Body mass index and cognitive functioning decline: Exploring the relationship
Bibliographic record
Abstract
BACKGROUND: Cognitive functions may play an important role in the management of obesity by promoting compliance towards lifestyle-related behaviours. This study aimed to identify cognitive deficits among adults and examine their association across different Body Mass Index (BMI) categories in an Indian setting. MATERIALS AND METHODS: The study is a cross-sectional survey of a sample attending a tertiary care hospital in northern India. The Montreal Cognitive Assessment (MoCA) scale was administered as part of an interview schedule to evaluate participants’ cognitive performance across eight domains. The responses were analyzed to investigate the association between BMI and total MoCA scores, as well as domain-specific MoCA scores. RESULTS: Three hundred forty-nine participants, with a mean age of 36.9 ± 10.9 years and a BMI of 26.7 ± 4.6 kg/m2, were recruited. BMI was found to be significantly associated with the total MoCA score, indicating a negative relationship ( P < 0.001). A significant negative association was found between six domain-specific scores, namely visuospatial, attention, language, abstraction, delayed recall ( P < 0.001), orientation ( P < 0.05), and BMI. CONCLUSION: An association between BMI and cognitive functioning (both overall and domain-specific) was observed, showing a dose-effect relationship. In these cases, visuospatial, attention, language, abstraction, delayed recall, and orientation were found to be affected.
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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 | 0.001 |
| 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 | 0.001 |
| 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".