Found programs: National Natural Science Foundation of China (No . 82471952) ; Research Project of Anhui Pro- vincial Institute of Translational Medicine (No . 2023zhyx-B02) ; Sicience Foundation for Clinical Research of An- hui Medical University (No . 2023xkj143) ; Basic and Clinical Collaborative Research Enhancement Project of An- hui Medical University (No . 2023xkjT025)
Authors：Wang Jie1 , 2 , Miao Ziyue3 , Chang Jiayue3 , Wu Xingwang1 , Zhu Jiajia1 , Cai Huanhuan1
Keywords：diabetes; magnetic resonance imaging; machine learning; brain age; cognition; aging
DOI：10.19405/j.cnki.issn1000-1492.2025.11.022
〔Abstract〕 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 . 61 9( - 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 .