Early coagulation changes and survival outcomes, a multi-perspective retrospective analysis post severe burn

Early coagulation changes and survival outcomes, a multi-perspective retrospective analysis post severe burn | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Early coagulation changes and survival outcomes, a multi-perspective retrospective analysis post severe burn Shengyu Huang, Qimin Ma, Xincheng Liao, Xi Yin, Tuo Shen, Xiaobin Liu, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3840342/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 May, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Coagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptome and clinical characteristic levels. At the transcriptional level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care. Health sciences/Diseases/Trauma Health sciences/Risk factors Health sciences/Medical research/Outcomes research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Severe burns trigger a cascade of systemic responses within the body, involving the activation of multiple physiological systems. These responses encompass significant early alterations in both the coagulation and fibrinolytic systems 1 . Early activation of coagulation not only promotes hemostasis within the locally impaired microcirculation, but is also appears to contribute to the mitigation of inflammatory processes 2 . However, the intricate interplay between coagulation, fibrinolysis, the complement system, and immuno-inflammation in vivo often leads to the overactivation of coagulation and, in extreme cases, the development of disseminated intravascular coagulation (DIC) 3 – 5 . A critical aspect to consider is that current evidence suggests that the presence of early coagulation disorders subsequent to severe burns constitutes a substantial risk factor for a range of adverse outcomes, including death 6 . Moreover, the coagulation changes identified by previous studies that are associated with mortality outcomes remain highly controversial 7 , and only a limited number of studies have leveraged early coagulation indicators for the prediction of mortality outcomes. The primary objective was to pinpoint coagulation indicators that significantly contribute to the risk of mortality, and then embark on a comprehensive exploration of survival related coagulation alterations during the initial post-burn period and delves into the role these alterations play in influencing clinical survival outcomes. We conducted an analysis within the Gene Expression Omnibus (GEO) database, utilizing bioinformatics methods to identify differentially expressed genes related to coagulation post-burn injury, referred to as differentially expressed coagulation-related genes (DECRGs). Subsequently, we proceeded to identify core DECRGs that exhibit robust associations with mortality outcomes. Furthermore, to ascertain the potential causality, we employed Mendelian randomization analysis to investigate the causal links between plasm DECRGs related proteins and burn injury. Finally, our investigation entailed a retrospective analysis of early clinical data drawn from twelve medical centers nationwide and assessed clinical coagulation indicators associated with mortality outcomes (Fig. 1 ). Our findings provide critical clinical evidence that underscores the significance of early coagulation alterations in the aftermath of burn injuries. Result Identification survival related DECRGs We conducted DEGs analysis and WGCNA respectively on two merged transcriptome datasets obtained from the GEO database. Our analysis resulted in the identification of 5527 DEGs. In the WGCNA analysis, we ultimately identified 10 co-expression modules (Fig. 2 A-E). Notably, the greenyellow and turquoise modules exhibited the strongest correlation with burn, and we extracted a total of 1913 core genes from these modules (Fig. 2 F-H). Coagulation-related genes were extracted from those co-occurring in DEGs and core genes, resulting in a set of 28 DECRGs (Fig. 3 A, Table S2 in supplementary). To validate our findings, we assessed the expression of these 28 DECRGs in an additional dataset, GSE37069. The results showed that all were differentially expressed (Figure S1 in supplementary). Further univariate Cox regression analysis of the 28 genes identified 6 genes that showed potential associations with survival outcomes (Fig. 3 B). Subsequent correlation analysis revealed a significant phase relationship between P2RX1, CYP4F2, and CD59 (Fig. 3 C). GO annotation of the six genes screened revealed that these genes are involved in neutrophil activation and function, where minimum set of genes was set to be 5. Building upon this finding, we employed three algorithms, namely LASSO, Random Forest, and XGBoost, to identify genes associated with the outcome. Ultimately, CYP4F2 and P2RX1 emerged as the genes associated with survival outcomes, as consistently identified by all three algorithms (Fig. 3 D-G). In our nomogram prediction model, we incorporated four variables: age, TBSA, CYP4F2 level, and P2RX1 level. The prediction model yielded a C-index of 0.818 with a 95% CI ranging from 0.680 to 0.957 and a P value of 6.58×10 − 06 . Furthermore, the calibration curve demonstrated consistency between predicted and observed values. The model achieved an area under the curve (AUC) value of 0.80 for predicting outcomes at 28, 60, and 90 days. Risk scores were stratified based on the model's criteria, with the optimal cutoff value determined as 1.728 using the R package maxstat. The significance of survival differences between subgroups, assessed via the logrank test method, yielded a P value of 6.40×10 − 8 in survival curves (Fig. 4 A-D). Causal relationship between burns and expression of DECRGs Given the intricate regulation of the in vivo coagulation system, we proceeded to investigate causal alterations in DECRGs related proteins following burn injuries using MR analysis. Among the 28 DECRGs, we conducted GWAS for plasma proteins regulated by 13 of these genes. Utilizing the IVW method, we identified a potential causal relationship between burn exposure and protein S, tissue factor pathway inhibitor (TFPI), and tissue factor (TF) (Fig. 4 E). Simultaneously, the Weighted mode method identified decay accelerating factor (DAF) and TF. Notably, DAF, protein S, and TFPI exhibited positive directional trends, while TF displayed a negative directional trend (Fig. 4 F). Importantly, none of the aforementioned positive results exhibited horizontal pleiotropy or heterogeneity following sensitivity analysis, except for DAF( P = 0.049) (Table S3 in supplementary). D-dimer and serum calcium as risk factors for death In this retrospective clinical study, we enrolled 583 patients with severe burns, among whom 86 (14.75%) unfortunately succumbed to their injuries. Regarding coagulation indicators, except thrombin time and fibrinogen, notable differences were observed between the groups. The death group exhibited prolonged prothrombin time (PT) and activated partial thromboplastin time (APTT), along with increased levels of D-dimer, INR, and platelets. The death group displayed a decrease in pH and albumin levels, along with an increase in total bilirubin (TLIB) and alkaline phosphatase. Statistically significant differences were not observed in other parameters, including the cause of injury, medical history, and the use of anticoagulant therapy (Table S4 in supplementary). For further analysis of influential factors, we incorporated coagulation-related indicators and assessed them for multicollinearity. The VIF for all variables was found to be less than 5 (Fig. 5 A). Through univariate and multivariate Cox regression analyses, we identified D-dimer (HR = 1.01, 95% CI: 1.00-1.02) and calcium (HR = 4.01, 95% CI: 1.82–8.83) as associated with mortality outcomes, establishing them as independent risk factors. Acknowledging the existence of inevitable interactions (Figure S2 in supplementary), we proceeded to screen the variables using elastic networks and the LASSO algorithm. The outcome remained significant ( P < 0.05) (Table 1 , Table S5 in supplementary). The inclusion of D-dimer and calcium in the survival outcome prediction model resulted in enhanced prediction accuracy across multiple time intervals: 28 days, 60 days, and 90 days (Fig. 5 B- C). Mediation analyses revealed that the level of D-dimer appeared to mediate the effect of TBSA on mortality outcomes (Table S6 in supplementary). Table 1 Univariate and multivariate analysis of risk factors (Cox regression) Methods D-dimer Calcium HR (95%CI) P value HR (95%CI) P value Univariable 1.02 (1.01–1.03) < 0.001 1.57 (0.88–2.78) 0.125 Model1 1.01 (1.00-1.02) 0.02 2.66 (1.40–5.04) 0.003 Model2 1.01 (1.00-1.02) 0.049 3.92 (1.83–8.38) < 0.001 LASSO 1.02 (1.01–1.03) 0.001 2.98 (1.43–6.23) 0.004 Stepwise-backward 1.01 (1.00-1.02) 0.038 3.83 (1.79–8.19) 0.001 Elastic Net 1.02 (1.01–1.03) 0.001 2.95 (1.41–6.17) 0.004 HR = Hazard Ratio, CI = Confidence Interval. Model1: adjusted for age, TBSA and inhalation injury; Model2: adjusted for age, TBSA, inhalation injury, PH, lactic acid, total bilirubin, creatinine, albumin and WBC. Nonlinear relationship between D-dimer, calcium and mortality outcome In clinical practice, the relationship between independent and dependent variables is frequently nonlinear, particularly when the independent variable is continuous. Categorizing continuous variables based on arbitrary truncation values can lead to missing data. First of all, the Schoenfeld test proves that the Cox model meets the proportional risk assumption (Figure S3 in supplementary). Furthermore, we observed that age and TBSA were independent risk factors for mortality. RCS analysis, adjusted for covariates age and TBSA, revealed a nonlinear dose-response relationship between D-dimer levels and the risk of death ( P < 0.05), with a cutoff point at 1.418 mg/L (Fig. 5 D). When D-dimer levels exceeded 1.418 mg/L, an incremental increase in D-dimer was associated with a progressively elevated risk of death. Segmented regression analysis at this cutoff point demonstrated that D-dimer levels exceeding 1.418 mg/L were statistically significant as a risk factor (HR = 1.02, 95% CI: 1.01–1.03, P < 0.001), whereas levels below 1.418 mg/L were not statistically significant. Likewise (Table 2 ), RCS analysis revealed a nonlinear association between calcium levels and mortality outcomes, with calcium levels below 2.04 mmol/L being a risk factor for death (HR = 3.53, 95% CI: 1.38–9.04, P = 0.008) (Fig. 5 E, Table 2 ). In survival curves, it is evident that D-dimer and calcium, stratified by the cutoff values, effectively differentiate survival risk (Fig. 5 F-G). Table 2 Effect of D-dimer and Calcium Level on Survival: Adjusted Hazard Ratios from Segmented Cox Regression Analysis Characteristic HR 95% CI P -value D-dimer (mg/L) < 1.418 0.45 0.10, 1.97 0.290 ≥ 1.418 1.02 1.01, 1.03 < 0.001 Calcium (mmol/L) < 2.04 3.53 1.38, 9.04 0.008 ≥ 2.04 2.5 0.24, 26.4 0.450 HR: Hazard Ratio, CI: Confidence Interval. HRs were adjusted for TBSA and Age Discussion Previous research has indicated that coagulation disturbances exhibit dynamic changes throughout the progression of burn injuries 7 . Early alterations in the coagulation system may be less discernible in patients with smaller burn areas. However, in patients with extensive burns, these changes can manifest within 24 hours of injury. The precise pathophysiology underlying early coagulation changes in severe burn patients remains uncertain, with endothelial injury and tissue hypoperfusion currently the main thought drivers 8 . Extensive vascular endothelial damage and capillary leakage lead to a self-protective activation of coagulation pathways within the body. Damage-associated molecular patterns (DAMPs), molecular fragments generated in stressed or damaged tissues following burn injuries, are detected by pattern recognition receptors on the surfaces of a wide array of cells throughout the body 9 . The binding of DAMPs not only induces TF expression and activates external coagulation pathways but also triggers complement cross-linking with the coagulation system 10 . Within the context of systemic multisystem crosstalk, coagulation exhibits significant heterogeneity. Due to the limited specificity, we proceeded to identify DECRGs with potential effects on survival outcomes at the transcriptional level. These 28 DECRGs encompass regulators and proteins associated with diverse coagulation pathways, encompassing the complement system, hemostatic processes, and fibrinolysis. GO analysis of the six DECRGs identified as survival-related through univariate Cox regression unveiled a predominant involvement of neutrophils and their associated functional components. In the setting of widespread activation of coagulation and immunoinflammatory systems following burn injuries in vivo, DAMPs induced early in the course of burn injuries trigger the expression of TF on the surface of innate immune cells and activate external coagulation pathways 11 . Neutrophils, as pivotal components of the innate immune system, serve as essential mediators and play a significant role in fibrin formation during the early post-burn period 12 . In a laser-induced vascular endothelial injury model, neutrophils are the initial cells to accumulate within the vessel wall, interact with and aggregate around damaged endothelial cells, and contribute to thrombus formation 13 . Importantly, DAMPs also stimulate neutrophil degranulation, leading to the release of serine proteases (such as elastase and histone G) and the formation of neutrophil extracellular traps 14 . Previous research has primarily associated the impact of neutrophil serine proteases on the coagulation process with their role in promoting the inactivation of TFPI and fibrin production 12 . Early activation of neutrophils contributes to hemostasis and local inflammatory responses, but excessive dysregulation can result in coagulation depletion and a systemic immune-inflammatory response that is more likely to lead to adverse outcomes. The expression of genes associated with fatal outcomes, namely P2RX1 and CYP4F2, seems to contribute to the process of neutrophil hyperactivation. P2X1 receptors, which are ATP-gated ion channels, are expressed on both platelets and neutrophils 15 , 16 . ATP ligands increase in the extracellular space of the endothelium following tissue injury. This channel exhibits high permeability to Ca 2+ , with approximately 10% of the action potential mediated by these ions 17 . In platelets, P2X1 receptors mediates the activation of ERK2 phosphorylation via increased intracellular Ca 2+ , thereby enhancing the aggregation response to thrombin 18 . Additionally, the accumulation and pro-thrombotic capacity of neutrophils rely on the P2X1 receptor. Mice lacking the P2X1 receptor exhibited a substantial reduction in neutrophil chemotaxis and fibronectin production at the site of endothelial injury when compared to wild mice 15 . CYP4F2 serves as a vitamin K1 oxidase and an ω-hydroxylase of menaquinone 4 (MK-4) 19 , 20 . Moreover, genetic polymorphisms in the CYP4F2 gene impact plasma concentrations of vitamin K1 and MK-4. Vitamin K, functioning as a cofactor for gamma-glutamyl carboxylase, plays a crucial role in the hepatic metabolism of proteins C and S, along with the synthesis of coagulation factors II, VII, IX, and X 21 . Furthermore, genetic polymorphisms in CYP4F2 are linked to the necessary dosage of warfarin 22 . Following MR analysis, it was observed that plasma TF levels decreased after exposure to burns, while there was an increase in the expression of negatively regulated factors such as TFPI, protein S, and DAF. TFPI plays a pivotal role as a major inhibitor of the FVIIa-TF complex within the exogenous coagulation pathway, and protein S functions as an inhibitory cofactor that enhances the inhibitory capacity of TFPI 1 . DAF serves as a negative regulator within the complement cascade 23 . In summary, following burn injuries, genetic variation perspectives indicate a somewhat heightened activation of negative regulatory mechanisms. This aligns with findings from previous studies focusing on early trauma 24 ; however, further examination is required within the context of burn patients. Given the dynamic nature of burn injuries, the insights we have presented may necessitate additional validation through rigorous animal or clinical trials. It has been proposed that early changes in severe burns involve the activation of coagulation pathways, a decrease in natural anticoagulants (protein C and protein S), and the initiation of fibrinolysis 1 . Nonetheless, some scholars contend that coagulation experiences heightened activation, leading to microvascular thrombosis subsequent to coagulation dysregulation caused by consumptive coagulation disorders 25 . This results in a state of hypocoagulability and increased fibrinolysis. In our study, we observed prolonged PT and APTT in the death group, supporting the notion that early hypocoagulation may hold predictive value for survival outcomes. Importantly, early fibrinolytic activation has been consistently observed in the majority of studies, and it represents a significant factor contributing to mortality in trauma patients, including those with burns 26 – 28 . This phenomenon may be attributed to the release of substantial levels of free tissue-type fibrinogen activator from damaged endothelial cells, ultimately resulting in an upregulation of fibrinogenesis 29 . In our multicenter population-based study, D-dimer, a marker reflecting activation of fibrinolysis, was significantly elevated in the death group and was an independent predictor of survival outcome. Previous investigations have demonstrated that D-dimer levels are elevated upon admission in burn patients 25 . Moreover, early elevation in D-dimer levels in trauma patients is believed to correlate not only with the severity of injury but also to predict multiple adverse outcomes, including hemorrhage, disseminated intravascular coagulation (DIC), and death 30 . In severely traumatized patients, Hayakawa et al identified an optimal cutoff value of 38 mg/L for 24-hour D-dimer levels associated with mortality outcomes using receiver operating characteristic (ROC) analysis 28 . In our study, we observed an association between D-dimer levels exceeding 1.418 mg/L and mortality outcomes, with a corresponding increase in the risk of death as D-dimer levels rose. Given that endothelial injury, tissue hypoperfusion, and systemic inflammation frequently co-occur in severe burns, a mild fibrinolytic status may serve as a predictive marker for adverse outcomes. Besides D-dimer, serum calcium levels below 2.03 mmol/L were also linked to an elevated risk of mortality. As a pivotal element of P2X1 receptors in the coagulation pathway, excessive depletion of blood calcium independently contributes to mortality risk. In addition, neutrophil activation after injury leads to increased intracellular calcium mobilization 31 . While previous studies have observed a decrease in serum calcium levels following trauma, they did not identify an association with adverse outcomes 32 . Likewise, in our study, we initially did not observe any disparity in calcium levels between the groups that survived and those that did not. Furthermore, no statistical significance was evident in the univariate Cox analysis. However, positive findings surfaced following variable screening. This may warrant further consideration of the interaction of serum calcium with other factors, which is also more reflective of the real-world impact of serum calcium levels. When combined with transcript levels and clinical characteristics, it was observed that patients in the death group exhibited elevated levels of both fibrinolysis and thrombosis. Furthermore, this phenomenon is associated with the early activation of neutrophils. Based on this observation, we formulated a hypothesis that adverse outcomes may be linked to early secondary coagulation disorders arising from hyperactivation of the coagulation system. Excessive coagulation consumption could potentially manifest as the depletion of various factors. However, it is important to note that the present study did not comprehensively and meticulously collect indicators such as coagulation factors and fibrinolysis from a large number of patients. This is an aspect that we intend to address more rigorously in our future investigations. Given the intricate interplay with various immune-inflammatory systems, it appears challenging to intervene directly in the coagulation pathway solely based on alterations in patient coagulation screening indicators. Furthermore, it is worth noting that extensive assessments of thrombosis and hemostasis have not yet been undertaken at the clinical level. Utilizing four valuable predictive indicators allows for the identification of individuals at risk, thereby enabling the implementation of additional interventions and tailored care for this population. There are also some limitations in this paper. Firstly, we encountered limitations in obtaining GWAS data for all DECRGs candidates, and our MR analysis was consequently restricted to the available metrics. Secondly, our study did not involve the analysis of variations in mRNA and protein expression levels of the core genes through cellular experiments. The current lack of open studies of burned patients still exists, and thus genetic diversity and potential differences between the different population cohorts in our study are still not well avoided. However, we provide a method of analysis that may be flawed with the current data, but also provides direction for future research. Lastly, we could only gather information regarding the patients' prior medical histories, particularly concerning coagulation deficiencies and pre-hospitalization anticoagulant medications, through verbal inquiries. Methods Bioinformatics analysis To identify DECRGs, we initiated our analysis from a bioinformatic perspective. Burn-related blood transcriptome sequencing data were retrieved from two GEO databases ( http://www.ncbi.nlm.nih.gov/geo/ ): GSE77791 (burn patients = 15, control = 13) and GSE19743 (burn patients = 57, control = 63). To ensure consistency, we selected the earliest available sequencing data from each patient, acquired within 24 hours post-injury, for further analysis. Genes associated with the human coagulation pathway were collected from three reputable databases: KEGG, AmiGo 33 , and GeneCards 34 . The search keywords employed included "coagulation," "fibrosis," and "hemostasis." We further refined our gene selection by including 268 genes that were concurrently present in two or more of these databases. Initially, we merged the two datasets employing the R package inSilicoMerging 35 . Subsequently, to eliminate any potential batch effects, we applied the methodology outlined by Johnson WE et al 36 . Differential expression genes (DEGs) analysis was conducted using the R package limma after log2 transformation of the data 37 . Genes were deemed differentially expressed if they exhibited an adjusted P value of less than 0.05 and an absolute fold change greater than 1.5. Weighted correlation network analysis (WGCNA) is a robust systems biology method employed to elucidate gene association patterns among samples 38 . It enables the identification of highly synergistic gene sets and the exploration of candidate biomarker genes or therapeutic targets by integrating gene-set integration and gene-set-phenotype associations. In our analysis, we selected a power threshold of R 2 = 0.85 and a soft threshold of β = 18. Gene significance was utilized to measure the correlation between genes and clinical phenotypes, while module membership captured the correlation between eigengene and gene expression profiles of modules. We identified core genes as those within modules demonstrating both clear significance and a high correlation with burn injury, with a module membership threshold exceeding 0.8. Identification survival related DECRGs Correlate limma-identified DEGs, WGCNA-identified hub genes, and coagulation-related genes with each other to identify DECRGs. Initially, we employed univariate Cox regression to identify genes potentially linked to mortality outcomes. Gene Ontology (GO) of the screened survival-associated DECRGs was performed by clusterProfiler package in R, and P value < 0.05 were considered statistically significant. Subsequently, we subjected the characterized genes to a comprehensive screening process. This screening involved the combination of least absolute shrinkage and selection operator (LASSO), Random Forest, and XGBoost algorithms. The LASSO regression algorithm, by adding a penalty term in the model estimation, can compress the regression coefficients of some unnecessary variables to zero and then eliminate them from the model to achieve the purpose of variable screening 39 . The DECRGs that emerged as survival-related were harnessed for survival prediction analysis. Finally, the expression of DECRGs was validated in an additional dataset GSE37069. Mendelian randomization analysis Changes following burn injuries are intricate, involving multiple systems and levels. Traditional methods often struggle to uncover the specific causal links between burn injuries and alterations in various coagulation indicators. To address this challenge, we employed an innovative epidemiological approach known as Mendelian randomization (MR) analysis 40 . MR analysis uses genetic variation as an instrumental variable to investigate causal relationships between exposures and outcomes 41 . In this study, we conducted a two-sample MR analysis with burn injury as the exposure factor and DECRGs related proteins as the outcome factor. Burn-related Genome-Wide Association Study (GWAS) data were acquired from the FinnGen database, encompassing 3134 cases and 306,020 controls. GWAS data for DECRGs related proteins in plasma were obtained from previous studies 42 – 44 . The populations included in these datasets were all of European origin (Table S1 in supplementary). The significance threshold for instrumental variable screening was 1×10 − 5 , considering that instrumental variables (single nucleotide polymorphism) require strong correlation with exposure factors but not with outcome factors 45 . We employed two distinct MR analysis methods, Inverse Variance Weighted (IVW) and Weighted Median, to assess the specific association of genetically predicted burns with DECRGs related proteins. In addition, we conducted a sensitivity analysis for the MR results by applying Cochran's Q-test to assess the heterogeneity between instrumental variables, and a P value less than 0.05 indicated the presence of heterogeneity. In cases where heterogeneity was detected, we employed MR Egger regression to further eliminate the potential effect of horizontal pleiotropy. Once again, a P value less than 0.05 indicated the presence of heterogeneity that required correction 46 . Analysis of clinical coagulation indicator influencing survival outcomes We recruited patients with burn injuries meeting the criteria of either a total body surface area (TBSA) of ≥ 30% burns or ≥ 10% third-degree burns. These patients were admitted to the hospital within 24 hours post-injury and completed relevant clinical laboratory tests from twelve prominent tertiary hospitals across mainland China during the period spanning January 01, 2015, to December 30, 2020. To assess the influence of early coagulation indicators on survival outcomes, we employed the Cox proportional-hazards model to identify potentially relevant variables. We also utilized the variance inflation factor (VIF) to evaluate potential multicollinearity among the variables 47 . The VIF is calculated using the formula: VIF = 1 / (1 - R 2 ) , where R 2 represents the coefficient of determination used by the other predictors in the model to predict the predictor to be evaluated. Each independent variable is assigned a VIF score. A VIF less than 5 is indicative of low multicollinearity, while a score falling between 5 and 10 suggests the presence of some multicollinearity. A VIF exceeding 10 indicates severe multicollinearity. Variables identified through this initial screening process were subsequently subjected to multivariate Cox regression analysis. To identify variables suitable for multifactorial analyses, we employed a systematic approach involving three main methods: 1) Variables that exhibited positive statistical significance following univariate Cox regression analysis were selected for further consideration. 2) In view of the small number of variables, we included all variables in the Multivariate analysis. 3) To address the potential impact of multicollinearity, we employed both LASSO and Elastic Net models to identify suitable variables for multifactorial analysis. The Elastic Network algorithm combines the strengths of Ridge regression and Lasso regression 48 . 10-fold cross-validation was used in variable screening to select lambda values with the smallest mean square error. Restricted cubic spline To investigate potential nonlinear associations between coagulation indicators and survival outcomes, we employed the restricted cubic spline (RCS) methodology 49 . A cubic spline represents a continuous, segmented cubic polynomial, where the selection of node positions and their number is done to ensure the spline function provides a smooth curve for the continuous variable X across its entire range of values. Knot value with the smallest AIC value was selected after modeling different nodes and determined the position based on the 10th and 50th percentile. Prior to the implementation of RCS, we check whether the Cox model constructed by the variables is consistent with the proportional risk assumption using the Schoenfeld test. Statistical analysis In this paper, categorical variables are expressed as numbers (%), and continuous variables were presented as either mean (SD) when they adhered to a normal distribution or median (interquartile range) when their distribution deviated from normality. All statistical analyses were performed using R software. Some graphs were visualized based on the Sanger Box analysis tool 50 . A two-sided P value < 0.05 was considered statistically significant. Conclusion This study has illuminated early coagulation disorders linked to fatal outcomes in severe burns from a multifaceted standpoint. Early coagulation depletion and fibrinolytic activation have emerged as significant associations with mortality outcomes. Furthermore, our investigation also determined the feasibility of early alterations in D-dimer, serum calcium, CYP4F2 and P2RX1 within 24h post-injury as predictors to build a robust predictive model that may provide early clinical targets for monitoring. Further validation of the predictive value of these metrics in cohort populations is necessary Declarations Ethics statement The retrospective cohort has been approved by the ethics committee of Changhai Hospital (grant NO. CHCE2022-243). Data Availability Statement Burn patient transcriptome data was obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo), accession numbers GSE77791, GSE19743, and GSE37069. GWAS data was obtained from the FinnGen (https://www.finngen.fi/en)，The coagulation-related gene set is derived from three major gene databases, KEGG (https://www.genome.jp/kegg/), AmiGo (https://amigo.geneontology.org/amigo/), and GeneCards (https://www.genecards.org/). Conflict of Interests The authors declare that they have no competing interests. Author contributions SH, QM, and XL Integrated and wrote manuscript content. TS, XL, WT, YW, LW, HX, XL, and LC collected, sorted and analyzed data. ZC and RL verified the data. CW and DW reviewed the results and revised the manuscript. FZ and GG selected the topic and set the structure. All authors read and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Fundings National Key R&D Program “Stem Cell and Transformation Research” Key Special Project (2019YFA0110601) (Feng Zhu); The Top-level Clinical Discipline Project of Shanghai Pudong (PWYgf2021-03) (Feng Zhu); National Natural Science Foundation of China (81701899, 82160376) (Guanghua Guo). Acknowledgments We are very grateful to GEO and the FinnGen for providing open datasets. We also thank the twelve hospitals for their cooperation. References Ball, R. L. et al. Burn-Induced Coagulopathies: a Comprehensive Review. Shock 54, 154–167, doi: 10.1097/shk.0000000000001484 (2020). Wilhelm, G. et al. The Crossroads of the Coagulation System and the Immune System: Interactions and Connections. Int J Mol Sci 24, doi: 10.3390/ijms241612563 (2023). Rawish, E., Sauter, M., Sauter, R., Nording, H. & Langer, H. F. Complement, inflammation and thrombosis. Br J Pharmacol 178, 2892–2904, doi: 10.1111/bph.15476 (2021). Korkmaz, H. I. et al. The Complexity of the Post-Burn Immune Response: An Overview of the Associated Local and Systemic Complications. Cells 12, doi: 10.3390/cells12030345 (2023). Lippi, G., Ippolito, L. & Cervellin, G. Disseminated intravascular coagulation in burn injury. Semin Thromb Hemost 36, 429–436, doi: 10.1055/s-0030-1254051 (2010). Geng, K. et al. Incidence and Prognostic Value of Acute Coagulopathy After Extensive Severe Burns. J Burn Care Res 41, 544–549, doi: 10.1093/jbcr/irz178 (2020). Guilabert, P. et al. Coagulation Alterations in Major Burn Patients: A Narrative Review. J Burn Care Res 44, 280–292, doi: 10.1093/jbcr/irac177 (2023). Sherren, P. B. et al. Acute burn induced coagulopathy. Burns 39, 1157–1161, doi: 10.1016/j.burns.2013.02.010 (2013). Relja, B. & Land, W. G. Damage-associated molecular patterns in trauma. Eur J Trauma Emerg Surg 46, 751–775, doi: 10.1007/s00068-019-01235-w (2020). Müller, I. et al. Intravascular tissue factor initiates coagulation via circulating microvesicles and platelets. Faseb j 17, 476–478, doi: 10.1096/fj.02-0574fje (2003). Maugeri, N. et al. Human polymorphonuclear leukocytes produce and express functional tissue factor upon stimulation. J Thromb Haemost 4, 1323–1330, doi: 10.1111/j.1538-7836.2006.01968.x (2006). Massberg, S. et al. Reciprocal coupling of coagulation and innate immunity via neutrophil serine proteases. Nature Medicine 16, 887–896, doi: 10.1038/nm.2184 (2010). Darbousset, R. et al. Tissue factor-positive neutrophils bind to injured endothelial wall and initiate thrombus formation. Blood 120, 2133–2143, doi: 10.1182/blood-2012-06-437772 (2012). Andrews, R. K., Arthur, J. F. & Gardiner, E. E. Neutrophil extracellular traps (NETs) and the role of platelets in infection. Thromb Haemost 112, 659–665, doi: 10.1160/TH14-05-0455 (2014). Darbousset, R. et al. P2X1 expressed on polymorphonuclear neutrophils and platelets is required for thrombosis in mice. Blood 124, 2575–2585, doi: 10.1182/blood-2014-04-571679 (2014). Lecut, C. et al. P2X1 ion channels promote neutrophil chemotaxis through Rho kinase activation. J Immunol 183, 2801–2809, doi: 10.4049/jimmunol.0804007 (2009). Hechler, B. et al. A role of the fast ATP-gated P2X1 cation channel in thrombosis of small arteries in vivo. J Exp Med 198, 661–667, doi: 10.1084/jem.20030144 (2003). Toth-Zsamboki, E. et al. P2X1-mediated ERK2 activation amplifies the collagen-induced platelet secretion by enhancing myosin light chain kinase activation. J Biol Chem 278, 46661–46667, doi: 10.1074/jbc.M308452200 (2003). McDonald, M. G., Rieder, M. J., Nakano, M., Hsia, C. K. & Rettie, A. E. CYP4F2 is a vitamin K1 oxidase: An explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol 75, 1337–1346, doi: 10.1124/mol.109.054833 (2009). Edson, K. Z. et al. Cytochrome P450-dependent catabolism of vitamin K: omega-hydroxylation catalyzed by human CYP4F2 and CYP4F11. Biochemistry 52, 8276–8285, doi: 10.1021/bi401208m (2013). Vermeer, C. Vitamin K: the effect on health beyond coagulation - an overview. Food Nutr Res 56, doi: 10.3402/fnr.v56i0.5329 (2012). Hirai, K. et al. Plasma vitamin K concentrations depend on CYP4F2 polymorphism and influence on anticoagulation in Japanese patients with warfarin therapy. Thromb Res 135, 861–866, doi: 10.1016/j.thromres.2015.02.019 (2015). Mannes, M., Schmidt, C. Q., Nilsson, B., Ekdahl, K. N. & Huber-Lang, M. Complement as driver of systemic inflammation and organ failure in trauma, burn, and sepsis. Semin Immunopathol 43, 773–788, doi: 10.1007/s00281-021-00872-x (2021). Wada, T. et al. Association of antithrombin with development of trauma-induced disseminated intravascular coagulation and outcomes. Front Immunol 13, 1026163, doi: 10.3389/fimmu.2022.1026163 (2022). Lin, J. C., Xu, Z. R., Chen, Z. H. & Chen, X. D. Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality. Burns 47, 1322–1332, doi: 10.1016/j.burns.2020.11.016 (2021). Lavrentieva, A. et al. Early coagulation disorders after severe burn injury: impact on mortality. Intensive Care Med 34, 700–706, doi: 10.1007/s00134-007-0976-5 (2008). Lavrentieva, A. Replacement of specific coagulation factors in patients with burn: a review. Burns 39, 543–548, doi: 10.1016/j.burns.2012.12.009 (2013). Hayakawa, M. et al. High D-Dimer Levels Predict a Poor Outcome in Patients with Severe Trauma, Even with High Fibrinogen Levels on Arrival: A Multicenter Retrospective Study. Shock 45, 308–314, doi: 10.1097/SHK.0000000000000542 (2016). Shimono, K. et al. Damage-associated molecular patterns and fibrinolysis perturbation are associated with lethal outcomes in traumatic injury. Thromb J 21, 91, doi: 10.1186/s12959-023-00536-w (2023). Jiang, R. M., Pourzanjani, A. A., Cohen, M. J. & Petzold, L. Associations of longitudinal D-Dimer and Factor II on early trauma survival risk. BMC Bioinformatics 22, 122, doi: 10.1186/s12859-021-04065-z (2021). Carrillo, C., Del Mar Cavia, M., Roelofs, H., Wanten, G. & Alonso-Torre, S. R. Activation of human neutrophils by oleic acid involves the production of reactive oxygen species and a rise in cytosolic calcium concentration: a comparison with N-6 polyunsaturated fatty acids. Cell Physiol Biochem 28, 329–338, doi: 10.1159/000331749 (2011). Burggraf, M., Payas, A., Kauther, M. D., Schoeneberg, C. & Lendemans, S. Evaluation of clotting factor activities early after severe multiple trauma and their correlation with coagulation tests and clinical data. World J Emerg Surg 10, 43, doi: 10.1186/s13017-015-0038-1 (2015). Carbon, S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–289, doi: 10.1093/bioinformatics/btn615 (2008). Stelzer, G. et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 54, 1.30.31–31.30.33, doi: 10.1002/cpbi.5 (2016). Taminau, J. et al. Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 13, 335, doi: 10.1186/1471-2105-13-335 (2012). Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127, doi: 10.1093/biostatistics/kxj037 (2007). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47, doi: 10.1093/nar/gkv007 (2015). Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559, doi: 10.1186/1471-2105-9-559 (2008). Tibshirani, R. The lasso method for variable selection in the Cox model. Stat Med 16, 385–395, doi: 10.1002/(sici)1097-0258(19970228)16:43.0.co;2-3 (1997). Xu, J. et al. Using Mendelian randomization as the cornerstone for causal inference in epidemiology. Environ Sci Pollut Res Int 29, 5827–5839, doi: 10.1007/s11356-021-15939-3 (2022). Benn, M. & Nordestgaard, B. G. From genome-wide association studies to Mendelian randomization: novel opportunities for understanding cardiovascular disease causality, pathogenesis, prevention, and treatment. Cardiovasc Res 114, 1192–1208, doi: 10.1093/cvr/cvy045 (2018). Gilly, A. et al. Whole-genome sequencing analysis of the cardiometabolic proteome. Nat Commun 11, 6336, doi: 10.1038/s41467-020-20079-2 (2020). Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun 8, 14357, doi: 10.1038/ncomms14357 (2017). Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79, doi: 10.1038/s41586-018-0175-2 (2018). Huang, S. et al. Gut Microbiota and Respiratory Infections: Insights from Mendelian Randomization. Microorganisms 11, doi: 10.3390/microorganisms11082108 (2023). Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44, 512–525, doi: 10.1093/ije/dyv080 (2015). Kim, J. H. Multicollinearity and misleading statistical results. Korean J Anesthesiol 72, 558–569, doi: 10.4097/kja.19087 (2019). Vidyasagar, M. Identifying predictive features in drug response using machine learning: opportunities and challenges. Annu Rev Pharmacol Toxicol 55, 15–34, doi: 10.1146/annurev-pharmtox-010814-124502 (2015). Desquilbet, L. & Mariotti, F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med 29, 1037–1057, doi: 10.1002/sim.3841 (2010). Shen, W. et al. Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. iMeta 1, e36, doi: 10.1002/imt2.36 (2022). Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 07 May, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Mar, 2024 Reviews received at journal 04 Mar, 2024 Reviewers agreed at journal 16 Feb, 2024 Reviews received at journal 04 Feb, 2024 Reviewers agreed at journal 23 Jan, 2024 Reviewers invited by journal 17 Jan, 2024 Editor assigned by journal 17 Jan, 2024 Editor invited by journal 16 Jan, 2024 Submission checks completed at journal 16 Jan, 2024 First submitted to journal 06 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3840342","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267627160,"identity":"e2aa13ba-6f4c-4c9e-ab46-5a00c30b19bd","order_by":0,"name":"Shengyu Huang","email":"","orcid":"","institution":"Medical Center of Burn plastic and wound repair, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Huang","suffix":""},{"id":267627161,"identity":"c08a8535-1a37-4240-9551-0d6f8b9292b1","order_by":1,"name":"Qimin Ma","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qimin","middleName":"","lastName":"Ma","suffix":""},{"id":267627162,"identity":"c2beba70-be91-4236-bae1-e4c201136d2b","order_by":2,"name":"Xincheng Liao","email":"","orcid":"","institution":"Medical Center of Burn plastic and wound repair, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xincheng","middleName":"","lastName":"Liao","suffix":""},{"id":267627163,"identity":"2cbe1376-72b3-448a-9c09-f40fda863218","order_by":3,"name":"Xi Yin","email":"","orcid":"","institution":"Department of Burns and Plastic Surgery, Zhangjiagang Hospital Affiliated to Soochow University/ The First People' s Hospital of Zhangjiagang City","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Yin","suffix":""},{"id":267627164,"identity":"c82835b8-d919-49bb-b408-b736ad3c897a","order_by":4,"name":"Tuo Shen","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Shen","suffix":""},{"id":267627165,"identity":"c4c17d8e-2cd9-45ea-9d48-f42084993298","order_by":5,"name":"Xiaobin Liu","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Liu","suffix":""},{"id":267627166,"identity":"1239aa8b-4c06-458f-a299-e6850231e6e8","order_by":6,"name":"Wenbin Tang","email":"","orcid":"","institution":"Department of Burns, Guangzhou Red Cross Hospital, Jinan University","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Tang","suffix":""},{"id":267627167,"identity":"d50517c3-5f95-439b-8ffc-ea23745cd54d","order_by":7,"name":"Yusong Wang","email":"","orcid":"","institution":"ICU of Burn and Trauma, The First Affiliated Hospital, Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yusong","middleName":"","lastName":"Wang","suffix":""},{"id":267627168,"identity":"ad269214-6170-4258-a09f-99ee075511f2","order_by":8,"name":"Lei Wang","email":"","orcid":"","institution":"Department of Burns and Plastic Surgery, Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""},{"id":267627169,"identity":"0dba4260-529f-4512-a355-d31c0bcd549b","order_by":9,"name":"Haiming Xin","email":"","orcid":"","institution":"Department of Burns and Plastic Surgery, the 924th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army","correspondingAuthor":false,"prefix":"","firstName":"Haiming","middleName":"","lastName":"Xin","suffix":""},{"id":267627170,"identity":"f1a1305b-c9c3-4b1b-ac5d-cdbf84a883ed","order_by":10,"name":"Xiaoliang Li","email":"","orcid":"","institution":"Department of Burns and Plastic Surgery, Zhengzhou First People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoliang","middleName":"","lastName":"Li","suffix":""},{"id":267627171,"identity":"5cc96472-8f00-4762-9d64-e118eaa7dc60","order_by":11,"name":"Liu Chang","email":"","orcid":"","institution":"Department of Burns and Plastic Surgery, Dalian The Fourth People' s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Chang","suffix":""},{"id":267627172,"identity":"9432fa00-3049-415b-b600-5b8350cc3cc1","order_by":12,"name":"Zhaohong Chen","email":"","orcid":"","institution":"Department of Burns, Fujian Medical University Union Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaohong","middleName":"","lastName":"Chen","suffix":""},{"id":267627173,"identity":"2b5adabb-07c4-4f24-aa1b-a78eb5e795e4","order_by":13,"name":"Rui Liu","email":"","orcid":"","institution":"Department of Burn Surgery, Heilongjiang Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Liu","suffix":""},{"id":267627174,"identity":"fb675837-8fd9-4d14-93a3-40984b778c05","order_by":14,"name":"Choulang Wu","email":"","orcid":"","institution":"Department of Burns, Taizhou Hospital of Zhejiang Province","correspondingAuthor":false,"prefix":"","firstName":"Choulang","middleName":"","lastName":"Wu","suffix":""},{"id":267627175,"identity":"b53cdf78-8974-4d7a-b2c7-bafa82aa6b10","order_by":15,"name":"Deyun Wang","email":"","orcid":"","institution":"Department of Burns, Wuhan Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Deyun","middleName":"","lastName":"Wang","suffix":""},{"id":267627176,"identity":"44bce079-25bc-4853-9c4d-f4439f2ef48f","order_by":16,"name":"Guanghua Guo","email":"","orcid":"","institution":"Medical Center of Burn plastic and wound repair, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Guanghua","middleName":"","lastName":"Guo","suffix":""},{"id":267627177,"identity":"f9654f63-121f-4696-a89f-b090dca96647","order_by":17,"name":"Feng Zhu","email":"data:image/png;base64,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","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-01-06 17:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3840342/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3840342/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-61194-0","type":"published","date":"2024-05-07T21:17:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49889529,"identity":"1e2178fb-dbbd-4ffa-b9d9-b3feef048d95","added_by":"auto","created_at":"2024-01-19 19:50:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":116507,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure138.png","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/6bb8ec936389f1117f04b06b.png"},{"id":49889531,"identity":"83cb09cb-bc67-4c00-891a-61fbe45882e5","added_by":"auto","created_at":"2024-01-19 19:50:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes after burn injury. \u003c/strong\u003e(A): Relative expression between datasets before and after removal of batch effects; (B): Distribution between data sets before and after removal of batch effects; (C) Volcano map of differentially expressed genes; (D): WGCNA threshold determination; (E): Cluster analysis of co-expression modules; (F): Correlation of co-expression modules with burn injuries; (G): Scatterplot of module membership versus gene significance for burn in greenyellow module; (H): Scatterplot of module membership versus gene significance for burn in turquoise module. MM: module membership.\u003c/p\u003e","description":"","filename":"figure238.png","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/3d0dc888820e91f8c37ed76a.png"},{"id":49889534,"identity":"2e8fcd88-79ae-473f-8150-d50ce24ff92d","added_by":"auto","created_at":"2024-01-19 19:50:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of survival related DECRGs.\u003c/strong\u003e(A): DECRGs identified by DEGs and WGCNA jointly; (B): Forest plot of the hazard ratio after univariate Cox regression; (C): Correlation between survival related DECRGs; (D): GO annotation of survival-related genes; (E): LASSO algorithm screens for survival related signature genes; (F): Random Forest algorithm screens for survival related signature genes; (G): Survival related DECRGs identified by all three algorithms. WGCNA: Weighted correlation network analysis; DECRGs: differentially expressed coagulation-related genes; DEGs: Differential expression genes; RF: Random Forest; LASSO: least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"figure322.png","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/447139835ae74b9a3f26d892.png"},{"id":49889530,"identity":"fa779da5-f742-4ce5-a651-c5d8c0b99dea","added_by":"auto","created_at":"2024-01-19 19:50:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival-related gene prediction models and causal validation of MR\u003c/strong\u003e. (A): Nomogram Incorporating Characterized Genes Predicts Risk of Death; (B): Calibration curve between observed and predicted outcome for nomogram; (C): ROC Comparison of the nomogram model for Survival Prediction; (D): Survival curves divided by risks core in the nomogram model; (E): Forest plot of positive results of MR analysis; (F): Scatterplot of MR positive results. TBSA: total body surface area; HR: Hazard Ratio; SNP: single nucleotide polymorphism; DAF:decay accelerating factor; TFPI: tissue factor pathway inhibitor; IVW: inverse variance weighted.\u003c/p\u003e","description":"","filename":"figure419.png","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/6cf0b0095fa2257d2b45702e.png"},{"id":49889533,"identity":"2dd948d2-62bb-4f28-85a3-5cb340523548","added_by":"auto","created_at":"2024-01-19 19:50:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eD-dimer and serum calcium are risk factors for death.\u003c/strong\u003e (A): VIF values after multicollinearity test for variables. (B): ROC Comparison of Age and TBSA for Survival Prediction. (C): ROC Comparison of Age, TBSA, D-dimer, and calcium for Survival Prediction. (D): Restricted cubic spline for D-dimer and survival outcomes. (E): Survival curves divided by D-dimer cutoffs. (F): Restricted cubic spline for calcium and survival outcomes. (G): Survival curves divided by calcium cutoffs. TBSA: total body surface area; APTT: activated partial thromboplastin time; INR: international normalized ratio; AUC: area under curve; HR: hazard ratio; CI: confidence interval.\u003c/p\u003e","description":"","filename":"figure514.png","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/0804f6b17abd72ccdd83fced.png"},{"id":56488065,"identity":"2811c625-d01a-4f1a-b390-2890d5cb8061","added_by":"auto","created_at":"2024-05-14 21:28:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1914901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/7085e1ce-b734-4d3b-8d62-2bb94c6cc064.pdf"},{"id":49889899,"identity":"bc3a2d63-61ae-4863-be88-c682812b06c6","added_by":"auto","created_at":"2024-01-19 19:58:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":738746,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3840342/v1/e8f74edbd3be1e58fc81035d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early coagulation changes and survival outcomes, a multi-perspective retrospective analysis post severe burn","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere burns trigger a cascade of systemic responses within the body, involving the activation of multiple physiological systems. These responses encompass significant early alterations in both the coagulation and fibrinolytic systems\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early activation of coagulation not only promotes hemostasis within the locally impaired microcirculation, but is also appears to contribute to the mitigation of inflammatory processes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the intricate interplay between coagulation, fibrinolysis, the complement system, and immuno-inflammation in vivo often leads to the overactivation of coagulation and, in extreme cases, the development of disseminated intravascular coagulation (DIC)\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. A critical aspect to consider is that current evidence suggests that the presence of early coagulation disorders subsequent to severe burns constitutes a substantial risk factor for a range of adverse outcomes, including death\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Moreover, the coagulation changes identified by previous studies that are associated with mortality outcomes remain highly controversial\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and only a limited number of studies have leveraged early coagulation indicators for the prediction of mortality outcomes.\u003c/p\u003e \u003cp\u003eThe primary objective was to pinpoint coagulation indicators that significantly contribute to the risk of mortality, and then embark on a comprehensive exploration of survival related coagulation alterations during the initial post-burn period and delves into the role these alterations play in influencing clinical survival outcomes. We conducted an analysis within the Gene Expression Omnibus (GEO) database, utilizing bioinformatics methods to identify differentially expressed genes related to coagulation post-burn injury, referred to as differentially expressed coagulation-related genes (DECRGs). Subsequently, we proceeded to identify core DECRGs that exhibit robust associations with mortality outcomes. Furthermore, to ascertain the potential causality, we employed Mendelian randomization analysis to investigate the causal links between plasm DECRGs related proteins and burn injury. Finally, our investigation entailed a retrospective analysis of early clinical data drawn from twelve medical centers nationwide and assessed clinical coagulation indicators associated with mortality outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our findings provide critical clinical evidence that underscores the significance of early coagulation alterations in the aftermath of burn injuries.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification survival related DECRGs\u003c/h2\u003e \u003cp\u003eWe conducted DEGs analysis and WGCNA respectively on two merged transcriptome datasets obtained from the GEO database. Our analysis resulted in the identification of 5527 DEGs. In the WGCNA analysis, we ultimately identified 10 co-expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-E). Notably, the greenyellow and turquoise modules exhibited the strongest correlation with burn, and we extracted a total of 1913 core genes from these modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-H). Coagulation-related genes were extracted from those co-occurring in DEGs and core genes, resulting in a set of 28 DECRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Table S2 in supplementary). To validate our findings, we assessed the expression of these 28 DECRGs in an additional dataset, GSE37069. The results showed that all were differentially expressed (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in supplementary).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther univariate Cox regression analysis of the 28 genes identified 6 genes that showed potential associations with survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Subsequent correlation analysis revealed a significant phase relationship between P2RX1, CYP4F2, and CD59 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). GO annotation of the six genes screened revealed that these genes are involved in neutrophil activation and function, where minimum set of genes was set to be 5. Building upon this finding, we employed three algorithms, namely LASSO, Random Forest, and XGBoost, to identify genes associated with the outcome. Ultimately, CYP4F2 and P2RX1 emerged as the genes associated with survival outcomes, as consistently identified by all three algorithms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-G). In our nomogram prediction model, we incorporated four variables: age, TBSA, CYP4F2 level, and P2RX1 level. The prediction model yielded a C-index of 0.818 with a 95% CI ranging from 0.680 to 0.957 and a \u003cem\u003eP\u003c/em\u003e value of 6.58\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e. Furthermore, the calibration curve demonstrated consistency between predicted and observed values. The model achieved an area under the curve (AUC) value of 0.80 for predicting outcomes at 28, 60, and 90 days. Risk scores were stratified based on the model's criteria, with the optimal cutoff value determined as 1.728 using the R package maxstat. The significance of survival differences between subgroups, assessed via the logrank test method, yielded a \u003cem\u003eP\u003c/em\u003e value of 6.40\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e in survival curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCausal relationship between burns and expression of DECRGs\u003c/h2\u003e \u003cp\u003eGiven the intricate regulation of the in vivo coagulation system, we proceeded to investigate causal alterations in DECRGs related proteins following burn injuries using MR analysis. Among the 28 DECRGs, we conducted GWAS for plasma proteins regulated by 13 of these genes. Utilizing the IVW method, we identified a potential causal relationship between burn exposure and protein S, tissue factor pathway inhibitor (TFPI), and tissue factor (TF) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Simultaneously, the Weighted mode method identified decay accelerating factor (DAF) and TF. Notably, DAF, protein S, and TFPI exhibited positive directional trends, while TF displayed a negative directional trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Importantly, none of the aforementioned positive results exhibited horizontal pleiotropy or heterogeneity following sensitivity analysis, except for DAF(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049) (Table S3 in supplementary).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eD-dimer and serum calcium as risk factors for death\u003c/h2\u003e \u003cp\u003eIn this retrospective clinical study, we enrolled 583 patients with severe burns, among whom 86 (14.75%) unfortunately succumbed to their injuries. Regarding coagulation indicators, except thrombin time and fibrinogen, notable differences were observed between the groups. The death group exhibited prolonged prothrombin time (PT) and activated partial thromboplastin time (APTT), along with increased levels of D-dimer, INR, and platelets. The death group displayed a decrease in pH and albumin levels, along with an increase in total bilirubin (TLIB) and alkaline phosphatase. Statistically significant differences were not observed in other parameters, including the cause of injury, medical history, and the use of anticoagulant therapy (Table S4 in supplementary).\u003c/p\u003e \u003cp\u003eFor further analysis of influential factors, we incorporated coagulation-related indicators and assessed them for multicollinearity. The VIF for all variables was found to be less than 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Through univariate and multivariate Cox regression analyses, we identified D-dimer (HR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00-1.02) and calcium (HR\u0026thinsp;=\u0026thinsp;4.01, 95% CI: 1.82\u0026ndash;8.83) as associated with mortality outcomes, establishing them as independent risk factors. Acknowledging the existence of inevitable interactions (Figure S2 in supplementary), we proceeded to screen the variables using elastic networks and the LASSO algorithm. The outcome remained significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S5 in supplementary). The inclusion of D-dimer and calcium in the survival outcome prediction model resulted in enhanced prediction accuracy across multiple time intervals: 28 days, 60 days, and 90 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB- C). Mediation analyses revealed that the level of D-dimer appeared to mediate the effect of TBSA on mortality outcomes (Table S6 in supplementary).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of risk factors (Cox regression)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eCalcium\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57 (0.88\u0026ndash;2.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.66 (1.40\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.92 (1.83\u0026ndash;8.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLASSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.98 (1.43\u0026ndash;6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStepwise-backward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00-1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.83 (1.79\u0026ndash;8.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElastic\u0026nbsp;Net\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.01\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95 (1.41\u0026ndash;6.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eHR\u0026thinsp;=\u0026thinsp;Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval. Model1: adjusted for age, TBSA and inhalation injury; Model2: adjusted for age, TBSA, inhalation injury, PH, lactic acid, total bilirubin, creatinine, albumin and WBC.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear relationship between D-dimer, calcium and mortality outcome\u003c/h2\u003e \u003cp\u003eIn clinical practice, the relationship between independent and dependent variables is frequently nonlinear, particularly when the independent variable is continuous. Categorizing continuous variables based on arbitrary truncation values can lead to missing data. First of all, the Schoenfeld test proves that the Cox model meets the proportional risk assumption (Figure S3 in supplementary). Furthermore, we observed that age and TBSA were independent risk factors for mortality. RCS analysis, adjusted for covariates age and TBSA, revealed a nonlinear dose-response relationship between D-dimer levels and the risk of death (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a cutoff point at 1.418 mg/L (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). When D-dimer levels exceeded 1.418 mg/L, an incremental increase in D-dimer was associated with a progressively elevated risk of death. Segmented regression analysis at this cutoff point demonstrated that D-dimer levels exceeding 1.418 mg/L were statistically significant as a risk factor (HR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.01\u0026ndash;1.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas levels below 1.418 mg/L were not statistically significant. Likewise (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), RCS analysis revealed a nonlinear association between calcium levels and mortality outcomes, with calcium levels below 2.04 mmol/L being a risk factor for death (HR\u0026thinsp;=\u0026thinsp;3.53, 95% CI: 1.38\u0026ndash;9.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In survival curves, it is evident that D-dimer and calcium, stratified by the cutoff values, effectively differentiate survival risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of D-dimer and Calcium Level on Survival: Adjusted Hazard Ratios from Segmented Cox Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10, 1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01, 1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38, 9.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24, 26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHR: Hazard Ratio, CI: Confidence Interval. HRs were adjusted for TBSA and Age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious research has indicated that coagulation disturbances exhibit dynamic changes throughout the progression of burn injuries\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Early alterations in the coagulation system may be less discernible in patients with smaller burn areas. However, in patients with extensive burns, these changes can manifest within 24 hours of injury. The precise pathophysiology underlying early coagulation changes in severe burn patients remains uncertain, with endothelial injury and tissue hypoperfusion currently the main thought drivers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Extensive vascular endothelial damage and capillary leakage lead to a self-protective activation of coagulation pathways within the body. Damage-associated molecular patterns (DAMPs), molecular fragments generated in stressed or damaged tissues following burn injuries, are detected by pattern recognition receptors on the surfaces of a wide array of cells throughout the body\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The binding of DAMPs not only induces TF expression and activates external coagulation pathways but also triggers complement cross-linking with the coagulation system\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Within the context of systemic multisystem crosstalk, coagulation exhibits significant heterogeneity.\u003c/p\u003e \u003cp\u003eDue to the limited specificity, we proceeded to identify DECRGs with potential effects on survival outcomes at the transcriptional level. These 28 DECRGs encompass regulators and proteins associated with diverse coagulation pathways, encompassing the complement system, hemostatic processes, and fibrinolysis. GO analysis of the six DECRGs identified as survival-related through univariate Cox regression unveiled a predominant involvement of neutrophils and their associated functional components. In the setting of widespread activation of coagulation and immunoinflammatory systems following burn injuries in vivo, DAMPs induced early in the course of burn injuries trigger the expression of TF on the surface of innate immune cells and activate external coagulation pathways\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Neutrophils, as pivotal components of the innate immune system, serve as essential mediators and play a significant role in fibrin formation during the early post-burn period\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In a laser-induced vascular endothelial injury model, neutrophils are the initial cells to accumulate within the vessel wall, interact with and aggregate around damaged endothelial cells, and contribute to thrombus formation\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Importantly, DAMPs also stimulate neutrophil degranulation, leading to the release of serine proteases (such as elastase and histone G) and the formation of neutrophil extracellular traps\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Previous research has primarily associated the impact of neutrophil serine proteases on the coagulation process with their role in promoting the inactivation of TFPI and fibrin production\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Early activation of neutrophils contributes to hemostasis and local inflammatory responses, but excessive dysregulation can result in coagulation depletion and a systemic immune-inflammatory response that is more likely to lead to adverse outcomes.\u003c/p\u003e \u003cp\u003eThe expression of genes associated with fatal outcomes, namely P2RX1 and CYP4F2, seems to contribute to the process of neutrophil hyperactivation. P2X1 receptors, which are ATP-gated ion channels, are expressed on both platelets and neutrophils\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. ATP ligands increase in the extracellular space of the endothelium following tissue injury. This channel exhibits high permeability to Ca\u003csup\u003e2+\u003c/sup\u003e, with approximately 10% of the action potential mediated by these ions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In platelets, P2X1 receptors mediates the activation of ERK2 phosphorylation via increased intracellular Ca\u003csup\u003e2+\u003c/sup\u003e, thereby enhancing the aggregation response to thrombin\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Additionally, the accumulation and pro-thrombotic capacity of neutrophils rely on the P2X1 receptor. Mice lacking the P2X1 receptor exhibited a substantial reduction in neutrophil chemotaxis and fibronectin production at the site of endothelial injury when compared to wild mice\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. CYP4F2 serves as a vitamin K1 oxidase and an ω-hydroxylase of menaquinone 4 (MK-4)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Moreover, genetic polymorphisms in the CYP4F2 gene impact plasma concentrations of vitamin K1 and MK-4. Vitamin K, functioning as a cofactor for gamma-glutamyl carboxylase, plays a crucial role in the hepatic metabolism of proteins C and S, along with the synthesis of coagulation factors II, VII, IX, and X\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, genetic polymorphisms in CYP4F2 are linked to the necessary dosage of warfarin\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFollowing MR analysis, it was observed that plasma TF levels decreased after exposure to burns, while there was an increase in the expression of negatively regulated factors such as TFPI, protein S, and DAF. TFPI plays a pivotal role as a major inhibitor of the FVIIa-TF complex within the exogenous coagulation pathway, and protein S functions as an inhibitory cofactor that enhances the inhibitory capacity of TFPI\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. DAF serves as a negative regulator within the complement cascade\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In summary, following burn injuries, genetic variation perspectives indicate a somewhat heightened activation of negative regulatory mechanisms. This aligns with findings from previous studies focusing on early trauma\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; however, further examination is required within the context of burn patients. Given the dynamic nature of burn injuries, the insights we have presented may necessitate additional validation through rigorous animal or clinical trials.\u003c/p\u003e \u003cp\u003eIt has been proposed that early changes in severe burns involve the activation of coagulation pathways, a decrease in natural anticoagulants (protein C and protein S), and the initiation of fibrinolysis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Nonetheless, some scholars contend that coagulation experiences heightened activation, leading to microvascular thrombosis subsequent to coagulation dysregulation caused by consumptive coagulation disorders\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This results in a state of hypocoagulability and increased fibrinolysis. In our study, we observed prolonged PT and APTT in the death group, supporting the notion that early hypocoagulation may hold predictive value for survival outcomes.\u003c/p\u003e \u003cp\u003eImportantly, early fibrinolytic activation has been consistently observed in the majority of studies, and it represents a significant factor contributing to mortality in trauma patients, including those with burns\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This phenomenon may be attributed to the release of substantial levels of free tissue-type fibrinogen activator from damaged endothelial cells, ultimately resulting in an upregulation of fibrinogenesis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In our multicenter population-based study, D-dimer, a marker reflecting activation of fibrinolysis, was significantly elevated in the death group and was an independent predictor of survival outcome. Previous investigations have demonstrated that D-dimer levels are elevated upon admission in burn patients\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Moreover, early elevation in D-dimer levels in trauma patients is believed to correlate not only with the severity of injury but also to predict multiple adverse outcomes, including hemorrhage, disseminated intravascular coagulation (DIC), and death\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In severely traumatized patients, Hayakawa et al identified an optimal cutoff value of 38 mg/L for 24-hour D-dimer levels associated with mortality outcomes using receiver operating characteristic (ROC) analysis\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In our study, we observed an association between D-dimer levels exceeding 1.418 mg/L and mortality outcomes, with a corresponding increase in the risk of death as D-dimer levels rose. Given that endothelial injury, tissue hypoperfusion, and systemic inflammation frequently co-occur in severe burns, a mild fibrinolytic status may serve as a predictive marker for adverse outcomes.\u003c/p\u003e \u003cp\u003eBesides D-dimer, serum calcium levels below 2.03 mmol/L were also linked to an elevated risk of mortality. As a pivotal element of P2X1 receptors in the coagulation pathway, excessive depletion of blood calcium independently contributes to mortality risk. In addition, neutrophil activation after injury leads to increased intracellular calcium mobilization\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. While previous studies have observed a decrease in serum calcium levels following trauma, they did not identify an association with adverse outcomes\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Likewise, in our study, we initially did not observe any disparity in calcium levels between the groups that survived and those that did not. Furthermore, no statistical significance was evident in the univariate Cox analysis. However, positive findings surfaced following variable screening. This may warrant further consideration of the interaction of serum calcium with other factors, which is also more reflective of the real-world impact of serum calcium levels.\u003c/p\u003e \u003cp\u003eWhen combined with transcript levels and clinical characteristics, it was observed that patients in the death group exhibited elevated levels of both fibrinolysis and thrombosis. Furthermore, this phenomenon is associated with the early activation of neutrophils. Based on this observation, we formulated a hypothesis that adverse outcomes may be linked to early secondary coagulation disorders arising from hyperactivation of the coagulation system. Excessive coagulation consumption could potentially manifest as the depletion of various factors. However, it is important to note that the present study did not comprehensively and meticulously collect indicators such as coagulation factors and fibrinolysis from a large number of patients. This is an aspect that we intend to address more rigorously in our future investigations. Given the intricate interplay with various immune-inflammatory systems, it appears challenging to intervene directly in the coagulation pathway solely based on alterations in patient coagulation screening indicators. Furthermore, it is worth noting that extensive assessments of thrombosis and hemostasis have not yet been undertaken at the clinical level. Utilizing four valuable predictive indicators allows for the identification of individuals at risk, thereby enabling the implementation of additional interventions and tailored care for this population.\u003c/p\u003e \u003cp\u003eThere are also some limitations in this paper. Firstly, we encountered limitations in obtaining GWAS data for all DECRGs candidates, and our MR analysis was consequently restricted to the available metrics. Secondly, our study did not involve the analysis of variations in mRNA and protein expression levels of the core genes through cellular experiments. The current lack of open studies of burned patients still exists, and thus genetic diversity and potential differences between the different population cohorts in our study are still not well avoided. However, we provide a method of analysis that may be flawed with the current data, but also provides direction for future research. Lastly, we could only gather information regarding the patients' prior medical histories, particularly concerning coagulation deficiencies and pre-hospitalization anticoagulant medications, through verbal inquiries.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eTo identify DECRGs, we initiated our analysis from a bioinformatic perspective. Burn-related blood transcriptome sequencing data were retrieved from two GEO databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): GSE77791 (burn patients\u0026thinsp;=\u0026thinsp;15, control\u0026thinsp;=\u0026thinsp;13) and GSE19743 (burn patients\u0026thinsp;=\u0026thinsp;57, control\u0026thinsp;=\u0026thinsp;63). To ensure consistency, we selected the earliest available sequencing data from each patient, acquired within 24 hours post-injury, for further analysis. Genes associated with the human coagulation pathway were collected from three reputable databases: KEGG, AmiGo\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and GeneCards\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The search keywords employed included \"coagulation,\" \"fibrosis,\" and \"hemostasis.\" We further refined our gene selection by including 268 genes that were concurrently present in two or more of these databases.\u003c/p\u003e \u003cp\u003eInitially, we merged the two datasets employing the R package inSilicoMerging\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Subsequently, to eliminate any potential batch effects, we applied the methodology outlined by Johnson WE et al\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Differential expression genes (DEGs) analysis was conducted using the R package limma after log2 transformation of the data\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Genes were deemed differentially expressed if they exhibited an adjusted \u003cem\u003eP\u003c/em\u003e value of less than 0.05 and an absolute fold change greater than 1.5.\u003c/p\u003e \u003cp\u003eWeighted correlation network analysis (WGCNA) is a robust systems biology method employed to elucidate gene association patterns among samples\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It enables the identification of highly synergistic gene sets and the exploration of candidate biomarker genes or therapeutic targets by integrating gene-set integration and gene-set-phenotype associations. In our analysis, we selected a power threshold of \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85 and a soft threshold of \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18. Gene significance was utilized to measure the correlation between genes and clinical phenotypes, while module membership captured the correlation between eigengene and gene expression profiles of modules. We identified core genes as those within modules demonstrating both clear significance and a high correlation with burn injury, with a module membership threshold exceeding 0.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification survival related DECRGs\u003c/h2\u003e \u003cp\u003eCorrelate limma-identified DEGs, WGCNA-identified hub genes, and coagulation-related genes with each other to identify DECRGs. Initially, we employed univariate Cox regression to identify genes potentially linked to mortality outcomes. Gene Ontology (GO) of the screened survival-associated DECRGs was performed by clusterProfiler package in R, and \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Subsequently, we subjected the characterized genes to a comprehensive screening process. This screening involved the combination of least absolute shrinkage and selection operator (LASSO), Random Forest, and XGBoost algorithms. The LASSO regression algorithm, by adding a penalty term in the model estimation, can compress the regression coefficients of some unnecessary variables to zero and then eliminate them from the model to achieve the purpose of variable screening\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The DECRGs that emerged as survival-related were harnessed for survival prediction analysis. Finally, the expression of DECRGs was validated in an additional dataset GSE37069.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eChanges following burn injuries are intricate, involving multiple systems and levels. Traditional methods often struggle to uncover the specific causal links between burn injuries and alterations in various coagulation indicators. To address this challenge, we employed an innovative epidemiological approach known as Mendelian randomization (MR) analysis\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. MR analysis uses genetic variation as an instrumental variable to investigate causal relationships between exposures and outcomes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In this study, we conducted a two-sample MR analysis with burn injury as the exposure factor and DECRGs related proteins as the outcome factor. Burn-related Genome-Wide Association Study (GWAS) data were acquired from the FinnGen database, encompassing 3134 cases and 306,020 controls. GWAS data for DECRGs related proteins in plasma were obtained from previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The populations included in these datasets were all of European origin (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in supplementary).\u003c/p\u003e \u003cp\u003eThe significance threshold for instrumental variable screening was 1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, considering that instrumental variables (single nucleotide polymorphism) require strong correlation with exposure factors but not with outcome factors\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. We employed two distinct MR analysis methods, Inverse Variance Weighted (IVW) and Weighted Median, to assess the specific association of genetically predicted burns with DECRGs related proteins. In addition, we conducted a sensitivity analysis for the MR results by applying Cochran's Q-test to assess the heterogeneity between instrumental variables, and a \u003cem\u003eP\u003c/em\u003e value less than 0.05 indicated the presence of heterogeneity. In cases where heterogeneity was detected, we employed MR Egger regression to further eliminate the potential effect of horizontal pleiotropy. Once again, a \u003cem\u003eP\u003c/em\u003e value less than 0.05 indicated the presence of heterogeneity that required correction\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of clinical coagulation indicator influencing survival outcomes\u003c/h2\u003e \u003cp\u003eWe recruited patients with burn injuries meeting the criteria of either a total body surface area (TBSA) of \u0026ge;\u0026thinsp;30% burns or \u0026ge;\u0026thinsp;10% third-degree burns. These patients were admitted to the hospital within 24 hours post-injury and completed relevant clinical laboratory tests from twelve prominent tertiary hospitals across mainland China during the period spanning January 01, 2015, to December 30, 2020. To assess the influence of early coagulation indicators on survival outcomes, we employed the Cox proportional-hazards model to identify potentially relevant variables. We also utilized the variance inflation factor (VIF) to evaluate potential multicollinearity among the variables\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The VIF is calculated using the formula: \u003cem\u003eVIF\u0026thinsp;=\u0026thinsp;1 / (1 - R\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e, where \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e represents the coefficient of determination used by the other predictors in the model to predict the predictor to be evaluated. Each independent variable is assigned a VIF score. A VIF less than 5 is indicative of low multicollinearity, while a score falling between 5 and 10 suggests the presence of some multicollinearity. A VIF exceeding 10 indicates severe multicollinearity. Variables identified through this initial screening process were subsequently subjected to multivariate Cox regression analysis. To identify variables suitable for multifactorial analyses, we employed a systematic approach involving three main methods: 1) Variables that exhibited positive statistical significance following univariate Cox regression analysis were selected for further consideration. 2) In view of the small number of variables, we included all variables in the Multivariate analysis. 3) To address the potential impact of multicollinearity, we employed both LASSO and Elastic Net models to identify suitable variables for multifactorial analysis. The Elastic Network algorithm combines the strengths of Ridge regression and Lasso regression\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. 10-fold cross-validation was used in variable screening to select lambda values with the smallest mean square error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRestricted cubic spline\u003c/h2\u003e \u003cp\u003eTo investigate potential nonlinear associations between coagulation indicators and survival outcomes, we employed the restricted cubic spline (RCS) methodology\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. A cubic spline represents a continuous, segmented cubic polynomial, where the selection of node positions and their number is done to ensure the spline function provides a smooth curve for the continuous variable X across its entire range of values. Knot value with the smallest AIC value was selected after modeling different nodes and determined the position based on the 10th and 50th percentile. Prior to the implementation of RCS, we check whether the Cox model constructed by the variables is consistent with the proportional risk assumption using the Schoenfeld test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this paper, categorical variables are expressed as numbers (%), and continuous variables were presented as either mean (SD) when they adhered to a normal distribution or median (interquartile range) when their distribution deviated from normality. All statistical analyses were performed using R software. Some graphs were visualized based on the Sanger Box analysis tool\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study has illuminated early coagulation disorders linked to fatal outcomes in severe burns from a multifaceted standpoint. Early coagulation depletion and fibrinolytic activation have emerged as significant associations with mortality outcomes. Furthermore, our investigation also determined the feasibility of early alterations in D-dimer, serum calcium, CYP4F2 and P2RX1 within 24h post-injury as predictors to build a robust predictive model that may provide early clinical targets for monitoring. Further validation of the predictive value of these metrics in cohort populations is necessary\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe retrospective cohort has been approved by the ethics committee of Changhai Hospital (grant NO. CHCE2022-243).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBurn patient transcriptome data was obtained from the GEO database (http://www.ncbi.nlm.nih.gov/geo), accession numbers GSE77791, GSE19743, and GSE37069. GWAS data was obtained from the FinnGen (https://www.finngen.fi/en)，The coagulation-related gene set is derived from three major gene databases, KEGG (https://www.genome.jp/kegg/), AmiGo (https://amigo.geneontology.org/amigo/), and GeneCards (https://www.genecards.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSH, QM, and XL Integrated and wrote manuscript content. TS, XL, WT, YW, LW, HX, XL, and LC collected, sorted and analyzed data. ZC and RL verified the data. CW and DW reviewed the results and revised the manuscript. FZ and GG selected the topic and set the structure. All authors read and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Key R\u0026amp;D Program \u0026ldquo;Stem Cell and Transformation Research\u0026rdquo; Key Special Project (2019YFA0110601) (Feng Zhu); The Top-level Clinical Discipline Project of Shanghai Pudong (PWYgf2021-03) (Feng Zhu); National Natural Science Foundation of China (81701899, 82160376) (Guanghua Guo).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to GEO and the FinnGen for providing open datasets. We also thank the twelve hospitals for their cooperation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBall, R. L. \u003cem\u003eet al.\u003c/em\u003e Burn-Induced Coagulopathies: a Comprehensive Review. Shock 54, 154\u0026ndash;167, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/shk.0000000000001484\u003c/span\u003e\u003cspan address=\"10.1097/shk.0000000000001484\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilhelm, G. \u003cem\u003eet al.\u003c/em\u003e The Crossroads of the Coagulation System and the Immune System: Interactions and Connections. Int J Mol Sci 24, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241612563\u003c/span\u003e\u003cspan address=\"10.3390/ijms241612563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawish, E., Sauter, M., Sauter, R., Nording, H. \u0026amp; Langer, H. F. Complement, inflammation and thrombosis. Br J Pharmacol 178, 2892\u0026ndash;2904, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/bph.15476\u003c/span\u003e\u003cspan address=\"10.1111/bph.15476\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorkmaz, H. I. \u003cem\u003eet al.\u003c/em\u003e The Complexity of the Post-Burn Immune Response: An Overview of the Associated Local and Systemic Complications. \u003cem\u003eCells\u003c/em\u003e 12, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells12030345\u003c/span\u003e\u003cspan address=\"10.3390/cells12030345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi, G., Ippolito, L. \u0026amp; Cervellin, G. Disseminated intravascular coagulation in burn injury. Semin Thromb Hemost 36, 429\u0026ndash;436, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-0030-1254051\u003c/span\u003e\u003cspan address=\"10.1055/s-0030-1254051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeng, K. \u003cem\u003eet al.\u003c/em\u003e Incidence and Prognostic Value of Acute Coagulopathy After Extensive Severe Burns. J Burn Care Res 41, 544\u0026ndash;549, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jbcr/irz178\u003c/span\u003e\u003cspan address=\"10.1093/jbcr/irz178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuilabert, P. \u003cem\u003eet al.\u003c/em\u003e Coagulation Alterations in Major Burn Patients: A Narrative Review. J Burn Care Res 44, 280\u0026ndash;292, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jbcr/irac177\u003c/span\u003e\u003cspan address=\"10.1093/jbcr/irac177\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSherren, P. B. \u003cem\u003eet al.\u003c/em\u003e Acute burn induced coagulopathy. Burns 39, 1157\u0026ndash;1161, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.burns.2013.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.burns.2013.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRelja, B. \u0026amp; Land, W. G. Damage-associated molecular patterns in trauma. Eur J Trauma Emerg Surg 46, 751\u0026ndash;775, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00068-019-01235-w\u003c/span\u003e\u003cspan address=\"10.1007/s00068-019-01235-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026uuml;ller, I. \u003cem\u003eet al.\u003c/em\u003e Intravascular tissue factor initiates coagulation via circulating microvesicles and platelets. Faseb j 17, 476\u0026ndash;478, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1096/fj.02-0574fje\u003c/span\u003e\u003cspan address=\"10.1096/fj.02-0574fje\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaugeri, N. \u003cem\u003eet al.\u003c/em\u003e Human polymorphonuclear leukocytes produce and express functional tissue factor upon stimulation. J Thromb Haemost 4, 1323\u0026ndash;1330, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1538-7836.2006.01968.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1538-7836.2006.01968.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassberg, S. \u003cem\u003eet al.\u003c/em\u003e Reciprocal coupling of coagulation and innate immunity via neutrophil serine proteases. Nature Medicine 16, 887\u0026ndash;896, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.2184\u003c/span\u003e\u003cspan address=\"10.1038/nm.2184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarbousset, R. \u003cem\u003eet al.\u003c/em\u003e Tissue factor-positive neutrophils bind to injured endothelial wall and initiate thrombus formation. Blood 120, 2133\u0026ndash;2143, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2012-06-437772\u003c/span\u003e\u003cspan address=\"10.1182/blood-2012-06-437772\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrews, R. K., Arthur, J. F. \u0026amp; Gardiner, E. E. Neutrophil extracellular traps (NETs) and the role of platelets in infection. Thromb Haemost 112, 659\u0026ndash;665, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1160/TH14-05-0455\u003c/span\u003e\u003cspan address=\"10.1160/TH14-05-0455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarbousset, R. \u003cem\u003eet al.\u003c/em\u003e P2X1 expressed on polymorphonuclear neutrophils and platelets is required for thrombosis in mice. Blood 124, 2575\u0026ndash;2585, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2014-04-571679\u003c/span\u003e\u003cspan address=\"10.1182/blood-2014-04-571679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLecut, C. \u003cem\u003eet al.\u003c/em\u003e P2X1 ion channels promote neutrophil chemotaxis through Rho kinase activation. J Immunol 183, 2801\u0026ndash;2809, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4049/jimmunol.0804007\u003c/span\u003e\u003cspan address=\"10.4049/jimmunol.0804007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHechler, B. \u003cem\u003eet al.\u003c/em\u003e A role of the fast ATP-gated P2X1 cation channel in thrombosis of small arteries in vivo. J Exp Med 198, 661\u0026ndash;667, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1084/jem.20030144\u003c/span\u003e\u003cspan address=\"10.1084/jem.20030144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToth-Zsamboki, E. \u003cem\u003eet al.\u003c/em\u003e P2X1-mediated ERK2 activation amplifies the collagen-induced platelet secretion by enhancing myosin light chain kinase activation. J Biol Chem 278, 46661\u0026ndash;46667, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1074/jbc.M308452200\u003c/span\u003e\u003cspan address=\"10.1074/jbc.M308452200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald, M. G., Rieder, M. J., Nakano, M., Hsia, C. K. \u0026amp; Rettie, A. E. CYP4F2 is a vitamin K1 oxidase: An explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol 75, 1337\u0026ndash;1346, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1124/mol.109.054833\u003c/span\u003e\u003cspan address=\"10.1124/mol.109.054833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdson, K. Z. \u003cem\u003eet al.\u003c/em\u003e Cytochrome P450-dependent catabolism of vitamin K: omega-hydroxylation catalyzed by human CYP4F2 and CYP4F11. Biochemistry 52, 8276\u0026ndash;8285, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/bi401208m\u003c/span\u003e\u003cspan address=\"10.1021/bi401208m\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVermeer, C. Vitamin K: the effect on health beyond coagulation - an overview. Food Nutr Res 56, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3402/fnr.v56i0.5329\u003c/span\u003e\u003cspan address=\"10.3402/fnr.v56i0.5329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirai, K. \u003cem\u003eet al.\u003c/em\u003e Plasma vitamin K concentrations depend on CYP4F2 polymorphism and influence on anticoagulation in Japanese patients with warfarin therapy. Thromb Res 135, 861\u0026ndash;866, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.thromres.2015.02.019\u003c/span\u003e\u003cspan address=\"10.1016/j.thromres.2015.02.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMannes, M., Schmidt, C. Q., Nilsson, B., Ekdahl, K. N. \u0026amp; Huber-Lang, M. Complement as driver of systemic inflammation and organ failure in trauma, burn, and sepsis. Semin Immunopathol 43, 773\u0026ndash;788, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00281-021-00872-x\u003c/span\u003e\u003cspan address=\"10.1007/s00281-021-00872-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWada, T. \u003cem\u003eet al.\u003c/em\u003e Association of antithrombin with development of trauma-induced disseminated intravascular coagulation and outcomes. Front Immunol 13, 1026163, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.1026163\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.1026163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, J. C., Xu, Z. R., Chen, Z. H. \u0026amp; Chen, X. D. Low-soluble TREM-like transcript-1 levels early after severe burn reflect increased coagulation disorders and predict 30-day mortality. Burns 47, 1322\u0026ndash;1332, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.burns.2020.11.016\u003c/span\u003e\u003cspan address=\"10.1016/j.burns.2020.11.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavrentieva, A. \u003cem\u003eet al.\u003c/em\u003e Early coagulation disorders after severe burn injury: impact on mortality. Intensive Care Med 34, 700\u0026ndash;706, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-007-0976-5\u003c/span\u003e\u003cspan address=\"10.1007/s00134-007-0976-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavrentieva, A. Replacement of specific coagulation factors in patients with burn: a review. Burns 39, 543\u0026ndash;548, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.burns.2012.12.009\u003c/span\u003e\u003cspan address=\"10.1016/j.burns.2012.12.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayakawa, M. \u003cem\u003eet al.\u003c/em\u003e High D-Dimer Levels Predict a Poor Outcome in Patients with Severe Trauma, Even with High Fibrinogen Levels on Arrival: A Multicenter Retrospective Study. \u003cem\u003eShock\u003c/em\u003e 45, 308\u0026ndash;314, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SHK.0000000000000542\u003c/span\u003e\u003cspan address=\"10.1097/SHK.0000000000000542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimono, K. \u003cem\u003eet al.\u003c/em\u003e Damage-associated molecular patterns and fibrinolysis perturbation are associated with lethal outcomes in traumatic injury. Thromb J 21, 91, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12959-023-00536-w\u003c/span\u003e\u003cspan address=\"10.1186/s12959-023-00536-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, R. M., Pourzanjani, A. A., Cohen, M. J. \u0026amp; Petzold, L. Associations of longitudinal D-Dimer and Factor II on early trauma survival risk. BMC Bioinformatics 22, 122, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12859-021-04065-z\u003c/span\u003e\u003cspan address=\"10.1186/s12859-021-04065-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarrillo, C., Del Mar Cavia, M., Roelofs, H., Wanten, G. \u0026amp; Alonso-Torre, S. R. Activation of human neutrophils by oleic acid involves the production of reactive oxygen species and a rise in cytosolic calcium concentration: a comparison with N-6 polyunsaturated fatty acids. Cell Physiol Biochem 28, 329\u0026ndash;338, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000331749\u003c/span\u003e\u003cspan address=\"10.1159/000331749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurggraf, M., Payas, A., Kauther, M. D., Schoeneberg, C. \u0026amp; Lendemans, S. Evaluation of clotting factor activities early after severe multiple trauma and their correlation with coagulation tests and clinical data. World J Emerg Surg 10, 43, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13017-015-0038-1\u003c/span\u003e\u003cspan address=\"10.1186/s13017-015-0038-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarbon, S. \u003cem\u003eet al.\u003c/em\u003e AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288\u0026ndash;289, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btn615\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btn615\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStelzer, G. \u003cem\u003eet al.\u003c/em\u003e The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 54, 1.30.31\u0026ndash;31.30.33, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cpbi.5\u003c/span\u003e\u003cspan address=\"10.1002/cpbi.5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaminau, J. \u003cem\u003eet al.\u003c/em\u003e Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 13, 335, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-13-335\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-13-335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson, W. E., Li, C. \u0026amp; Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118\u0026ndash;127, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/biostatistics/kxj037\u003c/span\u003e\u003cspan address=\"10.1093/biostatistics/kxj037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, M. E. \u003cem\u003eet al.\u003c/em\u003e limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43, e47, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gkv007\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkv007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangfelder, P. \u0026amp; Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2105-9-559\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-9-559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibshirani, R. The lasso method for variable selection in the Cox model. Stat Med 16, 385\u0026ndash;395, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/(sici)1097-0258(19970228)16:4\u0026lt;385::aid-sim380\u0026gt;3.0.co;2-3\u003c/span\u003e\u003cspan address=\"10.1002/(sici)1097-0258(19970228)16:4%3C385::aid-sim380%3E3.0.co;2-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, J. \u003cem\u003eet al.\u003c/em\u003e Using Mendelian randomization as the cornerstone for causal inference in epidemiology. Environ Sci Pollut Res Int 29, 5827\u0026ndash;5839, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11356-021-15939-3\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-15939-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenn, M. \u0026amp; Nordestgaard, B. G. From genome-wide association studies to Mendelian randomization: novel opportunities for understanding cardiovascular disease causality, pathogenesis, prevention, and treatment. Cardiovasc Res 114, 1192\u0026ndash;1208, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/cvr/cvy045\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvy045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilly, A. \u003cem\u003eet al.\u003c/em\u003e Whole-genome sequencing analysis of the cardiometabolic proteome. Nat Commun 11, 6336, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41467-020-20079-2\u003c/span\u003e\u003cspan address=\"10.1038/s41467-020-20079-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuhre, K. \u003cem\u003eet al.\u003c/em\u003e Connecting genetic risk to disease end points through the human blood plasma proteome. Nat Commun 8, 14357, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms14357\u003c/span\u003e\u003cspan address=\"10.1038/ncomms14357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, B. B. \u003cem\u003eet al.\u003c/em\u003e Genomic atlas of the human plasma proteome. Nature 558, 73\u0026ndash;79, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-018-0175-2\u003c/span\u003e\u003cspan address=\"10.1038/s41586-018-0175-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, S. \u003cem\u003eet al.\u003c/em\u003e Gut Microbiota and Respiratory Infections: Insights from Mendelian Randomization. \u003cem\u003eMicroorganisms\u003c/em\u003e 11, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/microorganisms11082108\u003c/span\u003e\u003cspan address=\"10.3390/microorganisms11082108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44, 512\u0026ndash;525, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyv080\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyv080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J. H. Multicollinearity and misleading statistical results. Korean J Anesthesiol 72, 558\u0026ndash;569, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4097/kja.19087\u003c/span\u003e\u003cspan address=\"10.4097/kja.19087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidyasagar, M. Identifying predictive features in drug response using machine learning: opportunities and challenges. Annu Rev Pharmacol Toxicol 55, 15\u0026ndash;34, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1146/annurev-pharmtox-010814-124502\u003c/span\u003e\u003cspan address=\"10.1146/annurev-pharmtox-010814-124502\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesquilbet, L. \u0026amp; Mariotti, F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med 29, 1037\u0026ndash;1057, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.3841\u003c/span\u003e\u003cspan address=\"10.1002/sim.3841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, W. \u003cem\u003eet al.\u003c/em\u003e Sangerbox: A comprehensive, interaction-friendly clinical bioinformatics analysis platform. \u003cem\u003eiMeta\u003c/em\u003e 1, e36, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/imt2.36\u003c/span\u003e\u003cspan address=\"10.1002/imt2.36\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3840342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3840342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoagulation alterations manifest early after severe burns and are closely linked to mortality outcomes. Nevertheless, the precise characterization of coagulation changes associated with early mortality remains elusive. We examined alterations in indicators linked to mortality outcomes at both the transcriptome and clinical characteristic levels. At the transcriptional level, we pinpointed 28 differentially expressed coagulation-related genes (DECRGs) following burn injuries and endeavored to validate their causal relationships through Mendelian randomization. DECRGs tied to survival exhibit a significant association with neutrophil function, wherein the expression of CYP4F2 and P2RX1 serves as robust predictors of fatal outcomes. In terms of clinical indicators, early levels of D-dimer and alterations in serum calcium show a strong correlation with mortality outcomes. Coagulation depletion and fibrinolytic activation, stemming from the hyperactivation of coagulation pathways post-severe burns, are strongly linked to patient mortality. Monitoring these early coagulation markers with predictive value can effectively identify individuals necessitating priority critical care.\u003c/p\u003e","manuscriptTitle":"Early coagulation changes and survival outcomes, a multi-perspective retrospective analysis post severe burn","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 19:50:06","doi":"10.21203/rs.3.rs-3840342/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-05T08:21:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-04T18:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"945cc155-69cd-4bdc-9de8-6488cc10893b","date":"2024-02-16T13:02:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-04T05:24:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"da42d343-c2b7-4a4c-b4e7-f1d9265ace9a","date":"2024-01-23T14:03:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-17T08:18:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-17T08:15:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-16T12:05:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-16T11:48:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-06T17:36:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c1f5fc2-29c7-4025-a80b-ab8a3e3a26f6","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28198041,"name":"Health sciences/Diseases/Trauma"},{"id":28198042,"name":"Health sciences/Risk factors"},{"id":28198043,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2024-05-14T21:19:53+00:00","versionOfRecord":{"articleIdentity":"rs-3840342","link":"https://doi.org/10.1038/s41598-024-61194-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-05-07 21:17:21","publishedOnDateReadable":"May 7th, 2024"},"versionCreatedAt":"2024-01-19 19:50:06","video":"","vorDoi":"10.1038/s41598-024-61194-0","vorDoiUrl":"https://doi.org/10.1038/s41598-024-61194-0","workflowStages":[]},"version":"v1","identity":"rs-3840342","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3840342","identity":"rs-3840342","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}