Evidence map›Paper›PMID 31891371›Full record

ReviewBrain : a journal of neurology2020

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

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Brain : a journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
98citing papers in PubMed, 1 pooled it
11.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

98 citing papers in PubMed, 1 synthesis or guideline pooled it, 155 citations in OpenAlex.

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38 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

AmanPreet BadhwarCentre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada.
G Peggy McFallDepartment of Psychology, University of Alberta, Edmonton, Canada.
Shraddha SapkotaHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada.
Sandra E BlackHurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, Toronto, Canada.
Howard ChertkowBaycrest Health Sciences and the Rotman Research Institute, University of Toronto, Toronto, Canada.
Simon DuchesneCentre CERVO, Quebec City Mental Health Institute, Quebec, Quebec City, Canada.
Mario MasellisDepartment of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.
Liang LiDepartment of Chemistry, University of Alberta, Edmonton, Canada.
Roger A DixonDepartment of Psychology, University of Alberta, Edmonton, Canada.
Pierre BellecCentre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Canada.
Sunnybrook Health Science Centre · CAUniversity of Alberta · CAInstitut Universitaire de Gériatrie de Montréal · CAInstitut Universitaire en Santé Mentale de Québec · CAUniversity of Toronto · CA

Funding

LONGITUDINAL STUDY OF COGNITIVE AGINGR01AG008235 · NIA · UNIVERSITY OF VICTORIA · PI DIXON, ROGER A · 1989 to 2017
$2.3M
CIHR 152548NIA NIH HHS R01 AG008235
6 · The paper itself

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.

Indexed as

Alzheimer DiseaseGenomicsHumansMetabolomicsNeuroimagingPrecision MedicineAlzheimer’s diseasemetabolite panelmultiomics biomarkersneuroimaging subtypepolygenic risk score

Identifiers

PMID31891371
PMCPMC7241959
OpenAlexW2997092212

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.