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Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial

BACKGROUND: Glucocorticoids are among the most commonly prescribed drugs, but there is no biomarker that can quantify their action. The aim of the study was to identify and validate circulating biomarkers of glucocorticoid action. METHODS: In a randomized, crossover, single-blind, discovery study, 1...

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Autores principales: Chantzichristos, Dimitrios, Svensson, Per-Arne, Garner, Terence, Glad, Camilla AM, Walker, Brian R, Bergthorsdottir, Ragnhildur, Ragnarsson, Oskar, Trimpou, Penelope, Stimson, Roland H, Borresen, Stina W, Feldt-Rasmussen, Ulla, Jansson, Per-Anders, Skrtic, Stanko, Stevens, Adam, Johannsson, Gudmundur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024021/
https://www.ncbi.nlm.nih.gov/pubmed/33821793
http://dx.doi.org/10.7554/eLife.62236
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author Chantzichristos, Dimitrios
Svensson, Per-Arne
Garner, Terence
Glad, Camilla AM
Walker, Brian R
Bergthorsdottir, Ragnhildur
Ragnarsson, Oskar
Trimpou, Penelope
Stimson, Roland H
Borresen, Stina W
Feldt-Rasmussen, Ulla
Jansson, Per-Anders
Skrtic, Stanko
Stevens, Adam
Johannsson, Gudmundur
author_facet Chantzichristos, Dimitrios
Svensson, Per-Arne
Garner, Terence
Glad, Camilla AM
Walker, Brian R
Bergthorsdottir, Ragnhildur
Ragnarsson, Oskar
Trimpou, Penelope
Stimson, Roland H
Borresen, Stina W
Feldt-Rasmussen, Ulla
Jansson, Per-Anders
Skrtic, Stanko
Stevens, Adam
Johannsson, Gudmundur
author_sort Chantzichristos, Dimitrios
collection PubMed
description BACKGROUND: Glucocorticoids are among the most commonly prescribed drugs, but there is no biomarker that can quantify their action. The aim of the study was to identify and validate circulating biomarkers of glucocorticoid action. METHODS: In a randomized, crossover, single-blind, discovery study, 10 subjects with primary adrenal insufficiency (and no other endocrinopathies) were admitted at the in-patient clinic and studied during physiological glucocorticoid exposure and withdrawal. A randomization plan before the first intervention was used. Besides mild physical and/or mental fatigue and salt craving, no serious adverse events were observed. The transcriptome in peripheral blood mononuclear cells and adipose tissue, plasma miRNAomic, and serum metabolomics were compared between the interventions using integrated multi-omic analysis. RESULTS: We identified a transcriptomic profile derived from two tissues and a multi-omic cluster, both predictive of glucocorticoid exposure. A microRNA (miR-122-5p) that was correlated with genes and metabolites regulated by glucocorticoid exposure was identified (p=0.009) and replicated in independent studies with varying glucocorticoid exposure (0.01 ≤ p≤0.05). CONCLUSIONS: We have generated results that construct the basis for successful discovery of biomarker(s) to measure effects of glucocorticoids, allowing strategies to individualize and optimize glucocorticoid therapy, and shedding light on disease etiology related to unphysiological glucocorticoid exposure, such as in cardiovascular disease and obesity. FUNDING: The Swedish Research Council (Grant 2015-02561 and 2019-01112); The Swedish federal government under the LUA/ALF agreement (Grant ALFGBG-719531); The Swedish Endocrinology Association; The Gothenburg Medical Society; Wellcome Trust; The Medical Research Council, UK; The Chief Scientist Office, UK; The Eva Madura’s Foundation; The Research Foundation of Copenhagen University Hospital; and The Danish Rheumatism Association. CLINICAL TRIAL NUMBER: NCT02152553.
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spelling pubmed-80240212021-04-07 Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial Chantzichristos, Dimitrios Svensson, Per-Arne Garner, Terence Glad, Camilla AM Walker, Brian R Bergthorsdottir, Ragnhildur Ragnarsson, Oskar Trimpou, Penelope Stimson, Roland H Borresen, Stina W Feldt-Rasmussen, Ulla Jansson, Per-Anders Skrtic, Stanko Stevens, Adam Johannsson, Gudmundur eLife Computational and Systems Biology BACKGROUND: Glucocorticoids are among the most commonly prescribed drugs, but there is no biomarker that can quantify their action. The aim of the study was to identify and validate circulating biomarkers of glucocorticoid action. METHODS: In a randomized, crossover, single-blind, discovery study, 10 subjects with primary adrenal insufficiency (and no other endocrinopathies) were admitted at the in-patient clinic and studied during physiological glucocorticoid exposure and withdrawal. A randomization plan before the first intervention was used. Besides mild physical and/or mental fatigue and salt craving, no serious adverse events were observed. The transcriptome in peripheral blood mononuclear cells and adipose tissue, plasma miRNAomic, and serum metabolomics were compared between the interventions using integrated multi-omic analysis. RESULTS: We identified a transcriptomic profile derived from two tissues and a multi-omic cluster, both predictive of glucocorticoid exposure. A microRNA (miR-122-5p) that was correlated with genes and metabolites regulated by glucocorticoid exposure was identified (p=0.009) and replicated in independent studies with varying glucocorticoid exposure (0.01 ≤ p≤0.05). CONCLUSIONS: We have generated results that construct the basis for successful discovery of biomarker(s) to measure effects of glucocorticoids, allowing strategies to individualize and optimize glucocorticoid therapy, and shedding light on disease etiology related to unphysiological glucocorticoid exposure, such as in cardiovascular disease and obesity. FUNDING: The Swedish Research Council (Grant 2015-02561 and 2019-01112); The Swedish federal government under the LUA/ALF agreement (Grant ALFGBG-719531); The Swedish Endocrinology Association; The Gothenburg Medical Society; Wellcome Trust; The Medical Research Council, UK; The Chief Scientist Office, UK; The Eva Madura’s Foundation; The Research Foundation of Copenhagen University Hospital; and The Danish Rheumatism Association. CLINICAL TRIAL NUMBER: NCT02152553. eLife Sciences Publications, Ltd 2021-04-06 /pmc/articles/PMC8024021/ /pubmed/33821793 http://dx.doi.org/10.7554/eLife.62236 Text en © 2021, Chantzichristos et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Chantzichristos, Dimitrios
Svensson, Per-Arne
Garner, Terence
Glad, Camilla AM
Walker, Brian R
Bergthorsdottir, Ragnhildur
Ragnarsson, Oskar
Trimpou, Penelope
Stimson, Roland H
Borresen, Stina W
Feldt-Rasmussen, Ulla
Jansson, Per-Anders
Skrtic, Stanko
Stevens, Adam
Johannsson, Gudmundur
Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title_full Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title_fullStr Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title_full_unstemmed Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title_short Identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
title_sort identification of human glucocorticoid response markers using integrated multi-omic analysis from a randomized crossover trial
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024021/
https://www.ncbi.nlm.nih.gov/pubmed/33821793
http://dx.doi.org/10.7554/eLife.62236
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