Presence of Brain Amyloidosis Can Be Ascertained from Cognitive, Imaging and Peripheral Blood Protein Measures (IN3-1.004)

OBJECTIVE: We investigated the accuracy of a novel unsupervised multimodal biomarker classifier for predicting brain amyloidosis in MCI subjects. By combining the predictive information of cognitive, imaging and peripheral blood protein biomarkers, we expected to achieve high accuracy in differentiating Pittsburgh compound B (PIB) positive (SUVR>1.6) from PIB negative (SUVR BACKGROUND: AD prevention trials will enroll cognitively normal amyloid PET-positive individuals. Amyloid PET screening is expensive. Being able to determine one9s likelihood of being amyloid-positive using clinical and blood biomarkers could save time and resources. DESIGN/METHODS: We used cognitive, imaging and proteomic data of 60 ADNI MCI participants (mean age=75.2±8.1, mean MMSE=27.2±2.2). Our automated support vector machine classifier was provided age, sex, education, MMSE, Trails B, AVLT delayed recall, plasma levels of ApoE, BDNF, TNFalpha, IL13, IL6 and clusterin, hippocampal volume and ApoE genotype. Amyloid positivity was defined as mean cortical, lateral parietal and precuneal SUVR≥1.6. Classifier performance was also tested in ApoE4-positive and negative subjects. RESULTS: The non-ApoE-stratified classifiers consistently ranked ApoE genotype as the most powerful predictor of brain amyloidosis. Greatest classifier accuracy was seen for the lateral parietal (AUC=0.87) and precuneal regions (AUC=0.89). The variables selected by these classifiers included ApoE genotype, MMSE, Trails B, AVLT delayed recall, clusterin, TNF alpha and BDNF. The precuneal classifier also included IL13 and ApoE protein. In the ApoE4-stratified analyses the classifier achieved highest accuracy for parietal PIB positivity in both ApoE+ (AUC=1) and ApoE- subjects (AUC=0.9). In addition to MMSE and Trails B, CLU and BDNF were selected in the Apo4+ and AVLT, CLU and TNFalpha were selected in the ApoE4- classifier. CONCLUSIONS: Automated classifiers based on cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with high accuracy. Such methods could have implications for clinical trial design and enrollment in the near future. Supported by: Easton Consortium for Alzheimer9s Drug Discovery and Biomarker Development, NIA R01 AG040770, NIA P50 AG1657 and NIA U01AG024904. Algorithm development was also supported, in part, by NIMH R01 MH097268 and NIA R01 AG040060. Disclosure: Dr. Apostolova has received personal compensation for activities with Eli Lilly & Company as a speaker. Dr. Apostolova has received compensation from CODA. Dr. Hwang has nothing to disclose. Dr. Kohannim has nothing to disclose. Dr. Jack has received personal compensation for activities with Janssen, Eisai Inc., General Electric, Johnson & Johnson, and Eli Lilly & Company. Dr. Jack has received research support from Pfizer Inc, Allon, and Baxter. Dr. Jack has received research support from Allon and Baxter. Dr. Shaw has received personal compensation for activities with Innogenetics/Fujirebio, and Janssen as a consultant and from Saladax for serving on the advisory board. Dr. Trojanowski has received personal compensation for activities with Pfizer, Johnson & Johnson, MetLife, and Bristol-Myers Squibb Company. Dr. Trojanowski has received royalty payments from PENN licenses. Dr. Trojanowski has received research support from AstraZeneca Pharmaceuticals, Inc., and Bristol-Myers Squibb Company. Dr Weiner is engaged in consulting activities for Astra Zeneca, Araclon, Medivation/Pfizer, Ipsen, TauRx Therapeutics LTD, Bayer Healthcare, Biogen Idec, Exonhit Therapeutics, Servier, Synarc, Pfizer, Janssen, Harvard University and KLJ Associates. Dr. We....Dr. Weiner owns stock options in Synarc and Elan.Dr. Weiner has received research support from Merck & Co., Inc. and Avid Radiopharmaceuticals. Dr. Thompson has nothing to disclose.