ISSN 1662-4009 (online)

ESPE Yearbook of Paediatric Endocrinology (2021) 18 8.7 | DOI: 10.1530/ey.18.8.7

J Clin Endocrinol Metab. 2021 Mar 25;106(4):1111–1128.

In this multicentre patient cohort study, the authors investigated the use of targeted metabolomics to discriminate primary hypertension (PHT) from endocrine forms of hypertension (EHT). They identified 16 metabolites that help to discriminate between PHT and EHT.

Arterial hypertension represents a global epidemic with an estimated prevalence ranging from 25% to 50%, according to region, population age and definition. Among secondary forms of hypertension, those caused by endocrine disorders are the most challenging to diagnose. The prevalence of EHT, such as primary hyperaldosteronism (PA), hormonally active pheochromocytoma/paraganglioma (PPGL) and Cushing syndrome (CS), is difficult to estimate. EHT remains largely unrecognized, even though the early diagnosis and treatment is effective and cost-effective (1). Metabolomic profiling is a relatively new strategy for the parallel and high-throughput identification and quantification of dozens to hundreds of low molecular weight molecules (metabolites). Targeted metabolomics refers to the targeted identification of previously identified specific metabolites (low molecular weight molecules). (2). Targeted metabolomics have been successfully used to investigate endocrine conditions associated with secondary hypertension, such as CS and PPGL (3, 4).

These authors performed retrospective analyses of 282 adult patients (52% female; mean age 49 years) with proven PHT (n=59) or EHT (n=223) from a European multicenter study (ENSAT-HT). They used liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) and flow injection analysis–electrospray ionization–tandem mass spectrometry (FIAESI-MS/MS) on stored blood samples to identify discriminating metabolites between the two forms of hypertension in a “Targeted Metabolomics” approach. Identified factors were assessed via a “classical approach” (univariate and multivariate analyses) and a “machine learning approach” (MLA) (random forest). From 155 eligible metabolites, 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 statistical approaches as discriminating between PHT and EHT (31 by the classical approach; 27 by MLA).

There is a strong clinical need for this approach. Under current recommendations, 50% of patients with arterial hypertension are eligible for screening for EHT (5, 6). New strategies are needed to preselect patients for referral to endocrine clinics. The lack of published data on the diagnostic performance of current recommendations EHT screening limits the possibility of comparing them with the proposed new method. However, based on the established prevalence of arterial hypertension (30%) and estimation that 10% of arterial hypertension patients have an underlying endocrine cause, the authors extrapolate a positive and negative predictive value of 4.3% and 98.6%, respectively – which suggests good performance as a screening tool. Furthermore, the capability of this methodology to identify specific metabolites in specific clinical entities can provide mechanistic links. In this regard, the authors highlight that in both primary and endocrine hypertension, high concentrations of long-chain acylcarnitines that have been associated with cardiovascular morbidity (7).

Reference: 1. Williams TA, Lenders JWM, Mulatero P, et al. Primary Aldosteronism Surgery Outcome (PASO) investigators. Outcomes after adrenalectomy for unilateral primary aldosteronism: an international consensus on outcome measures and analysis of remission rates in an international cohort. Lancet Diabetes Endocrinol. 2017; 5(9): 689–699.2. Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted metabolomics. Curr Protoc Mol Biol. 2012; Chapter 30: Unit 30.32 31–24.3. Di Dalmazi G, Quinkler M, Deutschbein T, et al. Cortisol-related metabolic alterations assessed by mass spectrometry assay in patients with Cushing’s syndrome. Eur J Endocrinol. 2017; 177(2): 227–237.4. Erlic Z, Kurlbaum M, Deutschbein T, et al. Metabolic impact of pheochromocytoma/paraganglioma: targeted metabolomics in patients before and after tumor removal. Eur J Endocrinol. 2019; 181(6): 647–657.5. Mulatero P, Sechi LA, Williams TA, Lenders JWM, Reincke M, Satoh F, Januszewicz A, Naruse M, Doumas M, Veglio F, Wu VC, Widimsky J. Subtype diagnosis, treatment, complications and outcomes of primary aldosteronism and future direction of research: a position statement and consensus of the Working Group on Endocrine Hypertension of the European Society of Hypertension. J Hypertens. 2020; 38(10): 1929–1936.6. Mulatero P, Monticone S, Deinum J, Amar L, Prejbisz A, Zennaro MC, Beuschlein F, Rossi GP, Nishikawa T, Morganti A, Seccia TM, Lin YH, Fallo F, Widimsky J. Genetics, prevalence, screening and confirmation of primary aldosteronism: a position statement and consensus of the Working Group on Endocrine Hypertension of The European Society of Hypertension. J Hypertens. 2020; 38(10): 1919–1928.7. McCoin CS, Knotts TA, Adams SH. Acylcarnitines–old actors auditioning for new roles in metabolic physiology. Nat Rev Endocrinol. 2015; 11(10): 617–625.

Article tools

My recent searches

No recent searches.