Partial Least Squares Regression Analysis of Alzheimer’s Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment
Affiliations: [a] Department of Psychology, The Ohio State University, Columbus, OH, USA
| [b] Chronic Brain Injury Initiative, The Ohio State University, Columbus, OH, USA
| [c] Centre for Addiction and Mental Health, Toronto, Canada
| [d] Department of Psychology, The University of Texas at Dallas, Dallas, TX, USA
Correspondence:
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Correspondence to: Scott M. Hayes, PhD, Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA. Tel.: +1 614 292 3385; E-mail: hayes.1074@osu.edu.
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://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: https://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:Prior work has shown that certain modifiable health, Alzheimer’s disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition. Objective:Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI). Methods:Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer’s Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)—a multivariate method which creates components that best predict an outcome—was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years. Results:At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function. Conclusion:These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change.
