A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort

Abstract

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 sources of variation: (A) deterministic interactions between biomarkers, (B) stochastic drive that can either correlate or decorrelate different biomarkers, and (C) an observation noise which combines measurement error and very rapid fluctuations in the dolphin's biomarkers. Importantly, the stochastically-driven correlations (type-B) are large in magnitude, often comparable to the obervation errors (type-C) and larger than the deterministic 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 sources of variation, we demonstrate that the dolphins exhibit several significant deterministic interactions (type-A), as well as many significant stochastically-driven correlations (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.