Data_Sheet_1_Predicting AT(N) pathologies in Alzheimer’s disease from blood-based proteomic data using neural networks.pdf

Background and objectiveBlood-based biomarkers represent a promising approach to help identify early Alzheimer’s disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.MethodsWe measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein–protein interaction enrichment analysis.ResultsAge and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase–protein kinase B/Akt signaling pathway.ConclusionCombined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.</p

Additional file 1 of GOIZ ZAINDU study: a FINGER-like multidomain lifestyle intervention feasibility randomized trial to prevent dementia in Southern Europe

Additional file 1: Table 1S. Adherence degrees to each intervention component. Table 2S. Baseline cognitive performance per group. Table 3S. Post-intervention characteristics per groups. Table 4S. Effect of intervention in cognitive change between pre-intervention and post-intervention visits per group. Table 5S. Effect of intervention in cognitive change between pre-intervention and post-intervention visits per groups. Table 6S. Baseline demographic, CAIDE, and Cognition characteristics differences between good and bad adherence groups. Table 7S. Cognitive domain z scores at pre-intervention and post-intervention visits. Table 8S. Cognitive z scores at pre-intervention and post-intervention visits per group. Figure 1S. Number of participants in each adherence category

Additional file 3: of The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics

Table S3. Biomarker protocol information for each cohort. Protocol information for PET, CSF and plasma biomarker collection (DOCX 100 kb

Additional file 4: of The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics

Table S4. Ethical approval committee of each center. Ethical approval committees in each of the participating centers (DOCX 109 kb

Additional file 1: of The EMIF-AD Multimodal Biomarker Discovery study: design, methods and cohort characteristics

Table S1. Diagnostic criteria per cohort. Cohorts, countries, number of subjects and diagnostic criteria used for NC, MCI and AD dementia (DOCX 90 kb