The Association of Sleep Disorder, Obesity Status, and Diabetes Mellitus among US Adults—The NHANES 2009-2010 Survey Results

To examine the association between sleep disorders, obesity status, and the risk of diabetes in adults, a total of 3668 individuals aged 40+ years fromtheNHANES 2009-2010 withoutmissing information on sleep-related questions,measurements related to diabetes, and BMI were included in this analysis. Subjects were categorized into three sleep groups based on two sleep questions: (a) no sleep problems; (b) sleep disturbance; and (c) sleep disorder. Diabetes was defined as having one of a diagnosis from a physician; an overnight fasting glucose > 125 mg/dL; Glycohemoglobin > 6.4%; or an oral glucose tolerance test > 199mg/dL. Overall, 19% of subjects were diabetics, 37% were obese, and 32% had either sleep disturbance or sleep disorder. Using multiple logistic regression models adjusting for covariates without including BMI, the odds ratios (OR, (95% CI)) of diabetes were 1.40 (1.06, 1.84) and 2.04 (1.40, 2.95) for those with sleep disturbance and with sleep disorder, respectively. When further adjusting for BMI, the ORs were similar for those with sleep disturbance 1.36 (1.06, 1.73) but greatly attenuated for those with sleep disorders (1.38 [0.95, 2.00]). In conclusion, the impact of sleep disorders on diabetes may be explained through the individuals’ obesity status.

The association between maternity insurance, residence status and selected perinatal outcomes among Chinese women

Objective: To investigate the impact of maternity insurance and maternal residence on birth outcomes in a Chinese population. Methods: Secondary data was analyzed from a perinatal cohort study conducted in the Beichen District of the city of Tianjin, China. A total of 2364 pregnant women participated in this study at approximately 12-week gestation upon registration for receiving prenatal care services. After accounting for missing information for relevant variables, a total of 2309 women with single birth were included in this analysis. Results: A total of 1190 (51.5%) women reported having maternity insurance, and 629 (27.2%) were rural residents. The abnormal birth outcomes were small for gestational age (SGA, n=217 (9.4%)), large for gestational age (LGA, n=248 (10.7%)), birth defect (n=48 (2.1%)) including congenital heart defect (n=32 (1.4%)). In urban areas, having maternal insurance increased the odds of SGA infants (1.32, 95%CI (0.85, 2.04), NS), but decreased the odds of LGA infants (0.92, 95%CI (0.62, 1.36), NS); also decreased the odds of birth defect (0.93, 95%CI (0.37, 2.33), NS), and congenital heart defect (0.65, 95%CI (0.21, 1.99), NS) after adjustment for covariates. In contrast to urban areas, having maternal insurance in rural areas reduced the odds of SGA infants (0.60, 95%CI (0.13, 2.73), NS); but increased the odds of LGA infants (2.16, 95%CI (0.92, 5.04), NS), birth defects (2.48, 95% CI (0.70, 8.80), NS), and congenital heart defect (2.18, 95%CI (0.48, 10.00), NS) after adjustment for the same covariates. Similar results were obtained from Bootstrap methods except that the odds ratio of LGA infants in rural areas for maternal insurance was significant (95%CI (1.13, 4.37)); urban residence was significantly related with lower odds of birth defect (95%CI (0.23, 0.89)) and congenital heart defect (95%CI (0.19, 0.91)). Conclusions: whether having maternal insurance did have an impact on perinatal outcomes, but the impact of maternal insurance on the perinatal outcomes showed differently between women with urban residence and women with rural residence status. However, it is not clear what are the reason causing the observed differences. Thus, more studies are needed.