Regional Brain Structure Alterations in Diabetes but Not Prediabetes
Bibliographic record
Abstract
CONTEXT: Diabetes (DM) affects brain volume and white matter hyperintensity (WMH), but whether regional gray matter volume (GMV) and tract-specific WMH progress in the prediabetes (PreDM) stage remains unclear. OBJECTIVE: We investigate brain structural changes across 3 distinct glycemic states. METHODS: We analyzed 512 participants (122 with DM, 109 with PreDM, and 281 controls) using advanced neuroimaging techniques. High-resolution structural T1-weighted magnetic resonance images and FLAIR (fluid-attenuated inversion recovery) images were acquired, complemented by cognitive assessments, grip strength measurements, and gait speed testing. We performed correlational analyses to examine the relationships between observed brain changes, cognitive performance, and motor function across different levels of glycemic states. RESULTS: We found substantial changes in GMV in DM, especially in areas responsible for movement and coordination, including the bilateral cerebellum, right precentral gyrus, and left postcentral gyrus (P < .001 uncorrected with cluster size > 500). These brain changes were associated with decreases in cognitive test scores (Montreal Cognitive Assessment; P = .04), gait speed (P < .05), and right-hand grip strength (P < .05)-effects not seen in the prediabetic group. We observed significantly higher WMH index across 20 tracts in DM brains (P < .05; false discovery rate [FDR]-corrected). Glycemic levels positively correlated with WMH index in multiple tracts (P < .05; FDR-corrected). CONCLUSION: This study illuminates DM as a powerful force in cerebral architecture, challenging the notion of a gradual decline beginning in PreDM. These insights not only underscore the critical importance of DM prevention but also hint at the brain's remarkable resilience in the face of early metabolic challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".