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Talukdar T, Zwilling CE, Barbey AK. Integrating Nutrient Biomarkers, Cognitive Function, and Structural MRI Data to Build Multivariate Phenotypes of Healthy Aging. J Nutr 2023; 153:1338-1346. [PMID: 36965693 DOI: 10.1016/j.tjnut.2023.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/20/2023] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
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. OBJECTIVE While prior research has primarily examined the bivariate relationship between nutrition and cognition, or nutrition and brain health, the present 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 Magnetic Resonance Imaging measures of structural brain volume) within a cross-sectional sample of 111 healthy older adults that had an average age of 69.1 years, were 62% female and had an average Body Mass Index of 26.0. RESULTS Data fusion uncovered 3 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. Hierarchical cluster analysis further revealed systematic differences between the observed latent factors, providing evidence for multivariate phenotypes that represent high versus low levels of performance across multiple health domains. The primary features that distinguish between each phenotype were: (i) nutrient biomarkers for monounsaturated and polyunsaturated fatty acids; (ii) cognitive measures of immediate, auditory, and delayed memory; and (iii) brain volumes within frontal, temporal, and parietal cortex. CONCLUSIONS By incorporating innovations in nutritional epidemiology (nutrient biomarker analysis), cognitive neuroscience (high-resolution structural brain imaging), and statistics (data fusion), the present 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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Affiliation(s)
- Tanveer Talukdar
- Decision Neuroscience Laboratory, Beckman Institute, University of Illinois, Urbana, IL. USA
| | - Christopher E Zwilling
- Decision Neuroscience Laboratory, Beckman Institute, University of Illinois, Urbana, IL. USA
| | - Aron K Barbey
- 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.
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Watson P, Paul E, Cooke G, Ward N, Monti J, Horecka K, Allen C, Hillman C, Cohen N, Kramer A, Barbey A. Cognitive and anatomical data in a healthy cohort of adults. Data Brief 2016; 7:1221-1227. [PMID: 28795120 PMCID: PMC5540669 DOI: 10.1016/j.dib.2016.03.100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 03/30/2016] [Accepted: 03/30/2016] [Indexed: 11/18/2022] Open
Abstract
We present data from a sample of 190 healthy adults including assessments of 4 cognitive factor scores, 12 cognitive tests, and 115 MRI-assessed neuroanatomical variables (cortical thicknesses, cortical and sub-cortical volumes, fractional anisotropy, and radial diffusivity). These data were used in estimating underlying sources of individual variation via independent component analysis (Watson et al., In press) [25].
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Affiliation(s)
- P.D. Watson
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Corresponding authors at: Decision Neuroscience Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, USA.Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-ChampaignUrbanaILUnited States http://DecisionNeuroscienceLab.org/
| | - E.J. Paul
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - G.E. Cooke
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - N. Ward
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - J.M. Monti
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - K.M. Horecka
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, USA
| | - C.M. Allen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - C.H. Hillman
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Kinesiology and Community Health, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - N.J. Cohen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, USA
| | - A.F. Kramer
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, USA
| | - A.K. Barbey
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Psychology, University of Illinois at Urbana–Champaign, Champaign, IL, USA
- Decision Neuroscience Laboratory, University of Illinois at Urbana–Champaign, Champaign, IL, USA
- Department of Bioengineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA
- Department of Internal Medicine, University of Illinois at Urbana–Champaign, Champaign, IL, USA
- Department of Speech and Hearing Science, University of Illinois at Urbana–Champaign, Champaign, IL, USA
- Neuroscience Program, University of Illinois at Urbana–Champaign, Champaign, IL, USA
- Corresponding authors at: Decision Neuroscience Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 North Mathews Avenue, Urbana, IL 61801, USA.Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-ChampaignUrbanaILUnited States http://DecisionNeuroscienceLab.org/
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