Machine learning applications for novel biomarker & target discovery.
Aging
The "geroscience hypothesis" postulates that aging itself is a common underlying risk factor for susceptibility to various aging-related chronic diseases [1]. Life expectancy for the US population in 2022 was found to be 77.5 years, a 1.1-year increase from 2021, and the top causes of death were attributed to common aging-related chronic diseases, including heart disease, cancer, Alzheimer's, diabetes, and liver and kidney diseases, among others [2]. A recent study also estimated that a populational increase in life expectancy by a year is associated with $38 trillion in healthcare costs [3].
Delaying the aging process and promoting healthy aging is expected to reduce the incidence or severity of chronic diseases widely. Our goal is to elucidate the underlying mechanisms of aging, in order to develop novel biomarkers for monitoring aging processes and identify molecular targets to prevent or prolong the onset of chronic diseases. We are currently working with blood and brain, and aim to expand to other tissues.
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[1] Sierra F, Kohanski R. Geroscience and the trans-NIH Geroscience Interest Group, GSIG. Geroscience. 2017 Feb;39(1):1-5. doi: 10.1007/s11357-016-9954-6. PMID: 28299635; PMCID: PMC5352582.
[2] Xu J, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2021. NCHS Data Brief. 2022 Dec;(456):1-8. PMID: 36598387.
[3] Scott AJ, Ellison M, Sinclair DA. The economic value of targeting aging. Nat Aging. 2021 Jul;1(7):616-623. doi: 10.1038/s43587-021-00080-0. Epub 2021 Jul 5. PMID: 37117804; PMCID: PMC10154220.