The Causal Effects of Inflammation and Coagulation-Related Biomarkers on Sepsis-Related Complications: A Mendelian Randomization Study

Abstract

Background

Sepsis and its complications pose a major global health burden. Mendelian randomization (MR) provides a genetic approach to assess causality.

Methods

We conducted a two-sample Mendelian randomization (MR) study using data from large-scale genome-wide association studies (GWAS). This study aimed to assess the causal effects of six key biomarkers (D-dimer, Galectin-3, activated protein C, tumor necrosis factor receptor 2, interleukin-6 receptor subunit alpha, and thrombomodulin) on six sepsis-related complications, including acute respiratory distress syndrome (ARDS) and acute renal failure. The primary analysis utilized the inverse-variance weighted (IVW) method, with comprehensive sensitivity analyses to assess for heterogeneity and pleiotropy. A False Discovery Rate (FDR) was applied to correct for multiple testing.

Results

The primary IVW analysis suggested several potential causal associations. Genetically predicted D-dimer was associated with a lower risk of ARDS (OR = 0.63, P = 0.025), IL-6Rα with a reduced risk of acute renal failure (OR = 0.85, P = 0.035), and Galectin-3 with a lower risk of streptococcal septicaemia (OR = 0.95, P = 0.039). Reverse MR analysis suggested that genetic liability to sepsis (critical care) was associated with lower D-dimer levels. However, after FDR correction, none of these associations remained statistically significant. Sensitivity analyses did not indicate the presence of significant horizontal pleiotropy.

Conclusion

This study did not find robust genetic evidence to support a causal relationship between the six selected biomarkers and the risk of sepsis-related complications after correction for multiple testing. The suggestive associations observed prior to correction warrant further investigation.

Introduction

Sepsis, a life-threatening organ dysfunction caused by a dysregulated host response to infection, represents a major global public health challenge, characterized by high morbidity and mortality. ¹ The clinical course of sepsis is often complicated by the onset of severe conditions such as acute respiratory distress syndrome (ARDS) and acute renal failure, which further worsen patient outcomes.^2–4 Understanding the risk factors that drive the progression of sepsis to these critical complications is paramount for developing effective preventative and therapeutic strategies.

The pathophysiology of sepsis is complex, involving an intricate interplay between the host's inflammatory and coagulation systems.^5–8 Several biomarkers have been implicated in this process.^9–12 For instance, D-dimer, a fibrin degradation product, is elevated during hypercoagulability states common in sepsis. Pro-inflammatory markers like interleukin-6 receptor subunit alpha (IL-6Rα) and tumor necrosis factor receptor 2 (TNFR2), along with Galectin-3, which mediates inflammation and immune responses, are also thought to play significant roles. Concurrently, anticoagulant pathways involving activated protein C (APC) and thrombomodulin can become impaired.

While numerous observational studies have linked these biomarkers to the risk and severity of sepsis, establishing a definitive causal relationship is challenging. Such studies are often limited by confounding factors (eg, underlying comorbidities, treatments) and the potential for reverse causality, where the disease process itself alters the biomarker levels. Mendelian randomization (MR) offers a robust approach to mitigate these limitations and strengthen causal inference in epidemiological studies. ¹³ By employing germline genetic variants, typically single nucleotide polymorphisms (SNPs), as instrumental variables (IVs) for an exposure, MR can assess the causal effect of the exposure on an outcome. Since genetic variants are randomly allocated at conception, they are less susceptible to environmental confounding and precede disease onset, thus minimizing reverse causation. In sepsis research, MR methods have been used to study inflammatory markers, genetic factors, and other potential risk factors. For example, genetic prediction of asthma is associated with an increased risk of multiple sepsis conditions, suggesting that more aggressive sepsis prevention and management may be justified for patients with asthma. ¹⁴

This study utilizes a two-sample MR design to systematically investigate the potential causal effects of six key inflammation and coagulation-related biomarkers on the risk of sepsis and its associated complications.

Methods

Study Design

We employed a two-sample Mendelian randomization design to assess the causal relationships between six biomarkers (exposures) and six sepsis-related complications (outcomes). This approach uses summary-level data from two independent genome-wide association studies (GWAS), one for the exposure and one for the outcome. The validity of MR analysis relies on three core assumptions: (1) the genetic IVs are robustly associated with the exposure; (2) the IVs are not associated with any confounding factors of the exposure-outcome relationship; and (3) the IVs influence the outcome only through the exposure. ¹⁵ A flowchart of the study design is presented in Figure 1.

Figure 1.

Flowchart of the two-sample Mendelian randomization study.

Data Sources

Summary-level GWAS data for the six exposures were obtained from the GWAS catalog and published literature. Data for Galectin-3, TNFR2, IL-6Rα, and thrombomodulin were sourced from a study by Folkersen et al ¹⁶ involving 21 758 individuals. Data for D-dimer and APC levels were from a study by Sabater-Lleal et al ¹⁷ on 3301 individuals.

GWAS summary statistics for the outcomes were sourced from the MRC-IEU consortium's analysis of the UK Biobank (UKB) cohort and the FinnGen consortium (Release R12). The outcomes included sepsis, sepsis (critical care), sepsis (28-day death), streptococcal septicaemia, acute respiratory distress syndrome (ARDS), and acute renal failure. All participating individuals in the source GWAS were of European ancestry, minimizing bias from population stratification. Detailed information on the GWAS datasets is provided in Supplementary Table 1.

Instrumental Variable Selection

To satisfy the first MR assumption, we selected SNPs strongly associated with each exposure. For Galectin-3, TNFR2, IL-6Rα, and thrombomodulin, we selected SNPs reaching a genome-wide significance threshold of P < 5 × 10⁻⁸. Due to a limited number of significant SNPs, this threshold was relaxed to P < 5 × 10^−6 18,19 for D-dimer, activated protein C, and the sepsis-related outcomes (for reverse MR).

To ensure the independence of IVs, we performed linkage disequilibrium (LD) clumping based on the European 1000 Genomes Project reference panel, using a stringent threshold of R² < 0.001 within a 10 000 kb window. We also filtered for SNPs with a minor allele frequency (MAF) > 0.01. When an IV was unavailable in the outcome GWAS, we used a proxy SNP with a high LD (R² > 0.8) ²⁰ where available. Finally, we calculated the F-statistic for each SNP using the formula F = R² × (N−2)/(1−R²) to assess instrument strength, excluding any IVs with an F-statistic <10 to avoid weak instrument bias.

MR Analysis

To assess the causal relationship between the six biomarkers and six sepsis-related complications, the random-effects inverse-variance weighted (IVW) method was employed as the cornerstone tool. ²¹ This method combines the Wald ratio estimates from each SNP in a meta-analysis framework, providing the most precise estimate when all instrumental variables (IVs) are valid. To explore whether the sepsis-related complications had any causal effect on the biomarkers, we also performed a reverse MR analysis.

To bolster the robustness of these findings, additional methods were invoked, encompassing the MR-Egger, weighted median, and weighted mode approaches. ²² The MR-Egger regression can provide a causal estimate adjusted for directional pleiotropy, though it has lower statistical power. In contrast, the weighted median technique provides a valid estimate relying on the assumption that at least half of the IVs are valid, while the weighted mode is robust when the largest subset of IVs providing a similar estimate are valid instruments. ²³

Sensitivity Analysis

We conducted multiple sensitivity analyses to test for violations of MR assumptions. First, we used Cochran's Q test to assess heterogeneity among the SNP-specific causal estimates, where significant heterogeneity may indicate pleiotropy. ²⁴ Second, we examined the intercept from the MR-Egger regression; a non-zero intercept (P < 0.05) is indicative of directional horizontal pleiotropy. ²⁵ Third, we employed the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test to detect and correct for potential outliers that may be biasing the results. ²⁶ Finally, a leave-one-out analysis ²⁷ was performed to identify if any single SNP was disproportionately driving the overall causal estimate.

Given the multiple exposure-outcome pairs tested, we applied the Benjamini-Hochberg False Discovery Rate (FDR) method to adjust P-values. An FDR-corrected P < 0.05 was considered statistically significant. ²⁸ All analyses were performed using the “TwoSampleMR” package in R (version 4.0.5).

Statistical Power Analysis

Statistical power for the forward MR analyses was evaluated using the mRnd online tool (https://shiny.cnsgenomics.com/mRnd/). Power calculations were based on the proportion of variance explained by genetic instruments (R²), the sample size of the outcome GWAS, the number of instruments, and the IVW effect estimates. Reverse MR analyses were not assessed due to insufficient information on explained variance. A power >80% was considered adequate to detect causal effects of the observed magnitude.

Results

Instrumental Variable Selection

Following our selection criteria, we identified a set of valid and independent single nucleotide polymorphisms (SNPs) to serve as instrumental variables (IVs) for each exposure and outcome. The number of IVs ranged from 5 to 14 across the different traits. Detailed information on the GWAS data sources for all outcomes is provided in Supplementary Table 1. All selected IVs demonstrated strong instrument strength, with F-statistics well above the conventional threshold of 10, thereby minimizing the risk of weak instrument bias. The specific F-statistics for each IV are detailed in Supplementary Table 2.

Forward Mendelian Randomization: Effects of Biomarkers on Sepsis Complications

The primary inverse-variance weighted (IVW) analysis identified three nominally significant associations between genetically predicted biomarker levels and the risk of sepsis-related complications. These key findings are presented in Table 1 and visualized in the forest plot in Figure 2. Specifically, a higher genetically predicted level of D-dimer was associated with a reduced risk of acute respiratory distress syndrome (OR = 0.63, 95% CI: 0.42–0.94, P = 0.025). Additionally, higher genetically predicted levels of IL-6Rα were associated with a lower risk of acute renal failure (OR = 0.85, 95% CI: 0.72–0.99, P = 0.035), and higher genetically predicted Galectin-3 was linked to a reduced risk of streptococcal septicaemia (OR = 0.95, 95% CI: 0.90–1.00, P = 0.039). However, after applying the Benjamini-Hochberg procedure to control the False Discovery Rate (FDR), none of these associations remained statistically significant (FDR = 0.474 for all three associations). No other significant causal relationships were identified between the other biomarkers and sepsis outcomes. The complete results for all forward MR analyses, including non-significant findings, are available in Supplementary Table 3. Statistical power analysis showed that the analyses of IL-6Rα (99.3%) and TNFR2 (89.0%) with acute renal failure achieved adequate power, whereas most other exposure-outcome pairs were underpowered (<50%; Supplementary Table 4).

Table 1.

Nominally Significant Causal Effects of Biomarkers on Sepsis-Related Complications from the Forward MR Analysis.

Id.exposure	Exposure	Outcome	Nsnp	Method	Or_ci	p	FDR
YDWcRf	D-dimer	Acute respiratory distress syndrome	9	IVW	0.63 (0.42–0.94)	0.025	0.474
ebi-a-GCST90012009	Galectin-3	Streptococcal septicaemia	8	IVW	0.95 (0.9–1)	0.039	0.474
ebi-a-GCST90012025	Interleukin-6 Receptor Subunit Alpha	Acute renal failure	7	IVW	0.85 (0.72–0.99)	0.035	0.474

Figure 2.

Forest plots of the nominally significant causal effects of biomarkers on sepsis-related complications. The plots show the causal associations of genetically predicted (A) D-dimer with acute respiratory distress syndrome, (B) Galectin-3 with streptococcal septicaemia, and (C) Interleukin-6 Receptor Subunit Alpha with acute renal failure.

Reverse Mendelian Randomization: Effects of Sepsis on Biomarker Levels

In the reverse MR analysis, we explored whether a genetic liability to sepsis-related conditions could causally affect biomarker levels. The IVW results, detailed in Table 2 and Figure 3, revealed two nominally significant associations. Genetic liability to Sepsis (critical care) was associated with lower levels of D-dimer (OR = 0.89, 95% CI: 0.81–0.98, P = 0.018) and Galectin-3 (OR = 0.96, 95% CI: 0.93–1.00, P = 0.037). As with the forward analysis, these associations did not withstand correction for multiple testing (FDR > 0.65) and were therefore not considered statistically significant. The full results for all reverse MR analyses are presented in Supplementary Table 7.

Table 2.

Nominally Significant Causal Effects of Sepsis-Related Complications on Biomarkers from the Reverse MR Analysis.

Exposure	Outcome	Nsnp	Method	Or_ci	p	FDR
Sepsis (critical care)	D-dimer	7	IVW	0.89 (0.81–0.98)	0.018	0.652
Sepsis (critical care)	Galectin-3	8	IVW	0.96 (0.93–1)	0.037	0.662
Acute respiratory distress syndrome	TNFR2	13	IVW	1.16 (1–1.34)	0.043	0.391
Streptococcal septicaemia	TNFR2	13	IVW	1.16 (1–1.34)	0.043	0.391

Figure 3.

Forest plots of the nominally significant causal effects of sepsis-related complications on biomarkers.

Sensitivity Analyses

To ensure the robustness of our findings, we conducted a comprehensive set of sensitivity analyses. The MR-Egger intercept test revealed no evidence of significant directional pleiotropy across any of the associations (all P > 0.05), with detailed results presented in Supplementary Tables 4 and 7. Although Cochran's Q test detected some heterogeneity, justifying the use of the random-effects IVW model (Supplementary Tables 4 and 7). The MR-PRESSO analysis did not identify any influential outliers in the forward MR (Supplementary Table 6). In the reverse analysis, a single outlier was found for the effect of ARDS on Galectin-3, but its removal did not substantively alter the null result (Supplementary Table 9). Furthermore, leave-one-out analyses confirmed that no individual SNP disproportionately drove the observed associations (Supplementary Figures 1 and 2).

Discussion

In this comprehensive two-sample Mendelian randomization study, we investigated the causal relationships between six important inflammation and coagulation biomarkers and the risk of sepsis and its related complications. Our primary analysis identified suggestive evidence for protective effects of D-dimer on ARDS, IL-6Rα on acute renal failure, and Galectin-3 on streptococcal septicaemia. However, these findings did not withstand correction for multiple testing, indicating a lack of robust evidence for causality. Similarly, while reverse MR analyses suggested that severe sepsis might influence levels of D-dimer and Galectin-3, these associations also lost significance after FDR correction. The largely null findings after multiple-testing correction suggest that these biomarkers are unlikely to serve as effective upstream therapeutic targets for preventing sepsis-related complications, but they remain clinically valuable for risk stratification and prognostic assessment in patients with sepsis.

Our study observed a nominal association between genetically predicted higher D-dimer levels and a reduced risk of ARDS, although this finding did not withstand correction for multiple testing. This direction contrasts with clinical evidence showing that elevated circulating D-dimer is a prognostic marker for in-hospital mortality in intensive care unit patients with sepsis. ²⁹ Higher D-dimer levels have also been linked with greater risk of ARDS and death in severe respiratory infections, including sepsis and COVID-19-associated ARDS.^30,31 Pathophysiologically, sepsis triggers systemic inflammation, endothelial injury, and immunothrombosis, leading to increased fibrin formation and secondary fibrinolysis, which in turn elevate circulating D-dimer levels and correlate with the severity of organ dysfunction.^32–34 In contrast, MR captures genetically determined baseline variation in D-dimer, independent of acute illness severity or treatment effects. The divergence between MR and cohort findings suggests that D-dimer elevation during sepsis reflects downstream thrombo-inflammatory activation. While clinically useful for risk stratification, baseline D-dimer levels do not appear to exert a causal influence on ARDS development.

Our study identified a nominal protective association between genetically predicted higher IL-6Rα levels and a reduced risk of acute renal failure, a finding that did not withstand correction for multiple testing. This contrasts with observational evidence showing that elevated circulating IL-6 levels are associated with acute kidney injury and adverse outcomes. ³⁵ IL-6 is rapidly upregulated in the early inflammatory response to sepsis, and higher circulating IL-6 levels are correlated with greater organ dysfunction and disease severity.^36,37 Experimental studies indicate that excessive IL-6 signaling exacerbates renal injury, ³⁸ and pharmacological blockade of the IL-6 receptor attenuates kidney damage in animal models of sepsis. ³⁹ These clinical and experimental observations reflect cytokine dynamics during active inflammation. By contrast, MR estimates the effect of genetically proxied variation in IL-6Rα signaling, not acute cytokine surges. The absence of a robust causal association suggests that IL-6Rα functions predominantly as a marker of inflammatory burden in sepsis, rather than as a primary driver of renal injury susceptibility.

Our analysis also revealed a nominal protective association between genetically predicted higher Galectin-3 levels and a reduced risk of streptococcal septicaemia. This finding contrasts with observational evidence showing that elevated circulating Galectin-3 is associated with increased disease severity and mortality in sepsis and is interpreted as a marker of heightened inflammatory burden. ⁴⁰ In addition, Galectin-3 exerts bactericidal activity against Gram-positive bacteria, including Streptococcus suis, through binding to teichoic acids on the bacterial surface and disruption of membrane integrity, ⁴¹ and plays a central player in neutrophil-mediated bacterial clearance. ⁴² Observational studies capture elevations of Galectin-3 during established severe infection. In contrast, the MR framework reflects constitutive differences in Galectin-3 levels. The observed nominal protective association is therefore biologically plausible in early host defense, whereas higher circulating levels in clinical cohorts likely reflect downstream inflammatory amplification during severe disease.

In the reverse MR analysis, we observed nominal associations between genetic predisposition to severe sepsis (critical care) and lower levels of D-dimer and Galectin-3. This observation is paradoxical when compared to clinical observations, in which increasing sepsis severity is typically accompanied by marked elevations of D-dimer and Galectin-3, reflecting uncontrolled inflammation and coagulation activation. ⁴³ However, biomarker regulation during sepsis is highly dynamic and non-linear. For example, in sepsis-associated disseminated intravascular coagulation, profound activation of coagulation pathways leads to consumption of endogenous anticoagulants, and reduced levels of activated protein C are associated with disease severity and mortality. ⁴⁴ The reverse MR findings may reflect genetically determined differences in baseline regulation or homeostatic set-points of immune and coagulation pathways. Consistent with this interpretation, prior genetic analyses have reported inverse associations between sepsis liability and the expression of several immune-related biomarkers, including LGALS9, another member of the galectin family. ⁴⁵ Although these reverse associations did not withstand correction for multiple testing, they suggest that genetic susceptibility to sepsis may involve complex regulatory feedback mechanisms influencing baseline biomarker levels.

In summary, this study systematically evaluated six key inflammation and coagulation biomarkers but found no robust genetic evidence that any single marker is an independent, direct causal driver of sepsis-related complications after correcting for multiple testing. This collective null finding points toward a unifying conclusion: although these biomarkers are potent clinical indicators of disease severity and prognosis, they likely reflect a complex, multifactorially driven downstream pathophysiological state rather than representing primary upstream causal targets for therapeutic intervention.

Strengths and Limitations

The main strength of our study is the use of the MR design, which reduces bias from confounding and reverse causation inherent in observational studies. We utilized summary statistics from large-scale GWAS consortia, increasing the statistical power and the precision of our estimates. Furthermore, we conducted a wide range of sensitivity analyses that confirmed the robustness of our results against violations of MR assumptions like horizontal pleiotropy.

However, our study also has several limitations. First, the GWAS data were primarily from individuals of European ancestry, which may limit the generalizability of our findings to other populations. In addition, comparable large-scale and harmonized GWAS data for sepsis-related outcomes in non-European populations are currently limited, precluding trans-ethnic MR analyses. Second, while we found no evidence of directional pleiotropy via the MR-Egger intercept, we cannot completely rule out the possibility of balanced or residual pleiotropy. Third, for some traits, we had to relax the P-value threshold to obtain sufficient instruments, which could potentially introduce weak instrument bias, although all instruments had F-statistics well above 10. Finally, only a limited number of exposure-outcome pairs achieved adequate statistical power. Therefore, small causal effects cannot be excluded due to limited sensitivity to detect modest effect sizes. The absence of statistically significant associations after multiple-testing correction suggests that strong causal effects are unlikely. The GWAS datasets used in this study represent the largest publicly available European-ancestry resources. However, future larger or multi-ancestry GWAS will be required to more definitively evaluate potential small effects.

Implications and Future Directions

Our findings have significant implications for both clinical practice and future research. For clinicians, this study reinforces the utility of these biomarkers for diagnosis and prognosis but cautions that therapeutic interventions aimed solely at modifying their circulating levels should not be expected to directly prevent sepsis-related complications. For researchers, our largely null results strongly suggest that future drug development efforts should be cautious about selecting these specific molecules as primary causal targets. The focus of investigation should perhaps shift towards more upstream regulators within the immune response and coagulation pathways, which may represent more promising targets for intervention. Future MR studies could also benefit from exploring a wider range of biomarkers and assessing their roles within specific, well-defined sepsis endotypes to uncover potentially context-dependent causal relationships.

Conclusion

In conclusion, this two-sample MR study did not find robust genetic evidence for a causal role of six key inflammation and coagulation-related biomarkers in the development of sepsis-related complications, or for a causal effect of sepsis on these biomarkers. Although some suggestive associations were observed, they did not survive correction for multiple testing. These findings underscore the complexity of the host response in sepsis and suggest that these biomarkers, while clinically useful for diagnosis and prognosis, may not be direct causal targets for therapeutic intervention in preventing sepsis complications.