Rethinking the Obesity Paradox: BMI and Longitudinal Cognitive Decline in Older Adults
Bibliographic record
Abstract
BACKGROUND: Higher body mass index (BMI) has often been linked to a heightened risk for cognitive decline; however, emerging evidence suggests a potential "obesity paradox," in which overweight or obese older adults may actually experience a slower rate of cognitive decline. Using a large, longitudinal cohort of older adults from 25 sites across the United States, we examined the relationship between BMI and cognition. METHOD: Data were drawn from the National Alzheimer's Coordinating Center (NACC) database over five annual visits. Cognition was measured by the Montreal Cognitive Assessment (MoCA). Clinically-assessed BMI was categorized as normal, overweight, and obese. Time-varying covariate measures included age and presence of clinically-assessed comorbidities (i.e., diabetes, hyperlipidemia, and hypertension). To test the association between MoCA and BMI, we used linear-mixed effects regression models with random intercepts and fixed effects for BMI, time, and their interaction with covariate measures. We performed sensitivity analyses stratified by age (55-75, and ≥ 76). RESULT: In the full sample (n =526 participants, p =2630 observations), total raw MoCA scores significantly decreased from visit 1 to visit 5 (β = -0.71, p<0.05). Age was negatively associated with MoCA (β = -0.09, p<0.5). There was no significant main effect of BMI on MoCA, however there were significant interactions between BMI categories and time. Specifically, obese (β = 0.86, p< 0.05) and overweight (β = 0.95, p <0.05) participants showed a slower rate of decline in MoCA over five visits compared to normal-weight individuals. The same pattern of results held for participants aged 55-75 as in the full sample. However, among those 76 years and older, being overweight or obese no longer significantly reduced the MoCA decline over the five visits. Comorbidities were not associated with MoCA in any of the models. CONCLUSION: In contrast to some previous research, our findings suggest a complex, and at times contradictory, relationship between weight status and MoCA score, where overweight or obesity may modestly buffer against cognitive decline based on age. Further research should explore potential age-dependent effects, clarify casual pathways, and determine whether targeted interventions around BMI might modify late-life cognitive trajectories.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".