Discovery of a DNA methylation episignature as a molecular biomarker for fetal alcohol syndrome
Bibliographic record
Abstract
PURPOSE: Fetal alcohol spectrum disorder (FASD) encompasses a range of clinical features and neurodevelopmental disorders in children exposed to alcohol in utero. Despite its global public health significance, FASD diagnosis remains challenging because of nonspecific clinical findings and the lack of an accurate molecular diagnostic biomarker. This study aimed to evaluate peripheral blood DNA methylation (DNAm) profiles as a potential diagnostic biomarker for fetal alcohol syndrome. METHODS: Genomic DNAm profiles from 93 individuals with suspected or confirmed FAS, including a clinically diagnosed FAS subgroup, were analyzed and compared with a large database of control and patient cohorts with previously reported DNAm episignatures. Functional analysis of these DNAm profiles was performed to identify episignatures and assess their potential diagnostic utility. RESULTS: A relatively sensitive and specific DNAm episignature for FAS was identified. Comparative epigenomic analysis revealed functional correlations between FAS and other rare genetic disorders, supporting the robustness of the identified DNAm profiles as a diagnostic tool. CONCLUSION: This study demonstrates that unique DNAm profiles provide a robust episignature biomarker for FAS. These findings contribute to the molecular understanding of FAS and hold promise for improving diagnostic accuracy for this complex disorder.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".