Multimodal Integration of Plasma, MRI, and Genetic Risk for Cerebral Amyloid Prediction

Alzheimers disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-{beta} (A{beta}) plaques. Although cerebral A{beta} positron emission tomography (A{beta}-PET) remains the gold standard for assessing cerebral A{beta} burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers serve as promising non-invasive predictors of cerebral A{beta} burden, but reliance on a single marker often leads to suboptimal predictive performance. To address this, we proposed a multimodal machine learning strategy that integrates readily accessible and non-invasive features--such as plasma biomarkers, structural magnetic resonance imaging (sMRI)-derived atrophy measures, diffusion tensor imaging (DTI)-based structural connectomes (SCs), and genetic risk profiles--to predict cerebral A{beta} burden and evaluate the relative contribution of each modality to predictive performance. Specifically, a random forest regressor was trained using data from the Alzheimers Disease Neuroimaging Initiative (ADNI; n = 150) and evaluated with leave-one-out cross-validation. Our results showed that integrating multimodal features improves the predictive power on cerebral amyloid burden: while the baseline model using plasma and clinical variables alone achieved an R{superscript 2} of 0.52, adding neuroimaging and apolipoprotein E (APOE) genotype features improved performance (R{superscript 2} = 0.617), and replacing APOE with polygenic risk scores (PRS) further enhanced accuracy (R{superscript 2} = 0.637). The predictive value of multimodal integration was also replicated in an independent cohort (SILCODE; n = 101). Moreover, a multiclass classifier trained with the same multimodal features achieved high accuracy in distinguishing clinical stages of A{beta} burden--normal controls (NC), mild cognitive impairment (MCI), and Alzheimers disease (AD)--with area under the curve (AUC) values of 0.86, 0.77, and 0.93, respectively. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral A{beta} burden, offering a potential alternative to PET imaging for early AD risk assessment.