Glucocorticoid receptors in human preadipocytes: regional and gender differences

Glucocorticoid excess causes visceral obesity and its accompanying insulin resistance, dyslipidemia and hypertension. Glucocorticoids enhance preadipocyte (PA) differentiation and increase their aromatase activity (oestrogen production) and there is regional variability in these PA processes. Therefore, we studied human PAs for the presence of, and any regional or gender differences in, glucocorticoid receptors (GRs). Confluent subcultured human subcutaneous (Sc) and visceral (Vis) PAs from both genders contained GRs as assessed by GR gene expression and specific glucocorticoid (dexamethasone) binding. The dissociation constant was similar to that of other human cells and there was no difference between Sc and Vis sites or between males and females. There was significantly less GR mRNA in Vis PAs compared with Sc PAs in females (P=0.008) but not in males. There was less glucocorticoid binding in Vis compared with Sc PAs in females, measured by maximal binding capacity (P=0.035) or single saturating dose glucocorticoid binding (Bssd) (P=0.019). There was no regional difference in specific glucocorticoid binding in males. There was a gender difference with fewer GRs in Vis PAs in females compared with males measured by Bssd (P=0.006). In summary, GRs are present in human PAs. There is a lower GR density in Vis compared with Sc PAs in females, and females have fewer GRs in Vis PAs compared with males. These differences are likely to affect regional aromatase activity and to contribute to the smaller visceral fat mass in females compared with males.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.