# Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome

> **NIH NIH F31** · DUKE UNIVERSITY · 2021 · $46,036

## Abstract

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.

## Key facts

- **NIH application ID:** 10228373
- **Project number:** 1F31NR019921-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Se Hee Min
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 1
- **Project period:** 2021-08-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10228373

## Citation

> US National Institutes of Health, RePORTER application 10228373, Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome (1F31NR019921-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10228373. Licensed CC0.

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