Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer’s dementia: a comparison of methods
for the Alzheimer Disease Neuroimaging Initiative, A Sörensen, Ganna Blazhenets, Florian Schiller, Philipp T. Meyer, Lars Frings
Abstract
Abstract Background Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer’s dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power. Methods Datasets were retrieved from the ADNI database. In a training dataset ( N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset ( N = 129), the predictive value of mean normalized 18 F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aβ 42 /Aβ 40 as predictors were compared by Cox models (corrected for age and sex). Results All four Aβ measures were significant predictors ( p < 0.001). Prediction accuracies (Harrell’s c ) showed step-wise significant increases from Cox-SUVR ( c = 0.71; HR = 1.84 per Z -score increase), composite SUVR ( c = 0.73; HR = 2.18), CSF Aβ 42 /Aβ 40 ( c = 0.75; HR = 3.89) to PES ( c = 0.77; HR = 2.71). Conclusion The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF Aβ 42 /Aβ 40 , with a slight but statistically significant advantage.