Evidence map›Paper›PMID 38313294›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Refinement of a published gene-physical activity interaction impacting HDL-cholesterol: role of sex and lipoprotein subfractions.

Kenneth E Westerman, Tuomas O Kilpeläinen, Magdalena Sevilla-Gonzalez, Margery A Connelly, Alexis C Wood, Michael Y Tsai, Kent D Taylor, Stephen S Rich, Jerome I Rotter, James D Otvos and 7 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 10 institutions in 3 countries.

Kenneth E WestermanClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
Tuomas O KilpeläinenNovo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DK.
Magdalena Sevilla-GonzalezClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
Margery A ConnellyLabCorp Diagnostics, Morrisville, NC, USA.
Alexis C WoodUSDA/ARS Children's Nutrition Center, Baylor College of Medicine, Houston, TX, USA.
Michael Y TsaiDepartment of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
Kent D TaylorThe Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
Stephen S RichCenter for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
James D OtvosLipoprotein Metabolism Laboratory, Translational Vascular Medicine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
Amy R BentleyCenter for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Samia MoraCenter for Lipid Metabolomics, Brigham and Women's Hospital, Boston, MA, USA.
Hugues AschardDepartment of Computational Biology, Institut Pasteur, Université de Paris, Paris, FR.
D C RaoDivision of Biostatistics, Washington University, St. Louis, MO, USA.
Charles GuDivision of Biostatistics, Washington University, St. Louis, MO, USA.
Daniel I ChasmanDivision of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Alisa K ManningClinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
Broad Institute · USBrigham and Women's Hospital · USNational Institutes of Health · USUCLA Medical Center · USWashington University in St. Louis · USChildren's Nutrition Research Center at Baylor College of Medicine · USHarvard University · USLabCorp (United States) · USUniversity of Minnesota · USUniversity of Virginia · US

Funding

Large Scale Sequencing and Analysis of GenomesU54HG003067 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY, LANDER, ERIC S · 2004 to 2015
$568.6M
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 ALAN R. SALTIEL · 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
Women's Health Study: Continued Follow-upR01CA047988 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI BURING, JULIE E., LEE, I-MIN · 1991 to 2014
$28.2M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
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
Studies of Rare Genetic Variation in the Isolated Population of SardiniaR01HL117626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2013 to 2016
$10.5M
CHARGE Consortium: Omics Discovery for CVD and Aging PhenotypesR01HL105756 · NHLBI · UNIVERSITY OF WASHINGTON · PI Bruce M Psaty, NICHOLAS L SMITH · 2011 to 2026
$9.5M
Rare variants and NHLBI traits in deeply phenotyped cohortsR01HL120393 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M, RICE, KENNETH M. · 2014 to 2016
$8.9M
A Multi-Ancestry Study of Gene-Lifestyle Interactions and Multi-Omics in Cardiometabolic TraitsR01HL156991 · NHLBI · WASHINGTON UNIVERSITY · PI RAO, DABEERU C · 2021 to 2024
$8.8M
NCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001881NCI NIH HHS R01 CA047988NCI NIH HHS UM1 CA182913NHGRI NIH HHS U54 HG003067NHLBI 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 K24 HL136852NHLBI 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 HL043851NHLBI NIH HHS R01 HL080467NHLBI NIH HHS R01 HL105756NHLBI NIH HHS R01 HL117626NHLBI NIH HHS R01 HL117861NHLBI NIH HHS R01 HL118305NHLBI NIH HHS R01 HL120393NHLBI NIH HHS R01 HL156991NHLBI NIH HHS R01 HL160799NIDDK NIH HHS K01 DK133637NIDDK NIH HHS P30 DK063491
6 · The paper itself

Abstract

Large-scale gene-environment interaction (GxE) discovery efforts often involve compromises in the definition of outcomes and choice of covariates for the sake of data harmonization and statistical power. Consequently, refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C). This GxE was originally identified by Kilpeläinen et al., with the strongest cohort-specific signal coming from the Women's Genome Health Study (WGHS). We thus explored this GxE further in the WGHS (N = 23,294), with follow-up in the UK Biobank (UKB; N = 281,380), and the Multi-Ethnic Study of Atherosclerosis (MESA; N = 4,587). Self-reported PA (MET-hrs/wk), genotypes at rs295849 (nearest gene:

Identifiers

PMID38313294
PMCPMC10836120
OpenAlexW4391228550

What Socratic holds

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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 Socratic graph.