Commonalities in cortical neurodegeneration between type 2 diabetes and Alzheimer's disease
Bibliographic record
Abstract
BackgroundType 2 diabetes (T2D) is a prevalent metabolic condition associated with increased risk of cognitive decline and dementia, including Alzheimer's disease (AD). While both T2D and AD are linked to neurodegeneration, the extent to which their patterns of brain atrophy overlap remain unclear.ObjectiveTo assess the similarities and differences in cortical atrophy patterns among individuals with controlled and uncontrolled T2D, mild cognitive impairment (MCI), and AD.MethodsStructural magnetic resonance imaging data from the UK Biobank (UKBB) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed. Participants aged 55 and older were selected. Linear regression models were applied to generate cortical thickness maps for each group, controlling for age and sex. Group comparisons were conducted using permutation-based tests accounting for spatial autocorrelation.ResultsThe study included 175 individuals with T2D (86 uncontrolled, 89 controlled) and 127 healthy controls without diabetes (HC) from UKBB, 334 individuals with MCI, 119 with AD and 315 cognitively healthy (CH) from ADNI. Uncontrolled T2D was associated with significant cortical atrophy in specific brain regions, with partial overlap in neurodegeneration patterns observed in MCI and AD. However, correlations between the cortical thinning patterns were weak and non-significant, suggesting distinct trajectories. Controlled T2D showed no significant cortical thinning, supporting the potential neuroprotective effects of glycemic control.ConclusionsUncontrolled T2D is linked to region-specific cortical atrophy that partially overlaps with MCI and AD but follows an independent neurodegenerative trajectory. Effective diabetes management may help preserve brain structure and reduce dementia risk, highlighting the importance of early metabolic intervention.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".