Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.
Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.
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在两个队列中使用机器学习识别和预测帕金森病亚型和进展。
DOI：
10.1038/s41531-022-00439-z
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发表时间：
2022-12-16
影响因子：
8.7
通讯作者：
Faghri, Faraz
中科院分区：
文献类型：
--
作者：
Dadu, Anant;Satone, Vipul;Kaur, Rachneet;Hashemi, Sayed Hadi;Leonard, Hampton;Iwaki, Hirotaka;Makarious, Mary B.;Billingsley, Kimberley J.;Bandres-Ciga, Sara;Sargent, Lana J.;Noyce, Alastair J.;Daneshmand, Ali;Blauwendraat, Cornelis;Marek, Ken;Scholz, Sonja W.;Singleton, Andrew B.;Nalls, Mike A.;Campbell, Roy H.;Faghri, Faraz
帕金森病（PD）的临床表现的特征在于发病年龄、疾病持续时间、进展速率以及运动与非运动特征的组合的异质性。对于不同疾病亚型的表征以及疾病过程的改进的个体化预测存在未满足的需求。我们使用无监督和有监督的机器学习方法对来自帕金森病进展标志物倡议（n = 294例）的全面纵向临床数据进行分析，以识别患者亚型并预测疾病进展。在帕金森病生物标志物项目的一个独立的、临床特征良好的队列中验证了所得模型（n = 263例）。我们的分析区分了三种不同的疾病亚型，具有高度可预测的进展率，对应于缓慢，中度和快速疾病进展。我们在初次诊断后5年实现了对疾病进展的高度准确预测，缓慢进展组（PDvec1）的平均曲线下面积（AUC）为0.92（95% CI：0.95 ± 0.01），中度进展组为0.87 ± 0.03，快速进展组为0.95 ± 0.02（PDvec3）。我们将血清神经丝光作为其他关键生物标志物中快速疾病进展的重要指标。我们在一个独立的队列中复制了这些发现，发布了分析代码，并以开放科学的方式开发了模型。我们的数据驱动研究为解构PD异质性提供了见解。这种方法可以通过改善对重要临床结果的检测来对临床试验产生直接影响。我们预计，机器学习模型将改善患者咨询、临床试验设计，并最终改善个性化的患者护理。
The clinical manifestations of Parkinson’s disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson’s Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson’s Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care.
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影响因子：
11
作者：
HUGHES, AJ;DANIEL, SE;LEES, AJ
通讯作者：
LEES, AJ
影响因子：
9.9
作者：
JANKOVIC, J;MCDERMOTT, M;WEINER, W
通讯作者：
WEINER, W
影响因子：
--
作者：
BENTON, AL;VARNEY, NR;HAMSHER, KD
通讯作者：
HAMSHER, KD
影响因子：
30.8
作者：
Faghri, Faraz;Brunn, Fabian;Dadu, Anant;Zucchi, Elisabetta;Martinelli, Ilaria;Mazzini, Letizia;Vasta, Rosario;Canosa, Antonio;Moglia, Cristina;Calvo, Andrea;Nalls, Michael A.;Campbell, Roy H.;Mandrioli, Jessica;Traynor, Bryan J.;Chio, Adriano
通讯作者：
Chio, Adriano
影响因子：
14.5
作者：
Fereshtehnejad, Seyed-Mohammad;Zeighami, Yashar;Postuma, Ronald B.
通讯作者：
Postuma, Ronald B.