Menopausal symptom severities and patterns are affected by medical profiles

This study involved a secondary data analysis of Health & Her app data to investigate medical profiles and menopausal symptom clusters.

Using a linear mixed effects model, we assessed how medical conditions, including Polyendocrine Metabolic Ovarian Syndrome (PMOS), premenstrual dysphoric disorder (PMDD), premature ovarian insufficiency (POI), cancers, depression, anxiety, uterine fibroids, endometriosis, adenomyosis and autoimmune disorders, predict menopausal symptom patterns. We also assessed medical procedures including bilateral oophorectomy, hysterectomy, unilateral oophorectomy, partial hysterectomy, endometrial ablation, and cancer treatment. Symptom clusters were identified via Principal Component Analysis. Linear mixed effects models analysed the effects of predictors on symptom outcomes.

Menopausal symptom clusters, and how menopausal symptom patterns and severities are associated with medical profiles.

Data from 210,489 app users from October 2020 to June 2024 identified symptom clusters, including joint aches, sleeping problems, low libido, weight gain, vasomotor, psychological/cognitive, vaginal and physical symptoms. Depression and anxiety were associated with the strongest increases in menopausal symptoms. Gynaecological surgeries were associated with increased vasomotor symptoms. POI and endometriosis were associated with vaginal symptoms. Autoimmune diseases had significant associations with joint aches, and physical and psychological/cognitive symptoms. PMOS, adenomyosis and PMDD were associated with reduced vasomotor symptoms. Undergoing cancer treatment was associated with reduced symptoms in most domains, albeit vaginal and vasomotor symptoms increased. Likewise, breast and gynaecological cancers were associated with low libido, and increased vasomotor and sleep symptoms.

Our findings highlight the importance of healthcare professionals considering comorbidities when supporting women with menopausal symptoms.