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Brain. 2019 Dec 31. pii: awz384. doi: 10.1093/brain/awz384. [Epub ahead of print]

A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap.

Author information

1
Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada.
2
Université de Montréal, Montreal, Canada.
3
Department of Psychology, University of Alberta, Edmonton, Canada.
4
Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada.
5
Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
6
Baycrest Health Sciences and the Rotman Research Institute, University of Toronto, Toronto, Canada.
7
Centre CERVO, Quebec City Mental Health Institute, Quebec, Quebec City, Canada.
8
Department of Radiology, Faculty of Medicine, Université Laval, Quebec City, Canada.
9
Department of Chemistry, University of Alberta, Edmonton, Canada.
10
Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada.

Abstract

Aetiological and clinical heterogeneity is increasingly recognized as a common characteristic of Alzheimer's disease and related dementias. This heterogeneity complicates diagnosis, treatment, and the design and testing of new drugs. An important line of research is discovery of multimodal biomarkers that will facilitate the targeting of subpopulations with homogeneous pathophysiological signatures. High-throughput 'omics' are unbiased data-driven techniques that probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular) and thereby account for pathophysiological heterogeneity in clinical populations. This review focuses on data reduction analyses that identify complementary disease-relevant perturbations for three omics techniques: neuroimaging-based subtypes, metabolomics-derived metabolite panels, and genomics-related polygenic risk scores. Neuroimaging can track accrued neurodegeneration and other sources of network impairments, metabolomics provides a global small-molecule snapshot that is sensitive to ongoing pathological processes, and genomics characterizes relatively invariant genetic risk factors representing key pathways associated with Alzheimer's disease. Following this focused review, we present a roadmap for assembling these multiomics measurements into a diagnostic tool highly predictive of individual clinical trajectories, to further the goal of personalized medicine in Alzheimer's disease.

KEYWORDS:

Alzheimer’s disease; metabolite panel; multiomics biomarkers; neuroimaging subtype; polygenic risk score

PMID:
31891371
DOI:
10.1093/brain/awz384

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