Leptin Unveiled: A Potential Biomarker for Acute Coronary Syndrome with Implications for Tailored Therapy in Patients with Type 2 Diabetes—Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Several studies evaluated the association between adipokines, including leptin, in patients with acute coronary syndrome (ACS). Nevertheless, the results have been inconclusive and conflicting. Therefore, we assessed the pertinent published studies and evaluated the association between leptin levels and ACS. In January 2023, we conducted a comprehensive systematic search using Web of Science, PubMed, Scopus, and Embase. Using the Newcastle-Ottawa Scale, we evaluated the quality of all the articles we included. The principal summary outcome was the mean difference (MD) in leptin levels. We included 16 studies in our systematic review, 10 of which were included in meta-analysis. The MD in leptin levels was then evaluated in each subgroup: the patients with ACS versus the controls, the patients with ACS versus the patients with stable angina pectoris (SAP), and the patients with type 2 diabetes mellitus (T2DM) and ACS versus the patients without diabetes, but with ACS. Respectively, the following MDs were obtained: 10.508 (95% CI 3.670-17.346); 2.408 (95% CI -0.150-4.966); and 17.089 (95% CI 5.565-28.612). The leptin levels were significantly higher in the patients with ACS compared to the healthy controls, as well as in the patients with ACS and T2DM compared to those without T2DM. However, no statistically significant increase in leptin levels was observed when comparing the patients with ACS to those with SAP.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".