Background: Existing studies which regarding to the association between individual socioeconomic status (SES) and obesity are still scarce in developing countries. The major aim of this study is to estimate such association in an adult population which was drawn from an economically prosperous province of China. Methods: Study population was determined by multilevel randomized sampling. Education and income were chosen as indicators of individual SES, general obesity and abdominal obesity were measured by body mass index (BMI) and waist circumference (WC). Descriptive statistical methods were used to depict overall and factor-specific distributions of general and abdominal obesity among 16,013 respondents. Two-step logistic regression models were fitted on gender basis. Results: The age-and-sex adjusted rates of general overweight, general obesity, abdominal overweight and abdominal obesity in study population were 28.9% (95%CI: 27.9%-29.9%), 7.5% (95%CI: 7.0%-8.1%), 32.2% (95%CI: 31.2%-33.3%) and 12.3% (95%CI: 11.6%-13.1%), respectively. Based on model fitting results, a significant inverse association between education and obesity only existed in women, while in men, income rather than education was positively related to obesity. Conclusions: The atypical SES-obesity relationship we found reflected the on-going social economy transformation in affluent regions of China. High-income men and poorly-educated women were at higher risk of obesity in Zhejiang province, thus merit intense focuses.

Domain-specific physical activities may have different correlates and health effects, but few large studies have examined these questions, especially their separate associations with adiposity. We analysed cross-sectional data of 466 605 adults without any prior chronic diseases, enrolled during 2004–8, from 10 diverse localities across China. Physical activity level in each of 4 domains (occupation, commuting, household, and active-recreation), calculated as metabolic equivalent (MET)-hr/day, was related to social-demographic factors and measures of adiposity (body mass index [BMI], waist circumference [WC], and bio-impedance derived percentage body fat), using multivariable linear and logistic regression models. The overall mean age was 50.8 years. The mean total physical activity was 21.7 MET-hr/day, mainly from occupation (62%) and household chores (26%), but little from active-recreation (4%), with women having a much higher household activity than men. Older participants had a lower level of occupational activity but a higher level of household and active-recreational activity, particularly after retirement. There was no linear association of occupational activity with adiposity, but working women tended to have a lower adiposity (e.g. 1.0 cm WC) than non-working women. In men, there was an inverse and apparently linear association between adiposity and levels of both commuting-related and household activities, with 3 MET-hr/day associated with -0.11 and -0.13 kg/m2 BMI, -0.42 and -0.62 cm WC, and -0.28 and -0.33 percentage points of body fat, respectively. In women, only household activity showed a linear, but weaker, association with adiposity. A higher adiposity was observed among men and women with higher levels of active-recreational activity. In Chinese adults, physical activity mainly involves occupation and housework, with little from active-recreational activity. Domain-specific physical activities varied by socio-demographic factors and had different associations with adiposity.

Obesity has been identified as a major risk factor for a large number of chronic diseases. Understanding factors related to adolescent obesity is critical for prevention of chronic diseases. The associations between sleep duration and obesity among adolescents in the existing literature are controversial. Our study was designed to determine the prevalence of short sleep duration, and assess the association of sleep duration and obesity, among middle and high school students in Zhejiang, China. 18,403 Students in 442 schools were recruited and surveyed using an anonymous, self-administered questionnaires. Weighted multivariable logistic regression models were used for data analyses. The mean (SD) age of the students was 15.9 (1.8) years. 49.7% of students were girls. The mean (SD) height and weight were 166.2 (8.5) cm and 54.6 (11.1) kg, respectively. The overall prevalence of obesity and overweight were 3.4% (95% CI: 3.0–3.8) and 7.8% (95% CI: 7.4–8.3), respectively. The overall prevalence of short sleep duration among students was 66.0% (95% CI: 63.8–68.1), higher among girls than boys (69.8% vs. 62.1%) (P < 0.0001). The figures for middle school, academic high school, and vocational high school were 59.0, 82.4 and 59.7%, respectively (P < 0.0001). As compared with girls who sleep 8 h per day (reference), the odds ratios (95% CI) of obesity for girls who sleep < 7 h, 7 h, 9 h and ≥ 10 h were 1.97 (1.15–3.38), 1.90 (1.18–3.04), 1.38 (0.86–2.20) and 2.12 (1.22–3.67) respectively, after adjustment for socio-demographic status, lifestyle factors, and mental health. The corresponding figures among boys were 1.45 (0.97–2.16), 1.13 (0.81–1.57), 1.25 (0.89–1.74), and 1.12 (0.81–1.54), respectively. Insufficient sleep is prevalent among students in Zhejiang China. A U-shaped relationship was found between sleep duration and obesity risk among girls, with the lowest risk among those who slept for 8 h, but not among boys. Adequate sleep duration may be an important component of obesity prevention initiatives among adolescents.

The incidence of type 2 diabetes mellitus (T2DM) has been increasing globally over the past two decades in children and adolescents. There are currently a dearth of comprehensive population-based estimates of T2DM incidence and time trends in Chinese youth. A population-based diabetes registry system in 30 representative districts in Zhejiang has been established for diabetes surveillance. All newly cases diagnosed by physicians in local hospitals and wards were registered using the registry system through web services and direct network report. The data were primarily abstracted from medical records in hospitals and wards. Annual incidence rates and their 95% confidence intervals (CIs) by age groups and sex were calculated per 100 000 person-years. Poisson regression models were applied to assess the effects of diagnosis year, age groups, sex and residence area on T2DM incidence and to examine the average annual percentage change in incidence. There were 392 newly diagnosed cases of T2DM (210 boys and 182 girls) over the study period. The mean annual age-standardized incidence was 1.96/100 000 person-years (95% CIs: 1.85–2.08). No statistically significant difference in incidence was found between boys and girls. However, the risk for T2DM was 1.49 times higher in urban area than in rural area. Besides, the mean annual incidence in youth increased with age. The age-standardized incidence was about 5 times higher in 2013 than in 2007. Steep rising incidence was observed, with an average annual increase of 26.6% in youth aged 10–19 years. The incidence of T2DM in children and adolescents was low in Zhejiang relative to other countries, whereas it increased markedly over the study period. Preventive strategies for T2DM are necessary in pediatric population.

Increasing evidence indicates that cigarette smoking is a strong predictor of electronic cigarettes (e-cigarettes) use, particularly in adolescents, yet the effects has not be systematically reviewed and quantified. Relevant studies were retrieved by searching three databases up to June 2015. The meta-analysis results were presented as pooled odds ratios (ORs) with 95% confidence intervals (CIs) calculated by a random-effects model. Current smokers were more likely to use e-cigarette currently (OR: 14.89, 95% CI: 7.70–28.78) and the probability was greater in adolescents than in adults (39.13 vs. 7.51). The probability of ever e-cigarettes use was significantly increased in smokers (OR: 14.67, 95% CI: 11.04–19.49). Compared with ever smokers and adults, the probabilities were much greater in current smokers (16.10 vs. 9.47) and adolescents (15.19 vs. 14.30), respectively. Cigarette smoking increases the probability of e-cigarettes use, especially in current smokers and adolescents.

Although exercise seems to be beneficial for type 2 diabetes mellitus (T2DM) patients, there is limited research elucidating the optimal accessible indices of adiposity and insulin resistance for identifying elderly T2DM patients with poor glycemic control, which could be improved by performing regular exercise. A community-based, cross-sectional study was conducted with 918 Chinese elderly individuals with T2DM in Zhejiang. Relevant risk factors for poor glycemic control, as determined using glycated haemoglobin A1c (HbA1c) > 7%, were explored using logistic regression analyses and included body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR), fasting blood glucose (FBG), triglycerides (TGs), total cholesterol (TC), the product of fasting triglycerides and glucose (TyG), visceral adiposity index (VAI), lipid accumulation product (LAP), TyG-BMI, and TyG-WC. Comparisons of the risk factors’ ability to discriminate poor glycemic control as well as their optimal cutoff values were determined using receiver operating characteristic (ROC) analyses, and then the extent of poor glycemic control risk reduction through regular exercise was examined using multivariate logistic regression analyses. The overall poor glycemic control rate was 49.3%. The factors associated with poor glycemic control included FBG > 3.869, TyG > 8.73, TyG-BMI > 222.45, and TyG-WC > 713.48 in logistic regression analyses. The optimal cutoff points of FBG, TyG, TyG-WC, and TyG-BMI in discriminating poor glycemic control were 7.38, 9.22, 813.33, and 227.77, and their corresponding areas under the ROC curves were 0.864(0.840–0.886), 0.684(0.653–0.714), 0.604(0.571–0.635), and 0.574(0.541–0.606), respectively. Occasional and regular exercise reduced the odds ratios (95% confidence interval) of poor glycemic control to 0.187 (0.063–0.557) and 0.183 (0.059–0.571) for subjects with TyG-WC > 813.33 (p = 0.008), to 0.349 (0.156–0.782) and 0.284 (0.123–0.652) for subjects with TyG > 9.22 (p = 0.011), and to 0.390 (0.175–0.869) and 0.300(0.130–0.688) for subjects with TyG-BMI > 227.77 (p = 0.017), respectively, after adjusting for multiple confounding factors. Among elderly individuals with T2DM, poor glycemic control risk might be identified using indices calculated from FBG, TG, BMI, and WC measurements, which are indicative of adiposity and insulin resistance. TyG-WC seems to be an accessible and useful indicator to identify which elderly T2DM patients would benefit from performing regular exercise to achieve good glycemic control.