Refine
Has Fulltext
- yes (4)
Is part of the Bibliography
- yes (4)
Document Type
- Journal article (4)
Language
- English (4) (remove)
Keywords
- metabolomics (3)
- Cushing syndrome (1)
- NMR (1)
- PPGL (1)
- age (1)
- biomarkers (1)
- confounders (1)
- hypertension (1)
- liquid chromatography tandem mass spectrometry (LC-MS/MS) (1)
- machine learning (1)
- metanephrine (1)
- methoxytyramine (1)
- multicenter (1)
- normetanephrine (1)
- operation (1)
- paired (1)
- pheochromocytoma/paraganglioma (1)
- plasma (1)
- plasma NMR (1)
- preanalytical conditions (1)
- primary aldosteronism (1)
- reference intervals (1)
Institute
Sonstige beteiligte Institutionen
Background
Plasma or urinary metanephrines are recommended for screening of pheochromocytomas and paragangliomas (PPGLs). Measurements of urinary free rather than deconjugated metanephrines and additional measurements of methoxytyramine represent other developments. For all measurements there is need for reference intervals.
Methods
Plasma free, urinary free and urinary deconjugated O-methylated catecholamine metabolites were measured by LC-MS/MS in specimens from 590 hypertensives and normotensives. Reference intervals were optimized using data from 2,056 patients tested for PPGLs.
Results
Multivariate analyses, correcting for age and body surface area, indicated higher plasma and urinary metanephrine in males than females and sex differences in urinary normetanephrine and free methoxytyramine that largely reflected body size variation. There were positive associations of age with plasma metabolites, but negative relationships with urinary free metanephrine and methoxytyramine. Plasma and urinary normetanephrine were higher in hypertensives than normotensives, but differences were small. Optimization of reference intervals using the data from patients tested for PPGLs indicated that age was the most important consideration for plasma normetanephrine and sex most practical for urinary metabolites.
Conclusion
This study clarifies impacts of demographic and anthropometric variables on catecholamine metabolites, verifies use of age-specific reference intervals for plasma normetanephrine and establishes sex-specific reference intervals for urinary metabolites.
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.
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.
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.