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Partial Least Squares Analysis of Alzheimer’s Disease Biomarkers, Modifiable Health Variables, and Cognition in Older Adults with Mild Cognitive Impairment

Published online by Cambridge University Press:  19 October 2021

Jessica Stark ,
Daniela J. Palombo ,
Jasmeet P. Hayes Open the ORCID record for Jasmeet P. Hayes [Opens in a new window] ,
Kelly J. Hiersche ,
Alexander N. Hasselbach ,
Scott M. Hayes Open the ORCID record for Scott M. Hayes [Opens in a new window]  and
for the Alzheimer’s Disease Neuroimaging Initiative
Jessica Stark
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Daniela J. Palombo
Affiliation:
Department of Psychology, University of British Columbia, Vancouver, BC, Canada
Jasmeet P. Hayes
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
Kelly J. Hiersche
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Alexander N. Hasselbach
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA
Scott M. Hayes*
Affiliation:
Department of Psychology, The Ohio State University, Columbus, OH, USA Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
*
Correspondence and reprint requests to: Scott M. Hayes, Ph.D., Department of Psychology, The Ohio State University, Psychology Building, 1835 Neil Ave., Columbus, OH 43210, USA. E-mail: [email protected]
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Abstract

Objectives:

To identify novel associations between modifiable physical and health variables, Alzheimer’s disease (AD) biomarkers, and cognitive function in a cohort of older adults with Mild Cognitive Impairment (MCI).

Methods:

Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, AD, and cognition were assessed in 154 MCI participants (Mean age = 74.1 years) from the Alzheimer’s Disease Neuroimaging Initiative. Partial Least Squares analysis was employed to examine associations among these physiological variables and cognition.

Results:

Latent variable 1 revealed a unique combination of AD biomarkers, neurotrophic/growth factors, education, and stress that were significantly associated with specific domains of cognitive function, including episodic memory, executive function, processing speed, and language, representing 45.2% of the cross-block covariance in the data. Age, body mass index, and metrics tapping basic attention or premorbid IQ were not significant.

Conclusions:

Our data-driven analysis highlights the significant relationships between metrics associated with AD pathology, neuroprotection, and neuroplasticity, primarily with tasks tapping episodic memory, executive function, processing speed, and verbal fluency rather than more basic tasks that do not require mental manipulation (basic attention and vocabulary). These data also indicate that biological metrics are more strongly associated with episodic memory, executive function, and processing speed than chronological age in older adults with MCI.

Keywords

Healthy AgingNeuropsychologyNeuroprotectionNeuroplasticityMemoryExecutive Function
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 28 , Issue 8 , September 2022 , pp. 781 - 789
Copyright
Copyright © INS. Published by Cambridge University Press, 2021

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Footnotes

*

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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