Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome

摘要 代谢综合征（MS）是指一组代谢异常，包括高血压、中枢神经系统疾病、高血压、高血压和高血压。 肥胖、胰岛素抵抗和致动脉粥样硬化性血脂异常。它与更高的风险有关， 发展为2型糖尿病、心血管疾病和心肌梗塞。估计有超过 三分之一的美国人口符合MS的标准。具体来说，其在女性中的患病率高于女性。 目前在美国有200多万女性受到影响。具有MS经验的个人 多种症状，如疼痛、睡眠障碍和影响患者结局的情绪改变。而 研究往往集中在单一的症状。患有慢性病的个体很少会出现 单一症状，而是经历多种症状或症状群。然而，人们对 在这个人群中的症状集群。因此，本研究的目的是了解复杂的 中年绝经妇女MS症状经历。本研究旨在：1）识别症状群 在患有MS和对症状群产生影响的关键症状的中年绝经妇女中，2） 探索随时间推移的症状经历轨迹，以将患有MS的中年绝经妇女分类为具有不同 症状经历和使用症状识别随着时间推移具有更大症状负担的高风险亚组 集群，以及3）检查与每个症状集群亚组相关的个体特征 会员资格这项回顾性、描述性的纵向研究将使用来自妇女研究的现有数据。 从基线至访视10的全国健康状况（SWAN）。基于机器学习的网络分析（NA） 用于识别症状群和在症状内和症状间产生影响的关键症状 集群将使用生长混合模型（GMM）对患有MS的中年绝经女性进行分类， 症状经历和识别出具有更大症状负担高风险亚组。回归模型将是 用于检查与每个症状群亚组成员资格相关的个体特征。的 这项研究是为了响应国家护理研究所在症状科学方面的战略计划。它 将有助于提供定量可视化和解释症状之间的关系， 症状聚类并通过基于机器学习的网络分析识别关键症状， 作为未来干预的潜在目标。它还将确定高风险亚组， 症状负担及其相关的个体特征，这将为未来的目标开发提供信息。 针对不同风险群体的症状干预。这项研究的结果将为下一阶段的症状提供信息。 通过应用新的分析技术和临床应用进行科学研究，以管理症状 MS中年绝经期妇女的主要症状。

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.