BMC Public Health 2009, 9:130 http://www.biomedcentral.com/1471-2458/9/130 D score of 0–15 was defined as no or minimal depression and a CES-D score ≥16 was defined as minor depres- sion.[32] Following a physical examination, BMI levels were measured and categorized as under weight (<18.5 kg/m2), normal weight (18.5 to <24.0 kg/m2) and over- weight/obese (≥24.0 kg/m2).[35] Three measurements of blood pressure were taken using an electronic blood pres- sure monitor and the mean of the last two measurements was used for analysis. Laboratory methods Overnight fasting blood samples were collected in tubes containing liquid ethylenediaminetetraacetic acid (EDTA), centrifuged at 4°C, and stored at -80°C until analysis. Total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) choles- terol, triglycerides (TG), and glucose were measured enzy- matically (Hitachi 7080, Japan). Plasma C-reactive protein (CRP) was measured by a particle-enhanced immunoturbidimetric assay (Ultrasensitive CRP kit, Orion Diagnostica, Espoo, Finland). Plasma adiponectin concentrations were measured by Luminex xMAP™ Tech- nology (Linco Research Inc, St Charles, Mo). All proce- dures followed the manufacture's instruction with quality controls in the expected ranges for each assay. Human Retinol Binding Protein 4 (RBP4) was measured by a sandwich ELISA developed in-house.[36] Interleuikin 6 (IL-6) was measured with high-sensitivity enzyme-linked immuno-sorbent assay (ELISA) (Quantikine HS IL-6 Immunoassay, R&D Systems, Inc., Minneapolis, Minne- sota). Plasma adiponectin, resistin and active plasmino- gen activator inhibitor-1 (PAI-1) concentrations were measured by Luminex xMAPTM Technology (Linco Research Inc, St Charles, Mo).[27] Fasting glucose was measured enzymatically on an automatic analyzer (Hitachi 7080, Japan). Fasting insulin was determined by radioimmunoassay (Linco Research, MO). Index of insu- lin resistance was calculated using updated homeostasis model assessment methods (HOMA2-IR, using the HOMA2 calculator, http://www.dtu.ox.ac.uk).[37] Statistical analyses We evaluated the distribution of sleep quality in the total population and how it varied by gender, geographic region and area of residence. Using a chi-square test, anal- ysis of variance or Wilcoxon rank-sum test and univariate analysis, we evaluated the associations between sleep quality and potential associated factors (selected based on previous literature or scientific knowledge). Factors that significantly modified sleep quality or those that were consistently reported as significant determinants in previ- ous publications were selected to be included in multivar- iate analysis applying an ordinal regression model. Multivariate analysis was repeated stratifying by gender, region and residence one at a time and by stratifying for two of these factors simultaneously. The associations between sleep quality and biomarkers of cardio-metabolic disease were tested on the total popula- tion and on a sub-sample of participants whom were free from CVD and diabetes with parametric t-tests (after log- arithmic transformation of the data to satisfy normality assumptions) or non-parametric Wilcoxon rank-sum tests. Data management and statistical analyses were performed with the SAS statistical package version 9.1 (SAS Institute, Cary, NC). Statistical tests were two-sided and at a 5% level of significance. Results Characteristics of the participants The mean age of the study sample was 58.6 years (Table 1). More than half of the participants were women (55.7%), 72% were non-smokers (94% women and 44% men), 72% were non alcohol drinkers (90% women and 48% men), 92% were involved in moderate or high levels of physical activity and 53% were overweight or obese. Three percent of the participants reported taking sleeping pills (4% women and 1% men), 42% reported taking a nap (56% in Beijing, 29% in Shanghai), 68% reported having 7–9 hours of daily sleep and 53% reported good sleep quality (Table 1). Thirty two percent of participants reported good/excellent self-rated health, 9% of partici- pants suffered from depressive symptoms (15% in Beijing and 4% in Shanghai), 14% had diabetes (19% in Beijing and 9% in Shanghai), 10% had CVD (15% in Beijing and 6% in Shanghai), 42% had metabolic syndrome (50% in Beijing and 35% in Shanghai), 14% had hyperlipidemia, 7% had pulmonary disease, 13% arthritis and 11% pros- tatisim (Table 1). Distribution of sleep quality More than half of the study population (53%) reported good sleep quality (Beijing 46% vs. Shanghai 60%, women 47% vs. 61% men). Almost a third of the popula- tion (31%) reported a 'common' level of sleep quality (Beijing 34% vs. 27% Shanghai, women 33% vs. 28% men) and the rest (16%) reported poor sleep quality (19% Beijing vs. 13% Shanghai, women 20% vs. men 11%) (Table 1, data for gender difference not shown). Eighty-eight percent of the participants who reported good sleep quality reported seven or more hours of daily sleep. In participants that reported poor sleep quality, 84% had fair, poor or very poor SRH. Three per cent of those with good sleep quality reported depressive symp- toms, compared to 27% of those who reported poor sleep quality Additionally, higher prevalence of CVD (20% vs. Page 3 of 11 (page number not for citation purposes)