[IC‐P‐050]: TOWARD DISCOVERY OF MULTI‐OMICS BIOTYPES OF ALZHEIMER's DISEASE: A FOCUSED REVIEW AND PROPOSED ROAD MAP (2017)

A major aim of the Canadian Consortium on Neurodegeneration in Aging (CCNA) is to characterize the pathophysiological basis of Alzheimer's disease (AD) and precisely differentiate it from other age-related neurodegenerative diseases. Accordingly, CCNA enrols a large cohort (n=1600) representative of the spectrum of neurodegeneration in aging. Participants will undergo extensive phenotyping for unbiased biomarker discovery, including neuroimaging (connectomics), genomics, and metabolomics. The high-dimensional nature and distinctive methods of these ‘omics’ modalities makes it challenging to extract clinically relevant information, and further integrate this information across modalities. Recent trends in omics data analysis have been to either reduce or select from the pertinent complex data. Examples include (1) neuroimaging advances in the identification of biotypes (i.e. groups of individuals who share similar brain characteristics) and (2) metabolomics advances in multi-pathway panels of diagnostic biomarkers. This focused review by the CCNA Biomarker Team discusses literature on the emerging field of multi-omics and proposes a “roadmap” to discovering multi-omics AD biotypes within the CCNA sample. We review recent AD literature for biotyping, pathway panels, and multiplicative risk indexes from connectomics, genomics, and metabolomics approaches. All searches were conducted in PUBMED and restricted to the last five years. We propose a model combining across biotyping methods and integrating with key biomarker candidates to advance the discrimination of AD from related dementia. Eight neuroimaging studies have reported biotypes that were found to be associated with differences in cognitive symptoms, cerebral amyloid deposition, glucose metabolism, biofluid-based biomarker profiles, and/or AD risk. Comparable biotypes appearing in recent genomics and metabolomics literature are identifiable through multi-modal interactions, polygenic risk indexes, and metabolomics diagnostic panels. We reviewed recent techniques to discover multi-omics biomarkers of AD. Although omics biomarkers are demonstrated separately by modality, we develop a “roadmap” for representing their combined effects and relevance to translation (Figure 1). In addition, the roadmap leads to (1) enriching multi-omics biomarkers with modifying factors (age, sex, vascular health), (2) validating their diagnostic power within the heterogeneous clinical sample of the CCNA, and (3) using multi-omics biotypes to predict progression of clinical symptoms in preclinical individuals during longitudinal follow-up. Proposed roadmap to discovering multi-omics AD biomarkers. Despite diagnostic labels, Alzheimer's disease and related dementias are inherently heterogeneous entities both in clinical presentation and underlying pathophysiology. We reviewed recent techniques to discover multi-omics biomarkers of AD. Biotypes derived from the integration of multi-omics data using semi-supervised machine learning techniques will better identify individuals on an AD spectrum trajectory. Abbreviations: Alzheimer's Disease (AD), FrontoTemporal Dementia spectrum (FTD), Lewy Body Disease (LBD), Vascular Cognitive Impairment (VCI), Mixed etiology dementia (Mixed), Healthy Controls (HC), Subjective Cognitive Impairment (SCI). Mild Cognitive Impairment (MCI).