Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.

BACKGROUND: Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS: We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS: We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (ß = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (ß = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (ß = 0.088, FDR = 0.010), whereas no such association was found with PRS (ß = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (ß = -0.042, FDR = 6.30 × 10-9), but not with PRS (ß = -0.003, FDR = 0.857). CONCLUSIONS: Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

An atlas of causal association between micronutrients and osteoarthritis.

OBJECTIVE: This study examines the causal relationships between serum micronutrients and site-specific osteoarthritis (OA) using Mendelian Randomization (MR). METHODS: This study performed a two-sample MR analysis to explore causal links between 21 micronutrients and 11 OA outcomes. These outcomes encompass overall OA, seven site-specific manifestations, and three joint replacement subtypes. Sensitivity analyses using MR methods, such as the weighted median, MR-Egger, and MR-PRESSO, assessed potential horizontal pleiotropy and heterogeneity. Genome-wide association summary statistical data were utilized for both exposure and outcome data, including up to 826,690 participants with 177,517 OA cases. All data was sourced from Genome-wide association studies datasets from 2009 to 2023. RESULTS: In the analysis of associations between 21 micronutrients and 11 OA outcomes, 15 showed Bonferroni-corrected significance (P < 0.000216), without significant heterogeneity or horizontal pleiotropy. Key findings include strong links between gamma-tocopherol and spine OA (OR = 1.70), and folate with hand OA in finger joints (OR = 1.15). For joint replacements, calcium showed a notable association with a reduced likelihood of total knee replacement (TKR) (OR = 0.52) and total joint replacement (TJR) (OR = 0.56). Serum iron was significantly associated with an increased risk of total hip replacement (THR) (OR = 1.23), while folate indicated a protective effect (OR = 0.95). Various sex-specific associations were also uncovered. CONCLUSION: These findings underscore the critical role of micronutrients in osteoarthritis, providing valuable insights for preventive care and potential enhancement of treatment outcomes.

Identifying novel chemical-related susceptibility genes for five psychiatric disorders through integrating genome-wide association study and tissue-specific 3'aQTL annotation datasets.

The establishment of 3'aQTLs comprehensive database provides an opportunity to help explore the functional interpretation from the genome-wide association study (GWAS) data of psychiatric disorders. In this study, we aim to search novel susceptibility genes, pathways, and related chemicals of five psychiatric disorders via GWAS and 3'aQTLs datasets. The GWAS datasets of five psychiatric disorders were collected from the open platform of Psychiatric Genomics Consortium (PGC, https://www.med.unc.edu/pgc/ ) and iPSYCH ( https://ipsych.dk/ ) (Demontis et al. in Nat Genet 51(1):63-75, 2019; Grove et al. in Nat Genet 51:431-444, 2019; Genomic Dissection of Bipolar Disorder and Schizophrenia in Cell 173: 1705-1715.e1716, 2018; Mullins et al. in Nat Genet 53: 817-829; Howard et al. in Nat Neurosci 22: 343-352, 2019). The 3'untranslated region (3'UTR) alternative polyadenylation (APA) quantitative trait loci (3'aQTLs) summary datasets of 12 brain regions were obtained from another public platform ( https://wlcb.oit.uci.edu/3aQTLatlas/ ) (Cui et al. in Nucleic Acids Res 50: D39-D45, 2022). First, we aligned the GWAS-associated SNPs of psychiatric disorders and datasets of 3'aQTLs, and then, the GWAS-associated 3'aQTLs were identified from the overlap. Second, gene ontology (GO) and pathway analysis was applied to investigate the potential biological functions of matching genes based on the methods provided by MAGMA. Finally, chemical-related gene-set analysis (GSA) was also conducted by MAGMA to explore the potential interaction of GWAS-associated 3'aQTLs and multiple chemicals in the mechanism of psychiatric disorders. A number of susceptibility genes with 3'aQTLs were found to be associated with psychiatric disorders and some of them had brain-region specificity. For schizophrenia (SCZ), HLA-A showed associated with psychiatric disorders in all 12 brain regions, such as cerebellar hemisphere (P = 1.58 × 10-36) and cortex (P = 1.58 × 10-36). GO and pathway analysis identified several associated pathways, such as Phenylpropanoid Metabolic Process (GO:0009698, P = 6.24 × 10-7 for SCZ). Chemical-related GSA detected several chemical-related gene sets associated with psychiatric disorders. For example, gene sets of Ferulic Acid (P = 6.24 × 10-7), Morin (P = 4.47 × 10-2) and Vanillic Acid (P = 6.24 × 10-7) were found to be associated with SCZ. By integrating the functional information from 3'aQTLs, we identified several susceptibility genes and associated pathways especially chemical-related gene sets for five psychiatric disorders. Our results provided new insights to understand the etiology and mechanism of psychiatric disorders.

Assessing the effect of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes.

Longitudinal changes in brain structure and lifestyle can affect sleep phenotypes. However, the influence of the interaction between longitudinal changes in brain structure and lifestyle on sleep phenotypes remains unclear. Genome-wide association study dataset of longitudinal changes in brain structure was obtained from published study. Phenotypic data of lifestyles and sleep phenotypes were obtained from UK Biobank cohort. Using genotype data from UK Biobank, we calculated polygenetic risk scores of longitudinal changes in brain structure phenotypes. Linear/logistic regression analysis was conducted to evaluate interactions between longitudinal changes in brain structure and lifestyles on sleep duration, chronotype, insomnia, snoring and daytime dozing. Multiple lifestyle × longitudinal changes in brain structure interactions were detected for 5 sleep phenotypes, such as physical activity×caudate_age2 for daytime dozing (OR = 1.0389, P = 8.84 × 10-3) in total samples, coffee intake×cerebellar white matter volume_age2 for daytime dozing (OR = 0.9652, P = 1.13 × 10-4) in females. Besides, we found 4 overlapping interactions in different sleep phenotypes. We conducted sex stratification analysis and identified one overlapping interaction between female and male. Our results support the moderate effects of interaction between lifestyle and longitudinal changes in brain structure on sleep phenotypes, and deepen our understanding of the pathogenesis of sleep disorders.

Assessing the association of coffee consumption on the relationship of chronic pain with depression and anxiety.

BACKGROUND: A bidirectional relationship between chronic pain (CP) and mental disorders has been reported, and coffee was believed to be associated with both. However, the association of coffee in this bidirectional relationship remains unclear. We aim to analyze the association of coffee consumption on the relationship of CP with depression and anxiety. METHODS: A total of 376,813 participants from UK Biobank were included. We collected data on anxiety, depression and CP from objects of our study population. The association of coffee consumption on the relationship of CP with depression and anxiety was assessed through logistic/linear regression models. Moreover, seemingly unrelated estimation test (SUEST) was used to compare whether the coefficients differed in two different groups. RESULTS: We observed significant associations of coffee consumption in the interaction of CP with depression and anxiety, such as the association of multisite chronic pain (MCP) on self-reported depression (ßcoffee = 0.421, ßnon-coffee = 0.488, PSUEST = 0.001), and the association of MCP on generalized anxiety disorder-7 (GAD-7) scores (ßcoffee = 0.561, ßnon-coffee = 0.678, PSUEST = 0.004) were significantly different between coffee drinking and non-coffee drinking groups. Furthermore, in analysis stratified by gender, we found headache (ßmale = 0.392, ßfemale = 0.214, PSUEST = 0.022) and hip pain (ßmale = 0.480, ßfemale = 0.191, PSUEST = 0.021) had significant associations with self-reported depression between males and females groups in coffee drinkers. CONCLUSIONS: Our results suggested that coffee consumption has a significant association on the relationship of CP with depression and anxiety.

A genome-wide gene-environmental interaction study identified novel loci for the relationship between ambient air pollution exposure and depression, anxiety.

BACKGROUND: Genetic factors and environmental exposures, including air pollution, contribute to the risk of depression and anxiety. While the association between air pollution and depression and anxiety has been established in the UK Biobank, there has been limited research exploring this relationship from a genetic perspective. METHODS: Based on individual genotypic and phenotypic data from a cohort of 104,385 participants in the UK Biobank, a polygenic risk score for depression and anxiety was constructed to explore the joint effects of nitric oxide (NO), nitrogen dioxide (NO2), particulate matter (PM) with a diameter of ⩽2.5 µm (PM2.5) and 2.5-10 µm (PMcoarse) with depression and anxiety by linear and logistic regression models. Subsequently, a genome-wide gene-environmental interaction study (GWEIS) was performed using PLINK 2.0 to identify the genes interacting with air pollution for depression and anxiety. RESULTS: A substantial risk of depression and anxiety development was detected in participants exposed to the high air pollution concomitantly with high genetic risk. GWEIS identified 166, 23, 18, and 164 significant candidate loci interacting with NO, NO2, PM2.5, and PMcoarse for Patient Health Questionnaire-9 (PHQ-9) score, and detected 44, 10, 10, and 114 candidate loci associated with NO, NO2, PM2.5, and PMcoarse for General Anxiety Disorder (GAD-7) score, respectively. And some significant genes overlapped among four air pollutants, like TSN (rs184699498, PNO2 = 3.47 × 10-9; rs139212326, PPM2.5 = 1.51 × 10-8) and HSP90AB7P(rs150987455, PNO2 = 1.63 × 10-11; rs150987455, PPM2.5 = 7.64 × 10-11), which were common genes affecting PHQ-9 score for both NO2 and PM2.5. CONCLUSION: Our study identified the joint effects of air pollution with genetic susceptibility on the risk of depression and anxiety, and provided several novel candidate genes for the interaction, contributing to an understanding of the genetic architecture of depression and anxiety.

Combined effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression: A cross-sectional study based on UK Biobank.

Infection-induced perturbation of immune homeostasis could promote psychopathology. Psychiatric sequelae have been observed after previous coronavirus outbreaks. However, limited studies were conducted to explore the potential interaction effects of inflammation and coronavirus disease 2019 (COVID-19) on the risks of anxiety and depression. In this study, first, polygenic risk scores (PRS) were calculated for eight COVID-19 clinical phenotypes using individual-level genotype data from the UK Biobank. Then, linear regression models were developed to assess the effects of COVID-19 PRS, C-reactive protein (CRP), systemic immune inflammation index (SII), and their interaction effects on the Generalized Anxiety Disorder-7 (GAD-7, 104 783 individuals) score and the Patient Health Questionnaire-9 (PHQ-9, 104 346 individuals) score. Several suggestive interactions between inflammation factors and COVID-19 clinical phenotypes were detected for PHQ-9 score, such as CRP/SII × Hospitalized/Not_Hospitalized in women group and CRP × Hospitalized/Unscreened in age >65 years group. For GAD-7 score, we also found several suggestive interactions, such as CRP × Positive/Unscreened in the age ≤65 years group. Our results suggest that not only COVID-19 and inflammation have important effects on anxiety and depression but also the interactions of COVID-19 and inflammation have serious risks for anxiety and depression.

A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank.

Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change. PURPOSE: This study aimed to identify the genetic architecture and potential biomarkers of BMD. METHODS: Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. RESULTS: A total of 52 genes associated with BMD trajectory mean were identified, of which the top three significant genes were WNT16 (P = 1.31 × 10-126), FAM3C (P = 4.18 × 10-108), and CPED1 (P = 8.48 × 10-106). In addition, 114 genes associated with BMD within-subject variability were also identified, such as AC092079.1 (P = 2.72 × 10-13) and RGS7 (P = 4.72 × 10-10). The associations for these candidate genes were confirmed in the previous GWASs and replicated successfully in the Asian participants. GSEA results of BMD change identified multiple GO terms related to skeletal development, such as SKELETAL SYSTEM DEVELOPMENT (Padjusted = 2.45 × 10-3) and REGULATION OF OSSIFICATION (Padjusted = 2.45 × 10-3). KEGG enrichment analysis showed that these genes were mainly enriched in WNT SIGNALING PATHWAY. CONCLUSIONS: Our findings indicated that the CPED1-WNT16-FAM3C locus plays a significant role in BMD mean trajectories and identified several novel candidate genes contributing to BMD within-subject variability, facilitating the understanding of the genetic architecture of BMD.

Assessing the interaction effects of mitochondrial DNA polymorphisms and lifestyle on heel bone mineral density.

BACKGROUND: Bone mineral density (BMD) is a major predictor of osteoporotic fractures, and previous studies have reported the effects of mitochondrial dysfunction and lifestyle on BMD, respectively. However, their interaction effects on BMD are still unclear. Therefore, we aimed to investigate the possible interaction of mitochondrial DNA (mtDNA) and common lifestyles contributing to osteoporosis. METHODS: Our analysis included 119,120 white participants (Nfemale=65,949 and Nmale=53,171) from the UK Biobank with heel BMD phenotype data. A generalized linear regression model of PLINK was performed to assess the interaction effects of mtDNA and five life environmental factors on heel BMD, including smoking, drinking, physical activity, dietary diversity score, and vitamin D. In addition, we also performed linear regression analysis for total body BMD. Finally, we assessed the potential causal relationships between mtDNA copy number (mtDNA-CN) and life environmental factors using Mendelian randomization (MR) analysis. RESULTS: Our study identified four mtDNA loci showing suggestive evidence of heel BMD, such as m.16356T>C (MT-DLOOP; P =1.50×10-3) in total samples. Multiple candidate mtDNA×lifetsyle interactions were also detected for heel BMD, such as MT-ND2×physical activity (P = 2.88×10-3) in total samples and MT-ND1×smoking (P = 8.54×10-4) in males. Notably, MT-CYB was a common candidate mtDNA loci for heel BMD to interact with five life environmental factors. Multivariable MR analysis indicated a causal effect of physical activity on heel BMD when mtDNA-CN was considered (P =1.13×10-3). CONCLUSIONS: Our study suggests the candidate interaction between mitochondria and lifestyles on heel BMD, providing novel clues for exploring the pathogenesis of osteoporosis.

Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma.

Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for 'fluid intelligence score' and 38 949 subjects for 'maximum digits remembered correctly') and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10-4 (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between 'fluid intelligence score' and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P brain = 1.21 × 10-8, P CSF = 1.10 × 10-7), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P CSF = 1.23 × 10-4). Other proteins showed close-to-significant associations with the trait of 'fluid intelligence score', such as plasma protease C1 inhibitor (P CSF = 4.19 × 10-4, P plasma = 6.97 × 10-4), and with the trait of 'maximum digits remembered correctly', such as tenascin (P plasma = 3.42 × 10-4). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: ß = 0.54, P = 1.64 × 10-61 in the brain; ß = 0.09, P = 1.60 × 10-12 in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.

Body shape from birth to adulthood is associated with skeletal development: A Mendelian randomization study.

BACKGROUND: Observational studies have shown that childhood obesity is associated with adult bone health but yield inconsistent results. We aimed to explore the potential causal association between body shape and skeletal development. METHODS: We used two-sample Mendelian randomization (MR) to estimate causal relationships between body shape from birth to adulthood and skeletal phenotypes, with exposures including placental weight, birth weight, childhood obesity, BMI, lean mass, fat mass, waist circumference, and hip circumference. Independent genetic instruments associated with the exposures at the genome-wide significance level (P < 5 × 10-8) were selected from corresponding large-scale genome-wide association studies. The inverse-variance weighted analysis was chosen as the primary method, and complementary MR analyses included the weighted median, MR-Egger, weighted mode, and simple mode. RESULTS: The MR analysis shows strong evidence that childhood (ß = -1.29 × 10-3, P = 8.61 × 10-5) and adulthood BMI (ß = -1.28 × 10-3, P = 1.45 × 10-10) were associated with humerus length. Tibiofemoral angle was negatively associated with childhood BMI (ß = -3.60 × 10-1, P = 3.00 × 10-5) and adolescent BMI (ß = -3.62 × 10-1, P = 2.68 × 10-3). In addition, genetically predicted levels of appendicular lean mass (ß = 1.16 × 10-3, P = 1.49 × 10-13), whole body fat mass (ß = 1.66 × 10-3, P = 1.35 × 10-9), waist circumference (ß = 1.72 × 10-3, P = 6.93 × 10-8) and hip circumference (ß =1.28 × 10-3, P = 4.34 × 10-6) were all associated with tibia length. However, we found no causal association between placental weight, birth weight and bone length/width. CONCLUSIONS: This large-scale MR analysis explores changes in growth patterns in the length/width of major bone sites, highlighting the important role of childhood body shape in bone development and providing insights into factors that may drive bone maturation.

Single-cell multiomics analysis reveals cell/tissue-specific associations in bipolar disorder.

This study investigates the cellular origin and tissue heterogeneity in bipolar disorder (BD) by integrating multiomics data. Four distinct datasets were employed, including single-cell RNA sequencing (scRNA-seq) data (embryonic and fetal brain, n = 8, 1,266 cells), BD Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (adult brain, n = 210), BD bulk RNA-seq data (adult brain, n = 314), and BD genome-wide association study (GWAS) summary data (n = 413,466). The integration of scRNA-seq data with multiomics data relevant to BD was accomplished using the single-cell disease relevance score (scDRS) algorithm. We have identified a novel brain cell cluster named ADCY1, which exhibits distinct genetic characteristics. From a high-resolution genetic perspective, glial cells emerge as the primary cytopathology associated with BD. Specifically, astrocytes were significantly related to BD at the RNA-seq level, while microglia showed a strong association with BD across multiple panels, including the transcriptome-wide association study (TWAS), ATAC-seq, and RNA-seq. Additionally, oligodendrocyte precursor cells displayed a significant association with BD in both ATAC-seq and RNA-seq panel. Notably, our investigation of brain regions affected by BD revealed significant associations between BD and all three types of glial cells in the dorsolateral prefrontal cortex (DLPFC). Through comprehensive analyses, we identified several BD-associated genes, including CRMP1, SYT4, UCHL1, and ZBTB18. In conclusion, our findings suggest that glial cells, particularly in specific brain regions such as the DLPFC, may play a significant role in the pathogenesis of BD. The integration of multiomics data has provided valuable insights into the etiology of BD, shedding light on potential mechanisms underlying this complex psychiatric disorder.

Identification of novel functional brain proteins for treatment-resistant schizophrenia: Based on a proteome-wide association study.

OBJECTIVE: Genetic approaches are increasingly advantageous in characterizing treatment-resistant schizophrenia (TRS). We aimed to identify TRS-associated functional brain proteins, providing a potential pathway for improving psychiatric classification and developing better-tailored therapeutic targets. METHODS: TRS-related proteome-wide association studies (PWAS) were conducted on genome-wide association studies (GWAS) from CLOZUK and the Psychiatric Genomics Consortium (PGC), which provided TRS individuals (n = 10,501) and non-TRS individuals (n = 20,325), respectively. The reference datasets for the human brain proteome were obtained from ROS/MAP and Banner, with 8,356 and 11,518 proteins collected, respectively. We then performed colocalization analysis and functional enrichment analysis to further explore the biological functions of the proteins identified by PWAS. RESULTS: In PWAS, two statistically significant proteins were identified using the ROS/MAP and then replicated using the Banner reference dataset, including CPT2 (PPWAS-ROS/MAP = 4.15 × 10-2 and PPWAS-Banner = 3.38 × 10-3) and APOL2 (PPWAS-ROS/MAP = 4.49 × 10-3 and PPWAS-Banner = 8.26 × 10-3). Colocalization analysis identified three variants that were causally related to protein expression in the human brain, including CCDC91 (PP4 = 0.981), PRDX1 (PP4 = 0.894), and WARS2 (PP4 = 0.757). We extended PWAS results from gene-based analysis to pathway-based analysis, identifying 14 gene ontology (GO) terms and the only candidate pathway for TRS, metabolic pathways (all P < 0.05). CONCLUSIONS: Our results identified two protein biomarkers, and cautiously support that the pathological mechanism of TRS is linked to lipid oxidation and inflammation, where mitochondria-related functions may play a role.

Association between electronic device use and health status among a middle-aged and elderly population: a cross-sectional analysis in the UK Biobank.

Aim: Few previous studies have investigated the impact of multiple types of electronic devices on health status, and the moderating effects of gender, age, and BMI. Our aim is to examine the relationships between the use of four types of electronics and three health status indicators in a middle-aged and elderly population, and how these relationships varied by gender, age, and BMI. Subject and methods: Using data from 376,806 participants aged 40-69 years in the UK Biobank, we conducted a multivariate linear regression to estimate the association between electronic device use and health status. Electronics use was categorized as TV watching, computer use, computer gaming, and mobile phone use, and health status included self-rated health (SRH), multisite chronic pain (MCP), and total physical activity (TPA). Interaction terms were utilized to assess whether the above associations were modified by BMI, gender, and age. Further stratified analysis was performed to explore the role of gender, age, and BMI. Results: Higher levels of TV watching (BSRH = 0.056, BMCP = 0.044, BTPA= -1.795), computer use (BSRH = 0.007, BTPA= -3.469), and computer gaming (BSRH = 0.055, BMCP = 0.058, BTPA= -6.076) were consistently associated with poorer health status (all P < 0.05). Contrastingly, earlier exposure to mobile phones (BSRH = -0.048, BTPA= 0.933, BMCP = 0.056) was inconsistent with health (all P < 0.05). Additionally, BMI (Bcomputer use-SRH= 0.0026, Bphone-SRH= 0.0049, BTV-MCP= 0.0031, and BTV-TPA= -0.0584) exacerbated the negative effects of electronics use, and male (Bphone-SRH = -0.0414, Bphone-MCP = -0.0537, Bphone-TPA= 2.8873) were healthier with earlier exposure to mobile phones (all P < 0.05). Conclusion: Our findings suggest that the adverse health effects associated with watching TV, computer use, and computer gaming were consistent and were moderated by BMI, gender, and age, which advances a comprehensive understanding of the association between multiple types of electronic devices and health status, and provides new perspectives for future research. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01886-5.

A systematical association analysis of 25 common virus infection and genetic susceptibility of COVID-19 infection.

OBJECTIVES: Previous studies identified a number of diseases were associated with 2019 coronavirus disease (COVID-19). However, the associations between these diseases related viral infections and COVID-19 remains unknown now. METHODS: In this study, we utilized single nucleotide polymorphisms (SNPs) related to COVID-19 from genome-wide association study (GWAS) and individual-level genotype data from the UK biobank to calculate polygenic risk scores (PRS) of 487,409 subjects for eight COVID-19 clinical phenotypes. Then, multiple logistic regression models were established to assess the correlation between serological measurements (positive/negative) of 25 viruses and the PRS of eight COVID-19 clinical phenotypes. And we performed stratified analyses by age and gender. RESULTS: In whole population, we identified 12 viruses associated with the PRS of COVID-19 clinical phenotypes, such as VZV seropositivity for Varicella Zoster Virus (Unscreened/Exposed_Negative: ß = 0.1361, P = 0.0142; Hospitalized/Unscreened: ß = 0.1167, P = 0.0385) and MCV seropositivity for Merkel Cell Polyomavirus (Unscreened/Exposed_Negative: ß = -0.0614, P = 0.0478). After age stratification, we identified seven viruses associated with the PRS of eight COVID-19 clinical phenotypes in the age < 65 years group. After gender stratification, we identified five viruses associated with the PRS of eight COVID-19 clinical phenotypes in the women group. CONCLUSION: Our study findings suggest that the genetic susceptibility to different COVID-19 clinical phenotypes is associated with the infection status of various common viruses.

Evaluating the role of anxiety on the association between irritable bowel syndrome and brain volumes: a mediation analysis in the UK Biobank cohort.

There is a strong link between irritable bowel syndrome and brain volumes, yet, to date, research examining the mediators of this association has been little. Based on the phenotypic data of 15 248 participants from the UK Biobank, a two-stage mediation analysis was performed to assess the association among brain volumes, anxiety, and irritable bowel syndrome. In the first stage, we identified the candidate mediating role of anxiety for irritable bowel syndrome associated with brain volumes using regression models. Then, we quantified the magnitude of the mediation effects by evaluating the average causal-mediated effect and proportion of mediation through performing mediation analyses in the R package in the second stage. In the first stage, we identified the partly mediating role of anxiety in the association between irritable bowel syndrome and the volume of thalamus (P left = 1.16 × 10-4, P right = 2.41 × 10-4), and grey matter (P left = 3.22 × 10-2, P right = 1.18 × 10-2) in the VIIIa cerebellum. In the second stage, we observed that the proportion of the total effect of irritable bowel syndrome on volume of thalamus mediated by anxiety was 14.3% for the left region (ß Average causal-mediated effect = -0.008, P Average causal-mediated effect = 0.004) and 14.6% for the right region (ß Average causal-mediated effect = -0.007, P Average causal-mediated effect = 0.006). Anxiety mediated 30.8% for the left region (ß Average causal-mediated effect = -0.013, P Average causal-mediated effect = 0.002) and 21.6% for the right region (ß Average causal-mediated effect = -0.010, P Average causal-mediated effect x= 0.018) of the total effect of irritable bowel syndrome on the volume of grey matter in the VIIIa cerebellum. Our study revealed the indirect mediating role of anxiety in the association between irritable bowel syndrome and brain volumes, promoting our understanding of the functional mechanisms of irritable bowel syndrome and its related psychosocial factors.

A retrospective case series of electroconvulsive therapy in the management of depression and suicidal symptoms in adolescents.

OBJECTIVE: Major depressive disorder (MDD) with suicidal symptoms is common in adolescents. Electroconvulsive therapy (ECT) is highly effective in the treatment of MDD. We have described its use and outcome in a case series of adolescents with depression and suicidal symptoms receiving ECT. METHODS: We analyzed 362 adolescents aged from 12 to 17 who had received ECT between year 2015 and 2021. A total of 278 subjects were found to meet the inclusion criteria, where depressive symptoms were assessed by HDRS and suicidal symptoms were assessed by HDRS item 3. Their sociodemographic, clinical, and treatment information were retrieved through these records for this study. RESULTS: The mean ± SD age of subjects was 15.41 ± 1.50 years and male sex was 14.7% (n = 41). Comorbid diagnoses were present in 104 patients (37.4%). The ECT sessions ranged from 6 to 12 times. All the patients took antidepressants, with sertraline (n = 182; 65.5%) being the most widely used. Majority of patients also received benzodiazepines. ECT was significantly effective in adolescents with depression and suicidal symptoms in evaluation by HDRS, HDRS item 3, CGI-S (p < .001) pre/post-ECT. The response rate of MDD patients was 52%, with suicidal ideation (SI) at 49%, and 54% in MDD with suicide attempt (SA). The change of CGI-S scores showed no significant differences between various subgroups of sex and comorbid (p>.05), but there were significant differences between subgroups of suicidal symptoms (p < .001). ECT was generally safe with subjective memory complaint (n = 189, 68.0%), headache (n = 150, 54.0%), body pain (n = 28, 10.1%), delirium (n = 95, 34.2%), and nausea (n = 31, 11.2%) as possible side effects following ECT. CONCLUSION: In this study, ECT was found to decrease depressive and suicidal symptoms in adolescents, and the side effect was acceptable. ECT showed better outcome for MDD with SA compared to MDD with SI.

Assessing the effect of interaction between gut microbiome and inflammatory bowel disease on the risks of depression.

Background: Gut microbiome and inflammatory bowel disease (IBD) are implicated in the development of depression, but the effect of their interactions on the risk of depression remains unclear. We aim to analyze the effect of interactions between gut microbiome and IBD on the risk of depression, and explore candidate genes involving the interactions. Methods: Using the individual genotype and depression traits data from the UK Biobank, we calculated the polygenetic risk scores (PRS) of 114 gut microbiome, ulcerative colitis (UC), Crohn's disease (CD), and total IBD (CD + UC) respectively. The effects of interactions between gut microbiome and IBD on depression were assessed through a linear regression model. Moreover, for observed significant interactions between gut microbiome PRS and IBD PRS, PLINK software was used to test pair-wise single nucleotide polymorphisms (SNPs) interaction of corresponding gut microbiome PRS and IBD PRS on depression. Results: We found 64 candidate interactions between gut microbiome and IBD on four phenotypes of depression, such as F_Lachnospiraceae (RNT) × (CD + UC) for patient health questionnaire-9 (PHQ-9) score (P = 1.48 × 10-3), F_Veillonellaceae (HB) × UC for self-reported depression (P = 2.83 × 10-3) and P_Firmicutes (RNT) × CD for age at first episode of depression (P = 8.50 × 10-3). We observed interactions of gut-microbiome-associated SNPs × IBD-associated SNPs, such as G_Alloprevotella (HB)-associated rs147650986 (GPM6A) × IBD-associated rs114471990 (QRICH1) (P = 2.26 × 10-4). Conclusion: Our results support the effects of interactions between gut microbiome and IBD on depression risk, and reported several novel candidate genes for depression.

Identification of Human Brain Proteins for Bitter-Sweet Taste Perception: A Joint Proteome-Wide and Transcriptome-Wide Association Study.

Objective: Bitter or sweet beverage perception is associated with alterations in brain structure and function. Our aim is to analyze the genetic association between bitter or sweet beverage perception and human brain proteins. Materials and methods: In our study, 8356 and 11,518 proteins were first collected from two reference datasets of human brain proteomes, the ROS/MAP and Banner. The bitter or sweet beverage perception-related proteome-wide association studies (PWAS) were then conducted by integrating recent genome-wide association study (GWAS) data (n = 422,300) of taste perception with human brain proteomes. The human brain gene expression profiles were collected from two reference datasets, including the brain RNA-seq (CBR) and brain RNA-seq splicing (CBRS). The taste perception-related transcriptome-wide association studies (TWAS) were finally performed by integrating the same GWAS data with human brain gene expression profiles to validate the PWAS findings. Results: In PWAS, four statistically significant proteins were identified using the ROS/MAP and then replicated using the Banner reference dataset (all permutated p < 0.05), including ABCG2 for total bitter beverages and tea, CPNE1 for total bitter beverage, ACTR1B for artificially sweetened beverages, FLOT2 for alcoholic bitter beverages and total sweet beverages. In TWAS analysis, six statistically significant genes were detected by CBR and confirmed by the CBRS reference dataset (all permutated p < 0.05), including PIGG for total bitter beverages and non-alcoholic bitter beverages, C3orf18 for total bitter beverages, ZSWIM7 for non-alcoholic bitter beverages, PEX7 for coffee, PKP4 for tea and RPLP2 for grape juice. Further comparison of the PWAS and TWAS found three common statistically significant proteins/genes identified from the Banner and CBR reference datasets, including THBS4 for total bitter beverages, CA4 for non-alcoholic bitter beverages, LIAS for non-grape juices. Conclusions: Our results support the potential effect of bitter or sweet beverage perception on brain function and identify several candidate brain proteins for bitter or sweet beverage perception.