Polymorphisms in genes within the IGF-axis influence antenatal and postnatal growth
Bibliographic record
Abstract
Two pregnancy cohorts were used to investigate the association between single-nucleotide polymorphisms (SNPs) in genes within the insulin-like growth factor (IGF)-axis and antenatal and postnatal growth from birth to adolescence. Longitudinal analyses were conducted in the Raine pregnancy cohort (n = 1162) using repeated measures of fetal head circumference (HC), abdominal circumference (AC) and femur length (FL) from 18 to 38 weeks gestation and eight measures of postnatal height and weight (1-17 years). Replications of significant associations up to birth were undertaken in the Generation R Study (n = 2642). Of the SNPs within the IGF-axis genes, 40% (n = 58) were associated with measures of antenatal growth (P ⩽ 0.05). The majority of these SNPs were in receptors; IGF-1R (23%; n = 34) and IGF-2R (13%; n = 9). Fifteen SNPs were associated with antenatal growth (either AC or HC or FL) in Raine (P ⩽ 0.005): five of which remained significant after adjusting for multiple testing. Four of these replicated in Generation R. Associations were identified between 38% (n = 55) of the IGF-axis SNPs and postnatal height and weight; 21% in IGF-1R (n = 31) and 9% in IGF-2R (n = 13). Twenty-six SNPs were significantly associated with both antenatal and postnatal growth; 17 with discordant effects and nine with concordant effects. Genetic variants in the IGF-axis appear to play a significant role in antenatal and postnatal growth. Further replication and new analytic methods are required in order to better understand this key metabolic pathway integrating biologic knowledge about the interaction between IGF-axis components.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".