Prevalence of Poor Mental Health and Cognitive Status among Middle-Aged Adults and Its Predictors in Relation to Polyphenols Intake
Bibliographic record
Abstract
Background: Decline in mental health and cognitive status starts to show its sign during middle-age and is affected by dietary factors, namely the polyphenols intake.Polyphenols have received attention in improving health issues related to aging, including decline in mental health and cognitive.The aim of this study is to determine the prevalence of poor mental health and cognitive status among middle-aged adults and its predictors in relation to polyphenols intake.Methods: Subjects' food intakes were calculated by using dietary history questionnaire and food frequency questionnaire for polyphenols.The subjects' mental health and cognitive status were measured by general health questionnaire-28 (GHQ-28) and Rey's auditory verbal learning test (RAVLT).Results: More than 40% of middle-aged adults were identified as having signs of poor mental health.A total of 67.9% of the subjects had poor cognitive status according to RAVLT immediate recall.Hierarchical binary logistic regression indicated that fat intake was associated with somatic symptoms for both men [adjusted odds ratio (AOR) = 1.04;P < 0.05] and women (AOR = 1.06;P < 0.05).Intake of lignan (AOR = 1.071;P < 0.05) was associated with better RAVLT immediate recall among women.Additionally, high cholesterol (AOR = 3.14; P < 0.05) was associated with poor score of RAVLT delayed recall for women.Conclusions: Early detection of poor mental health and cognitive is crucial to prevent Alzheimer's disease in old age.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".