UNLABELLED: The purpose of this study was to investigate the potential relationships between cerebrospinal fluid (CSF) measurements of beta-amyloid-1-42 (Abeta(1-42)) and total tau to (11)C-Pittsburgh compound B ((11)C-PiB) and 2-(1-{6-[(2-(18)F-fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene) malononitrile ((18)F-FDDNP) binding as measured using PET. METHODS: A total of 37 subjects were included, consisting of 15 patients with Alzheimer disease (AD), 12 patients with mild cognitive impairment, and 10 healthy controls. All subjects underwent a lumbar puncture and PET using both (11)C-PiB and (18)F-FDDNP. For both PET tracers, parametric images of binding potential were generated. Potential associations of CSF levels of Abeta(1-42) and tau with (11)C-PiB and (18)F-FDDNP binding were assessed using Pearson correlation coefficients and linear regression analyses. RESULTS: For both global (11)C-PiB and (18)F-FDDNP binding, significant correlations with CSF levels of Abeta(1-42) (r = -0.72 and -0.37, respectively) and tau (r = 0.58 and 0.56, respectively) were found across groups (all P < 0.001, except P < 0.05 for correlation between (18)F-FDDNP and Abeta(1-42)). Linear regression analyses showed that, adjusted for regional volume, age, sex, and diagnosis, global (11)C-PiB uptake had an inverse association with Abeta(1-42) CSF levels (standardized beta = -0.50, P < 0.001), whereas there was a positive association between global (18)F-FDDNP binding and tau CSF levels (standardized beta = 0.62, P < 0.01). CONCLUSION: The good agreement between these 2 different types of biomarkers (i.e., CSF and PET) provides converging evidence for their validity. The inverse association between (11)C-PiB and CSF tau Abeta(1-42) confirms that (11)C-PiB measures amyloid load in the brain. The positive association between (18)F-FDDNP and CSF tau suggests that at least part of the specific signal of (18)F-FDDNP in AD patients is due to tangle formation.
UNLABELLED: The purpose of this study was to investigate the potential relationships between cerebrospinal fluid (CSF) measurements of beta-amyloid-1-42 (Abeta(1-42)) and total tau to (11)C-Pittsburgh compound B ((11)C-PiB) and 2-(1-{6-[(2-(18)F-fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene) malononitrile ((18)F-FDDNP) binding as measured using PET. METHODS: A total of 37 subjects were included, consisting of 15 patients with Alzheimer disease (AD), 12 patients with mild cognitive impairment, and 10 healthy controls. All subjects underwent a lumbar puncture and PET using both (11)C-PiB and (18)F-FDDNP. For both PET tracers, parametric images of binding potential were generated. Potential associations of CSF levels of Abeta(1-42) and tau with (11)C-PiB and (18)F-FDDNP binding were assessed using Pearson correlation coefficients and linear regression analyses. RESULTS: For both global (11)C-PiB and (18)F-FDDNP binding, significant correlations with CSF levels of Abeta(1-42) (r = -0.72 and -0.37, respectively) and tau (r = 0.58 and 0.56, respectively) were found across groups (all P < 0.001, except P < 0.05 for correlation between (18)F-FDDNP and Abeta(1-42)). Linear regression analyses showed that, adjusted for regional volume, age, sex, and diagnosis, global (11)C-PiB uptake had an inverse association with Abeta(1-42) CSF levels (standardized beta = -0.50, P < 0.001), whereas there was a positive association between global (18)F-FDDNP binding and tauCSF levels (standardized beta = 0.62, P < 0.01). CONCLUSION: The good agreement between these 2 different types of biomarkers (i.e., CSF and PET) provides converging evidence for their validity. The inverse association between (11)C-PiB and CSFtauAbeta(1-42) confirms that (11)C-PiB measures amyloid load in the brain. The positive association between (18)F-FDDNP and CSFtau suggests that at least part of the specific signal of (18)F-FDDNP in ADpatients is due to tangle formation.
Authors: Anand Kumar; Vladimir Kepe; Jorge R Barrio; Prabha Siddarth; Vicki Manoukian; Virginia Elderkin-Thompson; Gary W Small Journal: Arch Gen Psychiatry Date: 2011-11
Authors: Stephen D Weigand; Prashanthi Vemuri; Heather J Wiste; Matthew L Senjem; Vernon S Pankratz; Paul S Aisen; Michael W Weiner; Ronald C Petersen; Leslie M Shaw; John Q Trojanowski; David S Knopman; Clifford R Jack Journal: Alzheimers Dement Date: 2011-02-01 Impact factor: 21.566
Authors: Clifford R Jack; Prashanthi Vemuri; Heather J Wiste; Stephen D Weigand; Paul S Aisen; John Q Trojanowski; Leslie M Shaw; Matthew A Bernstein; Ronald C Petersen; Michael W Weiner; David S Knopman Journal: Arch Neurol Date: 2011-08-08
Authors: Clifford R Jack; David S Knopman; William J Jagust; Leslie M Shaw; Paul S Aisen; Michael W Weiner; Ronald C Petersen; John Q Trojanowski Journal: Lancet Neurol Date: 2010-01 Impact factor: 44.182
Authors: Susan M Landau; Ming Lu; Abhinay D Joshi; Michael Pontecorvo; Mark A Mintun; John Q Trojanowski; Leslie M Shaw; William J Jagust Journal: Ann Neurol Date: 2013-12 Impact factor: 10.422