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Record W4391164436 · doi:10.1093/ecco-jcc/jjad212.0451

P321 Development and validation of an Integrated Risk Score for future risk of Crohn’s disease in healthy first-degree relatives: The CCC-GEM Project, a multicentre prospective cohort study

2024· article· en· W4391164436 on OpenAlexaffabout
Williams Turpin, Osvaldo Espin‐Garcia, Haim Leibovitzh, Meilan Xue, Juan A. Raygoza Garay, L. Grana, Michelle I. Smith, Ashleigh Goethel, Krishnamurthy L, Irit Avni‐Biron, Iris Dotan, Ben Horin Shomron, Anne M. Griffiths, A. Hillary Steinhart, Mark S. Silverberg, Dan Turner, Çharles N. Bernstein, Brian G. Feagan, P Moayeddi, Andrew D. Paterson, David S. Guttman, Wei Xu, Kenneth Croitoru

Bibliographic record

VenueJournal of Crohn s and Colitis · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMcMaster UniversityWestern UniversityUniversity of ManitobaPublic Health OntarioHospital for Sick ChildrenPopulation Health Research InstituteUniversity of AlbertaUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsCrohn's diseaseMedicineCohortProspective cohort studyDiseaseFramingham Risk ScoreCohort studyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Although the cause of Crohn’s disease (CD) is unknown, recent studies have identified a number of biomarkers associated with the risk of developing CD in healthy at-risk individuals. Establishing a combined prediction model that stratifies the future risk of CD in healthy at-risk individuals is the first step towards disease prevention and a better understanding of the preclinical phase of CD. Methods We recruited healthy first-degree relatives (FDRs) of persons with CD from 2008-2017 as part of the global multicenter prospective Crohn’s and Colitis Canada Genetic Environmental Microbial (CCC-GEM) Project. After collecting demographic information, blood, urine, and stool samples at recruitment, participants were followed for the development of CD. A GEM-integrative risk score (GEM-IRS), using random survival forest modeling that combined the baseline variables (demographics, measures of gut inflammation – fecal calprotectin, intestinal barrier function – urinary fractional excretion ratio of lactulose to mannitol, and fecal microbiome composition and predicted functional capacities – based upon 16S rDNA sequencing and PICRUSt2) to estimate time-to-CD onset, was derived and subsequently validated in two independent testing cohorts. Results Among 2,619 FDRs followed for a median of 6.8 years, 61 (2.3%) developed CD. The GEM-IRS, developed on the training cohort (n= 1,170), upon validation, showed a c-statistic of 0.789 in the pooled-testing cohorts (0.786 in the North American Testing cohort (n=1,141) and 0.804 in the Israeli Cohort (n=308)). The 4th quartile group compared to the rest had significantly increased risk of CD (hazard ratio [HR] 6.42, 95% confidence interval [CI], 3.10-13.30) in the pooled-testing cohorts (HR 5.89, 95% CI, 2.70-12.85 in North American Testing cohort; HR 10.7, 95% CI 1.24-92.65 in Israeli cohort). Furthermore, the GEM-IRS predicted CD in pre-specified subgroups of FDRs with minimal subclinical inflammation (fecal calprotectin<50 ug/g) or normal barrier function measures (lactulose/mannitol ratio<0.025), and up to 7 years before diagnosis in the pooled-testing cohort. Conclusion The GEM-IRS, which includes biomarkers of gut inflammation, intestinal barrier function, and the gut microbiome, is a valid risk stratification tool for predicting future development of CD in healthy first-degree relatives of persons with CD. The score may be used to guide preventative care for healthy first-degree relatives.

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)0.006
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.271
Teacher spread0.259 · 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 designObservational
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

Citations1
Published2024
Admission routes2
Has abstractyes

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