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.

Potential involvement of connective tissue growth factor in chondrocytes apoptosis of Kashin-Beck disease.

BACKGROUND: Kashin-Beck disease (KBD) is an endemic osteoarthropathy characterized by excessive chondrocytes apoptosis. T-2 toxin exposure has been proved to be its etiology. Connective tissue growth factor (CTGF) exerts a profound influence on cartilage growth and metabolism. We investigated the potential role of CTGF in KBD development and examined CTGF alterations under T-2 toxin stimulation. METHODS: The levels of CTGF and chondrocyte apoptosis-related markers in cartilage and primary chondrocytes from KBD and control groups were measured using qRT-PCR, Western blotting, immunohistochemistry, and immunofluorescence. We analyzed expression changes of these genes in response to T-2 toxin. Apoptosis rates of chondrocytes induced by T-2 toxin were measured by flow cytometry and TUNEL assay. The active pharmaceutical ingredient targeting CTGF was screened through Comparative Toxicogenomics Database, and molecular docking was performed using AutoDock Tools. RESULTS: The CTGF levels in KBD cartilage and chondrocytes were significantly elevated and positively associated with the levels of apoptosis-related genes. T-2 toxin exposure increased CTGF and apoptosis-related gene levels in chondrocytes, with apoptosis rates rising alongside T-2 toxin concentration. Curcumin was identified as targeting CTGF and exhibited effective binding. It could down-regulate CTGF, apoptosis-related genes, such as Cleaved caspase 3 and BAX, and also significantly reduce apoptosis rate in chondrocytes treated with T-2 toxin. CONCLUSION: CTGF plays a crucial role in the development of KBD. Curcumin has shown potential in inhibiting CTGF levels and reducing chondrocyte apoptosis, highlighting its promise as a therapeutic agent for preventing cartilage damage in KBD. Our findings provided valuable insights into the pathogenesis of KBD and could promote the development of novel therapeutic strategies for this debilitating disease.

Socioeconomic inequalities, genetic susceptibility, and risks of depression and anxiety: A large-observational study.

OBJECTIVES: This study aimed to investigate the interplay between genetic susceptibility and socioeconomic disparities on psychiatric disorders. METHODS: In this study, we utilized data from the UK Biobank to analyze the Generalized Anxiety Disorder (GAD)-7 scale (N = 74,425) and the Patient Health Questionnaire (PHQ)-9 (N = 74,101), along with the Index of Multiple Deprivation (IMD). The polygenic risk score (PRS) was calculated to assess the genetic risk associated with GAD-7/PHQ-9 scores, and the individuals were classified into low, medium, and high genetic risk groups according to tertiles of PRSs related to the GAD-7/PHQ-9. Linear regression models were used to explore the relationships between GAD-7/PHQ-9 scores and IMD scores in patients with different genetic susceptibilities. RESULTS: Disadvantaged socioeconomic status was associated with the risk of anxiety and depression across all strata of genetic risk, and stronger associations were shown for individuals with greater genetic susceptibility. From low to high genetic risk, the risk of psychiatric disorders increased for the GAD-7 (ß = 0.002 to 0.032) and PHQ-9 (ß = 0.003 to 0.045) scores. In addition, strong associations of high genetic risk with anxiety (ß = 0.875) and depression (ß = 1.152) were detected in the IMD quartile 4 group compared with the least deprivation quartile group. Furthermore, income and employment were estimated to contribute strongly to anxiety (ßemployment = 7.331, ßincome = 4.492) and depression (ßemployment = 9.951, ßincome = 6.453) in the high genetic risk group. CONCLUSION: The results suggest that we should pay more attention to psychiatric disorders with high genetic susceptibility and try to improve their socioeconomic status to prevent the development of psychiatric disorders.

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.

Air pollutants and the risk of incident hepatobiliary diseases: A large prospective cohort study in the UK Biobank.

The association between air pollutants and hepatobiliary pancreatic diseases remains inconclusive. This study analyzed up to 247,091 participants of White European ancestry (aged 37 to 73 years at recruitment) from the UK Biobank, a large-scale prospective cohort with open access. An air pollution score was utilized to assess the combined effect of PM2.5, PM2.5-10, PM10, NO2, and NOX on total hepatobiliary pancreatic diseases, liver diseases, cholecyst diseases, and pancreatic diseases. Cox proportional hazard models were employed to evaluate the relationships between air pollutants and the incidence of these diseases. Restricted cubic spline regressions were used to examine the dose-response association between air pollutants and the risk of hepatobiliary pancreatic diseases. We identified 4865 cases of total hepatobiliary pancreatic diseases, over a median follow-up of 10.86 years. The air pollution scores were moderately associated with increased liver disease risk (HR = 1.009, 95 % CI: 1.004, 1.014), but not with cholecyst and pancreatic diseases. Among the individual air pollutants, PM2.5 (HR = 1.069, 95 % CI: 1.025, 1.115) and PM10 (HR = 1.036, 95 % CI: 1.011, 1.061) significantly increased liver disease risk. Males showed a higher risk of liver diseases with PM2.5 (HR = 1.075, 95 % CI: 1.015, 1.139). Additionally, individuals with overweight (HR = 1.125, 95 % CI: 1.052, 1.203), age ≥ 60 and ≤73 (HR = 1.098, 95 % CI: 1.028, 1.172), and alcohol intake ≥ 14 unit/week (HR = 1.078, 95 % CI: 1.006, 1.155) had a higher risk of developing liver diseases at high expose to PM2.5. This study suggests that prolonged exposure to ambient air pollutants may elevate the risk of liver diseases.

Micronutrient-Associated Single Nucleotide Polymorphism and Mental Health: A Mendelian Randomization Study.

PURPOSE: Previous studies have demonstrated the link between micronutrients and mental health. However, it remains uncertain whether this connection is causal. We aim to investigate the potential causal effects of micronutrients on mental health based on linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) analysis. METHODS: Utilizing publicly available genome-wide association study (GWAS) summary datasets, we performed LDSC and MR analysis to identify candidate micronutrients with potential causal effects on mental health. Single nucleotide polymorphisms (SNPs) significantly linked with candidate micronutrients with a genome-wide significance level (p < 5 × 10-8) were selected as instrumental variables (IVs). To estimate the causal effect of candidate micronutrients on mental health, we employed inverse variance weighted (IVW) regression. Additionally, two sensitivity analyses, MR-Egger and weighted median, were performed to validate our results. RESULTS: We found evidence supporting significant causal associations between micronutrients and mental health. LDSC detected several candidate micronutrients, including serum iron (genetic correlation = -0.134, p = 0.032) and vitamin C (genetic correlation = -0.335, p < 0.001) for attention-deficit/hyperactivity disorder (ADHD), iron-binding capacity (genetic correlation = 0.210, p = 0.037) for Alzheimer's disease (AD), and vitamin B12 (genetic correlation = -0.178, p = 0.044) for major depressive disorder (MDD). Further MR analysis suggested a potential causal relationship between vitamin B12 and MDD (b = -0.139, p = 0.009). There was no significant heterogeneity or pleiotropy, indicating the validity of the findings. CONCLUSION: In this study, we identified underlying causal relationships between micronutrients and mental health. Notably, more research is necessary to clarify the underlying biological mechanisms by which micronutrients affect mental health.

Disadvantaged social status contributed to sleep disorders: An observational and genome-wide gene-environment interaction analysis.

BACKGROUND: Sleep is a natural and essential physiological need for individuals. Our study aimed to research the associations between accumulated social risks and sleep disorders. METHODS: In this study, we came up with a polysocial risk score (PsRS), which is a cumulative social risk index composed of 13 social determinants of health. This research includes 239,165 individuals with sleep disorders and social determinants of health data from the UK Biobank cohort. First, logistic regression models were performed to examine the associations of social determinants of health and sleep disorders, including chronotype, narcolepsy, insomnia, snoring, short and long sleep duration. Then, PsRS was calculated based on statistically significant social determinants of health for each sleep disorder. Third, a genome-wide gene-environment interaction study was conducted to explore the interactions between single-nucleotide polymorphisms and PsRS in relation to sleep disorders. RESULTS: Higher PsRS scores were associated with worse sleep status, with the adjusted odds ratio (OR) ranging from 1.10 (95% Confidence interval [CI]: 1.09-1.11) to 1.29 (95% CI: 1.27-1.30) for sleep disorders. Emotional stress (OR = 1.36, 95% CI: 1.28-1.43) and not in paid employment (OR = 2.62, 95% CI: 2.51-2.74) were found to have significant contributions for sleep disorders. Moreover, multiple single-nucleotide polymorphisms were discovered to have interactions with PsRS, such as FRAS1 (P = 2.57 × 10-14) and CACNA1A (P = 8.62 × 10-14) for narcolepsy, and ACKR3 (P = 1.24 × 10-8) for long sleep. CONCLUSIONS: Our findings suggested that cumulative social risks was associated with sleep disorders, while the interactions between genetic susceptibility and disadvantaged social status are risk factors for the development of sleep disorders.

A genome-wide association study identifies candidate genes for sleep disturbances in depressed individuals.

OBJECTIVE: This study aimed to identify candidate loci and genes related to sleep disturbances in depressed individuals and clarify the co-occurrence of sleep disturbances and depression from the genetic perspective. METHODS: The study subjects (including 58,256 self-reported depressed individuals and 6,576 participants with PHQ-9 score ≥ 10, respectively) were collected from the UK Biobank, which were determined based on the Patient Health Questionnaire (PHQ-9) and self-reported depression status, respectively. Sleep related traits included chronotype, insomnia, snoring and daytime dozing. Genome-wide association studies (GWASs) of sleep related traits in depressed individuals were conducted by PLINK 2.0 adjusting age, sex, Townsend deprivation index and 10 principal components as covariates. The CAUSALdb database was used to explore the mental traits associated with the candidate genes identified by the GWAS. RESULTS: GWAS detected 15 loci significantly associated with chronotype in the subjects with self-reported depression, such as rs12736689 at RNASEL (P = 1.00 × 10- 09), rs509476 at RGS16 (P = 1.58 × 10- 09) and rs1006751 at RFX4 (P = 1.54 × 10- 08). 9 candidate loci were identified in the subjects with PHQ-9 ≥ 10, of which 2 loci were associated with insomnia such as rs115379847 at EVC2 (P = 3.50 × 10- 08), and 7 loci were associated with daytime dozing, such as rs140876133 at SMYD3 (P = 3.88 × 10- 08) and rs139156969 at ROBO2 (P = 3.58 × 10- 08). Multiple identified genes, such as RNASEL, RGS16, RFX4 and ROBO2 were reported to be associated with chronotype, depression or cognition in previous studies. CONCLUSION: Our study identified several candidate genes related to sleep disturbances in depressed individuals, which provided new clues for understanding the biological mechanism underlying the co-occurrence of depression and sleep disorders.

Mendelian randomization study of the relationship between blood and urine biomarkers and schizophrenia in the UK Biobank cohort.

BACKGROUND: The identification of suitable biomarkers is of crucial clinical importance for the early diagnosis of treatment-resistant schizophrenia (TRS). This study aims to comprehensively analyze the association between TRS and blood and urine biomarkers. METHODS: Candidate TRS-related single nucleotide polymorphisms (SNPs) were obtained from a recent genome-wide association study. The UK Biobank cohort, comprising 376,807 subjects with blood and urine biomarker testing data, was used to calculate the polygenic risk score (PRS) for TRS. Pearson correlation analyses were performed to evaluate the correlation between TRS PRS and each of the biomarkers, using calculated TRS PRS as the instrumental variables. Bidirectional two-sample Mendelian randomization (MR) was used to assess potential causal associations between candidate biomarkers with TRS. RESULTS: Here we identify a significant association between TRS PRS and phosphate (r = 0.007, P = 1.96 × 10-4). Sex subgroup analyses identify seven and three candidate biomarkers associated with TRS PRS in male and female participants, respectively. For example, total protein and phosphate for males, creatinine and phosphate for females. Bidirectional two-sample MR analyses indicate that TRS is negatively associated with cholesterol (estimate = -0.363, P = 0.008). Conversely, TRS is positively associated with total protein (estimate = 0.137, P = 0.027), mean corpuscular volume (estimate = 0.032, P = 2.25 × 10-5), and mean corpuscular hemoglobin (estimate = 0.018, P = 0.007). CONCLUSIONS: Our findings provide insights into the roles of blood and urine biomarkers in the early detection and treatment of TRS.

People with schizophrenia experience periods of time during which they misperceive reality. Some people with schizophrenia do not respond well to the usual drugs that are used to relieve their symptoms. This type of schizophrenia is known as treatment-resistant schizophrenia (TRS). We looked at differences in the genes (inherited characteristics), blood and urine of a group of people in the UK with schizophrenia to see if people with TRS have particular characteristics that would enable them to be distinguished from patients with schizophrenia who tend to respond to usual treatment. We found several differences in the blood that could be used to predict which people might get TRS, including some that were specific to men or women. These discoveries are important because they can help doctors identify people who are more likely to develop TRS earlier, enabling them to avoid using treatments that might not work well for them.

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.

Long-term ambient air pollution and the risk of musculoskeletal diseases: A prospective cohort study.

Evidence of the associations of air pollution and musculoskeletal diseases is inconsistent. This study aimed to examine the associations between air pollutants and the risk of incident musculoskeletal diseases, such as degenerative joint diseases (n = 38,850) and inflammatory arthropathies (n = 20,108). An air pollution score was constructed to assess the combined effect of PM2.5, PM2.5-10, NO2, and NOX. Cox proportional hazard model was applied to assess the relationships between air pollutants and the incidence of each musculoskeletal disease. The air pollution scores exhibited the modest association with an increased risk of osteoporosis (HR = 1.006, 95% CI: 1.002-1.011). Among the individual air pollutants, PM2.5 and PM2.5-10 exhibited the most significant effect on elevated risk of musculoskeletal diseases, such as PM2.5 on osteoporosis (HR = 1.064, 95% CI: 1.020-1.110), PM2.5-10 on inflammatory arthropathies (HR = 1.059, 95% CI: 1.037-1.081). Females were found to have a higher risk of incident musculoskeletal diseases when exposed to air pollutants. Individuals with extreme BMI or lower socioeconomic status had a higher risk of developing musculoskeletal diseases. Our findings reveal that long-term exposure to ambient air pollutants may contribute to an increased risk of musculoskeletal diseases.

Associations of classical HLA alleles with depression and anxiety.

Immune dysregulation has been widely observed in patients with psychiatric disorders. This study aims to examine the association between HLA alleles and depression and anxiety. Using data from the UK Biobank, we performed regression analyses to assess the association of 359 HLA alleles with depression and anxiety, as determined by Patient Health Questionnaire (PHQ) score (n = 120,033), self-reported depression (n = 121,685), general anxiety disorder (GAD-7) score (n = 120,590), and self-reported anxiety (n = 108,310). Subsequently, we conducted gene environmental interaction study (GEIS) to evaluate the potential effects of interactions between HLA alleles and environmental factors on the risk of depression and anxiety. Sex stratification was implemented in all analysis. Our study identified two significant HLA alleles associated with self-reported depression, including HLA-C*07:01 (ß = -0.015, p = 5.54 × 10-5 ) and HLA-B*08:01 (ß = -0.015, p = 7.78 × 10-5 ). Additionally, we identified four significant HLA alleles associated with anxiety score, such as HLA-DRB1*07:01 (ß = 0.084, p = 9.28 × 10-5 ) and HLA-B*57:01 (ß = 0.139, p = 1.22 × 10-4 ). GEIS revealed that certain HLA alleles interacted with environmental factors to influence mental health outcomes. For instance, HLA-A*02:07 × cigarette smoking was associated with depression score (ß = 0.976, p = 1.88 × 10-6 ). Moreover, sex stratification analysis revealed significant sex-based differences in the interaction effects of certain HLA alleles with environmental factors. Our findings indicate the considerable impact of HLA alleles on the risks of depression and anxiety, providing valuable insights into the functional relevance of immune dysfunction in these conditions.

Identification of novel rare variants for anxiety: an exome-wide association study in the UK Biobank.

BACKGROUND: Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety. METHODS: Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety. RESULTS: Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests. CONCLUSIONS: Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety.

A multidimensional social risk atlas of depression and anxiety: An observational and genome-wide environmental interaction study.

Background: Mental disorders are largely socially determined, yet the combined impact of multidimensional social factors on the two most common mental disorders, depression and anxiety, remains unclear. Methods: We constructed a polysocial risk score (PsRS), a multidimensional social risk indicator including components from three domains: socioeconomic status, neighborhood and living environment and psychosocial factors. Supported by the UK Biobank cohort, we randomly divided 110 332 participants into the discovery cohort (60%; n = 66 200) and the replication cohort (40%; n = 44 134). We tested the associations between 13 single social factors with Patient Health Questionnaire (PHQ) score, Generalized Anxiety Disorder Scale (GAD) score and self-reported depression and anxiety. The significant social factors were used to calculate PsRS for each mental disorder by considering weights from the multivariable linear model. Generalized linear models were applied to explore the association between PsRS and depression and anxiety. Genome-wide environmental interaction study (GWEIS) was further performed to test the effect of interactions between PsRS and SNPs on the risk of mental phenotypes. Results: In the discovery cohort, PsRS was positively associated with PHQ score (ß = 0.37; 95% CI = 0.35-0.38), GAD score (ß = 0.27; 95% CI = 0.25-0.28), risk of self-reported depression (OR = 1.29; 95% CI = 1.28-1.31) and anxiety (OR = 1.19; 95% CI = 1.19-1.23). Similar results were observed in the replication cohort. Emotional stress, lack of social support and low household income were significantly associated with the development of depression and anxiety. GWEIS identified multiple candidate loci for PHQ score, such as rs149137169 (ST18) (Pdiscovery = 1.08 × 10-8, Preplication = 3.25 × 10-6) and rs3759812 (MYO9A) (Pdiscovery = 3.87 × 10-9, Preplication = 6.21 × 10-5). Additionally, seven loci were detected for GAD score, such as rs114006170 (TMPRSS11D) (Pdiscovery = 1.14 × 10-9, Preplication = 7.36 × 10-5) and rs77927903 (PIP4K2A) (Pdiscovery = 2.40 × 10-9, Preplication = 0.002). Conclusions: Our findings reveal the positive effects of multidimensional social factors on the risk of depression and anxiety. It is important to address key social disadvantage in mental health promotion and treatment.

Insight into the Causal Relationship between Gut Microbiota and Back Pain: A Two Sample Bidirectional Mendelian Randomization Study.

Observational studies have shown that alterations in gut microbiota composition are associated with low back pain. However, it remains unclear whether the association is causal. To reveal the causal association between gut microbiota and low back pain, a two-sample bidirectional Mendelian randomization (MR) analysis is performed. The inverse variance weighted regression (IVW) is performed as the principal MR analysis. MR-Egger and Weighted Median is further conducted as complementary analysis to validate the robustness of the results. Finally, a reverse MR analysis is performed to evaluate the possibility of reverse causation. The inverse variance weighted (IVW) method suggests that Peptostreptococcaceae (odds ratio [OR] 1.056, 95% confidence interval [CI] [1.015-1.098], P IVW = 0.010), and Lactobacillaceae (OR 1.070, 95% CI [1.026-1.115], P IVW = 0.003) are positively associated with back pain. The Ruminococcaceae (OR 0.923, 95% CI [0.849-0.997], P IVW = 0.033), Butyricicoccus (OR 0.920, 95% CI [0.868 - 0.972], P IVW = 0.002), and Lachnospiraceae (OR 0.948, 95% CI [0.903-0.994], P IVW = 0.022) are negatively associated with back pain. In this study, underlying causal relationships are identified among gut microbiota and low back pain. Notably, further research is needed on the biological mechanisms by which gut microbiota influences low back pain.

Dissecting the Association between Gut Microbiota and Brain Structure Change Rate: A Two-Sample Bidirectional Mendelian Randomization Study.

The connection between the gut microbiota and brain structure changes is still unclear. We conducted a Mendelian randomization (MR) study to examine the bidirectional causality between the gut microbiota (211 taxa, including 131 genera, 35 families, 20 orders, 16 classes and 9 phyla; N = 18,340 individuals) and age-independent/dependent longitudinal changes in brain structure across the lifespan (N = 15,640 individuals aged 4~99 years). We identified causal associations between the gut microbiota and age-independent/dependent longitudinal changes in brain structure, such as family Peptostreptococcaceae with age-independent longitudinal changes of cortical gray matter (GM) volume and genus Faecalibacterium with age-independent average cortical thickness and cortical GM volume. Taking age-independent longitudinal changes in brain structure across the lifespan as exposures, there were causal relationships between the surface area and genus Lachnospiraceae. Our findings may serve as fundamentals for further research on the genetic mechanisms and biological treatment of complex traits and diseases associated with the gut microbiota and the brain structure change rate.

Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia.

The aberrant aging hypothesis of schizophrenia (SCZ) and autism spectrum disorder (ASD) has been proposed, and the DNA methylation (DNAm) clock, which is a cumulative evaluation of DNAm levels at age-related CpGs, could serve as a biological aging indicator. This study evaluated epigenetic brain aging of ASD and SCZ using Horvath's epigenetic clock, based on two public genome-wide DNA methylation datasets of post-mortem brain samples (NASD = 222; NSCZ = 142). Total subjects were further divided into subgroups by gender and age. The epigenetic age acceleration (AgeAccel) for each sample was calculated as the residual value resulting from the regression model and compared between groups. Results showed DNAm age has a strong correlation with chronological age in both datasets across multiple brain regions (P < 0.05). When divided into equally sized age groups, the AgeAccel of the cerebellum (CB) region from people over 45 years of age was greater compared to the control sample (AgeAccel of ASD vs control: 5.069 vs -6.249; P < 0.001). And a decelerated epigenetic aging process was observed in the CB region of individuals with SCZ aged 50-70 years (AgeAccel of SCZ vs control: -3.171 vs 2.418; P < 0.05). However, our results showed no significant difference in AgeAccel between ASD and control groups, and between SCZ and control groups in the total and gender-specific groups (P > 0.05). This study's results revealed some evidence for aberrant epigenetic CB brain aging in old-aged patients with ASD and SCZ, indicating a different pattern of CB aging in older adults with these two diseases. However, further studies of larger ASD and SCZ cohorts are necessary to make definitive conclusions on this observation.

Mitochondria-wide association study observed significant interactions of mitochondrial respiratory and the inflammatory in the development of anxiety and depression.

The aim of this study was to investigate the possible interaction of mitochondrial dysfunction and inflammatory cytokines in the risk of anxiety and depression. We utilized the UK Biobank for the sample of this study. A mitochondria-wide association(MiWAS) and interaction analysis was performed to investigate the interaction effects of mitochondrial DNA (mtDNA)×C-reactive protein (CRP) on the risks of self-reported anxiety (N = 72,476), general anxiety disorder (GAD-7) scores (N = 80,853), self-reported depression (N = 80,778), Patient Health Questionnaire (PHQ-9) scores (N = 80,520) in total samples, females and males, respectively, adjusting for sex, age, Townsend deprivation index (TDI), education score, alcohol intake, smoking and 10 principal components. In all, 25 mtSNPs and 10 mtSNPs showed significant level of association with self-reported anxiety and GAD-7 score respectively. A total of seven significant mtDNA × CRP interactions were found for anxiety, such as m.3915G>A(MT-ND1) for self-reported anxiety in total subjects (P = 6.59 × 10-3), m.4561T>C(MT-ND2) (P = 3.04 × 10-3) for GAD-7 score in total subjects. For depression, MiWAS identified 17 significant mtSNPs for self-reported depression and 14 significant mtSNPs for PHQ-9 scores. 17 significant mtDNA associations (2 for self-reported depression and 15 for PHQ-9 score) was identified, such as m.14869G>A(MT-CYB; P = 2.22 × 10-3) associated with self-reported depression and m.4561T>C (MT-ND2; P value = 3.02 × 10-8) associated with PHQ-9 score in all subjects. In addition, 5 common mtDNA shared with anxiety and depression were found in MiWAS, and 4 common mtDNA variants were detected to interact with CRP for anxiety and depression, such as m.9899T>C(MT-CO3). Our study suggests the important interaction effects of mitochondrial function and CRP on the risks of anxiety and depression.

Integrative Analysis of Proteome-wide Association Studies and Functional Enrichment Analysis to Identify Genes and Chemicals Associated with Alcohol Dependence.

OBJECTIVES: Alcohol dependence accounts for a large proportion of the global burden of disease and disability. This study aims to investigate the candidate genes and chemicals associated with alcohol dependence. METHODS: Using data from published alcohol dependence genome-wide association studies, we first conducted a proteome-wide association study of alcohol dependence by integrating alcohol dependence genome-wide association studies with 2 human brain reference proteomes of dorsolateral prefrontal cortex from the Religious Order Study and Rush Memory and Aging Project and the Banner Sun Health Research Institute. Then, based on the identified genes in proteome-wide association study, we conducted functional enrichment analysis and chemical-related functional enrichment analysis to detect the related Gene Ontology terms and chemicals. RESULTS: Proteome-wide association study identified several potential candidate genes for alcohol dependence, such as GOT2 ( P = 7.59 × 10 -6 ) and C3orf33 ( P = 5.00 × 10 -3 ). Furthermore, functional enrichment analysis identified multiple candidate Gene Ontology terms associated with alcohol dependence, such as glyoxylate metabolic process (adjusted P = 2.99 × 10 -6 ) and oxoglutarate metabolic process (adjusted P = 9.95 × 10 -6 ). Chemical-related functional enrichment analysis detected several alcohol dependence-related candidate chemicals, such as pitavastatin ( P = 2.00 × 10 -4 ), cannabinoids ( P = 4.00 × 10 -4 ), 11-nor-Δ(9)-tetrahydrocannabinol-9-carboxylic acid ( P = 4.00 × 10 -4 ), and gabapentin ( P = 2.00 × 10 -3 ). CONCLUSIONS: Our study reports multiple candidate genes and chemicals associated with alcohol dependence, providing novel clues for understanding the biological mechanism of alcohol dependence.

Evaluating the interaction between 3'aQTL and alcohol consumption/smoking on anxiety and depression: 3'aQTL-by-environment interaction study in UK Biobank cohort.

BACKGROUND: Smoking and alcohol consumption were associated with the development of depression and anxiety. 3'UTR APA quantitative trait loci (3'aQTLs) have been associated with multiple health states and conditions. Our aim is to evaluate the interactive effects of 3'aQTLs-alcohol consumption/tobacco smoking on the risk of anxiety and depression. METHODS: The 3'aQTL data of 13 brain regions were extracted from the large-scale 3'aQTL atlas. The phenotype data (frequency of cigarette smoking and alcohol drinking, anxiety score, self-reported anxiety, depression score and self-reported depression) of 90,399-103,011 adults aged 40-69 years living in the UK and contributing to the UK Biobank during 2006-2010, were obtained from the UK Biobank cohort. The frequency of cigarette smoking and alcohol drinking of each subject were defined by the amount of smoking and alcohol drinking of self-reported, respectively. The continuous alcohol consumption/smoking terms were further categorized in tertiles. 3'aQTL-by-environmental interaction analysis was then performed to evaluate the associations of gene-smoking/alcohol consumption interactions with anxiety and depression using generalized linear model (GLM) of PLINK 2.0 with an additive mode of inheritance. Furthermore, GLM was also used to explore the relationship between alcohol consumption/smoking with hazard of anxiety/depression stratified by allele for the significant genotyped SNPs that modified the alcohol consumption/smoking-anxiety/depression association. RESULTS: The interaction analysis identified several candidate 3'aQTLs-alcohol consumption interactions, such as rs7602638 located in PPP3R1 (ß = 0.08, P = 6.50 × 10-6) for anxiety score; rs10925518 located in RYR2 (OR = 0.95, P = 3.06 × 10-5) for self-reported depression. Interestingly, we also observed that the interactions between TMOD1 (ß = 0.18, P = 3.30 × 10-8 for anxiety score; ß = 0.17, P = 1.42 × 10-6 for depression score), ZNF407 (ß = 0.17, P = 2.11 × 10-6 for anxiety score; ß = 0.15, P = 4.26 × 10-5 for depression score) and alcohol consumption was not only associated with anxiety, but related to depression. Besides, we found that relationship between alcohol consumption and hazard of anxiety/depression was significantly different for different SNPs genotypes, such as rs34505550 in TMOD1 (AA: OR = 1.03, P = 1.79 × 10-6; AG: OR = 1.00, P = 0.94; GG: OR = 1.00, P = 0.21) for self-reported anxiety. LIMITATIONS: The identified 3'aQTLs-alcohol consumption/smoking interactions were associated with depression and anxiety, and its potential biological mechanisms need to be further revealed. CONCLUSIONS: Our study identified important interactions between candidate 3'aQTL and alcohol consumption/smoking on depression and anxiety, and found that the 3'aQTL may modify the associations between consumption/smoking with depression and anxiety. These findings may help to further explore the pathogenesis of depression and anxiety.