Sphingolipids are structural components of cell membrane, displaying several functions in cell signalling. Extracellular vesicles (EV) are lipid bilayer membrane nanoparticle and their lipid composition may be different from parental cells, with a significant enrichment in sphingolipid species, especially in pathological conditions. We aimed at optimizing EV isolation from plasma and describing the differential lipid content of EV, as compared to whole plasma. As pilot study, we evaluated the diagnostic potential of lipidomic signature of circulating EV in patients with a diagnosis of ST-segment-elevation myocardial infarction (STEMI). STEMI patients were evaluated before reperfusion and 24-h after primary percutaneous coronary intervention. Twenty sphingolipid species were quantified by liquid-chromatography tandem-mass-spectrometry. EV-ceramides, -dihydroceramides, and -sphingomyelins increased in STEMI vs. matched controls and decreased after reperfusion. Their levels correlated to hs-troponin, leucocyte count, and ejection fraction. Plasma sphingolipids levels were 500-to-700-fold higher as compared to EV content; nevertheless, only sphingomyelins differed in STEMI vs. control patients. Different sphingolipid species were enriched in EV and their linear combination by machine learning algorithms accurately classified STEMI patients at pre-PCI evaluation. In conclusion, EV lipid signature discriminates STEMI patients. These findings may contribute to the identification of novel biomarkers and signaling mechanisms related to cardiac ischemia.
Primary aldosteronism (PA) is characterized by inappropriate aldosterone production. Chronic aldosterone excess has detrimental effects on cardiovascular system, including endothelial dysfunction and vascular inflammation. Circulating extracellular vesicles (EVs) are central players in the crosstalk between endothelium, vascular structures, and inflammatory cells. The aim of the study was to assess the potential role of EVs in aldosterone-related vascular damage by evaluating a comprehensive panel of 37 EV surface antigens. Serum EVs were isolated by immunocapture from 32 patients with PA, 29 patients with essential hypertension and from 22 normotensive controls. EVs were characterized by Western blotting, nanoparticle tracking analysis, transmission electron microscopy, and flow cytometry. Particle concentration was higher and diameter lower in patients with PA compared with controls and the number of particles decreased after unilateral adrenalectomy. Nineteen EV surface antigens were differentially expressed in patients with PA compared with patients with essential hypertension or normotensive controls, including markers of activated platelets, endothelial and immune/inflammatory cells. The specific EV surface signature discriminated patients with PA from controls, whereas after specific PA treatment the profile became similar to essential hypertension. Stimulation of human endothelial cells with PA-derived EVs resulted in the overexpression of 5 selected genes ( AKT1-CALR-CSNK2A1-FN1-PIK3R1 ), previously identified by bioinformatic analysis as targets of differentially expressed EV antigens. Overexpression of CALR and FN1 was confirmed also at protein level. Our data suggest that EVs represent biomarkers of vascular inflammation and endothelial dysfunction in patients with PA and also potential biovectors contributing to accelerated organ damage by multiple signaling processes.
Context The diagnostic work-up of primary aldosteronism (PA) includes screening and confirmation steps. Case confirmation is time-consuming, expensive, and there is no consensus on tests and thresholds to be used. Diagnostic algorithms to avoid confirmatory testing may be useful for the management of patients with PA. Objective Development and validation of diagnostic models to confirm or exclude PA diagnosis in patients with a positive screening test. Design, Patients and Setting We evaluated 1,024 patients who underwent confirmatory testing for PA. The diagnostic models were developed in a training cohort (n=522), and then tested on an internal validation cohort (n=174) and on an independent external prospective cohort (n=328). Main outcome measure Different diagnostic models and a 16-point score were developed by machine learning and regression analysis to discriminate patients with a confirmed diagnosis of PA. Results Male sex, antihypertensive medication, plasma renin activity, aldosterone, potassium levels and presence of organ damage were associated with a confirmed diagnosis of PA. Machine learning based models displayed an accuracy of 72.9-83.9%. The Primary Aldosteronism Confirmatory Testing (PACT) score correctly classified 84.1% at training and 83.9% or 81.1% at internal and external validation, respectively. A flow chart employing the PACT score to select patients for confirmatory testing, correctly managed all patients, and resulted in a 22.8% reduction in the number of confirmatory tests. Conclusions The integration of diagnostic modelling algorithms in clinical practice may improve the management of patients with PA by circumventing unnecessary confirmatory testing.
Objective – Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically-demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion. Design – Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models. Methods – Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a 19-point score system to stratify patients according to subtype diagnosis. Results – Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy 82.9-95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS. Conclusions – Our score could be integrated in clinical practice and guide decision-making in patients with unilateral successful AVS and contralateral suppression.
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