Identification of high-risk symptom cluster burden group among midlife peri-menopausal and post-menopausal women with metabolic syndrome using latent class growth analysis

• Type of Material: Article
• Authors: Se Hee Min, Columbia University School of Nursing; Sharron L Docherty, Duke University; Eun Im, Emory University; Xiao Hu, Emory University; Daniel Hatch, Duke University; Qing Yang, Duke University
• Language: English
• Date: 2023-01-01
• Publisher: Emory University Libraries
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1177/17455057231160955
• Title of Journal or Parent Work: Women's Health
• Volume: 19
• Start Page: 17455057231160955
• End Page: 17455057231160955
• Grant/Funding Information: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number 1F31NR019921-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
• Keywords: Female; Women's Health; women; Perimenopause; Menopause; midlife; Humans; Postmenopause; Metabolic Syndrome; symptom; menopause

Abstract

Background: Midlife peri-menopausal and post-menopausal women with metabolic syndrome experience multiple co-occurring symptoms or symptom clusters, which often result in significant symptom cluster burden. While they are a high-risk symptom burden group, there are no studies that have focused on identifying symptom cluster trajectories in midlife peri-menopausal and post-menopausal women with metabolic syndrome. Objectives: The objectives were to identify meaningful subgroups of midlife peri-menopausal and post-menopausal women with metabolic syndrome based on their distinct symptom cluster burden trajectories, and to describe the demographic, social, and clinical characteristics of different symptom cluster burden subgroups. Design: This is a secondary data analysis using the longitudinal data from Study of Women’s Health Across the Nation. Methods: Multi-trajectory analysis using latent class growth analysis was conducted to join the different developmental trajectories of symptom clusters to identify meaningful subgroups and high-risk subgroup for greater symptom cluster burden over time. Then, descriptive statistics were used to explain the demographic characteristics of each symptom cluster trajectory subgroup, and bivariate analysis to examine the association between each symptom cluster trajectory subgroup and demographic characteristics. Results: A total of four classes were identified: Class 1 (low symptom cluster burden), Classes 2 and 3 (moderate symptom cluster burden), and Class 4 (high symptom cluster burden). Social support was a significant predictor of high symptom cluster burden subgroup and highlights the need to provide routine assessment. Conclusion: An understanding and appreciation for the different symptom cluster trajectory subgroups and their dynamic nature will assist clinicians to offer targeted and routine symptom cluster assessment and management in clinical settings.