Refine
Has Fulltext
- yes (3)
Is part of the Bibliography
- yes (3)
Document Type
- Journal article (3)
Language
- English (3)
Keywords
- metabolomics (2)
- Biochemical-Diagnosis (1)
- Cushing syndrome (1)
- MASS (1)
- Metanephrine (1)
- NMR (1)
- Normetanephrine (1)
- PPGL (1)
- Paraganglioma (1)
- Plasma (1)
Institute
Sonstige beteiligte Institutionen
EU-Project number / Contract (GA) number
- 633983 (2)
Background: Pheochromocytomas and paragangliomas (PPGLs) are rare but potentially harmful tumors that can vary in their clinical presentation. Tumors may be found due to signs and symptoms, as part of a hereditary syndrome or following an imaging procedure. Objective: To investigate potential differences in clinical presentation between PPGLs discovered by imaging (iPPGLs), symptomatic cases (sPPGLs) and those diagnosed during follow-up because of earlier disease/known hereditary mutations (fPPGL). Design: Prospective study protocol, which has enrolled patients from six European centers with confirmed PPGLs. Data were analyzed from 235 patients (37 iPPGLs, 36 sPPGLs, 27% fPPGLs) and compared for tumor volume, biochemical profile, mutation status, presence of metastases and self-reported symptoms. iPPGL patients were diagnosed at a significantly higher age than fPPGLs (P<0.001), found to have larger tumors (P=0.003) and higher metanephrine and normetanephrine levels at diagnosis (P=0.021). Significantly lower than in sPPGL, there was a relevant number of self-reported symptoms in iPPGL (2.9 vs 4.3 symptoms, P< 0.001). In 16.2% of iPPGL, mutations in susceptibility genes were detected, although this proportion was lower than that in fPPGL (60.9%) and sPPGL (21.5%). Patients with PPGLs detected by imaging were older, have higher tumor volume and more excessive hormonal secretion in comparison to those found as part of a surveillance program. Presence of typical symptoms indicates that in a relevant proportion of those patients, the PPGL diagnosis had been delayed. Precis: Pheochromocytoma/paraganglioma discovered by imaging are often symptomatic and carry a significant proportion of germline mutations in susceptibility genes.
Predicting hypertension subtypes with machine learning using targeted metabolites and their ratios
(2022)
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.
Purpose
Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors.
Design
A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics.
Methods
Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods.
Results
Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected.
Conclusions
Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.