Alzheimer’s disease (AD) is a devastating brain illness that slowly robs older adults of their memory and independence. We currently have no cures or disease modifying therapies and only temporary and minimally effective treatments for certain symptoms. Biomarkers obtained from blood or other body fluids may provide key insights into the underlying pathobiology of AD, before the illness becomes evident. Biomarker analysis of the preclinical phase may inform us regarding the relevant pathobiology and thereby suggest novel therapies that might mitigate or prevent AD.
One of the major hurdles to biomarker research is the complexity of the clinical pathophysiology and by extension, the density of the data. It is clear that multiple levels of clinical, lifestyle, environmental, and biological information, over time, must be considered for accurate and early detection of preclinical (asymptomatic) AD. A multimodal “big data” analytic approach will be required to manage these mutually informative, but distinct datasets, in developing a better understanding of AD, and other human conditions.
In this talk, Drs. Fiandaca and Mapstone will describe their work in preclinical AD biomarker development. They will give an overview of their recent findings related to metabolomic markers of AD and present the potential for integration of other relevant –omics, including proteomics, transcriptomics, genomics, and epigenomics for enhanced understanding of the underlying pathobiology. They will also present work on metabolomics of successful cognitive aging which will be important when considering approaches to primary prevention of AD. This talk hopes to present the unique opportunities and challenges associated with complex multidimensional datasets and the growing need for big data analytic approaches in medicine.