Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

Zoran Erlic; Parminder Reel; Smarti Reel; 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; Katharina Langton; Paolo Mulatero; Martin Reincke; Livia Lenzini; Anne-Paule Gimenez-Roqueplo; Guillaume Assié; Anne Blanchard; Maria Christina Zennaro; Emily Jefferson; Felix Beuschlein

doi:10.1210/clinem/dgaa954

Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

Zoran Erlic, Parminder Reel, Smarti Reel, 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, Katharina Langton, Paolo Mulatero, Martin Reincke, Livia LenziniAnne-Paule Gimenez-Roqueplo, Guillaume Assié, Anne Blanchard, Maria Christina Zennaro, Emily Jefferson, Felix Beuschlein (Lead / Corresponding author)

Research output: Contribution to journal › Article › peer-review

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Abstract

Context: Identification 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.

Objective: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.

Methods: Retrospective 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.#

Results: From 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).

Conclusion: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

Original language	English
Pages (from-to)	1111-1128
Number of pages	18
Journal	Journal of Clinical Endocrinology and Metabolism
Volume	106
Issue number	4
Early online date	31 Dec 2020
DOIs	https://doi.org/10.1210/clinem/dgaa954
Publication status	Published - Apr 2021

Keywords

Cushing syndrome
arterial hypertension
pheochromocytoma
primary aldosteronism
screening
targeted metabolomics

ASJC Scopus subject areas

Endocrinology, Diabetes and Metabolism
Biochemistry
Endocrinology
Clinical Biochemistry
Biochemistry, medical

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1210/clinem/dgaa954Licence: CC BY-NC-ND

Final Published VersionFinal published version, 2.58 MBLicence: CC BY-NC-ND

Cite this

Erlic, Z., Reel, P., Reel, S., Amar, L., Pecori, A., Larsen, C. K., Tetti, M., Pamporaki, C., Prehn, C., Adamski, J., Prejbisz, A., Ceccato, F., Scaroni, C., Kroiss, M., Dennedy, M. C., Deinum, J., Langton, K., Mulatero, P., Reincke, M., ... Beuschlein, F. (2021). Targeted metabolomics as a tool in discriminating endocrine from primary hypertension. Journal of Clinical Endocrinology and Metabolism, 106(4), 1111-1128. https://doi.org/10.1210/clinem/dgaa954

@article{b7674b1f0e6c43418e397defead6a0fc,

title = "Targeted metabolomics as a tool in discriminating endocrine from primary hypertension",

abstract = "Context: Identification 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.Objective: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.Methods: Retrospective 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.#Results: From 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).Conclusion: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.",

keywords = "Cushing syndrome, arterial hypertension, pheochromocytoma, primary aldosteronism, screening, targeted metabolomics",

author = "Zoran Erlic and Parminder Reel and Smarti Reel and Laurence Amar and Alessio Pecori and Larsen, {Casper K.} and Martina Tetti and Christina Pamporaki and Cornelia Prehn and Jerzy Adamski and Aleksander Prejbisz and Filippo Ceccato and Carla Scaroni and Matthias Kroiss and Dennedy, {Michael C.} and Jaap Deinum and Katharina Langton and Paolo Mulatero and Martin Reincke and Livia Lenzini and Anne-Paule Gimenez-Roqueplo and Guillaume Assi{\'e} and Anne Blanchard and Zennaro, {Maria Christina} and Emily Jefferson and Felix Beuschlein",

note = "This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 633983 (ENSAT-HT to all authors, except MD), by the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to FB) and the Deutsche Forschungsgemeinschaft project number 314061271 (CRC/Transregio 205/1 “The Adrenal: Central relay of health and disease” to GE, MK, MR, FB).",

year = "2021",

month = apr,

doi = "10.1210/clinem/dgaa954",

language = "English",

volume = "106",

pages = "1111--1128",

journal = "Journal of Clinical Endocrinology and Metabolism",

issn = "0021-972X",

publisher = "Endocrine Society",

number = "4",

}

Erlic, Z, Reel, P , Reel, S, Amar, L, Pecori, A, Larsen, CK, Tetti, M, Pamporaki, C, Prehn, C, Adamski, J, Prejbisz, A, Ceccato, F, Scaroni, C, Kroiss, M, Dennedy, MC, Deinum, J, Langton, K, Mulatero, P, Reincke, M, Lenzini, L, Gimenez-Roqueplo, A-P, Assié, G, Blanchard, A, Zennaro, MC, Jefferson, E & Beuschlein, F 2021, 'Targeted metabolomics as a tool in discriminating endocrine from primary hypertension', Journal of Clinical Endocrinology and Metabolism, vol. 106, no. 4, pp. 1111-1128. https://doi.org/10.1210/clinem/dgaa954

TY - JOUR

T1 - Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

AU - Erlic, Zoran

AU - Reel, Parminder

AU - Reel, Smarti

AU - Amar, Laurence

AU - Pecori, Alessio

AU - Larsen, Casper K.

AU - Tetti, Martina

AU - Pamporaki, Christina

AU - Prehn, Cornelia

AU - Adamski, Jerzy

AU - Prejbisz, Aleksander

AU - Ceccato, Filippo

AU - Scaroni, Carla

AU - Kroiss, Matthias

AU - Dennedy, Michael C.

AU - Deinum, Jaap

AU - Langton, Katharina

AU - Mulatero, Paolo

AU - Reincke, Martin

AU - Lenzini, Livia

AU - Gimenez-Roqueplo, Anne-Paule

AU - Assié, Guillaume

AU - Blanchard, Anne

AU - Zennaro, Maria Christina

AU - Jefferson, Emily

AU - Beuschlein, Felix

N1 - This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 633983 (ENSAT-HT to all authors, except MD), by the Clinical Research Priority Program of the University of Zurich for the CRPP HYRENE (to FB) and the Deutsche Forschungsgemeinschaft project number 314061271 (CRC/Transregio 205/1 “The Adrenal: Central relay of health and disease” to GE, MK, MR, FB).

PY - 2021/4

Y1 - 2021/4

N2 - Context: Identification 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.Objective: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.Methods: Retrospective 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.#Results: From 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).Conclusion: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

AB - Context: Identification 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.Objective: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability.Methods: Retrospective 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.#Results: From 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).Conclusion: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.

KW - Cushing syndrome

KW - arterial hypertension

KW - pheochromocytoma

KW - primary aldosteronism

KW - screening

KW - targeted metabolomics

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DO - 10.1210/clinem/dgaa954

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JO - Journal of Clinical Endocrinology and Metabolism

JF - Journal of Clinical Endocrinology and Metabolism

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Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

Abstract

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Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)

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Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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Projects

Omics-Based Strategies for Improved Diagnosis and Treatment of Endocrine Hypertension (ENSAT-HT) (Joint with Inserm, University of Torino, University of Padua, University of Glasgow, University of Birmingham, Radboud University Medical Centre, SleekIT Limited and Inserm Transfert)

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