Your browser doesn't support javascript.
loading
Causal relationship between resting-state networks and depression: a bidirectional two-sample mendelian randomization study.
Huang, Dongmiao; Wu, Yuelin; Yue, Jihui; Wang, Xianglan.
Afiliação
  • Huang D; Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China.
  • Wu Y; Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China.
  • Yue J; Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China. 735826125@qq.com.
  • Wang X; Department of Psychiatry, the Fifth Affiliated Hospital of Sun Yat-sen University, No. 52, East Meihua Road, Zhuhai City, Guangdong Province, 519000, China. wxiangl@mail.sysu.edu.cn.
BMC Psychiatry ; 24(1): 402, 2024 May 29.
Article em En | MEDLINE | ID: mdl-38811927
ABSTRACT

BACKGROUND:

Cerebral resting-state networks were suggested to be strongly associated with depressive disorders. However, the causal relationship between cerebral networks and depressive disorders remains controversial. In this study, we aimed to investigate the effect of resting-state networks on depressive disorders using a bidirectional Mendelian randomization (MR) design.

METHODS:

Updated summary-level genome-wide association study (GWAS) data correlated with resting-state networks were obtained from a meta-analysis of European-descent GWAS from the Complex Trait Genetics Lab. Depression-related GWAS data were obtained from the FinnGen study involving participants with European ancestry. Resting-state functional magnetic resonance imaging and multiband diffusion imaging of the brain were performed to measure functional and structural connectivity in seven well-known networks. Inverse-variance weighting was used as the primary estimate, whereas the MR-Pleiotropy RESidual Sum and Outliers (PRESSO), MR-Egger, and weighted median were used to detect heterogeneity, sensitivity, and pleiotropy.

RESULTS:

In total, 20,928 functional and 20,573 structural connectivity data as well as depression-related GWAS data from 48,847 patients and 225,483 controls were analyzed. Evidence for a causal effect of the structural limbic network on depressive disorders was found in the inverse variance-weighted limbic network (odds ratio, [Formula see text]; 95% confidence interval, [Formula see text]; [Formula see text]), whereas the causal effect of depressive disorders on SC LN was not found(OR=1.0025; CI,1.0005-1.0046; P=0.012). No significant associations between functional connectivity of the resting-state networks and depressive disorders were found in this MR study.

CONCLUSIONS:

These results suggest that genetically determined structural connectivity of the limbic network has a causal effect on depressive disorders and may play a critical role in its neuropathology.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Female / Humans / Male Idioma: En Revista: BMC Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Imageamento por Ressonância Magnética / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Female / Humans / Male Idioma: En Revista: BMC Psychiatry Assunto da revista: PSIQUIATRIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China