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Keywords

menopause, obesity, quality of life, symptom management, women's health

 

Authors

  1. Min, Se Hee
  2. Yang, Qing
  3. Docherty, Sharron L.
  4. Im, Eun-Ok
  5. Hu, Xiao

Abstract

Background: Midlife perimenopausal and postmenopausal women with metabolic syndrome experience multiple symptoms concurrently.

 

Objective: The study objectives were to examine the relationship among symptoms through network visualization and identify and compare symptom clusters and key symptoms across symptom occurrence and symptom severity dimensions in midlife perimenopausal and postmenopausal women with and without metabolic syndrome.

 

Methods: Cross-sectional data from the Study of Women's Health Across the Nation (Visit 5) were used for analysis. A machine-learning-based network analysis and the Walktrap algorithm were used to fulfill the study objectives.

 

Results: The number and types of symptom clusters differed between the groups. Midlife perimenopausal and postmenopausal women with metabolic syndrome experienced the psychological/somatic/genital cluster (key symptom: frequent mood change), the sleep/urinary cluster (sleep disturbance), and the vasomotor cluster (cold sweat) in the symptom occurrence dimension and the psychological/somatic/sexual cluster (anxiety), the sleep/urinary cluster (sleep disturbance), and the vasomotor/genital cluster (night sweat) in the symptom severity dimension. In contrast, midlife perimenopausal and postmenopausal women without metabolic syndrome experienced the psychological cluster (anxiety), the sleep/somatic/genitourinary cluster (sleep disturbance), and the vasomotor cluster (night sweat) in the symptom occurrence dimension and the psychological/somatic cluster (anxiety), the sleep/urinary cluster (sleep disturbance), the vasomotor cluster (night sweat), and the sexual/genital cluster (vaginal dryness) in the symptom severity dimension.

 

Discussion: The study findings may serve as a knowledge basis for effective assessment and management of symptom clusters and key symptoms in clinical settings and provide directions for future development of targeted symptom management interventions.