Sex differences in the association between Body Mass Index and cognitive function in Parkinson disease: a cross-sectional study
Bibliographic record
Abstract
Objective This study utilized a binary logistic regression model to explore the relationship between Body Mass Index (BMI) and cognitive function in Parkinson’s disease (PD) patients. Methods In this cross-sectional study, data were obtained from 1,005 Parkinson’s patients enrolled in the Parkinson’s Progression Markers Initiative (PPMI) from 2010 to 2023, including 378 females and 627 males. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and the correlation between BMI and cognitive function was determined using binary logistic regression. Results The median age of enrollment was 63.6 (56.2, 69.6) years old, including 378 (37.6%) females and 627 (62.4%) males. In the final adjusted model, a significant positive correlation was found between BMI and the prevalence of cognitive impairment in females (OR = 1.06, 95% CI = 1.01 ~ 1.12, p = 0.022), while no correlation was found in males (OR = 1.03, 95% CI = 0.99 ~ 1.08, p = 0.165). The results after categorizing BMI indicate that, among females, the risk of cognitive impairment increases for both groups with BMI ≥ 30 kg/m2 and those with 25 ≤ BMI < 30 kg/m2 compared to the reference group with BMI < 25 kg/m2, with a p for trend <0.001 indicating a stable and strong association between BMI and cognitive impairment in females. In males, the results were not significant. The trend of linear fitting was consistent with the above results. Conclusion In female Parkinson’s patients, there is a positive correlation between BMI and cognitive impairment, while no correlation was found in male patients. This study provides new evidence of sex differences in the correlation between BMI and cognitive impairment among Parkinson’s patients. The role of sex differences in the relationship between BMI and cognitive impairment should be considered in future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".