Abstract
Allostatic load, or the physiological dysregulation accumulated due to senescence and stress, is an established predictor of human morbidity and mortality and has been proposed as a tool for monitoring health and welfare in captive wildlife. It is estimated by combining biomarkers from multiple somatic systems into allostatic load indices (ALIs), providing a score representing overall physiological dysregulation. Such ALIs have been shown to predict disease and mortality risk in western lowland gorillas. In these prior analyses, we were unable to include lipid markers, a potential limitation as they are key biomarkers in human models. Recently, we were able to assay serum cholesterol and triglycerides and add them to our previous ALI. We then re-examined associations with health outcomes using binomial generalized linear models. We constructed ALIs using 2 pooling strategies and 2 methods. By itself, a 1-unit increase in allostatic load was associated with higher odds of all-cause morbidity and mortality, but results were mixed for cardiac disease. However, the best fit models for all-cause morbidity and cardiac disease included only age and sex. Allostatic load was retained alongside age in the best fit models for mortality, with a 1-unit increase associated with 23% to 45% higher odds of death. Compared with previous results, ALIs containing cholesterol and triglycerides better predict disease risk in zoo-housed western lowland gorillas, as evidenced by larger effect sizes for some models and better goodness of fit for all ALIs. Based on these results, we address methodology for future allostatic load research on wildlife.