Cohort profile: Celiac disease genomic, environmental, microbiome and metabolome study; a prospective longitudinal birth cohort study of children at-risk for celiac disease
Bibliographic record
Abstract
The Celiac Disease Genomic, Environmental, Microbiome and Metabolomic (CDGEMM) study is an international prospective birth cohort in children at-risk of developing celiac disease (CD). The CDGEMM study has been designed to take a multi-omic approach to predicting CD onset in at-risk individuals. Participants are required to have a first-degree family member with biopsy diagnosed CD and must be enrolled prior to the introduction of solid food. Participation involves providing blood and stool samples longitudinally over a period of five years as well as answering questionnaires related to the participant, their family, and environment. Recruitment and data collection have been ongoing since 2014. As of 2022 we have a total of 554 participants and the average age of the cohort is 56.4 months. A total of 54 participants have developed positive antibodies for CD and 31 have confirmed CD. Approximately 80% of the 54 participants with CD have developed it by 3 years of age. To date we have identified several microbial strains, pathways, and metabolites occurring in increased abundance and detected before CD onset, which have previously been linked to autoimmune and inflammatory conditions while others occurred in decreased abundance before CD onset and are known to have anti-inflammatory effects. Our ongoing analysis includes expanding our metagenomic and metabolomic analyses, evaluating environmental risk factors linked to CD onset, and mechanistic studies investigating how alterations in the microbiome and metabolites may protect against or contribute to CD development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".