Gestational Weight Gain: Is the Role of Genetic Variants a Determinant? A Review
Bibliographic record
Abstract
Excessive or insufficient gestational weight gain (GWG) leads to diverse adverse maternal and neonatal outcomes. There is evidence that pregestational body mass index (pBMI) plays a role in GWG, but no genetic cause has been identified. In this review, we aim to analyze genotype variants associated with GWG. Results: We identified seven genotype variants that may be involved in GWG regulation that were analyzed in studies carried out in Brazil, Romania, the USA, Turkey, Ukraine, and Canada. Some genetic variants were only associated with GWG in certain races or depending on the pBMI. In women who were obese or overweight before gestation, some genetic variants were associated with GWG. Environmental and genetic factors together showed a greater association with GWG than genetic factors alone; for example, type of diet was observed to have a significant influence. Conclusions: We found little scientific evidence of an association between genotype variants in countries with a high prevalence of women of reproductive age who are overweight and obese, such as in Latin America. GWG may be more dependent on environmental factors than genetic variants. We suggest a deeper study of genetic variants, cytokines, and their possible association with GWG, always with the respective control of potential cofounding factors, such as pBMI, diet, and race.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".