Causal associations between blood metabolites and risk of five infections: a Mendelian randomization study. BMC Infectious Diseases


Depending on the initial instrument selection, the number of instrumental variables for metabolites ranged from 3 to 148 with a median of 13. Using these instrumental variables, we initially evaluated causal relationships of 452 metabolites to the five infections and detected a total of 71 suggestive ones. Association (P<0.05; (corresponding to 64 unique metabolites by IVW analysis), 40 associations in 36 known metabolites and 31 associations in 28 unknown metabolites (Supplementary Table 1). Among them, 11, 7, 7, 9, and 6 associations were found for known metabolites, and 10, 5, 6, 6, and 4 associations were found for unknown metabolites, which were associated with sepsis, pneumonia, URTI, and UTI, respectively. Were related to. SSTI. Importantly, the minimum F statistic was greater than 10 (ranging from 18.55 to 1431.87), indicating a low likelihood of weak instrument bias (Supplementary Table 1). After multiple-testing correction, we found 4, 2, 3, and 3 associations for known metabolites and 2, 1, 0, and 2 associations for unknown metabolites, respectively, which were significant (FDR) for sepsis, pneumonia, URTI, and <0.05). UTI (Figure 2). No metabolites significantly associated with SSTI were identified after multiple test corrections. Specifically, 7 metabolites associated with sepsis were glycerol (odds ratio).[OR]= 1.88, 95% confidence interval [CIs]OR: 1.178-2.999, FDR = 0.043), 1-stearoylglycerol (1-SG) (OR = 0.563, 95% CI: 0.374-0.849, FDR = 0.039), 3-carboxy-4-methyl-5-propyl-2- Furanpropanoate (CMPF) (OR = 0.806, 95% CI: 0.690-0.942, FDR = 0.033), Dihomo-linoleate (20:2N6) (OR = 2.283, 95% CI: 1.334-3.908, FDR = 0.013), X- 12407 (OR = 1.212, 95%CI: 1.017–1.445, FDR = 0.047), %CI: 1.096-1.613, FDR = 0.019).

Fig. 2
Figure 2

Forest plot for the causal effect of identified metabolites on the risk of 4 types of infection phenotypes (sepsis, pneumonia, URTI and UTI) obtained from inverse variance weighting (IVW). SNP, single nucleotide polymorphism; Or, odds ratio; CI, confidence interval; FDR, false discovery rate; URTI, upper respiratory tract infection; UTI, urinary tract infection

The three metabolites significantly associated with pneumonia were ursodeoxycholate (UDCA) (OR = 0.833, 95% CI: 0.726-0.957, FDR = 0.049), kynurenine (OR = 1.685, 95% CI: 1.245-2.282, FDR = 0.004), and oleate (OR = 1.685, 95% CI: 1.245-2.282, FDR = 0.004). X-14588 (OR = 6.202, 95% CI: 1.599-24.051, FDR = 0.042). The three metabolites associated with URTI are tryptophan (OR = 4.642, 95% CI: 1.709-12.608, FDR = 0.013), histidine (OR = 39.251, 95% CI: 2.640-583.549, FDR = 0.032), and serotonin (5HT). ) (OR = 0.322, 95% CI: 0.148-0.699, FDR = 0.021).

The five metabolites associated with UTI are phenylacetate (PA) (OR = 1.476, 95% CI: 1.114-1.956, FDR = 0.033), cysteine ​​(OR = 1.601, 95% CI: 1.207-2.214, FDR = 0.005), eicosanoate (OR = 1.601, 95% CI: 1.207-2.214, FDR = 0.005). (20:1N9 or 11) (OR = 1.544, 95%CI: 1.152-2.070, FDR = 0.018), X-11483 (OR = 1.176, 95%CI: 1.049-1.318, FDR = 0.027), and (OR = 1.283, 95% CI: 1.106-1.488, FDR = 0.005).

sensitivity analysis

A series of sensitivity analyzes were conducted to evaluate the robustness of our main analytical approach. Using the IVW analysis as a basis, we applied MR-Egger regression, the weighted average method, and MR-PRESSO to comprehensively evaluate the causal effects between blood metabolites and infections. The results showed that the analysis results for 12 known metabolites and 5 unknown metabolites were robust. Specifically, the consistent direction and magnitude between the three MR analysis methods are presented in Supplementary Table 2 and Supplementary Figure 1. After testing for many effects and variations, P-values ​​obtained from Cochran’s Q test and I2 No abnormality was indicated. Furthermore, we observed a negligible effect of horizontal pleiotropy as evidenced by the small intercept term in the MR-Egger analysis (Table 2). Furthermore, the absence of horizontal pleiotropy or instrumental outliers is supported by MR-PRESSO analysis (PNon>0.05). Furthermore, leave-one-out analysis did not reveal any high-impact SNPs that would affect the combined effect estimate (Supplementary Figure 2). Therefore, we identified these 17 metabolites as potential candidate metabolites for further analysis, and the specific results are shown in Table 2 and Figure 2. Furthermore, to confirm the direction of effect from metabolites to infection, we performed the Steger test, which showed that the identified causal relationships were not biased by reverse causation (Supplementary Table 2).

Table 2 Sensitivity analysis for causal relationship between blood metabolites and infection phenotype

confusing analysis

Although sensitivity analyzes found no evidence of bias that would invalidate the MR estimates, we conducted further manual checks on other metabolite characteristics (body mass index, body fat percentage, total cholesterol level and low-density lipoprotein cholesterol). -Related SNP. Using PhenoScanner, we removed one SNP (rs3741298) from 1-SG, which was associated with total cholesterol levels, and three SNPs (rs1260326, rs1412972, rs603446) from tryptophan, which were associated with body fat percentage and were associated with total cholesterol levels. After re-running the IVW analysis, the causal relationship from metabolites to infection remained significant. Specifically, 1-SG (IVW OR = 0.573, 95% CI: 0.380-0.863, FDR = 0.015) and tryptophan (IVW OR = 4.968, 95% CI: 1.789-13.790, FDR = 0.006) were significantly associated with sepsis and URTI. Were formally connected. , respectively.

Replication and meta-analysis

To reinforce the robustness of our findings, we conducted replication analyzes using four GWAS datasets from FinGen R8, which revealed comparable trends for some metabolites. The known metabolites, 2, 1, and 2, have been associated with a predisposition to sepsis, pneumonia, and UTI, respectively. Additionally, two unidentified metabolites, X-12407 and X-12847, were found to be associated with an elevated risk of sepsis. As shown in Figure 3, in particular, joint analysis of the UK Biobank and FinGen datasets further confirmed that high levels of 1-SG (OR = 0.746, 95% CI: 0.573–0.998, P= 0.049) and CMPF (OR = 0.875, 95% CI: 0.785–0.976, P= 0.017) were protective factors for sepsis, X-12407 (OR = 1.172, 95% CI: 1.028-1.336, P= 0.018) and X-12847 (OR = 1.183, 95% CI: 1.028-1.360, P= 0.019) are risk factors for sepsis. UDCA (OR = 0.906, 95% CI: 0.829–0.990, P= 0.029) was a protective factor for pneumonia. Higher levels of PA (OR = 1.287, 95% CI: 1.048-1.579, P= 0.016) and cysteine ​​(OR = 1.310, 95% CI: 1.082-1.586, P= 0.006) predicted higher risk of UTI.

picture 3
Figure 3

Meta-analysis of causal relationships between metabolites and 3 types of infection phenotypes (sepsis, pneumonia and UTI). Or, odds ratio; CI, confidence interval; UTI, urinary tract infection

We observed null estimates in tryptophan, serotonin (5HT), dihomo-linoleate (20:2n6), glycerol, kynurenine, histidine, eicosanoate (20:1n9 or 11), X-14588, X-11483, and X-11491. Meta-analysis. Furthermore, replication analysis using GWAS summary data from the FinGene database revealed divergent directions. Details can be found in Supplementary Figure 3.

Multivariate and reverse MR analysis

Additionally, meta-analysis findings suggest that several metabolites may influence both sepsis and pneumonia. To explore the unique effects of each metabolite on sepsis or pneumonia, we performed a multivariable MR analysis. Interestingly, we found that the causal effect of each metabolite was consistent in direction and magnitude with the unadjusted results obtained through the IVW method (Table 3). The four metabolites that had an independent effect on sepsis were 1-SG (OR = 0.561, 95% CI: 0.403-0.780). P<0.001), CMPF (OR = 0.780, 95%CI: 0.6899–0.883, P<0.001), 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). Furthermore, significant causal effects were observed for 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) on UTI.

Table 3 Estimated causal effects of metabolites on sepsis/UTI by multivariable Mendelian randomization analysis

Finally, to examine the causality between metabolites and infection phenotype, we performed a reverse MR analysis using instrumental variables representing sepsis, pneumonia, and UTI, respectively. By selecting the top independent SNP with a significance level of PUsing <1 × 10-5 as the instrumental variable and assessing MR, we sought to determine whether there was any evidence of reverse causal correlation of the four infections from the 7 identified metabolites. However, our analysis revealed limited support for such a relationship, as demonstrated by Supplementary Table 3.

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