Daily Endocrinology Research Analysis

Whether and how endometrial aging affects fertility remains unclear. In our in-house clinical cohort at the Center for Reproductive Medicine of Peking University Third Hospital (n = 1,149), we observed adverse pregnancy outcomes in the middle-aged group after excluding aneuploid embryos, implying the negative impact of endometrial aging on fertility. To understand endometrial aging, we performed comprehensive transcriptomic profiling of the mid-secretory endometrium of young (<35 years) and middle-aged (≥35 years) patients. This analysis revealed that H3K27ac loss is linked to impaired endometrial receptivity in the middle-aged group. We eliminated H3K27ac in young human endometrial stromal cells and observed reduced progesterone receptor (PGR), a critical regulator of endometrial receptivity. Lastly, we validated the association between H3K27ac/PGR loss and uterine aging in a mouse model. Our findings establish H3K27ac as a critical regulator of PGR and demonstrate that endometrial H3K27ac loss is associated with aging-related fertility decline. This work provides valuable insights into enhancing the safety and efficacy of assisted reproductive technologies in future clinical practices.

INTRODUCTION: Accurately classifying pediatric diabetes can be challenging for providers, and misclassification can result in suboptimal care. In recent years, type 1 diabetes (T1D) polygenic scores, which quantify one's genetic risk for T1D based on T1D risk allele burden, have been developed with good discriminating capacity between T1D and not-T1D. These tools have the potential to improve significantly diagnostic provider accuracy if used in clinic. METHODS: We applied T1D polygenic scores to a group of pediatric patients (n = 1,846) with genetic data available in the Boston Children's Hospital PrecisionLink Biobank, including 96 individuals diagnosed with T1D. RESULTS: Patients with a clinical diagnosis of T1D had higher T1D polygenic scores compared to controls (Wilcoxon rank-sum p < 0.0001). Sixty-nine of the 74 individuals with diabetes and a T1D polygenic score exceeding an externally validated cutoff for distinguishing T1D from not-T1D were confirmed to have T1D. There were multiple cases where T1D polygenic scores would have clinical utility. An elevated T1D polygenic score suggested T1D in a pancreatic autoantibody (PAA)-negative individual with negative maturity-onset diabetes of the young (MODY) genetic testing and a phenotype matching T1D. A low T1D polygenic score accurately indicated atypical diabetes in an individual found to have HNF1B-MODY. One individual had positive PAA, but the provider noted that the patient may not have classic T1D, as later suggested by a low T1D polygenic score. CONCLUSION: T1D polygenic scores already have clinical utility to aid in the accurate diagnosis of pediatric diabetes. Efforts are now needed to advance their use in clinical practice.

BACKGROUND: To determine the associations between sleep phenotypes and the risks of specific obesity types and weight gain in patients with type 2 diabetes (T2D), especially in different genetic risk groups. MATERIALS AND METHODS: We conducted a prospective study involving 58 890 participants. Sleep and napping were assessed according to the standardized questionnaire. General and abdominal obesity were defined by BMI or visceral fat area (VFA), respectively. Multivariable Cox regression, stratified, and joint analysis were performed to explore potential correlations. Furthermore, mediation models were constructed to figure out the mediating role of metabolic factors (blood pressure, UACR, and HbA1c). RESULTS: During a median 3.05-year follow-up period, short sleep increased the risk of obesity (HR 1.42, 95% CI 1.17-1.71; 1.33, 1.08-1.65) and weight gain (1.21, 1.09-1.34; 1.17, 1.06-1.29), while long sleep and napping were unrelated to abdominal obesity and weight gain. Mediation analysis showed that systolic blood pressure, UACR, and HbA1c mediated the statistical association between night sleep duration and general obesity with proportions (%) of 7.9, 1.8, and 8.8, respectively. Joint analysis showed both sleep and napping groups had no significance among the low genetic risk group, while long napping, short sleep, and long sleep increased the risk of general obesity in medium to high risk patients. CONCLUSIONS: Short sleep, long sleep, and long napping increased the risk of general obesity and BMI-defined weight gain, and were more pronounced in the medium to high genetic risk group. Napping was unrelated to abdominal obesity. Metabolic factors partially explain the mechanism between sleep and obesity.