Obesity impacts brain metabolism and structure independently of amyloid and tau pathology in healthy elderly
Bibliographic record
Abstract
Abstract Background: Mid-life obesity is related to increased risk for overall dementia and Alzheimer´s disease (AD) dementia. In the present work, we aimed to investigate the impact of obesity on brain structure, metabolism, and cerebrospinal fluid (CSF) biomarkers of amyloid (Aβ 1-42) and tau-pathology (total-tau and p-tau) in healthy elderly. Methods: We selected healthy controls from ADNI2 with available CSF AD biomarkers and/or fluorodeoxyglucose (FDG) PET and 3T-MRI. Participants without follow-up or with significant weight loss were excluded from the analyses. Brain cortical thickness (Cth) was evaluated with Freesurfer software and FDG uptake was measured with a surface-based method using both SPM and Freesurfer softwares. We performed regression analyses between FDG uptake, CTh, CSF AD biomarkers levels and BMI and interaction analyses with age by obesity/overweight status. Results: We included 147 individuals (mean age 73.3 years, mean BMI 27.4 Kg/m 2 ). Higher BMI was related to less cortical thickness and higher glucose metabolism in brain areas not typically involved in AD (FWE<0.05), with little overlap between them. There was no association between BMI and any of the CSF core AD biomarkers. The relationship between age and brain metabolism was modified by overweight/obesity status, but not that of age and brain structure or core CSF AD biomarkers. Conclusions: Our data support that obesity has differential effects on brain metabolism and structure independent of an underlying AD pathophysiology in cognitively unimpaired elderly.
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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 | 0.000 | 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".