This study aims to compare the prevalence of hypogonadism between male patients with early-onset type 2 diabetes mellitus (T2DM) and late-onset type 2 diabetes. A total of 122 male patients with early-onset T2DM (diagnosis age ≤40 years) and 100 male patients with late-onset T2DM (diagnosis age >40 years) were recruited from our in-patient department between 1 January 2013 and 28 December 2015. Serum FSH, LH, testosterone, lipid profile, uric acid, HbA1c, and beta-cell function were determined in blood samples. The diagnosis of hypogonadism was based on the levels of LH, FSH, and total testosterone. The mean onset age was 29.86 ± 6.31 and 54.47 ± 9.97 years old in the early-onset group and late-onset group, respectively. Compared with late-onset T2DM, those with early-onset T2DM had a higher proportion of new-onset diabetes, were more likely to be obese, and had worse glycemic control, lipid control, and lower sex hormone-binding globulin (SHBG). The prevalence of hypogonadism was much higher in the early-onset group than in the late-onset group (48.0% vs. 26.7%, p< 0.05). The rate of secondary hypogonadism in the early-onset group and late-onset group were 44.3% and 25.0%, respectively (p < 0.05). Obesity, waist circumference, and SHBG were significantly associated with serum total testosterone level in all, early-onset, and late-onset T2DM. Both all and early-onset T2DM groups had positive correlations between total testosterone and fasting C-peptide, total cholesterol, triglycerides, and uric acid. Our results indicate that in a population of admission to a large urban hospital in China, the prevalence of hypogonadism was higher in the patients with early-onset T2DM than that of late-onset T2DM. This prevalence might be attributable to greater obesity, worse lipid control, and lower SHBG levels in those patients.
The effects of dulaglutide and a calorie-restricted diet (CRD) on visceral adipose tissue (VAT) and metabolic profiles in polycystic ovary syndrome (PCOS) have not been extensively investigated. In this study, we investigated whether dulaglutide combined with CRD could further reduce VAT and promote clinical benefits as compared with a CRD regimen alone in overweight or obese PCOS-affected women. Between May 2021 and May 2022, this single-center, randomized, controlled, open-label clinical trial was conducted. Overall, 243 participants with PCOS were screened, of which 68 overweight or obese individuals were randomly randomized to undergo dulaglutide combined with CRD treatment (n = 35) or CRD treatment alone (n = 33). The duration of intervention was set as the time taken to achieve a 7% weight loss goal from baseline body weight, which was restricted to 6 months. The primary endpoint was the difference in the change in VAT area reduction between the groups. The secondary endpoints contained changes in menstrual frequency, metabolic profiles, hormonal parameters, liver fat, and body composition. As compared with the CRD group, the dulaglutide + CRD group had a considerably shorter median time to achieve 7% weight loss. There was no significant between-group difference in area change of VAT reduction (−0.97 cm2, 95% confidence interval from −14.36 to 12.42, p = 0.884). As compared with CRD alone, dulaglutide + CRD had significant advantages in reducing glycated hemoglobin A1c and postprandial plasma glucose levels. The results of the analyses showed different changes in menstruation frequency, additional metabolic profiles, hormonal markers, liver fat, and body composition between the two groups did not differ significantly. Nausea, vomiting, constipation, and loss of appetite were the main adverse events of dulaglutide. These results emphasize the value of dietary intervention as the first line of treatment for PCOS-affected women, while glucagon-like peptide 1 receptor agonist therapy provides an efficient and typically well tolerated adjuvant therapy to aid in reaching weight targets based on dietary therapy in the population of overweight/obese PCOS-affected women.
Study question How is the cumulative pregnancy probability of individual patients after IVF-ET,could we develop a visualized clinical model to predict it based on patient’s characteristics? Summary answer The visualized clinical mode incorporates five items of female age, number of oocytes, antral follicle count, endometrium thickness and basal FSH level. What is known already Many factors can result in infertility, prognosis prediction is clinically relevant for making the right therapeutic strategy while avoiding overtreatment. It is also helpful in counselling, making the patient aware of possible treatment duration and estimated expense and managing patient’s expectation. Visualized clinical mode and accurate prediction would also be helpful in designing clinical trials to evaluate new treatments. Study design, size, duration We conducted a retrospective analysis of a single-center database using prospectively collected data from women who underwent IVF/ICSI treatment from January 2013 to December 2015, All the participants were followed up for at least 2 years, 3538 IVF-ET cycles were included in the study.A total of 3538 IVF/ICSI cycles were included in the study. Participants/materials, setting, methods Data from a total of 2312 IVF/ICSI cycles from January 2013 to December 2014 were randomly split into training dataset (1550, 67%) and internal validation dataset (762, 33%). A total of 1226 IVF/ICSI cycles in 2015 was applied to external validation dataset (temporal validation) Main results and the role of chance Multivariable logistic regression model combined with restricted cubic splines function was used to test independent prognostic factors and estimate their effects on treatment outcome for patients treated with IVF/ICSI. Female age, number of oocytes retrieved, AFC, endometrium thickness and basal FSH were included the final model. The above model was used to calculate prediction scores for all women in the training and validation datasets. The C-index was 0.693 (95% CI: 0.692∼0.695) in training sets, 0.689 in internal validation sets and 0.710 in external validation sets, which denotes a good performance. Calibration curves suggest excellent model calibration, with an ideal agreement between the prediction and actual observation . The DCA showed that if the threshold probability is between 0 and 0.7, using the nomogram derived in the present study to predict cumulative pregnancy provided a greater benefit than either thetreat-all or the treat-none strategy. Limitations, reasons for caution it was a retrospective, single-center study.In the future, prospective, randomized controlled, multicenter clinical studies will be designed. Wider implications of the findings: The visualized nomogram model provides great predictive value for infertility patients in their first IVF/ICSI cycle, and predicts the pregnancy probability of individuals ,and could help clinicians improving clinical counselling. Trial registration number Not applicable
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