Analysis of 61 exclusive enteral nutrition formulas used in the management of active Crohn’s disease—new insights into dietary disease triggers
Bibliographic record
Abstract
BACKGROUND: Exclusive enteral nutrition (EEN) is an effective treatment for Crohn's disease. AIMS: To investigate the hypothesis that ingredients of EEN formulas are unlikely to initiate a disease flare and that their dietary elimination is not essential for disease amelioration. METHODS: We performed compositional analysis of EEN formulas with evidence of efficacy in management of active Crohn's disease. Macronutrient content was compared against the dietary reference values (DRV), the UK National Diet and Nutrition Survey (NDNS) and intake of Crohn's disease children. Food additives were cross-referenced against the FAO/WHO database. RESULTS: Sixty-one formulas were identified with variable composition (carbohydrates [22.8%-89.3%], protein [7.8%-30.1%], fat [0%-52.5%]). Maltodextrin, milk protein and vegetable/plant oils were the commonest macronutrient sources. Their n-6:n-3 fatty acid ratio varied from 0.25 to 46.5. 56 food additives were identified (median per formula: 11). All formulas were lactose-free, gluten-free, and 82% lacked fibre. The commonest food additives were emulsifiers, stabilisers, antioxidants, acidity regulators and thickeners. Food additives, implicated in Crohn's disease aetiology, were present in formulas (modified starches [100%], carrageenan [22%], carboxymethyl cellulose [13%] and polysorbate 80 [5%]). Remission rates did not differ between EEN formulas with and without those food additives. Analysis including only formulas from randomised controlled trials (RCTs) retained in the latest Cochrane meta-analysis produced similar findings. EEN formulas contained less energy from saturated fat than NDNS intake. CONCLUSION: We have identified food ingredients which are present in EEN formulas that are effective in Crohn's disease and challenge perceptions that these ingredients might be harmful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".