Betty M Tijms1, Eline A J Willemse2, Marissa D Zwan3, Sandra D Mulder3, Pieter Jelle Visser3,4, Bart N M van Berckel5, Wiesje M van der Flier3,6, Philip Scheltens3, Charlotte E Teunissen2. 1. Alzheimer Center and Department of Neurology, VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands; b.tijms@vumc.nl. 2. Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands. 3. Alzheimer Center and Department of Neurology, VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands. 4. Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands. 5. Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, VU University Medical Center, Amsterdam, the Netherlands. 6. Department of Epidemiology and Biostatistics, VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.
Abstract
BACKGROUND: Low cerebrospinal fluid (CSF) amyloid-β 1-42 (Aβ 1-42) concentrations indicate amyloid plaque accumulation in the brain, a pathological hallmark of Alzheimer disease (AD). Innotest assay values of Aβ 1-42 have gradually increased over the past 2 decades, which might lead to misclassification of AD when a single cutpoint for abnormality is used. We propose an unbiased approach to statistically correct for drift. METHODS: We determined year-specific cutpoints with Gaussian mixture modeling, based on the cross-section of bimodal distributions of Aβ 1-42 concentrations in 4397 memory clinic patients. This allowed us to realign year-specific cutpoints as an unbiased method to remove drift from the data. Sensitivity and specificity to detect AD dementia were compared between corrected and uncorrected values. RESULTS: Aβ 1-42 values increased 22 pg/mL annually, and this could not be explained by changes in cohort composition. Our approach removed time dependencies [β (SE) = 0.07 (0.59); P = 0.91]. Statistically correcting for drift improved the sensitivity to detect AD dementia to 0.90 (95% CI, 0.89-0.92) from at least 0.66 (95% CI, 0.64-0.69) based on uncorrected data. Specificity became lower (0.69; 95% CI, 0.67-0.70) vs at most 0.80 (95% CI, 0.79-0.82) for uncorrected data. CONCLUSIONS: This approach may also be useful to standardize Aβ 1-42 CSF concentrations across different centers and/or platforms, and to optimize use of CSF biomarker data collected over a long period.
BACKGROUND: Low cerebrospinal fluid (CSF) amyloid-β 1-42 (Aβ 1-42) concentrations indicate amyloid plaque accumulation in the brain, a pathological hallmark of Alzheimer disease (AD). Innotest assay values of Aβ 1-42 have gradually increased over the past 2 decades, which might lead to misclassification of AD when a single cutpoint for abnormality is used. We propose an unbiased approach to statistically correct for drift. METHODS: We determined year-specific cutpoints with Gaussian mixture modeling, based on the cross-section of bimodal distributions of Aβ 1-42 concentrations in 4397 memory clinic patients. This allowed us to realign year-specific cutpoints as an unbiased method to remove drift from the data. Sensitivity and specificity to detect AD dementia were compared between corrected and uncorrected values. RESULTS: Aβ 1-42 values increased 22 pg/mL annually, and this could not be explained by changes in cohort composition. Our approach removed time dependencies [β (SE) = 0.07 (0.59); P = 0.91]. Statistically correcting for drift improved the sensitivity to detect AD dementia to 0.90 (95% CI, 0.89-0.92) from at least 0.66 (95% CI, 0.64-0.69) based on uncorrected data. Specificity became lower (0.69; 95% CI, 0.67-0.70) vs at most 0.80 (95% CI, 0.79-0.82) for uncorrected data. CONCLUSIONS: This approach may also be useful to standardize Aβ 1-42 CSF concentrations across different centers and/or platforms, and to optimize use of CSF biomarker data collected over a long period.
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