BACKGROUND: Cholinergic projection systems degeneration is associated with dopamine nonresponsive features of Parkinson's disease (PD). Cholinergic deficits are variable in nondemented PD. Identification of cholinergic deficits in PD may help with selection of suitable patients for targeted cholinergic drug treatment in PD. The objective of this retrospective multivariate predictor analysis study was to identify clinical markers indicative of cholinergic deficits in PD patients, as assessed by acetylcholinesterase ([(11) C]PMP) positron emission tomography. METHODS: One hundred thirty-seven PD patients (34 female) participated; median modified Hoehn and Yahr score was 2.5 (range, 1-4), average age 65.6 ± 7.4 years, and average duration of motor disease symptoms of 6.0 ± 4.2 years. Subjects were dichotomized as "normocholinergic" or "hypocholinergic" based on a 5(th) percentile cutoff from normal for the basal forebrain-cortical and pedunculopontine nucleus-thalamic cholinergic projection systems. Previously identified clinical indices of cholinergic denervation were used for statistical prediction of cholinergic deficits. Logistic regression determined which risk factors predicted cholinergic deficits. Sensitivity, specificity, and accuracy were determined for the (combinations of) significant predictor variables. RESULTS: Forty-nine (35.8%) hypocholinergic PD subjects were identified. The combination of rapid eye movement (REM) sleep behavior disorder (RBD) symptoms and fall history showed highest diagnostic accuracy (81.1%) for predicting combined thalamic and cortical cholinergic deficits. A combined assessment of 8.5 m walk time and lower score on the Montreal cognitive assessment scale provided diagnostic accuracy of 80.7% for predicting isolated cortical cholinergic denervation. CONCLUSION: Assessment of clinical indices of cholinergic denervation may be useful for identifying suitable subjects for trials of targeted cholinergic drug treatments in PD.
BACKGROUND: Cholinergic projection systems degeneration is associated with dopamine nonresponsive features of Parkinson's disease (PD). Cholinergic deficits are variable in nondemented PD. Identification of cholinergic deficits in PD may help with selection of suitable patients for targeted cholinergic drug treatment in PD. The objective of this retrospective multivariate predictor analysis study was to identify clinical markers indicative of cholinergic deficits in PDpatients, as assessed by acetylcholinesterase ([(11) C]PMP) positron emission tomography. METHODS: One hundred thirty-seven PDpatients (34 female) participated; median modified Hoehn and Yahr score was 2.5 (range, 1-4), average age 65.6 ± 7.4 years, and average duration of motor disease symptoms of 6.0 ± 4.2 years. Subjects were dichotomized as "normocholinergic" or "hypocholinergic" based on a 5(th) percentile cutoff from normal for the basal forebrain-cortical and pedunculopontine nucleus-thalamic cholinergic projection systems. Previously identified clinical indices of cholinergic denervation were used for statistical prediction of cholinergic deficits. Logistic regression determined which risk factors predicted cholinergic deficits. Sensitivity, specificity, and accuracy were determined for the (combinations of) significant predictor variables. RESULTS: Forty-nine (35.8%) hypocholinergic PD subjects were identified. The combination of rapid eye movement (REM) sleep behavior disorder (RBD) symptoms and fall history showed highest diagnostic accuracy (81.1%) for predicting combined thalamic and cortical cholinergic deficits. A combined assessment of 8.5 m walk time and lower score on the Montreal cognitive assessment scale provided diagnostic accuracy of 80.7% for predicting isolated cortical cholinergic denervation. CONCLUSION: Assessment of clinical indices of cholinergic denervation may be useful for identifying suitable subjects for trials of targeted cholinergic drug treatments in PD.
Authors: Christopher G Goetz; Werner Poewe; Olivier Rascol; Cristina Sampaio; Glenn T Stebbins; Carl Counsell; Nir Giladi; Robert G Holloway; Charity G Moore; Gregor K Wenning; Melvin D Yahr; Lisa Seidl Journal: Mov Disord Date: 2004-09 Impact factor: 10.338
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Authors: Patrícia Dos Santos; Adriano D S Targa; Ana Carolina D Noseda; Lais S Rodrigues; Juliane Fagotti; Marcelo M S Lima Journal: Mol Neurobiol Date: 2016-09-22 Impact factor: 5.590
Authors: Shu-Ying Liu; Daryl J Wile; Jessie Fanglu Fu; Jason Valerio; Elham Shahinfard; Siobhan McCormick; Rostom Mabrouk; Nasim Vafai; Jess McKenzie; Nicole Neilson; Alexandra Perez-Soriano; Julieta E Arena; Mariya Cherkasova; Piu Chan; Jing Zhang; Cyrus P Zabetian; Jan O Aasly; Zbigniew K Wszolek; Martin J McKeown; Michael J Adam; Thomas J Ruth; Michael Schulzer; Vesna Sossi; A Jon Stoessl Journal: Lancet Neurol Date: 2018-02-16 Impact factor: 44.182