Evidence map›Paper›PMID 34882766›Full record

ReviewEndocrinology2022

Contributions of microRNAs to Peripheral Insulin Sensitivity.

Kang Ho Kim, Sean M Hartig

Open access · bronzeAbstract readReview
In one paragraph

Review in Endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Article
  6. Article
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

2 authors at 1 institution in 1 country.

Kang Ho KimDepartment of Anesthesiology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Sean M HartigDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0002-2695-2072
Baylor College of Medicine · US

Funding

METABOLIC IMPACTS OF TYPE II INTERFERON SIGNALS IN OBESITYR01DK114356 · NIDDK · BAYLOR COLLEGE OF MEDICINE · PI HARTIG, SEAN · 2017 to 2025
$4.6M
Metabolic impact of FGF-21 in adipose tissue and liver of PLWHR01DK126042 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI LAKE, JORDAN E · 2020 to 2024
$3.4M
THE ROLE OF HEPATOKINE ORM2 IN ADIPOSE TISSUE INFLAMMATIONR01DK126656 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KIM, KANGHO · 2021 to 2025
$1.9M
NIDDK NIH HHS R01 DK114356NIDDK NIH HHS R01 DK126042NIDDK NIH HHS R01 DK126656
6 · The paper itself

Abstract

An extensive literature base combined with advances in sequencing technologies demonstrate microRNA levels correlate with various metabolic diseases. Mechanistic studies also establish microRNAs regulate central metabolic pathways and thus play vital roles in maintaining organismal energy balance and metabolic homeostasis. This review highlights research progress on the roles and regulation of microRNAs in the peripheral tissues that confer insulin sensitivity. We discuss sequencing technologies used to comprehensively define the target spectrum of microRNAs in metabolic disease that complement studies reporting physiologic roles for microRNAs in the regulation of glucose and lipid metabolism in animal models. We also discuss the emerging roles of exosomal microRNAs as endocrine signals to regulate lipid and carbohydrate metabolism.

Indexed as

Adipose TissueAnimalsEnergy MetabolismGene Expression RegulationGlucoseHomeostasisHumansInsulin ResistanceLipid MetabolismMetabolic DiseasesMicroRNAsGlucoseMicroRNAsadipose tissueexosomeslivermicroRNAobesitytype 2 diabetes

Identifiers

PMID34882766
PMCPMC8758341
OpenAlexW4200497032

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.