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Background: Observational studies have suggested a possible association between hypothyroidism and increased risk of ischemic stroke. However, a causal relationship remains unclear. Methods: Data on single nucleotide polymorphisms (SNPs) associated with hypothyroidism and ischemic stroke were sourced from the FinnGens database and the UK Biobank of European descent. Both databases underwent separate two-sample Mendelian randomization (MR) analyses. A subsequent meta-analysis of MR results using a random-effects model was conducted to determine the causal relationship between hypothyroidism and ischemic stroke. Results: All five analyses indicated a positive causal relationship between hypothyroidism and ischemic stroke. MR analysis of the association between hypothyroidism and ischemic stroke yielded a result of the inverse variance weighted (IVW) method at 4.7411 (1.3598-16.5308), p = 0.0146. The analysis of ischemic stroke (without excluding controls) yielded a result of the IVW method of 4.5713 (1.3570-15.3986), p = 0.0142. MR analysis with cerebral infarction yielded a result of the IVW method at 1.0110 (1.0006-1.0215), p = 0.0373. The MR analysis with cerebrovascular disease sequelae yielded an IVW method result of 2.4556 (1.0291-5.8595), p = 0.0429. Analysis for the sequelae of cerebrovascular disease (without excluding controls) yielded an IVW method result of 2.4217 (1.0217-5.7402), p = 0.0446. No evidence of heterogeneity or horizontal pleiotropy was found. The meta-analysis of the five MR results was 2.24 (1.18-4.26), p = 0.025. Conclusion: Our two-sample Mendelian randomization study suggested a causal relationship between hypothyroidism and ischemic stroke, indicating that hypothyroidism could be a risk factor for ischemic stroke. However, further studies are required to elucidate the underlying biological mechanisms. [ABSTRACT FROM AUTHOR]

The relationships between gut microbiota and brain-related diseases/traits remains not fully understood. A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits, and evaluate the potential sex specific effects of gut microbiota. In discovery stage, we systematically scanned the relationships between 515 brain-related diseases/traits and gut microbiota through two-sample Mendelian randomisation analysis. Using ∼500,000 individuals derived from the UK Biobank, polygenetic risk scoring (PRS) analysis was performed to validate the associations detected in discovery stage. To evaluate the potential sex-specific effect of gut microbiota on brain-related disorders, PRS analysis was conducted in female and male, respectively. After systematically scanning diseases or traits, 41 of the 515 brain-related diseases/traits were identified to be associated with gut microbiota, such as Neuroticism score (P2-MR = 0.0018), worrier/anxious feelings (P2-MR = 0.0013), Suffer from 'nerves' (P2-MR = 0.0062) and Nervous feelings (P2-MR = 0.0158). 5 of 41 brain-related diseases or traits were successfully validated in UK Biobank, such as Neuroticism score (PUK = 0.0024, PUK-female = 0.0063, PUK-male = 0.1142), Nervous feelings (PUK = 0.0043, PUK-female = 0.0115, PUK-male = 0.1670) and Worrier/anxious feelings (PUK = 0.0166, PUK-female = 0.0196, PUK-male = 0.2930). Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males. A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits. Using the individuals derived from the UK Biobank, polygenetic risk scoring analysis was performed to validate the associations detected in the discovery stage. Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males. [ABSTRACT FROM AUTHOR]

We examine first language (L1) attrition among 30 L1 Spanish – L2 English speakers living in the United Kingdom. We also tested 30 recently-arrived Spaniards to the UK as a baseline. We present several key findings: 1) attrition fluctuates over time and does not affect all individuals equally; 2) entropy can explain said fluctuation of attritional affects over time such that while length of residence and age of arrival may affect the depth of attrition, how often one is exposed to her native language, how often she uses it and for how long each day, who her friends are, and to which types of input she is regularly exposed, seemingly aid in the maintenance or loss of linguistic knowledge; 3) though offline scalar interpretations among bilinguals were predicted by canonical sociolinguistic variables, the online data revealed an overall insensitivity to pragmatic violations. Thus, offline and online methods combine to be more explanatory regarding the comprehension and processing of implicature generating quantifiers. [ABSTRACT FROM AUTHOR]

Background: Herpes simplex virus‐1 (HSV‐1) infection is reported to be associated with depression. But limited efforts were made to investigate the relationship between HSV‐1 infection and the risk of depression, especially from the genetic perspective. Methods: In UK Biobank cohort, linear and logistic regression analyses were first performed to test the association of HSV‐1 seropositivity/antibody with depression, including depression status (N = 2951) and Patient Health Questionnaire (PHQ) score (N = 2839). Using individual genotypic and phenotypic data from the UK Biobank, genome‐wide environmental interaction study (GWEIS) was then conducted by PLINK2.0 to evaluate gene × HSV‐1 interacting effect on the risk of depression. Finally, gene set enrichment analysis was conducted to identify the biological pathways involved in the observed gene × HSV‐1 interaction for depression. Result: In UK Biobank cohort, significant associations were observed between depression status and HSV‐1 (odds ratio [OR] = 1.09; 95% confidence interval [CI], 1.02‐1.16; P = 2.40 × 10−2 for HSV‐1 antibody and OR = 1.28; 95% CI, 1.12‐1.47, P = 2.59 × 10−3 for HSV‐1 seropositivity). GWEIS revealed four significant gene × HSV‐1 interaction signals for PHQ score (all P < 5.0 × 10−8) and the leading loci was SULF2 (rs6094791, P = 8.60 × 10−9). Pathway analyses identified 21 pathways for PHQ score and 19 for depression status, including multiple neural development‐ and immune‐related ones, such as KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION (false discovery rate [FDR] = 3.18 × 10−2) for depression and LU_AGING_BRAIN_UP (FDR = 4.21 × 10−2) for PHQ score. Conclusion: Our results suggested that HSV‐1 was associated with the risk of depression, which was modulated by the several genes that were related to the nerve development or immune function. [ABSTRACT FROM AUTHOR]

Background: Recent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD. Methods: We performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait. Results: In total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction. Conclusion: Our study findings support the weak relevance of gut microbiota with the development of BMD. [ABSTRACT FROM AUTHOR]

Field coupled samples in soil and grass were collected to determine the concentrations and identify the uptake pathways of PCBs into the grass at a pasture from Scotland, UK. Concentrations of indicator PCBs (∑ 7 PCBs) in soils ranged from 0.20 to 0.88 ng g−1 dw (dry weight), with a mean of 0.33 ng g−1 dw, and in grass ranged from 0.20 to 2.14 ng g−1 dw, with a mean of 0.48 ng g−1 dw. The comprehensive factors of low concentrations and detection rate (PCB28: 18.8%; PCB52: 37.5%) of PCBs in soil, as well as continuously declined air concentrations of PCBs in the UK since the 1990s suggested that the secondary emission from the soil is becoming the supplied source of PCBs to air and grass. The significant correlations between bioconcentration factor (BCF) values and the log K OW (R = −0.850, p < 0.05) and log K OA (R = −0.860, p < 0.05) of indicator PCB congeners were found in the present study, indicating that these two parameters are likely to affect the bioaccumulation and uptake of grass. A generic one-compartment model was employed to identify uptake pathways of grass and evaluate the uptake amounts for PCBs. This suggested that the most important pathway for uptake of PCBs by grass was at the aerial part, and the difference of PCBs concentrations between leaves and roots was about four orders of magnitude. Removing and risk transfer of PCBs or other organic pollutants by grass need to be investigated and assessed further. • Low levels of PCBs in soil and grass found in Scottish pasture were related to declined PCBs in air. • The secondary emission from the soil is becoming the supplied source of PCBs to air and grass. • Weak variations of SOC lead to the insignificant difference of PCBs in spatial distribution. • Log K OW and log K OA were two crucial factors affecting bioaccumulation of PCBs into grass. • The present work showed that the uptake from air is a more important pathway than root uptake. [ABSTRACT FROM AUTHOR]

This study used water-quality monitoring data from seven monitoring stations in the Guo River Basin from 2003 to 2012 and three selected water-quality indexes (total phosphorus: TP, ammonium nitrogen: NH3–N, and permanganate index: CODMn) that were divided into five categories (classes I, II, III, IV, and V) according to Chinese environmental quality standards for surface water (GB 3838–2002). The results indicated that the concentrations of TP, NH3–N, and CODMn showed a significant decreasing trend at Xuanwu Sluice, Huangzhuang, Boxian Sluice, Guoyang, and Mengcheng Sluice by the Mann–Kendall trend analysis; the water pollution was worse in the upstream region, but it was relatively good in the downstream region, and the water pollutant concentrations improved significantly by the cluster analysis. In this study, a marginal distribution of water-quality indexes and a combinatorial probability distribution of the copula function are established based on the basic principle of the copula function. The Gaussian copula function was selected to calculate the combinatorial risk probability of the water pollutants by the fit test of copula function and the goodness-of-fit evaluation. Results show that the combinatorial risk probability varies substantially when the water-quality indexes reach different states under different combinations, such as the combinatorial risk probability showed a decreasing trend with increasing TP concentration when the NH3–N concentration was certain and CODMn reached the class III or class IV level at Xuanwu Sluice and Boxian Sluice; the combinatorial risk probability presented a decreasing trend with increasing TP concentration when the NH3–N and CODMn concentrations were certain at Mengcheng Sluice and Longkangji; the combinatorial risk probability presented a trend that first increased and then decreased with increasing CODMn concentration when the TP and NH3–N concentrations were certain. The temporal–spatial distribution characteristics and combinatorial risk probabilities obtained in this study will provide a basis for management, water pollution control, water resources protection, and sustainable development of the basin. [ABSTRACT FROM AUTHOR]

We aim to explore the mechanism of relationship between insomnia and liver metabolism by examining the gene × insomnia interactions. Individual level genotypic and phenotypic data were obtained from the UK Biobank cohort. Regression analysis was first conducted to test the association of insomnia with plasma total bilirubin (TBil; n = 186,793), direct bilirubin (DBil; n = 159,854) and total protein (TP; n = 171,574) in UK Biobank cohort. Second, genome-wide gene-environment interaction study (GWGEIS) was conducted by PLINK 2.0, and FUMA platform was used to identify enriched pathway terms. In UK Biobank cohort, we found that TP (P < 2.00 × 10−16), DBil (P = 1.72 × 10−3) and TBil (P = 3.38 × 10−5) were significantly associated with insomnia. GWGEIS of both DBil and TBil observed significant G × INSOMNIA effects between insomnia and UDP Glucuronosyltransferase Family 1 (rs6431558, P = 6.26 × 10−11) gene. GWGEIS of TP also detected several significant genes interacting with insomnia, such as KLF15, (rs70940816, P = 6.77 × 10−10) and DOK7, (rs2344205, P = 1.37 × 10−9). Multiple gene ontology (GO) terms were identified for bilirubin, such as GO_URONIC_ACID_METABOLIC_PROCESS (adjusted P = 4.15 × 10−26). Our study results suggested negative associations between insomnia and DBil and TBil; and a positive association between insomnia and TP. • Plasma TP, DBil and TBil associated with insomnia. • Insomnia could affect metabolism by Gene × insomnia interactions. • UGT1A might play a role in relationship between insomnia and bilirubin metabolism. • Gene participated in lipid and bile acid metabolism showed G × E effects on TP. [ABSTRACT FROM AUTHOR]

Maternal smoking during pregnancy (MSDP) has been reported to be associated with increased anxiety and depression behaviors in offspring. However, there is still scant evidence to support the link between MSDP and anxiety/depression. Using the subjects from the UK Biobank cohort (n = 371,903–432,881). Logistic regression analyses were first conducted to test the correlation between MSDP and anxiety/depression in offspring. Second, genome-wide gene-environment interaction study (GWGEIS) analyses were conducted by PLINK, using MSDP as environmental factor. Genetic correlation analysis of anxiety/depression and smoking was conducted by the LDSC software using the published genome-wide association study (GWAS) summary data of four smoking traits (n = 337,334–1,232,091), anxiety (n = 31,880) and depression (n = 490,359). Finally, pathway enrichment analysis was carried out to detect the pathway involved in the development of offspring anxiety caused by the interaction of MSDP × SNPs. Observational analyses showed that anxiety and depression status in offspring were significantly associated with MSDP (all p < 0.0001). Further GWEGI analyses observed significant MSDP-gene interaction effects at UNC80 gene for anxiety (p = 9.09 × 10−9). LDSC did not detect significant genetic correlation between anxiety and smoking traits. Pathway analysis identified 19 significant pathways for anxiety, such as MANALO_HYPOXIA_UP (FDR = 5.50 × 10−4), REACTOME_ADHERENS_JUNCTIONS_INTERACTIONS (FDR = 0.0304) and ONDER_CDH1_TARGETS_2_UP (FDR = 0.0371). Our study results suggested the important impact of MDSP on the risk of anxiety in offspring, partly attributing to environment-gene interactions effects. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are among the largest and fastest-growing categories of the global disease burden. However, limited effort has been made to further elucidate associations between socioeconomic factors and psychiatric disorders from a genetic perspective. We randomly divided 501,882 participants in the UK Biobank cohort with socioeconomic Townsend deprivation index (TDI) data into a discovery cohort and a replication cohort. For both cohorts, we first conducted regression analyses to evaluate the associations between the TDI and common psychiatric disorders or traits, including anxiety, bipolar disorder, self-harm, and depression (based on self-reported depression and Patient Health Questionnaire scores). We then performed a genome-wide gene-by-environment interaction study using PLINK 2.0 with the TDI as an environmental factor to explore interaction effects. In the discovery cohort, significant associations were observed between the TDI and psychiatric disorders (p < 4.00 × 10−16), including anxiety (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.07–1.10), bipolar disorder (OR = 1.42, 95% CI = 1.36–1.48), self-harm (OR = 1.21, 95% CI = 1.19–1.23), self-reported depression (OR = 1.22, 95% CI = 1.20–1.24), and Patient Health Questionnaire scores (β =.07, SE = 0.004). We observed similar significant associations in the replication cohort. In addition, multiple candidate loci were identified by the genome-wide gene-by-environment interaction study, including rs10886438 at 10q26.11 (GRK5) (p = 5.72 × 10−11) for Patient Health Questionnaire scores and rs162553 at 2p22.2 (CYP1B1) (p = 2.25 × 10−9) for self-harm. Our findings suggest the relevance of the TDI to psychiatric disorders. The genome-wide gene-by-environment interaction study identified several candidate genes interacting with the TDI, providing novel clues for understanding the biological mechanism of associations between the TDI and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Introduction: Serum urate is associated with BMD and may be a protective factor. However, the exact association and mechanism are still unclear. We performed a genome-wide gene-environmental interaction study (GWGEIS) to explore the interaction effects between gene and urate on BMD, using data from the UK Biobank cohort., Methods: A total of 4575 participants for femur total BMD, 4561 participants for L1-L4 BMD, and 237799 participants for heel BMD were included in the present study. Linear regression models were used to test for associations between urate and BMD (femur total BMD, L1-L4 BMD, heel BMD) by R software. GWGEIS was conducted by PLINK 2.0 using a generalize linear model, adjusted for age, sex, weight, smoking behavior, drinking behavior, physical activity and 10 principle components for population structure., Results: Results showed that urate was positively associated with femur total BMD, L1-L4 BMD and heel BMD and similar findings were observed in both the male and female subgroups. GWGEIS identified 261 genome-wide significant (P < 5.00 × 10 -8 ) SNP × urate interaction effects for femur total BMD (rs8192585 in NOTCH4, rs116080577 in PBX1, rs9409991 in COL5A1), 17 genome-wide significant SNP × urate interaction effects for heel BMD (rs145344540 in PDE11A and rs78485379 in DKK2), 17 suggestive genome-wide SNP × urate interaction effects (P < 1.00 × 10 -5 ) for L1-L4 BMD (rs10977015 in PTPRD). We also detected genome-wide significant and suggestive SNP × urate interaction effects for BMD in both the male and female subgroups., Conclusions: This study reported several novel candidate genes, and strengthen the evidence of the interactive effects between gene and urate on the variations of BMD., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The relationships between long-term antibiotic use during early life and mental traits remain elusive now. A total of 158,444 subjects from UK Biobank were used in this study. Linear regression analyses were first conducted to assess the correlations between long-term antibiotic use during early life and mental traits. Gene-environment-wide interaction study (GEWIS) was then performed by PLINK2.0 to detect the interaction effects between long-term antibiotic use during early life and genes on the risks of mental traits. Finally, DAVID tool was used to conduct gene ontology (GO) analysis of the identified genes interacting with long-term antibiotic use during early life. We found negative associations of long-term antibiotic use during early life with remembrance (p value=1.74 × 10 -6 , b = -0.10) and intelligence (p value=2.64 × 10 -26 , b = -0.13), and positive associations of long-term antibiotic use during early life with anxiety (p value = 2.75 × 10 -47 , b = 0.12) and depression (p value=2.01 × 10 -195 , b = 0.25). GEWIS identified multiple significant genes-long-term antibiotic use during early life interaction effects, such as ANK3 (rs773585997, p value = 1.78 × 10 -8 ) for anxiety and STRN (rs140049205, p value = 1.88 × 10 -8 ) for depression. GO enrichment analysis detected six GO terms enriched in the identified genes interacting with long-term antibiotic use during early life for anxiety, such as GO:0030425~dendrite (p value = 3.41 × 10 -2 ) and GO:0005886~plasma membrane (p value = 3.64 × 10 -3 ). Our study results suggest the impact of long-term antibiotic use during early life on the development of mental traits.

Shoulder impingement syndrome (SIS) is a common shoulder disorder with unclear genetic mechanism. In this study, Genome-wide Association Study (GWAS) was conducted to identify the candidate loci associated with SIS by using the UK Biobank samples (including 3,626 SIS patients and 3,626 control subjects). Based on the GWAS results, gene set enrichment analysis was further performed to detect the candidate gene ontology and pathways associated with SIS. We identified multiple risk loci associated with SIS, such as rs750968 ( P = 4.82 × 10 -8 ), rs754832 ( P = 4.83 × 10 -8 ) and rs1873119 ( P = 6.39 × 10 -8 ) of ANXA1 gene. Some candidate pathways have been identified related to SIS, including those linked to infection response and hypoxia, "ZHOU&#95;INFLAMMATORY&#95;RESPONSE&#95;FIMA&#95;DN" ( P = 0.012) and "MANALO&#95;HYPOXIA&#95;UP" ( P = 5.00 × 10 -5 ). Our results provide novel clues for understanding the genetic mechanism of SIS., (Copyright © 2020 Cheng et al.)

Background: Birth weight influences not only brain development, but also mental health outcomes, including depression, but the underlying mechanism is unclear., Methods: The phenotypic data of 12,872-91,009 participants (59.18-63.38% women) from UK Biobank were included to test the associations between the birth weight, depression, and brain volumes through the linear and logistic regression models. As birth weight is highly heritable, the polygenic risk scores (PRSs) of birth weight were calculated from the UK Biobank cohort (154,539 participants, 56.90% women) to estimate the effect of birth weight-related genetic variation on the development of depression and brain volumes. Finally, the mediation analyses of step approach and mediation analysis were used to estimate the role of brain volumes in the association between birth weight and depression. All analyses were conducted sex stratified to assess sex-specific role in the associations., Result: We observed associations between birth weight and depression (odds ratio [OR] = 0.968, 95% confidence interval [CI] = 0.957-0.979, p = 2.29 × 10-6). Positive associations were observed between birth weight and brain volumes, such as gray matter (B = 0.131, p = 3.51 × 10-74) and white matter (B = 0.129, p = 1.67 × 10-74). Depression was also associated with brain volume, such as left thalamus (OR = 0.891, 95% CI = 0.850-0.933, p = 4.46 × 10-5) and right thalamus (OR = 0.884, 95% CI = 0.841-0.928, p = 2.67 × 10-5). Additionally, significant mediation effects of brain volume were found for the associations between birth weight and depression through steps approach and mediation analysis, such as gray matter (B = -0.220, p = 0.020) and right thalamus (B = -0.207, p = 0.014)., Conclusions: Our results showed the associations among birth weight, depression, and brain volumes, and the mediation effect of brain volumes also provide evidence for the sex-specific of associations.

As a new concept in clinical research,the real world research(RWR) has attracted the attention of researchers in the world with its unique advantages. This research mainly analyzed it through visual methods,the specific steps were as follows. Firstly,the Chinese Biomedical Literature Database,China National Knowledge Infrastructure,Wan Fang Database,Medline and EMbase were searched and RWS were included,414 articles in Chinese and 2 158 articles in English were included in this research after layer-bylayer screening; secondly,the main information was extracted and sorted by BICOMS 2 software and generated its co-occurrence matrix; the network relationship diagram was drawn by Net Draw software; the cluster analysis was carried out by using g CLUTO software;finally,this research results show that the numbers of domestic and foreign literatures have shown an overall growth trend,but compared with foreign countries,China's research on the real world started late,the overall strength of research was not as good as abroad;and the domestic and foreign research on the real world was uneven in the region,the research in China was mainly distributed in developed provinces and cities,such as Beijing,Guangdong,Shanghai,etc,and there was a lack of close cooperation between provinces and cities; the foreign research are mainly distributed in developed countries such as the United States,the United Kingdom,Germany,et al; the cooperation between countries was relatively close. And the hotspots and core directions of domestic and foreign research were also different. This research was intended to provide reference for the further research of Chinese researchers through the current description of the themes and capability of the real world research in the world.