Evidence map›Paper›PMID 38912578›Full record

ArticleJCI insight2024

Preanalytical considerations in quantifying circulating miRNAs that predict end-stage kidney disease in diabetes.

Eiichiro Satake, Bozena Krolewski, Hiroki Kobayashi, Zaipul I Md Dom, Joseph Ricca, Jonathan M Wilson, Dave Sb Hoon, Kevin L Duffin, Marcus G Pezzolesi, Andrzej S Krolewski

Abstract read
In one paragraph

Article in JCI insight, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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  5. Review
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

10 authors.

Eiichiro SatakeResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.
Bozena KrolewskiResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.
Hiroki KobayashiResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.
Zaipul I Md DomResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.
Joseph RiccaResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.
Jonathan M WilsonEli Lilly and Company, Indianapolis, Indiana, USA.
Dave Sb HoonDepartment of Translational Molecular Medicine, Saint John's Cancer Institute, Providence Health and Service, Santa Monica, California, USA.
Kevin L DuffinEli Lilly and Company, Indianapolis, Indiana, USA.
Marcus G PezzolesiDepartment of Internal Medicine, Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Andrzej S KrolewskiResearch Division, Joslin Diabetes Center, Boston, Massachusetts, USA.

Funding

SPECIAL ASSAY COREP30DK036836 · NIDDK · JOSLIN DIABETES CENTER · PI ROHIT N. KULKARNI · 1986 to 2026
$50.5M
Causal connections between axon guidance proteins and early progressive kidney function decline in diabetesR01DK131061 · NIDDK · JOSLIN DIABETES CENTER · PI KROLEWSKI, ANDRZEJ S · 2022 to 2025
$2.7M
Development of Prognostic Algorithms to Identify Subjects at High Risk of ESKD in Type 2 DiabetesR01DK126799 · NIDDK · JOSLIN DIABETES CENTER · PI KROLEWSKI, ANDRZEJ S · 2021 to 2024
$2.7M
The Nuclear Receptor-Aster Pathway in Enterohepatic MetabolismR01DK126779 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TONTONOZ, PETER J · 2020 to 2024
$2.4M
The role of microRNAs in progressive renal decline in Type 1 diabetesR01DK110350 · NIDDK · UNIVERSITY OF UTAH · PI PEZZOLESI, MARCUS GUY · 2017 to 2021
$1.8M
NIDDK NIH HHS P30 DK036836NIDDK NIH HHS R01 DK110350NIDDK NIH HHS R01 DK126779NIDDK NIH HHS R01 DK126799NIDDK NIH HHS R01 DK131061
6 · The paper itself

Abstract

Our previous study identified 8 risk and 9 protective plasma miRNAs associated with progression to end-stage kidney disease (ESKD) in diabetes. This study aimed to elucidate preanalytical factors that influence the quantification of circulating miRNAs. Using the EdgeSeq platform, which quantifies 2,002 miRNAs in plasma, including ESKD-associated miRNAs, we compared miRNA profiles in whole plasma versus miRNA profiles in RNA extracted from the same plasma specimens. Less than half of the miRNAs were detected in standard RNA extraction from plasma. Detection of individual and concentrations of miRNAs were much lower when RNA extracted from plasma was quantified by RNA sequencing (RNA-Seq) or quantitative reverse transcription PCR (qRT-PCR) platforms compared with EdgeSeq. Plasma profiles of miRNAs determined by the EdgeSeq platform had excellent reproducibility in assessment and had no variation with age, sex, hemoglobin A1c, BMI, and cryostorage time. The risk ESKD-associated miRNAs were detected and measured accurately only in whole plasma and using the EdgeSeq platform. Protective ESKD-associated miRNAs were detected by all platforms except qRT-PCR; however, correlations among concentrations obtained with different platforms were weak or nonexistent. In conclusion, preanalytical factors have a profound effect on detection and quantification of circulating miRNAs in ESKD in diabetes. Quantification of miRNAs in whole plasma and using the EdgeSeq platform may be the preferable method to study profiles of circulating cell-free miRNAs associated with ESKD and possibly other diseases.

Indexed as

Circulating MicroRNAKidney Failure, ChronicAdultAgedBiomarkersDiabetes MellitusDiabetic NephropathiesDisease ProgressionFemaleHumansMaleMicroRNAsMiddle AgedReproducibility of ResultsBiomarkersCirculating MicroRNAMicroRNAsChronic kidney diseaseDiabetesNephrologyNoncoding RNAs

Identifiers

PMID38912578
PMCPMC11383361

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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.