Primary Aldosteronism Takes (KCNJ)Five!

International audienc

Identification de nouvelles options thérapeutiques et diagnostiques dans l'hyperaldostéronisme primaire

L hyperaldostéronisme primaire [HAP] résulte d une hypersécrétion d aldostérone d origine surrénale. La compréhension de la pathogénie de cette maladie, dont la prévalence est estimée à 10% de la population hypertendue, est essentielle pour le développement de nouveaux outils diagnostiques et thérapeutiques. Dans ce contexte, ce travail de doctorat avait pour but d identifier de nouvelles orientations thérapeutiques en testant un inhibiteur de l aldostérone synthase et de rechercher de nouveaux marqueurs diagnostiques par l étude du profil d expression des microARN [miRs]. Dans une étude de phase II, 14 patients présentant un HAP ont reçu un inhibiteur de l aldostérone synthase : le LCI699 pendant 4 semaines. Nous avons ainsi pu montrer que le LCI699 permet de diminuer les concentrations d aldostérone de 70 à 80% et de normaliser la kaliémie chez tous les patients. En revanche, il n a qu un effet modéré sur la pression artérielle et sur l élévation des concentrations de rénine, et n est que partiellement sélectif pour l aldostérone synthase. De plus son efficacité est moindre que celle de l éplérénone, antagoniste minéralocorticoide administré aux mêmes patients au décours du LCI699. Nous avons ensuite étudié l expression de 754 miRs dans des adénomes produisant de l aldostérone [APA] et dans des surrénales contrôles. L hypothèse était qu une dérégulation de leur expression pouvait être impliquée dans la tumorigénèse et la surproduction d aldostérone. L objectif secondaire était d identifier des miRs utilisables en tant que biomarqueurs. Cette analyse par carte microfluidique a révélé que 27 miRs sont significativement sous exprimés dans les APA et un seul miR est surexprimé. L expression différentielle de deux de ces miRs : miR 137 et miR 375 a pu être confirmée dans une cohorte de validation de 36 APA: Des résultats préliminaires in vitro indiquent que le miR 375 pourrait induire une diminution de la synthèse d aldostérone. Enfin, l analyse de l expression de ces miRs dans le plasma a permis de mettre en évidence une sous-expression du miR 375 chez les patients atteints d HAP en comparaison à des sujets sains. En conclusion, le blocage de la biosynthèse de l aldostérone représente une nouvelle option thérapeutiques, cependant il est nécessaire de développer une seconde génération de molécules : plus puissantes et plus sélectives. Les analyses effectuées sur les APA ouvrent de nouvelles perspectives pour l identification de nouveaux biomarqueurs tels que les miRs circulantsPrimary aldosteronism [PA] results from the hypersecretion of aldosterone by the adrenals. Understanding the pathogenesis of the disease is essential for identifying new diagnostic and therapeutic tools. In this context the purpose of my PHD was to investigate the effects of an aldosterone synthase inhibitor and second to investigate new diagnostic options by the extensive study of microRNA [miRNA]. In a phase II clinical study, 14 patients with PA were administered an aldosterone synthase inhibitor: LCI699. Four weeks of treatment lead to a 70 to 80% decrease in aldosterone concentration, associated with the cure of hypokalemia. However, there was only a mild effect on blood pressure and volemia (reflected by renin concentration). In addition, these results demonstrated an incomplete selectivity of LCI699 for aldosterone synthase in vivo, and showed that LCI699 is less potent than the blocker of the mineralocorticoid receptor: eplerenone . We also characterized the miRNA profile of Aldosterone producing adenomas [APA]. The hypothesis was that a dysregulation of the expression of miRNA could induce tumorigenesis and increase the production of aldosterone. The secondary aim of the study was to identify miRNA that could be measured in plasma as biomarkers. miRNA profiling of 754 miRNA using quantitative PCR Low Density array, revealed 28 miRNA whose expression was significantly different in APA. The differential expression of two miRNA: miRNA 137 and miRNA 375 was confirmed in a validation cohort of 36 APA. Preliminary in vitro studies showed that up-regulation of intracellular levels of miR 375 may reduce aldosterone secretion in H295R cells. Lastly, circulating plasma levels of miR 375 are differentially expressed between patients with PA and healthy volunteers. In conclusion, the blocking of the aldosterone pathway in hypertensive patients is a novel therapeutic option but second-generation drugs more potent and more selective of aldosterone synthase are required. Profiling miRNA in APA offers new prospect for the development of biomarkers, such as measuring circulating miRNA in plasmaPARIS5-Bibliotheque electronique (751069902) / SudocSudocFranceF

Circulating microRNAs as diagnostic markers in primary aldosteronism

Primary aldosteronism (PA) is a common and highly treatable condition, usually resulting from adrenocortical tumorous growth or hyperplasia. PA is currently underdiagnosed owing to its complex and protracted diagnostic procedures. A simplified biomarker-based test would be highly valuable in reducing cardiovascular morbidity and mortality. Circulating microRNAs are emerging as potential biomarkers for a number of conditions due to their stability and accessibility. PA is known to alter microRNA expression in adrenocortical tissue; if these changes or their effects are mirrored in the circulating miRNA profile, then this could be exploited by a diagnostic test. However, the reproducibility of studies to identify biomarker-circulating microRNAs has proved difficult for other conditions due to a series of technical challenges. Therefore, any studies seeking to definitively identify circulating microRNA biomarkers of PA must address this in their design. To this end, we are currently conducting the circulating microRNA arm of the ongoing ENS@T-HT study. In this review article, we present evidence to support the utility of circulating microRNAs as PA biomarkers, describe the practical challenges to this approach and, using ENS@T-HT as an example, discuss how these might be overcome

Retinoic acid receptor α as a novel contributor to adrenal cortex structure and function through interactions with Wnt and Vegfa signalling

International audiencePrimary aldosteronism (PA) is the most frequent form of secondary arterial hypertension. Mutations in different genes increase aldosterone production in PA, but additional mechanisms may contribute to increased cell proliferation and aldosterone producing adenoma (APA) development. We performed transcriptome analysis in APA and identified retinoic acid receptor alpha (RARα) signaling as a central molecular network involved in nodule formation. To understand how RARα modulates adrenal structure and function, we explored the adrenal phenotype of male and female Rarα knockout mice. inactivation of Rarα in mice led to significant structural disorganization of the adrenal cortex in both sexes, with increased adrenal cortex size in female mice and increased cell proliferation in males. Abnormalities of vessel architecture and extracellular matrix were due to decreased Vegfa expression and modifications in extracellular matrix components. On the molecular level, Rarα inactivation leads to inhibition of non-canonical Wnt signaling, without affecting the canonical Wnt pathway nor PKA signaling. Our study suggests that Rarα contributes to the maintenance of normal adrenal cortex structure and cell proliferation, by modulating Wnt signaling. Dysregulation of this interaction may contribute to abnormal cell proliferation, creating a propitious environment for the emergence of specific driver mutations in PA. Primary aldosteronism (PA) is the most common and curable form of secondary arterial hypertension, with prevalence estimations of up to 10% of cases in referred hypertensive patients, 4% of patients in primary care 1,2 and 20% of patients with resistant hypertension 3,4. Rapid diagnosis and treatment are important to prevent severe cardiovas-cular consequences of long term aldosterone exposure, which are independent of blood pressure levels and are du

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

Identification of glucocorticoid-related molecular signature by whole blood methylome analysis

Objective Cushing's syndrome represents a state of excessive glucocorticoids related to glucocorticoid treatments or to endogenous hypercortisolism. Cushing's syndrome is associated with high morbidity, with significant inter-individual variability. Likewise, adrenal insufficiency is a life-threatening condition of cortisol deprivation. Currently, hormone assays contribute to identify Cushing's syndrome or adrenal insufficiency. However, no biomarker directly quantifies the biological glucocorticoid action. The aim of this study was to identify such markers. Design We evaluated whole blood DNA methylome in 94 samples obtained from patients with different glucocorticoid states (Cushing's syndrome, eucortisolism, adrenal insufficiency). We used an independent cohort of 91 samples for validation. Methods Leukocyte DNA was obtained from whole blood samples. Methylome was determined using the Illumina methylation chip array (~850 000 CpG sites). Both unsupervised (principal component analysis) and supervised (Limma) methods were used to explore methylome profiles. A Lasso-penalized regression was used to select optimal discriminating features. Results Whole blood methylation profile was able to discriminate samples by their glucocorticoid status: glucocorticoid excess was associated with DNA hypomethylation, recovering within months after Cushing's syndrome correction. In Cushing's syndrome, an enrichment in hypomethylated CpG sites was observed in the region of FKBP5 gene locus. A methylation predictor of glucocorticoid excess was built on a training cohort and validated on two independent cohorts. Potential CpG sites associated with the risk for specific complications, such as glucocorticoid-related hypertension or osteoporosis, were identified, needing now to be confirmed on independent cohorts. Conclusions Whole blood DNA methylome is dynamically impacted by glucocorticoids. This biomarker could contribute to better assessment of glucocorticoid action beyond hormone assays

Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies

Background: The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias. Method: We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications. Results: We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure. Conclusions: Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting

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