Evidence map›Paper›PMID 28289238›Full record

Observational studyStroke2017

Stroke and Circulating Extracellular RNAs.

Eric Mick, Ravi Shah, Kahraman Tanriverdi, Venkatesh Murthy, Mark Gerstein, Joel Rozowsky, Robert Kitchen, Martin G Larson, Daniel Levy, Jane E Freedman

Open access · greenAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Stroke, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
1.9field-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

30 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

  1. Pooled it
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  11. SnoRNAs: Exploring Their Implication in Human Diseases.International journal of molecular sciences · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 5 institutions in 1 country.

Eric MickFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Ravi ShahFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Kahraman TanriverdiFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Venkatesh MurthyFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Mark GersteinFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Joel RozowskyFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Robert KitchenFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Martin G LarsonFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Daniel LevyFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.).
Jane E FreedmanFrom the Department of Quantitative Health Sciences (E.M.) and Department of Medicine (K.T., J.E.F.), University of Massachusetts Medical School, Worcester; Department of Cardiology, Massachusetts General Hospital, Boston (R.S.); Department of Cardiology, University of Michigan, Ann Arbor (V.M.); Yale University Medical School, Computational Biology, New Haven, CT (M.G., J.R., R.K.); The NHLBI's and Boston University's Framingham Heart Study, MA (M.G.L.); Biostatistics Department, Boston University School of Public Health, MA (M.G.L.); and The Framingham Heart Study, Population Sciences Branch, NHLBI, Bethesda, MD (D.L.). jane.freedman@umassmed.edu.
Massachusetts General Hospital · USYale University · USFramingham Heart Study · USBoston University · USUniversity of Michigan–Ann Arbor · US

Funding

Framingham Project/LevyZIAHL006001 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI LEVY, DANIEL · 2009 to 2025
$51.1M
University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
Racial and Ethnic Diversity in Human Extracellular RNAU01HL126495 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FREEDMAN, JANE E, GERSTEIN, MARK BENDER · 2014 to 2018
$3.4M
Extracellular RNAs: Biomarkers for Cardiovascular Risk and DiseaseUH3TR000921 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FREEDMAN, JANE E · 2015 to 2017
$3.1M
Extracellular RNAs: Biomarkers for Cardiovascular Risk and DiseaseUH2TR000921 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FREEDMAN, JANE E · 2013 to 2014
$1.0M
Myocardial tissue architecture in heart failure: implications on physiology and fitnessK23HL127099 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI SHAH, RAVI · 2016 to 2019
$668k
NCATS NIH HHS UH2 TR000921NCATS NIH HHS UH3 TR000921NCATS NIH HHS UL1 TR001453NHLBI NIH HHS K23 HL127099NHLBI NIH HHS U01 HL126495
6 · The paper itself

Abstract

background and purposeThere is increasing interest in extracellular RNAs (ex-RNAs), with numerous reports of associations between selected microRNAs (miRNAs) and a variety of cardiovascular disease phenotypes. Previous studies of ex-RNAs in relation to risk for cardiovascular disease have investigated small numbers of patients and assayed only candidate miRNAs. No human studies have investigated links between novel ex-RNAs and stroke.

methodsWe conducted unbiased next-generation sequencing using plasma from 40 participants of the FHS (Framingham Heart Study; Offspring Cohort Exam 8) followed by high-throughput polymerase chain reaction of 471 ex-RNAs. The reverse transcription quantitative polymerase chain reaction included 331 of the most abundant miRNAs, 43 small nucleolar RNAs, and 97 piwi-interacting RNAs in 2763 additional FHS participants and explored the relations of ex-RNAs and prevalent (n=63) and incident (n=51) stroke and coronary heart disease (prevalent=286, incident=69).

resultsAfter adjustment for multiple cardiovascular disease risk factors, 7 ex-RNAs were associated with stroke prevalence or incidence; there were no ex-RNA associated with prevalent or incident coronary heart disease. Statistically significant ex-RNA associations with stroke were specific, with no overlap between prevalent and incident events.

conclusionsThis is the largest study of ex-RNAs in relation to stroke using an unbiased approach in an observational cohort and the first large study to examine human small noncoding RNAs beyond miRNAs. These results demonstrate that when studied in a large observational cohort, extracellular miRNAs are associated with stroke risk.

Indexed as

AgedCohort StudiesCoronary DiseaseFemaleHigh-Throughput Nucleotide SequencingHumansIncidenceMaleMassachusettsMicroRNAsMiddle AgedPrevalenceRNA, Small InterferingRNA, Small NucleolarStrokeMicroRNAsRNA, Small InterferingRNA, Small Nucleolarcardiovascular diseasesgenesrisk factorsRNAsstroke

Identifiers

PMID28289238
PMCPMC5373984
OpenAlexW2596796425

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.