Carbonyl reductase 1 amplifies glucocorticoid action in adipose tissue and impairs glucose tolerance in lean mice

Objective: Carbonyl reductase 1 (Cbr1), a recently discovered contributor to tissue glucocorticoid metabolism converting corticosterone to 2013dihydrocorticosterone (2013-DHB), is upregulated in adipose tissue of obese humans and mice and may contribute to cardiometabolic complications of obesity. This study tested the hypothesis that Cbr1-mediated glucocorticoid metabolism influences glucocorticoid and mineralocorticoid receptor activation in adipose tissue and impacts glucose homeostasis in lean and obese states. Methods: The actions of 2013-DHB on corticosteroid receptors in adipose tissue were investigated first using a combination of in silico, in vitro, and transcriptomic techniques and then in vivo administration in combination with receptor antagonists. Mice lacking one Cbr1 allele and mice overexpressing Cbr1 in their adipose tissue underwent metabolic phenotyping before and after induction of obesity with high-fat feeding. Results: 2013-DHB activated both the glucocorticoid and mineralocorticoid receptor in adipose tissue and systemic administration to wild-type mice induced glucose intolerance, an effect that was ameliorated by both glucocorticoid and mineralocorticoid receptor antagonism. Cbr1 haploinsufficient lean male mice had lower fasting glucose and improved glucose tolerance compared with littermate controls, a difference that was abolished by administration of 2013-DHB and absent in female mice with higher baseline adipose 2013-DHB concentrations than male mice. Conversely, overexpression of Cbr1 in adipose tissue resulted in worsened glucose tolerance and higher fasting glucose in lean male and female mice. However, neither Cbr1 haploinsfficiency nor adipose overexpression affected glucose dyshomeostasis induced by high-fat feeding. Conclusions: Carbonyl reductase 1 is a novel regulator of glucocorticoid and mineralocorticoid receptor activation in adipose tissue that influences glucose homeostasis in lean mice. (c) 2021 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Diabetes mellitus: pathophysiological changes and therap

Modelos matemáticos para predição da chuva de projeto para regiões do Estado de Minas Gerais Mathematical models for the estimation of rainfall in selected regions of Minas Gerais State, Brazil

O uso de modelos matemáticos para predição da chuva é uma forma prática e precisa para determinação do valor a ser aplicado em projetos, sendo útil para localidades desprovidas de informações pluviométricas. Objetivou-se ajustar o método de Bell, que possui características de regionalização para a chuva de projeto, com base em equações de chuvas intensas e modelos de probabilidade de Gumbel de estações meteorológicas do Estado de Minas Gerais ajustando, também, um modelo para cada região do estado. Avaliaram-se os modelos considerando-se o coeficiente de determinação e os erros médios em relação aos dados originais. Para validação, trabalhou-se com três estações meteorológicas da região Norte não usadas para ajuste do respectivo modelo. Foram analisadas três metodologias para estimativa da chuva intensa padrão (h(60,2)), que pondera o método usado, ressaltando-se a média aritmética, a média ponderada pelo inverso do quadrado da distância e a predição geoestatística (krigagem). Observou-se que os modelos possuem bons indicadores estatísticos e a validação produziu erros baixos, mostrando que os modelos podem ser aplicados, especialmente se a krigagem for usada para estimativa do parâmetro h(60,2).<br>The use of mathematical models for predicting rainfall is a practical and accurate way of determining this parameter to be applied to regions which do not have any precipitation data. Based on the intense rainfall equations and Gumbel's probability model for maximum daily precipitation of meteorological stations in Minas Gerais State, the objective of this work was to adjust the Bell's Method, with regional features, for rainfall, adjusting one model for each region. The regional parameters were estimated by non-linear multiple regression, using Gauss-Newton's method. The goodness of the models was evaluated by the coefficient of determination and mean errors of prediction as compared to the original data. Data from three meteorological stations in the Northern region, which were not used to adjust the respective model, were used for validation purposes. The most frequent precipitation was tested by the arithmetic mean, the weighted mean by the inverse-square-distance and the geo-statistical prediction (kriging). The models produced good statistical parameters, with low mean errors, showing their accuracy, specially when the kriging method for estimating the most frequent precipitation was used