Sleep and Cardiometabolic Subgroup Discovery and Risk Prediction in United States Adolescents and Young Adults: A Multi-Study Multi-Domain Analysis of NHANES and NSRR

美国青少年和年轻人的睡眠和心脏代谢亚组发现和风险预测：NHANES 和 NSRR 的多研究多领域分析

• 批准号: 10639360
• 负责人: Bing Si
• 金额: $ 50.65万
• 依托单位: STATE UNIVERSITY OF NY,BINGHAMTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10639360
• 关键词: Address; Adolescent; Adolescent and Young Adult; Adult; African American population; Age; Algorithms; American; Asian Americans; Behavior; Cardiometabolic Disease; Cardiovascular Diseases; Cardiovascular system; Clinical; Collection; Data; Data Pooling; Data Set; Diabetes Mellitus; Dimensions; Early treatment; Evaluation; Health; Health Care Costs; Health Promotion; Healthcare; Heterogeneity; Hispanic Populations; Individual; Intervention; Knowledge; Latino Population; Measures; Metabolic; Metabolic Diseases; Modeling; Morbidity - disease rate; National Health and Nutrition Examination Survey; Obesity; Outcome; Patient Self-Report; Patient risk; Patients; Population; Prognosis; Prognostic Factor; Research; Risk; Risk Factors; Sleep; Sleep Deprivation; Sleep Disorders; Subgroup; United States; blood-based biomarker; cardiometabolic risk; cardiometabolism; caucasian American; cost; evidence based guidelines; future epidemic; health disparity; improved; insight; interest; lifestyle intervention; mortality; multilevel analysis; novel; nutrition; outcome prediction; physical inactivity; preventable death; research clinical testing; risk prediction; risk stratification; secondary analysis; sleep health; transfer learning; young adult

Project Summary/Abstract Cardiometabolic (CM) diseases including cardiovascular (CV) and metabolic diseases are the leading cause of preventable death in the United States and Worldwide. CM diseases are interconnected and positively associated with multi-domain Cardiometabolic Risk Factors (CMRFs) such as metabolic dysregulation, obesity, physical inactivity, poor nutrition, and other emerging factors (including and especially sleep disorders). CMRFs are highly and increasingly prevalent in adolescents and young adults, which foreshadows a future epidemic of incident CM diseases as they age. However, existing studies have primarily focused on the adult and senior population with little to no knowledge on the young population. CM data hold great promise to facilitate CM subgroup discovery for early risk stratification and precise prognosis. However, significant gaps exist in fully leveraging CM data. Gap I: Lack of inclusion of multi- domain CMRFs (especially sleep health). Gap II: Lack of “outcome-predictive” CM subgroups in early risk stratification. Gap III: Lack of “subgroup-specific” precise prognosis of “multi-dimensional” CM outcomes. Gap IV: Under-utilization of the rich but “incomplete” multi-domain CM data in NHANES and NSRR. We propose a multi-study multi-domain secondary analysis for CM subgroup discovery and risk prediction in U.S. adolescents (11-18) and young adults (19-39). The objective is to create 2 combined NHANES and NSRR datasets and examine multi-domain CMRFs including metabolic dysregulation, physical inactivity, poor nutrition, and multi- dimensional sleep measures for CM subgroup discovery and risk prediction in the large and diverse U.S. adolescent and young adult population of Hispanics/Latinos, African Americans, Caucasians, and Asian Americans. Aim 1. CM risk subgroup discovery at baseline for U.S. adolescents & young adults. (1.1) Develop a novel sparse Incomplete Multi-domain Mixed-typed Factor Mixture Model (IM2-FMM) for subgroup discovery from incomplete multi-domain mixed-typed CMRFs. (1.2) Apply IM2-FMM to identify, characterize, and evaluate CM subgroups in adolescents and young adults from incomplete multi-domain mixed-typed CMRFs at baseline including: (a) self-reported sleep measures in NHANES; (b) self-reported and objective sleep measures in NSRR. Aim 2. Subgroup-specific prediction of multi-dimensional longitudinal CM outcomes for young adults. (2.1) Develop a novel sparse Transfer Learning-based Generalized Multi-level Model (TL-GMM) to predict multi- dimensional longitudinal CM outcomes from clustered CMRFs at baseline. (2.2) Apply TL-GMM to young adults in NSRR to: (1) examine fixed effects and random effects of baseline CMRFs on CM outcomes; (2) provide subgroup-specific multi-dimensional prognosis of CM health from clustered CMRFs at baseline. Impact: Our study will generate novel insights into CM subgroup discovery to facilitate early and targeted interventions and help establish health promoting behaviors in adolescents and young adults, eventually improving CM health care in their transition to adulthood and reducing CM health disparities and costs as the young population ages.

项目总结/摘要 包括心血管（CV）和代谢性疾病在内的心脏代谢（CM）疾病是主要的 在美国和世界范围内可预防的死亡原因。CM疾病是相互关联的， 与多领域心脏代谢风险因素（CMRF）相关，如代谢失调，肥胖， 缺乏运动、营养不良和其他新出现的因素（包括尤其是睡眠障碍）。CMRF 在青少年和年轻人中越来越普遍，这预示着未来 随着年龄的增长，CM疾病的流行。然而，现有的研究主要集中在成人 和老年人，对年轻人知之甚少。 CM数据很有希望促进CM亚组的发现，以进行早期风险分层和精确的 预后然而，在充分利用变革管理数据方面存在重大差距。差距一：缺乏包容性， CMRFs（特别是睡眠健康）。差距II：早期风险中缺乏“结局预测性”CM亚组 分层差距三：缺乏“亚组特异性”的准确预后的“多维”CM的结果。间隙 四：未充分利用NHANES和NSRR中丰富但“不完整”的多领域CM数据。我们提出了一个 美国青少年CM亚组发现和风险预测的多研究多领域次要分析 （11-18岁）和年轻人（19-39岁）。目标是创建2个合并的NHANES和NSRR数据集， 检查多领域CMRF，包括代谢失调，身体活动不足，营养不良和多 多维睡眠测量在大而多样的美国CM亚组发现和风险预测 西班牙裔/拉丁裔、非裔美国人、高加索人和亚洲人的青少年和年轻成人人群 美国人目标1。美国青少年和年轻人基线时CM风险亚组发现。(1.1)发展 一种新的稀疏不完全多域混合型因子混合模型（IM 2-FMM） 从不完整的多域混合类型CMRF。(1.2)应用IM 2-FMM识别、表征和评估 基线时来自不完整多领域混合型CMRF的青少年和年轻成人中的CM亚组 包括：（a）NHANES中自我报告的睡眠测量;（B）NSRR中自我报告的客观睡眠测量。 目标二。亚组特异性预测年轻成人多维纵向CM结局（二、一） 提出了一种基于稀疏迁移学习的广义多水平模型（TL-GMM）， 基线时群集CMRF的三维纵向CM结局。(2.2)将TL-GMM应用于年轻人 在NSRR中：（1）检查基线CMRF对CM结局的固定效应和随机效应;（2）提供 根据基线时的CMRF聚类，对CM健康的亚组特异性多维预后。影响：我们的 研究将产生新的见解CM亚组发现，以促进早期和有针对性的干预措施， 帮助青少年和年轻人建立健康促进行为，最终改善CM卫生保健 在他们向成年过渡的过程中，随着年轻人口的老龄化，减少CM健康差距和成本。