Are dopamine-related genotypes risk factors for excessive gestational weight gain?
Bibliographic record
Abstract
BACKGROUND: Excessive gestational weight gain is associated with postpartum weight retention and downstream child obesity. Dopamine plays a critical role in the regulation of energy intake and body weight. The purpose of this study was to examine the relationship between excessive gestational weight gain and dopamine pathway-related polymorphisms, namely the variable nucleotide tandem repeat in the 3'untranslated region (UTR) region of the SLC6A3 (DAT-1) dopamine transporter gene and the 30-base pair variable nucleotide tandem repeat polymorphism of the 5'UTR of the monoamine oxidase-A (MAO-A) gene. METHODS: Ninety-three women of mean age 31.7 ± 4.2 years were recruited from the Ottawa and Kingston birth cohort and assessed at 12-20 weeks' gestation. Mean body mass index was 22.7 ± 2.5 kg/m(2). Excessive gestational weight gain was defined according to the 2009 Institute of Medicine guidelines based on body mass index. Genotype analyses were performed using polymerase chain reaction and agarose gel electrophoresis. RESULTS: There was no relationship between the prevalence or magnitude of excessive gestational weight gain among women with the 3' UTR single nucleotide polymorphism of the DAT-1 gene. However, 70% (19 of 27) of women carrying the MAO-A 4/4 (high activity) allele exceeded recommendations for gestational weight gain compared with 48% (32 of 60) of those with the pooled 3/3, 3/4, and 3/3.5 (low activity) alleles (P < 0.05). Similarly, those with the MAO-A 4/4 allele had significantly greater gestational weight gain than those with the 3/3, 3/4, or 3/3.5 pooled genotypes (19.3 ± 4.1 versus 17.0 ± 5.0 kg, P = 0.03). CONCLUSION: Carriers of the 4/4 variants of the MAO-A gene may be at increased risk for excessive gestational weight gain.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".