Alzheimer's disease progression model based on integrated biomarkers and clinical measures

Abstract

Aim:Biomarkers and image markers of Alzheimer's disease (AD), such as cerebrospinal fluid Aβ 42 and p-tau, are effective predictors of cognitive decline or dementia. The aim of this study was to integrate these markers with a disease progression model and to identify their abnormal ranges.Methods:The data of 395 participants, including 86 normal subjects, 108 early mild cognitive impairment (EMCI) subjects, 120 late mild cognitive impairment (LMCI) subjects, and 81 AD subjects were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. For the participants, baseline and long-term data on cerebrospinal fluid Aβ 42 and p-tau, hippocampal volume, and ADAS-cog were available. Various linear and nonlinear models were tested to determine the associations among the ratio of Aβ 42 to p-tau (the Ratio), hippocampal volume and ADAS-cog.Results:The most likely models for the Ratio, hippocampal volume, and ADAS-cog (logistic, E max, and linear models, respectively) were used to construct the final model. Baseline disease state had an impact on all the 3 endpoints (the Ratio, hippocampal volume, and ADAS-cog), while APOEε4 genotype and age only influence the Ratio and hippocampal volume.Conclusion:The Ratio can be used to identify the disease stage for an individual, and clinical measures integrated with the Ratio improve the accuracy of mild cognitive impairment (MCI) to AD conversion forecasting.