Supplemental Data and Methods: Subtyping of Primary Aldosteronism in the AVIS-2 Study: Assessment of Selectivity and Lateralisation

Context: Adrenal venous sampling (AVS) is the key test for subtyping primary aldosteronism (PA), but its interpretation varies widely across referral centers and this can adversely affect the management of PA patients. Objectives: To investigate in a real life study the rate of bilateral success, identification of unilateral aldosteronism and blood pressure outcomes in PA subtyped by AVS. Design and settings: in a retrospective analysis of the largest international registry of individual AVS data (AVIS-2 study) we investigated how different cut-off values of the selectivity (SI) and lateralization index (LI) affected rate of bilateral success, identification of unilateral aldosteronism and blood pressure outcomes. Results: AVIS-2 recruited 1625 individual AVS studies performed between 2000 and 2015 in 19 tertiary referral centers. Under unstimulated conditions, the rate of biochemically confirmed bilateral AVS success progressively decreased with increasing SI cut-offs; furthermore, with currently used LI cut-offs the rate of identified unilateral PA leading to adrenalectomy was as low as < 25%. A within-patient pairwise comparison of 402 AVS performed both under unstimulated and cosyntropin-stimulation conditions showed that cosyntropin increased the confirmed rate of bilateral selectivity for SI cutoffs ≥ 2.0, but with reduced lateralization rates (p < 0.001). Post-adrenalectomy outcomes were not improved by use of cosyntropin or more restrictive diagnostic criteria. Conclusion: Commonly used SI and LI cut-offs are associated with disappointingly low rates of biochemically defined AVS success and identified unilateral PA. Evidence-based protocols entailing less restrictive interpretative cut-offs might optimize the clinical use of this costly and invasive test

Expression and Functional Role of Urotensin-II and Its Receptor in the Adrenal Cortex and Medulla: Novel Insights for the Pathophysiology of Primary Aldosteronism

Abstract Context: The involvement of urotensin II, a vasoactive peptide acting via the G protein-coupled urotensin II receptor, in arterial hypertension remains contentious. Objective: We investigated the expression of urotensin II and urotensin II receptor in adrenocortical and adrenomedullary tumors and the functional effects of urotensin II receptor activation. Design: The expression of urotensin II and urotensin II receptor was measured by real time RT-PCR in aldosterone-producing adenoma (n = 22) and pheochromocytoma (n = 10), using histologically normal adrenocortical (n = 6) and normal adrenomedullary (n = 5) tissue as control. Urotensin II peptide and urotensin II receptor protein were investigated with immunohistochemistry and immunoblotting. To identify urotensin II-related and urotensin II receptor-related pathways, a whole transcriptome analysis was used. The adrenocortical effects of urotensin II receptor activation were also assessed by urotensin II infusion with/without the urotensin II receptor antagonist palosuran in rats. Results: Urotensin II was more expressed in pheochromocytoma than in aldosterone-producing adenoma tissue; the opposite was seen for the urotensin II receptor expression. Urotensin II receptor activation in vivo in rats enhanced (by 182 ± 9%; P &lt; 0.007) the adrenocortical expression of immunoreactive aldosterone synthase. Conclusions: Urotensin II is a putative mediator of the effects of the adrenal medulla and pheochromocytoma on the adrenocortical zona glomerulosa. This pathophysiological link might account for the reported causal relationship between pheochromocytoma and primary aldosteronism

Angiotensin peptides in the regulation of adrenal cortical function

The adrenal cortex plays a key role in the regulation of metabolism, salt and water homeostasis and sex differentiation by synthesizing glucocorticoid, mineralocorticoid and androgen hormones. Evidence exists that angiotensin II regulates adrenocortical function and it has been contended that angiotensin peptides of the non-canonical branch of the renin angiotensin system (RAS) might also modulate steroidogenesis in adrenals. Thus, the aim of this review is to examine the role of the RAS, and particularly of the angiotensin peptides and their receptors, in the regulation of adrenocortical hormones with particular focus on aldosterone production

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

Introduction: Endocrine 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. Methods: Retrospective 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. Results: After 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. Discussions: Our 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.</p

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

Introduction: Endocrine 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. Methods: Retrospective 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. Results: After 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. Discussions: Our 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.</p

Targeted metabolomics as a tool in discriminating endocrine from primary hypertension

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

Whole blood methylome-derived features to discriminate endocrine hypertension

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

High sodium intake, glomerular hyperfiltration and protein catabolism in patients with essential hypertension

Aims: A blood pressure-independent metabolic shift toward a catabolic state upon high sodium (Na+) diet, ultimately favouring body fluid preservation, has recently been described in pre-clinical controlled settings. We sought to investigate the real-life impact of high Na+ intake on measures of renal Na+/water handling and metabolic signatures, as surrogates for cardiovascular risk, in hypertensive patients. Methods and results: We analysed clinical and biochemical data from 766 consecutive patients with essential hypertension, collected at the time of screening for secondary causes. The systematic screening protocol included 24h urine collection on usual diet and avoidance of renin-angiotensin-aldosterone system-confounding medications. Urinary 24h-Na+ excretion, used to define classes of Na+ intake (Low ≤2.3g/d; Medium 2.3-5g/d; High &gt;5g/d), was an independent predictor of glomerular filtration rate after correction for age, sex, blood pressure, BMI, aldosterone and potassium excretion (p = 0.001; Low: 94.1 [69.9-118.8] vs High: 127.5 [108.3-147.8] ml/min/1.73m2). Renal Na+ and water handling diverged, with higher fractional excretion of Na+ and lower fractional excretion of water in those with evidence of High Na+ intake (FENa: Low 0.39% [0.30-0.47] vs. High 0.81% [0.73-0.98], p &lt; 0.001; FEwater: Low 1.13% [0.73-1.72] vs. High 0.89% [0.69-1.12], p = 0.015). Despite higher FENa, these patients showed higher absolute 24h Na+ reabsorption and higher associated tubular energy expenditure, estimated by tubular Na+/ATP stoichiometry, accordingly (ΔHigh-Low = 18 [12-24] kcal/d, p &lt; 0.001). At non-targeted LC/MS plasma metabolomics in an unselected subcohort (n = 67), metabolites which were more abundant in High vs. Low Na+ intake (p &lt; 0.05) mostly entailed intermediates or end products of protein catabolism/urea cycle. Conclusions: When exposed to high Na+ intake, kidneys dissociate Na+ and water handling. In hypertensive patients, this comes at the cost of higher glomerular filtration rate, increased tubular energy expenditure and protein catabolism from endogenous (muscle) or excess exogenous (dietary) sources. Glomerular hyperfiltration and the metabolic shift may have broad implications on global cardiovascular risk independent of blood pressure

Machine learning for classification of hypertension subtypes using multi-omics: a multi-centre, retrospective, data-driven study

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)