Therapeutic efficacy of botanicals in psychological disorders in menopausal women: a systematic and scoping review - Sultana, Arshiya; Bin Heyat, Md Belal; Rahman, Khaleequr; Ahmed, Zahoor; Akhtar, Faijan; Shamim, Ansari Uzma; Khaleeq, Khadija; Muaad, Abdullah Y. (Frontiers in Pharmacology, 2025)
IntroductionUnderstanding the emotional impact of menopause on women is of utmost importance, especially with the predictable estimate of 1.2 billion menopausal women globally by 2030. Depression, anxiety, and stress are common during menopause, and botanical medicines, particularly from Unani traditions, may offer effective, natural therapeutic options. This study combines systematic and scoping review methods to assess the clinical efficacy of botanical interventions and map the broader research landscape.MethodsA comprehensive literature search was conducted across PubMed, Science Direct and PROSPERO from 2000 to 2024, following both PRISMA 2020 and PRISMA-ScR guidelines. The scoping phase identified a wide range of botanicals and research trends, while the systematic review focused on randomized controlled trials evaluating efficacy. Cochrane risk-of-bias tool was used for methodological quality assessment. Network visualization and word cloud techniques were also employed to identify related terms from the prior studies included in the analysis.ResultsSixteen RCTs involving 1,112 participants (mean age ±SD: 69.5 ± 21.88) were included. Most studies had a low risk of bias. Bioactive compounds such as withaferin A, quercetin, rosmarinic acid, and thymoquinone demonstrated antidepressant, anxiolytic, and neuroprotective effects through antioxidant, anti-inflammatory, GABAergic, and serotonergic mechanisms. Machine learning approaches showed potential for identifying compound interactions and personalizing treatment.ConclusionBotanical medicines, especially from the Unani system, show promising efficacy in managing psychological symptoms during menopause. While current evidence is encouraging, further robust trials and mechanistic studies are needed. The integration of machine learning offers a novel direction for personalized phytotherapy.Systematic Review RegistrationIdentifier CRD42024514198.