Glucose tolerance tests have been devised to determine the speed of blood glucose clearance. Diabetes is often tested with the standard oral glucose tolerance test (OGTT), along with fasting glucose level. However, no single test may be sufficient for the diagnosis, and the World Health Organization (WHO)/International Diabetes Federation (IDF) has suggested composite criteria. Accordingly, a single multi-class trait was constructed with three of the fasting phenotypes and 1- and 2-hour OGTT phenotypes from the Korean Association Resource (KARE) project, and the genetic association was investigated. All of the 18 possible combinations made out of the 3 sets of classification for the individual phenotypes were taken into our analysis. These were possible due to a method that was recently developed by us for estimating genomic associations using a generalized index of dissimilarity. Eight single-nucleotide polymorphisms (SNPs) that were found to have the strongest main effect are reported with the corresponding genes. Four of them conform to previous reports, located in the CDKAL1 gene, while the other 4 SNPs are new findings. Two-order interacting SNP pairs of are also presented. One pair (rs2328549 and rs6486740) has a prominent association, where the two single-nucleotide polymorphism locations are CDKAL1 and GLT1D1. The latter has not been found to have a strong main effect. New findings may result from the proper construction and analysis of a composite trait.
Blood Glucose ; Classification ; Diagnosis ; Fasting* ; Glucose Tolerance Test* ; Glucose* ; Methods ; Phenotype ; Polymorphism, Single Nucleotide ; World Health Organization

To identify miRNA-mRNA interaction pairs associated with binary phenotypes, we propose a hierarchical structural component model for miRNA-mRNA integration (HisCoM-mimi). Information on known mRNA targets provided by TargetScan is used to perform HisCoM-mimi. However, multiple databases can be used to find miRNA-mRNA signatures with known biological information through different algorithms. To take these additional databases into account, we present our advanced application software for HisCoM-mimi for binary phenotypes. The proposed HisCoM-mimi supports both TargetScan and miRTarBase, which provides manually-verified information initially gathered by text-mining the literature. By integrating information from miRTarBase into HisCoM-mimi, a broad range of target information derived from the research literature can be analyzed. Another improvement of the new HisCoM-mimi approach is the inclusion of updated algorithms to provide the lasso and elastic-net penalties for users who want to fit a model with a smaller number of selected miRNAs and mRNAs. We expect that our HisCoM-mimi software will make advanced methods accessible to researchers who want to identify miRNA-mRNA interaction pairs related with binary phenotypes.
MicroRNAs ; Phenotype ; RNA, Messenger

BACKGROUND: For correct interpretation of the high-density lipoprotein cholesterol (HDL-C) data from the Korea National Health and Nutrition Examination Survey (KNHANES), the values should be comparable to reference values. We aimed to suggest a way to calibrate KNHANES HDL-C data from 2008 to 2015 to the Centers for Disease Control and Prevention (CDC) reference method values. METHODS: We derived three calibration equations based on comparisons between the HDL-C values of the KNHANES laboratory and the CDC reference method values in 2009, 2012, and 2015 using commutable frozen serum samples. The selection of calibration equation for correcting KNHANES HDL-C in each year was determined by the accuracy-based external quality assurance results of the KNHANES laboratory. RESULTS: Significant positive biases of HDL-C values were observed in all years (2.85-9.40%). We created the following calibration equations: standard HDL-C=0.872×[original KNHANES HDL-C]+2.460 for 2008, 2009, and 2010; standard HDL-C=0.952×[original KNHANES HDL-C]+1.096 for 2012, 2013, and 2014; and standard HDL-C=1.01×[original KNHANES HDL-C]-3.172 for 2011 and 2015. We calibrated the biases of KNHANES HDL-C data using the calibration equations. CONCLUSIONS: Since the KNHANES HDL-C values (2008-2015) showed substantial positive biases compared with the CDC reference method values, we suggested using calibration equations to correct KNHANES data from these years. Since the necessity for correcting the biases depends on the characteristics of research topics, each researcher should determine whether to calibrate KNHANES HDL-C data or not for each study.
Algorithms ; Calibration ; Cholesterol, HDL/*blood/standards ; Humans ; *Nutrition Surveys ; Reference Values ; Republic of Korea

Objective: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. Methods: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. Results: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. Conclusion This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

Objective: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. Methods: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. Results: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. Conclusion This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

Background: While the triglyceride-glucose (TyG) index is a measure of insulin resistance, its association with cardiovascular disease (CVD) has not been well elucidated. We evaluated the TyG index for prediction of CVDs in a prospective large communitybased cohort. Methods: Individuals 40 to 70 years old were prospectively followed for a median 15.6 years. The TyG index was calculated as the Ln [fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2]. CVDs included any acute myocardial infarction, coronary artery disease or cerebrovascular disease. We used a Cox proportional hazards model to estimate CVD risks according to quartiles of the TyG index and plotted the receiver operating characteristics curve for the incident CVD. Results: Among 8,511 subjects (age 51.9±8.8 years; 47.5% males), 931 (10.9%) had incident CVDs during the follow-up. After adjustment for age, sex, body mass index, diabetes mellitus, hypertension, total cholesterol, smoking, alcohol, exercise, and C-reactive protein, subjects in the highest TyG quartile had 36% increased risk of incident CVD compared with the lowest TyG quartile (hazard ratio, 1.36; 95% confidence interval, 1.10 to 1.68). Carotid plaque, assessed by ultrasonography was more frequent in subjects in the higher quartile of TyG index (P for trend=0.049 in men and P for trend <0.001 in women). The TyG index had a higher predictive power for CVDs than the homeostasis model assessment of insulin resistance (HOMA-IR) (area under the curve, 0.578 for TyG and 0.543 for HOMA-IR). Adding TyG index on diabetes or hypertension alone gave sounder predictability for CVDs. Conclusion The TyG index is independently associated with future CVDs in 16 years of follow-up in large, prospective Korean cohort.

Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. Methods: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). Results: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. Conclusion This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.