Association of circulating branched-chain amino acids with risk of pre-diabetes: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective Recent investigations have looked at the systemic concentrations of branched-chain amino acids (BCAA), which they consider prospective indicators for health conditions and the likelihood of chronic diseases. To elucidate the equivocal link between systemic BCAA concentrations and prediabetes, our study undertook a meta-analytical investigation. Materials and Methods Electronic databases were comprehensively searched in April 2024, and the study quality evaluation relied on the Newcastle-Ottawa Scale (NOS). The I2 statistic was used for heterogeneity assessment, and data analysis relied on Review Manager 5.4 and Stata 12.0. Standard mean difference (SMD) was used as the effect size to account for varying units of measurement across the included studies. Sensitivity assessment was instituted to evaluate result tenacity, and subgroup examinations were concomitantly carried out, with funnel plots, Egger’s regression analysis, and Begg’s rank-correlation methodology deployed to discern publication bias. PROSPERO registration (CRD42024572760) validates this review’s protocol compliance. Results Meta-analysis was conducted on 15 studies that involved 3,849 participants. Most individuals were over 40 years old. The prediabetes (PreDM) group exhibited significantly elevated levels of valine (Val) (SMD = 0.29; 95% confidence interval (CI) [0.14–0.45]; P = 0.0002), leucine (Leu) (SMD = 0.34; 95% CI [0.18–0.49]; P < 0.0001), and isoleucine (Ile) (SMD = 0.24; 95% CI [0.15–0.32]; P < 0.00001) compared to controls. Affirming the soundness of the results, sensitivity analysis indicated the lack of significant publication bias. Conclusions This meta-analysis supports the hypothesis that circulating BCAA levels increase in PreDM, suggesting that measuring BCAA levels could be investigated as a potential biomarker for the diagnosis of PreDM and a target for its treatment.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".