Plasma Levels of Alanine Aminotransferase in the First Trimester Identify High Risk Chinese Women for Gestational Diabetes
Bibliographic record
Abstract
Alanine aminotransferase (ALT) predicts type 2 diabetes but it is uncertain whether it also predicts gestational diabetes mellitus (GDM). We recruited 17359 Chinese women with ALT measured in their first trimester. At 24-28 weeks of gestation, all women underwent a 50-gram 1-hour glucose challenge test (GCT) followed by a 75-gram 2-hour oral glucose tolerance test if GCT result was ≥7.8 mmol/L. Restricted cubic spline analysis was used to examine full-range risk associations of ALT levels with GDM. Relative excess risk due to interaction, attributable proportion due to interaction and synergy index were used to estimate additive interaction between high ALT and overweight/obesity for GDM. Finally, 1332 (7.7%) women had GDM. ALT levels were positively associated with GDM risk without a clear threshold. Using ALT levels <22 U/L as the referent, the middle ALT levels (≥22 to <40 U/L) [odds ratio (OR) (95% confidence intervals): 1.41(1.21-1.65)] and high ALT levels (≥40 U/L) [1.62 (1.31-2.00)] were associated with increased GDM risk. Maternal overweight/obesity greatly enhanced the OR of ALT ≥22 U/L from 1.44 (1.23-1.69) to 3.46 (2.79-4.29) with significant additive interactions. In conclusion, elevated ALT levels in the first trimester even within normal range predicted GDM risk, further enhanced by overweight/obesity.
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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 | 0.001 | 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".