Evidence map›Paper›PMID 32816962›Full record

ArticleDiabetes2020

Anti-Insulin Receptor Antibodies Improve Hyperglycemia in a Mouse Model of Human Insulin Receptoropathy.

Gemma V Brierley, Hannah Webber, Eerika Rasijeff, Sarah Grocott, Kenneth Siddle, Robert K Semple

Open access · bronzeAbstract read
In one paragraph

Article in Diabetes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
  2. Observational
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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

6 authors at 1 institution in 1 country.

Gemma V BrierleyThe University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K. rsemple@ed.ac.uk gb466@cam.ac.uk.ORCID 0000-0002-4600-9970
Hannah WebberMRC Disease Model Core, Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
Eerika RasijeffMRC Disease Model Core, Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
Sarah GrocottMRC Disease Model Core, Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
Kenneth SiddleThe University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K.
Robert K SempleThe University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, U.K. rsemple@ed.ac.uk gb466@cam.ac.uk.ORCID 0000-0001-6539-3069
Wellcome/MRC Institute of Metabolic Science · GB

Funding

Medical Research Council MC_UU_00014/5Wellcome TrustWellcome Trust 210752/Z/18/Z
6 · The paper itself

Abstract

Loss-of-function mutations in both alleles of the human insulin receptor gene (INSR) cause extreme insulin resistance (IR) and usually death in childhood, with few effective therapeutic options. Bivalent antireceptor antibodies can elicit insulin-like signaling by mutant INSR in cultured cells, but whether this translates into meaningful metabolic benefits in vivo, wherein the dynamics of insulin signaling and receptor recycling are more complex, is unknown. To address this, we adopted a strategy to model human insulin receptoropathy in mice, using

Indexed as

AntibodiesDisease Models, AnimalAnimalsBlood GlucoseGene Expression RegulationHyperglycemiaInsulinLiverMaleMiceMice, KnockoutRandom AllocationReceptor, InsulinRNA, MessengerAntibodiesBlood GlucoseInsulinReceptor, InsulinRNA, Messenger

Identifiers

PMID32816962
PMCPMC7576564
OpenAlexW3049601710

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.