Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the...Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the mutual influences between gut microbiota and insomnia.Methods We conducted Mendelian randomisation(MR)analysis using genome-wide association studies datasets on insomnia(N=386533),gut microbiota data from the MiBioGen alliance(N=18340)and the Dutch Microbiome Project(N=8208).The inverse variance weighted(IVW)technique was selected as the primary approach.Then,Cochrane’s Q,Mendelian randomization-Egger(MR-Egger)and MR Pleiotropy RESidual Sum and Outlier test(MRPRESSO)tests were used to detect heterogeneity and pleiotropy.The leave-one-out method was used to test the stability of the MR results.In addition,we performed the Steiger test to thoroughly verify the causation.Results According to IVW,our results showed that 14 gut bacterial taxa may contribute to the risks of insomnia(odds ratio(OR):1.01 to 1.04),while 8 gut bacterial taxa displayed a protective effect on this condition(OR:0.97 to 0.99).Conversely,reverse MR analysis showed that insomnia may causally decrease the abundance of 7 taxa(OR:0.21 to 0.57)and increase the abundance of 12 taxa(OR:1.65 to 4.43).Notably,the genus Odoribacter showed a significant positive causal relationship after conducting the Steiger test.Cochrane’s Q test indicated no significant heterogeneity between most singlenucleotide polymorphisms.In addition,no significant level of pleiotropy was found according to MR-Egger and MRPRESSO.Conclusions Our study highlighted the reciprocal relationships between gut microbiota and insomnia,which may provide new insights into the treatment and prevention of insomnia.展开更多
Background Metabolic dysregulation has been implicated in major depressive disorder(MDD).Aims We aimed to explore the potential role of plasma metabolites in MDD.Methods We conducted Mendelian randomisation(MR)analysi...Background Metabolic dysregulation has been implicated in major depressive disorder(MDD).Aims We aimed to explore the potential role of plasma metabolites in MDD.Methods We conducted Mendelian randomisation(MR)analysis to evaluate the causal effects of 871 circulating metabolites on MDD,using the Genome-Wide Association Studies datasets of MDD(N=1035760)and metabolites(N=8299).Bayesian colocalisation and druggability analyses were employed to identify genetic variants contributing to both MDD and levels of metabolites in plasma and to pinpoint metabolites with therapeutic potential,respectively.Results MR analysis identified 11 metabolites associated with MDD(false discovery rate<0.05).Eight metabolites,including arachidonate(20:4n6)(odds ratio(OR):0.97),1-arachidonoyl-GPC(20:4n6)(OR:0.98),1-(1-enylpalmitoyl)-2-palmitoleoyl-GPC(P-16:0/16:1)(OR:0.97),succinoyltaurine(OR:0.98),3-methoxycatechol sulphate(1)(OR:0.98)and 11β-hydroxyandrosterone glucuronide(OR:0.97),showed protective effects against MDD.Three metabolites were associated with increased risk,namely,butyrylglycine(OR:1.03),3-carboxy-4-methyl-5-propyl-2-furanpropanoate(OR:1.02)and 1-(1-enyl-stearoyl)-2-oleoyl-GPE(P-18:0/18:1)(OR:1.02).Colocalisation analysis supported shared genetic signals between five lipid metabolites and MDD,particularly at loci harbouring FADS and ATP9A.Notably,a majority of metabolites associated with MDD are being explored as therapeutic targets for various psychiatric disorders.Conclusions Genetically predicted levels of certain circulating metabolites make a causal contribution to MDD.Further investigation of their roles may provide novel pathophysiological insights and give clues for targeted therapies.展开更多
Background We aimed to evaluate whether major depressive disorder(MDD)could aggravate the outcomes of coronavirus disease 2019(COVID-19)or whether the genetic liability to COVID-19 could trigger MDD.Aims We aimed to a...Background We aimed to evaluate whether major depressive disorder(MDD)could aggravate the outcomes of coronavirus disease 2019(COVID-19)or whether the genetic liability to COVID-19 could trigger MDD.Aims We aimed to assess bidirectional causal associations between MDD and COVID-19.Methods We performed genetic correlation and Mendelian randomisation(MR)analyses to assess potential associations between MDD and three COVID-19 outcomes.Literature-based network analysis was conducted to construct molecular pathways connecting MDD and COVID-19.Results We found that MDD has positive genetic correlations with COVID-19 outcomes(rg:0.10–0.15).Our MR analysis indicated that genetic liability to MDD is associated with increased risks of COVID-19 infection(odds ratio(OR)=1.05,95%confidence interval(CI):1.00 to 1.10,p=0.039).However,genetic liability to the three COVID-19 outcomes did not confer any causal effects on MDD.Pathway analysis identified a panel of immunity-related genes that may mediate the links between MDD and COVID-19.Conclusions Our study suggests that MDD may increase the susceptibility to COVID-19.Our findings emphasise the need to increase social support and improve mental health intervention networks for people with mood disorders during the pandemic.展开更多
Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims...Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,p<0.001),while genetic liability to ADHD2022 had a suggestive causal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,p<0.001).The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.展开更多
Background Previous studies have shown that educational attainment(EA),intelligence and income are key factors associated with mental disorders.However,the direct effects of each factor on major mental disorders are u...Background Previous studies have shown that educational attainment(EA),intelligence and income are key factors associated with mental disorders.However,the direct effects of each factor on major mental disorders are unclear.Aims We aimed to evaluate the overall and independent causal effects of the three psychosocial factors on common mental disorders.Methods Using genome-wide association study summary datasets,we performed Mendelian randomisation(MR)and multivariable MR(MVMR)analyses to assess potential associations between the 3 factors(EA,N=766345;household income,N=392422;intelligence,N=146808)and 13 common mental disorders,with sample sizes ranging from 9907 to 807553.Inverse-variance weighting was employed as the main method in the MR analysis.Results Our MR analysis showed that(1)higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa,obsessive-compulsive disorder(OCD),bipolar disorder(BD)and autism spectrum disorder(ASD);(2)higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD;(3)higher household income protected against 10 mental disorders but confers risk for anorexia nervosa.Our MVMR analysis showed that(1)higher EA was a direct protective factor for attention-deficit/hyperactivity disorder(ADHD)and insomnia but a direct risk factor for schizophrenia,BD and ASD;(2)higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder(MDD)and ASD;(3)higher income was a direct protective factor for seven mental disorders,including schizophrenia,BD,MDD,ASD,post-traumatic stress disorder,ADHD and anxiety disorder.Conclusions Our study reveals that education,intelligence and income intertwine with each other.For each factor,its independent effects on mental disorders present a more complex picture than its overall effects.展开更多
文摘Background Although studies in recent years have explored the impact of gut microbiota on various sleep characteristics,the interaction between gut microbiota and insomnia remains unclear.Aims We aimed to evaluate the mutual influences between gut microbiota and insomnia.Methods We conducted Mendelian randomisation(MR)analysis using genome-wide association studies datasets on insomnia(N=386533),gut microbiota data from the MiBioGen alliance(N=18340)and the Dutch Microbiome Project(N=8208).The inverse variance weighted(IVW)technique was selected as the primary approach.Then,Cochrane’s Q,Mendelian randomization-Egger(MR-Egger)and MR Pleiotropy RESidual Sum and Outlier test(MRPRESSO)tests were used to detect heterogeneity and pleiotropy.The leave-one-out method was used to test the stability of the MR results.In addition,we performed the Steiger test to thoroughly verify the causation.Results According to IVW,our results showed that 14 gut bacterial taxa may contribute to the risks of insomnia(odds ratio(OR):1.01 to 1.04),while 8 gut bacterial taxa displayed a protective effect on this condition(OR:0.97 to 0.99).Conversely,reverse MR analysis showed that insomnia may causally decrease the abundance of 7 taxa(OR:0.21 to 0.57)and increase the abundance of 12 taxa(OR:1.65 to 4.43).Notably,the genus Odoribacter showed a significant positive causal relationship after conducting the Steiger test.Cochrane’s Q test indicated no significant heterogeneity between most singlenucleotide polymorphisms.In addition,no significant level of pleiotropy was found according to MR-Egger and MRPRESSO.Conclusions Our study highlighted the reciprocal relationships between gut microbiota and insomnia,which may provide new insights into the treatment and prevention of insomnia.
文摘Background Metabolic dysregulation has been implicated in major depressive disorder(MDD).Aims We aimed to explore the potential role of plasma metabolites in MDD.Methods We conducted Mendelian randomisation(MR)analysis to evaluate the causal effects of 871 circulating metabolites on MDD,using the Genome-Wide Association Studies datasets of MDD(N=1035760)and metabolites(N=8299).Bayesian colocalisation and druggability analyses were employed to identify genetic variants contributing to both MDD and levels of metabolites in plasma and to pinpoint metabolites with therapeutic potential,respectively.Results MR analysis identified 11 metabolites associated with MDD(false discovery rate<0.05).Eight metabolites,including arachidonate(20:4n6)(odds ratio(OR):0.97),1-arachidonoyl-GPC(20:4n6)(OR:0.98),1-(1-enylpalmitoyl)-2-palmitoleoyl-GPC(P-16:0/16:1)(OR:0.97),succinoyltaurine(OR:0.98),3-methoxycatechol sulphate(1)(OR:0.98)and 11β-hydroxyandrosterone glucuronide(OR:0.97),showed protective effects against MDD.Three metabolites were associated with increased risk,namely,butyrylglycine(OR:1.03),3-carboxy-4-methyl-5-propyl-2-furanpropanoate(OR:1.02)and 1-(1-enyl-stearoyl)-2-oleoyl-GPE(P-18:0/18:1)(OR:1.02).Colocalisation analysis supported shared genetic signals between five lipid metabolites and MDD,particularly at loci harbouring FADS and ATP9A.Notably,a majority of metabolites associated with MDD are being explored as therapeutic targets for various psychiatric disorders.Conclusions Genetically predicted levels of certain circulating metabolites make a causal contribution to MDD.Further investigation of their roles may provide novel pathophysiological insights and give clues for targeted therapies.
文摘Background We aimed to evaluate whether major depressive disorder(MDD)could aggravate the outcomes of coronavirus disease 2019(COVID-19)or whether the genetic liability to COVID-19 could trigger MDD.Aims We aimed to assess bidirectional causal associations between MDD and COVID-19.Methods We performed genetic correlation and Mendelian randomisation(MR)analyses to assess potential associations between MDD and three COVID-19 outcomes.Literature-based network analysis was conducted to construct molecular pathways connecting MDD and COVID-19.Results We found that MDD has positive genetic correlations with COVID-19 outcomes(rg:0.10–0.15).Our MR analysis indicated that genetic liability to MDD is associated with increased risks of COVID-19 infection(odds ratio(OR)=1.05,95%confidence interval(CI):1.00 to 1.10,p=0.039).However,genetic liability to the three COVID-19 outcomes did not confer any causal effects on MDD.Pathway analysis identified a panel of immunity-related genes that may mediate the links between MDD and COVID-19.Conclusions Our study suggests that MDD may increase the susceptibility to COVID-19.Our findings emphasise the need to increase social support and improve mental health intervention networks for people with mood disorders during the pandemic.
文摘Background Type 2 diabetes(T2D)is a chronic metabolic disorder with high comorbidity with mental disorders.The genetic links between attention-deficit/hyperactivity disorder(ADHD)and T2D have yet to be elucidated.Aims We aim to assess shared genetics and potential associations between ADHD and T2D.Methods We performed genetic correlation,two-sample Mendelian randomisation and polygenic overlap analyses between ADHD and T2D.The genome-wide association study(GWAS)summary results of T2D(80154 cases and 853816 controls),ADHD2019(20183 cases and 35191 controls from the 2019 GWAS ADHD dataset)and ADHD2022(38691 cases and 275986 controls from the 2022 GWAS ADHD dataset)were used for the analyses.The T2D dataset was obtained from the DIAGRAM Consortium.The ADHD datasets were obtained from the Psychiatric Genomics Consortium.We compared genome-wide association signals to reveal shared genetic variation between T2D and ADHD using the larger ADHD2022 dataset.Moreover,molecular pathways were constructed based on large-scale literature data to understand the connection between ADHD and T2D.Results T2D has positive genetic correlations with ADHD2019(rg=0.33)and ADHD2022(rg=0.31).Genetic liability to ADHD2019 was associated with an increased risk for T2D(odds ratio(OR):1.30,p<0.001),while genetic liability to ADHD2022 had a suggestive causal effect on T2D(OR:1.30,p=0.086).Genetic liability to T2D was associated with a higher risk for ADHD2019(OR:1.05,p=0.001)and ADHD2022(OR:1.03,p<0.001).The polygenic overlap analysis showed that most causal variants of T2D are shared with ADHD2022.T2D and ADHD2022 have three overlapping loci.Molecular pathway analysis suggests that ADHD and T2D could promote the risk of each other through inflammatory pathways.Conclusions Our study demonstrates substantial shared genetics and bidirectional causal associations between ADHD and T2D.
基金Nanjing Medical Science and Technology Development Project(ZKX20027).
文摘Background Previous studies have shown that educational attainment(EA),intelligence and income are key factors associated with mental disorders.However,the direct effects of each factor on major mental disorders are unclear.Aims We aimed to evaluate the overall and independent causal effects of the three psychosocial factors on common mental disorders.Methods Using genome-wide association study summary datasets,we performed Mendelian randomisation(MR)and multivariable MR(MVMR)analyses to assess potential associations between the 3 factors(EA,N=766345;household income,N=392422;intelligence,N=146808)and 13 common mental disorders,with sample sizes ranging from 9907 to 807553.Inverse-variance weighting was employed as the main method in the MR analysis.Results Our MR analysis showed that(1)higher EA was a protective factor for eight mental disorders but contributed to anorexia nervosa,obsessive-compulsive disorder(OCD),bipolar disorder(BD)and autism spectrum disorder(ASD);(2)higher intelligence was a protective factor for five mental disorders but a risk factor for OCD and ASD;(3)higher household income protected against 10 mental disorders but confers risk for anorexia nervosa.Our MVMR analysis showed that(1)higher EA was a direct protective factor for attention-deficit/hyperactivity disorder(ADHD)and insomnia but a direct risk factor for schizophrenia,BD and ASD;(2)higher intelligence was a direct protective factor for schizophrenia but a direct risk factor for major depressive disorder(MDD)and ASD;(3)higher income was a direct protective factor for seven mental disorders,including schizophrenia,BD,MDD,ASD,post-traumatic stress disorder,ADHD and anxiety disorder.Conclusions Our study reveals that education,intelligence and income intertwine with each other.For each factor,its independent effects on mental disorders present a more complex picture than its overall effects.
