Diagnosis of Wilson Disease and Its Phenotypes by Using Artificial Intelligence.
Journal:
Biomolecules
PMID:
34439909
Abstract
WD is caused by 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 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 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.
Authors
Keywords
Adult
Algorithms
Artificial Intelligence
Brain
Ceruloplasmin
Copper
Copper-Transporting ATPases
Diagnosis, Computer-Assisted
DNA, Mitochondrial
Female
Fuzzy Logic
Glutamic Acid
Hepatolenticular Degeneration
Humans
Liver
Male
Medical Informatics
Middle Aged
Mutation
Neural Networks, Computer
Phenotype
Principal Component Analysis