Evaluation of brain aging in patients with type 2 diabetes mellitus by structural magnetic resonance-driven machine learning model
10.19405/j.cnki.issn1000-1492.2025.11.022
- VernacularTitle:结构磁共振机器学习脑龄预测模型评估 2 型糖尿病患者大脑早衰
- Author:
Jie Wang
1
;
Ziyue Miao
2
;
Jiayue Chang
2
;
Xingwang Wu
3
;
Jiajia Zhu
3
;
Huanhuan Cai
3
Author Information
1. Dept of Radiology , The First Afiliated Hospital of Anhui Medical University , Hefei 230022 ; Anhui Provincial Institute of Transitional Medical , Hefei 230032
2. The First School of Clinical Medicine of Anhui Medical University , Hefei 230032
3. Dept of Radiology , The First Afiliated Hospital of Anhui Medical University , Hefei 230022
- Publication Type:Journal Article
- Keywords:
diabetes;
magnetic resonance imaging;
machine learning;
brain age;
cognition;
aging
- From:
Acta Universitatis Medicinalis Anhui
2025;60(11):2153-2158,2165
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the brain-predicted age difference (Brain-PAD) in patients with type 2 diabetes mellitus (T2DM) by a machine learning prediction model based on structural magnetic resonance ( sMRI) in the Southwest University Adult Lifespan Dataset (SALD) , and to reveal the relationship between Brain-PAD and dura- tion of T2DM and cognition .
Methods:Group comparisons about demographic variables and cognitive function were conducted respectively in local database of 104 T2DM patients and 83 healthy controls (HC) . The prediction model via Gaussian process regression (GPR) was constructed by training sMRI data of 329 healthy volunteers in SALD , then its performance was validated and evaluated . Furthermore , Brain-PAD ( predicted age-chronological age) in the local database was calculated . Group comparisons of Brain-PAD between T2DM patients and HCs were conducted by Mann-Whitney U test. Finally , Pearson correlation coefficient (r) was calculated between Brain-PAD and duration of disease and cognition .
Results:Poor performance in auditory verbal learning test (AVLT)-delayed recall , AVLT-recognition , symbol digital modalities test (SDMT) (P < 0. 05) , and increased Brain-PAD were ob- served in T2DM patients , compared with HCs [1 . 619 ( - 4. 001 , 8. 272) years vs - 1 . 289 ( - 4. 128 , 4. 134) years , Z = 2. 056 , P = 0. 034] . Notably , the median of Brain-PAD in T2DM group was positive , indicating that the brain of T2DM patient maybe relatively “older”than his chronological age . Brain-PAD in T2DM group was as- sociated with performance in AVLT-immediate recall ( r = 0. 291 , P = 0. 003) , AVLT-delayed recall ( r = 0. 248 , P = 0. 011) , SDMT( r = 0. 376 , P = 0. 001) and trail making test (TMT)-A ( r = - 0. 206 , P = 0. 036) . However , the relationships between Brain-PAD and duration of T2DM were not explored .
Conclusion:Decreased cognitive function in patients with T2DM is demonstrated in this study . The machine learning prediction model based on sMRI supports the identification of brain aging objectively in patients with T2DM .
- Full text:2026030522344565172结构磁共振机器学习脑龄预测模型评估2型糖尿病患者大脑早衰_汪洁.pdf
