Insulin-Like Growth Factor Axis Biomarkers and Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The insulin-like growth factor (IGF) axis has been implicated in glucose homeostasis. It is plausible to hypothesize that the IGF axis is involved in the development of gestational diabetes mellitus (GDM). In a systematic review of the evidence on IGF axis biomarkers in relation to GDM, we searched the PubMed and EMBASE for publications up to May 31, 2018, on the associations of circulating IGF axis biomarkers with GDM. Eligible studies must meet the pre-specified quality assessment criteria. Meta-analyses were conducted where there were at least three studies on the same biomarker at the same gestational age window-early (<20 weeks), mid (20-29 weeks), or late (30+ weeks) gestation. Twelve studies were included (484 GDM, 1755 euglycemic pregnancies). Meta-analyses showed that GDM was consistently associated with higher IGF-I concentrations in mid-gestation (six studies) and late gestation (six studies). There were only two studies on IGF-I in early gestation and GDM with inconsistent findings. GDM was associated with lower IGFBP-2 concentrations in early, mid-, or late gestation, according to data from one or two studies. GDM was associated with higher IGFBP-3 concentrations in late gestation according to a meta-analysis of five studies. There was no association with GDM for IGFBP-3 in early or mid-gestation, according to data from one study. Other IGF axis biomarkers (IGF-II, IGFBP-1,-4,-5-6, and -7) showed no or inconsistent associations, and the data at early gestation were scanty or absent. Available evidence is suggestive but inconclusive concerning whether the IGF axis is involved in the development of GDM. More studies on IGF axis biomarkers in early gestation are warranted. If a specific IGF axis molecule is proven to be involved in the development of GDM, this may point to a new molecular target for designing interventions to reduce the incidence of GDM.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".