A Multimodal Risk Network Predicts Executive Function Trajectories in Non-demented Aging

Abstract

Background: Multiple modalities of Alzheimer's disease (AD) risk factors may operate through interacting networks to predict differential cognitive trajectories in asymptomatic aging. We test such a network in a series of three analytic steps. First, we test independent associations between three risk scores (functional-health, lifestyle-reserve, and a combined multimodal risk score) and cognitive [executive function (EF)] trajectories. Second, we test whether all three associations are moderated by the most penetrant AD genetic risk [Apolipoprotein E (APOE) ε4+ allele]. Third, we test whether a non-APOE AD genetic risk score further moderates these APOE × multimodal risk score associations. Methods: We assembled a longitudinal data set (spanning a 40-year band of aging, 53-95 years) with non-demented older adults (baseline n = 602; Mage = 70.63(8.70) years; 66% female) from the Victoria Longitudinal Study (VLS). The measures included for each modifiable risk score were: (1) functional-health [pulse pressure (PP), grip strength, and body mass index], (2) lifestyle-reserve (physical, social, cognitive-integrative, cognitive-novel activities, and education), and (3) the combination of functional-health and lifestyle-reserve risk scores. Two AD genetic risk markers included (1) APOE and (2) a combined AD-genetic risk score (AD-GRS) comprised of three single nucleotide polymorphisms (SNPs; Clusterin[rs11136000], Complement receptor 1[rs6656401], Phosphatidylinositol binding clathrin assembly protein[rs3851179]). The analytics included confirmatory factor analysis (CFA), longitudinal invariance testing, and latent growth curve modeling. Structural path analyses were deployed to test and compare prediction models for EF performance and change. Results: First, separate analyses showed that higher functional-health risk scores, lifestyle-reserve risk scores, and the combined score, predicted poorer EF performance and steeper decline. Second, APOE and AD-GRS moderated the association between functional-health risk score and the combined risk score, on EF performance and change. Specifically, only older adults in the APOEε4- group showed steeper EF decline with high risk scores on both functional-health and combined risk score. Both associations were further magnified for adults with high AD-GRS. Conclusion: The present multimodal AD risk network approach incorporated both modifiable and genetic risk scores to predict EF trajectories. The results add an additional degree of precision to risk profile calculations for asymptomatic aging populations.

Keywords: Alzheimer’s disease; Victoria Longitudinal Study; cognitive trajectories; genetic risk scores; modifiable risk factors; normal aging.

FIGURE 1

A multimodal risk network predicts executive function (EF) trajectories in non-demented aging. In this network, we include (1) a pool of eight modifiable risk factors [pulse pressure, grip strength, body mass index, physical activities, social activity, cognitive-integrative activity, cognitive-novel activity, and education], (2) four single nucleotide polymorphisms (SNPs) (APOE, CLU, CR1, and PICALM), and (3) four standard cognitive tests (Stroop, Color trails, Hayling, and Brixton). The modifiable risk factors are clustered into two main modifiable domain risk scores (functional-health and lifestyle-reserve) and a Modifiable-Composite Risk Score (M-CRS; functional-health + lifestyle-reserve). Functional-health risk score ranges from 0 to 5, lifestyle-reserve risk score ranges from 0 to 6, and the M-CRS ranges from 0 to 11. The SNPs are represented as a key AD genetic risk (APOE) and an Alzheimer’s disease genetic risk score (AD-GRS; CLU + CR1 + PICALM). The cognitive tests are combined to represent EF in aging. Within this network, first, we examine whether the three modifiable domain risk scores (functional-health, lifestyle-reserve, and a M-CRS) predict differential EF decline in aging. Second, we test whether each of the three predictions are moderated (1) by a key AD genetic risk factor (stratified into APOE ε4– versus ε4+) and (2) further moderated by an AD-GRS (as stratified into low and high AD-GRS), to predict differential EF performance and decline in non-demented aging.

FIGURE 2

Executive function trajectories for functional-health risk score as moderated by AD genetic risk. APOE ε4 non-carriers with increasing functional-health risk scores had poorer EF performance and steeper 40-year trajectories selectively in the high AD-GRS group. Functional-health risk score is coded from very low risk (dashed blue line) to very high risk (solid red line).