Comparing machine learning‐derived MRI‐based and blood‐based neurodegeneration biomarkers in predicting syndromal conversion in early AD
Yuan Cai, Xiang Fan, Lei Zhao, Wanting Liu, Yishan Luo, Alexander Yuk Lun Lau, Lisa Au, Lin Shi, Bonnie Lam, Ho Ko, Vincent Mok
Abstract
Abstract Introduction We compared the machine learning‐derived, MRI‐based Alzheimer's disease (AD) resemblance atrophy index (AD‐RAI) with plasma neurofilament light chain (NfL) level in predicting conversion of early AD among cognitively unimpaired (CU) and mild cognitive impairment (MCI) subjects. Methods We recruited participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had the following data: clinical features (age, gender, education, Montreal Cognitive Assessment [MoCA]), structural MRI, plasma biomarkers (p‐tau 181 , NfL), cerebrospinal fluid biomarkers (CSF) (Aβ42, p‐tau 181 ), and apolipoprotein E (APOE) ε4 genotype. We defined AD using CSF Aβ42 (A+) and p‐tau 181 (T+). We defined conversion (C+) if a subject progressed to the next syndromal stage within 4 years. Results Of 589 participants, 96 (16.3%) were A+T+C+. AD‐RAI performed better than plasma NfL when added on top of clinical features, plasma p‐tau 181 , and APOE ε4 genotype (area under the curve [AUC] = 0.832 vs. AUC = 0.650 among CU, AUC = 0.853 vs. AUC = 0.805 among MCI) in predicting A+T+C+. Discussion AD‐RAI outperformed plasma NfL in predicting syndromal conversion of early AD. Highlights AD‐RAI outperformed plasma NfL in predicting syndromal conversion among early AD. AD‐RAI showed better metrics than volumetric hippocampal measures in predicting syndromal conversion. Combining clinical features, plasma p‐tau 181 and apolipoprotein E (APOE) with AD‐RAI is the best model for predicting syndromal conversion.