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Background and AimsIt is commonly accepted that body fat distribution is associated with hypertension, but the strongest anthropometric indicator of the risk of hypertension is still controversial. Furthermore, no studies on the association of hypotension with anthropometric indices have been reported. The objectives of the present study were to determine the best predictors of hypertension and hypotension among various anthropometric indices and to assess the use of combined indices as a method of improving the predictive power in adult Korean women and men.MethodsFor 12789 subjects 21–85 years of age, we assessed 41 anthropometric indices using statistical analyses and data mining techniques to determine their ability to discriminate between hypertension and normotension as well as between hypotension and normotension. We evaluated the predictive power of combined indices using two machine learning algorithms and two variable subset selection techniques.ResultsThe best indicator for predicting hypertension was rib circumference in both women (p = <0.0001; OR = 1.813; AUC = 0.669) and men (p = <0.0001; OR = 1.601; AUC = 0.627); for hypotension, the strongest predictor was chest circumference in women (p = <0.0001; OR = 0.541; AUC = 0.657) and neck circumference in men (p = <0.0001; OR = 0.522; AUC = 0.672). In experiments using combined indices, the areas under the receiver operating characteristic curves (AUC) for the prediction of hypertension risk in women and men were 0.721 and 0.652, respectively, according to the logistic regression with wrapper-based variable selection; for hypotension, the corresponding values were 0.675 in women and 0.737 in men, according to the naïve Bayes with wrapper-based variable selection.ConclusionsThe best indicators of the risk of hypertension and the risk of hypotension may differ. The use of combined indices seems to slightly improve the predictive power for both hypertension and hypotension.

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.

BackgroundVisceral obesity is associated with facial characteristics and chronic disease, but no studies on the best predictor of visceral obesity based on facial characteristics have been reported. The aims of the present study were to investigate the association of visceral obesity with facial characteristics, to determine the best predictor of normal waist and visceral obesity among these characteristics, and to compare the predictive power of individual and combined characteristics.MethodsCross-sectional data were obtained from 11347 adult Korean men and women ranging from 18 to 80 years old. We examined 15 facial characteristics to identify the strongest predictor of normal and viscerally obese subjects and assessed the predictive power of the combined characteristics.ResultsFD_94_194 (the distance between both inferior ear lobes) was the best indicator of the normal and viscerally obese subjects in the following groups: Men-18-50 (p ≤ 0.0001, OR = 4.610, AUC = 0.821), Men-50-80 (p ≤ 0.0001, OR = 2.624, AUC = 0.735), and Women-18-50 (p ≤ 0.0001, OR = 2.979, AUC = 0.76). In contrast, FD_43_143 (mandibular width) was the strongest predictor in Women-50-80 (p ≤ 0.0001, OR = 2.099, AUC = 0.679). In a comparison of the combined characteristics, the area under the receiver operating characteristic curve (AUC) and the kappa values of the 4 groups ranged from 0.826 to 0.702 and from 0.483 to 0.279, respectively. The model for Men-18-50 showed the strongest predictive values and the model for Women-51-80 had the lowest predictive value for both the individual and combined characteristics.ConclusionsIn both men and women, the predictive power of the young and middle-age groups was better than that of the elderly groups for predicting normal waist and viscerally obese subjects for both the individual and combined characteristics. The predictive power appeared to increase slightly with the combined characteristics.

Obesity and overweight have become serious public health problems worldwide. Obesity and abdominal obesity are associated with type 2 diabetes, cardiovascular diseases, and metabolic syndrome. In this paper, we first suggest a method of predicting normal and overweight females according to body mass index (BMI) based on facial features. A total of 688 subjects participated in this study. We obtained the area under the ROC curve (AUC) value of 0.861 and kappa value of 0.521 in Female: 21–40 (females aged 21–40 years) group, and AUC value of 0.76 and kappa value of 0.401 in Female: 41–60 (females aged 41–60 years) group. In two groups, we found many features showing statistical differences between normal and overweight subjects by using an independent two-sample t-test. We demonstrated that it is possible to predict BMI status using facial characteristics. Our results provide useful information for studies of obesity and facial characteristics, and may provide useful clues in the development of applications for alternative diagnosis of obesity in remote healthcare.

It is well known that body fat distribution and obesity are important risk factors for type 2 diabetes. Prediction of type 2 diabetes using a combination of anthropometric measures remains a controversial issue. This study aims to predict the fasting plasma glucose (FPG) status that is used in the diagnosis of type 2 diabetes by a combination of various measures among Korean adults. A total of 4870 subjects (2955 females and 1915 males) participated in this study. Based on 37 anthropometric measures, we compared predictions of FPG status using individual versus combined measures using two machine-learning algorithms. The values of the area under the receiver operating characteristic curve in the predictions by logistic regression and naive Bayes classifier based on the combination of measures were 0.741 and 0.739 in females, respectively, and were 0.687 and 0.686 in males, respectively. Our results indicate that prediction of FPG status using a combination of anthropometric measures was superior to individual measures alone in both females and males. We show that using balanced data of normal and high FPG groups can improve the prediction and reduce the intrinsic bias of the model toward the majority class.

ObjectivesPeptic ulcer disease (PUD) is a common disorder, but whether an association exists between PUD and anthropometric indicators remains controversial. Furthermore, no studies on the association of PUD with anthropometric indices, blood parameters, and nutritional components have been reported. The aim of this study was to assess associations of anthropometrics, blood parameters, nutritional components, and lifestyle factors with PUD in the Korean population.MethodsData were collected from a nationally representative sample of the South Korean population using the Korea National Health and Nutrition Examination Survey. Logistic regression was used to examine associations of anthropometrics, blood parameters and nutritional components among patients with PUD.ResultsAge was the factor most strongly associated with PUD in women (p = <0.0001, odds ratio (OR) = 0.770 [0.683–0.869]) and men (p = <0.0001, OR = 0.715 [0.616–0.831]). In both crude and adjusted analyses, PUD was highly associated with weight (adjusted p = 0.0008, adjusted OR = 1.251 [95%CI: 1.098–1.426]), hip circumference (adjusted p = 0.005, adjusted OR = 1.198 [1.056–1.360]), and body mass index (adjusted p = 0.0001, adjusted OR = 1.303 [1.139–1.490]) in women and hip circumference (adjusted p = 0.0199, adjusted OR = 1.217 [1.031–1.435]) in men. PUD was significantly associated with intake of fiber (adjusted p = 0.0386, adjusted OR = 1.157 [1.008–1.328], vitamin B2 (adjusted p = 0.0477, adjusted OR = 1.155 [1.001–1.333]), sodium (adjusted p = 0.0154, adjusted OR = 1.191 [1.034–1.372]), calcium (adjusted p = 0.0079, adjusted OR = 1.243 [1.059–1.459]), and ash (adjusted p = 0.0468, adjusted OR = 1.152 [1.002–1.325] in women but not in men. None of the assessed blood parameters were associated with PUD in women, and only triglyceride level was associated with PUD in men (adjusted p = 0.0169, adjusted OR = 1.227 [1.037–1.451]).DiscussionWe found that obesity was associated with PUD in the Korean population; additionally, the association between nutritional components and PUD was greater in women than in men.

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