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Articles by Charlotte E. Teunissen
Total Records ( 3 ) for Charlotte E. Teunissen
  Niklas Mattsson , Ulf Andreasson , Staffan Persson , Maria C. Carrillo , Steven Collins , Sonia Chalbot , Neal Cutler , Diane Dufour- Rainfray , Anne M. Fagan , Niels H.H. Heegaard , Ging-Yuek Robin Hsiung , Bradley Hyman , Khalid Iqbal , D. Richard Lachno , Alberto Lleo , Piotr Lewczuk , Jose L. Molinuevo , Piero Parchi , Axel Regeniter , Robert Rissman , Hanna Rosenmann , Giuseppe Sancesario , Johannes Schroder , Leslie M. Shaw , Charlotte E. Teunissen , John Q. Trojanowski , Hugo Vanderstichele , Manu Vandijck , Marcel M. Verbeek , Henrik Zetterberg , Kaj Blennow and Stephan A. Kaser
  Background The cerebrospinal fluid (CSF) biomarkers amyloid beta 1–42, total tau, and phosphorylated tau are used increasingly for Alzheimer's disease (AD) research and patient management. However, there are large variations in biomarker measurements among and within laboratories. Methods Data from the first nine rounds of the Alzheimer's Association quality control program was used to define the extent and sources of analytical variability. In each round, three CSF samples prepared at the Clinical Neurochemistry Laboratory (Molndal, Sweden) were analyzed by single-analyte enzyme-linked immunosorbent assay (ELISA), a multiplexing xMAP assay, or an immunoassay with electrochemoluminescence detection. Results A total of 84 laboratories participated. Coefficients of variation (CVs) between laboratories were around 20% to 30%; within-run CVs, less than 5% to 10%; and longitudinal within-laboratory CVs, 5% to 19%. Interestingly, longitudinal within-laboratory CV differed between biomarkers at individual laboratories, suggesting that a component of it was assay dependent. Variability between kit lots and between laboratories both had a major influence on amyloid beta 1–42 measurements, but for total tau and phosphorylated tau, between-kit lot effects were much less than between-laboratory effects. Despite the measurement variability, the between-laboratory consistency in classification of samples (using prehoc-derived cutoffs for AD) was high (>90% in 15 of 18 samples for ELISA and in 12 of 18 samples for xMAP). Conclusions The overall variability remains too high to allow assignment of universal biomarker cutoff values for a specific intended use. Each laboratory must ensure longitudinal stability in its measurements and use internally qualified cutoff levels. Further standardization of laboratory procedures and improvement of kit performance will likely increase the usefulness of CSF AD biomarkers for researchers and clinicians.
  Petra E. Spies , Jurgen A.H.R. Claassen , Petronella G.M. Peer , Marinus A. Blankenstein , Charlotte E. Teunissen , Philip Scheltens , Wiesje M. van der Flier , Marcel G.M. Olde Rikkert and Marcel M. Verbeek
  Background We aimed to develop a prediction model based on cerebrospinal fluid (CSF) biomarkers, that would yield a single estimate representing the probability that dementia in a memory clinic patient is due to Alzheimer‘s disease (AD). Methods All patients suspected of dementia in whom the CSF biomarkers had been analyzed were selected from a memory clinic database. Clinical diagnosis was AD (n = 272) or non-AD (n = 289). The prediction model was developed with logistic regression analysis and included CSF amyloid β42, CSF phosphorylated tau181, and sex. Validation was performed on an independent data set from another memory clinic, containing 334 AD and 157 non-AD patients. Results The prediction model estimated the probability that AD is present as follows: p(AD) = 1/(1 + e – [–0.3315 + score]), where score is calculated from –1.9486 × ln(amyloid β42) + 2.7915 × ln(phosphorylated tau181) + 0.9178 × sex (male = 0, female = 1). When applied to the validation data set, the discriminative ability of the model was very good (area under the receiver operating characteristic curve: 0.85). The agreement between the probability of AD predicted by the model and the observed frequency of AD diagnoses was very good after taking into account the difference in AD prevalence between the two memory clinics. Conclusions We developed a prediction model that can accurately predict the probability of AD in a memory clinic population suspected of dementia based on CSF amyloid β42, CSF phosphorylated tau181, and sex.
  Wesley Jongbloed , Maartje I. Kester , Wiesje M. van der Flier , Robert Veerhuis , Philip Scheltens , Marinus A. Blankenstein and Charlotte E. Teunissen
  Background Multiplex assays such as xMAP have been proposed for the assessment of Alzheimer‘s disease (AD) biomarkers amyloid β 42 (Aβ42), tau (Tau), and phosphorylated tau (pTau) in cerebrospinal fluid (CSF). Here, we compared the traditional enzyme-linked immunosorbent assay (ELISA) and xMAP with respect to their: (1) absolute biomarker concentration, (2) ability to distinguish AD from nondemented subjects, (3) ability to monitor AD longitudinally, and (4) ability to predict progression from mild cognitive impairment (MCI) to AD. Methods We selected 68 AD, 62 MCI, and 24 nondemented subjects, performed clinical examinations, and obtained CSF at baseline and 2 years later. Aβ42, Tau, and pTau were measured with both ELISA and xMAP. Results Biomarker levels differed considerably between the two assays, and the differences were concentration dependent. No differences were observed in ability to distinguish nondemented subjects from AD patients between ELISA (area under curve of 0.84 for Aβ42, 0.79 for Tau, and 0.75 for pTau) and xMAP (area under curve of 0.82 for Aβ42, 0.75 for Tau, and 0.73 for pTau), all P < .05. Increased Aβ42 levels of AD patients at follow-up compared with baseline were detected with ELISA, whereas increased Tau levels for nondemented subjects and MCI patients were only detected with xMAP. The hazard ratios for progression from MCI to AD did not differ between the assays. Conclusion Both ELISA and multiplex assays can be used to measure AD biomarker levels in CSF to support clinical diagnosis and predict progression from MCI to AD with similar accuracy. Importantly, the assays‘ output in absolute biomarker concentrations is remarkably different, and this discrepancy cannot be reconciled with simple correction factors.
 
 
 
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