LAUSR

Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g., diet, socio-economic status, medication). Because bottlenose dolphins are long-lived mammals that exhibit several age-related phenomena similar to humans, we analyzed data from a well controlled 25-year longitudinal cohort of 144 dolphins. The data from this study has been reported on earlier, and consists of 44 clinically relevant biomarkers. This time-series data exhibits three starkly different influences: (A) directed interactions between biomarkers, (B) sources of biological variation that can either correlate or decorrelate different biomarkers, and (C) random observation-noise which combines measurement error and very rapid fluctuations in the dolphin’s biomarkers. Importantly, the sources of biological variation (type-B) are large in magnitude, often comparable to the observation errors (type-C) and larger than the effect of the directed interactions (type-A). Attempting to recover the type-A interactions without accounting for the type-B and type-C variation can result in an abundance of false-positives and false-negatives. Using a generalized regression which fits the longitudinal data with a linear model accounting for all three influences, we demonstrate that the dolphins exhibit many significant directed interactions (type-A), as well as strong correlated variation (type-B), between several pairs of biomarkers. Moreover, many of these interactions are associated with advanced age, suggesting that these interactions can be monitored and/or targeted to predict and potentially affect aging. 2 Author Summary The body is a very complicated system with many interacting components, the vast majority of which are practically impossible to measure. Furthermore, it is still not understood how many of the components that we can measure influence one another as the body ages. In this study we try and take a small step towards answering this question. We use longitudinal data from a carefully controlled cohort of dolphins to help us build a simple model of aging. While the longitudinal data we use does measure many important biomarkers, there are obviously a much larger number of biomarkers that haven’t been measured. Our simple model accounts for these ‘missing’ measurements by assuming that their accumulated effect is similar to a kind of ‘noise’ often used in the study of complicated dynamical systems. With this simple model we are able to find evidence of several significant interactions between these biomarkers. The interactions we find may also play a role in the aging of other long-lived mammals, and may be worth investigating further to better understand human aging.