Wei Zhengxiao, Xiong Qingqing, Huang Dan, Wu Zhangjun, Chen Zhu
Department of Clinical Laboratory, Chengdu Public Health Clinical Medical Center & Public Health Clinical Center of Chengdu University of Traditional Chinese Medicine, 377 Jingming Road, Jinjiang District, Chengdu, 610066, China.
Department of Scientific Research and Teaching, Chengdu Public Health Clinical Medical Center & Public Health Clinical Center of Chengdu University of Traditional Chinese Medicine, 377 Jingming Road, Jinjiang District, Chengdu, 610066, China.
BMC Infect Dis. 2023 Oct 7;23(1):663. 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.
传染病在全球公共卫生领域仍然构成重大威胁，而我们对其代谢发病机制的理解仍然有限。然而，全基因组关联研究（GWAS）的出现为揭示代谢物与感染之间的关系提供了前所未有的机会。
采用单变量和多变量孟德尔随机化（MR）方法来阐明血液代谢与五种高频感染表型之间的因果关系：败血症、肺炎、上呼吸道感染（URTI）、尿路感染（UTI）以及皮肤和皮下组织感染（SSTI）。感染的GWAS数据来自英国生物银行和芬兰基因联盟。主要分析在英国生物银行数据上使用逆方差加权法进行，并进行了一系列敏感性分析。随后，对芬兰基因联盟数据进行了重复分析和荟萃分析。
经过主要分析和一系列敏感性分析，从英国生物银行中确定了17种与五种感染存在因果关系的代谢物。与芬兰基因队列联合分析后，其中7种代谢物表现出一致的关联。随后，我们进行了多变量孟德尔随机化分析以确认这些代谢物的独立作用。在已知代谢物中，基因预测的1-硬脂酰甘油（1-SG）（优势比[OR]=0.561，95%置信区间[CI]：0.403-0.780，P<0.001）和3-羧基-4-甲基-5-丙基-2-呋喃丙酸（CMPF）（OR=0.780，95%CI：0.689-0.883，P<0.001）与败血症风险降低存在因果关联，而基因预测的苯乙酸（PA）（OR=1.426，95%CI：1.152-1.765，P=0.001）和半胱氨酸（OR=1.522，95%CI：1.170-1.980，P=0.002）与UTI风险增加有关。熊去氧胆酸（UDCA）（OR=0.906，95%CI：0.829-0.990，P=0.029）是预防肺炎的保护因素。两种未知代谢物，X-12407（OR=1.294，95%CI：1.131-1.481，P<0.001）和X-12847（OR=1.344，95%CI：1.152-1.568，P<0.001），也被确定为败血症的独立危险因素。
在这项MR研究中，我们证明了血液代谢物与发生败血症、肺炎和UTI风险之间的因果关系。然而，没有证据表明血液代谢物与URTI或SSTI风险之间存在因果联系，这表明需要进行更大规模的研究来进一步调查对某些感染表型的易感性。
