Late-life obesity is a protective factor for prodromal Alzheimer’s disease: a longitudinal study
Bibliographic record
Abstract
Higher body mass index (BMI) in late-life has recently been considered as a possible protective factor for Alzheimer's disease (AD), which yet remains conflicting. To test this hypothesis, we have evaluated the cross-sectional and longitudinal associations of BMI categories with CSF biomarkers, brain β-amyloid (Aβ) load, brain structure, and cognition and have assessed the effect of late-life BMI on AD risk in a large sample (n = 1,212) of non-demented elderly from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. At baseline, higher late-life BMI categories were associated with higher levels of CSF Aβ42 (p=0.037), lower levels of CSF total-tau (t-tau, p=0.026) and CSF t-tau/Aβ42 (p=0.008), lower load of Aβ in the right hippocampus (p=0.030), as well as larger volumes of hippocampus (p<0.0001), entorhinal cortex (p=0.009) and middle temporal lobe (p=0.040). But no association was found with CSF phosphorylated-tau (p-tau) or CSF p-tau/Aβ42. Longitudinal studies showed that higher BMI individuals experienced a slower decline in cognitive function. In addition, Kaplan-Meier survival analysis revealed that higher late-life BMI had a reduced risk of progression to AD over time (p = 0.009). Higher BMI in late-life decreased the risk of AD, and this process may be driven by AD-related biomarkers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".