Information Fusion of Cognition, and Brain MRI on Cardio-Metabolic Risk Factors in the Middle-Aged: the CARDIA Study

背景：从由代谢物组成的大型队列研究中，缺乏心脏代谢危险因素在脑健康中的作用的证据；具体地说，从生命过程的角度来看，心血管疾病的危险曲线不断增加。目的：本研究的主要目的是在30年的年轻成人冠状动脉风险发展(CARDIA)研究中，研究心脏代谢危险因素和认知与脑MRI特征之间的关联，并调查尿代谢物是否是这种关联的中介。将检查重大的流行病学暴露和目标尿代谢物，这些信息将被用于开发大脑健康(即认知)的风险评估工具。设计和人群：本研究重点是2015-16年30年606名中年参与者(年龄48-60岁，完成认知测试)的CARDIA研究子队列。606名受试者的高分辨率核磁共振波谱和LC-MS数据以及280名受试者的脑MRI数据都是可用的。方法：将使用贝叶斯模型和先进的网络分类模型来调查代谢表型和其他暴露。不同类型的重大心脏代谢风险因素、其他临床、社会人口学、生活方式暴露以及认知和脑磁共振测量的代谢物标记物将被整合(即融合)，以提供特定于心脏代谢风险的大脑健康综合地图。讨论：本研究将对大规模流行病学研究中不同类型或不同水平的数据采用一种新的融合分析方法的生物统计学方法。该方法可能能够识别尚未确定的跨疾病网络的复杂的多变量流行病学、病理学和表型关系。

Background: There is a lack of evidence of the role of cardio-metabolic risk factors in brain health from large cohort studies which are comprised of metabolites; specifically, from a life-course perspective with increasing CVD risk profiles. Aim: The primary aim of this study is to investigate associations between cardio-metabolic risk factors and cognition with brain MRI characteristics in the Coronary Artery Risk Development in Young Adults (CARDIA) Study at Year 30, and to investigate if the urinary metabolites are mediators of the associations. Significant epidemiological exposures and targeted urinary metabolites will be examined, and the information will be used to develop a risk assessment tool for brain health (i.e., cognition).Design and population: This present study focuses on the CARDIA Study sub-cohort of the 606 participants in middle-aged (aged 48-60 years who completed cognitive tests) at Year 30, 2015-16. The high-resolution nuclear magnetic resonance (NMR) spectroscopy and the liquid-chromatography mass spectrometry (LC-MS) data for the 606 participants and the brain MRI data for the 280 participant are available. Methods: Bayesian models and advanced network classification models will be employed to investigate metabolic phenotypes and other exposures. Different types of significant cardio-metabolic risk factors, other clinical, socio-demographic, lifestyle exposures and metabolite markers on cognition and brain MRI measures will be integrated (i.e., fusion) to provide a cardio-metabolic risk-specific integrated map of brain health. The longitudinal associations will be examined to explore the possibility of developing a novel, comprehensive risk assessment tool of brain health.Discussion: This study will employ a novel biostatistical approach of a fusion analytic method for the different types or levels of data on the large-scale epidemiological study. The approach may be able to recognise complex multivariate epidemiologic, pathologic and phenotypic relationships across the disease networks that are yet unidentified.