Methylation risk scores for childhood aeroallergen sensitization: Results from the LISA birth cohort
Bibliographic record
Abstract
Background It has been hypothesized that epigenomic modifications such as genomic methylation changes are an intermediate step linking environmental exposures with allergic disease development. Associations between individual DNA methylation CpG sites and allergic diseases have been reported, but they have not been assessed regarding their joint predictive capability. Methods Data were obtained from 240 children of the German LISA cohort. Blood-based DNA methylation was measured at six and ten years. Aeroallergen sensitization, at least RAST class 1, was measured in blood at six, ten and 15 years. We calculated six methylation risk scores (MRS) for allergy-related phenotypes based on available publications and assessed their performance both cross-sectionally and prospectively. Dose-response associations between aeroallergen sensitization and MRS, their correlation and mapping of common hits were evaluated. Results All six atopy-related MRS were highly correlated (r>0.86) and seven CpGs were included in more than one MRS. Cross-sectionally, we observed an 80% increased risk for aeroallergen sensitization at six years with an increased risk score by one standard deviation (best MRS: relative risk = 1.81, 95% confidence interval = [1.43; 2.27]). Significant associations were also seen at ten years and in prospective models, though the effect of the latter was attenuated when only including participants not sensitized at baseline. A clear dose-response relationship with RAST classes of aeroallergen sensitization could be established cross-sectionally, but not prospectively. Conclusion We found good classification and prediction capabilities of calculated allergy-related MRS, particularly cross-sectionally for the allergy prevalence, underlining the relevance of altered gene-regulation in allergic diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".