Multifactorial Modeling of Cognitive Trajectories Using an Advanced Regression Technique: Improving Our Understanding of Biomarkers and Modifiable Variables that Support Cognition

Article type: Article Commentary

Authors: Fletcher, Evan^{; *}

Affiliations: Department of Neurology, Imaging of Dementia and Aging (IDeA) Laboratory, University of California, Davis, Davis, CA, USA

Correspondence: [*] Correspondence to: Evan Fletcher, Department of Neurology, Imaging of Dementia and Aging (IDeA) Laboratory, University of California, Davis, Davis, CA 95616, USA. E-mail: [email protected].

Abstract: Current research trends emphasize complex models of cognitive outcomes, with multiple, interacting predictors, including factors amenable to interventions toward sustaining healthy cognitive aging. Such models often require advanced analysis techniques. The article by Stark et al., ‘Partial least squares regression analysis of Alzheimer’s disease biomarkers, modifiable health variables, and cognitive change in older adults with mild cognitive impairment’, uses partial least squares regression to examine the associations to memory and executive change of 29 biomarker and demographic variables. This commentary discusses the significance of their results and methods within the context of current research foci.

Keywords: Alzheimer’s disease, biomarkers, cognitive change, partial least squares analysis

DOI: 10.3233/JAD-230304

Journal: Journal of Alzheimer's Disease, vol. 93, no. 2, pp. 815-819, 2023