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

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.

Keywords: Episodic memory; executive function; healthy aging; inflammation; metabolic syndrome; neuronal plasticity; neuroprotection; neuropsychology.

Fig. 1.

VIP Scores represent whether a given input variable is an important predictor in the PLSR model (across all components). Variables with a VIP score > 1 (highlighted by the black horizontal line at VIP value = 1) are considered important to the PLS model’s prediction. A) VIP scores for all variables entered in the episodic memory model. B) VIP scores for all variables entered in the executive function model. BDNF, brain-derived neurotrophic factor; BMI, body mass index; BP, blood pressure; CSF, cerebrospinal fluid; HB-EGF-like-GF, heparin-binding epidermal growth factor like-growth factor; IL-6, interleukin-6; IGF, insulin-like growth factor; p-tau, phosphorylated tau₁₈₁; t-tau, total tau; VEGF, vascular endothelial growth factor.

Fig. 2.

Circle of correlation per model. Figures show how each individual input variable (labelled 1 – 29; see figure legend) is correlated with Components 1 (x-axis) and 2 (y-axis) of each model. Each variable is plotted in a standard coordinate plane. For instance, education (purple #20, A) is positively correlated with both Components 1 and 2, which places it on the right, upper half of the coordinate plane. Education was more highly correlated with Component 2, as shown by nearer distance of #20 from the y-axis, relative to the x-axis. A variable that is perfectly correlated with a given component would touch the edge (gray line) of the circle. A) Episodic Memory: AD biomarkers (orange) are largely associated with Component 1, and neurotrophic factors (blue) and education (purple #20) are largely associated with Component 2. B) Executive Function: AD biomarkers (orange) are associated with both Components 1 and 2, while cardiometabolic variables and HB-EGF-like-GF (#16) are largely associated with Component 2. BDNF, brain-derived neurotrophic factor; BMI, body mass index; BP, blood pressure; CSF, cerebrospinal fluid; HB-EGF-like-GF, heparin-binding epidermal growth factor like-growth factor; IL-6, interleukin-6; IGF, insulin-like growth factor; p-tau, phosphorylated tau₁₈₁; t-tau, total tau; VEGF, vascular endothelial growth factor.

Fig. 3.

Maps of participant loadings for Components 1 and 2. A) Episodic Memory Model: participants coded by diagnosis at two-year follow-up. B) Episodic Memory Model: participants coded by gender. C) Executive Function Model: participants coded by diagnosis at two-year follow-up. D) Executive Function Model: participants coded by gender.