Measures and markers of biological aging: ‘a great clamoring … of fleeting significance’: An answer to W. Dean and R.F. Morgan, this volume, pp. 191–210
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Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks
2019, Cell SystemsCitation Excerpt :This approach provides an accessible conceptual framework and has been applied to a number of data modalities, from DNA methylation to physiological parameters (Borkan and Norris, 1980; Horvath, 2013; Levine, 2013; Nakamura and Miyao, 2007), but it reduces the comparison of complex biological states to the comparison of single numbers, which destroys information. Because it assumes that most, or even all, relevant age-dependent differences between individuals can be captured by a single dimension, the concept of a single biological age is itself probably fundamentally flawed (Costa and McCrae, 1988). Fortunately, reducing a network of phenotypes to a single biological age is unnecessary given modern analytics; fully utilizing this type of network model requires departing from reductionism, embracing complexity and the idea of emergent phenomena, and navigating high-dimensional analysis (Cohen, 2016), but the reward is a far more accurate picture of reality.
Epigenetic Biomarkers for Biological Age
2018, Epigenetics of Aging and Longevity: Translational Epigenetics vol 4Body weight, hormones and T cell subsets as predictors of life span in genetically heterogeneous mice
2004, Mechanisms of Ageing and DevelopmentBasic results for assessment of human ageing
1991, Archives of Gerontology and GeriatricsDe-standardising ageing? Shifting regimes of age measurement
2016, Ageing and SocietyRecent Advances in the Systems Biology of Aging
2018, Antioxidants and Redox Signaling