Brain amyloidosis ascertainment from cognitive, imaging, and peripheral blood protein measures

Apostolova, L. G.; Hwang, K. S.; Avila, D.; Elashoff, D.; Kohannim, O.; Teng, E.; Sokolow, S.; Jack, C. R.; Jagust, W. J.; Shaw, L.; Trojanowski, J. Q.; Weiner, M. W.; Thompson, P. M.; Stern, Yaakov; Alzheimer's Disease Neuroimaging Initiative

Background: The goal of this study was to identify a clinical biomarker signature of brain amyloidosis in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI1) mild cognitive impairment (MCI) cohort. Methods: We developed a multimodal biomarker classifier for predicting brain amyloidosis using cognitive, imaging, and peripheral blood protein ADNI1 MCI data. We used CSF b-amyloid 1–42 (Ab42) #192 pg/mL as proxy measure for Pittsburgh compound B (PiB)-PET standard uptake value ratio $1.5. We trained our classifier in the subcohort with CSF Ab42 but no PiB-PET data and tested its performance in the subcohort with PiB-PET but no CSF Ab42 data. We also examined the utility of our biomarker signature for predicting disease progression from MCI to Alzheimer dementia. Results: The CSF training classifier selected Mini-Mental State Examination, Trails B, Auditory Verbal Learning Test delayed recall, education, APOE genotype, interleukin 6 receptor, clusterin, and ApoE protein, and achieved leave-one-out accuracy of 85% (area under the curve [AUC] 5 0.8). The PiB testing classifier achieved an AUC of 0.72, and when classifier selftuning was allowed, AUC 5 0.74. The 36-month disease-progression classifier achieved AUC 5 0.75 and accuracy 5 71%. Conclusions: Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with a modest level of accuracy. Such methods could have implications for clinical trial design and enrollment in the near future.



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February 11, 2022