Despite intensive research, effective treatments for many common and devastating diseases are lacking. For example, huge efforts and billions of dollars have been invested in Alzheimer's disease (AD), which affects over 50 million people worldwide. However, there is still no effective drug that can slow or cure AD. This relates, in part, to the absence of an animal model or cellular system that incorporates all the relevant features of the disease. Therefore, large scale studies on human populations and tissues will be key to better understanding dementia and developing methods to prevent or treat it. This is especially difficult because the dementia phenotype can result from many different processes and is likely to be affected by multiple personal and environmental variables. We hypothesize that analyzing massive volumes of demographic data that are currently available and combining this with genomic, proteomic, and metabolomic profiles of AD patients and their families, new insights into pathophysiology and treatment of AD may arise. While this requires much coordination and cooperation among large institutions, the potential for advancement would be life-changing for millions of people. In many ways this represents the next step in the information revolution started by the Human Genome Project.
Citation: Mark M. Stecker, Morgan R. Peltier, Allison B. Reiss. The role of massive demographic databases in intractable illnesses: Denomics for dementia[J]. AIMS Public Health, 2022, 9(3): 618-629. doi: 10.3934/publichealth.2022043
Despite intensive research, effective treatments for many common and devastating diseases are lacking. For example, huge efforts and billions of dollars have been invested in Alzheimer's disease (AD), which affects over 50 million people worldwide. However, there is still no effective drug that can slow or cure AD. This relates, in part, to the absence of an animal model or cellular system that incorporates all the relevant features of the disease. Therefore, large scale studies on human populations and tissues will be key to better understanding dementia and developing methods to prevent or treat it. This is especially difficult because the dementia phenotype can result from many different processes and is likely to be affected by multiple personal and environmental variables. We hypothesize that analyzing massive volumes of demographic data that are currently available and combining this with genomic, proteomic, and metabolomic profiles of AD patients and their families, new insights into pathophysiology and treatment of AD may arise. While this requires much coordination and cooperation among large institutions, the potential for advancement would be life-changing for millions of people. In many ways this represents the next step in the information revolution started by the Human Genome Project.
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