Association of Circulating Branched-Chain Amino Acids with Gestational Diabetes Mellitus: A Meta-Analysis
Bibliographic record
Abstract
CONTEXT: Recently, the relationship between branched-chain amino acids (BCAAs) and diabetes mellitus (DM) has attracted worldwide attention. However, the results related to plasma BCAAs concentrations and gestational diabetes mellitus (GDM) lack statistical power due to the small sample size of a single article. OBJECTIVES: This study quantitatively summarized current observational studies to evaluate the association between plasma BCAAs concentration levels and GDM. METHODS: A systematic search was performed to select eligible publications using PubMed and EMBASE databases until July 23, 2018. The references of relevant articles were also manually searched. The quality evaluation of included studies was according to the guidelines of the Newcastle-Ottawa Scale (NOS). Data were analyzed with Review Manager 5.3 and STATA 14.0 software. In total, seven articles (including eight studies) involving 432 subjects were included. RESULTS: The results showed that all three-individual plasma BCAAs concentration levels in the GDM group were higher than those in the control group (leucine: SMD = 3.76, 95% CI: 1.70 - 5.82, P (SMD) < 0.001; isoleucine: SMD = 3.15, 95% CI: 1.42 - 4.87, P (SMD) < 0.001; valine: SMD = 2.77, 95% CI: 1.21 - 4.32, P (SMD) = 0.001), and the differences were statistically significant. In addition, subgroup analysis indicated that age, body mass index (BMI), publication year, and ethnicity were positively associated with plasma BCAAs concentrations in GDM. CONCLUSIONS: Plasma BCAAs, as potential biomarkers, might be associated with GDM risk, which provides useful information for the prevention and early diagnosis 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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.005 | 0.007 |
| 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) | 0.003 | 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".