Evidence map›Paper›PMID 34913723›Full record

ArticleCirculation2022

AtheroSpectrum Reveals Novel Macrophage Foam Cell Gene Signatures Associated With Atherosclerotic Cardiovascular Disease Risk.

Chuan Li, Lili Qu, Alyssa J Matz, Patrick A Murphy, Yongmei Liu, Ani W Manichaikul, Derek Aguiar, Stephen S Rich, David M Herrington, David Vu and 6 more

Open access · bronzeAbstract read
In one paragraph

Article in Circulation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 5% 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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

  1. Pooled it
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  19. Adopting artificial intelligence in cardiovascular medicine: a scoping review.Hypertension research : official journal of the Japanese Society of Hypertension · 2024
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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

16 authors at 9 institutions in 3 countries.

Chuan Li *Department of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0001-9116-450X
Lili Qu *Department of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0002-5253-6742
Alyssa J Matz *Department of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0003-0310-5097
Patrick A MurphyCenter for Vascular Biology (P.A.M., A.R.-O.), University of Connecticut, Farmington.ORCID 0000-0002-2956-1042
Yongmei LiuDepartment of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC (Y.L.).
Ani W ManichaikulCenter for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville (A.W.M., S.S.R.).ORCID 0000-0002-5998-795X
Derek AguiarDepartment of Computer Science and Engineering, University of Connecticut, Storrs (D.A.).ORCID 0000-0001-9166-8783
Stephen S RichCenter for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville (A.W.M., S.S.R.).ORCID 0000-0003-3872-7793
David M HerringtonDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC (D.M.H.).ORCID 0000-0002-7670-6400
David VuDepartment of Biostatistics, University of Washington, Seattle (D.V., W.C.J.).
W Craig JohnsonDepartment of Biostatistics, University of Washington, Seattle (D.V., W.C.J.).ORCID 0000-0002-3161-3753
Jerome I RotterInstitute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.I.R.).ORCID 0000-0001-7191-1723
Wendy S PostDepartment of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0002-8655-5204
Anthony T VellaDepartment of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0003-4600-5504
Annabelle Rodriguez-OquendoCenter for Vascular Biology (P.A.M., A.R.-O.), University of Connecticut, Farmington.ORCID 0000-0002-3917-8565
Beiyan ZhouDepartment of Immunology (C.L., L.Q., A.J.M., A.T.V., B.Z.), University of Connecticut, Farmington.ORCID 0000-0003-0638-2428
UConn Health · USUniversity of Connecticut · USOffice of Public Health Genomics · USUniversity of Washington · USDuke University · USJohns Hopkins University · USTwitter (United States) · USUCLA Medical Center · USWake Forest University · US

Funding

UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PETER J TONTONOZ · 2003 to 2026
$40.4M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
Genomic and Proteomic Architecture of AtherosclerosisR01HL111362 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HERRINGTON, DAVID MCLEOD · 2012 to 2021
$17.2M
Task Area A Core Study Operations.Task Area A shall encompass annual follow-up of cohort members, clinical endpoints ascertainment, study coordination activities, maintenance of the database and biosp75N92020D00001 · NHLBI · UNIVERSITY OF WASHINGTON · PI MCCLELLAND, ROBYN LEAGH · 2020 to 2025
$17.2M
A Longitudinal Epigenetic Study of AtherosclerosisR01HL135009 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI LIU, YONGMEI · 2017 to 2020
$5.8M
Task Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00005 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WATSON, KAROL E · 2020 to 2025
$5.1M
TO EXERCISE OPTION PERIOD ONE (1) FOR TASK AREA A - MESA CORE OPERATIONS, FIELD CENTER.75N92020D00004 · NHLBI · NORTHWESTERN UNIVERSITY · PI SIEGEL, JONATHAN H · 2020 to 2025
$4.5M
Task Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00006 · NHLBI · UNIVERSITY OF MINNESOTA · PI PANKOW, JAMES S · 2020 to 2025
$4.4M
Cell-specific genomic features of Alzheimer's disease progressionRF1AG054474 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI DING, JINGZHONG, LIU, YONGMEI · 2017 to 2019
$4.1M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001881NHLBI NIH HHS 75N92020D00001NHLBI NIH HHS 75N92020D00002NHLBI NIH HHS 75N92020D00003NHLBI NIH HHS 75N92020D00004NHLBI NIH HHS 75N92020D00005NHLBI NIH HHS 75N92020D00006NHLBI NIH HHS 75N92020D00007NHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL101250NHLBI NIH HHS R01 HL111362NHLBI NIH HHS R01 HL126477NHLBI NIH HHS R01 HL131862NHLBI NIH HHS R01 HL135009NHLBI NIH HHS R01 HL150362NIA NIH HHS RF1 AG054474NIDDK NIH HHS P30 DK063491NIDDK NIH HHS R01 DK101921NIDDK NIH HHS R01 DK121805
6 · The paper itself

Abstract

backgroundWhereas several interventions can effectively lower lipid levels in people at risk for atherosclerotic cardiovascular disease (ASCVD), cardiovascular event risks remain, suggesting an unmet medical need to identify factors contributing to cardiovascular event risk. Monocytes and macrophages play central roles in atherosclerosis, but studies have yet to provide a detailed view of macrophage populations involved in increased ASCVD risk.

methodsA novel macrophage foaming analytics tool, AtheroSpectrum, was developed using 2 quantitative indices depicting lipid metabolism and the inflammatory status of macrophages. A machine learning algorithm was developed to analyze gene expression patterns in the peripheral monocyte transcriptome of MESA participants (Multi-Ethnic Study of Atherosclerosis; set 1; n=911). A list of 30 genes was generated and integrated with traditional risk factors to create an ASCVD risk prediction model (30-gene cardiovascular disease risk score [CR-30]), which was subsequently validated in the remaining MESA participants (set 2; n=228); performance of CR-30 was also tested in 2 independent human atherosclerotic tissue transcriptome data sets (GTEx [Genotype-Tissue Expression] and GSE43292).

resultsUsing single-cell transcriptomic profiles (GSE97310, GSE116240, GSE97941, and FR-FCM-Z23S), AtheroSpectrum detected 2 distinct programs in plaque macrophages-homeostatic foaming and inflammatory pathogenic foaming-the latter of which was positively associated with severity of atherosclerosis in multiple studies. A pool of 2209 pathogenic foaming genes was extracted and screened to select a subset of 30 genes correlated with cardiovascular event in MESA set 1. A cardiovascular disease risk score model (CR-30) was then developed by incorporating this gene set with traditional variables sensitive to cardiovascular event in MESA set 1 after cross-validation generalizability analysis. The performance of CR-30 was then tested in MESA set 2 (

conclusionsOur novel computational program (AtheroSpectrum) identified a specific gene expression profile associated with inflammatory macrophage foam cells. A subset of 30 genes expressed in circulating monocytes jointly contributed to prediction of symptomatic atherosclerotic vascular disease. Incorporating a pathogenic foaming gene set with known risk factors can significantly strengthen the power to predict ASCVD risk. Our programs may facilitate both mechanistic investigations and development of therapeutic and prognostic strategies for ASCVD risk.

Indexed as

AgedAged, 80 and overAtherosclerosisCardiovascular DiseasesCoronary Artery DiseaseFemaleFoam CellsHumansMacrophagesMaleMiddle AgedPlaque, AtheroscleroticRiskROC CurveVascular Calcificationatherosclerosisfoam cellsmacrophagesrisk

Identifiers

PMID34913723
PMCPMC8766929
OpenAlexW4200282656

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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.