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 - 2020/12/31
Y1 - 2020/12/31
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.Design: Retrospective analyses of PHT and EHT patients from a European multicentre 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.Patients: 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 15 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 ROC 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).Conclusions: 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.Design: Retrospective analyses of PHT and EHT patients from a European multicentre 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.Patients: 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 15 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 ROC 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).Conclusions: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.
KW - targeted metabolomics
KW - arterial hypertension
KW - screening
KW - Cushing syndrome
KW - primary aldosteronism
KW - pheochromocytoma
U2 - 10.1210/clinem/dgaa954
DO - 10.1210/clinem/dgaa954
M3 - Article
C2 - 33382876
JO - Journal of Clinical Endocrinology and Metabolism
JF - Journal of Clinical Endocrinology and Metabolism
SN - 0021-972X
ER -