Investigating the relationships between unfavourable habitual sleep and metabolomic traits:evidence from multi-cohort multivariable regression and Mendelian randomization analyses

BACKGROUND: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease.METHODS: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions.RESULTS: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (- 0.08 standard deviation (SD)[95% confidence interval (CI) - 0.12, - 0.03] in AMV and - 0.03SD [- 0.07, - 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (- 0.04SD [- 0.08, 0.00] in AMV and - 0.05SD [- 0.09, - 0.02] in MR), and lower phospholipids in very large HDL particles (- 0.04SD [- 0.08, 0.002] in AMV and - 0.05SD [- 0.08, - 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures.CONCLUSIONS: Whilst our results suggested that unfavourable sleep traits may not cause widespread metabolic disruption, some notable effects were observed. The evidence for possible effects of insomnia symptoms on glycoprotein acetyls and citrate and longer total sleep duration on creatinine and isoleucine might explain some of the effects, found in MR analyses of these sleep traits on coronary heart disease, which warrant further investigation.</p

Meta-EWAS results

Offspring methylation ~ Paternal BMI + Paternal age + Maternal age + Paternal smoking + Maternal smoking + Paternal SEP + Maternal parity + estimated cell proportions + 20 surrogate variables These files contains meta-analysis summary statistics for all Illumina 450k or EPIC probes for which at least one cohort supplied data. No filtering has taken place. For the paper describing this work, some probes (e.g. cross-hybridising probes, or probes on the sex chromosomes) were removed after meta-analysis, before any downstream analysis

Simulated multi-omic dataset including DNA methylation, proteins and metabolites

Dataset simulated for use in a short course in molecular epidemiology

Co3O4-ZnO P-N Heterostructure Nanomaterials Film and its Enhanced Photoelectric Response to Visible Lights at Near Room Temperature

In this paper, Co3O4-ZnO nanomaterials with Co3O4 doping mass fractions of 0%, 2.13%, 4.13%, and 6.13% were prepared by sol-gel method. In order to explain and confirm the influence of the incorporation of Co3O4 on the surface morphology and gas sensitivity of ZnO at a relatively low gas concentration, additional studies such as XRD, XPS, SEM, EDS and UV-vis spectroscopy were performed. And its photoelectric response to 100 ppm acetone at near room temperature and visible light irradiation was studied. Due to the formation of P-N heterojunctions, the Co3O4-ZnO heterostructural nanoparticles has a highe response to low concentrations of acetone gas than undoped ZnO nanoparticles even at operating temperatures as low as 30ºC. The addition of Co3O4 improves the sensitivity and selectivity of ZnO thick films. The sensitivity of the 4.13wt% Co3O4-ZnO sample to 100 ppm acetone at a working temperature of 30ºC was 24.36. The light excitation effect was significantly enhanced. Under visible light irradiation, the sensitivity can reach 37.21. In addition, the Co3O4-ZnO P-N heterojunction model was combined with visible light excitation theory to further explore the mechanism of gas sensing reaction

Additional file 1 of Assessing the effects of hyperparameters on knowledge graph embedding quality

Additional file 1: A .zip file containing all supplementary tables, in gzip compressed .csv format, that are referenced in this manuscript.Supplementary table 1 provides the possible values that each of the tested hyperparameters could take. Supplementary table 2 describes the 233 total jobs that were attempted to be run in LibKGE in terms of: dataset, KGE method, training method, and loss function. Supplementary table 3 provides the raw data used to generate Figure 2. Supplementary table 4 shows the results of an inverse-relation-leakage analysis that was run on the UMLS dataset. Supplementary tables 5.1, 5.2, and 5.3 are the raw symmetrical adjacency matrices used to visualise the network data in figures 3A, 3B, and 3C respectively.

In vitro study on the effect of peucedanol on the activity of cytochrome P450 enzymes

Peucedanol is a major extract of Peucedanum japonicum Thunb. (Apiaceae) roots, which is a commonly used herb in paediatrics. Its interaction with cytochrome P450 enzymes (CYP450s) would lead to adverse effects or even failure of therapy. The interaction between peucedanol and CYP450s was investigated. Peucedanol (0, 2.5, 5, 10, 25, 50, and 100 μM) was incubated with eight human liver CYP isoforms (CYP1A2, 2A6, 3A4, 2C8, 2C9, 2C19, 2D6, and 2E1), in pooled human liver microsomes (HLMs) for 30 min with specific inhibitors as positive controls and untreated HLMs as negative controls. The enzyme kinetics and time-dependent study (0, 5, 10, 15, and 30 min) were performed to obtain corresponding parameters in vitro. Peucedanol significantly inhibited the activity of CYP1A2, 2D6, and 3A4 in a dose-dependent manner with IC50 values of 6.03, 13.57, and 7.58 μM, respectively. Peucedanol served as a non-competitive inhibitor of CYP3A4 with a Ki value of 4.07 μM and a competitive inhibitor of CYP1A2 and 2D6 with a Ki values of 3.39 and 6.77 μM, respectively. Moreover, the inhibition of CYP3A4 was time-dependent with the Ki/Kinact value of 5.44/0.046 min/μM. In vitro inhibitory effect of peucedanol on the activity of CYP1A2, 2A6, and 3A4 was reported in this study. As these CYPs are involved in the metabolism of various drugs, these results implied potential drug-drug interactions between peucedanol and drugs metabolized by CYP1A2, 2D6, and 3A4, which needs further in vivo validation

Additional file 1 of Sex steroid hormones and risk of breast cancer: a two-sample Mendelian randomization study, Version 1

Additional file 1. S1. Exposure instruments to proxy hormone levels in the overall breast cancer incidence MR analysis. S2. The strength of the instruments for each hormone including the variance explained and the F statistic. S3. Hormone and breast cancer incidence associations using the alternative MR methods: MR Egger, weighted median, weighted mode and MR-RAPS. S4. MR intercept calculations to identify evidence of pleiotropy. S5. Genetic correlation results between hormones. S6. Exposure instruments to proxy hormone levels in the subtype specific MR analysis. S7. Exposure instruments to proxy hormone levels in the BRCA1 mutated TNBC MR analysis. S8. Power calculations for each hormone and overall and subtype specific BC analysis

Additional file 1 of Sex steroid hormones and risk of breast cancer: a two-sample Mendelian randomization study

Additional file 1. S1. Exposure instruments to proxy hormone levels in the overall breast cancer incidence MR analysis. S2. The strength of the instruments for each hormone including the variance explained and the F statistic. S3. Hormone and breast cancer incidence associations using the alternative MR methods: MR Egger, weighted median, weighted mode and MR-RAPS. S4. MR intercept calculations to identify evidence of pleiotropy. S5. Genetic correlation results between hormones. S6. Exposure instruments to proxy hormone levels in the subtype specific MR analysis. S7. Exposure instruments to proxy hormone levels in the BRCA1 mutated TNBC MR analysis. S8. Power calculations for each hormone and overall and subtype specific BC analysis

Data from: Perceiving the evil eye: investigating hostile interpretation of ambiguous facial emotional expression in violent and non-violent offenders

Research into the causal and perpetuating factors influencing aggression has partly focused on the general tendency of aggression-prone individuals to infer hostile intent in others, even in ambiguous circumstances. This is referred to as the 'hostile interpretation bias'. Whether this hostile interpretation bias also exists in basal information processing, such as perception of facial emotion, is not yet known, especially with respect to the perception of ambiguous expressions. In addition, little is known about how this potential bias in facial emotion perception is related to specific characteristics of aggression. In the present study, conducted in a penitentiary setting with detained male adults, we investigated if violent offenders (n = 71) show a stronger tendency to interpret ambiguous facial expressions on a computer task as angry rather than happy, compared to non-violent offenders (n = 14) and to a control group of healthy volunteers (n = 32). We also investigated if hostile perception of facial expressions is related to specific characteristics of aggression, such as proactive and reactive aggression. No clear statistical evidence was found that violent offenders perceived facial emotional expressions as more angry than non-violent offenders or healthy volunteers. A regression analysis in the violent offender group showed that only age and a self-report measure of hostility predicted outcome on the emotion perception task. Other traits, such as psychopathic traits, intelligence, attention and a tendency to jump to conclusions were not associated with interpretation of anger in facial emotional expressions. We discuss the possible impact of the study design and population studied on our results, as well as implications for future studies.,2017.10.23 Perceiving Evil Eye Data SheetIn this observational study, conducted in a penitentiary setting in the Netherlands with detained male adults, we investigated if violent offenders (n = 71) show a stronger tendency to interpret ambiguous facial expressions on a computer task as angry rather than happy, compared to non-violent offenders (n = 14) and to a control group of healthy volunteers (n = 32). We also investigated if hostile perception of facial expressions is associated with specific characteristics of aggression, such as proactive and reactive aggression, based on multiple self-report questionnaires (Buss-Perry Aggression Questionnaire, Reactive-Proactive Aggression Qurestionnaire) and behavioural observations (Social Dysfunction and Aggression Scale - SDAS, rated once a week over a period of 4 weeks). Other relevant variables, such as psychopathic traits (Psychopathic Personality Inventory-revised), intelligence (Raven Standard Progressive Matrices), attention (Trail Makint Test-A/B) and a tendency to jump to conclusions (beads-in-a-jar task) were also assessed. The study protocol was not published online. Participants completed a neuropsychological test battery, of which the threshold score of the emotion perception task was the primary outcome measure. The threshold score of the emotion perception task is scored so that lower scores reflect a tendency to rate the faces as angry, while higher scores reflect a tendency to rate the faces as happy. The data dictionary defines the contents of the data sheet comprehensively. The data set comprises group, age-categories, data on the above mentioned questionnaires and neuropsychological tests (including threshold score). Conviction data were not included in the data file to ensure anonymity of the participants. The deposit includes the following files: Data Dictionary Data Sheet Data files and data dictionary can be opened in Excel or SPSS software.2017.10.23 Perceiving Evil Eye Data DictionaryThis files shows all data descriptions

Additional file 2 of Identifying the potential causal role of insomnia symptoms on 11,409 health-related outcomes: a phenome-wide Mendelian randomisation analysis in UK Biobank

Additional file 2: Table S1. GWAS significant SNPs in 23&amp;Me used to construct GRS for PheWAS and used to conduct two-sample MR follow-up. *rs28780988 used as a proxy for rs28458909 which was identified as an independent GWAs significant SNP in the clumping of the 23andMe GWAS results but was not available in UK biobank. Table S2. SNPs which were GWAS significant in both UK Biobank/23andMe meta-analysis and 23&amp;Me, used to construct GRS for sensitivity PheWAS analyses. Table S3. Results from the MR-PheWAS and sensitivity analysis 1 and 2 for each outcome (Ordered by the p-value from the main MR-PheWAS). For linear regressions the beta is the mean difference per one standard deviation increase in GRS and for all others the beta is the odds ratio per one standard deviation increase in GRS. Table S4. Quantified details of the total number in each category, number and percentage of outcomes reaching criteria for potential causal effect in each category. Table S5. Quantified details of the total number in each subcategory, number and percentage of outcomes reaching criteria for potential causal effect in each subcategory. Table S6. Follow-up information for associations with Insomnia that passed the Bonferroni-corrected significance threshold in the main MR-PheWAS. Table S7. List of GWAS from TwoSampleMR package v0.5.6 included in follow-up. The potential causal effects from the MR-PheWAS these relate to are in the Outcome column separated by semicolons. Table S8. List of GWAS from TwoSampleMR package v0.5.6 returned in search but not included in follow-up. The Reason column gives the reason for exclusion. Table S9. Results from two-sample MR follow-up for binary outcomes. Table S10. Results from two-sample MR follow-up for continuous outcomes. Table S11. Articles identified in systematic search and included after screening, with a summary of findings. Table S12. Articles identified in systematic search and excluded at full text screening