Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence
WD is caused by <i>ATP7B</i> variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and <i>ATP7B</i> variant type. Currently it is diagnosed b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/11/8/1243 |
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| author | Valentina Medici Anna Czlonkowska Tomasz Litwin Cecilia Giulivi |
| author_facet | Valentina Medici Anna Czlonkowska Tomasz Litwin Cecilia Giulivi |
| author_sort | Valentina Medici |
| collection | DOAJ |
| description | WD is caused by <i>ATP7B</i> variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and <i>ATP7B</i> variant type. Currently it is diagnosed by a combination of clinical symptoms/signs, aberrant copper metabolism parameters (e.g., low ceruloplasmin serum levels and high urinary and hepatic copper concentrations), and genetic evidence of <i>ATP7B</i> mutations when available. As early diagnosis and treatment are key to favorable outcomes, it is critical to identify subjects before the onset of overtly detrimental clinical manifestations. To this end, we sought to improve WD diagnosis using artificial neural network algorithms (part of artificial intelligence) by integrating available clinical and molecular parameters. Surprisingly, WD diagnosis was based on plasma levels of glutamate, asparagine, taurine, and Fischer’s ratio. As these amino acids are linked to the urea–Krebs’ cycles, our study not only underscores the central role of hepatic mitochondria in WD pathology but also that most WD patients have underlying hepatic dysfunction. Our study provides novel evidence that artificial intelligence utilized for integrated analysis for WD may result in earlier diagnosis and mechanistically relevant treatments for patients with WD. |
| first_indexed | 2024-03-10T08:58:56Z |
| format | Article |
| id | doaj.art-f055d13881ba441caff0e499490d4fc0 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-10T08:58:56Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-f055d13881ba441caff0e499490d4fc02023-11-22T06:56:53ZengMDPI AGBiomolecules2218-273X2021-08-01118124310.3390/biom11081243Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial IntelligenceValentina Medici0Anna Czlonkowska1Tomasz Litwin2Cecilia Giulivi3Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of California Davis, Sacramento, CA 95817, USASecond Department of Neurology, Institute of Psychiatry and Neurology, 02-957 Warsaw, PolandSecond Department of Neurology, Institute of Psychiatry and Neurology, 02-957 Warsaw, PolandDepartment of Molecular Biosciences, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USAWD is caused by <i>ATP7B</i> variants disrupting copper efflux resulting in excessive copper accumulation mainly in liver and brain. The diagnosis of WD is challenged by its variable clinical course, onset, morbidity, and <i>ATP7B</i> variant type. Currently it is diagnosed by a combination of clinical symptoms/signs, aberrant copper metabolism parameters (e.g., low ceruloplasmin serum levels and high urinary and hepatic copper concentrations), and genetic evidence of <i>ATP7B</i> mutations when available. As early diagnosis and treatment are key to favorable outcomes, it is critical to identify subjects before the onset of overtly detrimental clinical manifestations. To this end, we sought to improve WD diagnosis using artificial neural network algorithms (part of artificial intelligence) by integrating available clinical and molecular parameters. Surprisingly, WD diagnosis was based on plasma levels of glutamate, asparagine, taurine, and Fischer’s ratio. As these amino acids are linked to the urea–Krebs’ cycles, our study not only underscores the central role of hepatic mitochondria in WD pathology but also that most WD patients have underlying hepatic dysfunction. Our study provides novel evidence that artificial intelligence utilized for integrated analysis for WD may result in earlier diagnosis and mechanistically relevant treatments for patients with WD.https://www.mdpi.com/2218-273X/11/8/1243Wilson diseasecoppermitochondrialiverintermediary metabolismurea cycle |
| spellingShingle | Valentina Medici Anna Czlonkowska Tomasz Litwin Cecilia Giulivi Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence Biomolecules Wilson disease copper mitochondria liver intermediary metabolism urea cycle |
| title | Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence |
| title_full | Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence |
| title_fullStr | Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence |
| title_full_unstemmed | Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence |
| title_short | Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence |
| title_sort | diagnosis of wilson disease and its phenotypes by using artificial intelligence |
| topic | Wilson disease copper mitochondria liver intermediary metabolism urea cycle |
| url | https://www.mdpi.com/2218-273X/11/8/1243 |
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