Disease Progression Prediction Incorporating Genotype-Environment Interactions: A Longitudinal Neurodegenerative Disorder Study

Disease progression prediction is a fundamental yet challenging task in neurodegenerative disorders. Despite extensive research endeavors, disease progression fitting on brain imaging data alone may yield suboptimal performance due to the effect of potential interactions between genetic variations, proteomic expressions and environmental exposures on the disease progression. To fill this gap, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a fresh and powerful scheme, referred to as Mutual-Assistance Disease Progression fitting and Genotype-by-Environment interaction identification approach (MA-DPxGE). Specifically, our model jointly performs disease progression fitting using longitudinal imaging phenotypes and identification of genotype-by-environment interaction factors. To ensure stability and interpretability, we employ innovative penalties to discern significant risk factors. Moreover, we meticulously design adaptive mechanisms for lossterm reweighting, ensuring fair adjustments for each prediction task. Furthermore, due to high-dimensional genotype-by-environment interactions, we devise a rapid and efficient strategy to reduce runtime, ensuring practical availability and applicability. Experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset reveal that MA-DPxGE demonstrates superior performance compared to state-of the-art approaches, while maintaining exceptional interpretability. This outcome is pivotal in elucidating disease progression patterns and establishing effective strategies to mitigate or halt disease advancement.