# Visceral fat area as a predictor for macrovascular complications in patients with type 2 diabetes mellitus

## TL;DR

This study shows that visceral fat area is a strong predictor of macrovascular complications in type 2 diabetes patients, outperforming traditional measures like BMI.

## Contribution

The study introduces a predictive model using visceral fat area and other clinical parameters to accurately identify high-risk T2DM patients for macrovascular complications.

## Key findings

- Visceral fat area significantly increases with macrovascular complications in T2DM patients.
- A predictive model using VFA and other factors achieved 86% specificity and 72% sensitivity for macrovascular complications.
- VFA outperforms BMI as a predictor of macrovascular complications in T2DM patients.

## Abstract

This study aimed to investigate the association between visceral fat area (VFA) and markers of atherosclerosis in patients with type 2 diabetes mellitus (T2DM), with or without macrovascular complications (MVC). Additionally, the study sought to determine the optimal VFA threshold for predicting MVC in individuals with T2DM.

This retrospective study included 1, 176 patients with T2DM and 289 healthy individuals enrolled between August 2018 and May 2022. Participants were classified into three groups: healthy controls, T2DM without MVC, and T2DM with MVC. Demographic characteristics, clinical parameters, and laboratory data were collected. VFA, carotid intima-media thickness (CIMT), and brachial-ankle pulse wave velocity (baPWV) were measured. Univariate analysis was conducted for variable selection, followed by multivariate logistic regression to identify independent predictors. A risk prediction model was constructed. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, and predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).

Baseline comparisons across the three groups revealed a progressive increase in VFA from healthy controls to individuals with T2DM alone and to those with T2DM and MVC (P < 0.001). Subgroup analysis within the T2DM group showed significant differences in VFA between the CIMT(-)baPWV(+) and CIMT(-)baPWV(-) subgroups, as well as between the CIMT(+)baPWV(+) and CIMT(-)baPWV(-) subgroups. Multivariate logistic regression identified age, systolic blood pressure, weight, body mass index, VFA, and triglycerides as independent predictors of MVC in T2DM (all P < 0.05). The predictive model was defined as: Logit(P) = –11.942 + 0.083 × age + 0.035 × systolic blood pressure - 0.030 × weight - 0.109 × BMI + 0.054 × VFA + 0.154 × triglycerides. The model achieved an AUC of 0.867, with a sensitivity of 72% and a specificity of 86%.

VFA is an independent predictor of MVC in patients with T2DM, demonstrating superior predictive value compared to traditional indicators such as BMI. The predictive model developed in this study shows high accuracy, supporting early identification of high-risk individuals and enabling implementation of personalized interventions.