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Articles by Leslie M. Shaw
Total Records ( 7 ) for Leslie M. Shaw
  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 and Clifford R. Jack
  Background Positron-emission tomography (PET) imaging of amyloid with Pittsburgh Compound B (PIB) and Aβ42 levels in the cerebrospinal fluid (CSF Aβ42) demonstrate a highly significant inverse correlation. Both these techniques are presumed to measure brain Aβ amyloid load. The objectives of this study were to develop a method to transform CSF Aβ42 measures into calculated PIB measures (PIBcalc) of Aβ amyloid load, and to partially validate the method in an independent sample of subjects. Methods In all, 41 subjects from the Alzheimer‘s Disease Neuroimaging Initiative (ADNI) underwent PIB PET imaging and lumbar puncture (LP) at the same time. This sample, referred to as the ”training“ sample (nine cognitively normal subjects, 22 subjects with mild cognitive impairment, and 10 subjects with Alzheimer‘s disease), was used to develop a regression model by which CSF Aβ42 (with apolipoprotein E ɛ4 carrier status as a covariate) was transformed into units of PIB PET (PIBcalc). An independent ”supporting“ sample of 362 ADNI subjects (105 cognitively normal subjects, 164 subjects with mild cognitive impairment, and 93 subjects with Alzheime‘s disease) who underwent LP but not PIB PET imaging had their CSF Aβ42 values converted to PIBcalc. These values were compared with the overall PIB PET distribution found in the ADNI subjects (n = 102). Results A linear regression model demonstrates good prediction of actual PIB PET from CSF Aβ42 measures obtained in the training sample (R2 = 0.77, P < .001). PIBcalc data (derived from CSF Aβ42) in the supporting sample of 362 ADNI subjects who underwent LP but not PIB PET imaging demonstrate group-wise distributions that are highly consistent with the larger ADNI PIB PET distribution and with published PIB PET imaging studies. Conclusion Although the precise parameters of this model are specific for the ADNI sample, we conclude that CSF Aβ42 can be transformed into PIBcalc measures of Aβ amyloid load. Brain Aβ amyloid load can be ascertained at baseline in therapeutic or observational studies by either CSF or amyloid PET imaging and the data can be pooled using well-established multiple imputation techniques that account for the uncertainty in a CSF-based PIBcalc value.
  Niklas Mattsson , Ulf Andreasson , Staffan Persson , Hiroyuki Arai , Sat Dev Batish , Sergio Bernardini , Luisella Bocchio- Chiavetto , Marinus A. Blankenstein , Maria C. Carrillo , Sonia Chalbot , Els Coart , Davide Chiasserini , Neal Cutler , Gunilla Dahlfors , Stefan Duller , Anne M. Fagan , Orestes Forlenza , Giovanni B. Frisoni , Douglas Galasko , Daniela Galimberti , Harald Hampel , Aase Handberg , Michael T. Heneka , Adrianna Z. Herskovits , Sanna-Kaisa Herukka , David M. Holtzman , Christian Humpel , Bradley T. Hyman , Khalid Iqbal , Khalid Iqbal , Stephan A. Kaeser , Elmar Kaiser , Elisabeth Kapaki , Daniel Kidd , Peter Klivenyi , Cindy S. Knudsen , Markus P. Kummer , James Lui , Albert Llado , Piotr Lewczuk , Qiao-Xin Li , Ralph Martins , Colin Masters , John McAuliffe , Marc Mercken , Abhay Moghekar , Jose Luis Molinuevo , Thomas J. Montine , William Nowatzke , Richard O’Brien , Markus Otto , George P. Paraskevas , Lucilla Parnetti , Ronald C. Petersen , David Prvulovic , Herman P.M. de Reus , Robert A. Rissman , Elio Scarpini , Alessandro Stefani , Hilkka Soininen , Johannes Schroder , Leslie M. Shaw , Anders Skinningsrud , Brith Skrogstad and Annette Spreer
  Background The cerebrospinal fluid (CSF) biomarkers amyloid β (Aβ)-42, total-tau (T-tau), and phosphorylated-tau (P-tau) demonstrate good diagnostic accuracy for Alzheimer‘s disease (AD). However, there are large variations in biomarker measurements between studies, and between and within laboratories. The Alzheimer‘s Association has initiated a global quality control program to estimate and monitor variability of measurements, quantify batch-to-batch assay variations, and identify sources of variability. In this article, we present the results from the first two rounds of the program. Methods The program is open for laboratories using commercially available kits for Aβ, T-tau, or P-tau. CSF samples (aliquots of pooled CSF) are sent for analysis several times a year from the Clinical Neurochemistry Laboratory at the Molndal campus of the University of Gothenburg, Sweden. Each round consists of three quality control samples. Results Forty laboratories participated. Twenty-six used INNOTEST enzyme-linked immunosorbent assay kits, 14 used Luminex xMAP with the INNO-BIA AlzBio3 kit (both measure Aβ-(1-42), P-tau(181P), and T-tau), and 5 used Meso Scale Discovery with the Aβ triplex (AβN-42, AβN-40, and AβN-38) or T-tau kits. The total coefficients of variation between the laboratories were 13% to 36%. Five laboratories analyzed the samples six times on different occasions. Within-laboratory precisions differed considerably between biomarkers within individual laboratories. Conclusions Measurements of CSF AD biomarkers show large between-laboratory variability, likely caused by factors related to analytical procedures and the analytical kits. Standardization of laboratory procedures and efforts by kit vendors to increase kit performance might lower variability, and will likely increase the usefulness of CSF AD biomarkers.
  Hugo Vanderstichele , Mirko Bibl , Sebastiaan Engelborghs , Nathalie Le Bastard , Piotr Lewczuk , Jose Luis Molinuevo , Lucilla Parnetti , Armand Perret- Liaudet , Leslie M. Shaw , Charlotte Teunissen , Dirk Wouters and Kaj Blennow
  Background Numerous studies show that the cerebrospinal fluid biomarkers total tau (T-tau), tau phosphorylated at threonine 181 (P-tau181P), and amyloid-β (1–42) (Aβ1–42) have high diagnostic accuracy for Alzheimer‘s disease. Variability in concentrations for Aβ1–42, T-tau, and P-tau181P drives the need for standardization. Methods Key issues were identified and discussed before the first meeting of the members of the Alzheimer‘s Biomarkers Standardization Initiative (ABSI). Subsequent ABSI consensus meetings focused on preanalytical issues. Results Consensus was reached on preanalytical issues such as the effects of fasting, different tube types, centrifugation, time and temperature before storage, storage temperature, repeated freeze/thaw cycles, and length of storage on concentrations of Aβ1–42, T-tau, and P-tau181P in cerebrospinal fluid. Conclusions The consensus reached on preanalytical issues and the recommendations put forward during the ABSI consensus meetings are presented in this paper.
  Michal J. Figurski , Teresa Waligorska , Jon Toledo , Hugo Vanderstichele , Magdalena Korecka , Virginia M.Y. Lee , John Q. Trojanowski and Leslie M. Shaw
  Background The interassay variability and inconsistency of plasma β-amyloid (Aβ) measurements among centers are major factors precluding the interpretation of results and a substantial obstacle in the meta-analysis across studies of this biomarker. The goal of this investigation was to address these problems by improving the performance of the bioanalytical method. Methods We used the Luminex immunoassay platform with a multiplex microsphere-based reagent kit from Innogenetics. A robotic pipetting system was used to perform crucial steps of the procedure. The performance of this method was evaluated using two kit control samples and two quality control plasma samples from volunteer donors, and by retesting previously assayed patient samples in each run. This setup was applied to process 2454 patient plasma samples from the Alzheimer‘s Disease Neuroimaging Initiative study biofluid repository. We have additionally evaluated the correlations between our results and cerebrospinal fluid (CSF) biomarker data using mixed-effects modeling. Results The average precision values of the kit controls were 8.3% for Aβ1-40 and 4.0% for Aβ1-42, whereas the values for the plasma quality controls were 6.4% for Aβ1-40 and 4.8% for Aβ1-42. From the test–retest evaluation, the average precision was 7.2% for Aβ1-40 and 4.5% for Aβ1-42. The range of final plasma results for Alzheimer‘s Disease Neuroimaging Initiative patients was 13 to 372 pg/mL (median: 164 pg/mL) for Aβ1-40 and 3.5 to 103 pg/mL (median: 39.3 pg/mL) for Aβ1-42. We found that sample collection parameters (blood volume and time to freeze) have a small, but significant, influence on the result. No significant difference was found between plasma Aβ levels for patients with Alzheimer‘s disease and healthy control subjects. We have determined multiple significant correlations of plasma Aβ1-42 levels with CSF biomarkers. The relatively strongest, although modest, correlation was found between plasma Aβ1-42 levels and CSF p-tau181/Aβ1-42 ratio in patients with mild cognitive impairment. Plasma Aβ1-40 correlations with CSF biomarkers were weaker and diminished completely when we used longitudinal data. No significant correlations were found for the plasma Aβ1-42/Aβ1-40 ratio. Conclusions The precision of our robotized method represents a substantial improvement over results reported in the literature. Multiple significant correlations between plasma and CSF biomarkers were found. Although these correlations are not strong enough to support the use of plasma Aβ measurement as a diagnostic screening test, plasma Aβ1-42 levels are well suited for use as a pharmacodynamic marker.
  Jon B. Toledo , Estefania Toledo , Michael W. Weiner , Clifford R. Jack , William Jagust , Virginia M.-Y. Lee , Leslie M. Shaw and John Q. Trojanowski
  Background There is epidemiological evidence that cardiovascular risk factors (CVRF) also are risk factors for Alzheimer‘s disease, but there is limited information on this from neuropathological studies, and even less from in vivo studies. Therefore, we examined the relationship between CVRF and amyloid-β (Aβ) brain burden measured by Pittsburgh Compound B-positron emission tomography (PiB-PET) studies in the Alzheimer‘s Disease Neuroimaging Initiative. Methods Ninety-nine subjects from the Alzheimer‘s Disease Neuroimaging Initiative cohort who had a PiB-PET study measure, apolipoprotein E genotyping data, and information available on CVRF (body mass index [BMI], systolic blood pressure, diastolic blood pressure [DBP], and cholesterol and fasting glucose test results) were included. Eighty-one subjects also had plasma cortisol, C-reactive protein, and superoxide dismutase 1 measurements. Stepwise regression models were used to assess the relation between the CVRF and the composite PiB-PET score. Results The first model included the following as baseline variables: age, clinical diagnosis, number of apolipoprotein ɛ4 alleles, BMI (P = .023), and DBP (P = .012). BMI showed an inverse relation with PiB-PET score, and DBP had a positive relation with PiB-PET score. In the second adjusted model, cortisol plasma levels were also associated with PiB-PET score (P = .004). Systolic blood pressure, cholesterol, or impaired fasting glucose were not found to be associated with PiB-PET values. Conclusion In this cross-sectional study, we found an association between Aβ brain burden measured in vivo and DBP and cortisol, indicating a possible link between these CVRF and Aβ burden measured by PiB-PET. These findings highlight the utility of biomarkers to explore potential pathways linking diverse Alzheimer‘s disease risk factors.
  Maria C. Carrillo , Kaj Blennow , Holly Soares , Piotr Lewczuk , Niklas Mattsson , Pankaj Oberoi , Robert Umek , Manu Vandijck , Salvatore Salamone , Tobias Bittner , Leslie M. Shaw , Diane Stephenson , Lisa Bain and Henrik Zetterberg
  Recognizing that international collaboration is critical for the acceleration of biomarker standardization efforts and the efficient development of improved diagnosis and therapy, the Alzheimer's Association created the Global Biomarkers Standardization Consortium (GBSC) in 2010. The consortium brings together representatives of academic centers, industry, and the regulatory community with the common goal of developing internationally accepted common reference standards and reference methods for the assessment of cerebrospinal fluid (CSF) amyloid β42 (Aβ42) and tau biomarkers. Such standards are essential to ensure that analytical measurements are reproducible and consistent across multiple laboratories and across multiple kit manufacturers. Analytical harmonization for CSF Aβ42 and tau will help reduce confusion in the AD community regarding the absolute values associated with the clinical interpretation of CSF biomarker results and enable worldwide comparison of CSF biomarker results across AD clinical studies.
  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.
 
 
 
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