Association of type 2 diabetes with anthropometrics, bone mineral density, and body composition in a large-scale screening study of Korean adults.

OBJECTIVES: Type 2 diabetes mellitus (T2DM) is a common, chronic disease that is closely associated with anthropometric indices related to obesity. However, no study published to date has simultaneously examined the associations of T2DM with anthropometrics, bone mineral density (BMD), and body composition variables. The present study aimed to evaluate the associations of T2DM with anthropometrics, BMD and body composition variables and to identify the best indicator of T2DM in Korean adults. METHODS: The data used in this study were obtained from the Korea National Health and Nutrition Examination Survey conducted from 2008 to 2011. A total of 7,835 participants aged from 40 to 90 years were included in this study. A binary logistic regression analysis was performed to examine the significance of differences between the groups with and without T2DM, and the areas under the receiver operating characteristic (AUCs) curves were calculated to compare the predictive power of all variables. RESULTS: In men, waist-to-height ratio (WHtR) displayed the strongest association with T2DM (adjusted odds ratio (OR) = 1.838 [1.513-2.233], adjusted p<0.001), and waist circumference (WC) and WHtR were the best indicators (WC: AUC = 0.662 [0.639-0.685], WHtR: AUC = 0.680 [0.658-0.703]) of T2DM among all the variables. In women, left leg (LL) and right leg (RL) fat displayed strong negative associations with T2DM (LL fat: adjusted OR = 0.367 [0.321-0.419], adjusted p<0.001, RL fat: adjusted OR = 0.375 [0.329-0.428], adjusted p<0.001), and WC and WHtR were excellent indicators (WC: AUC = 0.730 [0.709-0.750], WHtR: AUC = 0.747 [0.728-0.766]) of T2DM among all the variables. In particular, the WHtR in men and LL and RL fat in women exhibited the strongest associations with T2DM, and the predictive power of the WC and WHtR was stronger than BMD, fat, and muscle mass variables in both men and women. Additionally, the predictive power of the WC and WHtR was stronger in women than in men. DISCUSSION: Of the anthropometric indices, BMD, and body fat and muscle variables, the best indicators of T2DM were WC and WHtR in both Korean men and women. The results of the present investigation will provide basic information for clinical studies of patients with T2DM and evidence for the prevention and management of T2DM.

Identification of hypertriglyceridemia based on bone density, body fat mass, and anthropometry in a Korean population.

BACKGROUND: Hypertriglyceridemia is strongly associated with the risks of cardiovascular disease, coronary heart disease, and metabolic syndrome. The relationship between hypertriglyceridemia or high triglyceride levels and bone mineral density remains controversial. Furthermore, to date, no study has simultaneously examined the association among hypertriglyceridemia, bone area, bone mineral content, bone mineral density, body fat mass, and anthropometrics. The present study aimed to evaluate the association among hypertriglyceridemia, anthropometrics and various bone density and body fat composition variables to identify the best indicator of hypertriglyceridemia in a Korean population. METHODS: The data were obtained from the fifth Korea National Health and Nutrition Examination Survey. In total, 3918 subjects aged 20-80 years participated in this study. In the variable analysis of the waist circumference (WC), trunk fat mass (Trk-Ft), body mass index, etc., a binary logistic regression analysis was performed to examine the significance of the differences between the normal group and hypertriglyceridemia groups. RESULTS: In both men and women, the WC showed the strongest association with hypertriglyceridemia in the crude analysis (odds ratio (OR) = 1.738 [confidence interval = 1.529-1.976] and OR = 2.075 [1.797-2.397]), but the Trk-Ft was the most strongly associated with the disease after adjusting for age and body mass index (adjusted OR = 1.565 [1.262-1.941] and adjusted OR = 1.730 [1.291-2.319]). In particular, the Pelvis area (Plv-A) was the most significant among the bone variables in women (adjusted OR = 0.641 [0.515-0.796]). In the predictive power analysis, the best indicator of hypertriglyceridemia was WC in women (the area under the receiver operating characteristic curve (AUC) = 0.718 [0.685-0.751]) and Trk-Ft in men (AUC = 0.672 [0.643-0.702]). The WC was also the most predictive among the anthropometric variables in men (AUC = 0.670 [0.641-0.700]). The strength of the association and predictive power was stronger in women than in men. CONCLUSIONS: The WC in women and Trk-Ft in men exhibited the best predictive power for hypertriglyceridemia. Our findings support the use of basic information for the identification of hypertriglyceridemia or high triglyceride levels in initial health screening efforts.

Constructing RBAC based security model in u-healthcare service platform.

In today's era of aging society, people want to handle personal health care by themselves in everyday life. In particular, the evolution of medical and IT convergence technology and mobile smart devices has made it possible for people to gather information on their health status anytime and anywhere easily using biometric information acquisition devices. Healthcare information systems can contribute to the improvement of the nation's healthcare quality and the reduction of related cost. However, there are no perfect security models or mechanisms for healthcare service applications, and privacy information can therefore be leaked. In this paper, we examine security requirements related to privacy protection in u-healthcare service and propose an extended RBAC based security model. We propose and design u-healthcare service integration platform (u-HCSIP) applying RBAC security model. The proposed u-HCSIP performs four main functions: storing and exchanging personal health records (PHR), recommending meals and exercise, buying/selling private health information or experience, and managing personal health data using smart devices.

Identification of protein functions using a machine-learning approach based on sequence-derived properties.

BACKGROUND: Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities. RESULTS: A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function. CONCLUSION: We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new PNPRD features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.