The correlation between vitamin D, glucose homeostasis and androgen level among polycystic ovary syndrome patients: a cross-sectional study.

MATERIAL AND METHODS: A total of 290 women with PCOS participated in this cross-sectional study. Glucose homeostasis was assessed by a 75-g oral glucose tolerance test and the concentration of serum 25-hydroxy vitamin D was determined among all subjects. The homeostasis model assessment of insulin resistance (HOMA-IR) was taken as the indicator of insulin resistance. Beta cell function was estimated using the insulinogenic index and the disposition index. Free androgen index (FAI) was used to represent the androgen level. RESULTS: In our study, 7.2% of the patients had Vit D severe deficiency, 75.2% had Vit D deficiency and 15.5% had vit D insufficiency. The level of serum 25(OH)D showed a significant positive association with insulinogenic index (r = 0.147, p < .05), disposition index (r = 0.280, p < .05), and SHBG (r = 0.178, p < .05) but exhibited a negative association with HOMA-IR (r = -0.198, p < .05), FAI (r = -0.178, p < .05). Adjusted age and BMI, 25(OH)D would be the dependent variable on disposition index [B = 0.259, 95%CI(0.041,0.477)] and FAI [B = -0.125, 95%CI(-0.232, -0.017)]. CONCLUSIONS: According to our results, the low levels of serum 25(OH)D were common in women with PCOS, which was speculated to be associated with glucose homeostasis and the androgen level.

Value of Capillary Glucose Profiles in Assessing Risk of Nocturnal Hypoglycemia in Type 1 Diabetes Based on Continuous Glucose Monitoring.

INTRODUCTION: This study aimed to evaluate the occurrence of nocturnal hypoglycemia in type 1 diabetes (T1D) based on continuous glucose monitoring (CGM), and to explore the value of capillary glucose profiles in assessing the risk of nocturnal hypoglycemia. The study also intended to develop a predictive model to identify people with high risk of nocturnal hypoglycemia. METHODS: A total of 169 participants with T1D received 3 days of blinded CGM; meanwhile, their self-monitoring blood glucose (SMBG) profiles were recorded. Logistic regression analyses were used to evaluate contributory factors of nocturnal hypoglycemia. Potential indicators were estimated using area under receiver operator curve (AUC) analyses. RESULTS: During the retrospective CGM period, 95 (56.2%) participants with T1D reported 238 events of hypoglycemia, and 69 (29.0%) of these episodes occurred during the nighttime. Increased risk of nocturnal hypoglycemia correlated with lower HbA1c, glycated albumin, and mean blood glucose (OR = 0.790, 0.940, 0.651, respectively; P < 0.05) and higher standard deviation, mean amplitude of glycemic excursions, and low blood glucose index (OR = 1.463, 1.168, 4.035, respectively; P < 0.05) after adjustment for age and duration. Of the daily SMBG profiles, fasting blood glucose (OR = 0.643, P = 0.001) and blood glucose at bedtime (OR = 0.851, P = 0.037) were associated with the occurrence of nocturnal hypoglycemia. The BGn model, which was derived from the variation of capillary glucose, could discriminate individuals with increased risk of nocturnal hypoglycemia (AUC = 0.774). CONCLUSIONS: Nocturnal hypoglycemia constitutes nearly one-third of hypoglycemic events in people with T1D. Strict glycemic control and great fluctuation of glucose are potential contributory factors. Daily SMBG profiles and the BGn model could help assess the risk of nocturnal hypoglycemia in T1D, which may support further development of preventive strategies.