SynthesisCirculation2018

LPA Variants Are Associated With Residual Cardiovascular Risk in Patients Receiving Statins.

Wei-Qi Wei, Xiaohui Li, Qiping Feng, Michiaki Kubo, Iftikhar J Kullo, Peggy L Peissig, Elizabeth W Karlson, Gail P Jarvik, Ming Ta Michael Lee, Ning Shang and 23 more

Open access · bronzeAbstract readMeta-AnalysisMulticenter Study
In one paragraph

Synthesis in Circulation, 2018. The graph read 1 number from its abstract, feeding 1 cell of the map, but none could be read as for or against, so it casts no vote. It also reports an association that does not count as treatment evidence, such as OR 1.58 (1.35 to 1.86) for lipids. Cited by 43 papers, 2 of them syntheses that pooled it.

1number the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 2 pooled it
11.1field-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.

Read, but not usablea number the graph found but could not read as for or against

Lipidsan association or prognostic statement, not a treatment comparison · ascvd, dyslipidemiafeeds one cell of the map
OR 1.581.35 to 1.86
The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10 CONCLUSIONS: Genetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering.

clause the extractor read what became the number

2 · Its place on the map

Where it lands on the map

Rows are treatments, columns are outcomes. The coloured squares are the cells this paper feeds, coloured by the vote it casts there. Click one to jump to what this paper adds to it.

supports the treatmentfavours the comparatorno clear differenceread, but no usable result
3 · What it changes

What it adds to each cell

For every cell the paper feeds: the belief in the claim with and without this paper, and this paper's estimate drawn against every other readable study in the cell. The ringed dot is this paper.

Statins×lipids

No readable resultOpen on the map →What to test next →

38 readable studies in this cell: 26 favour the treatment, 5 find no difference, 7 favour the comparator.

Belief with this paper
0.50contested · 21 families support, 7 contradict · against placebo
Without itNot a counted family in this claim, so removing it changes nothing.
← favours the treatmentfavours the comparator →
0 · no effect
NCT002899002,340 enrolled · 2006
Δ -13.2-16.8 to -9.60
reduced -66.0-73.0 to -58.0
NCT02546323543 enrolled · 2015
Δ -35.5-40.2 to -30.7
NCT01678820299 enrolled · 2012
Δ 0.50-4.80 to 5.80
NCT01218204287 enrolled · 2010
Δ 5.47-15.7 to 26.7
NCT0093525931 enrolled · 2009
Δ -51.7
reductions -33.6-38.8 to -28.4

This paper's own estimate is on a different scale from the rest of the cell, so it is not drawn here.

4 · 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.

5 · Its place in the literature

Who cites it

43 citing papers in PubMed, 2 syntheses or guidelines pooled it, 91 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
  5. Review
  6. Article
  7. Biomedical literature-based clinical phenotype definition discovery using large language models.Database : the journal of biological databases and curation · 2025
    Article
  8. Review
  9. Review
  10. Article
  11. Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. Article
6 · The record

Corrections and comments

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

7 · Who and what money

Authors and funding

33 authors at 15 institutions in 3 countries.

Wei-Qi WeiDepartment of Biomedical Informatics (W.-Q.W., J.D.M., J.C.D.), Vanderbilt University Medical Center, Nashville, TN.
Xiaohui LiThe Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (X.L., J.I.R.).
Qiping FengDivision of Clinical Pharmacology (Q.F., C.S., J.D.M., C.M.S., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.
Michiaki KuboRIKEN Center for Integrative Medical Sciences, Yokohama, Japan (M.K., S.M., M.H., Y.M.).
Iftikhar J KulloDepartment of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (I.J.K.).
Peggy L PeissigCenter for Precision Medicine Research, Marshfield Clinic Research Institute, WI (P.L.P.).
Elizabeth W KarlsonDivision of Rheumatology, Immunology and Allergy, Brigham & Women's Hospital and Harvard Medical School, Boston, MA (E.W.K.).
Gail P JarvikDepartments of Medicine (Medical Genetics) and Genome Sciences (G.P.J., D.C.), University of Washington, Seattle.
Ming Ta Michael LeeGenomic Medicine Institute, Geisinger, Danville, PA (M.T.M.L., M.S.W.).
Ning ShangDepartment of Biomedical Informatics, Columbia University, New York, NY (N.S., G.H.).
Eric A LarsonSanford School of Medicine, University of South Dakota, Sioux Falls (E.A.L., R.A.W.).
Todd EdwardsVanderbilt Genetics Institute and the Division of Genetic Medicine, Vanderbilt University, Nashville, TN (T.E., N.J.C.).
Christian M ShafferDivision of Clinical Pharmacology (Q.F., C.S., J.D.M., C.M.S., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.
Jonathan D MosleyDepartment of Biomedical Informatics (W.-Q.W., J.D.M., J.C.D.), Vanderbilt University Medical Center, Nashville, TN.
Shiro MaedaRIKEN Center for Integrative Medical Sciences, Yokohama, Japan (M.K., S.M., M.H., Y.M.).
Momoko HorikoshiRIKEN Center for Integrative Medical Sciences, Yokohama, Japan (M.K., S.M., M.H., Y.M.).
Marylyn RitchieCenter for Translational Bioinformatics, Institute for Biomedical Informatics, Institute for Biomedical Informatics, Center for Precision Medicine, University of Pennsylvania, Philadelphia (M.R.).
Marc S WilliamsGenomic Medicine Institute, Geisinger, Danville, PA (M.T.M.L., M.S.W.).
Eric B LarsonKaiser Permanente Washington Health Research Institute, Seattle (E.B.L., D.C.).
David R CrosslinDepartment of Biomedical Informatics and Medical Education (D.R.C.), University of Washington, Seattle.
Harris T BlandVanderbilt Institute for Clinical and Translational Research (S.T.B.), Vanderbilt University Medical Center, Nashville, TN.
Jennifer A PachecoFeinberg School of Medicine, Northwestern University, Chicago, IL (J.A.P., L.J.R.-T.).
Laura J Rasmussen-TorvikFeinberg School of Medicine, Northwestern University, Chicago, IL (J.A.P., L.J.R.-T.).
David CronkiteDepartments of Medicine (Medical Genetics) and Genome Sciences (G.P.J., D.C.), University of Washington, Seattle.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY (N.S., G.H.).
Nancy J CoxVanderbilt Genetics Institute and the Division of Genetic Medicine, Vanderbilt University, Nashville, TN (T.E., N.J.C.).
Russell A WilkeSanford School of Medicine, University of South Dakota, Sioux Falls (E.A.L., R.A.W.).
C Michael SteinDivision of Clinical Pharmacology (Q.F., C.S., J.D.M., C.M.S., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Departments of Pediatrics and Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA (X.L., J.I.R.).
Yukihide MomozawaRIKEN Center for Integrative Medical Sciences, Yokohama, Japan (M.K., S.M., M.H., Y.M.).
Dan M RodenDivision of Clinical Pharmacology (Q.F., C.S., J.D.M., C.M.S., D.M.R.), Vanderbilt University Medical Center, Nashville, TN.
Ronald M KraussChildren's Hospital Oakland Research Institute, CA (R.M.K.).
Joshua C DennyDepartment of Biomedical Informatics (W.-Q.W., J.D.M., J.C.D.), Vanderbilt University Medical Center, Nashville, TN.
Vanderbilt University Medical Center · USRIKEN Center for Integrative Medical Sciences · JPUniversity of South Dakota · USUniversity of Washington Medical Center · USColumbia University · USGeisinger Neuroscience Institute · USNorthwestern University · USUCLA Medical Center · USVanderbilt University · USBrigham and Women's Hospital · USChildren’s Institute · USInstitute for Medical Informatics and Biostatistics · CHMarshfield Clinic · USMayo Clinic · USUniversity of the Ryukyus · JP

Funding

EHR-based Genome-Informed Risk Assessment and CommunicationU01HG008680 · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2025 to 2025
$817k
eMERGE IV Northwest: A partnership to evaluate the use of genomic information in health careU01HG008657 · UNIVERSITY OF WASHINGTON · 2025 to 2025
$802k
EHR-based Genomic Discovery and Implementation [Funded Extension]U01HG006379 · MAYO CLINIC ROCHESTER · 2025 to 2025
$724k
eMERGE Phase IV Clinical Center at Mass General BrighamU01HG008685 · BRIGHAM AND WOMEN'S HOSPITAL · 2025 to 2025
$693k
American Heart Association-American Stroke Association 16FTF30130005NHGRI NIH HHS U01 HG006379NHGRI NIH HHS U01 HG008657NHGRI NIH HHS U01 HG008664NHGRI NIH HHS U01 HG008666NHGRI NIH HHS U01 HG008672NHGRI NIH HHS U01 HG008673NHGRI NIH HHS U01 HG008676NHGRI NIH HHS U01 HG008679NHGRI NIH HHS U01 HG008680NHGRI NIH HHS U01 HG008684NHGRI NIH HHS U01 HG008685NHGRI NIH HHS U01 HG008701NHLBI NIH HHS R01 HL133786NIGMS NIH HHS P50 GM115305NIGMS NIH HHS R01 GM103859NIGMS NIH HHS R01 GM105688NIGMS NIH HHS R01 GM120523NLM NIH HHS R01 LM010685
8 · The paper itself

Abstract

The marked sentences are the ones the graph read a number from.

backgroundCoronary heart disease (CHD) is a leading cause of death globally. Although therapy with statins decreases circulating levels of low-density lipoprotein cholesterol and the incidence of CHD, additional events occur despite statin therapy in some individuals. The genetic determinants of this residual cardiovascular risk remain unknown.

methodsWe performed a 2-stage genome-wide association study of CHD events during statin therapy. We first identified 3099 cases who experienced CHD events (defined as acute myocardial infarction or the need for coronary revascularization) during statin therapy and 7681 controls without CHD events during comparable intensity and duration of statin therapy from 4 sites in the Electronic Medical Records and Genomics Network. We then sought replication of candidate variants in another 160 cases and 1112 controls from a fifth Electronic Medical Records and Genomics site, which joined the network after the initial genome-wide association study. Finally, we performed a phenome-wide association study for other traits linked to the most significant locus.

resultsThe meta-analysis identified 7 single nucleotide polymorphisms at a genome-wide level of significance within the LPA/PLG locus associated with CHD events on statin treatment. The most significant association was for an intronic single nucleotide polymorphism within LPA/PLG (rs10455872; minor allele frequency, 0.069; odds ratio, 1.58; 95% confidence interval, 1.35-1.86; P=2.6×10

conclusionsGenetic variations at the LPA locus are associated with CHD events during statin therapy independently of the extent of low-density lipoprotein cholesterol lowering. This finding provides support for exploring strategies targeting circulating concentrations of lipoprotein(a) to reduce CHD events in patients receiving statins.

Indexed as

Polymorphism, Single NucleotideCase-Control StudiesCoronary DiseaseDatabases, GeneticDyslipidemiasElectronic Health RecordsGene FrequencyGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansHydroxymethylglutaryl-CoA Reductase InhibitorsLipoprotein(a)PhenotypeRisk AssessmentRisk FactorsTime FactorsHydroxymethylglutaryl-CoA Reductase InhibitorsLipoprotein(a)cholesterolcoronary diseaseelectronic health recordshydroxymethylglutaryl-CoALDL reductase inhibitorslysophosphatidic acid

Identifiers

PMID29703846
PMCPMC6202211
OpenAlexW2801591490

What Socratic holds

Texttitle and abstract
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 Socratic graph.