The international journal of neuropsychopharmacology · 2020
Clinical and Clinical-Pharmacogenetic Models for Prediction of the Most Common Psychiatric Complications Due to Dopaminergic Treatment in Parkinson's Disease.
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Redenšek Sara, Jenko Bizjan Barbara, Trošt Maja, Dolžan Vita
Аннотация
BACKGROUND: The most common psychiatric complications due to dopaminergic treatment in Parkinson's disease are visual hallucinations and impulse control disorders. Their development depends on clinical and genetic factors.
METHODS: We evaluated the simultaneous effect of 16 clinical and 34 genetic variables on the occurrence of visual hallucinations and impulse control disorders. Altogether, 214 Parkinson's disease patients were enrolled. Their demographic, clinical, and genotype data were obtained. Clinical and clinical-pharmacogenetic models were built by The Least Absolute Shrinkage and Selection Operator penalized logistic regression. The predictive capacity was evaluated with the cross-validated area under the receiver operating characteristic curve (AUC).
RESULTS: The clinical-pharmacogenetic index for prediction of visual hallucinations encompassed age at diagnosis (OR = 0.99), rapid eye movement (REM) sleep behavior disorder (OR = 2.27), depression (OR = 1.0002), IL6 rs1800795 (OR = 0.99), GPX1 s1050450 (OR = 1.07), COMT rs165815 (OR = 0.69), MAOB rs1799836 (OR = 0.97), DRD3 rs6280 (OR = 1.32), and BIRC5 rs8073069 (OR = 0.94). The clinical-pharmacogenetic index for prediction of impulse control disorders encompassed age at diagnosis (OR = 0.95), depression (OR = 1.75), beta-blockers (OR = 0.99), coffee consumption (OR = 0.97), NOS1 rs2682826 (OR = 1.15), SLC6A3 rs393795 (OR = 1.27), SLC22A1 rs628031 (OR = 1.19), DRD2 rs1799732 (OR = 0.88), DRD3 rs6280 (OR = 0.88), and NRG1 rs3924999 (OR = 0.96). The cross-validated AUCs of clinical and clinical-pharmacogenetic models for visual hallucinations were 0.60 and 0.59, respectively. The AUCs of clinical and clinical-pharmacogenetic models for impulse control disorders were 0.72 and 0.71, respectively. The AUCs show that the addition of selected genetic variables to the analysis does not contribute to better prediction of visual hallucinations and impulse control disorders.
CONCLUSIONS: Models could be improved by a larger cohort and by addition of other types of Parkinson's disease biomarkers to the analysis.
METHODS: We evaluated the simultaneous effect of 16 clinical and 34 genetic variables on the occurrence of visual hallucinations and impulse control disorders. Altogether, 214 Parkinson's disease patients were enrolled. Their demographic, clinical, and genotype data were obtained. Clinical and clinical-pharmacogenetic models were built by The Least Absolute Shrinkage and Selection Operator penalized logistic regression. The predictive capacity was evaluated with the cross-validated area under the receiver operating characteristic curve (AUC).
RESULTS: The clinical-pharmacogenetic index for prediction of visual hallucinations encompassed age at diagnosis (OR = 0.99), rapid eye movement (REM) sleep behavior disorder (OR = 2.27), depression (OR = 1.0002), IL6 rs1800795 (OR = 0.99), GPX1 s1050450 (OR = 1.07), COMT rs165815 (OR = 0.69), MAOB rs1799836 (OR = 0.97), DRD3 rs6280 (OR = 1.32), and BIRC5 rs8073069 (OR = 0.94). The clinical-pharmacogenetic index for prediction of impulse control disorders encompassed age at diagnosis (OR = 0.95), depression (OR = 1.75), beta-blockers (OR = 0.99), coffee consumption (OR = 0.97), NOS1 rs2682826 (OR = 1.15), SLC6A3 rs393795 (OR = 1.27), SLC22A1 rs628031 (OR = 1.19), DRD2 rs1799732 (OR = 0.88), DRD3 rs6280 (OR = 0.88), and NRG1 rs3924999 (OR = 0.96). The cross-validated AUCs of clinical and clinical-pharmacogenetic models for visual hallucinations were 0.60 and 0.59, respectively. The AUCs of clinical and clinical-pharmacogenetic models for impulse control disorders were 0.72 and 0.71, respectively. The AUCs show that the addition of selected genetic variables to the analysis does not contribute to better prediction of visual hallucinations and impulse control disorders.
CONCLUSIONS: Models could be improved by a larger cohort and by addition of other types of Parkinson's disease biomarkers to the analysis.
Предметные рубрики MeSH
Age FactorsAgedAntiparkinson AgentsDisruptive, Impulse Control, and Conduct DisordersDopamine AgentsFemaleGenetic Predisposition to DiseaseHallucinationsHumansMaleMiddle AgedParkinson DiseasePharmacogeneticsPharmacogenomic TestingPharmacogenomic VariantsPolymorphism, Single NucleotideRisk AssessmentRisk Factors