Impact of Short Sleep Duration on the Incidence of Obesity and Overweight among Children and Adolescents.

Background and Objectives: The prevalence of obesity among children is increasing and is highlighting many problems. Lack of sleep is common among children and adolescents. Although several studies have investigated sleep duration and overweight and obesity from a sex perspective, data regarding age and sex effects remain limited and inconclusive. This study aimed to evaluate the risk(s) for overweight or obesity according to sleep duration among children and adolescents; to evaluate the effect of short sleep duration on the incidence of overweight/obesity among children and adolescents; and to evaluate sex differences in the risk of overweight or obesity with shorter sleep durations. Materials and Methods: The PubMed database was searched for relevant studies published up to June 30, 2021. Odds ratios for obesity/overweight were estimated for short compared with long sleep duration. Subgroup analysis based on sleep duration, sex, and study location was also performed. Results: The estimated odds ratio for combined obesity and overweight was 1.171 (95% confidence interval (CI) 1.092−1.256) according to short sleep duration. Obesity/overweight with short sleep duration was significantly prevalent in the <6 and 6−10 years' subgroups (odds ratio 1.226 (95% CI 1.083−1.387) and 1.341 (95% CI 1.175−1.530), respectively). Among boys, short sleep duration was significantly correlated with a high occurrence of obesity/overweight (odds ratio 1.294 (95% CI 1.153−1.452)); no such correlation was found among girls. Conclusions: Short sleep duration may increase risk of obesity among children and adolescents, especially those <6 and 6−10 years of age. In the subgroup analysis, the incidence of obesity/overweight for short sleep time revealed significant results among Asians and boys.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population.

BACKGROUND: Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data. METHODS: The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions. RESULTS: The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes. CONCLUSIONS: Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

Phorbol 12-Myristate 13-Acetate Enhances Long-Term Potentiation in the Hippocampus through Activation of Protein Kinase Cδ and ε.

Many intracellular proteins and signaling cascades contribute to the sensitivity of N-methyl-D-aspartate receptors (NMDARs). One such putative contributor is the serine/threonine kinase, protein kinase C (PKC). Activation of PKC by phorbol 12-myristate 13-acetate (PMA) causes activation of extracellular signal-regulated kinase (ERK) and promotes the formation of new spines in cultured hippocampal neurons. The purpose of this study was to examine which PKC isoforms are responsible for the PMA-induced augmentation of long-term potentiation (LTP) in the CA1 stratum radiatum of the hippocampus in vitro and verify that this facilitation requires NMDAR activation. We found that PMA enhanced the induction of LTP by a single episode of theta-burst stimulation in a concentration-dependent manner without affecting to magnitude of baseline field excitatory postsynaptic potentials. Facilitation of LTP by PMA (200 nM) was blocked by the nonspecific PKC inhibitor, Ro 31-8220 (10µM); the selective PKCδ inhibitor, rottlerin (1µM); and the PKCε inhibitor, TAT-εV1-2 peptide (500 nM). Moreover, the NMDAR blocker DL-APV (50µM) prevented enhancement of LTP by PMA. Our results suggest that PMA contributes to synaptic plasticity in the nervous system via activation of PKCδ and/or PKCε, and confirm that NMDAR activity is required for this effect.

Identification of the associations between genes and quantitative traits using entropy-based kernel density estimation.

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

Trans-Anethole Alleviates Trimethyltin Chloride-Induced Impairments in Long-Term Potentiation.

Trans-anethole is an aromatic compound that has been studied for its anti-inflammation, anticonvulsant, antinociceptive, and anticancer effects. A recent report found that trans-anethole exerted neuroprotective effects on the brain via multiple pathways. Since noxious stimuli may both induce neuronal cell injury and affect synaptic functions (e.g., synaptic transmission or plasticity), it is important to understand whether the neuroprotective effect of trans-anethole extends to synaptic plasticity. Here, the effects of trimethyltin (TMT), which is a neurotoxic organotin compound, was investigated using the field recording method on hippocampal slice of mice. The influence of trans-anethole on long-term potentiation (LTP) was also studied for both NMDA receptor-dependent and NMDA receptor-independent cases. The action of trans-anethole on TMT-induced LTP impairment was examined, too. These results revealed that trans-anethole enhances NMDA receptor-dependent and -independent LTP and alleviates TMT-induced LTP impairment. These results suggest that trans-anethole modulates hippocampal LTP induction, prompting us to speculate that it may be helpful for improving cognitive impairment arising from neurodegenerative diseases, including Alzheimer's disease.

Impairment of long-term depression induced by chronic brain inflammation in rats.

Although deficits in synaptic plasticity have been identified in aged or neuroinflamed animals with memory impairments, few studies have examined the cellular basis of plasticity in such animals. Here, we examined whether chronic neuroinflammation altered long-term depression (LTD) and studied the underlying mechanism of LTD impairment by neuroinflammation. Chronic neuroinflammation was induced by administration of lipopolysaccharide (LPS) to the fourth ventricle. Excitatory postsynaptic potentials were recorded extracellularly in the rat hippocampal CA1 area to examine alterations in synaptic plasticity. Chronic administration of LPS induced remarkable memory impairment in the Morris water maze test. N-methyl-d-aspartate receptor (NMDAR)-dependent LTD was almost absent in LPS-infused animals. The AMPA receptor (AMPAR)-mediated synaptic response was reduced in the LPS-infused group. These results suggest that reduction in NMDAR-dependent LTD might arise because of alterations in postsynaptic AMPARs as well as NMDARs and that such changes may be present in mild and early forms of Alzheimer-type dementia.

Effects of CAY10404 on the PKB/Akt and MAPK pathway and apoptosis in non-small cell lung cancer cells.

BACKGROUND AND OBJECTIVE: Lung cancer is the most common cause of cancer death in men and women worldwide. The mechanism of cell death induced by CAY10404, a highly selective cyclooxygenase-2 inhibitor, was evaluated in three non-small cell lung cancer (NSCLC) cell lines (H460, H358, H1703). METHODS: To measure the effects of CAY10404 on proliferation of NSCLC cells, 3 x 10(3) cells/well were plated in 96-well plates and allowed to adhere overnight at 37 degrees C. After treatment with CAY10404 for 3 days, cell proliferation was measured by the 3- (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. In the H460 NSCLC cells, evidence of apoptosis was sought using the terminal deoxynucleotidyl transferase deoxyuridine triphosphate (dUTP) nick end labelling (TUNEL) assay and western blot analysis. RESULTS: Treatment with CAY10404 in the range of 10-100 microM caused dose-dependent growth inhibition, with an average 50% inhibitory concentration (IC(50)) of 60-100 micromol/L, depending on the cell line. Western blot analysis of CAY10404-treated cells showed cleavage of poly (ADP-ribose) polymerase (PARP) and procaspase-3, signifying caspase activity and apoptotic cell death. CAY10404 treatment inhibited the phosphorylation of Akt, glycogen synthase kinase-3beta and extracellular signal-regulated kinases 1/2 in H460 and H358 cells. CONCLUSIONS: These results suggest that CAY10404 is a potent inducer of apoptosis in NSCLC cells, and that it may act by suppressing multiple protein kinase B/Akt and mitogen-activated protein kinase pathways.

Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes.

Genome-wide association studies (GWAS) have been successful in identifying genetic variants associated with complex diseases. However, association analyses between genotypes and phenotypes are not straightforward due to the complex relationships between genetic and environmental factors. Moreover, multiple correlated phenotypes further complicate such analyses. To resolve this complexity, we present an analysis using structural equation modeling (SEM). Unlike current methods that focus only on identifying direct associations between diseases and genetic variants such as single-nucleotide polymorphisms (SNPs), our method introduces the effects of intermediate phenotypes, which are related phenotypes distinct from the target, into the systematic genetic study of diseases. Moreover, we consider multiple diseases simultaneously in a single model. The procedure can be summarized in four steps: 1) selection of informative SNPs, 2) extraction of latent variables from the selected SNPs, 3) investigation of the relationships among intermediate phenotypes and diseases, and 4) construction of an SEM. As a result, a quantitative map can be drawn that simultaneously shows the relationship among multiple SNPs, phenotypes, and diseases. In this study, we considered two correlated diseases, hypertension and type 2 diabetes (T2D), which are known to have a substantial overlap in their disease mechanism and have significant public health implications. As intermediate phenotypes for these diseases, we considered three obesity-related phenotypes-subscapular skin fold thickness, body mass index, and waist circumference-as traits representing subcutaneous adiposity, overall adiposity, and abdominal adiposity, respectively. Using GWAS data collected from the Korea Association Resource (KARE) project, we applied the proposed SEM process. Among 327,872 SNPs, 24 informative SNPs were selected in the first step (p<1.0E-05). Ten latent variables were generated in step 2. After an exploratory analysis, we established a path diagram among phenotypes and diseases in step 3. Finally, in step 4, we produced a quantitative map with paths moving from specific SNPs to hypertension through intermediate phenotypes and T2D. The resulting model had high goodness-of-fit measures (χ2 = 536.52, NFI = 0.997, CFI = 0.998, GFI = 0.995, AGFI = 0.993, RMSEA = 0.012).

Significant role of gene-gene interactions of clock genes in mood disorder.

BACKGROUND: The genetic interactions in the circadian rhythm biological system are promising as a source of pathophysiology in mood disorder. We examined the role of the gene-gene interactions of clock genes in mood disorder. METHODS: We included 413 patients with mood disorder and 1294 controls. The clock genes investigated were BHLHB2, CLOCK, CSNK1E, NR1D1, PER2, PER3, and TIMELESS. Allele, genotype, and haplotype associations were tested. Gene--gene interactions were analyzed using the non-parametric model-free multifactor-dimensionality reduction (MDR) method. RESULTS: TIMELESS rs4630333 and CSNK1E rs135745 were significantly associated with both major depressive disorder and bipolar disorder. The CLOCK haplotype was also strongly associated. The genetic roles of these SNPs were consistent from the allele and genotypic associations to the MDR interaction results. In MDR analysis, the combination of TIMELESS rs4630333 and CSNK1E rs135745 exhibited the most significant association with mood disorders in the two-locus model. BHLHB2 rs2137947 for major depressive disorder and CLOCK rs12649507 for bipolar disorder were the most significant third loci in the three-locus combination model. The four-locus SNP combination model showed the best balanced accuracy (BA), but its cross-validation consistency (CVC) was unsatisfactory. LIMITATIONS: We included only 17 SNPs for seven circadian genes due to our limited resources; all subjects were ethnically Korean. CONCLUSIONS: Our results suggest significant single-gene associations and gene-gene interactions of circadian genes with mood disorder. Gene-gene interactions play a crucial role in mood disorder, even when individual clock genes do not have significant roles.

Genetic Association Analysis of Fasting and 1- and 2-Hour Glucose Tolerance Test Data Using a Generalized Index of Dissimilarity Measure for the Korean Population.

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.

Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes.

To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.

Detecting Genetic Interactions for Quantitative Traits Using m-Spacing Entropy Measure.

A number of statistical methods for detecting gene-gene interactions have been developed in genetic association studies with binary traits. However, many phenotype measures are intrinsically quantitative and categorizing continuous traits may not always be straightforward and meaningful. Association of gene-gene interactions with an observed distribution of such phenotypes needs to be investigated directly without categorization. Information gain based on entropy measure has previously been successful in identifying genetic associations with binary traits. We extend the usefulness of this information gain by proposing a nonparametric evaluation method of conditional entropy of a quantitative phenotype associated with a given genotype. Hence, the information gain can be obtained for any phenotype distribution. Because any functional form, such as Gaussian, is not assumed for the entire distribution of a trait or a given genotype, this method is expected to be robust enough to be applied to any phenotypic association data. Here, we show its use to successfully identify the main effect, as well as the genetic interactions, associated with a quantitative trait.

A modified entropy-based approach for identifying gene-gene interactions in case-control study.

Gene-gene interactions may play an important role in the genetics of a complex disease. Detection and characterization of gene-gene interactions is a challenging issue that has stimulated the development of various statistical methods to address it. In this study, we introduce a method to measure gene interactions using entropy-based statistics from a contingency table of trait and genotype combinations. We also developed an exploration procedure by using graphs. We propose a standardized relative information gain (RIG) measure to evaluate the interactions between single nucleotide polymorphism (SNP) combinations. To identify the k (th) order interactions, contingency tables of trait and genotype combinations of k SNPs are constructed, with which RIGs are calculated. The RIGs are standardized using the mean and standard deviation from the permuted datasets. SNP combinations yielding high standardized RIG are chosen for gene-gene interactions. Detection of high-order interactions and comparison of interaction strengths between different orders are made possible by using standardized RIG. We have applied the proposed standardized entropy-based method to two types of data sets from a simulation study and a real genetic association study. We have compared our method and the multifactor dimensionality reduction (MDR) method through power analysis of eight different genetic models with varying penetrance rates, number of SNPs, and sample sizes. Our method shows successful identification of genetic associations and gene-gene interactions both in simulation and real genetic data. Simulation results suggest that the proposed entropy-based method is better able to detect high-order interactions and is superior to the MDR method in most cases. The proposed method is well suited for detecting interactions without main effects as well as for models including main effects.

External cross-validation of bioelectrical impedance analysis for the assessment of body composition in Korean adults.

Bioelectrical impedance analysis (BIA) models must be validated against a reference method in a representative population sample before they can be accepted as accurate and applicable. The purpose of this study was to compare the eight-electrode BIA method with DEXA as a reference method in the assessment of body composition in Korean adults and to investigate the predictive accuracy and applicability of the eight-electrode BIA model. A total of 174 apparently healthy adults participated. The study was designed as a cross-sectional study. FM, %fat, and FFM were estimated by an eight-electrode BIA model and were measured by DEXA. Correlations between BIA_%fat and DEXA_%fat were 0.956 for men and 0.960 for women with a total error of 2.1%fat in men and 2.3%fat in women. The mean difference between BIA_%fat and DEXA_%fat was small but significant (P < 0.05), which resulted in an overestimation of 1.2 ± 2.2%fat (95% CI: -3.2-6.2%fat) in men and an underestimation of -2.0 ± 2.4%fat (95% CI: -2.3-7.1%fat) in women. In the Bland-Altman analysis, the %fat of 86.3% of men was accurately estimated and the %fat of 66.0% of women was accurately estimated to within 3.5%fat. The BIA had good agreement for prediction of %fat in Korean adults. However, the eight-electrode BIA had small, but systemic, errors of %fat in the predictive accuracy for individual estimation. The total errors led to an overestimation of %fat in lean men and an underestimation of %fat in obese women.