The Adherence to Mediterranean Diet Moderates the Association Between Medical Multi-morbidity and Depressive Symptoms in Elderly Outpatients
Bibliographic record
Abstract
Background Depressive symptoms in the elderly are related to the advancing of age, loss of life purpose, medical multi-morbidity, cognitive decline and social-economic problems mounting evidence suggests that lifestyle behaviors and certain dietary patterns may improve mood and overall well-being in older adults. In the present study we investigated (i) the association of adherence to Med-Diet with depressive symptoms and multi-morbidity in a cohort of geriatric medical outpatients and (ii) the role of Med-Diet in mediating the association between depressive symptoms and multi-morbidity. Methods Morbidity was assessed using the severity index of cumulative illness rating scale for geriatrics (CIRSG-SI). Montreal cognitive assessment (MoCA) and geriatric depression scale (GDS) were administrated to evaluate cognitive and depressive symptoms. Adherence to Med-Diet was evaluated using the Med-Diet 14-Item questionnaire (MDQ). Pearson correlation was used to test association between variables. The Preacher and Hayes’ strategy was used to test the mediational model. Results One hundred and forty-three subjects were included in the study. Significant inverse correlations of MDQ with GDS (r = -0.317; P < 0.001) and CIRSG-SI (r = -0.247; P = 0.003) were found, with and without adjustment for potential confounders. A direct correlation between CIRSG-SI and GDS was also observed (r = 0.304; P = 0.001), with this association being moderated by MDQ (b = 0.386; P = 0.047). Conclusion These findings (i) add to the accumulating evidence that Med-Diet is crucially involved in the regulation of physical and mental health of elderly people, and (ii) suggest that a Mediterranean-style diet may contribute to protect elderly subjects with higher levels of polypathology/multi-morbidity from the development of depressive symptoms. Disclosure of interest The authors have not supplied their declaration of competing interest.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".