Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome

摘要 代谢综合征(MS)指的是一系列代谢异常，包括高血压、中枢神经系统 肥胖、胰岛素抵抗和致动脉粥样硬化性血脂异常。它与更高的风险相关 发展为2型糖尿病、心血管疾病和心肌梗塞。据估计，超过 三分之一的美国人口符合多发性硬化症的标准，尤其是女性多发性硬化症的患病率高于 目前，美国受影响的男性和女性人数增加了200万。具有多发性硬化症经验的个人 影响患者预后的多种症状，如疼痛、睡眠障碍和情绪改变。而当 研究往往集中在单一症状上。患有慢性病的人很少会出现一种 只有一个症状，而是经历多个症状或症状群。然而，人们对此知之甚少。 这一人群中的症状群。因此，本研究的目的就是了解这一情结 中年更年期女性多发性硬化的症状体验本研究旨在：1)确定症状群 中年更年期女性多发性硬化症与关键症状(S)对症状群的影响，2) 探索症状体验随时间变化的轨迹，对患有不同类型多发性硬化症的中年更年期妇女进行分类 症状体验和识别高危亚群，以便随着时间的推移使用症状增加症状负担 分类，以及3)检查与每个症状分类亚组相关联的个体特征 会员制。这项回溯性、描述性的纵向研究将使用来自妇女研究的现有数据 从基准到访问的全国健康(SWAN)10.基于机器学习的网络分析(NA)将 用于识别症状簇和在症状内部和症状之间产生影响的关键症状 集群。生长混合模型(GMM)将用于中年更年期女性多发性硬化症的分类 症状体验和识别症状负担更大的高危亚群。回归模型将是 用于检查与每个症状簇子组成员相关联的个体特征。这个 建议的研究是为了响应国家护理研究所症状科学战略计划。它 将有助于提供症状和症状之间的关系的量化可视化和解释 症状聚类和通过基于机器学习的网络分析识别关键症状(S) 作为未来干预的潜在目标。它还将识别高风险的子组，以获得更大的 症状负担及其相关的个人特征，将指导目标的未来发展 针对不同危险人群的症状干预。这项研究的发现将为下一阶段的症状提供信息 运用新的分析技术进行科学研究和临床应用管理症状 患有多发性硬化的中年更年期妇女的聚集性和主要症状。

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.