A growing body of epidemiological research show that Bisphenol A (BPA) is positively correlated with obesity and metabolic disorders. However, the mechanisms of BPA on adiposity remain largely unknown. In this study, we found that 5-week-old male and female C57BL/6J mice exposed to four dosages of BPA (5, 50, 500, and 5000 μg/kg/d) by oral intake for 30 days showed significantly increased body weight and fat mass in a nonmonotonic dose-dependent manner when fed a chow diet. The effect occurred even at the lowest concentration (5μg/kg/d), lower than the tolerable daily intake of 50 μg/kg/day for BPA. However, no significant difference in body weight and fat mass was observed in either male or female mice fed a high-fat diet, suggesting that BPA may interact with diet in promoting obesity risk. In vitro study showed that BPA treatment drives the differentiation of white adipocyte progenitors from the stromal vascular fraction, partially through glucocorticoid receptor. BPA exposure increased circulating inflammatory factors and the local inflammation in white adipose tissues in both genders fed a chow diet, but not under high-fat diet. We further found that BPA concentration was associated with increased circulating inflammatory factors, including leptin and TNFα, in lean female subjects (body mass index < 23.0 kg/m(2)) but not in lean male subjects or in both sexes of overweight/obese subjects (body mass index > 25.0 kg/m(2)). In conclusion, we demonstrated the nonmonotonic dose effects of BPA on adiposity and chronic inflammation in 5-week-old mice, which is related to caloric uptake.

Prolactin plays an important role in maintaining a normal glucose homeostasis during pregnancy and beyond. Studies investigating the association between prolactin and type 2 diabetes beyond pregnancy are rare and none is prospective. We aimed to examine whether prolactin associates with type 2 diabetes prospectively in a Chinese population. In 2009, 2,377 participants aged 40 years or older were enrolled from Shanghai, China. Among 1,596 diabetes-free participants at baseline, 1,510 completed the follow-up investigation in 2013. Participants who had a fasting plasma glucose ≥126 mg/dL and/or a 2-hour plasma glucose ≥200 mg/dL during a 75-g oral glucose tolerance test had a definite diagnosis of type 2 diabetes or received antidiabetic therapies during follow-up were classified as having type 2 diabetes. During a mean follow-up of 3.7 years, 189 new cases of type 2 diabetes were documented. After multivariate adjustment, women in the highest quartile of prolactin showed the lowest risk for diabetes compared with those in the lowest quartile (hazard ratio = 0.48, 95% confidence interval: 0.26, 0.90). However, such significant associations were not observed in men. Prolactin may be a mediator in the pathogenesis of type 2 diabetes in women; however, more studies are needed to elucidate the underlying sex-specific mechanism.

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Bile acid metabolism was reported to be involved in glucose metabolism homeostasis. However, the exact relationship between bile acid and glucose metabolism as well as insulin sensitivity is not clarified. Therefore, we sought to investigate the association between insulin sensitivity and hyperbileacidemia in type 2 diabetic and nondiabetic population.This community-based cross-sectional study included 9603 residents from Jiading, Shanghai, China, who were 40 years and older. Standardized questionnaire, anthropometric measurements and laboratory tests were conducted. Homeostasis model assessment of insulin resistance (HOMA-IR) ≥ 2.7 was defined as insulin resistance and fasting TBA ≥ 10 mmol/L was defined as hyperbileacidemia.Multivariate stepwise regression analysis revealed that HOMA-IR, age, and male sex were positively associated with hyperbileacidemia in both nondiabetic and diabetic participants. In multivariate logistic models, participants with insulin resistance had significantly higher risk of hyperbileacidemia compared to those who have no insulin resistance, in both nondiabetic and diabetic population (nondiabetic: OR = 1.76; 95% CI 1.42–2.19; P < 0.001; diabetic: OR = 1.56; 95% CI 1.06 – 2.31; P = 0.025, respectively). Further adjustment for the HbA1c level in diabetic population did not change the significant association (OR = 1.59; 95% CI 1.06 − 2.40; P = 0.024). In nondiabetic participants, each 1-unit increment of HOMA-IR conferred an 18% higher risk of hyperbileacidemia (95% CI 1.04–1.35; P = 0.013), whereas in diabetic participants, this association was similar but not significant (95% CI 0.95–1.59; P = 0.117).Insulin resistance was positively associated with hyperbileacidemia in both nondiabetic and diabetic population. The increase in the bile acid level in insulin-resistant population regardless of status of diabetes and glucose level indicated the important role of insulin resistance in the regulation of bile acid metabolism in human.

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The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.

ContextLimited population-based study focused on relationship between eosinophil and type 2 diabetes (T2D).ObjectivesWe aimed to evaluate the relationship between peripheral eosinophil percentage and glucose metabolism and insulin resistance in a large sample size of Chinese population aged 40 and older.Design and MethodsA cross-sectional study was performed among 9,111 Chinese adults including 3,561 men and 5,550 women. The glucose metabolism status was confirmed by 75-g oral glucose tolerance test. Homeostasis model assessment of insulin resistance index and serum insulin levels were used to evaluate insulin resistance. Homeostasis model assessment-B was used to evaluate β cell function.ResultsThe average age of participants was 58.5 years. The prevalence of T2D decreased across the tertiles of eosinophil percentage (21.3%, 18.2% and 16.9%, P<0.0001). Each one tertile increase of eosinophil percentage inversely associated with risk of T2D when referred not only to normal glucose tolerance (NGT) (odds ratio (OR) 0.81, 95% CI 0.76–0.87, P< 0.0001), but also to impaired glucose regulation (OR 0.89, 95% CI 0.83–0.97, P = 0.006), respectively, after adjustment for the confounding factors. Compared with the first tertile, the third tertile of eosinophil percentage associated with a 23% decrease of insulin resistance in NGT participants after full adjustments (P = 0.005). Each 1-standard deviation of increment of eosinophil percentage associated with a 37% decrease of insulin resistance (P = 0.005).ConclusionsHigher peripheral eosinophil percentage was associated with decreased risk of T2D. The inverse relation to insulin resistance was detected in NGT participants.