Abdallah M Eteleeb, Brenna C Novotny, Carolina Soriano Tarraga, Christopher Sohn, Eliza Dhungel, Logan Brase, Aasritha Nallapu, Jared Buss, Fabiana Farias, Kristy Bergmann, Joseph Bradley, Joanne Norton, Jen Gentsch, Fengxian Wang, Albert A Davis, John C Morris, Celeste M Karch, Richard J Perrin, Bruno A Benitez, Oscar Harari
Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles...
April 30, 2024: PLoS Biology