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1. Association of adrenal steroids with metabolomic profiles in patients with primary and endocrine hypertension

Author

Robin Knuchel, Zoran Erlic, Sven Gruber, Laurence Amar, Casper K. Larsen, Anne-Paule Gimenez-Roqueplo, Paolo Mulatero, Martina Tetti, Alessio Pecori, Christina Pamporaki, Katharina Langton, Mirko Peitzsch, Filippo Ceccato, Aleksander Prejbisz, Andrzej Januszewicz, Christian Adolf, Hanna Remde, Livia Lenzini, Michael Dennedy, Jaap Deinum, Emily Jefferson, Anne Blanchard, Maria-Christina Zennaro, Graeme Eisenhofer, and Felix Beuschlein

Subjects
metabolomics, adrenal steroids, endocrine hypertension, primary hypertension, pheochromocytoma, primary aldosteronism, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract

IntroductionEndocrine hypertension (EHT) due to pheochromocytoma/paraganglioma (PPGL), Cushing’s syndrome (CS), or primary aldosteronism (PA) is linked to a variety of metabolic alterations and comorbidities. Accordingly, patients with EHT and primary hypertension (PHT) are characterized by distinct metabolic profiles. However, it remains unclear whether the metabolomic differences relate solely to the disease-defining hormonal parameters. Therefore, our objective was to study the association of disease defining hormonal excess and concomitant adrenal steroids with metabolomic alterations in patients with EHT.MethodsRetrospective European multicenter study of 263 patients (mean age 49 years, 50% females; 58 PHT, 69 PPGL, 37 CS, 99 PA) in whom targeted metabolomic and adrenal steroid profiling was available. The association of 13 adrenal steroids with differences in 79 metabolites between PPGL, CS, PA and PHT was examined after correction for age, sex, BMI, and presence of diabetes mellitus.ResultsAfter adjustment for BMI and diabetes mellitus significant association between adrenal steroids and metabolites – 18 in PPGL, 15 in CS, and 23 in PA – were revealed. In PPGL, the majority of metabolite associations were linked to catecholamine excess, whereas in PA, only one metabolite was associated with aldosterone. In contrast, cortisone (16 metabolites), cortisol (6 metabolites), and DHEA (8 metabolites) had the highest number of associated metabolites in PA. In CS, 18-hydroxycortisol significantly influenced 5 metabolites, cortisol affected 4, and cortisone, 11-deoxycortisol, and DHEA each were linked to 3 metabolites.DiscussionsOur study indicates cortisol, cortisone, and catecholamine excess are significantly associated with metabolomic variances in EHT versus PHT patients. Notably, catecholamine excess is key to PPGL’s metabolomic changes, whereas in PA, other non-defining adrenal steroids mainly account for metabolomic differences. In CS, cortisol, alongside other non-defining adrenal hormones, contributes to these differences, suggesting that metabolic disorders and cardiovascular morbidity in these conditions could also be affected by various adrenal steroids.

Published
2024
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2. Identification of risk loci for primary aldosteronism in genome-wide association studies

Author

Edith Le Floch, Teresa Cosentino, Casper K. Larsen, Felix Beuschlein, Martin Reincke, Laurence Amar, Gian-Paolo Rossi, Kelly De Sousa, Stéphanie Baron, Sophie Chantalat, Benjamin Saintpierre, Livia Lenzini, Arthur Frouin, Isabelle Giscos-Douriez, Matthis Ferey, Alaa B. Abdellatif, Tchao Meatchi, Jean-Philippe Empana, Xavier Jouven, Christian Gieger, Melanie Waldenberger, Annette Peters, Daniele Cusi, Erika Salvi, Pierre Meneton, Mathilde Touvier, Mélanie Deschasaux, Nathalie Druesne-Pecollo, Sheerazed Boulkroun, Fabio L. Fernandes-Rosa, Jean-François Deleuze, Xavier Jeunemaitre, and Maria-Christina Zennaro

Subjects
Science
Abstract

Detection of primary aldosteronism, the most common form of secondary arterial hypertension, is essential for targeted management and prevention of cardiovascular complications. Here, the authors identify genetic loci associated with primary aldosteronism, suggesting new mechanisms of disease.

Published
2022
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3. Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

Author

Parminder S. Reel, Smarti Reel, Josie C. van Kralingen, Katharina Langton, Katharina Lang, Zoran Erlic, Casper K. Larsen, Laurence Amar, Christina Pamporaki, Paolo Mulatero, Anne Blanchard, Marek Kabat, Stacy Robertson, Scott M. MacKenzie, Angela E. Taylor, Mirko Peitzsch, Filippo Ceccato, Carla Scaroni, Martin Reincke, Matthias Kroiss, Michael C. Dennedy, Alessio Pecori, Silvia Monticone, Jaap Deinum, Gian Paolo Rossi, Livia Lenzini, John D. McClure, Thomas Nind, Alexandra Riddell, Anthony Stell, Christian Cole, Isabella Sudano, Cornelia Prehn, Jerzy Adamski, Anne-Paule Gimenez-Roqueplo, Guillaume Assié, Wiebke Arlt, Felix Beuschlein, Graeme Eisenhofer, Eleanor Davies, Maria-Christina Zennaro, and Emily Jefferson

Subjects
Machine learning, Multi-omics, Hypertension, Primary aldosteronism, Pheochromocytoma/paraganglioma, Cushing syndrome, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. Methods: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. Findings: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1) features were found to be most discriminating for all disease combinations. Overall, the MOmics-based classifiers were able to provide better classification performance in comparison to mono-omics classifiers. Interpretation: We have developed a ML pipeline to distinguish different EHT subtypes from PHT using multi-omics data. This innovative approach to stratification is an advancement towards the development of a diagnostic tool for EHT patients, significantly increasing testing throughput and accelerating administration of appropriate treatment. Funding: European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 633983, Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to Z.E. and F.B.), and Deutsche Forschungsgemeinschaft (CRC/Transregio 205/1).

Published
2022
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4. Predicting Hypertension Subtypes with Machine Learning Using Targeted Metabolites and Their Ratios

Author

Smarti Reel, Parminder S. Reel, Zoran Erlic, Laurence Amar, Alessio Pecori, Casper K. Larsen, Martina Tetti, Christina Pamporaki, Cornelia Prehn, Jerzy Adamski, Aleksander Prejbisz, Filippo Ceccato, Carla Scaroni, Matthias Kroiss, Michael C. Dennedy, Jaap Deinum, Graeme Eisenhofer, Katharina Langton, Paolo Mulatero, Martin Reincke, Gian Paolo Rossi, Livia Lenzini, Eleanor Davies, Anne-Paule Gimenez-Roqueplo, Guillaume Assié, Anne Blanchard, Maria-Christina Zennaro, Felix Beuschlein, and Emily R. Jefferson

Subjects
metabolomics, machine learning, hypertension, primary aldosteronism, pheochromocytoma/paraganglioma, Cushing syndrome, Microbiology, QR1-502
Abstract

Hypertension is a major global health problem with high prevalence and complex associated health risks. Primary hypertension (PHT) is most common and the reasons behind primary hypertension are largely unknown. Endocrine hypertension (EHT) is another complex form of hypertension with an estimated prevalence varying from 3 to 20% depending on the population studied. It occurs due to underlying conditions associated with hormonal excess mainly related to adrenal tumours and sub-categorised: primary aldosteronism (PA), Cushing’s syndrome (CS), pheochromocytoma or functional paraganglioma (PPGL). Endocrine hypertension is often misdiagnosed as primary hypertension, causing delays in treatment for the underlying condition, reduced quality of life, and costly antihypertensive treatment that is often ineffective. This study systematically used targeted metabolomics and high-throughput machine learning methods to predict the key biomarkers in classifying and distinguishing the various subtypes of endocrine and primary hypertension. The trained models successfully classified CS from PHT and EHT from PHT with 92% specificity on the test set. The most prominent targeted metabolites and metabolite ratios for hypertension identification for different disease comparisons were C18:1, C18:2, and Orn/Arg. Sex was identified as an important feature in CS vs. PHT classification.

Published
2022
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5. Targeted Metabolomics as a Tool in Discriminating Endocrine From Primary Hypertension

Author

Parminder Singh Reel, Anne Blanchard, Casper K Larsen, Paolo Mulatero, Guillaume Assié, Michael Conall Dennedy, Smarti Reel, Zoran Erlic, Cornelia Prehn, Martin Reincke, Anne-Paule Gimenez-Roqueplo, Alessio Pecori, Katharina Langton, Jaap Deinum, Jerzy Adamski, Christina Pamporaki, Felix Beuschlein, Maria-Christina Zennaro, Filippo Ceccato, Livia Lenzini, Laurence Amar, Martina Tetti, Matthias Kroiss, Carla Scaroni, Aleksander Prejbisz, Emily Jefferson, University of Zurich, Beuschlein, Felix, Universitätsspital Zürich (USZ), University of Dundee, Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Università degli studi di Torino = University of Turin (UNITO), University Hospital Carl Gustav Carus [Dresden, Germany], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Helmholtz Zentrum München = German Research Center for Environmental Health, Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), National University of Singapore (NUS), National Institute of Cardiology [Varsovie, Pologne], Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Universitätsklinikum Würzburg, University of Würzburg = Universität Würzburg, Ludwig-Maximilians-Universität München (LMU), National University of Ireland [Galway] (NUI Galway), Radboud University Medical Center [Nijmegen], Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CIC - HEGP (CIC 1418), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), and HULOT, Jean-Sébastien

Subjects
Oncology, Male, 1303 Biochemistry, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], Clinical Biochemistry, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], 10265 Clinic for Endocrinology and Diabetology, Adrenal Gland Neoplasms, 030204 cardiovascular system & hematology, 1308 Clinical Biochemistry, Biochemistry, Cushing syndrome, chemistry.chemical_compound, 0302 clinical medicine, Endocrinology, Primary aldosteronism, targeted metabolomics, arterial hypertension, pheochromocytoma, primary aldosteronism, screening, Area under the curve, Ornithine, Middle Aged, Hyperaldosteronism, ddc, 3. Good health, 1310 Endocrinology, [SDV] Life Sciences [q-bio], Europe, 2712 Endocrinology, Diabetes and Metabolism, Hypertension, Cushing Syndrome, Arterial Hypertension, Pheochromocytoma, Primary Aldosteronism, Screening, Targeted Metabolomics, Female, Essential Hypertension, AcademicSubjects/MED00250, Adult, medicine.medical_specialty, Context (language use), 030209 endocrinology & metabolism, 610 Medicine & health, 2704 Biochemistry (medical), Endocrine System Diseases, Diagnosis, Differential, Diagnostic Techniques, Endocrine, Paraganglioma, 03 medical and health sciences, Internal medicine, medicine, Endocrine system, Humans, Metabolomics, Clinical Research Articles, Aged, Retrospective Studies, Receiver operating characteristic, business.industry, Biochemistry (medical), medicine.disease, chemistry, business
Abstract

ContextIdentification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension.ObjectiveUse TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.MethodsRetrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a “classical approach” (CA) (performing a series of univariate and multivariate analyses) and a “machine learning approach” (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively.ResultsFrom 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83).ConclusionTM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Published
2021

6. Dietary K + acts as a genuine diuretic

Author

Samuel L. Svendsen, Simon Kornvig, Peder Berg, Iben S. Jensen, Isabela B. B. A. de Araujo, Casper K. Larsen, Jens Leipziger, and Mads V. Sørensen

Subjects
Mammals, Physiology, anti-diuretic hormone, K+ homeostasis, Diet, Diuresis, Kidney/metabolism, Mice, Diuretics/pharmacology, Sodium/metabolism, Animals, Direct K+ effect, Urinary K+ excretion, diuresis
Abstract

K+ balance in mammals relies on regulated renal K+ excretion matching unregulated fluctuating K+ intake. Upon a K+ rich meal, rapid and powerful K+ excretion is needed. Renal K+ secretion is stimulated by increased tubular flow. We speculated that high K+ intake acutely increases urinary flow to stimulate K+ excretion.Methods: Mice were K+ challenged through diets or gavage. Post K+ loading urinary output, osmolarity, [K+ ]u , [Na+ ]u , plasma osmolarity, [copeptin]p , [K+ ]p , and [Na+ ]p were measured. To locate the mechanism of K+ -induced diuresis in the glomerular/tubular system we measured creatinine excretion and assessed functional transport in isolated perfused TALs and CDs during an acute [K+ ]bl switch from 3.6 to 6.5 mM. Molecular adaptations of transport proteins involved in water reabsorption were investigated by immunoblotting.Results: 1) Mice switched from a 1% to 2% K+ diet increased diuresis within 12h and reciprocally reduced diuresis when switched from 1% to 0.01% K+ diet. 2) A single K+ gavage load, corresponding to 25-50% of daily K+ intake, induced 100% increase in diuresis within 30 minutes. This occurred despite augmented plasma osmolarity and AVP synthesis. 3) K+ gavage did not change GFR. 4) In isolated perfused TALs, shifting [K+ ]bl from 3.6 to 6.5 mM did not affect AVP-induced NaCl transport. 5) In sharp contrast, in isolated perfused CDs, shifting [K+ ]bl from 3.6 to 6.5 mM markedly reduced CD AVP-sensitivity, i.e. inhibited water absorption.Conclusion: Dietary K+ loading induces a rapidly on-setting diuresis. The mechanism of K+ -induced diuresis involves desensitization of the CD to AVP.

Published
2022

7. Whole blood methylome-derived features to discriminate endocrine hypertension

Author

Roberta Armignacco, Parminder S. Reel, Smarti Reel, Anne Jouinot, Amandine Septier, Cassandra Gaspar, Karine Perlemoine, Casper K. Larsen, Lucas Bouys, Leah Braun, Anna Riester, Matthias Kroiss, Fidéline Bonnet-Serrano, Laurence Amar, Anne Blanchard, Anne-Paule Gimenez-Roqueplo, Aleksander Prejbisz, Andrzej Januszewicz, Piotr Dobrowolski, Eleanor Davies, Scott M. MacKenzie, Gian Paolo Rossi, Livia Lenzini, Filippo Ceccato, Carla Scaroni, Paolo Mulatero, Tracy A. Williams, Alessio Pecori, Silvia Monticone, Felix Beuschlein, Martin Reincke, Maria-Christina Zennaro, Jérôme Bertherat, Emily Jefferson, Guillaume Assié, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of Dundee, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme Post-génomique de la Pitié-Salpêtrière (PASS-P3S), Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Ludwig-Maximilians University [Munich] (LMU), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Institute of Cardiology (WARSAW - Cardiology), Institute of Cardiology, University of Glasgow, Università degli Studi di Padova = University of Padua (Unipd), Università degli studi di Torino = University of Turin (UNITO), Universität Zürich [Zürich] = University of Zurich (UZH), and BRUNEL, Nadège

Subjects
Circulating biomarker, Endocrine hypertension, Whole blood methylome, Adrenal Gland Neoplasms, Pheochromocytoma, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, DNA Methylation, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Epigenome, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Hypertension, Genetics, Humans, Molecular Biology, Genetics (clinical), Biomarkers, Developmental Biology
Abstract

Background Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. Results Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods—Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine—predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. Conclusions The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder.

Published
2022

8. Identification of glucocorticoid-related molecular signature by whole blood methylome analysis

Author

Roberta Armignacco, Anne Jouinot, Lucas Bouys, Amandine Septier, Thomas Lartigue, Mario Neou, Cassandra Gaspar, Karine Perlemoine, Leah Braun, Anna Riester, Fidéline Bonnet-Serrano, Anne Blanchard, Laurence Amar, Carla Scaroni, Filippo Ceccato, Gian Paolo Rossi, Tracy Ann Williams, Casper K Larsen, Stéphanie Allassonnière, Maria-Christina Zennaro, Felix Beuschlein, Martin Reincke, Jérôme Bertherat, Guillaume Assié, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Plateforme Post-génomique de la Pitié-Salpêtrière (PASS-P3S), Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Ludwig Maximilian University [Munich] (LMU), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CIC AP-HP (hegp Ex-Broussais)/inserm, Institut National de la Santé et de la Recherche Médicale (INSERM), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Service d'HYPERVASC [CHU HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Azienda Ospedaliera di Padova, Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Department of Medical Sciences [Turin, Italy] (DMS), Università degli studi di Torino = University of Turin (UNITO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Service de Génétique [CHU HEGP], Universitätsspital Zürich (USZ), Service d'Endocrinologie [CHU Cochin], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 826421,TVB-Cloud (2018), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University Hospital of Padua, University of Turin, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), and University of Zurich

Subjects
Adult, Male, Adolescent, Hydrocortisone, Endocrinology, Diabetes and Metabolism, 10265 Clinic for Endocrinology and Diabetology, 610 Medicine & health, Tacrolimus Binding Proteins, Adrenal Insufficiency, Aged, Biomarkers, CpG Islands, Cushing Syndrome, DNA, DNA Methylation, Epigenome, Female, Glucocorticoids, Humans, Leukocytes, Middle Aged, Saliva, Endocrinology, Circulating biomarker, Whole blood methylome, General Medicine, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, 1310 Endocrinology, 2712 Endocrinology, Diabetes and Metabolism, Cushing’s syndrome, Clinical Study
Abstract

Objective Cushing’s syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing’s syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing’s syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. Design We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing’s syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. Methods Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. Results Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing’s syndrome correction. In Cushing’s syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. Conclusions Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays.

Published
2021

9. Dietary K

Author

Samuel L, Svendsen, Simon, Kornvig, Peder, Berg, Iben S, Jensen, Isabela B B A, de Araujo, Casper K, Larsen, Jens, Leipziger, and Mads V, Sørensen

Subjects
Mammals, Mice, Sodium, Animals, Diuretics, Kidney, Diet, Diuresis
Abstract

KMice were K(1) Mice switched from a 1% to 2% KDietary K

Published
2021

10. Le profil de méthylome du sang total comme biomarqueur de l’excès des glucocorticoïdes

Author

Laurence Amar, Fideline Bonnet-Serrano, Anna Riester, Roberta Armignacco, Karine Perlemoine, Jérôme Bertherat, Leah T. Braun, Mario Neou, Guillaume Assié, Martin Reincke, Filippo Ceccato, Lucas Bouys, Anne Blanchard, Casper K Larsen, Maria-Christina Zennaro, Tracy Ann Williams, Anne Jouinot, F Beuschlein, Amandine Septier, Gian Paolo Rossi, Carla Scaroni, Stéphanie Allassonnière, Thomas Lartigue, Cassandra Gaspar, Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Plateforme Post-génomique de la Pitié-Salpêtrière (PASS-P3S), Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Ludwig Maximilian University [Munich] (LMU), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CIC AP-HP (hegp Ex-Broussais)/inserm, Institut National de la Santé et de la Recherche Médicale (INSERM), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Azienda Ospedaliera di Padova, Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Università degli studi di Torino = University of Turin (UNITO), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Universitätsspital Zürich (USZ), Université Paris Cité (UPCité), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPC), Université Paris Cité (UPC), Université Paris Cité - UFR Médecine Paris Centre [Santé] (UPC Médecine Paris Centre), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University Hospital of Padua, Division of Internal Medicine and Hypertension Unit, Department of Medical Sciences, University of Turin, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Université de Paris (UP), Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and University of Turin

Subjects
03 medical and health sciences, 0302 clinical medicine, Endocrinology, 030220 oncology & carcinogenesis, Endocrinology, Diabetes and Metabolism, [SDV]Life Sciences [q-bio], 030209 endocrinology & metabolism, General Medicine, ComputingMilieux_MISCELLANEOUS
Abstract

Les dosages hormonaux sur lesquels s’appuie le diagnostic d’hypercortisolisme ne refletent pas les effets individuels d’impregnation en glucocorticoides. Pour identifier des biomarqueurs specifiques, le methylome du sang total a ete analyse (puce Illumina-850 K CpGs) dans 94 echantillons de 47 patients avec different statut cortisolique -syndrome de Cushing, eucortisolisme, insuffisance surrenalienne. Le profil de methylation des plus variable CpGs (ecart type) discrimine les echantillons selon leur statut, avec une marque d’hypomethylation dans les Cushing. Cette marque montre une cinetique de modification dans le temps, avec une recuperation du niveau de methylation plusieurs mois apres traitement. Un groupe de 29 CpGs, selectionnes par regression penalisee, discrimine le statut de Cushing dans un sous-cohorte d’entrainement de 60 echantillons (30 Cushing et 30 eucortisolisme/insuffisances surrenaliennes). La performance de ce predicteur est validee dans le reste des echantillons (n = 34 ; OR : 1,58) et dans une deuxieme cohorte de validation independante de 91 echantillons -dont 26 Cushing et 65 eucortisolisme (OR : 1,10). Le niveau de methylation d’un seul des 29 CpGs, associe au promoteur du gene FKBP5, recapitule la discrimination par statut cortisolique dans les deux cohortes. Le taux de neutrophiles infere a partir du methylome s’associe, comme attendu, au statut cortisolique. Cependant, dans un modele d’analyse multivarie, le predicteur de 29 CpGs reste fortement significatif quand combine au taux de neutrophiles (p Conclusion le methylome reflete l’impregnation en glucocorticoides. Perspectives optimiser le methylome pour la routine, l’evaluer dans les hypercortisolismes a minima, et le tester pour predire les complications de l’hypercortisolisme.

Published
2021

11. SUN-LB97 Targeted Metabolomics as a Screening Tool in the Diagnosis of Endocrine Hypertension

Author

Paolo Mulatero, Zoran Erlic, Martina Tetti, Anne Blanchard, Casper K Larsen, Marco Boscaro, Cornelia Prehn, Felix Beuschlein, Laurence Amar, Christina Pamporaki, Guillaume Assié, Jerzy Adamski, Maria-Christina Zennaro, Aleksander Prejbisz, and Graeme Eisenhofer

Subjects
business.industry, Endocrine Hypertension and Aldosterone Excess II, Endocrinology, Diabetes and Metabolism, Medicine, Screening tool, business, Bioinformatics, Endocrine hypertension, AcademicSubjects/MED00250, Targeted metabolomics, Cardiovascular Endocrinology
Abstract

Arterial hypertension [HT] is a global epidemic that requires adequate treatment to reduce cardiovascular morbidity and mortality. Secondary causes of HT and specifically endocrine hypertension [EHT] (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL] and Cushing syndrome [CS]) can potentially be cured by surgery or treated by targeted medication. However, diagnosis of EHT requires expertise in test selection and interpretation of test results. The availability of experts outnumbers its demand. Thus, preselecting tools are necessary to identify patients who require further referral to an expert. Since targeted metabolomics [TM] is a new method showing promising results in profiling cardiovascular diseases and endocrine conditions associated with HT, we tested the ability of TM in discriminating primary hypertension [PHT] from EHT cases. The study included 282 adult patients (52% female; mean age 49 years) from the European multicentre consortium ENSAT-HT (www.ensat-ht.eu). Of these, 59 were diagnosed with PHT and 223 with EHT (40 CS, 107 PA and 76 PPGL). TM was performed on stored blood samples with a mass spectrometry based approach using the AbsoluteIDQTM p180 Kit (BIOCRATES Life Sciences, Austria). In total, 188 metabolites were determined, of which 155 were eligible for statistical analyses according to established selection criteria. To identify relevant discriminating metabolites, a series of univariate and multivariate analyses were applied. Since the distribution of the patients between the clinical entities was different according to sex (p

Published
2020

12. The renal and blood pressure response to low sodium diet in P2X4 receptor knockout mice

Author

Eilidh, Craigie, Robert I, Menzies, Casper K, Larsen, Grégory, Jacquillet, Monique, Carrel, Scott S, Wildman, Johannes, Loffing, Jens, Leipziger, David G, Shirley, Matthew A, Bailey, and Robert J, Unwin

Subjects
Hypertension, Renal, hypertension, Sodium, ENaC, Blood Pressure, Diet, Sodium-Restricted, Kidney, Renal Reabsorption, Signalling Pathways, Mice, Inbred C57BL, purinoceptor, Mice, Renal Absorption, Reabsorption and Secretion, salt‐sensitivity, Membrane Physiology, Animals, purinergic, Epithelial Sodium Channels, Receptors, Purinergic P2X4, Original Research
Abstract

In the kidney, purinergic (P2) receptor‐mediated ATP signaling has been shown to be an important local regulator of epithelial sodium transport. Appropriate sodium regulation is crucial for blood pressure (BP) control and disturbances in sodium balance can lead to hypo‐ or hypertension. Links have already been established between P2 receptor signaling and the development of hypertension, attributed mainly to vascular and/or inflammatory effects. A transgenic mouse model with deletion of the P2X4 receptor (P2X4−/−) is known to have hypertension, which is thought to reflect endothelial dysfunction and impaired nitric oxide (NO) release. However, renal function in this model has not been characterized; moreover, studies in vitro have shown that the P2X4 receptor can regulate renal epithelial Na+ channel (ENaC) activity. Therefore, in the present study we investigated renal function and sodium handling in P2X4−/− mice, focusing on ENaC‐mediated Na+ reabsorption. We confirmed an elevated BP in P2X4−/− mice compared with wild‐type mice, but found that ENaC‐mediated Na+ reabsorption is no different from wild‐type and does not contribute to the raised BP observed in the knockout. However, when P2X4−/− mice were placed on a low sodium diet, BP normalized. Plasma aldosterone concentration tended to increase according to sodium restriction status in both genotypes; in contrast to wild‐types, P2X4−/− mice did not show an increase in functional ENaC activity. Thus, although the increased BP in P2X4−/− mice has been attributed to endothelial dysfunction and impaired NO release, there is also a sodium‐sensitive component.

Published
2018

13. Intact colonic K

Author

Casper K, Larsen, Helle A, Praetorius, Jens, Leipziger, and Mads V, Sorensen

Subjects
Mice, Knockout, K+ secretion, Colon, Large-Conductance Calcium-Activated Potassium Channel beta Subunits, KCa1.1 beta2 subunit, Biological Transport, Mice, Regulatory Pathways, Membrane Physiology, Potassium, Animals, LRCC26, Intestinal Mucosa, KCa1.1, Large-Conductance Calcium-Activated Potassium Channel alpha Subunits, Aldosterone, BK channel, Digestive Absorption and Secretion, K+ Homeostasis, Original Research, Maxi K+ channel
Abstract

Mammalian potassium homeostasis results from tightly regulated renal and colonic excretion, which balances the unregulated dietary K+ intake. Colonic K+ secretion follows the pump‐leak model, in which the large conductance Ca2+‐activated K+ channel (KC a1.1) is well established as the sole, but highly regulated apical K+ conductance. The physiological importance of auxiliary β and γ subunits of the pore‐forming α‐subunit of the KC a1.1 channel is not yet fully established. This study investigates colonic K+ secretion in a global knockout mouse of the KC a1.1‐β2‐subunit (KCNMB2−/−), which apparently is the only β‐subunit of the colonic enterocyte KC a1.1 channel. We can report that: (1) Neither KC a1.1 α‐ nor the remaining β‐subunits were compensatory transcriptional regulated in colonic epithelia of KCNMB2−/− mice. (2) Colonic epithelia from KCNMB2−/− mice displayed equal basal and ATP‐induced KC a1.1‐mediated K+ conductance as compared to KCNMB2+/+. (3) K+ secretion was increased in KCNMB2−/− epithelia compared to wild‐type epithelia from animals fed an aldosterone‐inducing diet. (4) Importantly, the apical K+ conductance was abolished by the specific blocker of KC a1.1 channel iberiotoxin in both KCNMB2+/+ and KCNMB2−/− mice. Recently a novel family of auxiliary γ‐subunits of the KC a1.1 channel has been described. (5) We detected the γ1‐subunit (LRRC26) mRNA in colonic epithelia. To investigate the physiological role of the γ1‐subunit of KC a1.1 channels in colonic K+ secretion, we acquired an LRRC26 knockout mouse. (6) Unexpectedly, LRRC26 mice had a perinatal lethal phenotype, thus preventing functional measurements. On this basis we conclude that colonic K+ secretion is intact or even increased in mice lacking the β2‐subunit of KC a1.1 channel complex despite no additional compensatory induction of KC a1.1 β‐subunits.

Published
2017

14. Glucocorticoid Excess in Patients with Pheochromocytoma Compared with Paraganglioma and Other Forms of Hypertension

Author

Jaap Deinum, Mirko Peitzsch, Richard O. Sinnott, Felix Beuschlein, Guillaume Assié, Katharina Langton, Laurence Amar, Stephanie M. J. Fliedner, Michael Conall Dennedy, Anne Paule Gimenez-Roqueplo, Casper K. Larsen, Georgiana Constantinescu, Anne Blanchard, Andrzej Januszewicz, Graeme Eisenhofer, Filippo Ceccato, Martin Reincke, Catleen Conrad, Maria-Christina Zennaro, Tracy Ann Williams, Paolo Mulatero, Stefan R. Bornstein, Martin Fassnacht, Aleksander Prejbisz, HULOT, Jean-Sébastien, Technische Universität Dresden = Dresden University of Technology (TU Dresden), University of Medicine and Pharmacy 'Grigore T.Popa' Iasi (UMF lasi), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), CIC - HEGP (CIC 1418), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Università degli studi di Torino = University of Turin (UNITO), Klinikum der Universitat Munchen, Ludwig-Maximilians-Universität München (LMU), Institute of Cardiology (WARSAW - Cardiology), Institute of Cardiology, University of Würzburg = Universität Würzburg, King‘s College London, University hospital of Zurich [Zurich], Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), University Medical Center of Schleswig–Holstein = Universitätsklinikum Schleswig-Holstein (UKSH), Kiel University, National University of Ireland [Galway] (NUI Galway), University of Melbourne, Radboud University Medical Center [Nijmegen], and University of Zurich

Subjects
Male, Endocrinology, Diabetes and Metabolism, medicine.medical_treatment, Hypertension, Metanephrines, Paraganglioma, Pheochromocytoma, Steroids, Adrenal Gland Neoplasms, Adult, Aged, Cross-Sectional Studies, Europe, Female, Glucocorticoids, Humans, Hyperaldosteronism, Middle Aged, Retrospective Studies, [SDV]Life Sciences [q-bio], Clinical Biochemistry, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], 10265 Clinic for Endocrinology and Diabetology, Biochemistry, Cushing syndrome, chemistry.chemical_compound, paraganglioma, 0302 clinical medicine, Endocrinology, Primary aldosteronism, pheochromocytoma, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, hypertension, metanephrines, steroids, AcademicSubjects/MED00250, medicine.medical_specialty, endocrine system, Urology, 030209 endocrinology & metabolism, 610 Medicine & health, 03 medical and health sciences, Internal medicine, medicine, Metanephrine, Clinical Research Articles, business.industry, Adrenalectomy, Biochemistry (medical), medicine.disease, chemistry, business
Abstract

Context Catecholamines and adrenocortical steroids are important regulators of blood pressure. Bidirectional relationships between adrenal steroids and catecholamines have been established but whether this is relevant to patients with pheochromocytoma is unclear. Objective This study addresses the hypothesis that patients with pheochromocytoma and paraganglioma (PPGL) have altered steroid production compared with patients with primary hypertension. Design Multicenter cross-sectional study. Setting Twelve European referral centers. Patients Subjects included 182 patients with pheochromocytoma, 36 with paraganglioma and 270 patients with primary hypertension. Patients with primary aldosteronism (n = 461) and Cushing syndrome (n = 124) were included for additional comparisons. Intervention In patients with PPGLs, surgical resection of tumors. Outcome measures Differences in mass spectrometry–based profiles of 15 adrenal steroids between groups and after surgical resection of PPGLs. Relationships of steroids to plasma and urinary metanephrines and urinary catecholamines. Results Patients with pheochromocytoma had higher (P < .05) circulating concentrations of cortisol, 11-deoxycortisol, 11-deoxycorticosterone, and corticosterone than patients with primary hypertension. Concentrations of cortisol, 11-deoxycortisol, and corticosterone were also higher (P < .05) in patients with pheochromocytoma than with paraganglioma. These steroids correlated positively with plasma and urinary metanephrines and catecholamines in patients with pheochromocytoma, but not paraganglioma. After adrenalectomy, there were significant decreases in cortisol, 11-deoxycortisol, corticosterone, 11-deoxycorticosterone, aldosterone, and 18-oxocortisol. Conclusions This is the first large study in patients with PPGLs that supports in a clinical setting the concept of adrenal cortical–medullary interactions involving an influence of catecholamines on adrenal steroids. These findings could have implications for the cardiovascular complications of PPGLs and the clinical management of patients with the tumors.

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