"Association of Leptin with Polycystic Ovary Syndrome: a Systematic Review and Meta-Analysis"

“瘦素与多囊卵巢综合征的相关性:系统评价和荟萃分析”

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Abstract

BACKGROUND: Serum leptin has been considered as an important measurable diagnostic and prognostic biomarker for polycystic ovarian syndrome (PCOS), although its evidence for use in clinical practice is limited. We aim to synthesize the available evidence on the clinical use of serum leptin values in PCOS by doing a systematic review and meta-analysis of studies. OBJECTIVE: To conduct a meta-analysis to determine the pooled effect size of the association of leptin levels in patients with PCOS. METHODS: We searched electronic databases, i.e., PubMed, Google Scholar, Web of Science, ClinicalTrials.gov, and Medline from inception to September 2020, keeping filters for human studies and published in the English language. We used the random-effects model if heterogeneity between the studies was > 50%; otherwise, a fixed-effect model was applied to determine the standardized mean difference with 95% CI for comparison of leptin level between cases and controls. All the statistical analyses were completed using software STATA version 13. RESULTS: The meta-analysis included a total of 35 studies involving 2015 cases and 1767 controls that suggested statistically significantly higher leptin levels in the women with PCOS as compared to controls (SMD, 1.76, 95% CI 1.28 to 2.23, P < 0.001). In the stratified analysis when only high methodological quality studies were included, we did not observe a statistically significant difference in the leptin level between PCOS and controls (SMD 0.68, 95% CI -0.09 to 1.46). Analysis restricted to low methodological quality studies observed statistically significant high leptin levels in PCOS women as compared to controls (SMD 2.24, 95% CI 1.65 to 2.83). CONCLUSION: The available evidence suggests that elevated leptin levels may be associated with risk of PCOS as compared to controls; however, failure to observe the similar association in high methodological quality studies demands further well-designed adequately powered studies to validate the findings.

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