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

Beyzaei, Zahra; Mehrzadeh, Arman; Hashemi, Niko; Geramizadeh, Bita

doi:10.1016/j.ymgmr.2023.101034

Cited by 3 publications

(1 citation statement)

References 28 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The disease is caused by homozygous or compound heterozygous mutations in ATP7B, which encodes the transmembrane copper-transporting ATPase 2 (commonly referred to as ATP7B). Abnormal function of ATP7B can lead to impaired copper homeostasis and copper overload in the brain, liver and other organs ( 3 ). Neurological symptoms are one of the most common types of symptoms associated with this disease.…”

Section: Introductionmentioning

confidence: 99%

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Yang,

Wang,

Fang

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

ObjectivesWilson disease (WD) is a rare autosomal recessive disorder caused by a mutation in the ATP7B gene. Neurological symptoms are one of the most common symptoms of WD. This study aims to construct a model that can predict the occurrence of neurological symptoms by combining clinical multidimensional indicators with machine learning methods.MethodsThe study population consisted of WD patients who received treatment at the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from July 2021 to September 2023 and had a Leipzig score ≥ 4 points. Indicators such as general clinical information, imaging, blood and urine tests, and clinical scale measurements were collected from patients, and machine learning methods were employed to construct a prediction model for neurological symptoms. Additionally, the SHAP method was utilized to analyze clinical information to determine which indicators are associated with neurological symptoms.ResultsIn this study, 185 patients with WD (of whom 163 had neurological symptoms) were analyzed. It was found that using the eXtreme Gradient Boosting (XGB) to predict achieved good performance, with an MCC value of 0.556, ACC value of 0.929, AUROC value of 0.835, and AUPRC value of 0.975. Brainstem damage, blood creatinine (Cr), age, indirect bilirubin (IBIL), and ceruloplasmin (CP) were the top five important predictors. Meanwhile, the presence of brainstem damage and the higher the values of Cr, Age, and IBIL, the more likely neurological symptoms were to occur, while the lower the CP value, the more likely neurological symptoms were to occur.ConclusionsTo sum up, the prediction model constructed using machine learning methods to predict WD cirrhosis has high accuracy. The most important indicators in the prediction model were brainstem damage, Cr, age, IBIL, and CP. It provides assistance for clinical decision-making.

show abstract

Section: Introductionmentioning

confidence: 99%

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Yang,

Wang,

Fang

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

show abstract

Clinical characteristics and prognosis of early diagnosed Wilson's disease: A large cohort study

Li,

Lin,

Chen

et al. 2024

Liver International

View full text Add to dashboard Cite

Background and AimsFew studies have focused on the outcomes of Wilson's disease (WD) diagnosed before age of 5 years. This study aimed to summarize the clinical features of early diagnosed WD and analyse treatment outcomes and the risk factors associated with treatment failure.MethodsA total of 139 children confirmed with WD before 5 years were enrolled in this study. Only patients with follow‐up over 1 year were analysed with Kaplan–Meier survival analysis. The composite outcomes included death, progression to liver failure or acute hepatitis, development of renal or neurological symptoms and persistent elevation of alanine aminotransferase (ALT). The treatment failure was defined as occurrence of at least one of above outcomes.ResultsAmong 139 WD patients at diagnosis, two (1.4%) WD patients presented with symptomatic liver disease, whereas 137 (98.6%) were phenotypically asymptomatic, including 135 with elevated ALT and 2 with normal liver function. Median serum ceruloplasmin (Cp) was 3.1 mg/dL, and urinary copper excretion was 87.4 μg/24‐h. There were 71 variants identified in the the copper‐transporting ATPase beta gene, and 29 were loss of function (LOF). 51 patients with LOF variant were younger at diagnosis and had lower Cp than 88 patients without LOF. Among 93 patients with over 1 year of follow‐up, 19 (20.4%) received zinc monotherapy, and 74 (79.6%) received a zinc/D‐penicillamine combination therapy. 14 (15.1%) patients underwent treatment failure, and its occurrence was associated with poor compliance (p < .01).ConclusionsCp is a reliable biomarker for early diagnosis, and zinc monotherapy is an effective treatment for WD during early childhood. Good treatment compliance is critical to achieve a favourable outcome.

show abstract

Wilson’s disease

Svetel,

Kresojevic,

Tomic

et al. 2024

Srp Arh Celok Lek

View full text Add to dashboard Cite

Wilson?s disease (WD) is an autosomal recessive inherited disorder of copper metabolism caused by mutations in the ATP7B gene, which is located on chromosome 13q14.3. The global genetic prevalence of WD at birth is approximately 13.9?15.4 per 100,000 population. Although WD is a rare condition associated with treatment efficacy, mortality rates in patients with WD (5?6.1%) are higher than healthy controls. Prevalent features of WD include hepatic, neurologic, and psychiatric syndromes, even though various signs and symptoms of the disease have been depicted to this point. If diagnosed and treated at an early stage, WD patients would likely improve and be often largely asymptomatic for the rest of their lives. Prompt diagnosis and lifelong treatment substantially affect outcome. We aimed to summarize current knowledge about WD epidemiology, genetics, clinical manifestations, diagnostic workup, and current WD management.

show abstract

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

Cited by 3 publications

References 28 publications

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Decoding Wilson disease: a machine learning approach to predict neurological symptoms

Clinical characteristics and prognosis of early diagnosed Wilson's disease: A large cohort study

Wilson’s disease

Contact Info

Product

Resources

About