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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".