Epigenetic Dysregulation of Insulin-like Growth Factor (IGF)-related Genes and Adverse Pregnancy Outcomes: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA) are leading causes of neonatal mortality and morbidity around the world. Epigenetic alterations of the human genome may be involved in the causal chain of adverse pregnancy outcomes. In this systematic review we investigated whether PTB, LBW and SGA are associated with epigenetic dysregulation of insulin-like growth factor-related genes (IGF). METHODS: We searched MEDLINE and EMBASE for peer-reviewed articles about IGF and PTB, LBW and SGA published up to February 2015. Two independent reviewers selected original, controlled, human studies published in any language and graded them using the Newcastle-Ottawa Quality Assessment Scale. Disagreements were resolved by consensus with a third reviewer. RESULTS: Eighteen observational studies of low-to-moderate quality met the eligibility criteria out of 210 unique studies. There was substantial heterogeneity across studies. Most studies reported no, limited or borderline association between epigenetic changes (methylation or imprinting) of IGF-related genes and LBW or SGA. There were no IGF-related epigenetic studies of PTB. CONCLUSIONS: Overall, evidence of an association between epigenetic abnormalities of IGF-related genes and LBW or SGA was weak and inconsistent. Methodological concerns limited results validity.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".