Body mass index predicts cognitive aging trajectories selectively for females: Evidence from the Victoria Longitudinal Study.
Bibliographic record
Abstract
OBJECTIVE: Elevated body weight in midlife is an established risk factor for accelerated cognitive decline, impairment, and dementia. Research examining the impact of later-life body mass index (BMI) on normal cognitive aging has produced mixed results. There is a need for longitudinal designs, replication across multiple cognitive domains, and consideration of BMI effects in the context of important moderators. The present research examined (a) BMI prediction of neuropsychological performance and decline in executive function (EF), neurocognitive speed, and memory and (b) sex stratification of BMI effects. METHOD: age = 71.75, range = 53-85 years) were older adults from the Victoria Longitudinal Study. Latent growth modeling was used to examine BMI as a predictor of level and change in three latent variables of cognition. The data were then stratified by sex to test whether BMI effects differed for females and males. We adjusted for selected medical, psychosocial, and demographic characteristics. RESULTS: Higher BMI predicted less decline in EF, neurocognitive speed, and memory. Interestingly, when the data were stratified by sex, higher BMI predicted less neuropsychological decline across domains for females only. BMI was unrelated to cognitive aging trajectories for males. CONCLUSIONS: We found that elevated BMI was a risk-reducing factor for cognitive decline only for females. Results may be used to enhance the precision with which intervention protocols may target specific subgroups of older adults. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".