Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity

Abstract

Type 2 diabetes mellitus (T2DM) is closely linked to cognitive decline and alterations in brain structure and function. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to diagnose neurodegenerative diseases, such as cognitive impairment (CI), Alzheimer’s disease (AD), and vascular dementia (VaD). However, whether the functional connectivity (FC) of patients with T2DM and mild cognitive impairment (T2DM-MCI) is conducive to early diagnosis remains unclear. To answer this question, we analyzed the rs-fMRI data of 37 patients with T2DM and mild cognitive impairment (T2DM-MCI), 93 patients with T2DM but no cognitive impairment (T2DM-NCI), and 69 normal controls (NC). We achieved an accuracy of 87.91% in T2DM-MCI versus T2DM-NCI classification and 80% in T2DM-NCI versus NC classification using the XGBoost model. The thalamus, angular, caudate nucleus, and paracentral lobule contributed most to the classification outcome. Our findings provide valuable knowledge to classify and predict T2DM-related CI, can help with early clinical diagnosis of T2DM-MCI, and provide a basis for future studies.

Type 2 Diabetes Mellitus: Early Detection using Machine Learning Classification

“…The results showed that the nomogram achieved high accuracy in predicting T2DM risk and could be clinically applicable for the early detection and prevention of T2DM. Another study by Wu et al ( 2023) [32] proposed a novel machine learning algorithm that combines deep neural networks (DNNs) and attention mechanisms to predict T2DM. The authors used electronic health record data from a large hospital in China and compared their model's performance to other widely used models.…”