Dietary Inflammatory Index in relation to psoriasis risk, cardiovascular risk factors, and clinical outcomes: a case-control study in psoriasis patients
Bibliographic record
Abstract
Psoriasis is an inflammatory skin disease. Despite the understanding of disease pathogenesis, the link between diet-induced inflammation and the risk of psoriasis remains underexplored. Therefore, we examined the capability of the literature-derived energy-adjusted Dietary Inflammatory Index (E-DII) as a predictive tool for inflammation, incidence, and severity of psoriasis (as indexed by the Psoriasis Area Severity Index (PASI)). We conducted a case-control study of 149 adults (75 cases and 74 controls). The E-DII score was calculated based on the dietary intake that was evaluated using a validated 168 item quantity food-frequency questionnaire. The E-DII tertile cut-offs were categorized based on the following cut points: tertiles 1 ≤ −1.99; tertiles 2 = −2.00 to 0.60; tertile 3 ≥ 0.61. Logistic regression models were used to estimate the multivariable odds ratio (OR) adjusted for confounders. Patients with higher pro-inflammatory E-DII had a 3.60-times increased risk of psoriasis relative to patients in tertiles 1 (E-DIIT3 vs E-DIIT1: OR = 3.64; 95% confidence interval (CI) 1.51 to 8.79, P = 0.005). The severity of disease as indexed by PASI remained associated with E-DII (E-DIIT3 vs E-DIIT1: OR = 3.64; 95% CI 1.74 to 7.57, P = 0.015). For each unit increase in E-DII, the probability of disease severity is increased 3 times. Patients consuming a more pro-inflammatory diet were at a greater risk of psoriasis. These patients also demonstrated increased disease severity relative to individuals consuming a more anti-inflammatory diet. Novelty: A pro-inflammatory diet is associated with higher psoriasis incidence. Subjects with higher DII scores had higher inflammatory markers levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".