Stéphanie Battini1, Alessio Imperiale2, David Taïeb3, Karim Elbayed1, A Ercument Cicek4, Frédéric Sebag5, Laurent Brunaud6, Izzie-Jacques Namer7. 1. ICube, UMR 7357 University of Strasbourg/CNRS, Strasbourg, France. 2. ICube, UMR 7357 University of Strasbourg/CNRS, Strasbourg, France; Department of Biophysics and Nuclear Medicine, Hautepierre Hospital, University Hospitals of Strasbourg, Strasbourg, France; FMTS, Faculty of Medicine, Strasbourg, France. 3. La Timone University Hospital, European Center for Research in Medical Imaging, Aix-Marseille University, Marseille, France. 4. Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA; Computer Engineering Department, Bilkent University, Ankara, Turkey. 5. Department of Endocrine Surgery, Aix-Marseille University, Marseille, France. 6. Department of Digestive, Hepato-Biliary and Endocrine Surgery, Brabois University Hospital, Nancy, France. 7. ICube, UMR 7357 University of Strasbourg/CNRS, Strasbourg, France; Department of Biophysics and Nuclear Medicine, Hautepierre Hospital, University Hospitals of Strasbourg, Strasbourg, France; FMTS, Faculty of Medicine, Strasbourg, France. Electronic address: Izzie.Jacques.NAMER@chru-strasbourg.fr.
Abstract
BACKGROUND: Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis. METHODS: The method used was (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study. RESULTS: Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R(2)Y = 0.85, Q(2) = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R(2)Y = 0.96, Q(2) = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease. CONCLUSION: This study shows for the first time that (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies.
BACKGROUND:Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis. METHODS: The method used was (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study. RESULTS: Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R(2)Y = 0.85, Q(2) = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R(2)Y = 0.96, Q(2) = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease. CONCLUSION: This study shows for the first time that (1)H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies.
Authors: Michael A Morris; Babak Saboury; Mark Ahlman; Ashkan A Malayeri; Elizabeth C Jones; Clara C Chen; Corina Millo Journal: Front Endocrinol (Lausanne) Date: 2022-02-25 Impact factor: 5.555