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Branched-chain amino acids and risk of major depressive disorder: a Mendelian randomization and colocalization study
Xiang Li1,2,3, Jianyi Wang4
1Medical College, Guangxi University, Nanning, China.
Aims:
Depression is associated with numerous metabolic pathway abnormalities, and several studies have suggested a link between depression and branched chain amino acids (BCAAs) metabolic disorders. However, the precise causality and direction remain inconclusive. Consequently, this study aims to ascertain the relationship between the risk of depression and BCAAs levels using two-sample Mendelian randomization (MR) analysis.
Materials And Methods:
Single nucleotide polymorphisms associated with the BCAAs were extracted from the IEU OpenGWAS. Pooled level data for major depressive disorder (MDD) was obtained from the Psychiatric Genomics Consortium. We performed genome-wide linkage disequilibrium score regression, MR analyses, and colocalization analyses using summary genome-wide association study data across European population to probe genetic causality between BCAAs and MDD.
Results:
Our results showed a causal effect of MDD risk on the increasing valine levels (IVW OR = 1.043, 95% CI = 1.006-1.082, P = 0.024) and a genetic correlation between MDD and valine. However, leucine, isoleucine, and total BCAAs were not causally associated with the risk of MDD (P > 0.05). The sensitivity analyses indicated that there was no heterogeneity or horizontal pleiotropy in our findings. The linkage disequilibrium score regression demonstrated significant evidence of shared genetic architecture between MDD and valine, with the genetic correlation estimated to be 0.112 (P = 0.002). Colocalization analysis did not provide any evidence of a shared causal variant between MDD and valine.
Conclusions:
It was revealed that valine metabolism may be significantly affected by depression through a two-sample MR analysis, while no significant connection was identified between other branched-chain amino acids and depression. This result provided new insights into the metabolic processes involved in depression.
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