Blood biomarkers in dynamic prediction of conversion to Alzheimer's disease: An application of joint modeling

Objectives To investigate the accuracy of longitudinal trajectories of blood biomarkers for predicting future onset of AD among MCI participants as well as to demonstrate dynamic prediction of the individual conversion risk applying joint modeling. Methods A total of 446 participants with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative database were included. We introduced joint modeling to analyze the effects of the longitudinal blood biomarkers on the conversion risk to AD, and further to build individual‐specific prediction risk model. Results During the follow‐up, 345 participants remained with MCI and 101 progressed to AD, and were categorized as non‐progression and progression group, respectively. Longitudinally, the positive association of the concentration dynamics of plasma p‐tau181 and NfL with the conversion risk to AD from MCI was also demonstrated, with Hazard Ratio (HR) = 5.83 and HR = 4.18, respectively. When incorporating plasma p‐tau181 and NfL together to predict AD progression, we observed improved performance (AUC = 0.701, Brier Score = 0.119). Two participants were chosen to exemplify the individual‐specific risk prediction at different follow‐up time for comparative analysis. Conclusions Plasma p‐tau181 and NfL could serve as biomarkers for the prediction of AD onset, and the individualized prediction opens up the possibility to provide clinical information at a personal level. Key points Dynamic risk prediction of AD from MCI applying joint modeling using blood biomarkers and easily accessible participant characteristics. Plasma p‐tau181 and NfL could serve as biomarkers for the prediction of AD onset in the primary care settings and yield significant public health benefit.