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Zhi-Ying Wu
Second Affiliated Hospital,Zhejiang University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2106-572X
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Background: National Institute on Aging—Alzheimer’s Association (NIA-AA) proposed the AT(N) system based on β-amyloid deposition, pathologic tau, and neurodegeneration, which considered the definition of Alzheimer’s disease (AD) as a biological construct. However, the associations between different AT(N) combinations and clinical stage and progression have been poorly explored systematically. The aim of this study is to compare different AT(N) combinations using recognized biomarkers within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
Methods: A total of 341 participants from ADNI cohort were classified into AT(N) groups, including 200 cognitively unimpaired (CU) participants and 141 cognitively impaired (CI) participants (101 mild cognitive impairment [MCI] and 40 Alzheimer’s disease [AD]). CSF Aβ42 and amyloid-PET ([18F]flutemetamol) were used as biomarkers for A; CSF phosphorylated tau (p-tau) and tau-PET ([18F]flortaucipir) were used as biomarkers for T; CSF total tau (t-tau), FDG-PET, hippocampal volume, temporal cortical thickness and plasma neurofilament light (NfL) were used as biomarkers for (N). Binarization of biomarkers was acquired from Youden index and public cutoffs. The relationship between different AT(N) biomarkers combinations and cognitive changes (longitudinal Mini-Mental State Examination scores and Clinical Dementia Rating Sum of Boxes) was examined using linear mixed modeling and coefficient of variation.
Results: Among CU participants, A−T−(N)− variants were most common. More T+ cases were shown using p-tau than tau PET, and more N+ cases were shown using fluid biomarkers than neuroimaging. Among CI participants, A+T+(N)+ was more common. Tau PET combined with cortical thickness best predicted longitudinal cognitive decline in CI and MRI measurements in CU participants.
Conclusion: These findings suggest that optimal combinations of biomarkers to determine AT(N) are differed by clinical stage. Different biomarkers within a specific component for defining AT(N) cannot be used identically. Furthermore, different strategies for discontinuous biomarkers will be an important area for the future studies.
Keywords
Alzheimer disease, biomarkers, AT(N) system, longitudinal cognition
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This is a list of supplementary files associated with this preprint. Click to download.
- SupplementFigure1.jpg
Supplement Figure 1. Prevalence of different AT(N) categories in different AT(N) variants between MCI and AD participants
- SupplementFigure1.jpg
Supplement Figure 1. Prevalence of different AT(N) categories in different AT(N) variants between MCI and AD participants
- Supplementarycontents.pdf
Supplement Table 1. Characteristic, cognition and AT(N) biomarkers of participants Supplement Table 2. Cutoffs derived from Youden index and 2 alternative strategies Supplement Table 3. Prevalence of different AT variants among 4 groups Supplement Table 4. Prevalence of different AT(N) variants among 4 groups Supplement Table 5. Linear-mixed effect model for longitudinal cognition using single AT(N) biomarkers Supplement Table 6. Linear-mixed effect model with interactions for longitudinal cognition in CI using single AT(N) biomarkers Supplement Table 7. Linear-mixed effect model with interactions for longitudinal cognition in CI using AT(N) variants Supplement Table 8. Sensitivity analysis for AT(N) prevalence using alternative cutoffs
- Supplementarycontents.pdf
Supplement Table 1. Characteristic, cognition and AT(N) biomarkers of participants Supplement Table 2. Cutoffs derived from Youden index and 2 alternative strategies Supplement Table 3. Prevalence of different AT variants among 4 groups Supplement Table 4. Prevalence of different AT(N) variants among 4 groups Supplement Table 5. Linear-mixed effect model for longitudinal cognition using single AT(N) biomarkers Supplement Table 6. Linear-mixed effect model with interactions for longitudinal cognition in CI using single AT(N) biomarkers Supplement Table 7. Linear-mixed effect model with interactions for longitudinal cognition in CI using AT(N) variants Supplement Table 8. Sensitivity analysis for AT(N) prevalence using alternative cutoffs
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Zhi-Ying Wu
Second Affiliated Hospital,Zhejiang University School of Medicine
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2106-572X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 03 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cognitive Neuroscience
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: National Institute on Aging—Alzheimer’s Association (NIA-AA) proposed the AT(N) system based on β-amyloid deposition, pathologic tau, and neurodegeneration, which considered the definition of Alzheimer’s disease (AD) as a biological construct. However, the associations between different AT(N) combinations and clinical stage and progression have been poorly explored systematically. The aim of this study is to compare different AT(N) combinations using recognized biomarkers within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
Methods: A total of 341 participants from ADNI cohort were classified into AT(N) groups, including 200 cognitively unimpaired (CU) participants and 141 cognitively impaired (CI) participants (101 mild cognitive impairment [MCI] and 40 Alzheimer’s disease [AD]). CSF Aβ42 and amyloid-PET ([18F]flutemetamol) were used as biomarkers for A; CSF phosphorylated tau (p-tau) and tau-PET ([18F]flortaucipir) were used as biomarkers for T; CSF total tau (t-tau), FDG-PET, hippocampal volume, temporal cortical thickness and plasma neurofilament light (NfL) were used as biomarkers for (N). Binarization of biomarkers was acquired from Youden index and public cutoffs. The relationship between different AT(N) biomarkers combinations and cognitive changes (longitudinal Mini-Mental State Examination scores and Clinical Dementia Rating Sum of Boxes) was examined using linear mixed modeling and coefficient of variation.
Results: Among CU participants, A−T−(N)− variants were most common. More T+ cases were shown using p-tau than tau PET, and more N+ cases were shown using fluid biomarkers than neuroimaging. Among CI participants, A+T+(N)+ was more common. Tau PET combined with cortical thickness best predicted longitudinal cognitive decline in CI and MRI measurements in CU participants.
Conclusion: These findings suggest that optimal combinations of biomarkers to determine AT(N) are differed by clinical stage. Different biomarkers within a specific component for defining AT(N) cannot be used identically. Furthermore, different strategies for discontinuous biomarkers will be an important area for the future studies.
Figure 1
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
Figure 4
Figure 5
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- SupplementFigure1.jpg
Supplement Figure 1. Prevalence of different AT(N) categories in different AT(N) variants between MCI and AD participants
- SupplementFigure1.jpg
Supplement Figure 1. Prevalence of different AT(N) categories in different AT(N) variants between MCI and AD participants
- Supplementarycontents.pdf
Supplement Table 1. Characteristic, cognition and AT(N) biomarkers of participants Supplement Table 2. Cutoffs derived from Youden index and 2 alternative strategies Supplement Table 3. Prevalence of different AT variants among 4 groups Supplement Table 4. Prevalence of different AT(N) variants among 4 groups Supplement Table 5. Linear-mixed effect model for longitudinal cognition using single AT(N) biomarkers Supplement Table 6. Linear-mixed effect model with interactions for longitudinal cognition in CI using single AT(N) biomarkers Supplement Table 7. Linear-mixed effect model with interactions for longitudinal cognition in CI using AT(N) variants Supplement Table 8. Sensitivity analysis for AT(N) prevalence using alternative cutoffs
- Supplementarycontents.pdf
Supplement Table 1. Characteristic, cognition and AT(N) biomarkers of participants Supplement Table 2. Cutoffs derived from Youden index and 2 alternative strategies Supplement Table 3. Prevalence of different AT variants among 4 groups Supplement Table 4. Prevalence of different AT(N) variants among 4 groups Supplement Table 5. Linear-mixed effect model for longitudinal cognition using single AT(N) biomarkers Supplement Table 6. Linear-mixed effect model with interactions for longitudinal cognition in CI using single AT(N) biomarkers Supplement Table 7. Linear-mixed effect model with interactions for longitudinal cognition in CI using AT(N) variants Supplement Table 8. Sensitivity analysis for AT(N) prevalence using alternative cutoffs
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