Assessing the joint effects of mitochondrial genes and physical activity on the psychiatric phenotype of subjective well-being based on the UK Biobank data.

Subjective well-being (SWB) is an important measure for mental health status. Previous research has shown that physical activity can affect an individual's well-being, yet the underlying molecular mechanism remains to be clarified. In this study, we aim to evaluate the potential interactions between mitochondrial genes and physical activity (PA) as well as their combined effects on individual well-being. SWB phenotype data in UK Biobank were enrolled for this study including nine aspects such as work/job satisfaction, health satisfaction, family relationship satisfaction, friendships satisfaction, financial situation satisfaction, ever depressed for a whole week, general happiness, general happiness with own health and belief that own life is meaningful. We made analysis for each aspects separately. Firstly, mitochondria-wide association studies (MiWAS) was conducted to assess the association of mitochondrial Single Nucleotide Polymorphisms SNP with each aspect of SWB. Then an interaction analysis of mitochondrial DNA (mtDNA) mutation and PA was performed to evaluate their joint effect on SWB status. Meanwhile, these two analysis were made for female and male group separately as well as the total samples, all under the control of possible confounding factors including gender, age, Townsend Deprivation Index (TDI), education, alcohol consumption, smoking habits, and 10 principal components. MiWAS analysis identified 45 mtSNPs associated with 9 phenotypes of SWB. For example, m.15218A > G on MT-CYB in the health satisfaction phenotype of the total subjects. Gender-specific analyses found 30 mtSNPs in females and 58 in males, involving 13 mtGenes. In mtDNA-PA interaction analysis, we also identified 10 significant mtDNA-PA interaction sets for SWB. For instance, m.13020 T > C (MT-ND5) was associated with the SWB financial situation satisfaction phenotype in all subjects (P = 0.00577). In addition, MiWAS analysis identified 12 mtGene variants associated with SWB, as MT-ND1 and MT-ND2. However, in mtDNA-PA interactions we detected 7 mtDNA affecting psychiatric disorders occurring, as in the friendships satisfaction phenotype (m.3394 T > C on MT-ND1). Our study results suggest an implication of the interaction between mitochondrial function and physical activity in the risk of psychiatric disorder development.

Association between white matter hyperintensity and anxiety/depression.

Although previous studies have explored the associations of white matter hyperintensity with psychiatric disorders, the sample size is small and the conclusions are inconsistent. The present study aimed to further systematically explore the association in a larger sample. All data were extracted from the UK Biobank. First, general linear regression models and logistic regression models were used to assess the association between white matter hyperintensity volume and anxiety/depression. White matter hyperintensity has been classified into periventricular white matter hyperintensity and deep white matter hyperintensity. Anxiety was determined by General Anxiety Disorder-7 score (n = 17,221) and self-reported anxiety (n = 15,333), depression was determined by Patient Health Questionnaire-9 score (n = 17,175), and self-reported depression (n = 14,519). Moreover, we employed Cox proportional hazard models to explore the association between white matter hyperintensity volume and anxiety/depression. The covariates included in fully adjusted model are age, gender, body mass index, Townsend deprivation index, healthy physical activity, cigarette consumption, alcohol consumption, educational attainment, diabetes, hypertension, and coronary heart disease. The results of the fully adjusted model showed that white matter hyperintensity volume was significantly associated with General Anxiety Disorder-7 score (periventricular white matter hyperintensity: ß = 0.152, deep white matter hyperintensity: ß = 0.094) and Patient Health Questionnaire-9 score (periventricular white matter hyperintensity: ß = 0.168). Logistic regression analysis results indicated that periventricular white matter hyperintensity volume (odds ratio = 1.153) was significantly associated with self-reported anxiety. After applying the Cox proportional hazard models, we found that larger white matter hyperintensity volume was associated with increased risk of depression (periventricular white matter hyperintensity: hazard ratio = 1.589, deep white matter hyperintensity: hazard ratio = 1.200), but not anxiety. In summary, our findings support a positive association between white matter hyperintensity volume and depression.

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.

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.