A Multiomics Approach to Heterogeneity in Alzheimer's Disease

Focused Review and Roadmap

AmanPreet Badhwar; G. Peggy McFall; Shraddha Sapkota; Sandra E. Black; Howard Chertkow; Simon Duchesne; Mario Masellis; Liang Li; Roger A. Dixon; Pierre Bellec

Disclosures

Brain. 2020;143(5):1315-1331. 

In This Article

Abstract and Introduction

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.

Introduction

Alzheimer's disease is a complex, multifactorial pathology that manifests itself along a continuum of conditions, ranging from asymptomatic, to mild cognitive impairment (MCI), to dementia (specifically Alzheimer's disease dementia). Trials of disease-modifying therapies remain unsuccessful, and these persistent failures have been attributed to (i) intervention late in the disease process (i.e. symptomatic stage), by which time extensive irreversible damage has accrued; and (ii) lack of precision intervention targets in a multifactorial condition. Accordingly, an important line of current research is directed at the discovery of multimodal biomarkers that will help facilitate the detection of Alzheimer's disease in asymptomatic populations, and the adaptation of intervention regimens to different target subpopulations in prevention trials (Anstey et al., 2015; Olanrewaju et al., 2015). This work reviews recent data-driven approaches to biomarker discovery, in three omics fields that capture complementary aspects of neurodegeneration and Alzheimer's disease risk factors. We further propose a roadmap for integrating these multiomics biomarkers to advance our understanding of heterogeneity in Alzheimer's disease and other age-related dementias, and promote efficacy in intervention trials.

Established Alzheimer's disease biomarkers currently capture three facets of the disease pathophysiology: amyloidosis (A), tauopathy (T), and specific aspects of neurodegeneration (N) or A/T/N (Jack et al., 2018). Although these biomarkers have been usefully applied to the crucial goal of early Alzheimer's disease detection (Sperling et al., 2011), they fall short in explaining the heterogeneity of individual clinical trajectories, and their ability to predict differential cognitive decline is modest (Dumurgier et al., 2017). Predicting progression to dementia is challenging, as patients diagnosed with probable Alzheimer's disease dementia show considerable heterogeneity in the cognitive domains impaired (Scheltens et al., 2016), and the presence or severity of established Alzheimer's disease biomarkers. For example, amyloidosis-and-tauopathy-defined 'pure Alzheimer's disease neuropathology' is observed in only 30–50% of patients with probable Alzheimer's disease dementia (Beach et al., 2012; Robinson et al., 2018). The remaining cases show co-occurrence of multiple brain pathologies that overlap with other neurodegenerative diseases (NDDs) of ageing, such as cerebral small vessel disease, and Lewy body dementia. Minimum to above-threshold levels of Alzheimer's disease pathology are also observed in a considerable proportion (39%) of dementia patients not clinically diagnosed as probable Alzheimer's disease (Beach et al., 2012). Alzheimer's disease pathology has also been demonstrated in post-mortem studies of cognitively normal older adults (Bennett et al., 2006), and it remains unclear whether such individuals would have developed Alzheimer's disease symptoms with time, should they have lived longer (Jagust, 2013). Overall, 'top-down' clinical labels, based primarily on cognitive symptoms, imperfectly align with biomarkers of neurodegeneration. Additional biomarkers are thus urgently needed to characterize the clinicopathological heterogeneity of Alzheimer's disease, and to disambiguate it from other age-related NDDs and normal ageing (Jack et al., 2018).

A radically different paradigm to NDDs is to move away from 'top-down' clinical labels, and concentrate on pathological signatures built 'bottom up' using unsupervised machine learning algorithms and high-throughput 'omics' metrics that screen global facets of an organism. 'Omics' refers to several areas of study in biology, all of which end in the suffix -omics, and implies a comprehensive (or global) assessment of the subject using high-throughput technologies. For example, the study of an organism's entire collection of genes (the genome) versus a single or a few genes is termed genomics (Hasin et al., 2017). Data-driven approaches on 'omics' technologies-generated data provide new opportunities to probe the complex aetiology of Alzheimer's disease from multiple levels (e.g. network, cellular, and molecular), and to identify biomarker signatures with high diagnostic/prognostic value. This review focuses on the following omics approaches: brain connectomics, metabolomics, and genomics. These omics data capture complementary information on Alzheimer's disease emergence and progression: brain connectomics (and morphometry) 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 (Jack et al., 2018). The high-dimensional nature of omics 'big data' can prove challenging to process, manipulate, and visualize, even when a single modality is involved. Multiple redundancies are often present in these measures, and not all data points provide independent information as they tend to co-vary because of shared biological processes. We focus this review on three omics data reduction techniques that capture disease-relevant population heterogeneity with a limited number of indicators: neuroimaging-based subtypes, metabolite panels, and polygenic risk score (PRS). Neuroimaging subtypes are based on data-driven algorithms that identify patient subgroups with homogeneous brain imaging features. Metabolite panels are developed via data-driven algorithms applied to thousands of small molecules representing global biochemical events and distinguishing clinical phenotypes. PRS and other empirically derived representations of interactive or multi-gene risk may represent key domains of mechanisms and pathways to Alzheimer's disease. Following this focused review, we discuss the rationale and challenges for assembling multiomics diagnostic tools highly predictive of individual clinical trajectories in the context of Alzheimer's disease, and in particular, the importance of pathophysiological heterogeneity in research clinical cohorts, with the intent to extend these findings to the discrimination of Alzheimer's disease, as well as other dementia related subtypes.

Comments

3090D553-9492-4563-8681-AD288FA52ACE

processing....