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Using a machine learning approach, we determine relative importance of Alzheimer’s disease (AD) biomarkers to predict neurodegeneration as stratified by Apolipoprotein E (APOE) ɛ4+ status.

Recent biomarker research on neurodegenerative diseases has focused on combinations of multiple risk factors to predict development of cognitive impairment and dementia. We combine (1) global brain atrophy as measured by brain parenchymal fraction (BPF), (2) leading AD genetic risk marker (APOE), and (3) commonly examined AD biomarkers to understand the influence of risk factors in relation to differential patterns of atrophy in AD.

We used a ~2-year longitudinal sample (followed annually) of diagnosed AD patients (baseline N=187; mean age=70.57 years; range=37–89 years) from the Sunnybrook Dementia Study. We used (1) latent growth modeling and class analyses to …