Tessandra Stewart1, Min Shi1, Aanchal Mehrotra1, Patrick Aro1, David Soltys1, Kathleen F Kerr2, Cyrus P Zabetian3,4, Elaine R Peskind5,6, Peggy Taylor7, Leslie M Shaw8, John Q Trojanowski9,10, Jing Zhang1. 1. Department of Pathology, University of Washington School of Medicine, Seattle, WA, USA. 2. Department of Biostatistics, University of Washington, Seattle, WA, USA. 3. Parkinson's Disease Research and Geriatric Research, Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA. 4. Department of Neurology, University of Washington School of Medicine, Seattle, WA, USA. 5. Veterans Affairs Northwest Network, Mental Illness Research Education and Clinical Center, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA. 6. Department of Psychiatry and Behavioral Science, University of Washington School of Medicine, Seattle, WA, USA. 7. BioLegend, Dedham, MA, USA. 8. Department of Pathology and Laboratory Medicine and Center for Neurodegenerative Disease Research, Institute on Aging, University of Pennsylvania, Philadelphia, PA, USA. 9. Center for Neurodegenerative Disease Research (CNDR), University of Pennsylvania School of Medicine, Philadelphia, PA, USA. 10. Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Abstract
BACKGROUND: Neurodegenerative diseases require characterization based on underlying biology using biochemical biomarkers. Mixed pathology complicates discovery of biomarkers and characterization of cohorts, but inclusion of greater numbers of patients with different, related diseases with frequently co-occurring pathology could allow better accuracy. Combining cohorts collected from different studies would be a more efficient use of resources than recruiting subjects from each population of interest for each study. OBJECTIVE: To explore the possibility of combining existing datasets by controlling pre-analytic variables in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI) studies. METHODS: Cerebrospinal fluid (CSF) was collected and processed from 30 subjects according to both the ADNI and PPMI protocols. Relationships between reported levels of Alzheimer's disease (AD) and Parkinson's disease (PD) biomarkers in the same subject under each protocol were examined. RESULTS: Protocol-related differences were observed for Aβ, but not t-tau or α-syn, and trended different for p-tau and pS129. Values of α-syn differed by platform. Conversion of α-syn values between ADNI and PPMI platforms did not completely eliminate differences in distribution. DISCUSSION: Factors not captured in the pre-analytical sample handling influence reported biomarker values. Assay standardization and better harmonized characterization of cohorts should be included in future studies of CSF biomarkers.
BACKGROUND:Neurodegenerative diseases require characterization based on underlying biology using biochemical biomarkers. Mixed pathology complicates discovery of biomarkers and characterization of cohorts, but inclusion of greater numbers of patients with different, related diseases with frequently co-occurring pathology could allow better accuracy. Combining cohorts collected from different studies would be a more efficient use of resources than recruiting subjects from each population of interest for each study. OBJECTIVE: To explore the possibility of combining existing datasets by controlling pre-analytic variables in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Parkinson's Progression Markers Initiative (PPMI) studies. METHODS:Cerebrospinal fluid (CSF) was collected and processed from 30 subjects according to both the ADNI and PPMI protocols. Relationships between reported levels of Alzheimer's disease (AD) and Parkinson's disease (PD) biomarkers in the same subject under each protocol were examined. RESULTS: Protocol-related differences were observed for Aβ, but not t-tau or α-syn, and trended different for p-tau and pS129. Values of α-syn differed by platform. Conversion of α-syn values between ADNI and PPMI platforms did not completely eliminate differences in distribution. DISCUSSION: Factors not captured in the pre-analytical sample handling influence reported biomarker values. Assay standardization and better harmonized characterization of cohorts should be included in future studies of CSF biomarkers.
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