Edes, A. N., Brown, J. L., Edwards, K. L. 2023. Evaluating individual biomarkers for predicting health risks in zoo-housed chimpanzees (Pan troglodytes) and bonobos (Pan paniscus). American Journal of Primatology 85(3), e23457.

Although biomarkers are often used for predicting morbidity and mortality in humans, similar data are lacking in our closest relatives. This study analyzed 16 biomarkers in zoo-housed chimpanzees and bonobos from serum samples collected during both routine and nonroutine veterinary immobilizations. Generalized linear and generalized linear mixed models were used to determine the efficacy of each biomarker to predict all-cause morbidity, defined as the presence of at least one chronic condition, or cardiac disease as a subset of all-cause morbidity. Cox proportional hazards models were used to examine associations between biomarkers and mortality risk from any cause. Analyses were conducted using two data sets for each species, one with all values retained (chimpanzees: n = 148; bonobos: n = 33) and the other from samples collected during routine immobilizations only (chimpanzees: n = 95; bonobos: n = 23). Consistent results across both data sets in chimpanzees included associations of higher cortisol with all-cause morbidity risk, lower creatinine with cardiac disease risk, and higher creatinine with mortality risk, and in bonobos were increased cardiac disease risk with higher cortisol and lower dehydroepiandrosterone-sulfate, fructosamine, and triglycerides. However, there were some inconsistencies between data sets, such as tumor necrosis factor-α predicting mortality risk positively in chimpanzees when all values were retained, but negatively for routine values only. Despite the close evolutionary relationships between chimpanzees and bonobos, the only result observed in both species was a negative association between albumin and mortality risk in the all values retained data sets. Thus, data suggest some biomarkers may be useful predictors of future health outcomes, although a better understanding of both individual and species variation in biomarkers and their contribution to health risks is needed.