Evidence map›Paper›PMID 23716066›Full record

ArticleAnnals of the rheumatic diseases2014

Association between low density lipoprotein and rheumatoid arthritis genetic factors with low density lipoprotein levels in rheumatoid arthritis and non-rheumatoid arthritis controls.

Katherine P Liao, Dorothée Diogo, Jing Cui, Tianxi Cai, Yukinori Okada, Vivian S Gainer, Shawn N Murphy, Namrata Gupta, Daniel Mirel, Ashwin N Ananthakrishnan and 7 more

Open access · greenAbstract read
In one paragraph

Article in Annals of the rheumatic diseases, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 41% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping.Journal of machine learning research : JMLR · 2022
    Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. High-throughput multimodal automated phenotyping (MAP) with application to PheWAS.Journal of the American Medical Informatics Association : JAMIA · 2019
    Article
  10. PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Thromboprophylaxis is associated with reduced post-hospitalization venous thromboembolic events in patients with inflammatory bowel diseases.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2014
    Article
  17. Article
  18. Article
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

17 authors at 6 institutions in 2 countries.

Katherine P LiaoDivision of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, , Boston, Massachusetts, USA.
Dorothée Diogo
Jing Cui
Tianxi Cai
Yukinori Okada
Vivian S Gainer
Shawn N Murphy
Namrata Gupta
Daniel Mirel
Ashwin N Ananthakrishnan
Peter Szolovits
Stanley Y Shaw
Soumya Raychaudhuri
Susanne Churchill
Isaac Kohane
Elizabeth W Karlson
Robert M Plenge
Brigham and Women's Hospital · USHarvard University · USMass General Brigham · USBroad Institute · USMassachusetts General Hospital · USMassachusetts Institute of Technology · US

Funding

Informatics for Integrating Biology/ the Bedside (RMI)U54LM008748 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI KOHANE, ISAAC S. · 2004 to 2013
$39.0M
Pilot & Feasibility ProgramP30DK043351 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Ramnik J Xavier · 1991 to 2026
$35.3M
Trial of risk mangement for prevention of accelerated atherosclerosis in SLEP60AR047782 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI KATZ, JEFFREY NEIL · 2001 to 2016
$18.8M
Novel Biomarkers and Prediction Models for Rheumatoid ArthritisR01AR049880 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI COSTENBADER, KAREN H, KARLSON, ELIZABETH W · 2003 to 2018
$8.7M
Genetic predictors of response to anti-TNF therapy in rheumatoid arthritisU01GM092691 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI BRENNER, MICHAEL B., RAYCHAUDHURI, SOUMYA · 2010 to 2014
$7.5M
Dissection of two rheumatoid arthritis risk loci from genome-wide studiesR01AR056768 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI BRENNER, MICHAEL B. · 2009 to 2013
$1.9M
Genetic Predictors of Early Atherosclerosis in SLEK24AR052403 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI KARLSON, ELIZABETH W · 2005 to 2014
$1.5M
Bioinformatics and Population Genetics to Identify Rheumatoid Arthritis GenesK08AR055688 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI RAYCHAUDHURI, SOUMYA · 2008 to 2012
$724k
Genetic Risk Factors for Coronary Artery Disease in Rheumatoid ArthritisK08AR060257 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI LIAO, KATHERINE PHOENIX · 2011 to 2015
$649k
Meta-analysis of GWAS data to identify novel rheumatoid arthritis risk lociR01AR057108 · NIAMS · BRIGHAM AND WOMEN'S HOSPITAL · PI PLENGE, ROBERT M · 2009 to 2010
$557k
Deep Re-Sequencing of Rheumatoid Arthritis Risk LociR01AR059648 · NIAMS · BROAD INSTITUTE, INC. · PI PLENGE, ROBERT M · 2010 to 2011
$493k
NHGRI NIH HHS U01HG0070033NIAMS NIH HHS K08 AR055688NIAMS NIH HHS K08 AR060257NIAMS NIH HHS K08-AR060257NIAMS NIH HHS K24 AR052403NIAMS NIH HHS K24-AR052403NIAMS NIH HHS P60 AR047782NIAMS NIH HHS P60-AR047782NIAMS NIH HHS R01 AR049880NIAMS NIH HHS R01-AR049880NIAMS NIH HHS R01 AR056768NIAMS NIH HHS R01-AR056768NIAMS NIH HHS R01 AR057108NIAMS NIH HHS R01-AR057108NIAMS NIH HHS R01 AR059648NIAMS NIH HHS R01-AR059648NIDDK NIH HHS P30 DK043351NIGMS NIH HHS U01 GM092691NIGMS NIH HHS U01-GM092691NLM NIH HHS U54 LM008748NLM NIH HHS U54-LM008748
6 · The paper itself

Abstract

objectivesWhile genetic determinants of low density lipoprotein (LDL) cholesterol levels are well characterised in the general population, they are understudied in rheumatoid arthritis (RA). Our objective was to determine the association of established LDL and RA genetic alleles with LDL levels in RA cases compared with non-RA controls.

methodsUsing data from electronic medical records, we linked validated RA cases and non-RA controls to discarded blood samples. For each individual, we extracted data on: first LDL measurement, age, gender and year of LDL measurement. We genotyped subjects for 11 LDL and 44 non-HLA RA alleles, and calculated RA and LDL genetic risk scores (GRS). We tested the association between each GRS and LDL level using multivariate linear regression models adjusted for age, gender, year of LDL measurement and RA status.

resultsAmong 567 RA cases and 979 controls, 80% were female and mean age at the first LDL measurement was 55 years. RA cases had significantly lower mean LDL levels than controls (117.2 vs 125.6 mg/dl, respectively, p<0.0001). Each unit increase in LDL GRS was associated with 0.8 mg/dl higher LDL levels in both RA cases and controls (p=3.0×10(-7)). Each unit increase in RA GRS was associated with 4.3 mg/dl lower LDL levels in both groups (p=0.01).

conclusionsLDL alleles were associated with higher LDL levels in RA. RA alleles were associated with lower LDL levels in both RA cases and controls. As RA cases carry more RA alleles, these findings suggest a genetic basis for epidemiological observations of lower LDL levels in RA.

Indexed as

AdultAgedArthritis, RheumatoidCase-Control StudiesDyslipidemiasFemaleGenetic Predisposition to DiseaseGenotypeHumansLipoproteins, LDLMaleMiddle AgedPolymorphism, Single NucleotideLipoproteins, LDLGene PolymorphismLipidsRheumatoid Arthritis

Identifiers

PMID23716066
PMCPMC3815491
OpenAlexW1588589581

What OpenQuestion holds

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LicenceTDM
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.