Evidence map›Paper›PMID 27385318›Full record

ArticleMolecular medicine (Cambridge, Mass.)2016

Identification of matrix metalloproteinase-12 as a candidate molecule for prevention and treatment of cardiometabolic disease.

M Amor, V Moreno Viedma, A Sarabi, N G Grün, B Itariu, L Leitner, I Steiner, M Bilban, K Kodama, A J Butte and 3 more

Open access · goldAbstract read
In one paragraph

Article in Molecular medicine (Cambridge, Mass.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

10 citing papers in PubMed, 33 citations in OpenAlex.

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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

13 authors at 3 institutions in 2 countries.

M AmorChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
V Moreno ViedmaChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
A SarabiChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
N G GrünChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
B ItariuChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
L LeitnerChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
I SteinerCenter for Medical Statistics, Informatics, and Intelligent Systems, Section for Medical Statistics, Medical University of Vienna, Austria.
M BilbanCore Facility Genomics, Core Facilities, Medical University of Vienna, Vienna, Austria.
K KodamaInstitute for Computational Health Sciences. University of California, San Francisco, EEUU.
A J ButteInstitute for Computational Health Sciences. University of California, San Francisco, EEUU.
G StafflerAFFiRiS GmbH, Vienna, Austria.
M ZeydaDepartment of Pediatrics and Adolescent Medicine, Clinical Division of Pediatric Pulmonology, Allergology and Endocrinology, Medical University of Vienna.
T M StulnigChristian Doppler Laboratory for Cardio-Metabolic Immunotherapy and Clinical Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Vienna, Austria.
Medical University of Vienna · ATUniversity of California, San Francisco · USAFFiRiS (Austria) · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obesity is strongly associated with metabolic syndrome, a combination of risk factors that predispose to the development of the cardiometabolic diseases: atherosclerotic cardiovascular disease and type 2 diabetes mellitus. Prevention of metabolic syndrome requires novel interventions to address this health challenge. The objective of this study was the identification of candidate molecules for the prevention and treatment of insulin resistance and atherosclerosis, conditions that underlie type 2 diabetes mellitus and cardiovascular disease, respectively. We used an unbiased bioinformatics approach to identify molecules that are upregulated in both conditions by combining murine and human data from a microarray experiment and meta-analyses. We obtained a pool of eight genes that were upregulated in all the databases analysed. This included well known and novel molecules involved in the pathophysiology of type 2 diabetes mellitus and cardiovascular disease. Notably,

Indexed as

Cardiovascular diseasesDiabetes Mellitus, Type 2Insulin ResistanceMeta-AnalysisMetabolic SyndromeMicroarray Analysis

Identifiers

PMID27385318
PMCPMC5072410
OpenAlexW2490679308

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.