Evidence map›Paper›PMID 34502360›Full record

ArticleInternational journal of molecular sciences2021

Circulating microRNAs Signature for Predicting Response to GLP1-RA Therapy in Type 2 Diabetic Patients: A Pilot Study.

Caterina Formichi, Daniela Fignani, Laura Nigi, Giuseppina Emanuela Grieco, Noemi Brusco, Giada Licata, Claudia Sabato, Elisabetta Ferretti, Guido Sebastiani, Francesco Dotta

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 6% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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  16. Polyagonists in Type 2 Diabetes Management.Current diabetes reports · 2024
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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

10 authors at 2 institutions in 1 country.

Caterina FormichiDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
Daniela FignaniDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
Laura NigiDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
Giuseppina Emanuela GriecoDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-7325-6128
Noemi BruscoDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
Giada LicataDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-6995-8581
Claudia SabatoDepartment of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0001-7533-4660
Elisabetta FerrettiDepartment of Experimental Medicine, Sapienza University of Rome, 00161 Rome, Italy.ORCID 0000-0001-7265-6429
Guido SebastianiDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
Francesco DottaDiabetes Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, 53100 Siena, Italy.
University of Siena · ITSapienza University of Rome · IT

Funding

Italian ministry of Health PROMETEOItalian Ministry of Health "Ricerca Finalizzata 2018" GR-2018-12365577Italian Ministry of University and Research 201793XZ5A_006Italian Ministry of University and Research 2268-2019-DF-CONRICMIUR PRIN2017_001PON "Ricerca e Innovazione" 2014-2020 (ARS01_01270- IDF SHARID - Innovative Devices For SHAping the RIsk of Diabetes)
6 · The paper itself

Abstract

Type 2 diabetes (T2D) represents one of the major health issues of this century. Despite the availability of an increasing number of anti-hyperglycemic drugs, a significant proportion of patients are inadequately controlled, thus highlighting the need for novel biomarkers to guide treatment selection. MicroRNAs (miRNAs) are small non-coding RNAs, proposed as useful diagnostic/prognostic markers. The aim of our study was to identify a miRNA signature occurring in responders to glucagon-like peptide 1 receptor agonists (GLP1-RA) therapy. We investigated the expression profile of eight T2D-associated circulating miRNAs in 26 prospectively evaluated diabetic patients in whom GLP1-RA was added to metformin. As expected, GLP1-RA treatment induced significant reductions of HbA1c and body weight, both after 6 and 12 months of therapy. Of note, baseline expression levels of the selected miRNAs revealed two distinct patient clusters: "high expressing" and "low expressing". Interestingly, a significantly higher percentage of patients in the high expression group reached the glycemic target after 12 months of treatment. Our findings suggest that the evaluation of miRNA expression could be used to predict the likelihood of an early treatment response to GLP1-RA and to select patients in whom to start such treatment, paving the way to a personalized medicine approach.

Indexed as

AdultBiomarkers, PharmacologicalBlood GlucoseCirculating MicroRNADiabetes Mellitus, Type 2FemaleGene ExpressionGene Expression ProfilingGlucagon-Like Peptide 1Glucagon-Like Peptide-1 ReceptorGlucagon-Like Peptide-1 Receptor AgonistsGlucoseHumansHypoglycemic AgentsMaleMicroRNAsBiomarkers, PharmacologicalBlood GlucoseCirculating MicroRNAGLP1R protein, humanGlucagon-Like Peptide 1Glucagon-Like Peptide-1 ReceptorGlucagon-Like Peptide-1 Receptor AgonistsGlucoseHypoglycemic AgentsMicroRNAsGLP1-RAmicroRNAsobesitypersonalized medicinetype 2 diabetes

Identifiers

PMID34502360
PMCPMC8431190
OpenAlexW3197804642

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.