Obesity-related brain atrophy is independent of Alzheimer's disease protein pathways
Bibliographic record
Abstract
Background Obesity increases the risk for Alzheimer's disease (AD) and other dementias. Obesity causes structural brain injury, and it has been suggested that this may contribute to the development of AD pathology. Neurodegeneration in AD results from the aggregation of misfolded and dysfunctional tau and amyloid-β. However, it remains unknown whether adiposity-related brain injury acts through tau and amyloid deposition or as an independent cause of neurodegeneration. Objective Here, we tested whether obesity, cerebrovascular disease, and obesity-related metabolic risk score were associated with structural brain and cognitive changes via the same mechanisms as AD or independent of them. Methods We used the UK Biobank sample of over 33,000 individuals aged 64 years on average. We tested the influence of the microtubule-associated protein tau ( MAPT) and apolipoprotein E ( APOE) risk alleles involved in tau and amyloid-β synthesis, folding, and clearance, as well as AD polygenic risk score (PRS) to define genetic risk of AD. Specifically, we investigated whether these genetic risk factors moderated the relationship between obesity and brain structure and cognition. Results We found that MAPT and APOE status and AD PRS did not moderate the relationship between obesity and brain atrophy. We also found limited evidence for the moderation of MAPT and APOE of the cerebrovascular disease-brain structure relationship as well as the metabolic risk score-brain structure relationship. Conclusions We conclude that the mechanisms linking obesity with brain atrophy are most likely independent of the ones governing AD-related protein deposition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".