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0463 Cluster Analysis in Perimenopausal and Menopausal Women with Insomnia


 
 
 Women in perimenopause or menopause report higher rates of insomnia, with depression, pain and sleep apnea common comorbidities. Identifying clinically relevant subtypes of women with similar symptom patterns might help target treatment more precisely and optimize outcomes more successfully.
 
 
 
 Participants were woman >50 years with insomnia (ISI>10) who were recruited from 12,108 patients visiting the Cleveland Clinic Sleep Disorders Center between 2008-2012. Patients completed questionnaires at initial clinic visit, and comorbidity data was extracted from medical records. K-method cluster analysis of cross-sectional data with PAM (portioning around medoids) was performed to identify clusters of patients based on insomnia (ISI), depression (PHQ2), and pain (EQ5D) symptoms as well as presence or absence of diabetes or sleep disorders (OSA, RLS). Silhouette widths and visualization using factoextra in R identified the optimal number of clusters. Characteristics of each cluster were compared using Pearson chi-square, Kruskal-Wallis or ANOVA models in SAS.
 
 
 
 Sample comprised 374 women. Average age was 60.5 years and 81.6% were White. A three-cluster solution was the most plausible. Clusters with mild (N=155, ISI=14.1±1.9), moderate (N=131, ISI=19.7±1.6) and severe (N=88, ISI=25.4±1.9) insomnia showed significant differences in characteristic. Clusters differed on depression level (PHQ2≥4 mild 19%, moderate 38%, severe 60%), and pain (ED5D=3 mild 3%, moderate 12%, severe 23%). Although the mild insomnia cluster reported better overall health, it showed higher rates of OSA compared to the moderate insomnia cluster, along with significantly older age and higher BMI.
 
 
 
 Perimenopausal and menopausal women divided into three clusters with mild, moderate and severe insomnia, with levels of reported depression and pain symptoms increasing with insomnia symptoms. Clusters also differed on age, BMI and prevalence of OSA, suggesting that specific symptom clusters might indicate more precise and targeted treatment of common comorbid conditions during menopause transition.
 
 
 
  
