Detect the disrupted brain structural connectivity in type 2 diabetes mellitus patients without cognitive impairment.

Background: Cognitive decline in type 2 diabetes mellitus (T2DM) occurs years before the onset of clinical symptoms. Early detection of this incipient cognitive decline stage, which is T2DM without mild cognitive impairment, is critical for clinical intervention, yet it remains elusive and challenging to identify.

Aim: To identify structural changes in the brains of T2DM patients without cognitive impairment to gain insights into the early-stage cognitive decline.

Methods: Using diffusion tensor imaging (DTI), we constructed structural brain networks in 47 T2DM patients and 47 age-/sex-matched healthy controls. Machine learning models incorporating connectivity features were developed to classify T2DM brains and predict disease duration.

Results: T2DM patients exhibited reduced global/local efficiency and small-worldness, alongside weakened connectivity in cortical regions but enhanced subcortical-frontal connections, suggesting compensatory mechanisms. A classification model leveraging 18 connectivity features achieved 92.5% accuracy in distinguishing T2DM brains. Structural connectivity patterns further predicted disease onset with an error of ± 1.9 years.

Conclusion: Our findings reveal early-stage brain network reorganization in T2DM, highlighting subcortical-frontal connectivity as a compensatory biomarker. The high-accuracy models demonstrate the potential of DTI-based biomarkers for preclinical cognitive decline detection.