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Article

Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids

1
Department of Clinical Pharmacy, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
2
Metabolomic Medicine, Health Clinic for Autoimmune and Chronic Diseases, 10674 Athens, Greece
3
European Institute of Nutritional Medicine (E.I.Nu.M.), 00198 Rome, Italy
4
The Golden Helix Foundation, London WC2N 5AP, UK
5
Laboratory of Health Economics & Management, Economics Department, University of Piraeus, 18534 Piraeus, Greece
6
Laboratory of Toxicology and Forensic Sciences, Medical School, University of Crete, 71003 Heraklion, Greece
7
Department of Analytical and Forensic Medical Toxicology, Sechenov University, 119991 Moscow, Russia
8
Department of Biochemistry, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
9
ENT Department, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
*
Authors to whom correspondence should be addressed.
These authors contribute equally to this work.
Metabolites 2020, 10(12), 502; https://doi.org/10.3390/metabo10120502
Received: 11 September 2020 / Revised: 30 November 2020 / Accepted: 4 December 2020 / Published: 8 December 2020
(This article belongs to the Special Issue Metabolomics and Its Application in Human Diseases Volume 2)
Autoimmune diseases (ADs) are chronic disorders characterized by the loss of self-tolerance, and although being heterogeneous, they share common pathogenic mechanisms. Self-antigens and inflammation markers are established diagnostic tools; however, the metabolic imbalances that underlie ADs are poorly described. The study aimed to employ metabolomics for the detection of disease-related changes in autoimmune diseases that could have predictive value. Quantitative analysis of 28 urine organic acids was performed using Gas Chromatography-Mass Spectrometry in a group of 392 participants. Autoimmune thyroiditis, inflammatory bowel disease, psoriasis and rheumatoid arthritis were the most prevalent autoimmune diseases of the study. Statistically significant differences were observed in the tricarboxylate cycle metabolites, succinate, methylcitrate and malate, the pyroglutamate and 2-hydroxybutyrate from the glutathione cycle and the metabolites methylmalonate, 4-hydroxyphenylpyruvate, 2-hydroxyglutarate and 2-hydroxyisobutyrate between the AD group and the control. Artificial neural networks and Binary logistic regression resulted in the highest predictive accuracy scores (66.7% and 74.9%, respectively), while Methylmalonate, 2-Hydroxyglutarate and 2-hydroxybutyrate were proposed as potential biomarkers for autoimmune diseases. Urine organic acid levels related to the mechanisms of energy production and detoxification were associated with the presence of autoimmune diseases and could be an adjunct tool for early diagnosis and prediction. View Full-Text
Keywords: autoimmune diseases; metabolomics; organic acids; tricarboxylate cycle; glutathione cycle; disease prediction; artificial intelligence autoimmune diseases; metabolomics; organic acids; tricarboxylate cycle; glutathione cycle; disease prediction; artificial intelligence
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MDPI and ACS Style

Tsoukalas, D.; Fragoulakis, V.; Papakonstantinou, E.; Antonaki, M.; Vozikis, A.; Tsatsakis, A.; Buga, A.M.; Mitroi, M.; Calina, D. Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids. Metabolites 2020, 10, 502. https://doi.org/10.3390/metabo10120502

AMA Style

Tsoukalas D, Fragoulakis V, Papakonstantinou E, Antonaki M, Vozikis A, Tsatsakis A, Buga AM, Mitroi M, Calina D. Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids. Metabolites. 2020; 10(12):502. https://doi.org/10.3390/metabo10120502

Chicago/Turabian Style

Tsoukalas, Dimitris, Vassileios Fragoulakis, Evangelos Papakonstantinou, Maria Antonaki, Athanassios Vozikis, Aristidis Tsatsakis, Ana M. Buga, Mihaela Mitroi, and Daniela Calina. 2020. "Prediction of Autoimmune Diseases by Targeted Metabolomic Assay of Urinary Organic Acids" Metabolites 10, no. 12: 502. https://doi.org/10.3390/metabo10120502

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