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

被引:13
|
作者
Vavougios, George D. [1 ]
Doskas, Triantafyllos [1 ]
Kormas, Constantinos [2 ]
Krogfelt, Karen A. [3 ]
Zarogiannis, Sotirios G. [4 ]
Stefanis, Leonidas [5 ,6 ]
机构
[1] Athens Naval Hosp, Dept Neurol, Athens 70, Greece
[2] Natl & Kapodistrian Univ Athens, Eginit Hosp, Dept Neurol 1, Athens, Greece
[3] State Serum Inst, 5 Artillerivej, DK-2300 Copenhagen, Denmark
[4] Univ Thessaly, Dept Physiol, Fac Med, Biopolis 411105, Larissa, Greece
[5] Acad Athens, Biomed Res Fdn, Div Basic Neurosci, Athens, Greece
[6] Natl & Kapodistrian Univ Athens, Sch Med, Dept Neurol 2, Athens, Greece
关键词
Parkinson's disease; Progression; Biomarkers; Cluster analysis; Phenotypes; GROWTH-FACTOR-I; COGNITIVE IMPAIRMENT; VERBAL FLUENCY; SYMPTOMS; DEFICITS; AGE;
D O I
10.1016/j.jns.2018.01.025
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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 VO4 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 VO4 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.
引用
收藏
页码:103 / 108
页数:6
相关论文
共 50 条
  • [1] Identification of a prospective rapid motor progression cluster of Parkinson's disease: data from the PPMI study
    Vavougios, G.
    Doskas, T.
    Kormas, C.
    Krogfelt, K.
    Zarogiannis, S.
    Stefanis, L.
    [J]. EUROPEAN JOURNAL OF NEUROLOGY, 2018, 25 : 525 - 525
  • [2] Baseline predictors of clinical and imaging progression in Parkinson's disease: Results from the PPMI study
    Chahine, L.
    Siderowf, A.
    Caspell-Garcia, C.
    Barnes, J.
    Seedorff, N.
    Coffey, C.
    Galasko, D.
    Mollenhauer, B.
    Arnedo, V.
    Daegele, N.
    Frasier, M.
    Tanner, C.
    Kieburtz, K.
    Marek, K.
    [J]. MOVEMENT DISORDERS, 2018, 33 : S383 - S383
  • [3] NON-MOTOR SYMPTOMS OF PARKINSON DISEASE IN THE PPMI STUDY: A DISEASE PROGRESSION MODELING ANALYSIS.
    Conrado, D. J.
    Chen, Z.
    Burton, J.
    Akalu, M.
    Hynds, R.
    Marek, K.
    Stephenson, D.
    Romero, K.
    [J]. CLINICAL PHARMACOLOGY & THERAPEUTICS, 2019, 105 : S34 - S34
  • [4] Cognitive and non-motor phenotypes in Parkinson's disease with REM sleep behaviour disorder: data from the PPMI study
    Vavougios, G. D.
    Kalampokini, S.
    Artemiadis, A.
    Zis, P.
    Stavrou, V.
    Gourgoulianis, K. I.
    Hadjigeorgiou, G.
    Bargiotas, P.
    [J]. JOURNAL OF SLEEP RESEARCH, 2022, 31
  • [5] Predictors of motor complications in early Parkinson's disease: A prospective cohort study
    Kelly, Mark J.
    Lawton, Michael A.
    Baig, Fahd
    Ruffmann, Claudio
    Barber, Thomas R.
    Lo, Christine
    Klein, Johannes C.
    Ben-Shlomo, Yoav
    Hu, Michele T.
    [J]. MOVEMENT DISORDERS, 2019, 34 (08) : 1174 - 1183
  • [6] Association between CSF biomarkers and clinical phenotype of early Parkinson's disease in the Parkinson's Progression Marker Initiative (PPMI)
    Kang, J. H.
    Caspell, C.
    Coffey, C.
    Taylor, P.
    Frasier, M.
    Marek, K.
    Trojanowski, J. Q.
    Shaw, L. M.
    [J]. MOVEMENT DISORDERS, 2012, 27 : S34 - S35
  • [7] Course of psychiatric symptoms and cognitive performance in early Parkinson's disease: Results from the PPMI study
    de la Riva, P.
    Smith, K.
    Xie, S. X.
    Weintraub, D.
    [J]. MOVEMENT DISORDERS, 2014, 29 : S313 - S314
  • [8] Phenotypes of cognitive decline in idiopathic Parkinson's disease: a data driven analysis of the Parkinson's Progression Markers Initiative (PPMI)
    Vavougios, G.
    Krogfelt, K.
    Doskas, T.
    Stefanis, L.
    [J]. EUROPEAN JOURNAL OF NEUROLOGY, 2019, 26 : 849 - 849
  • [9] Cognitive performance and psychiatric symptoms in early, untreated Parkinson's disease: Results from the PPMI study
    Weintraub, D.
    Simuni, T.
    Siderowf, A.
    Troyer, M.
    Coffey, C.
    Foster, E.
    Hawkins, K.
    [J]. MOVEMENT DISORDERS, 2013, 28 : S193 - S194
  • [10] Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort
    Chen, Xi
    He, Chentao
    Ma, Jianrui
    Yang, Rui
    Qi, Qi
    Gao, Ziqi
    Du, Tingyue
    Zhang, Piao
    Li, Yan
    Cai, Mengfei
    Zhang, Yuhu
    [J]. CNS NEUROSCIENCE & THERAPEUTICS, 2024, 30 (08)