Diet-Related Inflammation is Associated with Major Depressive Disorder in Bahraini Adults: Results of a Case-Control Study Using the Dietary Inflammatory Index
Bibliographic record
Abstract
PURPOSE: This case-control study aimed to examine the association between the inflammation potential of the diet and depression. PATIENTS AND METHODS: ) scores were calculated based on the participants' responses to a detailed nutritional assessment using a quantitative food frequency questionnaire. Descriptive results were reported. Logistic regression was used to estimate odds ratios and 95% confidence intervals associated with E-DII scores and depression adjusting for covariates including age, sex, body mass index, comorbidities, level of education, and employment. RESULTS: The mean E-DII score of participants in the study was 1.32±1.08, indicating a generally pro-inflammatory diet. Patients with depression had elevated E-DII scores compared to age- and sex-matched controls with E-DII scores of 1.72± 1.09 vs 0.90±0.90, respectively (P=0.001). Results of logistic regression analysis revealed that patients with depression were nearly three times more likely to be in the extreme ends of pro-inflammatory diet (OR 2.75; 95% CI 1.82-4.15). CONCLUSION: Adult Bahraini patients with depression have higher dietary inflammation potential compared to the general population controls. Further research is needed to confirm these results and provide effective interventions to reduce the burden of this dietary inflammation.
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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 | 0.000 |
| 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".