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Despite increasing evidence of the importance of metabolic dysregulation in AD, the underlying metabolic changes that may impact amyloid plaque formation are not understood, particularly for late\u2010onset AD. This study analyzed genome\u2010wide association studies (GWAS), transcriptomics, and proteomics data obtained from several data repositories to obtain differentially expressed (DE) multi\u2010omics elements in mouse models of AD. We characterized the metabolic modulation in these data sets using gene ontology, transcription factor, pathway, and cell\u2010type enrichment analyses. A predicted lipid signature was extracted from genome\u2010scale metabolic networks (GSMN) and subsequently validated in a lipidomic data set derived from cortical tissue of ABCA\u20107 null mice, a mouse model of one of the genes associated with late\u2010onset AD. Moreover, a metabolome\u2010wide association study (MWAS) was performed to further characterize the association between dysregulated lipid metabolism in human blood serum and genes associated with AD risk. We found 203 DE transcripts, 164 DE proteins, and 58 DE GWAS\u2010derived mouse orthologs associated with significantly enriched metabolic biological processes. Lipid and bioenergetic metabolic pathways were significantly over\u2010represented across the AD multi\u2010omics data sets. Microglia and astrocytes were significantly enriched in the lipid\u2010predominant AD\u2010metabolic transcriptome. We also extracted a predicted lipid signature that was validated and robustly modeled class separation in the ABCA7 mice cortical lipidome, with 11 of these lipid species exhibiting statistically significant modulations. MWAS revealed 298\u2009AD single nucleotide polymorphisms\u2010metabolite associations, of which 70% corresponded to lipid classes. These results support the importance of lipid metabolism dysregulation in AD and highlight the suitability of mapping AD multi\u2010omics data into GSMNs to identify metabolic alterations.\n                    <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                      <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/jnc15719-fig-0008-m.png\">\n                        <jats:alt-text>image<\/jats:alt-text>\n                      <\/jats:graphic>\n                    <\/jats:boxed-text>\n                  <\/jats:p>","DOI":"10.1111\/jnc.15719","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T10:18:25Z","timestamp":1667470705000},"page":"57-76","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Pathway\u2010based integration of 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