Methylation risk scores for childhood aeroallergen sensitization: Results from the LISA birth cohort
Bibliographic record
Abstract
BACKGROUND: Epigenomic (e.g., DNA methylation [DNAm]) changes have been hypothesized as intermediate step linking environmental exposures with allergic disease. Associations between individual DNAm at CpGs and allergic diseases have been reported, but their joint predictive capability is unknown. METHODS: Data were obtained from 240 children of the German LISA cohort. DNAm was measured in blood clots at 6 (N = 234) and 10 years (N = 227) using the Illumina EPIC chip. Presence of aeroallergen sensitization was measured in blood at 6, 10, and 15 years. We calculated six methylation risk scores (MRS) for allergy-related phenotypes, like total and specific IgE, asthma, or any allergies, based on available publications and assessed their performances both cross-sectionally (biomarker) and prospectively (predictor of the disease). Dose-response associations between aeroallergen sensitization and MRS were evaluated. RESULTS: All six allergy-related MRS were highly correlated (r > .86), and seven CpGs were included in more than one MRS. Cross-sectionally, we observed an 81% increased risk for aeroallergen sensitization at 6 years with an increased MRS by one standard deviation (best-performing MRS, 95% confidence interval = [43%; 227%]). Significant associations were also seen cross-sectionally at 10 years and prospectively, though the effect of the latter was attenuated when restricted to participants not sensitized at baseline. A clear dose-response relationship with levels of aeroallergen sensitization could be established cross-sectionally, but not prospectively. CONCLUSION: We found good classification and prediction capabilities of calculated allergy-related MRS cross-sectionally, underlining the relevance of altered gene-regulation in allergic diseases and providing insights into potential DNAm biomarkers of aeroallergen sensitization.
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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 | 0.001 | 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".