BackgroundThe relationship between obesity and type 2 diabetes mellitus (T2DM) varies with geographical area and race.ObjectivesTo investigate the prevalence of T2DM and the proportion of subjects with undiagnosed T2DM. In addition, to compare the associations between different obesity indices and T2DM for middle-aged and elderly people from six communities in Jinan, China.SettingA cross-sectional study was designed and the study subjects were chosen from blocks which were randomly selected in the 6 communities of Jinan, China in 2011–2012.ParticipantsA total of 3277 residents aged ≥50 years were eligible for this study, but 1563 people were excluded because they did not provide anthropometric data such as height, weight, waist circumference (WC), hip circumference, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, triglyceride (TG), total cholesterol (TC) or information about their current medication use. Hence, 1714 participants were included in the final data analysis.ResultsThe prevalence of T2DM among people aged ≥50 years was 16.6% (19.3% for men and 15.3% for women) and the proportion of patients with undiagnosed T2DM was 32.7%. Compared with the lowest levels of body mass index (BMI), WC, waist-to-hip ratio or waist-to-stature ratio (WSR), the ORs and 95% CIs of the highest levels for men, after adjusting for age, smoking, alcohol drinking, regular exercise, hypertension, TG and TC, were 1.607 (0.804 to 3.210), 2.189 (1.118 to 4.285), 1.873 (0.968 to 3.623) and 2.572 (1.301 to 5.083), respectively, and for women, 2.764 (1.622 to 4.712), 2.407 (1.455 to 3.985), 2.500 (1.484 to 4.211) and 2.452 (1.447 to 4.155), respectively.ConclusionsAmong adults aged ≥50 years in Jinan, China, the best indicator of the relationship between obesity and T2DM is WSR for men and BMI for women, respectively.

Predicting the passenger flow of metro networks is of great importance for traffic management and public safety. However, such predictions are very challenging, as passenger flow is affected by complex spatial dependencies (nearby and distant) and temporal dependencies (recent and periodic). In this paper, we propose a novel deep-learning-based approach, named STGCNNmetro (spatiotemporal graph convolutional neural networks for metro), to collectively predict two types of passenger flow volumes-inflow and outflow-in each metro station of a city. Specifically, instead of representing metro stations by grids and employing conventional convolutional neural networks (CNNs) to capture spatiotemporal dependencies, STGCNNmetro transforms the city metro network to a graph and makes predictions using graph convolutional neural networks (GCNNs). First, we apply stereogram graph convolution operations to seamlessly capture the irregular spatiotemporal dependencies along the metro network. Second, a deep structure composed of GCNNs is constructed to capture the distant spatiotemporal dependencies at the citywide level. Finally, we integrate three temporal patterns (recent, daily, and weekly) and fuse the spatiotemporal dependencies captured from these patterns to form the final prediction values. The STGCNNmetro model is an end-to-end framework which can accept raw passenger flow-volume data, automatically capture the effective features of the citywide metro network, and output predictions. We test this model by predicting the short-term passenger flow volume in the citywide metro network of Shanghai, China. Experiments show that the STGCNNmetro model outperforms seven well-known baseline models (LSVR, PCA-kNN, NMF-kNN, Bayesian, MLR, M-CNN, and LSTM). We additionally explore the sensitivity of the model to its parameters and discuss the distribution of prediction errors.

BackgroundIt has been reported that the prevalence of chronic diseases is high among old people and they have poor chronic diseases knowledge. This study was therefore designed to evaluate the awareness rate of chronic diseases knowledge among people aged over 60 years, to explore its related factors and to provide evidence for future health education.MethodsA cross-sectional study was conducted from April to August in 2011. People aged 60 years and above from 3 communities in Jinan were selected by cluster sampling. Nine hundred and twenty five participants were interviewed face-to-face using a structured questionnaire.ResultsThe awareness rates of chronic diseases knowledge varied from 29.5% to 90.2%. Four healthy lifestyles including quitting smoking and less drinking, keeping broad-minded, maintaining balanced diet and moderate physical activity were best known (from 86.3% to 90.2%). The least known knowledge were 2 complications of hypertension: nephropathy (29.5%) and retinopathy (37.2%). Participants with the following characteristics or behaviors were more likely to have higher chronic diseases knowledge: younger age, female, Han Chinese, higher level of education, having health insurance, participating in societies, having family history of chronic diseases, frequently gathering with friends/relatives, usually going to provincial hospitals/hospitals affiliated with medical universities, usually going to municipal hospitals and usually going to community health center/station.ConclusionsOld people in Jinan had incomplete chronic diseases knowledge and the overall awareness rate was not high. The older people’s chronic diseases knowledge should be improved and health education programs should target males, older people with lower educational level, having no health insurance, having no family history of chronic diseases, participating in no societies, and less frequently gathering with friends/relatives. Also, lower level medical facilities should improve their skills of health education.

IL-1β (rs16944) and IL-18 (rs1946518) may be served as potential predictors for AML.