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Articles by Magdalena Korecka
Total Records ( 2 ) for Magdalena Korecka
  John Q. Trojanowski , Hugo Vandeerstichele , Magdalena Korecka , Christopher M. Clark , Paul S. Aisen , Ronald C. Petersen , Kaj Blennow , Holly Soares , Adam Simon , Piotr Lewczuk , Robert Dean , Eric Siemers , William Z. Potter , Michael W. Weiner , Clifford R. Jack Jr. , William Jagust , Arthur W. Toga , Virginia M.-Y. Lee and Leslie M. Shaw
  Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1-42 and total tau (T-tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F-18 fluorodeoxyglucose-positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.
  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.
 
 
 
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