Evidence map›Paper›PMID 30328514›Full record

ReviewCurrent cardiology reports2018

How Genomics Is Personalizing the Management of Dyslipidemia and Cardiovascular Disease Prevention.

Lane B Benes, Daniel J Brandt, Eric J Brandt, Michael H Davidson

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current cardiology reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. Article
  3. Review
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

4 authors at 3 institutions in 1 country.

Lane B BenesSection of Cardiology, The University of Chicago Medicine, 5841 S Maryland Avenue, MC 6080, Chicago, IL, 60637, USA.
Daniel J BrandtDepartment of Epidemiology, The University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Eric J BrandtSection of Cardiovascular Medicine, Yale University School of Medicine, 789 Howard Avenue, New Haven, CT, 06519, USA.
Michael H DavidsonSection of Cardiology, The University of Chicago Medicine, 5841 S Maryland Avenue, MC 6080, Chicago, IL, 60637, USA. mdavidso@bsd.uchicago.edu.
University of Chicago · USUniversity of Michigan–Ann Arbor · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of the reviewTo summarize advances in genomic medicine and anticipated future directions to improve cardiovascular risk reduction. RECENT

findingsMendelian randomization and genome-wide association studies have given significant insights into the role of genetics in dyslipidemia and cardiovascular disease (CVD), with over 160 gene loci found to be associated with coronary artery disease to date. This has enabled the creation of genetic risk scores that have demonstrated improved risk prediction when added to clinical markers of CVD risk. Incorporation of genomic data into clinical patient care is on the horizon. Genomic medicine is expected to offer improved risk assessment, determination of targeted treatment strategies, and improved detection of lipid disorders causal to CVD development.

Indexed as

Precision MedicinePrimary PreventionCardiovascular DiseasesDyslipidemiasEarly DiagnosisGenome-Wide Association StudyGenomicsHumansMendelian Randomization AnalysisMolecular Targeted TherapyRisk AssessmentCardiovascular preventionGenetic risk score for coronary artery diseaseGenome-wide association studiesGenomicsPolygenic lipid disorder

Identifiers

PMID30328514
OpenAlexW2896593166

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.