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.

Long-Term Follow-Up of Patients With Elevated Aldosterone-to-Renin Ratio but Negative Confirmatory Test: The Progression of Primary Aldosteronism Phenotypes.

BACKGROUND: About 10% of patients with arterial hypertension have a positive screening test for primary aldosteronism (PA) and 50% to 70% of them have a negative confirmatory test: the appropriate follow-up of these patients is currently unknown. We investigated the incidence of PA in patients with previous negative confirmatory testing, after at least a 2-year follow-up. METHODS: One hundred eighty-four patients with a previously elevated aldosterone-to-renin ratio followed by a negative confirmatory test were recruited in 2 hypertension centers (Torino and Munich). We repeated the screening test for PA and, if positive, the confirmatory test (seated saline infusion test or captopril challenge test). Primary end point of the study was the incidence of newly diagnosed overt PA, as defined by a positive confirmatory test. RESULTS: After a mean follow-up of 5 years, 20% of patients developed overt PA. When subtype diagnosis was offered systematically, one-third of patients displayed unilateral PA. Patients who developed PA showed worsening of blood pressure control and a higher rate of cardiac organ damage, despite similar implementation of antihypertensive therapy, compared with patients without PA. A mild progression of autonomous aldosterone secretion was evident even in patients without confirmed PA but with relatively stable control of blood pressure levels over time. CONCLUSIONS: About one-fifth of patients with a negative confirmatory test develop overt PA over time. A clinical follow-up of patients with a negative confirmatory test is advisable, along with the repetition of PA investigation, primarily in patients with worsening of blood pressure control.

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.

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).

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

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.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Preanalytical Considerations and Outpatient Versus Inpatient Tests of Plasma Metanephrines to Diagnose Pheochromocytoma.

CONTEXT: Sampling of blood in the supine position for diagnosis of pheochromocytoma and paraganglioma (PPGL) results in lower rates of false positives for plasma normetanephrine than seated sampling. It is unclear how inpatient vs outpatient testing and other preanalytical factors impact false positives. OBJECTIVE: We aimed to identify preanalytical precautions to minimize false-positive results for plasma metanephrines. METHODS: Impacts of different blood sampling conditions on plasma metanephrines were evaluated, including outpatient vs inpatient testing, sampling of blood in semi- vs fully recumbent positions, use of cannulae vs direct venipuncture, and differences in outside temperature. A total of 3147 patients at 10 tertiary referral centers were tested for PPGL, including 278 with and 2869 without tumors. Rates of false-positive results were analyzed. RESULTS: Outpatient rather than inpatient sampling resulted in 44% higher plasma concentrations and a 3.4-fold increase in false-positive results for normetanephrine. Low temperature, a semi-recumbent position, and direct venipuncture also resulted in significantly higher plasma concentrations and rates of false-positive results for plasma normetanephrine than alternative sampling conditions, although with less impact than outpatient sampling. Higher concentrations and rates of false-positive results for plasma normetanephrine with low compared with warm temperatures were only apparent for outpatient sampling. Preanalytical factors were without impact on plasma metanephrines in patients with PPGL. CONCLUSION: Although inpatient blood sampling is largely impractical for screening patients with suspected PPGL, other preanalytical precautions (eg, cannulae, warm testing conditions) may be useful. Inpatient sampling may be reserved for follow-up of patients with difficult to distinguish true- from false-positive results.

Mineralocorticoid Receptor Antagonist Effect on Aldosterone to Renin Ratio in Patients With Primary Aldosteronism.

CONTEXT: Although current international guidelines recommend to avoid mineralocortcoid receptor antagonists in patients undergoing screening test for primary aldosteronism, a recent report suggested that mineralocorticoid receptor antagonist treatment can be continued without significant influence on screening results. OBJECTIVE: We aimed to evaluate the effect of mineralocorticoid receptor antagonists on the aldosterone to renin ratio in patients with primary aldosteronism. METHODS: We prospectively enrolled 121 patients with confirmed primary aldosteronism who started mineralocorticoid receptor antagonist (canrenone) treatment. Eighteen patients (11 with unilateral and 7 with bilateral primary aldosteronism) constituted the short-term study cohort and underwent aldosterone, renin, and potassium measurement after 2 and 8 weeks of canrenone therapy. The long-term cohort comprised 102 patients (16 with unilateral and 67 with bilateral primary aldosteronism, and 19 with undetermined subtype) who underwent hormonal and biochemical re-assessment after 2 to 12 months of canrenone therapy. RESULTS: Renin and potassium levels showed a significant increase, and the aldosterone to renin ratio displayed a significant reduction compared with baseline after both a short- and long-term treatment. These effects were progressively more evident with higher doses of canrenone and after longer periods of treatment. We demonstrated that canrenone exerted a deep impact on the diagnostic accuracy of the screening test for primary aldosteronism: the rate of false negative tests was raised to 16.7%, 38.9%, 54.5%, and 72.5% after 2 weeks, 8 weeks, 2 to 6 months, and 7 to 12 months of mineralocorticoid receptor antagonist treatment, respectively. CONCLUSION: Mineralocorticoid receptor antagonists should be avoided in patients with hypertension before measurement of renin and aldosterone for screening of primary aldosteronism.

Quality of life in primary aldosteronism: A prospective observational study.

BACKGROUND: Previous studies suggested that patients affected by primary aldosteronism (PA) have impaired quality of life (QOL) compared to the general population, but a direct comparison with patients affected by essential hypertension (EH) has never been performed. The aim of the study was to compare the QOL of patients affected by PA to the QOL of patients affected by EH. MATERIAL AND METHODS: We designed a prospective observational study comparing the QOL of patients with PA and carefully matched patients with EH before and after treatment. We recruited 70 patients with PA and 70 patients with EH, matched for age, sex, blood pressure levels and intensity of antihypertensive treatment. We assessed QOL at baseline and after specific treatment for PA or after optimization of medical therapy for patients with EH. RESULTS: Patients with PA displayed impaired QOL compared with the general healthy population, but similar to patients with EH. Both laparoscopic adrenalectomy and treatment with mineralocorticoid receptor antagonist allowed an improvement of QOL in patients with PA, that was more pronounced after surgical treatment. Optimization of blood pressure control by implementation of antihypertensive treatment (without MR antagonists) allowed a minimal improvement in only one of eight domains in patients with EH. CONCLUSIONS: Patients with PA have impaired QOL, which is likely caused by uncontrolled hypertension and the effects of intensive antihypertensive treatment. Surgical and medical treatment of PA allows a significant improvement of QOL, by amelioration of blood pressure control and, after surgical treatment, by reduction of antihypertensive treatment.

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.

Recent Advances in Histopathological and Molecular Diagnosis in Pheochromocytoma and Paraganglioma: Challenges for Predicting Metastasis in Individual Patients.

Pheochromocytomas and paragangliomas (PHEO/PGL) are rare but occasionally life-threatening neoplasms, and are potentially malignant according to WHO classification in 2017. However, it is also well known that histopathological risk stratification to predict clinical outcome has not yet been established. The first histopathological diagnostic algorithm for PHEO, "PASS", was proposed in 2002 by Thompson et al. Another algorithm, GAPP, was then proposed by Kimura et al. in 2014. However, neither algorithm has necessarily been regarded a 'gold standard' for predicting post-operative clinical behavior of tumors. This is because the histopathological features of PHEO/PGL are rather diverse and independent of their hormonal activities, as well as the clinical course of patients. On the other hand, recent developments in wide-scale genetic analysis using next-generation sequencing have revealed the molecular characteristics of pheochromocytomas and paragangliomas. More than 30%-40% of PHEO/PGL are reported to be associated with hereditary genetic abnormalities involving > 20 genes, including SDHXs, RET, VHL, NF1, TMEM127, MAX, and others. Such genetic alterations are mainly involved in the pathogenesis of pseudohypoxia, Wnt, and kinase signaling, and other intracellular signaling cascades. In addition, recurrent somatic mutations are frequently detected and overlapped with the presence of genetic alterations associated with hereditary diseases. In addition, therapeutic strategies specifically targeting such genetic abnormalities have been proposed, but they are not clinically applicable at this time. Therefore, we herein review recent advances in relevant studies, including histopathological and molecular analyses, to summarize the current status of potential prognostic factors in patients with PHEO/PGL.

Histopathological Analysis of Tumor Microenvironment and Angiogenesis in Pheochromocytoma.

Pheochromocytomas (PHEOs) are relatively rare catecholamine-producing tumors derived from adrenal medulla. Tumor microenvironment (TME) including neoangiogenesis has been explored in many human neoplasms but not necessarily in PHEOs. Therefore, in this study, we examined tumor infiltrating lymphocytes (CD4 and CD8), tumor associated macrophages (CD68 and CD163), sustentacular cells (S100p), and angiogenic markers (CD31 and areas of intratumoral hemorrhage) in 39 cases of PHEOs in the quantitative fashion. We then compared the results with pheochromocytoma of the adrenal gland scaled score (PASS), grading system for pheochromocytoma and paraganglioma (GAPP) and the status of intra-tumoral catecholamine-synthesizing enzymes (TH, DDC, and PNMT) as well as their clinicopathological factors. Intratumoral CD8 (p = 0.0256), CD31 (p = 0.0400), and PNMT (p = 0.0498) status was significantly higher in PHEOs with PASS <4 than PASS ≧4. In addition, intratumoral CD8+ lymphocytes were also significantly more abundant in well-than moderately differentiated PHEO according to GAPP score (p = 0.0108) and inversely correlated with tumor size (p = 0.0257). Intratumoral CD68+ cells were significantly higher in PHEOs with regular or normal histological patterns than those not (p = 0.0370) and inversely correlated with tumor size (p = 0.0457). The status of CD163 was significantly positively correlated with that of CD8 positive cells (p = 0.0032). The proportion of intratumoral hemorrhage areas was significantly higher in PHEOs with PASS ≧4 (p = 0.0172). DDC immunoreactivity in tumor cells was significantly positively correlated with PASS score (p = 0.0356) and TH status was significantly higher in PHEOs harboring normal histological patterns (p = 0.0236) and cellular monotony (p = 0.0219) than those not. Results of our present study did demonstrate that abundant CD8+ and CD68+ cells could represent a histologically low-scored tumor. In particular, PHEOs with increased intratumoral hemorrhage should be considered rather malignant. In addition, abnormal catecholamine-producing status of tumor cells such as deficient PNMT and TH and increased DDC could also represent more aggressive PHEOs.

The spectrum of low-renin hypertension.

Low-renin hypertension (LRH) is a frequent condition in patients with arterial hypertension, accounting for 30% of patients. Monogenic forms can cause LRH in a minority of cases. However, in the large majority of patients, LRH is caused by the combined effects of congenital and acquired factors, comprising dietary habits. Several genetic variants have been proposed as co-factors in the pathogenesis of LRH with normal-low serum aldosterone. Emerging evidences support the hypothesis that a large proportion of LRH with normal-high serum aldosterone is associated with subclinical primary aldosteronism (PA). The recent identification of aldosterone-producing cell clusters (APCCs) as the possible cause of subclinical PA, further supported the concept of a continuous spectrum of autonomous aldosterone secretion, from subclinical forms towards overt PA. In this review we describe the main aspects of LRH, focusing on molecular basis, clinical risk profile and patients' management.