Xu WeiXiong, Xie DanDan, Zhang ZhenZhu, Du PiBo, Ye YongJian, Dai XuBo
Department of Clinical Laboratory, 910th Hospital of the Chinese People's Liberation Army Joint Logistics Support Force, Quanzhou, 362000, Fujian, China.
Discov Ment Health. 2025 Aug 19;5(1):126. doi: 10.1007/s44192-025-00275-6.
This study aimed to explore how serum metabolites affect the risk of substance use disorders (SUD).
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.
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.
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".
本研究旨在探讨血清代谢物如何影响物质使用障碍(SUD)的风险。
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之间的关系。主要使用逆方差加权、Wald比数比和95%置信区间来评估因果关系,并使用错误发现率进行多重比较校正。通过 Cochr an's Q检验和MR-PRESSO进行敏感性分析。使用MR-Steiger检验来检验反向因果关系。在验证阶段,我们寻求关于SUD的额外全基因组关联研究(GWAS)数据以验证初始结果。此外,对在两个阶段均与SUD表现出因果关系的已知代谢物进行通路富集分析。
在初始阶段,我们的分析表明这77种代谢物可能与SUD存在潜在因果关联,包括14种代谢物比值和63种代谢物(49种已知和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种比值),确认的关联可能表明对SUD发病率有因果影响。综合分析结果表明,联合代谢物的总体效应与初步分析一致,其中两种被确定为SUD的危险因素,四种为保护因素。具体而言,赤藓糖酸盐水平、1-(1-烯基硬脂酰基)-2-油酰基-GPE(P-18:0/18:1)水平、天冬氨酸与瓜氨酸比值以及顺式-4-癸烯酸(10:1n6)水平与SUD呈负相关,而γ-谷氨酰-α-赖氨酸和α-葡萄糖苷乙酯水平与疾病发病率呈正相关。在两个阶段均与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、亮氨酸和异亮氨酸生物合成;牛磺酸和亚牛磺酸代谢;组氨酸代谢;以及甘油olipid代谢。
循环代谢物可能与SUD风险存在因果关系。“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制定”。