Lower Body Mass Index at Baseline Is Related to Steeper Cognitive Decline in the Alzheimer’s Disease Neuroimaging Initiative Cohort
Bibliographic record
Abstract
OBJECTIVE: Midlife obesity is a risk factor for dementia, whereas obesity in older age may be protective of cognition, a phenomenon known as the "obesity paradox." The mechanisms underlying this phenomenon and the relationship between body mass index (BMI) and cognitive function over time remain unclear. METHODS: In 1399 adults with and without mild cognitive impairment (median age 73.6 years) from the Alzheimer's Disease Neuroimaging Initiative, we modeled the effects of baseline BMI on within-person trajectories of cognitive decline using Latent Growth Curve Modeling. We also tested if the effects of BMI on cognitive decline are global or specific to memory, executive function, or language. RESULTS: Higher baseline BMI was associated with better memory ( βBMI = 0.06, p < .05) and worse executive function ( βBMI = -0.05, p < .05) and not associated with language. Independent of baseline diagnosis, higher baseline BMI was associated with slower rate of decline in executive function, memory, and language ( βBMI = 0.13, 0.12, and 0.12, respectively; p < .01). Higher BMI was not associated with the intercept ( βBMI = 0.04, p = .059) or change ( βBMI = 0.04, p = .415) in a global cognitive factor. CONCLUSIONS: We found that higher baseline BMI was associated with slower cognitive decline in participants with and without mild cognitive impairment diagnosis. Higher BMI in this context seems to be protective of cognitive function for people at risk for dementia. Our findings also support domain-specific effects of obesity on various cognitive functions rather than a final common pathway.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".