Serum Newborn Screening Blood Metabolites Are not Associated With Childhood-onset Inflammatory Bowel Disease: A Population-based Matched Case-control Study
Bibliographic record
Abstract
BACKGROUND: Originally used for screening of inborn errors of metabolism, routine metabolite profiles of newborns have also been associated with prematurity and some childhood diseases. We sought to determine whether metabolites measured during routine newborn screening could identify infants who develop inflammatory bowel disease (IBD) in childhood. METHODS: We conducted a population-based matched case-control study using health administrative data from Ontario, Canada. Children born 2006 to 2015 with IBD were identified using a validated algorithm and matched to 5 controls based on birth date, sex, rural/urban household, and mean neighborhood income quintile at birth. Cases and controls were linked deterministically to metabolic profiles from Newborn Screening Ontario. We fit a lasso penalized logistic regression model and used 10-fold cross-validation to obtain internally valid performance measures. Models included metabolites, amino acids, and endocrine markers. Models also included ratios of metabolites, gestational age, birth weight, mode of delivery, age at serum collection, maternal age at delivery, maternal history of IBD, and parity. RESULTS: Three hundred eight cases of IBD, diagnosed at 5.5 ± 2.8 years, were matched to 1540 controls. No individual metabolites were associated with IBD. The c-statistic was 0.50 for the training data. After 10-fold cross-validation the C statistic was 0.50, indicating no significant association between metabolites and IBD diagnosis. CONCLUSIONS: Newborn screening serum metabolites could not identify children who will develop IBD in this population-based cohort. Future studies with an expanded panel of metabolites may provide improved prediction of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".