整合营养生物标志物、认知功能和结构 MRI 数据，构建健康老龄化的多变量表型。
Integrating Nutrient Biomarkers, Cognitive Function, and Structural MRI Data to Build Multivariate Phenotypes of Healthy Aging.
机构信息
Decision Neuroscience Laboratory, Beckman Institute, University of Illinois, Urbana, IL, USA(†).
Decision Neuroscience Laboratory, Beckman Institute, University of Illinois, Urbana, IL, USA(†); Department of Psychology, University of Illinois, Urbana, IL, USA; Carl R. Woese Institute for Genomic Biology, University of Illinois, Champaign, IL, USA; Department of Bioengineering, University of Illinois, Champaign, IL, USA; Division of Nutritional Sciences, University of Illinois, Champaign, IL, USA; Neuroscience Program, University of Illinois, Champaign, IL, USA.
出版信息
J Nutr. 2023 May;153(5):1338-1346. doi: 10.1016/j.tjnut.2023.03.016. Epub 2023 Mar 23.
BACKGROUND
Research in the emerging field of nutritional cognitive neuroscience demonstrates that many aspects of nutrition-from entire diets to specific nutrients-affect cognitive performance and brain health.
OBJECTIVES
Although previous research has primarily examined the bivariate relationship between nutrition and cognition or nutrition and brain health, this study sought to investigate the joint relationship between these essential and interactive elements of human health.
METHODS
We applied a state-of-the-art data fusion method, coupled matrix tensor factorization, to characterize the joint association between measures of nutrition (52 nutrient biomarkers), cognition (Wechsler Abbreviated Test of Intelligence and Wechsler Memory Scale), and brain health (high-resolution MRI measures of structural brain volume) within a cross-sectional sample of 111 healthy older adults, with an average age of 69.1 y, 62% being female, and an average body mass index of 26.0 kg/m.
RESULTS
Data fusion uncovered latent factors that capture the joint association between specific nutrient profiles, cognitive measures, and cortical volumes, demonstrating the respects in which these health domains are coupled. A hierarchical cluster analysis further revealed systematic differences between a subset of variables contributing to the underlying latent factors, providing evidence for multivariate phenotypes that represent high and low levels of performance across multiple health domains. The primary features that distinguish between each phenotype were as follows: 1) nutrient biomarkers for monounsaturated and polyunsaturated fatty acids; 2) cognitive measures of immediate, auditory, and delayed memory; and 3) brain volumes within frontal, temporal, and parietal cortexes.
CONCLUSIONS
By incorporating innovations in nutritional epidemiology (nutrient biomarker analysis), cognitive neuroscience (high-resolution structural brain imaging), and statistics (data fusion), this study provides an interdisciplinary synthesis of methods that elucidate how nutrition, cognition, and brain health are integrated through lifestyle choices that affect healthy aging.
背景
营养认知神经科学这一新兴领域的研究表明，营养的多个方面——从整个饮食到特定营养素——都会影响认知表现和大脑健康。
目的
尽管先前的研究主要考察了营养与认知或营养与大脑健康之间的二元关系，但本研究旨在探讨这些人类健康的基本且相互作用的要素之间的联合关系。
方法
我们应用了一种最先进的数据融合方法，即耦合矩阵张量分解，以描述 111 名健康老年人的横断面样本中营养（52 种营养生物标志物）、认知（韦氏简明智力测试和韦氏记忆量表）和大脑健康（高分辨率 MRI 测量的大脑结构体积）之间的联合关联，平均年龄为 69.1 岁，62%为女性，平均身体质量指数为 26.0kg/m²。
结果
数据融合揭示了特定营养谱、认知测量和皮质体积之间联合关联的潜在因素，表明了这些健康领域相互关联的方面。层次聚类分析进一步显示了对潜在因素有贡献的变量子集之间存在系统差异，为代表多个健康领域表现的高低水平的多变量表型提供了证据。区分每个表型的主要特征如下：1）单不饱和和多不饱和脂肪酸的营养生物标志物；2）即时、听觉和延迟记忆的认知测量；3）额叶、颞叶和顶叶皮质内的大脑体积。
结论
通过将营养流行病学（营养生物标志物分析）、认知神经科学（高分辨率结构脑成像）和统计学（数据融合）的创新相结合，本研究提供了一种跨学科的方法综合，阐明了通过影响健康衰老的生活方式选择，营养、认知和大脑健康是如何整合的。
Zotero 插件