Causal relationship between blood metabolites and risk of five infections: a Mendelian randomization study

• Published in: BMC infectious diseases Vol. 23; no. 1; pp. 1 - 663
• Main Authors: Wei, Zhengxiao, Xiong, Qingqing, Huang, Dan, Wu, Zhangjun, Chen, Zhu
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 07.10.2023; BioMed Central; BMC
• Subjects: Analysis; Biobanks; Blood; Blood metabolites; Body fat; Body mass index; Causation; Cholesterol; Cholesterol, LDL; Consortia; Disease prevention; Drug resistance in microorganisms; Genome-wide association studies; Genomes; Health aspects; High-throughput screening (Biochemical assaying); Infections; Infectious diseases; Measurement; Medical screening; Mendelian randomization; Meta-analysis; Metabolism; Metabolites; Methods; Pathogenesis; Phenotypes; Pneumonia; Prevention; Public health; Randomization; Respiratory tract; Respiratory tract infection; Risk factors; Sensitivity analysis; Sepsis; Urinary tract; Urinary tract diseases; Urinary tract infection; Urinary tract infections; Urogenital system; China; United Kingdom > UK
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-023-08662-6

Infectious diseases continue to pose a significant threat in the field of global public health, and our understanding of their metabolic pathogenesis remains limited. However, the advent of genome-wide association studies (GWAS) offers an unprecedented opportunity to unravel the relationship between metabolites and infections. Univariable and multivariable Mendelian randomization (MR) was commandeered to elucidate the causal relationship between blood metabolism and five high-frequency infection phenotypes: sepsis, pneumonia, upper respiratory tract infections (URTI), urinary tract infections (UTI), and skin and subcutaneous tissue infection (SSTI). GWAS data for infections were derived from UK Biobank and the FinnGen consortium. The primary analysis was conducted using the inverse variance weighted method on the UK Biobank data, along with a series of sensitivity analyses. Subsequently, replication and meta-analysis were performed on the FinnGen consortium data. After primary analysis and a series of sensitivity analyses, 17 metabolites were identified from UK Biobank that have a causal relationship with five infections. Upon joint analysis with the FinGen cohort, 7 of these metabolites demonstrated consistent associations. Subsequently, we conducted a multivariable Mendelian randomization analysis to confirm the independent effects of these metabolites. Among known metabolites, genetically predicted 1-stearoylglycerol (1-SG) (odds ratio [OR] = 0.561, 95% confidence interval [CI]: 0.403-0.780, P < 0.001) and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (OR = 0.780, 95%CI: 0.689-0.883, P < 0.001) was causatively associated with a lower risk of sepsis, and genetically predicted phenylacetate (PA) (OR = 1.426, 95%CI: 1.152-1.765, P = 0.001) and cysteine (OR = 1.522, 95%CI: 1.170-1.980, P = 0.002) were associated with an increased risk of UTI. Ursodeoxycholate (UDCA) (OR = 0.906, 95%CI: 0.829-0.990, P = 0.029) is a protective factor against pneumonia. Two unknown metabolites, X-12407 (OR = 1.294, 95%CI: 1.131-1.481, P < 0.001), and X-12847 (OR = 1.344, 95%CI: 1.152-1.568, P < 0.001), were also identified as independent risk factors for sepsis. In this MR study, we demonstrated a causal relationship between blood metabolites and the risk of developing sepsis, pneumonia, and UTI. However, there was no evidence of a causal connection between blood metabolites and the risk of URTI or SSTI, indicating a need for larger-scale studies to further investigate susceptibility to certain infection phenotypes.