Association Between Change in Inflammatory Aspects of Diet and Change in IBD-related Inflammation and Symptoms Over 1 Year: The Manitoba Living With IBD Study
Bibliographic record
Abstract
BACKGROUND: We aimed to investigate (1) the stability of inflammatory aspects of diet over 1 year among persons with inflammatory bowel disease (IBD) and (2) the impact of change in diet on changes in inflammation and IBD symptoms over 1 year. METHODS: Participants were recruited to the Manitoba Living with IBD Study and completed the Harvard Food Frequency Questionnaire (FFQ). The Dietary Inflammatory Index (DII) and the Empirical Dietary Inflammatory Index (EDII) were used to calculate the inflammatory potential of the diet. Inflammation was measured by fecal calprotectin (≥250 µg/g). Symptoms were measured by the IBD Symptom Inventory (IBDSI). All measures were obtained at baseline and 1 year. Dietary Inflammatory Index and Empirical Dietary Inflammatory Index scores >0 and <0 reflect pro- and anti-inflammatory diet, respectively. Variance components analyses were used to describe diet stability. Associations between changes in diet and changes in active inflammation and symptoms were assessed using ordinal logistic regression and multilevel linear regression modeling. RESULTS: One hundred thirty-five participants (66% CD) were included. Approximately one third of the variance in EDII (36%) and DII (33%) scores was explained by changes in diet over time. Each unit increase in the change in EDII (baseline to follow-up) was associated with a greater odds of FCAL, indicating active inflammation (>250 µg/g; odds ratio, 3.1; 95% confidence interval [CI], 1.02-9.93; P = 0.04) and with a rise in IBDSI of 6.7 (95% CI, 1.0-12.4; P = 0.022; theoretical IBDSI range, 0-81). There was no association between changes in DII and changes in FCAL or IBDSI. CONCLUSION: The EDII, but not the DII, may have utility to identify the inflammatory potential of diet. This inflammatory potential can contribute to inflammation and/or disease symptoms in persons with IBD.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".