BACKGROUND:Endocrine science remains underrepresented in European Union research programmes despite the fundamental role of hormone health in human wellbeing. Analysis of the CORDIS database reveals a persistent gap between the societal impact of endocrine disorders and their research prioritization. At national funding level, endocrine societies report limited or little attention of national research funding towards endocrinology. The EndoCompass project-a joint initiative between the European Society of Endocrinology and the European Society of Paediatric Endocrinology, aimed to identify and promote strategic research priorities in endocrine science to address critical hormone-related health challenges. METHODS:Research priorities were established through comprehensive analysis of the EU CORDIS database covering the Horizon 2020 framework period (2014-2020). Expert analysis examined current challenges and opportunities in hormone measurements, focusing on analytical quality, method validation, and emerging technologies to ensure reliable research and clinical care. RESULTS:Research priorities encompass optimization of pre-analytical processes, standardization and harmonization of endocrine tests, development of personalised reference intervals and clinical decision limits considering diversity, biological variation and environmental factors, innovation in biomarker discovery and point-of-care testing, and implementation of sustainable laboratory practices. Special emphasis is placed on leveraging artificial intelligence and health economics while maintaining analytical quality. CONCLUSIONS:This component of the EndoCompass project provides an evidence-based roadmap for advancing endocrine laboratory medicine. The findings support strategic investment in quality assurance and innovative technologies to enhance both research reliability and clinical outcomes, ultimately improving patient care in endocrine-related diseases.

BACKGROUND:Endocrine science remains underrepresented in European Union research programs despite the fundamental role of hormone health in human wellbeing. Analysis of the CORDIS database reveals a persistent gap between the societal impact of endocrine disorders and their research prioritization. At national funding level, endocrine societies report limited or little attention of national research funding towards endocrinology. The EndoCompass project-a joint initiative between the European Society of Endocrinology and the European Society of Paediatric Endocrinology, aimed to identify and promote strategic research priorities in endocrine science to address critical hormone-related health challenges. METHODS:Research priorities were established through comprehensive analysis of the EU CORDIS database covering the Horizon 2020 framework period (2014-2020). Expert analysis examined the current landscape of artificial intelligence (AI) applications in endocrinology, focusing on data sharing frameworks, fairness considerations, and training needs. RESULTS:Research priorities encompass 3 domains: establishing compliant frameworks for clinical and omic data sharing in endocrinology; developing fair and unbiased AI systems that account for demographic and clinical diversity while preventing physician de-skilling; and creating comprehensive AI training programs for endocrinologists at all career stages. Special emphasis is placed on coordinating AI initiatives across medical specialties while maintaining endocrine-specific requirements. CONCLUSIONS:This component of the EndoCompass project provides an evidence-based roadmap for integrating AI into endocrine practice and research. The analysis demonstrates the need for balanced approaches that leverage AI capabilities while preserving clinical expertise. The findings support strategic investment in AI infrastructure, training, and fairness-aware system development.

OBJECTIVE:Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are classified as "primary hypertension" (PHT), endocrine hypertension (EHT) accounts for ∼10% of cases and is caused by underlying conditions such as primary aldosteronism (PA), Cushing's syndrome (CS), pheochromocytoma or paraganglioma (PPGL). EHT is often misdiagnosed as PHT leading to delays in treatment for the underlying condition, reduced quality of life and costly, often ineffective, antihypertensive treatment. MicroRNA (miRNA) circulating in the plasma is emerging as an attractive potential biomarker for various clinical conditions due to its ease of sampling, the accuracy of its measurement and the correlation of particular disease states with circulating levels of specific miRNAs. METHODS:This study systematically presents the most discriminating circulating miRNA features responsible for classifying and distinguishing EHT and its subtypes (PA, PPGL, and CS) from PHT using 8 different supervised machine learning (ML) methods for the prediction. RESULTS:The trained models successfully classified PPGL, CS, and EHT from PHT with area under the curve (AUC) of 0.9 and PA from PHT with AUC 0.8 from the test set. The most prominent circulating miRNA features for hypertension identification of different disease combinations were hsa-miR-15a-5p and hsa-miR-32-5p. CONCLUSIONS:This study confirms the potential of circulating miRNAs to serve as diagnostic biomarkers for EHT and the viability of ML as a tool for identifying the most informative miRNA species.

BACKGROUND:Endocrine science remains underrepresented in European Union research programmes despite the fundamental role of hormone health in human well-being. Analysis of the CORDIS database reveals a persistent gap between the societal impact of endocrine disorders and their research prioritization. At national funding level, endocrine societies report limited or little attention of national research funding towards endocrinology. The EndoCompass project-a joint initiative between the European Society of Endocrinology and the European Society of Paediatric Endocrinology, aimed to identify and promote strategic research priorities in endocrine science to address critical hormone-related health challenges. METHODS:Research priorities were established through comprehensive analysis of the EU CORDIS database covering the Horizon 2020 framework period (2014-2020). Expert consultation in adrenal endocrinology was conducted to identify key research priorities, followed by broader stakeholder engagement including society members and patient advocacy groups. RESULTS:For adrenal disorders, research priorities span primary and secondary adrenal insufficiency, adrenal tumours, and endocrine hypertension. Key areas include development of biomarkers and replacement therapies, improved understanding of disease mechanisms, diagnostic procedure optimization, and establishment of pan-European registries. Special emphasis is placed on personalized treatment approaches. CONCLUSIONS:The adrenal component of the EndoCompass project provides an evidence-based roadmap for strategic research investment. This framework identifies crucial investigation areas into adrenal disease pathophysiology, prevention, and treatment strategies, ultimately aimed at reducing the burden of adrenal disorders on individuals and society. The findings support the broader EndoCompass objective of aligning research funding with areas of highest potential impact in endocrine health.

Abstract Disclosure: L.A. Birch: None. J.L. Fullerton: None. S.M. MacKenzie: None. L.M. Work: None. E. Davies: None. Vascular cognitive impairment (VCI) encompasses all disorders of vascular origin that result in cognitive deficits. Stroke is a leading cause of death, and doubles risk of VCI, in turn those with VCI have an increased risk of stroke. Hypertension is the most significant modifiable risk factor for both stroke and VCI. The mineralocorticoid aldosterone is an essential regulator of blood pressure, acting via the mineralocorticoid receptor (MR), and hyperaldosteronism increases risk of cerebrovascular disease including stroke and VCI. In addition to its epithelial role, MR is distributed throughout the vasculature and brain and has potential as a twofold target for VCI given that MR antagonism has proved beneficial in both stroke and VCI. Expression of the MR gene, NR3C2, can be repressed by microRNAs post-transcriptionally binding NR3C2 mRNA at its 3’ untranslated region (3’UTR). MicroRNAs circulate within extracellular vesicles (EVs), which have shown promise in therapeutic delivery and can cross the blood-brain barrier. This project aims to target MR expression by EV-mediated delivery of miRNAs targeting the NR3C2-3’UTR In silico analysis identified 74 microRNAs as potentially binding the NR3C2-3’UTR. Several were also highlighted in a recent review of circulating microRNAs implicated in stroke, including the brain-specific microRNA-124-3p (miR-124-3p). Pathway analysis also revealed additional mRNA targets of miR-124-3p relevant to VCI and aldosterone signalling, including serum/glucocorticoid regulated kinase 1 (SGK1), a protein kinase downstream of MR that mediates aldosterone action. We confirmed binding of miR-124-3p to the NR3C2-3’UTR in vitro by dual luciferase assay, with significantly decreased reporter gene expression observed after treatment with miR-124-3p precursor. Following induction of ischaemic stroke in stroke-prone spontaneously hypertensive rats by the transient middle cerebral artery occlusion model, brain miR-124-3p levels were significantly altered, with reduced expression confined to the infarct region. EVs were then isolated from rat plasma using size exclusion chromatography and loaded with miR-124-3p mimic by electroporation. When incubated with B50 rat neuronal cells, these EVs resulted in a significant 2-fold increase in miR-124-3p level, indicating successful delivery. No change in NR3C2 or SGK1 mRNA level was observed. Similarly, following oxygen-glucose deprivation, loaded EV treatment increased miR-124-3p levels but NR3C2 and SGK1 mRNA levels were unchanged. MR protein levels are still to be quantified. In conclusion, miRNA-124-3p is a promising therapeutic mediator of NR3C2 expression. Ongoing studies will include further in vitro characterization of its effects involving additional cell types of the neurovascular unit and exploration of neuroprotective effects in an in vivo bilateral carotid artery stenosis model of VCI. Presentation: 6/3/2024

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Aldosterone is a cardiovascular hormone with a key role in blood pressure regulation, among other processes, mediated through its targeting of the mineralocorticoid receptor in the renal tubule and selected other tissues. Its secretion from the adrenal gland is a highly controlled process subject to regulatory influence from the renin-angiotensin system and the hypothalamic-pituitary-adrenal axis. MicroRNAs are small endogenous non-coding RNA molecules capable of regulating gene expression post-transcriptionally through stimulation of mRNA degradation or suppression of translation. Several studies have now identified that microRNA levels are changed in cases of aldosterone dysregulation and that microRNAs are capable of regulating the expression of various genes involved in aldosterone production and action. In this article we summarise the major studies concerning this topic. We also discuss the potential role for circulating microRNAs as diagnostic biomarkers for primary aldosteronism, a highly treatable form of secondary hypertension, which would be highly desirable given the current underdiagnosis of this condition.

Abstract Disclosure: S. Lamprou: None. S.M. MacKenzie: None. J.D. McClure: None. S. Robertson: None. A. Riddell: None. J.C. van Kralingen: None. A. Gimenez-Roqueplo: None. P. Reel: None. S. Reel: None. G. Assié: None. A. Laurence: None. F. Beuschlein: None. G. Rossi: None. J. Deinum: None. M. Reincke: None. G. Eisenhofer: None. E. Jefferson: None. M. Zennaro: None. E. Davies: None. Primary Aldosteronism (PA) is the most common endocrine hypertension subtype, caused by autonomous aldosterone secretion. While readily treatable, accurate diagnosis of PA – particularly its differentiation from primary hypertension (PHT) – is a complex and protracted process that can entail delays in effective treatment and lead to more severe comorbidities. Given the established role of microRNAs (miRNA) in regulating aldosterone biosynthesis, we hypothesize that circulating miRNAs are potentially viable biomarkers for PA that could inform more accurate and rapid diagnosis. To detect changes in circulating miRNA profiles specific to PA, we analyzed plasma levels of 172 selected microRNAs in a retrospectively-generated cohort of PHT (n=111) and PA patients (n=109). The dataset consisted of ΔCt values for each miRNA (normalized to multiple endogenous miRNAs), as determined by real time RT-qPCR assay. Two-sample t-tests were used to assess whether the means of the two disease groups are equal, corrected using False Discovery Rate (FDR; significance level q < 0.05). Ingenuity Pathway Analysis software (QIAGEN) was employed to analyze the miRNAs bioinformatically and identify mRNA targets involved in blood pressure regulation. Pathway analysis was performed to discover associations between PA and differentially-expressed miRNAs. We identified 83 circulating microRNAs that showed significant differential expression: 27 were upregulated and 56 downregulated in PA relative to PHT. The upregulated miRNA demonstrating the greatest fold change was hsa-miR-451a (FC:1.8, q=0.001), while the most downregulated was hsa-miR-409-3p (FC:0.5, q=0.001). Bioinformatics analysis predicted several dysregulated miRNAs to target mRNAs in PA-related pathways. For example, the most upregulated miRNA, hsa-miR-451a, is predicted to target mRNAs in biochemical pathways relevant to PA pathophysiology, including SGK1 and MAP3K1. SGK1 contributes to the signaling cascade induced by the mineralocorticoid receptor-aldosterone complex that stimulates Na+ reabsorption in the kidney, while MAP3K1 participates in the Renin-Angiotensin system, a key regulator of aldosterone secretion, as part of a signaling cascade that leads to the activation of the AP1 complex in the nucleus. In addition to hsa-miR-451a, other differentially-expressed miRNAs were also revealed to target multiple mRNAs with direct and indirect connections to PA. In summary, analysis has identified miRNAs that are significantly differentially expressed in the plasma of PA patients compared to PHT patients, indicating promise for a biomarker-based approach to PA diagnosis utilizing distinctive circulating microRNA signatures. Further analysis is now being conducted to validate our findings in an independent population of hypertensive patients. Presentation: Friday, June 16, 2023

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

The population of people identifying as transgender has grown rapidly in recent years, resulting in a substantive increase in individuals obtaining gender-affirming medical care to align their secondary sex characteristics with their gender identity. This has established benefits for patients including improvements in gender dysphoria and psychosocial functioning, while reducing adverse mental health outcomes. Despite these potential advantages, recent evidence has suggested that gender-affirming hormone therapy (GAHT) may increase the risk of car-diovascular disease. However, owing to a paucity of research, the mechanisms underpinning these increased risks are poorly understood. Moreover, previous research has been limited by heterogenous methodologies, being underpowered, and lacking appropriate control populations. Consequently, the need for evidence regarding cardiovascular health in LGBTQ + individuals has been recognised as a critical area for future research to facilitate better healthcare and guidance. Recent research investigating the effect of transmasculine (testos-terone) GAHT on cardiovascular disease risk points to testosterone effecting the nitric oxide pathway, triggering inflammation, and promoting endothelial dysfunction. Equivalent studies focussing on transfeminine (oestrogen) GAHT are required, representing a crucial area of future research. Furthermore, when examining the effects of GAHT on the vasculature, it cannot be ignored that there are multiple factors that may increase the burden of cardiovascular disease in the transgender population. Such stressors include major psychological stress; increased adverse health behaviours, such as smoking; discrimination; and lowered socioeconomic status; all of which undoubtedly impact upon cardiovascular disease risk and offers the opportunity for intervention.

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.

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.

BackgroundArterial 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.MethodsThis 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.FindingsComplete 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.InterpretationWe 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.FundingEuropean 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).

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.

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 in-patient versus out-patient testing and other preanalytical factors impact false-positives. OBJECTIVE Identify preanalytical precautions to minimize false-positive results for plasma metanephrines. DESIGN Impacts of different blood sampling conditions on plasma metanephrines were evaluated, including out-patient versus in-patient testing, sampling of blood in semi- versus fully recumbent positions, use of cannulae versus direct venipuncture and differences in outside temperature. SETTING Ten tertiary referral centers. PATIENTS 3147 patients tested for PPGL, including 278 with and 2869 without tumors. INTERVENTIONS None. OUTCOME MEASURES Plasma metanephrines and rates of false-positive results. RESULTS Out-patient rather than in-patient 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, though with less impact than out-patient sampling. Higher concentrations and rates of false-positive results for plasma normetanephrine with low than warm temperatures were only apparent for out-patient sampling. Preanalytical factors were without impact on plasma metanephrines in patients with PPGL. CONCLUSIONS Although in-patient blood sampling is largely impractical for screening patients with suspected PPGL, other pre-analytical precautions (e.g., cannulae, warm testing conditions) may be useful. In-patient sampling may be reserved for follow-up of patients with difficult to distinguish true- from false-positive results.

L’hypertension artérielle est un facteur de risque majeur de morbidité et mortalité cardiovasculaire. Contrastant avec l’hypertension essentielle, majoritaire, l’hypertension peut être secondaire au syndrome de Cushing (CS), à l’hyperaldosteronisme primaire (PA), au pheochromocytome/paragangliome (PPGL). Leur détection efficace reste fondamentale pour orienter les patients vers un traitement spécifique. Des biomarqueurs facilement mesurables, en complément ou en remplacement des dosages hormonaux actuels, pourraient améliorer la détection de ces hypertensions endocrines. Le méthylome du sang total a été analysé (puce Illumina-850K) pour 255 patients avec une hypertension soit essentielle (n = 42) soit endocrine (n = 213, dont 57 CS, 101 PA, 55 PPGL). Le profil de méthylation des CpGs les plus variables (écart type) discrimine les échantillons selon leur statut, avec une marque d’hypomethylation dans les Cushing. En comparant de façon supervisée hypertension endocrine et essentielle, on identifie des CpGs et des gènes différentiellements méthylés dans chaque groupe (p-value ajoustée < 0,05), dont FKBP5 dans le CS parmi les plus significatifs. Quatre méthodes différentes de machine-learning (Lasso, Regression Logistic, Random Forest, Support Vector Machine) ont été utilisées pour la construction de modèles de prédiction d’hypertension endocrine. Les CpGs sélectionnés sur les sous-cohortes d’entraînement (80 % d’échantillons pour chaque groupe) ont été testés sur le reste d’échantillons. Le score de prédiction (exactitude ajustée) varie entre 0,55–0,74 pour l’hypertension endocrine (considérée comme groupe unique), 0,85–0,95 pour CS, 0,66–0,88 pour PA, et 0,70–0,83 pour PPGL. Le méthylome du sang total discrimine l’hypertension endocrine, avec des signatures de méthylation spécifiques à chaque type d’hypersécrétion hormonale.