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Profiling microRNAs in individuals at risk of progression to rheumatoid arthritis

Overview of attention for article published in Arthritis Research & Therapy, December 2017
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Title
Profiling microRNAs in individuals at risk of progression to rheumatoid arthritis
Published in
Arthritis Research & Therapy, December 2017
DOI 10.1186/s13075-017-1492-9
Pubmed ID
Authors

L. Ouboussad, L. Hunt, E. M. A. Hensor, J. L. Nam, N. A. Barnes, P. Emery, M. F. McDermott, M. H. Buch

Abstract

Individuals at risk of rheumatoid arthritis (RA) demonstrate systemic autoimmunity in the form of anti-citrullinated peptide antibodies (ACPA). MicroRNAs (miRNAs) are implicated in established RA. This study aimed to (1) compare miRNA expression between healthy individuals and those at risk of and those that develop RA, (2) evaluate the change in expression of miRNA from "at-risk" to early RA and (3) explore whether these miRNAs could inform a signature predictive of progression from "at-risk" to RA. We performed global profiling of 754 miRNAs per patient on a matched serum sample cohort of 12 anti-cyclic citrullinated peptide (CCP) + "at-risk" individuals that progressed to RA. Each individual had a serum sample from baseline and at time of detection of synovitis, forming the matched element. Healthy controls were also studied. miRNAs with a fold difference/fold change of four in expression level met our primary criterion for selection as candidate miRNAs. Validation of the miRNAs of interest was conducted using custom miRNA array cards on matched samples (baseline and follow up) in 24 CCP+ individuals; 12 RA progressors and 12 RA non-progressors. We report on the first study to use matched serum samples and a comprehensive miRNA array approach to identify in particular, three miRNAs (miR-22, miR-486-3p, and miR-382) associated with progression from systemic autoimmunity to RA inflammation. MiR-22 demonstrated significant fold difference between progressors and non-progressors indicating a potential biomarker role for at-risk individuals. This first study using a cohort with matched serum samples provides important mechanistic insights in the transition from systemic autoimmunity to inflammatory disease for future investigation, and with further evaluation, might also serve as a predictive biomarker.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 20%
Student > Bachelor 5 14%
Professor 4 11%
Student > Ph. D. Student 4 11%
Professor > Associate Professor 3 9%
Other 5 14%
Unknown 7 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 29%
Medicine and Dentistry 9 26%
Agricultural and Biological Sciences 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Economics, Econometrics and Finance 1 3%
Other 1 3%
Unknown 10 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 December 2017.
All research outputs
#17,923,510
of 23,012,811 outputs
Outputs from Arthritis Research & Therapy
#2,466
of 2,994 outputs
Outputs of similar age
#308,869
of 440,933 outputs
Outputs of similar age from Arthritis Research & Therapy
#42
of 46 outputs
Altmetric has tracked 23,012,811 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,994 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 440,933 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.