About this Research Topic
In 2018, for the first time in human history, there were more elderly people over 65 than children under five years old. The United Nations (UN) predicts that by 2050, one in four people living in Europe or North America will be over the age of 65. This massive increase in the elderly proportion of the population is likely to create a health-care burden with elderly patients 75 years and older costing five times more than a patient aged 34-44 in the US. This has sparked a resurgence in the quest to discover, develop and validate aging interventions by both academic and industrial sectors. However, unlike other disease intervention strategies, the efficacy of an aging intervention is particularly difficult to measure.
In this Research Topic, we are excited to hear about new technologies or strategies that allow the screening of drug candidates for their potential as aging interventions. These technologies or strategies may include but are not limited to innovative screening strategies, high through-put screening platforms, the use of model organisms, and computational algorithms (i.e. machine learning-based) that help with candidate identification. We are particularly interested in approaches that compare strategies that have already been widely vetted such as measurement of telomere length or senescence markers with the relatively recent emergence of AI-based prediction systems, like the methylation clocks of Steve Horvath or even more sophisticated approaches making use of complex datasets. Finally, we invite researchers that have developed biomarker panels or similar technologies that measure human aging and that can readily be used to measure the impact of aging interventions in patients.
In this Research Topic, we are excited to hear about new technologies or strategies that allow the screening of drug candidates for their potential as aging interventions. These technologies or strategies may include but are not limited to innovative screening strategies, high through-put screening platforms, the use of model organisms, and computational algorithms (i.e. machine learning-based) that help with candidate identification. We are particularly interested in approaches that compare strategies that have already been widely vetted such as measurement of telomere length or senescence markers with the relatively recent emergence of AI-based prediction systems, like the methylation clocks of Steve Horvath or even more sophisticated approaches making use of complex datasets. Finally, we invite researchers that have developed biomarker panels or similar technologies that measure human aging and that can readily be used to measure the impact of aging interventions in patients.
Keywords: biomarkers, screening strategies, machine learning, aging, interventions
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