ArticleFrontiers in neurology2025
DMPS-induced neurological deterioration in neurological Wilson's disease patients: a retrospective case-control study on clinical characteristics and risk factors.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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6 authors.
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Abstract
Background and aim: Wilson's disease (WD), an autosomal recessive copper metabolism defect, causes pathological copper deposition in hepatic and neurological systems, culminating in cirrhosis and neuropsychiatric manifestations. Our understanding of neurological deterioration in neurological WD patients following sodium dimercaptopropanesulfonate (DMPS) treatment is limited. Thus, this study aims to analyze the phenotypic spectrum and predictors of DMPS-induced neurological deterioration in neurological WD. Methods: Demographic (age, gender, weight), clinical (K-F ring, duration of illness), and biochemical parameters [alanine aminotransferase, aspartate aminotransferase, albumin, serum ceruloplasmin, blood urea nitrogen, serum creatinine, 24 h urinary copper, lactate, homocysteine (HCY)] were systematically evaluated alongside neuroimaging data, followed by receiver operating characteristic (ROC) curve analysis to identify predictive biomarkers for neurological deterioration in DMPS-induced neurological WD patients. Results: A total of 277 neurological WD patients were enrolled, among whom 24.5% (68/277) developed neurological deterioration. Notably, 70.6% (48/68) of the patients experiencing neurological worsening were male. Among the patients, 91.2% (62/68) exhibited mild deterioration, while 8.8% (6/68) experienced severe deterioration. Multivariate logistic regression analysis indicated that sex [odds ratio (OR) = 0.41[95% confidence interval (CI) = 0.18-0.94], Conclusion: Neurological deterioration in DMPS-induced neurological WD patients is not rare and predominantly occurs in males. We identified three independent risk factors for this deterioration: sex, brain MRI score, and HCY. A composite risk model incorporating these parameters achieved superior predictive accuracy compared to individual biomarker.
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