Background: The ENSAT-HT project aims to establish a signature for different forms of adrenal hypertension based on a multi-omics approach, including untargeted metabolomics of plasma. In the past, metabolic profiling using Nuclear Magnetic Resonance Spectroscopy (NMR) led to the discovery of biomarkers for many diseases, such as inborn errors of metabolism. Our aim within the scope of ENSAT-HT is to define the metabolic signature of patients suffering from Primary Aldosteronism (PA), Cushing’s Syndrome (CS) and Pheochromocytoma and Paraganglioma (PPGL), and compare it to primary hypertensives (PHT) and healthy volunteers (HV).

Methods: Heparin plasma samples were collected from patients and distributed to all centers collaborating on ENSAT-HT. These samples were analyzed in a randomized order, along with Quality Control samples (QC) using 1H-NMR spectroscopy. The resulting spectra from both sample types were processed so as to convert the spectra to a readable table by multivariate statistical analysis (MVA) tools. As a first step in MVA, Principal Component Analysis (PCA) was applied as an unsupervised method to check for outliers and trends within the datasets.

Results: Upon inspection of the PCA score plot of NMR plasma data, a tight clustering of the QC samples was observed, indicating the analytical robustness of the complete methodological approach. The PCA model of the definitive dataset gave away a tendency for separation between all three main groups, PHTs (n=106), HVs (n=132) and Adrenal Hypertensives (PA, PPGL, CS, n=230).

Conclusions: We applied untargeted NMR metabolomics on human plasma samples. The initial analysis suggests that patient classification based on the presence of either adrenal or primary hypertension appears to be feasible. Future plans include diminishing the effects of confounders, such as sample origin and medication, the analysis of urine 1H-NMR and UHPLC-QTOF plasma data, and the application of additional MVA algorithms, both unsupervised and supervised, to further understand the metabolism of patients with PA, PPGL and CS.