Type 2 diabetes and Alzheimer's disease share common cortical neurodegenerative patterns
Bibliographic record
Abstract
Abstract Background Type 2 diabetes (T2D) is a prevalent health condition associated with cognitive impairment and dementia. T2D induces adverse effects not only on the pancreas but also on the liver, kidneys, muscles, fat cells, and, notably, the brain. Both T2D and Alzheimer's disease (AD) exhibit associations with neurodegeneration, yet the extent of their shared patterns of brain atrophy remains poorly understood, potentially indicating common pathways. The primary objectives were to assess the similarity between neural degeneration patterns in T2D and AD and to identify commonalities between these two diseases. Method We employed linear models to compare neurodegeneration patterns linked to T2D and AD. A total of 43,172 participants from the United Kingdom Biobank (UKBB) (3,479 with T2D) and 8,378 from ADNI (871 with AD) were included in our study. We selected Free Surfer‐derived cortical thickness measures extracted from T1‐weighted MRI, age, sex, and blood biomarkers of T2D. The age range for UKBB was 40‐70 years, while for ADNI, it was 55‐97 years. We filtered data for individuals aged 55 and older, normalized features, and created age‐ and sex‐matched groups. We then created cortical atrophy maps using linear regression models for each group. Comparison of the AD and T2D maps was performed using permutation‐based tests accounting for spatial autocorrelation. Result Our findings revealed similar patterns of neural degeneration in T2D and AD brain maps, particularly in the frontal, parietal, temporal, and occipital lobes, suggesting shared neuroanatomical alterations. Statistically significant overlap was observed in atrophy patterns between AD and T2D patients. T2D individuals exhibited greater atrophy in brain regions affected in AD, such as the hippocampus. Permutation analysis indicated no significant difference between the impact of T2D and AD on brain regions, supporting the notion that T2D is a substantial risk factor for AD. Conclusion This study enhances our understanding of the similarities in how T2D and AD impact the brain, highlighting the potential connection between these conditions. Further research is warranted to explore underlying mechanisms and implications for clinical interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".