Correlation of ATP7B gene mutations with clinical phenotype and radiological features in Indian Wilson disease patients

The mutation spectrum and ethnic distribution of Wilson disease, a review

Wilson's disease is a complicated medical condition caused by the accumulation of copper, mostly in the liver and brain. The genetic basis of Wilson's disease is attributed to the presence of pathogenic variants in the ATP7B copper-transporting gene, which prevents the excretion of copper through the biliary tract. To date, ATP7B remains the only identified gene that has been linked to the development of this disease. Our understanding of the disease has been associated with the identification of particular disease-causing variants that present specific impairments in copper transporters. It is crucial to identify the most frequent variant in terms of ethnicity to facilitate testing of its functionality. This study represents the initial comprehensive analysis of ATP7B variants, providing insights into the extensive range of disease-causing mutations. Here, we describe the 1275 distinct ATP7B variants documented so far, with particular emphasis on their regional and ethnic prevalence. The H1069Q missense variant is the most frequently reported in Europe, Northern America, and North Africa, whereas the R778L, C271*, and M645R variants are the most prevalent in the East Asian, Middle Eastern-South Asian, and South American populations, respectively. Acquiring such knowledge would facilitate the implementation of a selective mutation screening approach, targeting the most predominant variant identified within a specific ethnic group or geographic region for better diagnosis of the disease.

Liver cirrhosis prediction for patients with Wilson disease based on machine learning: a case-control study from southwest China

OBJECTIVES

Wilson disease (WD) is a rare autosomal recessive disease caused by an ATP7B gene mutation. Liver cirrhosis is an important issue that affects the clinical management and prognosis of WD patients. Blood routine examination is a potential biomarker for predicting the occurrence of liver cirrhosis in WD. We aim to construct a predictive model for the occurrence of liver cirrhosis using general clinical information, blood routine examination, urine copper, and serum ceruloplasmin through a machine learning approach.

METHODS

Case-control study of WD patients admitted to West China Fourth Hospital between 2005 and 2020. Patients with a score of at least four in scoring system of WD were enrolled. A machine learning model was constructed by EmpowerStats software according to the general clinical data, blood routine examination, 24 h urinary copper, and serum ceruloplasmin.

RESULTS

This study analyzed 346 WD patients, of which 246 were without liver cirrhosis. And we found platelet large cell count (P-LCC), red cell distribution width CV (RDW-CV), serum ceruloplasmin, age at diagnosis, and mean corpuscular volume (MCV) were the top five important predictors. Moreover, the model was of high accuracy, with an area under the receiver operating characteristic curve of 0.9998 in the training set and 0.7873 in the testing set.

CONCLUSIONS

In conclusion, the predictive model for predicting liver cirrhosis in WD, constructed by machine learning, had a higher accuracy. And the most important indices in the predictive model were P-LCC, RDW-CV, serum ceruloplasmin, age at diagnosis, and MCV.