Additional file 1: Supplementary Methods. of Alzheimer disease pathology and the cerebrospinal fluid proteome

Table S1. Demographics and clinical characteristics of subjects removed from the statistical analyses. Table S2. Non-AD versus AD CSF biomarker profile group comparison after selection in all subjects of 26 proteins with LASSO. Table S3. Non-AD versus AD CSF biomarker profile group comparison after selection in subjects with cognitive impairment of 18 proteins with LASSO. Table S4. Correlation of CSF proteins with CSF Aβ1-42. Table S5. Correlation of CSF proteins with CSF tau. Table S6. Correlation of CSF proteins with CSF P-tau181. Table S7. Group comparisons of CSF protein measurements for AD versus non-AD CSF biomarker profiles in all subjects. Table S8. Group comparisons of CSF protein measurements for AD versus non-AD CSF biomarker profiles in subjects with cognitive impairment. Figure S1. Box-plots of CSF proteins (selected with LASSO analyses) for positive and negative CSF profiles of AD pathology in all subjects and subjects with cognitive impairment. Figure S2. Pairwise correlation heatmap of the 26 CSF proteins selected with LASSO for classification of non-AD versus AD CSF biomarker profiles for all subjects. Figure S3. Pairwise correlation heatmap of the 18 CSF proteins selected with LASSO for classification of non-AD versus AD CSF biomarker profiles for subjects with cognitive impairment. Figure S4. Correlations of CSF neurogranin and neuromodulin with CSF tau and P-tau181. Figure S5. Chord diagram of the relationships of 59 CSF proteins with CSF tau, P-tau181, and/or Aβ1-42. Figure S6. Venn diagrams of CSF proteins with significant group comparison differences between AD versus non-AD CSF biomarker profiles and those correlating with CSF Aβ1-42, tau, and P-tau181. Figure S7. Venn diagrams of CSF proteins selected with LASSO to classify non-AD versus AD CSF biomarker profiles and those correlating with CSF Aβ1-42, tau, and P-tau181. (DOCX 2575 kb

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 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