Evidence map›Paper›PMID 28842730›Full record

ArticleDiabetologia2017

Evidence-based prioritisation and enrichment of genes interacting with metformin in type 2 diabetes.

Adem Y Dawed, Ashfaq Ali, Kaixin Zhou, Ewan R Pearson, Paul W Franks

Open access · hybridFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetologia, 2017. 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
0.4field-weighted citation impact, top 36% 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, 5 citations in OpenAlex.

  1. Article
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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

5 authors at 4 institutions in 3 countries.

Adem Y DawedDivision of Molecular and Clinical Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Level 5, Mailbox 12, University of Dundee, Dundee, DD1 9SY, UK. a.y.dawed@dundee.ac.uk.
Ashfaq AliDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden.
Kaixin ZhouDivision of Molecular and Clinical Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Level 5, Mailbox 12, University of Dundee, Dundee, DD1 9SY, UK.
Ewan R PearsonDivision of Molecular and Clinical Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Level 5, Mailbox 12, University of Dundee, Dundee, DD1 9SY, UK.
Paul W FranksDepartment of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden.
University of Dundee · GBHarvard University · USNinewells Hospital · GBSkåne University Hospital · SE

Funding

Innovative Medicines Initiative 115317
6 · The paper itself

Abstract

aims/hypothesisThere is an extensive body of literature suggesting the involvement of multiple loci in regulating the action of metformin; most findings lack replication, without which distinguishing true-positive from false-positive findings is difficult. To address this, we undertook evidence-based, multiple data integration to determine the validity of published evidence.

methodsWe (1) built a database of published data on gene-metformin interactions using an automated text-mining approach (n = 5963 publications), (2) generated evidence scores for each reported locus, (3) from which a rank-ordered gene set was generated, and (4) determined the extent to which this gene set was enriched for glycaemic response through replication analyses in a well-powered independent genome-wide association study (GWAS) dataset from the Genetics of Diabetes and Audit Research Tayside Study (GoDARTS).

resultsFrom the literature search, seven genes were identified that are related to the clinical outcomes of metformin. Fifteen genes were linked with either metformin pharmacokinetics or pharmacodynamics, and the expression profiles of a further 51 genes were found to be responsive to metformin. Gene-set enrichment analysis consisting of the three sets and two more composite sets derived from the above three showed no significant enrichment in four of the gene sets. However, we detected significant enrichment of genes in the least prioritised category (a gene set in which their expression is affected by metformin) with glycaemic response to metformin (p = 0.03). This gene set includes novel candidate genes such as SLC2A4 (p = 3.24 × 10 CONCLUSIONS/

interpretationWe have described a semi-automated text-mining and evidence-scoring algorithm that facilitates the organisation and extraction of useful information about gene-drug interactions. We further validated the output of this algorithm in a drug-response GWAS dataset, providing novel candidate loci for gene-metformin interactions.

Indexed as

AlgorithmsBlood GlucoseDiabetes Mellitus, Type 2Genome-Wide Association StudyGenotypeGlucose Transporter Type 4HumansMetforminPolymorphism, Single NucleotideBlood GlucoseGlucose Transporter Type 4MetforminG6PCGene-set enrichmentMetforminSLC2A4Text-miningType 2 diabetes

Identifiers

PMID28842730
PMCPMC6448905
OpenAlexW2749667694

What Socratic holds

Textfull text, public
LicenceCC BY
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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.