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.

项目概要/摘要 包括心血管（CV）和代谢疾病在内的心脏代谢（CM）疾病是主要的疾病 美国和世界范围内可预防的死亡原因是相互关联且积极的。 与多领域心脏代谢危险因素（CMRF）相关，例如代谢失调、肥胖、 缺乏身体活动、营养不良和其他新出现的因素（包括特别是睡眠障碍）。 在青少年和年轻人中越来越普遍，这预示着未来 然而，现有的研究主要集中在成人身上。 老年人口对年轻人口知之甚少甚至一无所知。 CM 数据有望促进 CM 亚组发现，从而实现早期风险分层和精确 然而，在充分利用 CM 数据方面存在重大差距：缺乏包容性。 领域 CMRF（尤其是睡眠健康）：早期风险中缺乏“结果预测”CM 亚组。 差距 III：缺乏“多维”CM 结果的“亚组特异性”精确预测。 IV：NHANES 和 NSRR 中丰富但“不完整”的多域 CM 数据的利用不足。 美国青少年 CM 亚组发现和风险预测的多研究多领域二次分析 (11-18) 和年轻人 (19-39) 的目标是创建 2 个组合的 NHANES 和 NSRR 数据集和 检查多域 CMRF，包括代谢失调、缺乏身体活动、营养不良和多 在幅员辽阔、多元化的美国，用于 CM 亚组发现和风险预测的维度睡眠测量 西班牙裔/拉丁裔、非裔美国人、白种人和亚洲人的青少年和年轻成年人口 目标 1. 发现美国青少年和年轻人的基线 CM 风险亚组 (1.1)。 一种用于子组发现的新型稀疏不完全多域混合类型因子混合模型（IM2-FMM） 来自不完整的多域混合类型 CMRF (1.2) 应用 IM2-FMM 来识别、表征和评估。 基线时不完整的多域混合型 CMRF 中青少年和年轻人的 CM 亚组 包括：(a) NHANES 中的自我报告睡眠测量；(b) NSRR 中的自我报告和客观睡眠测量。 目标 2. 年轻人多维纵向 CM 结果的亚组特异性预测 (2.1)。 开发一种新颖的基于稀疏迁移学习的广义多级模型（TL-GMM）来预测多层次 (2.2) 将TL-GMM应用于年轻人 在 NSRR 中：(1) 检查基线 CMRF 对 CM 结果的固定效应和随机效应；(2) 提供 根据基线聚类 CMRF 对 CM 健康状况进行亚组特异性多维预后：我们的影响。 研究将对 CM 亚组发现产生新的见解，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 医疗保健 帮助他们向成年过渡，并随着年轻人口老龄化减少 CM 健康差异和成本。