Pre- and posttransplant abnormalities of insulin secretion and sensitivity are significant predictors of PTDM. Corticosteroid cumulative dose may affect the incidence of PTDM during the first 2 months after transplantation. CsA treatment increases insulin secretion in patients with a high pretransplant risk of PTDM.

No abstract

Thyrostimulin is a heterodimeric hormone comprised of two glycoprotein hormone subunits, namely glycoprotein hormone subunit alpha2 and glycoprotein hormone subunit beta5 (GPB5). Immunological studies have revealed that both subunits colocalize in human pituitary corticotroph cells. Although recombinant thyrostimulin protein selectively activates the TSH receptor and has thyrotropic activity in rats, its biological functions have not been clarified. To explore the physiological regulators for the GPB5, the 5'-flanking region of the GPB5 coding sequence up to 3-kb upstream was analyzed by luciferase reporter assays. We found that nuclear factor-kappaB (NF-kappaB) markedly activated GPB5 transcription. Disruption of the putative NF-kappaB-binding motifs in the GPB5 5'-flanking region silenced the GPB5 activation by p65. Chromatin immunoprecipitation assays revealed that recombinant p65 bound to the predicted NF-kappaB-binding sites. Because NF-kappaB is known to associate with acute phase inflammatory cytokines, we examined whether TNFalpha or IL-1beta could regulate GPB5. Both these cytokines activated GPB5 transcription by 2- to 3-fold, and their effects were abolished by the addition of MG132, a NF-kappaB inhibitor. Our results suggest that inflammatory cytokines positively regulate thyrostimulin through NF-kappaB activation.

Time series analysis was carried out on monthly data of body weight and blood pressure of one male subject from 29 to 65 years of age by the program Census X-11 and DECOMP. Body weight increased from 55 kg to 70 kg from 30 to 50 years, then decreased to 60 kg afterward. The trend factor of body weight obtained by X-11 fluctuated markedly even after the removal of seasonal and irregular components. The trend factor of systolic blood pressure increased from 120 mmHg to 140 mmHg from 40 to 55 years, while diastolic blood pressure increased from 70 mmHg to 90 mmHg around 40 years of age. These changes were also accompanied by many fluctuations. The program DECOMP suggested that the fluctuations observed in X-11 were expressed by an autoregressive model. From the viewpoint of the most appropriate autoregressive model, it is possible that at younger ages body weight and systolic blood pressure may be affected by the values of the previous several months. On the other hand, they may be affected by the values of the previous month or two at older ages. Spectral analysis was also applied to the autoregressive model. Peaks at about 20, 30, or 40 months were observed in body weight changes. The pattern of the seasonal factor changed with age in body weight and blood pressure. In body weight, the peak appeared in the summer at younger ages, but in the autumn or winter at older ages. In blood pressure, the peak did not always appear in the winter. Am. J. Hum. Biol. 12:526-541, 2000. Copyright 2000 Wiley-Liss, Inc.

Eighty-eight second grade students of a senior high school in Saitama prefecture in Japan participated in a prospective study to predict cigarette smoking behavior 3.5 years later. Predictor variables include sex, knowledge, beliefs and attitudes toward smoking, previous smoking behavior, and smoking behavior of their families. Stepwise discriminant analyses revealed that 90% of the smokers and 65% of the non-smokers were correctly classified. In this model, previous smoking behavior proved to be the best predictor. Attitude toward adult male's smoking, sex and smoking behavior of subjects' families were also related. These four variables explained 35% of the variance in smoking behavior. As for stepwise discriminant analyses among those who had not smoked at baseline, 78% of the smokers and 76% of the non-smokers were correctly classified. Attitude toward adult male's smoking, sex, knowledge about long-term effects of cigarette smoking and smoking behavior of their families entered the model in this order. These four variables explained 37% of the variance. Implications of this study for smoking prevention programs in Japan are discussed.