Evidence map›Paper›PMID 32778826›Full record

ArticleNature medicine2020

A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia.

Yuan Luo, Alal Eran, Nathan Palmer, Paul Avillach, Ami Levy-Moonshine, Peter Szolovits, Isaac S Kohane

Abstract read
PubMed Publisher
In one paragraph

Article in Nature medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
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

39 citing papers in PubMed.

  1. Article
  2. Sterol biosynthesis, brain development, and disease.The Journal of clinical investigation · 2026
    Review
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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

7 authors.

Yuan Luo *Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Alal Eran *Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0001-6784-7597
Nathan PalmerDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Paul AvillachDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-0235-7543
Ami Levy-MoonshineBroad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
Peter SzolovitsComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0001-8411-6403
Isaac S KohaneDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. isaac_kohane@harvard.edu.ORCID http://orcid.org/0000-0003-2192-5160

Funding

Northwestern University Clinical and Translational Science Institute (NUCATS)UL1TR001422 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI D'AQUILA, RICHARD · 2015 to 2023
$56.8M
Coordinating Center for the Undiagnosed Diseases NetworkU01HG007530 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2022
$30.2M
TrainingU54HG007963 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S., MURPHY, SHAWN N · 2014 to 2018
$18.3M
Research ProjectP50MH106933 · NIMH · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2018
$17.0M
The Integration of Trans-omics for Precision Medicine (TOPMED) and Other Heart, Lung, Blood and Sleep (HLBS) Data Sets with the Data CommonsOT3HL142480 · NHLBI · HARVARD MEDICAL SCHOOL · PI AVILLACH, PAUL · 2017 to 2022
$8.8M
Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
Mouse Neurodevelopmental Behavior CoreU54HD090255 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI STEVENS, BETH ANN · 2016 to 2020
$5.2M
Patient-Centric Information Commons under FAIR Principles (PIC-FAIR)OT3OD025466 · OD · HARVARD MEDICAL SCHOOL · PI AVILLACH, PAUL, KOHANE, ISAAC S. · 2017 to 2018
$771k
Bayesian Generative Methods for Extracting and Modeling Relations in EHR NarrativesR21LM012618 · NLM · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LUO, YUAN · 2017 to 2018
$390k
NCATS NIH HHS U01 TR002623NCATS NIH HHS UL1 TR001422NHGRI NIH HHS U01 HG007530NHGRI NIH HHS U54 HG007963NHLBI NIH HHS OT3 HL142480NICHD NIH HHS U54 HD090255NIH HHS OT3 OD025466NIMH NIH HHS P50 MH106933NLM NIH HHS R21 LM012618
6 · The paper itself

Abstract

The promise of precision medicine lies in data diversity. More than the sheer size of biomedical data, it is the layering of multiple data modalities, offering complementary perspectives, that is thought to enable the identification of patient subgroups with shared pathophysiology. In the present study, we use autism to test this notion. By combining healthcare claims, electronic health records, familial whole-exome sequences and neurodevelopmental gene expression patterns, we identified a subgroup of patients with dyslipidemia-associated autism.

Indexed as

Autistic DisorderDyslipidemiasElectronic Health RecordsExomeExome SequencingFemaleGenetic Predisposition to DiseaseHumansLipidsMaleMolecular Diagnostic TechniquesPrecision MedicineLipids

Identifiers

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.