The Association between Energy‐Adjusted Dietary Inflammatory Index, Body Composition, and Anthropometric Indices in COVID‐19‐Infected Patients: A Case‐Control Study in Shiraz, Iran
Bibliographic record
Abstract
Background and Aims. Inflammation is strongly associated with the severity and mortality rate of SARS‐CoV‐2 disease (COVID‐19). Dietary factors have a crucial role in preventing chronic and systemic inflammation. This study aimed to evaluate the association between energy‐adjusted dietary inflammatory index (E‐DII) scores and body composition parameters in COVID‐19‐infected patients compared to noninfected controls. Methods. A total of 133 COVID‐19‐infected patients and 322 noninfected controls were selected and enrolled from the Cohort Study of Employees of Shiraz University of Medical Sciences. E‐DII score was calculated based on a validated food frequency questionnaire (FFQ) and body composition was measured using In‐Body 770 equipment. Logistic regression models were utilized to estimate the odds ratio (OR). Results. In the control group, the mean E‐DII score was significantly lower than the case group (−2.05 vs. −0.30, P ≤ 0.001), indicating that the diet of COVID‐19‐infected subjects was more proinflammatory than the controls. For every 1 unit increase in E‐DII score, the odds of infection with COVID‐19 was nearly triple (OR: 2.86, CI: 2.30, 3.35, P ≤ 0.001). Moreover, for each unit increase in body mass index (BMI), the odds of infection to COVID‐19 increased by 7% (OR: 1.07, CI: 1.01, 1.13, P = 0.02). No significant difference was observed for other anthropometric parameters. Conclusion. The findings revealed that obese people and those consuming a more proinflammatory diet were more susceptible to coronavirus infection. Therefore, maintaining ideal body weight and consuming a more anti‐inflammatory diet can decrease the probability of COVID‐19 infection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".