Androgen responsiveness to competition in humans: the role of cognitive variables

Although androgens are commonly seen as male sex hormones, it has been established over the years that in both sexes, androgens also respond to social challenges. To explain the socially driven changes in androgens, two theoretical models have been proposed: the biosocial model and the challenge hypothesis. These models are typically seen as partly overlapping; however, they generate different predictions that are clarified here. In humans, sports competition and nonmetabolic competitive tasks have been used in the laboratory setting, as a proxy for agonistic interactions in animals. The results reviewed here show that the testosterone (T) response to competition in humans is highly variable – the studies present postcompetition T levels and changes in T that depend on the contest outcome and that cannot be predicted by the current theoretical models. These conflicting results bring to the foreground the importance of considering cognitive factors that could moderate the androgen response to competition. Among these variables, we elect cognitive appraisal and its components as a key candidate modulating factor. It is known that T also modulates the cognitive processes that are relevant to performance in competition. In this article, we reviewed the evidence arising from studies investigating the effect of administering exogenous T and compare those results with the findings from studies that measured endogenous T levels. Finally, we summarized the importance of also considering the interaction between androgens and other hormones, such as cortisol, when investigating the social modulation of T, as proposed by the dual-hormone hypothesis.

Coreferentiality: A New Method for the Hypothesis-Based Analysis of Phenotypes Characterized by Multivariate Data

Many multifactorial biologic effects, particularly in the context of complex human diseases, are still poorly understood. At the same time, the systematic acquisition of multivariate data has become increasingly easy. The use of such data to analyze and model complex phenotypes, however, remains a challenge. Here, a new analytic approach is described, termed coreferentiality, together with an appropriate statistical test. Coreferentiality is the indirect relation of two variables of functional interest in respect to whether they parallel each other in their respective relatedness to multivariate reference data, which can be informative for a complex effect or phenotype. It is shown that the power of coreferentiality testing is comparable to multiple regression analysis, sufficient even when reference data are informative only to a relatively small extent of 2.5%, and clearly exceeding the power of simple bivariate correlation testing. Thus, coreferentiality testing uses the increased power of multivariate analysis, however, in order to address a more straightforward interpretable bivariate relatedness. Systematic application of this approach could substantially improve the analysis and modeling of complex phenotypes, particularly in the context of human study where addressing functional hypotheses by direct experimentation is often difficult.