Background: Best practice standards for measuring analyte levels in saliva recommend that all biospecimens be tested in replicate with mean concentrations used in statistical analyses. This approach prioritizes minimizing laboratory-based measurement error but, in the process, expends considerable resources. We explore the possibility that, due to advances in salivary assay precision, the contribution of laboratory-based measurement error in salivary analyte data is very small relative to more important and meaningful variability in analyte levels across biological replicates (i.e., between different specimens). To evaluate this possibility, we examine the utility of the repeatability intra-class correlation (rICC) as an additional index of salivary analyte data precision. Using randomly selected subsamples (Ns=200 and 60) of salivary analyte data collected as part of a larger epidemiologic study, we compute the rICCs for seven commonly assayed salivary measures in biobehavioral research – cortisol, alpha-amylase, c-reactive protein, interlekin-6, uric acid, secretory immunoglobulin A, and testosterone. We assess the sensitivity of rICC estimates to assay type and the unique distributions of the underlying analyte data. We also use simulations to examine the bias, precision, and coverage probability of rICC estimates calculated for small to large sample sizes. For each analyte, the rICCs revealed that less than 5% of variation in analyte levels was attributable to laboratory-based measurement error. rICC estimates were similar across all analytes despite differences in analyte levels, average intra-assay coefficients of variation, and in the distributional properties of the data. Guidelines for calculating rICC are provided to enable investigators and laboratory staff to apply this metric and more accurately quantify, and communicate, the magnitude of laboratory-based measurement error in their data. By helping investigators scale measurement error relative to more scientifically meaningful variability between biological replicates, the application of the rICC has the potential to influence research strategies and tactics such that resources (e.g., finances, effort, number/volume of biospecimens) are allocated more efficiently and effectively.
This study tests a biosocial model of the link between testosterone and proactive-reactive aggression in youth at varying levels of harsh discipline. Given that proactive aggression is used to gain power and status and the importance of social learning in its formation, we hypothesized that testosterone would be associated with proactive aggression at higher levels of harsh discipline, and that this relationship would be more pronounced in boys than girls. Participants (n = 445; 50% male; M age = 11.92 years; 80% African-American) and their caregivers completed questionnaires including demographics, conflict tactics, and proactive-reactive aggression. Youth also provided a saliva sample for testosterone. Analyses revealed an interaction between testosterone and harsh discipline on proactive aggression in both boys and girls, and an interaction between testosterone and harsh discipline on reactive aggression in boys only. For those experiencing high levels of harsh discipline, testosterone was positively associated with proactive aggression, with the magnitude of the association increasing as harsh discipline increased. For below average levels of harsh discipline, there were protective effects of high testosterone for boy’s reactive aggression and for girl’s proactive aggression. The findings support basic tenets of the biosocial model which suggest that links between testosterone and aggressive behavior are dependent on contextual forces, highlighting the complex relationship between hormones, social context, and aggression. Novel findings include protective effects of high testosterone for those exposed to low levels of harsh discipline. Findings are discussed in light of the context-contingency effect and also within the differential susceptibility framework.
We recently established daily, free-living profiles of the adrenal hormone cortisol, the (primarily adrenal) anabolic precursor dehydroepiandrosterone (DHEA) and the (primarily gonadal) anabolic hormone testosterone in elite military men. A prevailing view is that adrenal and gonadal systems reciprocally modulate each other; however, recent paradigm shifts prompted the characterization of these systems as parallel, cooperative processes (i.e. the “positive coupling” hypothesis). In this study, we tested the positive coupling hypothesis in 57 elite military men by evaluating associations between adrenal and gonadal biomarkers across the day. Salivary DHEA was moderately and positively coupled with salivary cortisol, as was salivary testosterone. Anabolic processes (i.e. salivary DHEA and testosterone) were also positively and reliably coupled across the day. In multivariate models, salivary DHEA and cortisol combined to account for substantial variance in salivary testosterone concentrations across the day, but this was driven almost exclusively by DHEA. This may reflect choreographed adrenal release of DHEA with testicular and/or adrenal release of testosterone, systemic conversion of DHEA to testosterone, or both. DHEA and testosterone modestly and less robustly predicted cortisol concentrations; this was confined to the morning, and testosterone was the primary predictor. Altogether, top-down co-activation of adrenal and gonadal hormone secretion may complement bottom-up counter-regulatory functions to foster anabolic balance and neuronal survival; hence, the “yin and yang” of adrenal and gonadal systems. This may be an adaptive process that is amplified by stress, competition, and/or dominance hierarchy.
We recently established stable daily profiles of the anabolic hormones dehydroepiandrosterone (DHEA) and testosterone in 57 elite military men. In this follow-on study, we explored associations of salivary anabolic hormone profiles with demographic (i.e., age, body mass index [BMI]) and biobehavioral health indices (i.e., blood pressure, sleep, perceived stress, fatigue) via correlational models. Next, nuanced patterns were constructed using quartile splits followed by one-way analysis of variance and post hoc subgroup comparisons. Both DHEA (r range: -0.33 to -0.49) and testosterone (r range: -0.19 to -0.41) were inversely associated with age. Quartile comparisons revealed that age-related declines in DHEA were linear, curvilinear, or sigmoidal, depending on the summary parameter of interest. Anabolic hormone profiles did not associate with BMI, blood pressure, or sleep efficiency. Robust linear associations were observed between testosterone and perceived stress (r range: -0.29 to -0.36); concentration-dependent patterns were less discernible. Lower DHEA (r range: -0.22 to -0.30) and testosterone (r range: -0.22 to -0.36) concentrations associated with higher fatigue. Subsequent quartile comparisons suggested a concentration-dependent threshold with respect to evening testosterone. Specifically, those individuals within the lowest quartile (≤68.4pg/mL) endorsed the highest fatigue of the four groups (p=0.01), while the remaining three groups did not differ from each other. This study not only showed that anabolic hormone profiles have distinctive age trajectories, but are also valuable predictors of stress and fatigue in elite military men. This highlights the importance of routine monitoring of anabolic hormone profiles to sustain and optimize health and readiness in chronically stressed populations.