Abstract
INTRODUCTION: Understanding molecular, neuroanatomical, and neurophysiological changes in cognitive decline is crucial for comprehending Alzheimer's disease (AD) progression and facilitating objective staging and early screening. METHODS: We enrolled 277 participants and employed a multimodal approach, integrating genomics, metagenomics, metabolomics, magnetic resonance imaging (MRI), and electroencephalogram (EEG) to investigate the AD continuum, from subjective cognitive decline (SCD) through mild cognitive impairment (MCI) to advanced AD. RESULTS: Key markers and mechanisms were identified for each stage: initial neurophysiological deficits in SCD with compensatory metabolomic responses, gut-brain axis dysregulation in MCI, and extensive metabolic disruption and multisystem breakdown in AD. Using random forest models, we identified specific feature combinations that achieved predictive areas under the curve (AUCs) of 0.78 for SCD, 0.84 for MCI, and 0.98 for AD, highlighting EEG as a particularly effective early screening tool. DISCUSSION: This study elucidates AD's pathophysiological progression and highlights the potential of machine learning-assisted multimodal strategies for early detection and staging. HIGHLIGHTS: Early electroencephalogram (EEG) changes and compensatory metabolomic responses define subjective cognitive decline (SCD) stage. In mild cognitive impairment (MCI), gut-brain axis dysfunction alters microbial diversity and functional pathways. In Alzheimer's disease (AD), systemic breakdown disruption enables near-perfect machine learning (ML) detection. Random forest models yield predictive areas under the curve (AUCs) of 0.78 (SCD), 0.84 (MCI), 0.98 (AD). EEG is a convenient, cost-efficient marker for early screening.