Novel circling SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data

Abstract Background Recent advances in deep learning have allowed multi‐omics data to be used to detect early stages of Alzheimer's disease (AD). The metabolome, as the end‐product of biological cascades including the genome, transcriptome, and proteome, may have informative elements that could serve as potential AD biomarkers. We report a novel two‐step deep learning method to detect AD and progression using lipidomics data. Method We used serum‐based cross‐sectional lipidome data with 781 lipids from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) including 216 cognitively normal (CN), 635 MCI, and 382 dementia (AD). Phenotype influence scores (PIS) was derived by deep learning‐based circling Sliding Window Association Test approach (Circling SWAT) (Fig. 1‐A), an extension of SWAT (Jo et al., 2022) with correlation heatmap and dendrogram analysis for omics data with minimal features. We used convolutional neural networks (CNN) to classify AD from CN based on top‐scoring metabolites. The AD classification model was used to predict conversion of MCI to AD within two years using 146 MCI‐convertors who developed AD and 190 MCI‐Stable who did not develop AD over two years from baseline. We removed two deDE lipids, deDE(18:2) and deDE(20:4), because their associations with AD were driven mainly by AD‐related medication. Result Phenotype influence scores (PIS) were calculated for each lipid using circling SWAT to determine their impacts on AD. The metabolites with the highest PIS were LPE(22:6), PC(39:5), and PC(15‐MHDA_22:6). When CNN was applied to the top 50 lipids by PIS, AD and CN classification accuracy was 73.6% (Fig. 1‐B), which was 5.1% higher than the accuracy obtained using only age, sex, and APOE ε4. For MCI conversion to AD, the AD/CN classifier accuracy was 69.9% or 8.8% higher than a model using only age, sex, and APOE ε4. Conclusion The circling SWAT method appears promising for the analysis of lipidome data as it was able to identify several AD‐related lipids and predict disease progression.