Julia Greenberg , Kelly Astudillo , Steven J. Frucht , Adeen Flinker , Giulietta M. Riboldi
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Assessing the predictive value of certain clinical traits for the <em>GBA</em>-variant carrier status will help target genetic testing in clinical settings where cost and access limit its availability.</p></div><div><h3>Methods</h3><p>In-depth clinical characterization through standardized rating scales for motor and non-motor symptoms and self-reported binomial information of a cohort of subjects with PD (n = 100) from our center and from the larger cohort of the Parkinson’s Progression Marker Initiative (PPMI) was utilized to evaluate the predictive values of clinical traits for <em>GBA</em> variant carrier status. The model was cross-validated across the two cohorts.</p></div><div><h3>Results</h3><p>Leveraging non-motor symptoms of PD, we established successful discrimination of <em>GBA</em> variants in the PPMI cohort and study cohort (AUC 0.897 and 0.738, respectively). The PPMI cohort model successfully generalized to the study cohort data using both MDS-UPDRS scores and binomial data (AUC 0.740 and 0.734, respectively) while the study cohort model did not.</p></div><div><h3>Conclusions</h3><p>We assessed the predictive value of non-motor symptoms of PD for identifying <em>GBA</em> carrier status in the general PD population. These data can be used to determine a simple, clinically oriented model using either the MDS-UPDRS or subjective symptom reporting from patients. 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Assessing the predictive value of certain clinical traits for the <em>GBA</em>-variant carrier status will help target genetic testing in clinical settings where cost and access limit its availability.</p></div><div><h3>Methods</h3><p>In-depth clinical characterization through standardized rating scales for motor and non-motor symptoms and self-reported binomial information of a cohort of subjects with PD (n = 100) from our center and from the larger cohort of the Parkinson’s Progression Marker Initiative (PPMI) was utilized to evaluate the predictive values of clinical traits for <em>GBA</em> variant carrier status. The model was cross-validated across the two cohorts.</p></div><div><h3>Results</h3><p>Leveraging non-motor symptoms of PD, we established successful discrimination of <em>GBA</em> variants in the PPMI cohort and study cohort (AUC 0.897 and 0.738, respectively). The PPMI cohort model successfully generalized to the study cohort data using both MDS-UPDRS scores and binomial data (AUC 0.740 and 0.734, respectively) while the study cohort model did not.</p></div><div><h3>Conclusions</h3><p>We assessed the predictive value of non-motor symptoms of PD for identifying <em>GBA</em> carrier status in the general PD population. These data can be used to determine a simple, clinically oriented model using either the MDS-UPDRS or subjective symptom reporting from patients. 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引用次数: 0
摘要
导言鉴于GBA相关帕金森病(GBA-PD)独特的自然病史以及该人群接受新型治疗的潜力,确定GBA-PD患者基因检测的优先顺序对于预后、个体化治疗和临床试验分层至关重要。方法通过对本中心和帕金森病进展标志物倡议(PPMI)更大队列中的一组帕金森病受试者(n = 100)的运动症状和非运动症状的标准化评分量表以及自我报告的二项式信息进行深入的临床特征描述,评估临床特征对 GBA 变异携带者状态的预测价值。结果从帕金森病的非运动症状出发,我们在 PPMI 队列和研究队列中成功区分了 GBA 变异体(AUC 分别为 0.897 和 0.738)。使用 MDS-UPDRS 评分和二项式数据,PPMI 队列模型成功地推广到了研究队列数据(AUC 分别为 0.740 和 0.734),而研究队列模型则没有。这些数据可用于利用 MDS-UPDRS 或患者的主观症状报告确定一个简单的临床导向模型。我们的研究结果可为患者提供有关预期携带者风险的咨询,并为预期鉴定 GBA 变异的检测优先顺序提供信息。
Clinical prediction of GBA carrier status in Parkinson’s disease
Introduction
Given the unique natural history of GBA-related Parkinson’s disease (GBA-PD) and the potential for novel treatments in this population, genetic testing prioritization for the identification of GBA-PD patients is crucial for prognostication, individualizing treatment, and stratification for clinical trials. Assessing the predictive value of certain clinical traits for the GBA-variant carrier status will help target genetic testing in clinical settings where cost and access limit its availability.
Methods
In-depth clinical characterization through standardized rating scales for motor and non-motor symptoms and self-reported binomial information of a cohort of subjects with PD (n = 100) from our center and from the larger cohort of the Parkinson’s Progression Marker Initiative (PPMI) was utilized to evaluate the predictive values of clinical traits for GBA variant carrier status. The model was cross-validated across the two cohorts.
Results
Leveraging non-motor symptoms of PD, we established successful discrimination of GBA variants in the PPMI cohort and study cohort (AUC 0.897 and 0.738, respectively). The PPMI cohort model successfully generalized to the study cohort data using both MDS-UPDRS scores and binomial data (AUC 0.740 and 0.734, respectively) while the study cohort model did not.
Conclusions
We assessed the predictive value of non-motor symptoms of PD for identifying GBA carrier status in the general PD population. These data can be used to determine a simple, clinically oriented model using either the MDS-UPDRS or subjective symptom reporting from patients. Our results can inform patient counseling about the expected carrier risk and test prioritization for the expected identification of GBA variants.