Severe obesity and menopause symptoms are associated with cognitive impairment in postmenopausal women from Latin America
Bibliographic record
Abstract
Objective This study aimed to evaluate the association between obesity and cognitive impairment.Methods This study is a sub-analysis of an observational, cross-sectional study in nine Latin American counties. Sociodemographic, clinical and anthropometric data were collected, and cognition was assessed using the Montreal Cognitive Assessment (MoCA) tool in 722 postmenopausal women.Results The mean age, body mass index (BMI) and years of education of the cohort were 56.9 years, 26.8 kg/m2 and 13.6 years, respectively. Women with cognitive impairment, compared to those without, had a higher BMI (27.8 ± 5.9 vs. 26.6 ± 4.9 kg/m2, p = 0.037), had more children (3.1 ± 2.4 vs. 2.5 ± 1.7, p = 0.023), experienced more severe menopausal symptoms (56.3% vs. 31.9%, p < 0.001) and presented more comorbidities (60.0% vs. 43.8%, p = 0.006). They also had fewer years of study (10.8 ± 5.1 vs. 13.9 ± 4.9 years, p = 0.001), were less physically active (35.0% vs. 49.1%, p = 0.018) and were less likely to use menopausal hormone therapy (MHT) (11.3% vs. 28.8%, p = 0.001). In binary logistic regression analysis, BMI ≥ 35.0 kg/m2 (odds ratio [OR] 2.27, 95% confidence interval [CI] 1.08–4.76) and severe menopausal symptoms (OR 2.10, 95% CI 1.29–3.43) were associated with cognitive impairment. In the model, factors related to lower risk were ever use of MHT (OR 0.44, 95% CI 0.21–0.92) and having more years of education (OR 0.38, 95% CI 0.20–0.64).Conclusion Severe obesity and severe menopausal symptoms increased the risk of cognitive impairment in postmenopausal women, while higher education and ever use of MHT were protective factors.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".