Serum Sulfur-Containing Amino Acids and Risk of Maternal Gestational Diabetes and Adverse Growth Patterns in Offspring
Bibliographic record
Abstract
BACKGROUND: To estimate associations of sulfur-containing amino acids (SAAs) in the early trimester of pregnancy and gestational diabetes mellitus (GDM) and estimate associations of maternal SAAs with adverse growth patterns in offspring. METHODS: We established a 1:1 matched case-control study (n = 486) from our cohort of pregnant women, and 401 children were followed up at ages 1 to 8 years. We conducted binary conditional logistic regression to estimate the risk associations of serum SAAs with GDM. Multinomial logistic regression was implemented to explore associations of maternal SAAs with adverse growth patterns in the offspring. RESULTS: High serum methionine and cystine were independently associated with increased GDM risk (OR: 1.92, 95%CI: 1.18-3.13 and 2.69, 1.59-4.53). Conversely, a low level of serum taurine was independently associated with increased GDM risk (2.61, 1.64-4.16). Maternal high cystine and low taurine were also associated with an increased risk of persistent obesity growth pattern (POGP) in offspring (OR: 2.79, 95%CI: 1.09-7.17 and 3.92, 1.11-13.89) and the effect was largely independent of GDM. CONCLUSIONS: High serum methionine, cystine and low serum taurine in the early trimester of pregnancy were associated with a greatly increased risk of GDM. Maternal high cystine and low taurine were associated with elevated risk of offspring POGP, largely independent 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".