early death. This study aimed to establish a predictive model for the 10-year incidence of T2DM based on novel anthropometric indices.

Methods

This was a prospective cohort study comparing people with (n = 1256) and without (n = 5193) diabetes mellitus in phase II of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.

The association of several anthropometric indices in phase I, including Body Mass Index (BMI), Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Visceral Adiposity Index (VAI), Weight-Adjusted-Waist Index (WWI), Body Roundness Index (BRI), Body Surface Area (BSA), Conicity Index (C-Index) and Lipid Accumulation Product (LAP) with T2DM incidence (in phase II) were examined; using Logistic Regression (LR) and Decision Tree (DT) analysis.

Results

BMI followed by VAI and LAP were the best predictors of T2DM incidence. Participants with BMI < 21.25 kg/m² and VAI $\le$ 5.9 had a lower chance of diabetes than those with higher BMI and VAI levels (0.033 vs. 0.967 incident rate). For BMI > 25 kg/m², the chance of diabetes rapidly increased (OR = 2.27).

Conclusions

BMI, VAI, and LAP were the best predictors of T2DM incidence.