Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome

摘要

ABSTRACT Metabolic syndrome (MS) refers to a cluster of metabolic abnormalities that includes hypertension, central obesity, insulin resistance, and atherogenic dyslipidemia. It has been associated with a higher risk of developing type 2 diabetes, cardiovascular disease, and myocardial infarction. It is estimated that more than one third of the U.S. population meets criteria for MS. Specifically, its prevalence is higher in women than in men with currently 2 million more women being affected in the United States. Individuals with MS experience multiple symptoms such as pain, sleep disturbance, and altered mood that affect patient outcomes. While research often focuses on single symptoms. it is rare that an individual with a chronic condition presents with a single symptom but rather experiences multiple symptoms or symptom clusters. However, little is known about symptom clusters in this population. Therefore, the purpose of this study is to understand the complex symptom experience of midlife menopausal women with MS. This study aims to: 1) identify symptom clusters in midlife menopausal women with MS and key symptom(s) that exert influence on symptom clusters, 2) explore symptom experience trajectory over time to classify midlife menopausal women with MS with distinct symptom experience and identify subgroup at high-risk for greater symptom burden over time using symptom clusters, and 3) examine individual characteristics associated with each symptom cluster subgroup membership. This retrospective, descriptive longitudinal study will use existing data from the Study of Women’s Health Across the Nation (SWAN) from Baseline to Visit 10. Machine learning based network analysis (NA) will be used to identify symptom clusters and key symptoms that exert influence within and among symptom clusters. Growth Mixture Model (GMM) will be used to classify midlife menopausal women with MS with distinct symptom experience and identify subgroup at high-risk for greater symptom burden. Regression model will be used to examine individual characteristics associated with each symptom cluster subgroup membership. The proposed study is in response to National Institute of Nursing Research Strategic Plan in Symptom Science. It will assist in providing quantitative visualization and interpretation of the relationships among symptoms and symptom clusters and identifying key symptom(s) through machine learning based network analysis that may serve as a potential target for future interventions. It will also identify subgroups at high risk for greater symptom burden and their associated individual characteristics that will inform future development of targeted symptom interventions for different risk groups. Findings from this study will inform the next stage of symptom science research through application of new analytic techniques and clinical application to manage symptom clusters and key symptoms in midlife menopausal women with MS.