调查血清代谢物在物质使用障碍风险中的因果作用:一项整合孟德尔随机化和综合分析的研究。

IF 2.7
WeiXiong Xu, DanDan Xie, ZhenZhu Zhang, PiBo Du, YongJian Ye, XuBo Dai
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引用次数: 0

摘要

目的:探讨血清代谢物对物质使用障碍(SUD)风险的影响。方法:在初始阶段,采用孟德尔随机化方法评估1400种血清代谢物与SUD的关系。反方差加权、瓦尔德比值比和95%置信区间主要用于评估因果关系,错误发现率用于多次比较修正。采用Cochran’s Q检验和MR-PRESSO进行敏感性分析。MR-Steiger检验用于检验反向因果关系。在验证阶段,我们寻求SUD的额外GWAS数据来验证初始结果。此外,对已知的代谢产物进行了途径富集分析,这些代谢产物在这两个阶段都与SUD有因果关系。结果:在初始阶段,我们的分析表明,这77种代谢物可能与SUD有潜在的因果关系,包括14种代谢物比例和63种代谢物(49种已知,14种未知)。在验证阶段,对于57种代谢物(38种已知,6种未知,13种比例),已证实的关联可能表明与SUD发病率的因果关系。综合分析结果表明,联合代谢物的总体效果与初步分析一致,其中2个被确定为SUD的危险因素,4个被确定为SUD的保护因素。具体而言,赤氨酸水平、1-(1-烯基-硬脂酰)-2-油酰- gpe (P-18:0/18:1)水平、天冬氨酸与瓜氨酸的比例和顺式-4-十烯酸(10:1n6)水平与SUD呈负相关,而γ -谷氨酰- α -赖氨酸和乙基- α -葡萄糖苷水平与疾病发病率呈正相关。在这两个阶段,与SUD风险相关的代谢物主要富集在几种代谢途径中,包括泛酸和辅酶a生物合成;嘧啶代谢;缬氨酸、亮氨酸和异亮氨酸的生物合成;牛磺酸和次牛磺酸代谢;组氨酸代谢;甘油脂代谢。结论:循环代谢物可能与SUD的发生有因果关系。“特定代谢物可能是SUD的潜在生物标志物,有助于风险预测和个性化治疗策略的发展。”
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Investigating the causal role of serum metabolites in substance use disorder risk: a study integrating Mendelian randomization and synthesis analysis.

Investigating the causal role of serum metabolites in substance use disorder risk: a study integrating Mendelian randomization and synthesis analysis.

Investigating the causal role of serum metabolites in substance use disorder risk: a study integrating Mendelian randomization and synthesis analysis.

Investigating the causal role of serum metabolites in substance use disorder risk: a study integrating Mendelian randomization and synthesis analysis.

Objective: This study aimed to explore how serum metabolites affect the risk of substance use disorders (SUD).

Methods: In the initial stage, Mendelian randomization was applied to assess the relationship between 1,400 serum metabolites and SUD. Inverse variance weighting, the Wald ratio odds ratio, and 95% confidence intervals were primarily used to evaluate causal relationships, and the false discovery rate was used for multiple comparison corrections. Sensitivity analysis was conducted via Cochran's Q test and MR-PRESSO. The MR-Steiger test was used to examine reverse causality. In the validation stage, we sought additional GWAS data on SUD to verify the initial results. Furthermore, the pathway enrichment analysis was conducted for known metabolites that exhibited causal relationships with SUD in both phases.

Results: In the initial phase, we analysis suggests that these 77 metabolites may have potential causal associations with SUD, including 14 metabolite ratios and 63 metabolites (49 known and 14 unknown). In the validation phase, for 57 metabolites (38 known, 6 unknown, 13 ratios), confirmed associations may indicate causal effects on SUD incidence. The synthesis analysis results indicated that the overall effect of the combined metabolites was consistent with the primary analysis with two identified as risk factors and four as protective factors for SUD. Specifically, Erythronate levels, 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) levels, aspartate to citrulline ratios, and cis-4-decenoate (10:1n6) levels were negatively correlated with SUD, whereas gamma-glutamyl-alpha-lysine and ethyl alpha-glucopyranoside levels were positively correlated with disease incidence. The metabolites linked to the risk of SUD in both phases were primarily enriched in several metabolic pathways, including pantothenate and CoA biosynthesis; pyrimidine metabolism; biosynthesis of valine, leucine, and isoleucine; taurine and hypotaurine metabolism; histidine metabolism; and glycerolipid metabolism.

Conclusion: Circulating metabolites may have a causal relationship with the risk of SUD. "Specific metabolites may be potential biomarkers for SUD, contributing to risk prediction and the development of personalized treatment strategies".

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