Ultra-Processed Food, Disease Activity, and Inflammation in Ulcerative Colitis: The Manitoba Living With IBD Study
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to investigate the relationship between ultra-processed food (UPF) consumption and (i) symptomatic disease and (ii) intestinal inflammation among adults with inflammatory bowel disease (IBD). METHODS: We identified participants (Crohn's disease [CD] and ulcerative colitis [UC]) from the Manitoba Living with IBD study. Active disease was defined using the IBD Symptom Inventory (score >14 for CD; >13 for UC); fecal calprotectin was measured for intestinal inflammation (>250 μg/g). Diet data were collected using the Harvard Food Frequency Questionnaire. UPF consumption was determined by the NOVA classification system. Percentage of energy consumption from UPFs was calculated and divided into 3 tertiles (T1 = low; T3 = high). Multiple linear regression analysis was used for active disease and inflammation predicted by UPF consumption. RESULTS: Among 135 participants (65% with CD), mean number of episodes of active disease (14.2 vs 6.21) and active inflammation (1.6 vs 0.6) was significantly higher among participants with UC in T3 compared with T1 of UPF consumption ( P < 0.05). When adjusting for age, sex, disease type, and duration, number of episodes of active disease was lower in T1 compared with T3 (β = -7.11, P = 0.02); similarly, number of episodes of intestinal inflammation was lower in T1 (β = -0.95, P = 0.03). No significant differences were observed among participants with CD. DISCUSSION: UPF consumption may be a predictor of active symptomatic disease and inflammation among participants with UC. Reducing UPF consumption is a dietary strategy that can be suggested for minimizing symptoms and inflammation among people living 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 | 0.000 | 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 | 0.000 | 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".