Nonlinear biomarker interactions in conversion from mild cognitive impairment to Alzheimer's disease

Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid‐β42, total tau, phosphorylated tau), [18F]florbetapir, hippocampal volume and brain‐age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p

Alzheimer's disease progression is multi‐faceted. Here, we used demographic, genetic, neuroimaging and CSF measures to predict progression to Alzheimer's in people with mild cognitive impairment. We found that all measures provide unique information to improve predictions. Furthermore, by modelling nonlinear interactions between biomarkers, improved fit of predictive models was achieved.