Synthetic glucocorticoids are clinically used to treat auto-immune and inflammatory disease. Despite the high efficacy, glucocorticoid treatments causes side effects such as obesity and insulin... Show moreSynthetic glucocorticoids are clinically used to treat auto-immune and inflammatory disease. Despite the high efficacy, glucocorticoid treatments causes side effects such as obesity and insulin resistance in many patients. Via their pharmacological target, the glucocorticoid receptor (GR), glucocorticoids suppress endogenous glucocorticoid secretion. Endogenous, but not synthetic, glucocorticoids activate the mineralocorticoid receptor (MR) and side effects of synthetic glucocorticoids may thus not only result from GR hyperactivation but also from MR hypoactivation. Here, we tested the hypothesis that reactivation of MR with corticosterone add-on treatment can attenuate the metabolic effects of the synthetic glucocorticoid dexamethasone. Male 8-week-old C57Bl/6J mice received a high-fat diet supplemented with dexamethasone or vehicle, and were subcutaneously implanted with low-dose corticosterone- or vehicle-containing pellets. Dexamethasone strongly reduced body weight and fat mass gain, while corticosterone add-on partially normalized this. Dexamethasone-induced hyperglycemia and hyperinsulinemia were exacerbated by corticosterone add-on, which was prevented by MR antagonism. In subcutaneous white adipose tissue, corticosterone add-on prevented the dexamethasone-induced expression of intracellular lipolysis genes. In brown adipose tissue, dexamethasone also upregulated gene expression of brown adipose tissue identity markers, lipid transporters and lipolysis enzymes, which was prevented by corticosterone add-on. In conclusion, corticosterone add-on treatment prevents several, while exacerbating other metabolic effects of dexamethasone. While the exact role of MR remains elusive, this study suggests that corticosterone suppression by dexamethasone contributes to its effects in mice. Show less
Blankenstein, N.E.; Rooij, M. de; Ginkel, J. van; Wilderjans, T.F.; Ruigh, E.L. de; Oldenhof, H.C.; ... ; Jansen, L.M.C. 2021
Background Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous... Show moreBackground Antisociality across adolescence and young adulthood puts individuals at high risk of developing a variety of problems. Prior research has linked antisociality to autonomic nervous system and endocrinological functioning. However, there is large heterogeneity in antisocial behaviors, and these neurobiological measures are rarely studied conjointly, limited to small specific studies with narrow age ranges, and yield mixed findings due to the type of behavior examined. Methods We harmonized data from 1489 participants (9-27 years, 67% male), from six heterogeneous samples. In the resulting dataset, we tested relations between distinct dimensions of antisociality and heart rate, pre-ejection period (PEP), respiratory sinus arrhythmia, respiration rate, skin conductance levels, testosterone, basal cortisol, and the cortisol awakening response (CAR), and test the role of age throughout adolescence and young adulthood. Results Three dimensions of antisociality were uncovered: 'callous-unemotional (CU)/manipulative traits', 'intentional aggression/conduct', and 'reactivity/impulsivity/irritability'. Shorter PEPs and higher testosterone were related to CU/manipulative traits, and a higher CAR is related to both CU/manipulative traits and intentional aggression/conduct. These effects were stable across age. Conclusions Across a heterogeneous sample and consistent across development, the CAR may be a valuable measure to link to CU/manipulative traits and intentional aggression, while sympathetic arousal and testosterone are additionally valuable to understand CU/manipulative traits. Together, these findings deepen our understanding of the fundamental mechanisms underlying different components of antisociality. Finally, we illustrate the potential of using current statistical techniques for combining multiple datasets to draw robust conclusions about biobehavioral associations. Show less
