LipidClock: A Lipid-Based Predictor of Biological Age - Unfried, Maximilian; Ng, Li Fang; Cazenave-Gassiot, Amaury; Batchu, Krishna Chaithanya; Kennedy, Brian K.; Wenk, Markus R.; Tolwinski, Nicholas; Gruber, Jan (Frontiers in Aging, 2022)
Complexity is a fundamental feature of biological systems. Omics techniques like lipidomics can simultaneously quantify many thousands of molecules, thereby directly capturing the underlying biological complexity. However, this approach transfers the original biological complexity to the resulting datasets, posing challenges in data reduction and analysis. Aging is a prime example of a process that exhibits complex behaviour across multiple scales of biological organisation. The aging process is characterised by slow, cumulative and detrimental changes that are driven by intrinsic biological stochasticity and mediated through non-linear interactions and feedback within and between these levels of organization (ranging from metabolites, macromolecules, organelles and cells to tissue and organs). Only collectively and over long timeframes do these changes manifest as the exponential increases in morbidity and mortality that define biological aging, making aging a problem more difficult to study than the aetiologies of specific diseases. But aging’s time dependence can also be exploited to extract key insights into its underlying biology. Here we explore this idea by using data on changes in lipid composition across the lifespan of an organism to construct and test “lipid-clocks” for model organisms.