Evidence map›Paper›PMID 30985365›Full record

ReviewCurrent opinion in lipidology2019

Genetics-driven discovery of novel regulators of lipid metabolism.

Elizabeth E Ha, Andrew G Van Camp, Robert C Bauer

Open access · greenAbstract readReview
In one paragraph

Review in Current opinion in lipidology, 2019. 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
0.3field-weighted citation impact, top 48% 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, 8 citations in OpenAlex.

  1. Article
  2. Review
  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

3 authors at 1 institution in 1 country.

Elizabeth E HaCardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University, New York, New York, USA.
Andrew G Van Camp
Robert C Bauer
Columbia University · US

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007367 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI REINER, STEVEN L · 1985 to 2021
$25.1M
Regulation of lipoprotein metabolism by adipose-specific Tribbles-1R01HL141745 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAUER, ROBERT CLAYTON · 2018 to 2022
$2.0M
NHLBI NIH HHS R01 HL141745NIGMS NIH HHS T32 GM007367
6 · The paper itself

Abstract

purpose of reviewResidual cardiovascular disease risk and increasing metabolic syndrome risk underscores a need for novel therapeutics targeting lipid metabolism in humans. Unbiased human genetic screens have proven powerful in identifying novel genomic loci, and this review discusses recent developments in such discovery. RECENT

findingsRecent human genome-wide association studies have been completed in incredibly large, detailed cohorts, allowing for the identification of more than 300 genomic loci that participate in the regulation of plasma lipid metabolism. However, the discovery of these loci has greatly outpaced the elucidation of the underlying functional mechanisms. The identification of novel roles for long noncoding RNAs, such as CHROME, LeXis, and MeXis, in lipid metabolism suggests that noncoding RNAs should be included in the functional translation of GWAS loci. SUMMARY: Unbiased genetic studies appear to have unearthed a great deal of novel biology with respect to lipid metabolism, yet translation of these findings into actionable mechanisms has been slow. Increased focus on the translation, rather than the discovery, of these loci, with new attention paid to lncRNAs, can help spur the development of novel therapeutics targeting lipid metabolism.

Indexed as

Genome-Wide Association StudyAnimalsGenetic LociHumansLipid Metabolism

Identifiers

PMID30985365
PMCPMC6681899
OpenAlexW2938146179

What OpenQuestion holds

Textmetadata
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.