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.

项目摘要/摘要 心血管疾病（CM）包括心血管（CV）和代谢性疾病是领先的 在美国和全球可预防死亡的原因。 CM疾病是相互联系的 与多域心脏代谢风险因素（CMRF）相关，例如代谢失调，肥胖， 身体上的不活跃，营养不良和其他新兴因素（包括尤其是睡眠障碍）。 CMRFS 在青少年和年轻人中越来越普遍，预示着未来 随着年龄的增长，CM疾病的流行。但是，现有研究主要集中于成年人 和老年人，对年轻人的人口几乎不了解。 CM数据有很大的希望，可以促进CM亚组发现以提早风险分层和精度 预后。但是，在完全利用CM数据中存在明显的差距。 GAP I：缺乏多种多样的 域CMRF（尤其是睡眠健康）。 GAP II：缺乏早期风险中的“结果预测” CM亚组 分层。 GAP III：缺乏“多维” CM结果的“亚组特异性”精度预后。差距 IV：NHANES和NSRR中富裕但“不完整”的多域CM数据的利用不足。我们建议 CM亚组发现和风险预测的多阶级多域二级分析 （11-18）和年轻人（19-39）。目的是创建2个组合的NHANES和NSRR数据集以及 检查多域CMRF，包括代谢失调，身体不活跃，营养不良和多域 美国大型和潜水员的CM亚组发现和风险预测的维度睡眠度量 西班牙裔/拉丁美洲人，非裔美国人，高加索人和亚洲的青少年和年轻人人口 美国人。 AIM1。CM风险亚组发现在美国青少年和年轻人的基线。 （1.1）发展 一种新型稀疏的不完整多域混合因子混合模型（IM2-FMM），用于亚组发现 来自不完整的多域混合类型CMRF。 （1.2）应用IM2-FMM识别，表征和评估 基线时不完整的多域混合型CMRF的青少年和年轻人的CM亚组 包括：（a）NHANES的自我报告的睡眠度量； （b）NSRR中的自我报告和客观的睡眠度量。 目标2。针对年轻人的多维纵向CM结果的亚组特异性预测。 （2.1） 开发一种新型的基于稀疏传递学习的广义多层次模型（TL-GMM），以预测多种 基线群集CMRF的尺寸纵向CM结果。 （2.2）将TL-GMM应用于年轻人 在NSRR中：（1）检查基线CMRFS对CM结果的固定效应和随机影响； （2）提供 基线聚类CMRF的CM健康的亚组特异性多维预后。影响：我们的 研究将产生对CM亚组发现的新见解，以促进早期和有针对性的干预措施以及 帮助建立青少年和年轻人的健康促进行为，最终改善商业保健 随着年轻人的年龄，他们过渡到成年并降低了CM健康差异和成本。