Literature DB >> 33277338

Baseline Assessment of Circulating MicroRNAs Near Diagnosis of Type 1 Diabetes Predicts Future Stimulated Insulin Secretion.

Isaac Snowhite1, Ricardo Pastori1,2, Jay Sosenko2, Shari Messinger Cayetano3, Alberto Pugliese4,2,5.   

Abstract

Type 1 diabetes is an autoimmune disease resulting in severely impaired insulin secretion. We investigated whether circulating microRNAs (miRNAs) are associated with residual insulin secretion at diagnosis and predict the severity of its future decline. We studied 53 newly diagnosed subjects enrolled in placebo groups of TrialNet clinical trials. We measured serum levels of 2,083 miRNAs, using RNA sequencing technology, in fasting samples from the baseline visit (<100 days from diagnosis), during which residual insulin secretion was measured with a mixed meal tolerance test (MMTT). Area under the curve (AUC) C-peptide and peak C-peptide were stratified by quartiles of expression of 31 miRNAs. After adjustment for baseline C-peptide, age, BMI, and sex, baseline levels of miR-3187-3p, miR-4302, and the miRNA combination of miR-3187-3p/miR-103a-3p predicted differences in MMTT C-peptide AUC/peak levels at the 12-month visit; the combination miR-3187-3p/miR-4723-5p predicted proportions of subjects above/below the 200 pmol/L clinical trial eligibility threshold at the 12-month visit. Thus, miRNA assessment at baseline identifies associations with C-peptide and stratifies subjects for future severity of C-peptide loss after 1 year. We suggest that miRNAs may be useful in predicting future C-peptide decline for improved subject stratification in clinical trials.
© 2020 by the American Diabetes Association.

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Year:  2020        PMID: 33277338      PMCID: PMC7881864          DOI: 10.2337/db20-0817

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  57 in total

1.  Identification of microRNA biomarkers in type 2 diabetes: a meta-analysis of controlled profiling studies.

Authors:  Hongmei Zhu; Siu Wai Leung
Journal:  Diabetologia       Date:  2015-02-13       Impact factor: 10.122

2.  A miRNA181a/NFAT5 axis links impaired T cell tolerance induction with autoimmune type 1 diabetes.

Authors:  Isabelle Serr; Martin G Scherm; Adam M Zahm; Jonathan Schug; Victoria K Flynn; Markus Hippich; Stefanie Kälin; Maike Becker; Peter Achenbach; Alexei Nikolaev; Katharina Gerlach; Nicole Liebsch; Brigitta Loretz; Claus-Michael Lehr; Benedikt Kirchner; Melanie Spornraft; Bettina Haase; James Segars; Christoph Küper; Ralf Palmisano; Ari Waisman; Richard A Willis; Wan-Uk Kim; Benno Weigmann; Klaus H Kaestner; Anette-Gabriele Ziegler; Carolin Daniel
Journal:  Sci Transl Med       Date:  2018-01-03       Impact factor: 17.956

3.  Multiple microRNAs within the 14q32 cluster target the mRNAs of major type 1 diabetes autoantigens IA-2, IA-2β, and GAD65.

Authors:  Liron Abuhatzira; Huanyu Xu; Georges Tahhan; Afroditi Boulougoura; Alejandro A Schäffer; Abner L Notkins
Journal:  FASEB J       Date:  2015-07-06       Impact factor: 5.191

4.  Circulating miR-103 family as potential biomarkers for type 2 diabetes through targeting CAV-1 and SFRP4.

Authors:  Mao Luo; Chunrong Xu; Yulin Luo; Gang Wang; Jianbo Wu; Qin Wan
Journal:  Acta Diabetol       Date:  2019-10-03       Impact factor: 4.280

5.  Mixed-meal tolerance test versus glucagon stimulation test for the assessment of beta-cell function in therapeutic trials in type 1 diabetes.

Authors:  Carla J Greenbaum; Thomas Mandrup-Poulsen; Paula Friedenberg McGee; Tadej Battelino; Burkhard Haastert; Johnny Ludvigsson; Paolo Pozzilli; John M Lachin; Hubert Kolb
Journal:  Diabetes Care       Date:  2008-07-15       Impact factor: 19.112

6.  HTSeq--a Python framework to work with high-throughput sequencing data.

Authors:  Simon Anders; Paul Theodor Pyl; Wolfgang Huber
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

7.  miRBase: from microRNA sequences to function.

Authors:  Ana Kozomara; Maria Birgaoanu; Sam Griffiths-Jones
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

8.  A Network of microRNAs Acts to Promote Cell Cycle Exit and Differentiation of Human Pancreatic Endocrine Cells.

Authors:  Wen Jin; Francesca Mulas; Bjoern Gaertner; Yinghui Sui; Jinzhao Wang; Ileana Matta; Chun Zeng; Nicholas Vinckier; Allen Wang; Kim-Vy Nguyen-Ngoc; Joshua Chiou; Klaus H Kaestner; Kelly A Frazer; Andrea C Carrano; Hung-Ping Shih; Maike Sander
Journal:  iScience       Date:  2019-11-01

9.  MicroRNA expression in alpha and beta cells of human pancreatic islets.

Authors:  Dagmar Klein; Ryosuke Misawa; Valia Bravo-Egana; Nancy Vargas; Samuel Rosero; Julieta Piroso; Hirohito Ichii; Oliver Umland; Jiang Zhijie; Nicholas Tsinoremas; Camillo Ricordi; Luca Inverardi; Juan Domínguez-Bendala; Ricardo L Pastori
Journal:  PLoS One       Date:  2013-01-29       Impact factor: 3.240

10.  miRNA-375 a Sensor of Glucotoxicity Is Altered in the Serum of Children with Newly Diagnosed Type 1 Diabetes.

Authors:  Lucien Marchand; Audrey Jalabert; Emmanuelle Meugnier; Kathleen Van den Hende; Nicole Fabien; Marc Nicolino; Anne-Marie Madec; Charles Thivolet; Sophie Rome
Journal:  J Diabetes Res       Date:  2016-05-24       Impact factor: 4.011

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  2 in total

1.  The Deterrence of Rapid Metabolic Decline Within 3 Months After Teplizumab Treatment in Individuals at High Risk for Type 1 Diabetes.

Authors:  Emily K Sims; David Cuthbertson; Kevan C Herold; Jay M Sosenko
Journal:  Diabetes       Date:  2021-09-22       Impact factor: 9.461

2.  Type 1 Diabetes Mellitus-Related circRNAs Regulate CD4+ T Cell Functions.

Authors:  Jianni Chen; Guanfei Jia; Xue Lv; Shufa Li
Journal:  Biomed Res Int       Date:  2022-08-24       Impact factor: 3.246

  2 in total

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