Integrating Nutrient Biomarkers, Cognitive Function, and Structural MRI Data to Build Multivariate Phenotypes of Healthy Aging.

J Nutr

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. Electronic address:

Published: May 2023

AI Article Synopsis

  • The study explores the interrelationship between nutrition, cognition, and brain health, emphasizing their collective impact rather than examining them in isolation.
  • A cutting-edge data fusion method was used to analyze nutritional biomarkers, cognitive assessments, and MRI brain volume data from 111 healthy older adults, revealing how these elements are interconnected.
  • Results indicated distinct phenotypes associated with differing levels of nutrient profiles, memory capabilities, and cortical volumes, highlighting the importance of specific nutrients like fatty acids in cognitive and brain health performance.

Article Abstract

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.

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Source
http://dx.doi.org/10.1016/j.tjnut.2023.03.016DOI Listing

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