Evidence map›Paper›PMID 32174972›Full record

ArticleFrontiers in genetics2020

Multi-Omics Analysis Reveals MicroRNAs Associated With Cardiometabolic Traits.

Michelle M J Mens, Silvana C E Maas, Jaco Klap, Gerrit Jan Weverling, Paul Klatser, Just P J Brakenhoff, Joyce B J van Meurs, André G Uitterlinden, M Arfan Ikram, Maryam Kavousi and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 24% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Accelerated epigenetic age in hypertension: a systematic review and meta-analysis.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
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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

11 authors at 2 institutions in 1 country.

Michelle M J MensDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
Silvana C E MaasDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
Jaco KlapWorld Without Disease Accelerator, Data Sciences & Prevention Biomarkers, Johnson & Johnson, Leiden, Netherlands.
Gerrit Jan WeverlingWorld Without Disease Accelerator, Data Sciences & Prevention Biomarkers, Johnson & Johnson, Leiden, Netherlands.
Paul KlatserWorld Without Disease Accelerator, Data Sciences & Prevention Biomarkers, Johnson & Johnson, Leiden, Netherlands.
Just P J BrakenhoffWorld Without Disease Accelerator, Data Sciences & Prevention Biomarkers, Johnson & Johnson, Leiden, Netherlands.
Joyce B J van MeursDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
André G UitterlindenDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
M Arfan IkramDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
Maryam KavousiDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
Mohsen GhanbariDepartment of Epidemiology, Erasmus MC University Medical Center Rotterdam, Rotterdam, Netherlands.
Erasmus MC · NLJohnson & Johnson (Netherlands) · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are non-coding RNA molecules that regulate gene expression. Extensive research has explored the role of miRNAs in the risk for type 2 diabetes (T2D) and coronary heart disease (CHD) using single-omics data, but much less by leveraging population-based omics data. Here we aimed to conduct a multi-omics analysis to identify miRNAs associated with cardiometabolic risk factors and diseases. First, we used publicly available summary statistics from large-scale genome-wide association studies to find genetic variants in miRNA-related sequences associated with various cardiometabolic traits, including lipid and obesity-related traits, glycemic indices, blood pressure, and disease prevalence of T2D and CHD. Then, we used DNA methylation and miRNA expression data from participants of the Rotterdam Study to further investigate the link between associated miRNAs and cardiometabolic traits. After correcting for multiple testing, 180 genetic variants annotated to 67 independent miRNAs were associated with the studied traits. Alterations in DNA methylation levels of CpG sites annotated to 38 of these miRNAs were associated with the same trait(s). Moreover, we found that plasma expression levels of 8 of the 67 identified miRNAs were also associated with the same trait. Integrating the results of different omics data showed miR-10b-5p, miR-148a-3p, miR-125b-5p, and miR-100-5p to be strongly linked to lipid traits. Collectively, our multi-omics analysis revealed multiple miRNAs that could be considered as potential biomarkers for early diagnosis and progression of cardiometabolic diseases.

Indexed as

cardiometabolic traitsEWASGWASmicroRNAmulti-omics data

Identifiers

PMID32174972
PMCPMC7056871
OpenAlexW3008939625

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.