Allostatic load is the wear-and-tear organisms accumulate due to senescence and stress; it is measured by combining biomarkers from multiple somatic systems into allostatic load indices (ALIs). Frequently used in human research, ALIs have shown consistent results across samples despite different biomarkers and methods. However, determining optimal models likely is necessary if ALIs are to be feasible research tools in other species. Herein, we build on prior research in western lowland gorillas to explore one potential method for determining which biomarkers may be best for estimating allostatic load. After narrowing down which biomarkers to include using a combination of forward stepwise regression and independent biomarker associations with project variables, we estimated allostatic load using both the traditional one-tailed quartile method as well as a multi-method approach. There was a significant positive association between allostatic load and triglycerides, but not cholesterol, both of which are commonly used as diagnostic markers of poor health. Using binomial generalized linear models, a one-unit increase in allostatic load was associated with increased risk of all-cause morbidity and mortality, but reduced risk of cardiac disease. Although conclusions were similar, compared to our original ALIs, these new ALIs had weaker effect sizes and poorer relative goodness of fit, suggesting this method for identifying the best possible list of biomarkers to include in an index was not effective. This report continues the development of ALIs as a clinical tool in wildlife while systematically testing one possible method for determining an optimal ALI for a particular species.
Keywords: Great apes; allostatic overload; cardiovascular disease; chronic conditions; chronic disease; nonhuman primates.