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Record W2999349368 · doi:10.1093/ecco-jcc/jjz203.820

P692 Exploring relationships between microbiome, faecal calprotectin and healthy eating index in patients with ulcerative colitis: Interim analyses of a randomised controlled trial

2020· article· en· W2999349368 on OpenAlexaffabout
Maitreyi Raman, Lorian Taylor, Alana Schick, Christina Ohland, Kathy D. McCoy, Sandeep Kaur, Remo Panaccione, Raylene A. Reimer

Bibliographic record

VenueJournal of Crohn s and Colitis · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineInternal medicineCalprotectinUlcerative colitisRandomized controlled trialFaecal calprotectinBody mass indexMicrobiomeInflammatory bowel diseaseGastroenterologyPhysical therapyDiseaseBiologyBioinformatics

Abstract

fetched live from OpenAlex

Abstract Background This study explored relationships between gut microbiome, faecal calprotectin (FCP) and an adapted Canadian healthy eating index (CHEI) in ulcerative colitis (UC) patients enrolled in a randomised controlled dietary intervention trial. Methods Patients with both active and quiescent disease were recruited from the Foothills Medical Center in Calgary, Alberta, Canada and randomised to either an 8-week reduced sulfur anti-inflammatory diet intervention (INT; n = 14) or conventional management control group (CM; n = 10). Each INT patient met with a registered dietitian for diet teaching, in person at baseline, over the phone at 2 weeks, and in person at 4 weeks. Stool samples and 24-h dietary recalls were collected at baseline and 8 weeks. DNA from stool samples was extracted and the V4 region of the 16S gene was sequenced. FCP was extracted and analyzed using the EK-CAL ELISA. An adapted CHEI was generated from diet recalls using previously validated scoring guidelines. Relationships between variables were analyzed using analysis of variance and chi-squared. Results Mean age of the sample was 36.3 (SD=8.7) and 58% were male. Baseline medications included aminosalicylates (71%), steroids (50%), biologics (33%), immunosuppressants (25%) and 21% of patients had taken antibiotics within the last 3 months. Α-diversity, or within-community diversity, significantly increased in the CM group and remained stable in the INT group over time (p = 0.005). Β-diversity, or between-community diversity appeared to increase in the intervention group over time (p < 0.01); however, this may have been influenced by antibiotic use in five patients. Significant differential features between the CM and INT groups (p < 0.01) at the genus level were identified in the phyla Firmicutes and Proteobacteria, specifically Catenibacterium, Parvimonas, Coprococcus_2 and Desulfovibrio. FCP appeared to be different between the groups (p = 0.05) with a greater percentage of INT patients moving from high FCP (>250 mg/g) to low FCP (<250 mg/g); 50% of INT patients and 20% of CM patients normalised FCP levels. As CHEI increased in the whole sample, indicating higher diet quality, FCP decreased significantly (p = 0.04). Conclusion In an interim analysis, a dietary intervention shows efficacy in manipulating the microbiome. Higher diet scores representing a healthier diet were also related to lower faecal calprotectin levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.059
GPT teacher head0.309
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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