Literature DB >> 29571843

Identification of a prospective early motor progression cluster of Parkinson's disease: Data from the PPMI study.

George D Vavougios1, Triantafyllos Doskas2, Constantinos Kormas3, Karen A Krogfelt4, Sotirios G Zarogiannis5, Leonidas Stefanis6.   

Abstract

AIM: The aim of our study is to phenotype PD motor progression, and to detect whether serum, cerebrospinal fluid (CSF), neuroimaging biomarkers and neuropsychological measures characterize PD motor progression phenotypes.
METHODS: We defined motor progression as a difference of at least one point in the Hoehn & Yahr (H&Y) scale between the baseline (Visit 0, V0), 12 months (Visit 04, V04) and 36 months (Visit 08, V08) milestones of the Progression Markers Initiative (PPMI) study. H&Y progression events were recorded at each milestone in order to be used as cluster analysis variables, in order to produce progression phenotypes. Subsequently, cross-cluster comparisons prior to and following (pairwise) propensity score matching were performed in order to assess phenotype - defining characteristics.
RESULTS: Four progression clusters where identified: SPPD: Secondarily Progressive PD, H&Y progression between V04 and V08; EPPD: Early Progressive PD. H&Y progression between V0 and V04; NPPD: Non Progressive PD, no H&Y progression; MIPD: Minimally Improving PD, i.e. Minimal H&Y improvement H&Y progression between V04 and V08;. Independent Samples Mann Whitney U tests determined CSF aSyn (p = 0.006, adj p-value = 0.036. I) and Semantic Animal fluency T-score (SFT, p = 0.003, adjusted p-value = 0.016.) as statistically significant cross-cluster characteristics. Following Propensity Score Matching, SFT, Hopkins Verbal Learning Test (Retention/Recall), Serum IGF1, CSF aSyn, DaT-SPECT binding ratios (SBRs) and the Benton Judgement of Line Orientation Test (BJLOT) were determined as statistically significant predictors of cluster differentiation (p < 0.05). DISCUSSION: SFT, Serum IGF1, CSF aSyn and DaT-SPECT-derived, basal ganglia Striatal Binding Ratios warrant further investigation as possible motor progression biomarkers.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarkers; Cluster analysis; Parkinson's disease; Phenotypes; Progression

Mesh:

Substances:

Year:  2018        PMID: 29571843     DOI: 10.1016/j.jns.2018.01.025

Source DB:  PubMed          Journal:  J Neurol Sci        ISSN: 0022-510X            Impact factor:   3.181


  4 in total

1.  COVID-19 Phenotypes and Comorbidity: A Data-Driven, Pattern Recognition Approach Using National Representative Data from the United States.

Authors:  George D Vavougios; Vasileios T Stavrou; Christoforos Konstantatos; Pavlos-Christoforos Sinigalias; Sotirios G Zarogiannis; Konstantinos Kolomvatsos; George Stamoulis; Konstantinos I Gourgoulianis
Journal:  Int J Environ Res Public Health       Date:  2022-04-12       Impact factor: 4.614

Review 2.  Subtyping of Parkinson's Disease - Where Are We Up To?

Authors:  Elizabeth Qian; Yue Huang
Journal:  Aging Dis       Date:  2019-10-01       Impact factor: 6.745

3.  Dysautonomia and REM sleep behavior disorder contributions to progression of Parkinson's disease phenotypes.

Authors:  Giulietta Maria Riboldi; Marco J Russo; Ling Pan; Kristen Watkins; Un Jung Kang
Journal:  NPJ Parkinsons Dis       Date:  2022-08-30

Review 4.  Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering.

Authors:  Seung Hyun Lee; Sang-Min Park; Sang Seok Yeo; Ojin Kwon; Mi-Kyung Lee; Horyong Yoo; Eun Kyoung Ahn; Jae Young Jang; Jung-Hee Jang
Journal:  Diagnostics (Basel)       Date:  2022-01-04
  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.