September 24th, Switzerland

Free Registration, Part of MipTec 2014, Congress Center Basel

Monday, November 16, 2015


Many strategies that can be pursued to test the efficacy of geroprotectors in humans, including large scale publicly-funded supplement studies and studies designed to address a specific set of biomarkers of aging (Hefti and Bales, 2006; Le Couteur et al., 2012; Scott and DeFrancesco, 2015).

Even though major advances have been made since the final ICD-10 meeting in tracking aging at all levels of organization (Sprott, 2010; Le Couteur et al., 2012; Hatse et al., 2014; Wu et al., 2015), there is no universal set of biomarkers and guidelines for measuring aging as a system. Gerontologists have previously struggled to extrapolate biomarkers from animal models to humans (Butler et al., 2004). But with the advent of Big Data, it is now possible to track aging on the epigenetic level and measure accelerated aging in many diseases (Horvath et al., 2014; Horvath and Levine, 2015). There are also promising studies of transcriptomic (Nakamura et al., 2012; Dhahbi, 2014), telomere-length (Zhang et al., 2014; Shamir, 2015) and multi-variate (Sanders et al., 2014) blood-based biomarkers that may lead to minimally invasive diagnostic tests. It is now also possible to use system-wide biomarkers like heart rate variability (HRV) as biomarkers of aging (Corino et al., 2007). There is a rapidly growing body of evidence that biomarkers of aging contribute to and are very similar to the many age-related diseases on all levels of organization, and it is possible to multiplex epigenetic, transcriptomic, proteomic, signalomic, metabolomic, metagenomic, and phenotypic biomarkers to track the progression of aging as a disease.

The paper is available via the Frontiers open access system:
Paper Citation: Zhavoronkov A and Bhullar B (2015) Classifying aging as a disease in the context of ICD-11.Front. Genet. 6:326. doi: 10.3389/fgene.2015.00326


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