Balancing metabolic optimization and reproductive safety in Polycystic Ovary Syndrome: a Bayesian-informed framework for GLP-1 receptor agonists
Abstract
Background: GLP-1 receptor agonists (GLP-1RAs) address key metabolic drivers of Polycystic Ovary Syndrome (PCOS), yet their integration into fertility management remains challenging because reproductive efficacy is variably reported, preconception washout is required, and standardized clinical pathways are lacking.
Main body: We present a structured, fertility-centered, hypothesis-generating narrative synthesis of the metabolic-reproductive-pregnancy continuum in PCOS, integrating evidence from randomized trials, observational studies, reviews, and clinical guidance. Heterogeneous findings are organized by intervention/comparator, follow-up window, and key effect modifiers, including baseline obesity/insulin-resistance phenotype, concomitant metformin, and lifestyle co-interventions. The clinical framework is derived from this narrative synthesis rather than from quantitative reproductive meta-analysis. As a methodological proof-of-concept only, we additionally performed a Bayesian network meta-analysis restricted to body-weight change within a prespecified 12-16-weeks window in the largest connected treatment network. Because ovulation, clinical pregnancy, time to pregnancy, and live birth were inconsistently defined and sparsely reported across trials, no confirmatory quantitative synthesis or treatment ranking was undertaken for these reproductive outcomes. We therefore distinguish evidence domains explicitly, with relatively robust support for short-term metabolic…
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