Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study

Abstract Parkinson’s disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xi Zhang, Jingyuan Chou, Jian Liang, Cao Xiao, Yize Zhao, Harini Sarva, Claire Henchcliffe, Fei Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff625ebf1c6a430db23123280e2cc6c6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ff625ebf1c6a430db23123280e2cc6c6
record_format	dspace
spelling	oai:doaj.org-article:ff625ebf1c6a430db23123280e2cc6c62021-12-02T15:08:20ZData-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study10.1038/s41598-018-37545-z2045-2322https://doaj.org/article/ff625ebf1c6a430db23123280e2cc6c62019-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-37545-zhttps://doaj.org/toc/2045-2322Abstract Parkinson’s disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson’s Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson’s disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.Xi ZhangJingyuan ChouJian LiangCao XiaoYize ZhaoHarini SarvaClaire HenchcliffeFei WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xi Zhang Jingyuan Chou Jian Liang Cao Xiao Yize Zhao Harini Sarva Claire Henchcliffe Fei Wang Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
description	Abstract Parkinson’s disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson’s Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson’s disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.
format	article
author	Xi Zhang Jingyuan Chou Jian Liang Cao Xiao Yize Zhao Harini Sarva Claire Henchcliffe Fei Wang
author_facet	Xi Zhang Jingyuan Chou Jian Liang Cao Xiao Yize Zhao Harini Sarva Claire Henchcliffe Fei Wang
author_sort	Xi Zhang
title	Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
title_short	Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
title_full	Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
title_fullStr	Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
title_full_unstemmed	Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study
title_sort	data-driven subtyping of parkinson’s disease using longitudinal clinical records: a cohort study
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ff625ebf1c6a430db23123280e2cc6c6
work_keys_str_mv	AT xizhang datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT jingyuanchou datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT jianliang datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT caoxiao datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT yizezhao datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT harinisarva datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT clairehenchcliffe datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy AT feiwang datadrivensubtypingofparkinsonsdiseaseusinglongitudinalclinicalrecordsacohortstudy
_version_	1718388230256066560

Data-Driven Subtyping of Parkinson’s Disease Using Longitudinal Clinical Records: A Cohort Study

Ejemplares similares