Cerebrospinal Biomarker Cut-off Methods Defined Only by Alzheimer’s Disease Predict More Precisely Conversions of Mild Cognitive Impairment
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Background and Purpose The cerebrospinal fluid (CSF) biomarkers play an important supportive role as diagnostic and predictive indicators of Alzheimer’s disease (AD). About 30% of controls in old age show abnormal values of CSF biomarkers and display a higher risk for AD compared with those showing normal values. The cut-off values are determined by their diagnostic accuracy. However, the current cut-off values may be less accurate, because controls include high-risk groups of AD. We sought to develop models of patients with AD, who are homogenous for CSF biomarkers. Methods We included participants who had CSF biomarker data in the Alzheimer’s Disease Neuroimaging Initiative database. We investigated the factors related to CSF biomarkers in patients with AD using linear mixed models. Using the factors, we developed models corresponding to CSF biomarkers to classify patients with mild cognitive impairment (MCI) into high risk and low risk and analyzed the conversion from MCI to AD using the Cox proportional hazards model. Results APOE ε4 status and age were significantly related to CSF Aβ1-42. CSF t-tau, APOE ε2 status and sex were significant factors. The CSF p-tau181 was associated with age and frequency of diagnosis. Accordingly, we modeled the three CSF biomarkers of AD. In MCI without APOE ε4, our models were better predictors of conversion. Conclusions We can interpret CSF biomarkers based on the models derived from the data obtained from patients with AD.
Abstract
Background and Purpose The cerebrospinal fluid (CSF) biomarkers play an important supportive role as diagnostic and predictive indicators of Alzheimer’s disease (AD). About 30% of controls in old age show abnormal values of CSF biomarkers and display a higher risk for AD compared with those showing normal values. The cut-off values are determined by their diagnostic accuracy. However, the current cut-off values may be less accurate, because controls include high-risk groups of AD. We sought to develop models of patients with AD, who are homogenous for CSF biomarkers. Methods We included participants who had CSF biomarker data in the Alzheimer’s Disease Neuroimaging Initiative database. We investigated the factors related to CSF biomarkers in patients with AD using linear mixed models. Using the factors, we developed models corresponding to CSF biomarkers to classify patients with mild cognitive impairment (MCI) into high risk and low risk and analyzed the conversion from MCI to AD using the Cox proportional hazards model. Results APOE ε4 status and age were significantly related to CSF Aβ1-42. CSF t-tau, APOE ε2 status and sex were significant factors. The CSF p-tau181 was associated with age and frequency of diagnosis. Accordingly, we modeled the three CSF biomarkers of AD. In MCI without APOE ε4, our models were better predictors of conversion. Conclusions We can interpret CSF biomarkers based on the models derived from the data obtained from patients with AD.
참고문헌 (16)
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