The circadian clock plays an important role in several aspects of female reproductive biology. Evidence linking circadian clock-related genes to pregnancy outcomes has been inconsistent. We sought to examine whether variations in single nucleotide polymorphisms (SNPs) of circadian clock genes are associated with PA risk.
Maternal blood samples were collected from 470 PA case and 473 controls. Genotyping was performed using the Illumina Cardio-MetaboChip platform. We examined 119 SNPs in 13 candidate genes known to control circadian rhythms (e.g., CRY2, ARNTL, and RORA). Univariate and penalized logistic regression models were fit to estimate odds ratios (ORs); and the combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score (wGRS).
A common SNP in the RORA gene (rs2899663) was associated with a 21% reduced odds of PA (P<0.05). The odds of PA increased with increasing wGRS (Ptrend< 0.001). The corresponding ORs were 1.00, 1.83, 2.81 and 5.13 across wGRS quartiles. Participants in the highest wGRS quartile had a 5.13-fold (95% confidence interval: 3.21–8.21) higher odds of PA compared to those in the lowest quartile. Although the test for interaction was not significant, the odds of PA was substantially elevated for preeclamptics with the highest wGRS quartile (OR=14.44, 95%CI: 6.62–31.53) compared to normotensive women in the lowest wGRS quartile.
Genetic variants in circadian rhythm genes may be associated with PA risk. Larger studies are needed to corroborate these findings and to further elucidate the pathogenesis of this important obstetrical complication.
Globally, common psychiatric disorders such as depression and anxiety are among the leading causes of morbidity and mortality. The 12-item General Health Questionnaire (GHQ-12) is a widely used questionnaire for screening or detecting common psychiatric disorders. The purpose of this study was to examine the reliability, construct validity and factor structure of the GHQ-12 in a large sample of African, Asian and South American young adults.
A cross-sectional study was conducted among 9,077 undergraduate students from Chile, Ethiopia, Peru and Thailand. Students aged 18–35 years were invited to complete a self-administered questionnaire that collected information about lifestyle, demographics, and GHQ-12. In each country, the construct validity and factorial structures of the GHQ-12 questionnaire were tested through exploratory and confirmatory factor analyses (EFA and CFA).
Overall the GHQ-12 items showed good internal consistency across all countries as reflected by the Cronbach's alpha: Chile (0.86), Ethiopia (0.83), Peru (0.85), and Thailand (0.82). Results from EFA showed that the GHQ-12 had a two-factor solution in Chile, Ethiopia and Thailand, although a three-factor solution was found in Peru. These findings were corroborated by CFA. Indicators of goodness of fit, comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean squared residual, were all in acceptable ranges across study sites. The CFI values for Chile, Ethiopia, Peru and Thailand were 0.964, 0.951, 0.949, and 0.931, respectively. The corresponding RMSEA values were 0.051, 0.050, 0.059, and 0.059.
Overall, we documented cross-cultural comparability of the GHQ-12 for assessing common psychiatric disorders such as symptoms of depressive and anxiety disorders among young adults. Although the GHQ-12 is typically used as single-factor questionnaire, the results of our EFA and CFA revealed the multi- dimensionality of the scale. Future studies are needed to further evaluate the specific cut points for assessing each component within the multiple factors.
GHQ-12; factor structure; confirmatory factor analysis; exploratory factor analysis; common psychiatric disorders
Associating changes in protein levels with the onset of cancer has been widely investigated to identify clinically relevant diagnostic biomarkers. In the present study, we analyzed sera from 205 patients recruited in the U.S. and Egypt for biomarker discovery using label-free proteomic analysis by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We performed untargeted proteomic analysis of sera to identify candidate proteins with statistically significant differences between hepatocellular carcinoma (HCC) and patients with liver cirrhosis. We further evaluated the significance of 101 proteins in sera from the same 205 patients through targeted quantitation by multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer. This led to the identification of 21 candidate protein biomarkers that were significantly altered in both the U.S. and Egyptian cohorts. Among the 21 candidates, 10 were previously reported as HCC-associated proteins (eight exhibiting consistent trends with our observation), whereas 11 are new candidates discovered by this study. Pathway analysis based on the significant proteins reveals up-regulation of the complement and coagulation cascades pathway and down-regulation of the antigen processing and presentation pathway in HCC cases versus patients with liver cirrhosis. The results of this study demonstrate the power of combining untargeted and targeted quantitation methods for a comprehensive serum proteomic analysis, to evaluate changes in protein levels and discover novel diagnostic biomarkers.
Cancer biomarker discovery; Hepatocellular carcinoma; Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS); Liver cirrhosis; Multiple reaction monitoring
Repeat pregnancies with different perinatal outcomes minimize underlying maternal genetic diversity and provide unique opportunities to investigate nongenetic risk factors and epigenetic mechanisms of pregnancy complications. We investigated gestational diabetes mellitus (GDM)-related differential DNA methylation in early pregnancy peripheral blood samples collected from women who had a change in GDM status in repeat pregnancies. Six study participants were randomly selected from among women who had 2 consecutive pregnancies, only 1 of which was complicated by GDM (case pregnancy) and the other was not (control pregnancy). Epigenome-wide DNA methylation was profiled using Illumina HumanMethylation 27 BeadChips. Differential Identification using Mixture Ensemble and false discovery rate (<10%) cutoffs were used to identify differentially methylated targets between the 2 pregnancies of each participant. Overall, 27 target sites, 17 hypomethylated (fold change [FC] range: 0.77-0.99) and 10 hypermethylated (FC range: 1.01-1.09), were differentially methylated between GDM and control pregnancies among 5 or more study participants. Novel genes were related to identified hypomethylated (such as NDUFC1, HAPLN3, HHLA3, and RHOG) or hypermethylated sites (such as SEP11, ZAR1, and DDR). Genes related to identified sites participated in cell morphology, cellular assembly, cellular organization, cellular compromise, and cell cycle. Our findings support early pregnancy peripheral blood DNA methylation differences in repeat pregnancies with change in GDM status. Similar, larger, and repeat pregnancy studies can enhance biomarker discovery and mechanistic studies of GDM.
repeat pregnancies; DNA methylation; gestational diabetes mellitus; pregnancy; peripheral blood
We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement.
Liquid chromatography; mass spectrometry; normalization; bayesian hierarchical model
To investigate the relationship between common psychiatric disorders (CPDs) and sleep characteristics (evening chronotype, poor sleep quality and daytime sleepiness) among Thai college students.
A cross-sectional study was conducted among 2,970 undergraduate students in Thailand. Students were asked to complete a self-administered questionnaire that collected information about lifestyle and demographic characteristics. The Horne and Ostberg Morningess-Eveningeness Questionnaire (MEQ), Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS), were used to evaluate circadian preference, sleep quality and daytime sleepiness respectively. The General Health Questionnaire-12 (GHQ-12) was used to evaluate presence of CPDs. Logistic regression models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) of CPDs in relation to the covariates of interest.
A total of 337 students were classified as having CPDs (11.2%; 95% CI 10.1–12.3%). Evening chronotype (OR=3.35; 95% CI 2.09–5.37), poor sleep quality (OR=4.89; 95% CI 3.66–6.54) and daytime sleepiness (OR=1.95; 95% CI 1.54–2.47) were statistically significantly associated with CPDs.
Our study demonstrated that CPDs are common among Thai college students. Further, evening chronotype, poor sleep quality and excessive daytime sleepiness were strongly associated with increased risk of CPDs. These findings highlight the importance of educating students and school administrators about the importance of sleep and its impact on mental health.
The genetic architecture of placental abruption (PA) remains poorly understood. We examined variations in SNPs of circadian clock-related genes in placenta with PA risk. We also explored placental and maternal genomic contributions to PA risk. Placental genomic DNA samples were isolated from 280 PA cases and 244 controls. Genotyping was performed using the Illumina Cardio-MetaboChip. We examined 116 SNPs in 13 genes known to moderate circadian rhythms. Logistic regression models were fit to estimate odds ratios (ORs). The combined effect of multiple SNPs on PA risk was estimated using a weighted genetic risk score. We examined independent and joint associations of wGRS derived from placental and maternal genomes with PA. Seven SNPs in five genes (ARNTL2, CRY2, DEC1, PER3 and RORA), in the placental genome, were associated with PA risk. Each copy of the minor allele (G) of a SNP in the RORA gene (rs2899663) was associated with a 30% reduced odds of PA (95% CI 0.52-0.95). The odds of PA increased with increasing placental-wGRS (Ptrend<0.001). The ORs were 1.00, 2.16, 3.24 and 4.48 across quartiles. Associations persisted after the maternal-wGRS was included in the model. There was evidence of an additive contribution of placental and maternal genetic contributions to PA risk. Participants with placental- and maternal-wGRS in the highest quartile, compared with those in the lowest quartile, had a 15.57-fold (95% CI 3.34-72.60) increased odds of PA. Placental variants in circadian clock-related genes are associated with PA risk; and the association persists after control of genetic variants in the maternal genome.
Circadian gene; placental abruption; pregnancy; placentae; SNPs
The Berlin and Epworth Sleepiness Scale (ESS) are simple, validated, and widely used questionnaires designed to assess symptoms of obstructive sleep apnea syndrome (OSAS) a common but often unrecognized cause of morbidity and mortality.
A cross-sectional study was conducted among 2,639 college students to examine the extent to which symptoms of OSAS are associated with the odds of common mental disorders (CMDs). The General Health Questionnaire (GHQ-12) was used to evaluate the presence of CMDs while the Berlin and ESS were used to assess high-risk for obstructive sleep apnea (OSA) and excessive daytime sleepiness, respectively. Logistic regression procedures were used to derive odds ratios (OR) and 95% confidence intervals (CI) assessing the independent and joint associations of high-risk for OSA and excessive daytime sleepiness with odds of CMDs.
Approximately 19% of students had high-risk for OSA while 26.4% had excessive daytime sleepiness. Compared to students without high-risk for OSA and without excessive daytime sleepiness (referent group), students with excessive daytime sleepiness only (OR=2.01; 95%CI: 1.60-2.52) had increased odds of CMDs. The odds of CMDs for students with high-risk OSA only was 1.26 (OR=1.26; 95%CI 0.94-1.68). Students with both high-risk for OSA and excessive daytime sleepiness, compared to the referent group, had the highest odds of CMDs (OR=2.45; 95%CI: 1.69-3.56).
Our findings indicate that symptoms of OSAS are associated with increased risk of CMDs. These findings emphasize the comorbidity of sleep disorders and CMDs and suggest that there may be benefits to investing in educational programs that extend the knowledge of sleep disorders in young adults.
Sleep apnea syndrome; OSAS; College students; Common mental disorders; GHQ; Ethiopia
Biological network inference is a major challenge in systems biology. Traditional correlation-based network analysis results in too many spurious edges since correlation cannot distinguish between direct and indirect associations. To address this issue, Gaussian graphical models (GGM) were proposed and have been widely used. Though they can significantly reduce the number of spurious edges, GGM are insufficient to uncover a network structure faithfully due to the fact that they only consider the full order partial correlation. Moreover, when the number of samples is smaller than the number of variables, further technique based on sparse regularization needs to be incorporated into GGM to solve the singular covariance inversion problem. In this paper, we propose an efficient and mathematically solid algorithm that infers biological networks by computing low order partial correlation (LOPC) up to the second order. The bias introduced by the low order constraint is minimal compared to the more reliable approximation of the network structure achieved. In addition, the algorithm is suitable for a dataset with small sample size but large number of variables. Simulation results show that LOPC yields far less spurious edges and works well under various conditions commonly seen in practice. The application to a real metabolomics dataset further validates the performance of LOPC and suggests its potential power in detecting novel biomarkers for complex disease.
Systems biology; undirected network inference; correlation; Gaussian graphical models; low order partial correlation; biomarker discovery
clinically relevant biomarkers for early stage hepatocellular
carcinoma (HCC) in a high-risk population of cirrhotic patients has
potentially far-reaching implications for disease management and patient
health. Changes in glycan levels have been associated with the onset
of numerous diseases including cancer. In the present study, we used
liquid chromatography coupled with electrospray ionization mass spectrometry
(LC–ESI-MS) to analyze N-glycans in sera from 183 participants
recruited in Egypt and the U.S. and identified candidate biomarkers
that distinguish HCC cases from cirrhotic controls. N-Glycans were
released from serum proteins and permethylated prior to the LC–ESI-MS
analysis. Through two complementary LC–ESI-MS quantitation
approaches, global profiling and targeted quantitation, we identified
11 N-glycans with statistically significant differences between HCC
cases and cirrhotic controls. These glycans can further be categorized
into four structurally related clusters, matching closely with the
implications of important glycosyltransferases in cancer progression
and metastasis. The results of this study illustrate the power of
the integrative approach combining complementary LC–ESI-MS
based quantitation approaches to investigate changes in N-glycan levels
between HCC cases and patients with liver cirrhosis.
cancer biomarker discovery; glycomics; hepatocellular
carcinoma; liver cirrhosis; mass spectrometry; multiple reaction monitoring
We evaluated the extent to which use of a hypothesized imperfect gold
standard, the Composite International Diagnostic Interview (CIDI), biases
the estimates of diagnostic accuracy of the Patient Health Questionnaire-9
(PHQ-9). We also evaluate how statistical correction can be used to address
The study was conducted among 926 adults where structured interviews
were conducted to collect information about participants’ current
major depressive disorder (MDD) using PHQ-9 and CIDI instruments. First, we
evaluated the relative psychometric properties of PHQ-9 using CIDI as a gold
standard. Next, we employed a Bayesian latent-class model to correct for the
In comparison with CIDI, the relative sensitivity and specificity of
the PHQ-9 for detecting MDD at a cut point of ≥10 were 53.1%
(95%CI: 45.4–60.8%) and 77.5%
(95%CI: 74.5–80.5%), respectively. Using a Bayesian
latent-class model to correct for the bias arising from the use of an
imperfect gold standard increased the sensitivity and specificity of PHQ-9
to 79.8% (95% Bayesian credible interval (BCI):
64.9–90.8%) and 79.1%
Our results provided evidence that assessing diagnostic validity of
mental health screening instrument, where application of a gold standard
might not be available, can be accomplished by using appropriate statistical
This study evaluates changes in metabolite levels in hepatocellular carcinoma (HCC) cases vs. patients with liver cirrhosis by analysis of human blood plasma using gas chromatography coupled with mass spectrometry (GC-MS). Untargeted metabolomic analysis of plasma samples from participants recruited in Egypt was performed using two GC-MS platforms: a GC coupled to single quadruple mass spectrometer (GC-qMS) and a GC coupled to a time-of-flight mass spectrometer (GC-TOFMS). Analytes that showed statistically significant changes in ion intensities were selected using ANOVA models. These analytes and other candidates selected from related studies were further evaluated by targeted analysis in plasma samples from the same participants as in the untargeted metabolomic analysis. The targeted analysis was performed using the GC-qMS in selected ion monitoring (SIM) mode. The method confirmed significant changes in the levels of glutamic acid, citric acid, lactic acid, valine, isoleucine, leucine, alpha tocopherol, cholesterol, and sorbose in HCC cases vs. patients with liver cirrhosis. Specifically, our findings indicate up-regulation of metabolites involved in branched-chain amino acid (BCAA) metabolism. Although BCAAs are increasingly used as a treatment for cancer cachexia, others have shown that BCAA supplementation caused significant enhancement of tumor growth via activation of mTOR/AKT pathway, which is consistent with our results that BCAAs are up-regulated in HCC.
Prediction of functional modules is indispensable for detecting
protein deregulation in human complex diseases such as cancer. Bayesian
network (BN) is one of the most commonly used models to integrate
heterogeneous data from multiple sources such as protein domain,
interactome, functional annotation, genome-wide gene expression, and the
Methods and Results
In this paper, we present a BN classifier that is customized to: 1)
increase the ability to integrate diverse information from different
sources, 2) effectively predict protein-protein interactions, 3) infer
aberrant networks with scale-free and small world properties, and 4) group
molecules into functional modules or pathways based on the primary function
and biological features. Application of this model on discovering protein
biomarkers of hepatocelluar carcinoma (HCC) leads to the identification of
functional modules that provide insights into the mechanism of the
development and progression of HCC. These functional modules include cell
cycle deregulation, increased angiogenesis (e.g., vascular endothelial
growth factor, blood vessel morphogenesis), oxidative metabolic alterations,
and aberrant activation of signaling pathways involved in cellular
proliferation, survival, and differentiation.
The discoveries and conclusions derived from our customized BN
classifier are consistent with previously published results. The proposed
approach for determining BN structure facilitates the integration of
heterogeneous data from multiple sources to elucidate the mechanisms of
systems biology; statistical model; genomics; genetics; bioinformatics; bioinformatics; functional genomics; gene expression; statistical model; computational biology; protein-protein interaction
Although in the past decade occidental countries have increasingly recognized the personal and societal burden of migraine, it remains poorly understood in Africa. No study has evaluated the impact of sleep disturbances and the quality of life (QOL) in sub-Saharan Africans with migraine.
This was a cross-sectional study evaluating adults, ≥ 18 years of age, attending outpatient clinics in Ethiopia. Standardized questionnaires were utilized to collect demographic, headache, sleep, lifestyle, and QOL characteristics in all participants. Migraine classification was based on International Classification of Headache Disorders (ICHD)-II criteria. The Pittsburgh Sleep Quality Index (PSQI) and the World Health Organization Quality of Life (WHOQOL-BREF) questionnaires were utilized to assess sleep quality and QOL characteristics, respectively. Multivariable logistic regression models were fit to estimate adjusted odds ratio (OR) and 95% confidence intervals (95% CI).
Of 1,060 participants, 145 (14%) met ICHD-II criteria for migraine. Approximately three-fifth of the study participants (60.5%) were found to have poor sleep quality. After adjustments, migraineurs had over a two-fold increased odds (OR = 2.24, 95% CI 1.49-3.38) of overall poor sleep quality (PSQI global score >5) as compared with non-migraineurs. Compared with non-migraineurs, migraineurs were also more likely to experience short sleep duration (≤7 hours) (OR = 2.07, 95% CI 1.43-3.00), long sleep latency (≥30 min) (OR = 1.97, 95% CI 1.36-2.85), daytime dysfunction due to sleepiness (OR = 1.51, 95% CI 1.12-2.02), and poor sleep efficiency (<85%) (OR = 1.93, 95% CI 1.31-2.88). Similar to occidental countries, Ethiopian migraineurs reported a reduced QOL as compared to non-migraineurs. Specifically Ethiopian migraineurs were more likely to experience poor physical (OR = 1.56, 95% CI 1.08-2.25) and psychological health (OR = 1.75, 95% CI 1.20-2.56), as well as poor social relationships (OR = 1.56, 95% CI 1.08-2.25), and living environments (OR = 1.41, 95% CI 0.97-2.05) as compared to those without migraine.
Similar to occidental countries, migraine is highly prevalent among Ethiopians and is associated with poor sleep quality and a lower QOL. These findings support the need for physicians and policy makers to take action to improve the quality of headache care and access to treatment in Ethiopia.
Migraine; Sleep quality; Quality of life; Ethiopia
A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.
Bayesian Hierarchical Models; Comparative Genomic Hybridization Arrays; Gene Expression; Hidden Markov Models; Measurement Error; Variable Selection
The effects of hepatocellular carcinoma (HCC) on liver metabolism and circulating metabolites have been subjected to continuing investigation. This study compares the levels of selected metabolites in sera of HCC cases versus patients with liver cirrhosis and evaluates the influence of gender, race, and alcoholic cirrhosis on the performance of the metabolites as candidate biomarkers for HCC.
Targeted quantitation of 15 metabolites is performed by selected research monitoring (SRM) in sera from 89 Egyptian subjects (40 HCC cases and 49 cirrhotic controls) and 110 US subjects (56 HCC cases and 54 cirrhotic controls). Logistic regression models are used to evaluate the ability of these metabolites in distinguishing HCC cases from cirrhotic controls. The influences of gender, race, and alcoholic cirrhosis on the performance of the metabolites are analyzed by stratified logistic regression.
Two metabolites are selected based on their significance to both cohorts. While both metabolites discriminate HCC cases from cirrhotic controls in males and Caucasians, they are insignificant in females and African Americans. One metabolite is significant in patients with alcoholic cirrhosis and the other in non-alcoholic cirrhosis.
The study demonstrates the potential of two metabolites as candidate biomarkers for HCC by combining them with α-fetoprotein and gender. Stratified statistical analyses reveal that gender, race, and alcoholic cirrhosis affect the relative levels of small molecules in serum.
The findings of this study contribute to a better understanding of the influence of gender, race, and alcoholic cirrhosis in investigating small molecules as biomarkers for HCC.
Mass spectrometry; metabolomics; cancer biomarker; liver cirrhosis; health disparity
The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example.
Biological pathways; system biology; high-throughput omics data; cancer biomarker.
The Pittsburgh Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS) are questionnaires used to assess sleep quality and excessive daytime sleepiness in clinical and population-based studies. The present study aimed to evaluate the construct validity and factor structure of the PSQI and ESS questionnaires among young adults in four countries (Chile, Ethiopia, Peru and Thailand).
A cross-sectional study was conducted among 8,481 undergraduate students. Students were invited to complete a self-administered questionnaire that collected information about lifestyle, demographic, and sleep characteristics. In each country, the construct validity and factorial structures of PSQI and ESS questionnaires were tested through exploratory and confirmatory factor analyses (EFA and CFA).
The largest component-total correlation coefficient for sleep quality as assessed using PSQI was noted in Chile (r = 0.71) while the smallest component-total correlation coefficient was noted for sleep medication use in Peru (r = 0.28). The largest component-total correlation coefficient for excessive daytime sleepiness as assessed using ESS was found for item 1 (sitting/reading) in Chile (r = 0.65) while the lowest item-total correlation was observed for item 6 (sitting and talking to someone) in Thailand (r = 0.35). Using both EFA and CFA a two-factor model was found for PSQI questionnaire in Chile, Ethiopia and Thailand while a three-factor model was found for Peru. For the ESS questionnaire, we noted two factors for all four countries
Overall, we documented cross-cultural comparability of sleep quality and excessive daytime sleepiness measures using the PSQI and ESS questionnaires among Asian, South American and African young adults. Although both the PSQI and ESS were originally developed as single-factor questionnaires, the results of our EFA and CFA revealed the multi- dimensionality of the scales suggesting limited usefulness of the global PSQI and ESS scores to assess sleep quality and excessive daytime sleepiness.
While available evidence supports the role of genetics in the pathogenesis of placental abruption (PA), PA-related placental genome variations and maternal-placental genetic interactions have not been investigated. Maternal blood and placental samples collected from participants in the Peruvian Abruptio Placentae Epidemiology study were genotyped using Illumina’s Cardio-Metabochip platform. We examined 118,782 genome-wide SNPs and 333 SNPs in 32 candidate genes from mitochondrial biogenesis and oxidative phosphorylation pathways in placental DNA from 280 PA cases and 244 controls. We assessed maternal-placental interactions in the candidate gene SNPS and two imprinted regions (IGF2/H19 and C19MC). Univariate and penalized logistic regression models were fit to estimate odds ratios. We examined the combined effect of multiple SNPs on PA risk using weighted genetic risk scores (WGRS) with repeated ten-fold cross-validations. A multinomial model was used to investigate maternal-placental genetic interactions. In placental genome-wide and candidate gene analyses, no SNP was significant after false discovery rate correction. The top genome-wide association study (GWAS) hits were rs544201, rs1484464 (CTNNA2), rs4149570 (TNFRSF1A) and rs13055470 (ZNRF3) (p-values: 1.11e-05 to 3.54e-05). The top 200 SNPs of the GWAS overrepresented genes involved in cell cycle, growth and proliferation. The top candidate gene hits were rs16949118 (COX10) and rs7609948 (THRB) (p-values: 6.00e-03 and 8.19e-03). Participants in the highest quartile of WGRS based on cross-validations using SNPs selected from the GWAS and candidate gene analyses had a 8.40-fold (95% CI: 5.8–12.56) and a 4.46-fold (95% CI: 2.94–6.72) higher odds of PA compared to participants in the lowest quartile. We found maternal-placental genetic interactions on PA risk for two SNPs in PPARG (chr3∶12313450 and chr3∶12412978) and maternal imprinting effects for multiple SNPs in the C19MC and IGF2/H19 regions. Variations in the placental genome and interactions between maternal-placental genetic variations may contribute to PA risk. Larger studies may help advance our understanding of PA pathogenesis.
Motivation: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various ‘-omic’ studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data.
Results: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information.
Availability and implementation: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html
Supplementary data are available at Bioinformatics online.
We conducted this study to evaluate the prevalence of daytime sleepiness and evening chronotype, and to assess the extent to which both are associated with the use of caffeinated stimulants among 3,000 Thai college students. Demographic and behavioral characteristics were collected using a self-administered questionnaire. The Epworth Sleepiness Scale and the Horne and Ostberg Morningness-Eveningness Questionnaire were used to evaluate prevalence of daytime sleepiness and circadian preference. Multivariable logistic regression models were used to evaluate the association between sleep disorders and consumption of caffeinated beverages. Overall, the prevalence of daytime sleepiness was 27.9 % (95% CI: 26.2–29.5%) while the prevalence of evening chronotype was 13% (95% CI: 11.8–14.2%). Students who use energy drinks were more likely to be evening types. For instance, the use of M100/M150 energy drinks was associated with a more than 3-fold increased odds of evening chronotype (OR 3.50; 95% CI 1.90–6.44), while Red Bull users were more than twice as likely to have evening chronotype (OR 2.39; 95% CI 1.02–5.58). Additionally, those who consumed any energy drinks were more likely to be daytime sleepers. For example, Red Bull (OR 1.72; 95% CI 1.08–2.75) or M100/M150 (OR 1.52; 95% CI 1.10–2.11) consumption was associated with increased odds of daytime sleepiness. Our findings emphasize the importance of implementing educational and prevention programs targeted toward improving sleep hygiene and reducing the consumption of energy drinks among young adults
Poor sleep and heavy use of caffeinated beverages have been implicated as risk factors for a number of adverse health outcomes. Caffeine consumption and use of other stimulants are common among college students globally. However, to our knowledge, no studies have examined the influence of caffeinated beverages on sleep quality of college students in Southeast Asian populations. We conducted this study to evaluate the patterns of sleep quality; and to examine the extent to which poor sleep quality is associated with consumption of energy drinks, caffeinated beverages and other stimulants among 2,854 Thai college students.
A questionnaire was administered to ascertain demographic and behavioral characteristics. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep habits and quality. Chi-square tests and multivariate logistic regression models were used to identify statistically significant associations.
Overall, the prevalence of poor sleep quality was found to be 48.1%. A significant percent of students used stimulant beverages (58.0%). Stimulant use (OR 1.50; 95%CI 1.28-1.77) was found to be statistically significant and positively associated with poor sleep quality. Alcohol consumption (OR 3.10; 95% CI 1.72-5.59) and cigarette smoking (OR 1.43; 95% CI 1.02-1.98) also had statistically significant association with increased daytime dysfunction. In conclusion, stimulant use is common among Thai college students and is associated with several indices of poor sleep quality.
Our findings underscore the need to educate students on the importance of sleep and the influences of dietary and lifestyle choices on their sleep quality and overall health.
Sleep; Energy Drinks; Alcohol; Caffeine; Students; Cigarettes
To estimate the prevalence of daytime sleepiness and circadian preferences, and to examine the extent to which caffeine consumption and Khat (a herbal stimulant) use are associated with daytime sleepiness and evening chronotype among Ethiopian college students.
A cross-sectional study was conducted among 2,410 college students. A self-administered questionnaire was used to collect information about sleep, behavioral risk factors such as caffeinated beverages, tobacco, alcohol, and Khat consumption. Daytime sleepiness and chronotype were assessed using the Epworth Sleepiness Scale (ESS) and the Horne & Ostberg Morningness /Eveningness Questionnaire (MEQ), respectively. Linear and logistic regression models were used to evaluate associations.
Daytime sleepiness (ESS≥10) was present in 26% of the students (95% CI: 24.4–27.8%) with 25.9% in males and 25.5% in females. A total of 30 (0.8%) students were classified as evening chronotypes (0.7% in females and 0.9% in males). Overall, Overall, Khat consumption, excessive alcohol use and cigarette smoking status were associated with evening chronotype. Use of any caffeinated beverages (OR=2.18; 95%CI: 0.82–5.77) and Khat consumption (OR=7.43; 95%CI: 3.28–16.98) increased the odds of evening chronotype.
The prevalence of daytime sleepiness among our study population was high while few were classified as evening chronotypes. We also found increased odds of evening chronotype with caffeine consumption and Khat use amongst Ethiopian college students. Prospective cohort studies that examine the effects of caffeinated beverages and Khat use on sleep disorders among young adults are needed.
A Bayesian alignment model (BAM) is proposed for alignment of liquid chromatography-mass spectrometry (LC-MS) data. BAM belongs to the category of profile-based approaches, which are composed of two major components: a prototype function and a set of mapping functions. Appropriate estimation of these functions is crucial for good alignment results. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler and 2) an adaptive selection of knots. A block Metropolis-Hastings algorithm that mitigates the problem of the MCMC sampler getting stuck at local modes of the posterior distribution is used for the update of the mapping function coefficients. In addition, a stochastic search variable selection (SSVS) methodology is used to determine the number and positions of knots. We applied BAM to a simulated data set, an LC-MS proteomic data set, and two LC-MS metabolomic data sets, and compared its performance with the Bayesian hierarchical curve registration (BHCR) model, the dynamic time-warping (DTW) model, and the continuous profile model (CPM). The advantage of applying appropriate profile-based retention time correction prior to performing a feature-based approach is also demonstrated through the metabolomic data sets.
Alignment; Bayesian inference; block Metropolis-Hastings algorithm; liquid chromatography-mass spectrometry (LC-MS); Markov chain Monte Carlo (MCMC); stochastic search variable selection (SSVS)
Supplemental Digital Content is available in the text.
Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic.
We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response.
Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated.
Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.
DMET; gemcitabine; NCI-60; pancreatic cancer; probabilistic networks