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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.
Serum liquid chromatography–tandem mass spectrometry (LC–MS/MS) steroid profiling is used for the diagnosis of adrenocortical carcinoma (ACC). Guidelines recommend endocrine work-up in addition to radiological imaging for follow-up in ACC, but data on this topic are scarce. Patients were included in this retrospective study if pre-therapeutic hormone values, regular tumour evaluation by imaging, steroid measurements by LC–MS/MS, and details on therapies were available. The utility of steroid profiles in detecting recurrence or disease progression was assessed, whereby “endocrine progress” was defined by an elevation of at least 3 of 13 analysed hormones. Cohort A included 47 patients after R0 resection, of whom 15 experienced recurrence and 32 did not. In cohort B, 52 patients with advanced disease (including 7 patients of cohort A with recurrence) could be evaluated on 74 visits when progressive disease was documented. In 20 of 89 cases with documented disease progression, “endocrine progress” was detectable prior to radiological progress. In these cases, recurrence/progression was detected at a median of 32 days earlier by steroid measurement than by imaging, with 11-deoxycortisol and testosterone being the most sensitive markers. Notably, these patients had significantly larger tumour burden. In conclusion, steroid profiling by LC–MS/MS is of value in detecting recurrent/progressive disease in ACC.
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.