Regular consumption of fruits, vegetables, and whole grain foods is associated with fewer depressive symptoms in older adults: a cross-sectional analysis of EpiFloripa Aging cohort study, Brazil
Bibliographic record
Abstract
Diet has been postulated as a modifiable risk factor for the onset of depression. Here, we tested the hypothesis that weekly and daily consumption of healthy food decreases the prevalence ratio (PR) of depressive symptoms in older adults. Data from 1197 participants from the third wave of data collection of the EpiFloripa Aging cohort study (2017-2019) were used. Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS-15); the frequency of consumption of healthy foods (fruits, vegetables, whole food, fish, and beans) were collected through a questionnaire to evaluate the regular consumption of the food groups (≥5 times/week for fruits, vegetables, beans and whole grain; ≥2 times/week for fish; ≥5 times/day for fruits and vegetables combined). Poisson regression was used to examine the associations between food intake and depressive symptoms. A Directed Acyclic Graph (DAG) was created to define the minimal adjustment model. The prevalence of depressive symptoms was 14.6%. A statistically significant inverse association was found between regular consumption of healthy food and depressive symptoms: ≥5 times/week for fruits (PR = 0.71 [95%CI: 0.56, 0.90]), vegetables (0.81 [0.68, 0.96]), beans (0.84 [0.74, 0.96]), and whole grains (0.86 [0.74, 0.99]); once a week for fish consumption (0.82 [0.71, 0.95]); 2-4 times/day (0.80 [0.65, 0.97]) and ≥5 times/day (0.75 [0.58, 0.96]) for fruits and vegetables (FV). Our results suggest that older adults who regularly consume healthy food are less likely to experience depressive symptoms. Further longitudinal studies are necessary to understand the underlying mechanisms in the relationship between diet and depression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".