Factors associated with sexual dysfunction in dialysis patients: a gender-specific systematic review and meta-analysis.

INTRODUCTION: Sexual dysfunction (SD) is an important complication in dialysis patients that affects quality of life but remains under-recognized in clinical practice. Previous reviews have mainly focused on prevalence, whereas systematic quantification of associated factors, sex-stratified comparisons, and evidence certainty evaluations remain limited. OBJECTIVES: This meta-analysis aimed to identify factors associated with SD in dialysis patients and explore sex-specific differences. METHODS: This systematic review and meta-analysis included observational studies of adult dialysis patients identified from database inception to April 20, 2025. Extracted data included adjusted odds ratios and 95% confidence intervals. Study quality was assessed using the Agency for Healthcare Research and Quality checklist or the Newcastle-Ottawa Scale. Sensitivity analyses, subgroup analyses, publication bias assessment, and GRADE evaluations were conducted to assess the robustness and certainty of the evidence. RESULTS: A total of 19 studies involving 4989 patients were included. The primary analysis showed that factors associated with SD in males included age, diabetes mellitus, smoking history, dialysis vintage, depression, diabetic nephropathy, alcohol consumption, and inadequate dialysis. In females, factors associated with SD included age, depression, beta-blockers, low educational attainment, menopausal status, chronic diseases, and diabetic nephropathy, whereas recombinant human erythropoietin was associated with lower odds of SD. After REML+HKSJ adjustment, age, diabetes mellitus, smoking history, dialysis vintage, depression, and diabetic nephropathy in males, and depression, lower educational attainment, and diabetic nephropathy in females remained statistically significant. CONCLUSIONS: Factors associated with SD in dialysis patients differed by sex. However, evidence certainty was limited by observational designs, heterogeneity, potential publication bias, and variations in measurement tools, diagnostic thresholds, and exposure definitions. Therefore, these findings reflect statistical associations rather than causal conclusions.