■代谢失调代表2型糖尿病(T2DM)的定义特征。然而,目前尚无大量证据证明循环血液代谢产物与促进或预防T2DM之间存在直接的因果关系.在解决这一差距时,我们采用孟德尔随机化(MR)分析来调查1,091种血液代谢物之间的潜在因果关系,309个代谢物比率,2型糖尿病的发生。
■包含1,091种血液代谢物和309种代谢物比率的单核苷酸多态性(SNPs)的数据来自加拿大全基因组关联研究(GWAS),涉及8,299名参与者。为了评估这些代谢物与2型糖尿病(T2DM)之间的因果关系,多种方法,包括逆方差加权(IVW),加权中位数,Egger先生,加权模式,采用了简单模式。利用错误发现率(FDR)对p值进行校正。灵敏度分析结合了CochranQ检验,MR-Egger截距测试,MR-PRESSO,Steiger测试,遗漏分析,和单SNP分析。因果效应是通过Circos图可视化的,森林地块,和散点图。此外,值得注意的是,使用独立的T2DMGWAS数据集(GCST006867)进行复制分析.使用MetaboAnalyst5.0进行紧密相关代谢物的代谢途径分析。
■本研究中使用的IVW分析方法揭示了88种血液代谢物和37种代谢物比率,表明与T2DM存在显著的因果关系(p<0.05)。值得注意的是,观察到特定代谢物与T2DM有很强的因果关系:1-亚油酰基-GPE(18:2)(IVW:OR:0.930,95%CI:0.899-0.962,p=2.16×10-5),1,2-二油酰基-GPE(18:2/18:2)(IVW:OR:0.942,95%CI:0.917-0.968,p=1.64×10-5),甘露糖(IVW:OR:1.133,95%CI:1.072-1.197,p=1.02×10-5),X-21829(IVW:OR:1.036,95%CI:1.036-1.122,p=9.44×10-5),磷酸盐与甘露糖之比(IVW:OR:0.870,95%CI:0.818-0.926,p=1.29×10-5,FDR=0.008)。此外,代谢途径分析强调了与T2DM发展相关的六个重要途径:缬氨酸,亮氨酸和异亮氨酸的生物合成,苯丙氨酸代谢,甘油磷脂代谢,α-亚麻酸代谢,鞘脂代谢,和丙氨酸,天冬氨酸,和谷氨酸代谢。
■这项研究确定了保护性和风险相关的代谢物,它们在T2DM的发展中起因果作用。通过整合基因组学和代谢组学,它为T2DM的发病机制提供了新的见解。这些发现对早期筛查具有潜在的意义,预防措施,2型糖尿病的治疗策略。
UNASSIGNED: Metabolic dysregulation represents a defining characteristic of Type 2 diabetes (T2DM). Nevertheless, there remains an absence of substantial evidence establishing a direct causal link between circulating blood metabolites and the promotion or prevention of T2DM. In addressing this gap, we employed Mendelian randomization (MR) analysis to investigate the potential causal association between 1,091 blood metabolites, 309 metabolite ratios, and the occurrence of T2DM.
UNASSIGNED: Data encompassing single-nucleotide polymorphisms (SNPs) for 1,091 blood metabolites and 309 metabolite ratios were extracted from a Canadian Genome-wide association study (GWAS) involving 8,299 participants. To evaluate the causal link between these metabolites and Type 2 diabetes (T2DM), multiple methods including Inverse Variance Weighted (IVW), Weighted Median, MR Egger, Weighted Mode, and Simple Mode were employed. p-values underwent correction utilizing False Discovery Rates (FDR). Sensitivity analyses incorporated Cochran\'s Q test, MR-Egger intercept test, MR-PRESSO, Steiger test, leave-one-out analysis, and single SNP analysis. The causal effects were visualized via Circos plot, forest plot, and scatter plot. Furthermore, for noteworthy, an independent T2DM GWAS dataset (GCST006867) was utilized for replication analysis. Metabolic pathway analysis of closely correlated metabolites was conducted using MetaboAnalyst 5.0.
UNASSIGNED: The IVW analysis method utilized in this study revealed 88 blood metabolites and 37 metabolite ratios demonstrating a significant causal relationship with T2DM (p < 0.05). Notably, strong causal associations with T2DM were observed for specific metabolites: 1-linoleoyl-GPE (18:2) (IVW: OR:0.930, 95% CI: 0.899-0.962, p = 2.16 × 10-5), 1,2-dilinoleoyl-GPE (18:2/18:2) (IVW: OR:0.942, 95% CI: 0.917-0.968, p = 1.64 × 10-5), Mannose (IVW: OR:1.133, 95% CI: 1.072-1.197, p = 1.02 × 10-5), X-21829 (IVW: OR:1.036, 95% CI: 1.036-1.122, p = 9.44 × 10-5), and Phosphate to mannose ratio (IVW: OR:0.870, 95% CI: 0.818-0.926, p = 1.29 × 10-5, FDR = 0.008). Additionally, metabolic pathway analysis highlighted six significant pathways associated with T2DM development: Valine, leucine and isoleucine biosynthesis, Phenylalanine metabolism, Glycerophospholipid metabolism, Alpha-Linolenic acid metabolism, Sphingolipid metabolism, and Alanine, aspartate, and glutamate metabolism.
UNASSIGNED: This study identifies both protective and risk-associated metabolites that play a causal role in the development of T2DM. By integrating genomics and metabolomics, it presents novel insights into the pathogenesis of T2DM. These findings hold potential implications for early screening, preventive measures, and treatment strategies for T2DM.