Symptom Cluster of Midlife Menopausal Women with Metabolic Syndrome

中年更年期女性代谢综合症的症状群

基本信息

批准号：
10228373
负责人：
Se Hee Min
金额：
$ 4.6万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10228373
关键词：
Address Affect Aging Biochemical Cardiovascular Diseases Central obesity Characteristics Chronic Chronic Disease Cluster Analysis Complex Data Development Dimensions Dyslipidemias Face Factor Analysis Fatigue Future Goals Growth Health Hormonal Change Hypertension Individual Insulin Resistance Intervention Knowledge Left Longitudinal Studies Machine Learning Malignant Neoplasms Menopausal Status Menopause Metabolic Metabolic syndrome Methods Modeling Moods Myocardial Infarction National Institute of Nursing Research Nature Network-based Non-Insulin-Dependent Diabetes Mellitus Oncology Outcome Pain Pathway Analysis Patient-Focused Outcomes Patients Population Prevalence Public Health Quality of life Research Research Design Retrospective Studies Risk Severities Site Sleep disturbances Strategic Planning Study of Women&apos s Health Across the Nation Subgroup Symptoms Techniques Theoretical model Time United States Visit Visualization Woman associated symptom base biopsychosocial factor clinical application clinically significant experience high risk improved men middle age response symptom cluster symptom management symptom science time use

项目摘要

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.

抽象的代谢综合征（MS）是指一系列代谢异常，包括高血压、中枢性肥胖、胰岛素抵抗和动脉粥样硬化性血脂异常与较高的风险有关。据估计，罹患2型糖尿病、心血管疾病和心肌梗塞的人数超过。三分之一的美国人口符合多发性硬化症的标准，具体而言，女性的患病率高于女性。目前，美国有 200 万女性患有多发性硬化症。疼痛、睡眠障碍和情绪等多种症状会影响患者的治疗结果。研究通常集中于单一症状，慢性病患者很少出现单一症状。单一症状，而是经历多种症状或症状群。然而，人们对此知之甚少。因此，本研究的目的是了解该人群的复杂症状。患有多发性硬化症的中年更年期女性的症状经历本研究旨在：1) 确定症状群。患有多发性硬化症和对症状群产生影响的关键症状的中年更年期女性，2) 探索一段时间内的症状经历轨迹，以将患有多发性硬化症的中年更年期女性分类为具有不同特征的症状经历并使用症状识别随着时间的推移症状负担更大的高风险亚组集群，3) 检查与每个症状集群亚组相关的个体特征这项回顾性、描述性纵向研究将使用妇女研究的现有数据。从基线到访问 10 的全国健康 (SWAN)。基于机器学习的网络分析 (NA) 将用于识别症状群以及在症状内部和症状之间产生影响的关键症状生长混合模型 (GMM) 将用于对患有 MS 的中年更年期女性进行分类。症状经历并确定高风险亚组，以获得更大的症状负担。用于检查与每个症状群亚组成员资格相关的个体特征。拟议的研究是为了响应国家护理研究所症状科学战略计划。将有助于提供症状和症状之间关系的定量可视化和解释通过基于机器学习的网络分析来识别症状群并识别关键症状它还将作为未来干预措施的潜在目标。症状负担及其相关的个体特征将为未来的有针对性的发展提供信息针对不同风险群体的症状干预措施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来管理症状进行科学研究患有多发性硬化症的中年更年期女性的集群和关键症状。