Chemokines in Gestational Diabetes Mellitus
Bibliographic record
Abstract
Background: Studies investigating chemokines in gestational diabetes mellitus (GDM) have yielded mixed results. The purpose of this meta-analysis was to explore whether concentrations of chemokines in patients with GDM differed from that of the controls. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched Web of Science, Embase, Cochrane Library, and PubMed databases for articles, published in any language, on chemokines and GDM through August 1st, 2021. The difference in concentrations of chemokines between patients with GDM and controls was determined by a standardized mean difference (SMD) with a 95% confidence interval (CI), calculated in the meta-analysis of the eligible studies using a random-effects model with restricted maximum-likelihood estimator. Results: Seventeen studies met the inclusion criteria for the meta-analysis. Altogether, they included nine different chemokines comparisons involving 5,158 participants (1,934 GDM patients and 3,224 controls). Results showed a significant increase of these chemokines (CCL2, CXCL1, CXCL8, CXCL9, and CXCL12) in the GDM patients compared with the controls. However, there was a significant decrease of the chemokines, CCL4, CCL11 and CXCL10, in the GDM patients compared with the controls. Moreover, subgroup analysis revealed a potential role of chemokines as biomarkers in relation to laboratory detection (different sample type and assay methods) and clinical characteristics of GDM patients (ethnicity and body mass index). Conclusion: GDM is associated with several chemokines (CCL2, CCL4, CCL11, CXCL1, CXCL8, CXCL9, CXCL10 and CXCL12). Therefore, consideration of these chemokines as potential targets or biomarkers in the pathophysiology of GDM development is necessary. Notably, the information of subgroup analysis underscores the importance of exploring putative mechanisms underlying this association, in order to develop new individualized clinical and therapeutic strategies.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".