Exploration of the causal relationship between obesity and sepsis and investigation of the mechanism of anoikis in sepsis

doi:10.21203/rs.3.rs-4949529/v1

Objective To explore the causal relationship between common obesity indicators (body mass index, hip circumference, waist circumference) and sepsis based on Mendelian randomization analysis. Furthermore, the mechanism of the role of anoikis in sepsis was explored based on the bioinformatics mining.

Methods In the first part, SNPs strongly associated with body mass index, Hip circumference, and Waist circumference were downloaded from the genome-wide association study(GWAS) database and screened as instrumental variables, and sepsis was used as an outcome variable. IVW was used as the primary analysis method to assess causal associations, with Weighted median and Mr-Egger as complementary methods. Heterogeneity among genetic variants was detected using Cochran's Q test and funnel plot analysis, horizontal pleiotropy was detected using Mr-Egger-intercept, and sensitivity analyses were performed using the "leave-one-out" method. In the second part, the biological functions and mechanisms of anoikis in sepsis were investigated based on R-analysis downloaded from the GEO database.

Resuts The body mass index(BMI), hip circumference(HC), and waist circumference (WC) were risk factors for sepsis. The core ARDEGs SERPINB1, MERTK and CEACAM8 were significantly up-regulated in sepsis and showed good diagnostic efficacy. The risk model based on ARDEGs showed good potential for clinical application. SERPINB1 may be involved in the regulation of inflammatory responses in sepsis through the NLRC4/CASP1-inflammatory effects signaling pathway.

Conclusion There is a causal association between obesity and sepsis and obesity is a risk factor for sepsis. The anoikis genes SERPINB1, MERTK and CEACAM8 are potential diagnostic targets for sepsis. And SERPINB1 may be involved in the regulation of inflammatory effects in sepsis through the NLRC4/CASP1- inflammatory effects signaling pathway.

sepsis

Mendelian randomization

anoikis

BMI

hip circumference

waist circumference

Sepsis is a syndrome in which a bacterial infection leads to a systemic hyperinfectious response in the body, which in turn causes abnormalities in the coagulation system, vascular endothelial damage, and the development of multiorgan failure, which is rapidly life-threatening. The mortality rate of severe sepsis has decreased slightly in recent years, but is still not less than 30%[1–3]. The injured organs and tissues often involved include the heart, lungs, kidneys, liver, blood vessels, and so on[4]. At present, the gold standard for the diagnosis of sepsis is still blood bacterial culture. And early diagnosis and treatment of sepsis is extremely important for the prognosis of sepsis patients[5, 6].

In the pathogenesis of sepsis the vascular endothelium sequentially undergoes cell swelling, production of inflammatory factors, alteration of vascular permeability, occurrence of endothelial cell apoptosis under the action of endotoxin, followed by systemic inflammatory syndrome (SIRS). This is one of the important basic mechanisms for the development of sepsis into multi-organ failure[7]. And with continuous deepening, it is found that the occurrence and development of sepsis is a comprehensive process, and its mechanism involves many processes such as tissue hypoxia, oxidative stress, immunosuppression and apoptosis dysregulation[8].

Obesity has been found to be a risk factor for a variety of metabolic diseases and tumors, and clinical findings have shown that there seems to be a correlation between obesity and sepsis, but so to date there have been no large relevant studies that have actually demonstrated a correlation between the two. Some studies have mentioned an increased risk of sepsis due to immune system dysfunction triggered by chronic low-grade inflammation in obesity[9]. The concept of Obesity Paradox has also been proposed[10], which mentions that obese patients seem to have a better survival benefit compared to lean individuals[11–14]. The results of this study seem to contradict the results of the previous studies, so the association between obesity and sepsis seems to be unclear and needs to be analyzed further.

Mendelian randomization (MR) is a novel epidemiological method that uses single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to reveal causal relationships[15]. Because of its very high level of evidence, which is second only to randomized controlled trials, MR is widely used to assess the association between exposure factors and outcome variables.

Since the association between obesity and sepsis seems to be unclear, this study was based on MR to further explore the relationship between the two. In addition, although the diagnosis and treatment of sepsis have been diversified and integrated, the high morbidity and mortality of sepsis suggests that the diagnosis and treatment of sepsis are still facing great difficulties. Lactic acid is the most commonly used biomarker to identify sepsis, but its diagnostic sensitivity is still not able to meet the clinical needs. However, the diagnostic sensitivity of these biomarkers still cannot meet the clinical needs. The future of sepsis diagnosis and treatment still lies in the combined diagnosis of multiple markers, so the discovery of new biomarkers and therapeutic targets is still imminent[16].

Abnormal apoptosis is one of the important features in the progression of sepsis. Vascular endothelial cell apoptosis is induced by a variety of inflammatory factors in the chain reaction of sepsis, but there are very few studies on its correlation, so the present study also explored the association of loss-of-nest apoptosis with sepsis based on public databases. In order to be able to discover new biomarkers to explain the development of sepsis and be able to recognize the disease at an earlier stage. Thus reducing the mortality associated with severe sepsis.

1.Subjects of study

Part I: Data were mainly obtained from the UK Biobank (https: //gwas.mrcieu.ac.uk) to obtain SNPs for the exposure factors: BMI, HC and WC, the outcome variable, sepsis, and all the details of the data are shown in Table S1.

Part II: The sepsis mRNA-Seq data (GSE49756, GSE57065 and GSE236713) were downloaded from the GEO (https://www.ncbi.nlm. nih.gov/geo/) database.

The data in both the GWAS and GEO databases are open to all, and there are no disputes of interest or infringement; therefore, this study did not require the approval of the local ethical committee, and this study followed the data access policy and publication guidelines of GEO and GWAS.

2. R-based analysis

All analyses involved in this study were done using R 4.2.2. The first part of the analysis used P<5.0E-8 to screen for SNPs associated with exposure factors, setting conditions of r2 >0.01 and kb<10,000 for the removal of linkage disequilibrium. Strong instrumental variables (IVs) were set when F > 10, and finally confounders were excluded using the online database Phenoscanner (http://www.phenoscanner. medschl.cam.ac.uk/). All IVs were strongly associated with exposure (association assumption). There was no association with known confounders (independence assumption). The IVs could only influence the outcome through exposure and were not directly associated with the outcome (exclusion assumption), and Mendelian randomization analyses were performed using the R "TwoSample MR" package.

In the second part, differential expression analysis was performed by GEO2R online analysis, R "limma" package and WGCNA, respectively. GO and KEGG analyses of the intersected genes were performed using the R "clusterProfiler" package to explore the biological functions and signaling pathways of the intersected differential genes (P<0.05). Support vector machine (SVM-RFE), random forest algorithm (RF) and Lasso-Cox regression analysis were used sequentially to further screen the characterized genes. The diagnostic efficacy of the characterized genes was analyzed using the "pROC" package; The "limma" and "ggplot2" packages were used for expression verification; The correlation of the characterized genes with the inflammasome and its ligands was analyzed using the "corrplot" package; The abundance of immune cell infiltration in normal and sepsis was analyzed using "CIBERSORT".

3.Genmania based analysis

The pathways and functions involved in anoikis-related core genes in sepsis were predicted using the online tool Genmania (https://genemania.org/search/ homo- sapiens/).

4. KEGG2 based analysis

The pathways involved in the core genes in sepsis were explored using KEGG2 (https://www.kegg.jp/kegg/kegg2.html).

5. Statistical analysis

Statistical analysis and plotting were performed using R 4.2.2, and the measurement information that conformed to normal distribution was expressed as mean ± standard deviation. Comparisons between groups were made using the Wilcoxon rank sum test; Comparisons between groups for count data were made using the chi-square test; Simple correlation analysis for measures that did not conform to normal distribution was performed using Spearman's correlation analysis, and P<0.05 was statistically significant.

Part I Results

1.Mendelian randomization analysis

Exp	Method	Nsnp	B	SE	P	OR(CI 95%)
BMI	IVW	432	0.40609	0.04292	3.05E-21	1.50093(1.37983-1.63271)
	MR Egger	432	0.38617	0.11651	0.00099	1.47134(1.17097-1.84875)
	WM	432	0.38231	0.07932	1.44E-06	1.46565(1.25463-1.71216)
HC	IVW	399	0.31202	0.04537	6.09E-12	1.36618(1.24994-1.49322)
	MR Egger	399	0.27233	0.12574	0.03092	1.31303(1.02621-1.68001)
	WM	399	0.37041	0.07136	2.09E-07	1.44832(1.25928-1.66574)
WC	IVW	357	0.53017	0.05401	9.44E-23	1.69923(1.52857-1.88894)
	MR Egger	357	0.41554	0.15475	0.00759	1.51521(1.11877-2.05209)
	WM	357	0.49593	0.09516	1.87E-07	1.64202(1.36264-1.97869)

Firstly, the causal association between obesity-related indexes and the development of sepsis was analyzed in this study based on MR. Based on the above screening conditions, 432 SNPs, 399 SNPs, and 357 SNPs were obtained from the data obtained for BMI, HC, and WC, respectively. These IVs were used to analyze with the outcomes, respectively, and the results showed that regardless of Inverse variance weighted (IVW), MR Egger or Weighted median (WM) showed that Body mass index (BMI), Hip circumference (HC) and Waist circumference (WC) were positively correlated with sepsis (P<0.05, OR>1), suggesting that BMI, HC and WC are risk factors for sepsis, as shown in Table 1.

Table 1. Results of causal analysis between BMI/HC/WC and sepsis

Notes: Inverse variance weighted(IVW); Weighted median(WM); Body mass index(BMI); Hip circumference(HC); Waist circumference(WC).

Exposure	Method	Q	Q_df	P
BMI	MR Egger	454.04163	430	0.20394
BMI	IVW	454.07735	431	0.21330
HC	MR Egger	446.94904	397	0.04211
HC	IVW	447.07797	398	0.04493
WC	MR Egger	367.33016	355	0.31475
WC	IVW	367.97674	356	0.31960

Table 2. Heterogeneity tests

Notes: Inverse variance weighted(IVW); Weighted median(WM); Body mass index(BMI); Hip circumference(HC); Waist circumference(WC).

Exposure	Egger_intercept	SE	P
BMI	0.00039	0.00211	0.85416
HC	0.00078	0.00231	0.73523
WC	0.00189	0.00239	0.42977

2. Heterogeneity test and pleiotropy test

To verify the reliability of the results, the study proceeded with the heterogeneity test using Cochran's Q test. The results showed that there was no heterogeneity of single nucleotide polymorphisms between IVW and MR Egger ( P>0.05), as shown in Table 2. And a symmetrical distribution of SNPs was also seen in the funnel plot, suggesting that MR analysis did not show pleiotropy (Figure S1). And the results of MR Egger intercept test showed that MR analysis did not show horizontal pleiotropy (intercept values:BMI 0.00039,P=0.85416; HC 0.0007, P=0.73523; WC 0.00189, P=0.42977).

Table 3. Egger_intercept

Notes: Inverse variance weighted(IVW); Weighted median(WM); Body mass index(BMI); Hip circumference(HC); Waist circumference(WC).

Exposure	Egger_intercept	SE	P
BMI	0.00039	0.00211	0.85416
HC	0.00078	0.00231	0.73523
WC	0.00189	0.00239	0.42977

3. Sensitivity analysis

At the end of the MR analysis, the study further evaluated the presence of selection bias through the "leave-one-out" sensitivity analysis. No single SNP was found to have a significant impact on the overall results, and no bias was caused by a single SNP, suggesting that the results of the MR analysis were reliable (Figure S2).

Figure 1. Differential expression analysis. A. Heat map of the results of differential expression analysis of GES49756 using GEO2R; B. Heat map of the results of differential expression analysis of GES57065 using GEO2R; C. Heat map of the results of differential expression analysis of GES236713 using GEO2R; D. Volcanogram of the results of differential expression analysis of GES49756; E. Volcanogram of the results of the differential expression analysis of GES57065; F. Volcanogram of the results of the differential expression analysis of GES49756; G. Intersection Venn diagram of up-regulated genes taken from the results of differential expression analysis of the three datasets; H. Intersection Venn diagram of down-regulated genes taken from the results of differential expression analysis of the three datasets; I. Heatmap of the results of differential expression analysis using the "limma" package after data merging and normalization.

Part II Results

1. Differential expression analysis

Three sepsis datasets, GSE49756, GSE57065 and GSE236713, were downloaded from the GEO database. The three datasets included in the study were firstly analyzed for differential expression analysis (|log(FC)|>0.585) using GEO2R, an analysis tool that comes with the GEO database, and the resultant heatmaps are shown in Figure 1.A-C,and the volcano plots are shown in Figure 1.D-F. The up-regulated differentially expressed genes and down-regulated differentially expressed genes obtained from the three datasets were intersected, and the genes appearing in the two different datasets were defined as differentially expressed genes filtered by "GEO2R" (up-regulated Figure 1.G, down-regulated Figure 1.H), and a total of 580 differentially expressed genes were obtained.

Next, the three datasets were normalized and merged, and differential expression analysis was performed using the "limma" package (|log(FC)|>0.585), resulting in 2144 differentially expressed genes. The heatmap of some of the genes is shown in Figure 1.I. Finally, the combined dataset was analyzed using WGCNA to explore the clustering of sepsis with normal related genes. Combined with the module independence test ( Figure 2.A), Gene dendrogram and module colors (Figure 2.B) and Module-trait relationships analysis (Figure 2.C) in the WGCNA analysis results, the purple module showed better sepsis correlation (cor=0.57, p=5e-45), and Module membership vs. gene significance correlation was also better (cor=0.77, p=5.1e-64), therefore, the corresponding gene module of purple was selected for the subsequent analysis, and 320 sepsis-related genes were obtained.

Figure 2. WGCNA analysis. A. Module independence test in the results. B. Gene dendrogram and module colors; C. Heatmap of Module-trait relationships analysis; D. Correlation scatterplot of Module membership vs. gene significance for normal samples in the purple module; E. Correlation scatterplot of Module membership vs. gene significance for sepsis samples in the purple module.

2.Screening and enrichment analysis of anoikis core genes

The differentially expressed genes obtained by the three methods were taken to be intersected with anoikis genes at the same time to determine the final anoikis related-differentially expressed genes (ARDEGs) in sepsis, and 6 ARDEGs were obtained, as shown in Figure 3.A. The GO function and KEGG pathway enrichment analysis showed that these ARDEGs were mainly involved in the negative regulation of cytokine production, negative regulation of interleukin-1 production, negative regulation of interleukin-1 production and negative regulation of myeloid leukocyte differentiation at the biological process(BP) level. Granule-related functions at the cell component(CC) level. And at the MF level is mainly involved in peptidase inhibitor activity, endopeptidase inhibitor activity and protein phosphatase binding. And in the aspect of pathway, it is mainly involved in the pathway related to Arrhythmogenic right ventricular cardiomyopathy, Acute myeloid leukemia and other diseases (Figure 3.B).

Considering that the 6 ARDEGs did not show good disease correlation, then this study further screened the 6 ARDEGs using support vector machine (SVM-RFE), random forest tree algorithm (RF), and lasso-cox regression analysis, respectively. And the results showed that the SVM-RFE got the minimum worse validation at the validation coefficient of 6, and the 6 ARDEGs were obtained (Figure 3.C). RF got the minimum error at trees=157, taking the genes with significance score greater than 20 and got 3 ARDEGs (Figure 3.D,E). The lasso-cox gets the minimum cross-validation bias at 4, yielding 4 ARDEGs (Figure 3.F). The intersection of the results of the three algorithms was taken to finally obtain 3 core ARDEGs ( Figure 3.G).

Figure 3. screening and enrichment analysis of loss-of-nest apoptotic core genes. A. Wayne plots of the results obtained from the three differential expression analyses with loss-of-nest apoptosis genes taken as intersections; B. Bar graph of GO and KEGG enrichment analysis of ARDEGs; C. Screening of core genes using support vector machine (SVM-RFE); D. Random Forest Tree (RF) algorithm for screening core genes; E. RF-based gene importance circle map; F. Screening of core genes using lasso-Cox regression analysis; G. Wayne's plot of the intersection of the results of the three methods of screening genes taken.

3. Risk nomogram construction and validation

Next, the expression of the 3 core ARDEGs was again validated using the merged and normalized sepsis mRNA-Seq data, and the results showed that SERPINB1, MERTK and CEACAM8 showed a significant up-regulation in sepsis (Figure 4.A-C,P<0.001). The diagnostic efficacy of the 3 core ARDEGs was evaluated, and the results showed that the ROC-AUC of all three was greater than 0.8, suggesting that the three had good diagnostic efficacy for sepsis (Figure 4.D-F). Based on the good diagnostic efficacy of SERPINB1, MERTK and CEACAM8, this study attempted to construct a risk prediction nomogram based on these three core ARDEGs, as shown in Figure 4.G. Then, the predictive efficacy of the model was verified using calibration curves, and the results showed that the calibration curves exhibited a good fit (Figure 4.H). Decision curve analysis (DCA) was also plotted in this study to assess the predictive efficacy of the model, and the results showed that the predictive efficacy of the model was high and stable (Figure 4.I). The predictive value of the model was also observed to be very close to the true positive rate in the clinical impact curve, suggesting higher predictive efficacy and higher patient benefit (Figure 4.J).

Figure 4. Risk model construction and validation. A. The validation box plot for the SERPINB1 expression analysis; B. The MERTK expression analysis validation box plot; C. The CEACAM8 expression analysis validation box plot; D. The diagnostic ROC curve for SERPINB1; E. The diagnostic ROC curve for MERTK; F. The diagnostic ROC curve for CEACAM8; G. The nomogram of risk prediction models based on SERPINB1, MERT and CEACAM8; H. Calibration curves; I. Decision curves; J. Clinical impact curves.

4. immune cell profile in sepsis

The current study also explored the distribution of immune cells in patients with sepsis based on CIBERSORET, and the proportions of the composition of the various immune cells are shown in Figure 5.A. The study also analyzed the correlation between the various immune cells, and the results showed that the relationship between the various immune cells was generally dominated by a negative correlation, the details of which are shown in Figure 5.B. In order to further clarify the trend of various immune cells in sepsis, this study analyzed the levels of each immune cell in the normal and sepsis groups. The results showed that the levels of Plasma cells, T cells CD4 naive, T cells gamma delta, Macrophages M0, Macrophages M2, Mast cells activated and Neutrophils were raised in the sepsis group. While the levels of T cells CD8, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs), NK cells resting, Dendritic cells resting, etc. levels were decreased in the sepsis group as shown in Figure 5.C.

Figure 5. Overview of the immune environment in sepsis. A. Bar graph of the composition of various immune cells in the normal sepsis group; B. Heatmap of correlation analysis between various immune cells in sepsis; C. Comparative violin plots of the levels of various immune cells in the normal group sepsis group.

5. Functions and signaling pathways of core ARDEGs

Based on the Genmania online database, we investigated the functions and pathways of the 3 core ARDEGs. The results showed that MERTK was mainly involved in phagocytosis, regulation of cytokine-mediated signaling pathway, leukocyte apoptotic process, regulation of dendritic cell apoptotic process, and dendritic cell apoptotic process. apoptotic process, dendritic cell apoptotic process, positive regulation of natural killer cell activation, regulation of leukocyte apoptotic process (Figure 6.A). CEACAM8 is mainly involved in cell killing, plasma membrane signaling receptor complex, macrophage activation, negative regulation of cytokine production, protein complex involved in cell adhesion, humoral immune response, defense response to bacterium (Figure 6.B). While SERPINB1 is mainly involved in inflammasome complex, cysteine-type endopeptidase activity involved in apoptotic process (Figure 6.C). It can be seen that the three core ARDEGs are mainly involved in the apoptotic process of various immune cells. It is noteworthy that SERPINB1 is mainly involved in inflammasome complex, and considering the close association of sepsis with inflammation in the organism, this study further explored the correlation between SERPINB1 and classical inflammasome and its related ligands, and the results showed that SERPINB1 was associated with NLRP3, NLRC4, NLRP12, AIM2, CASP1, CASP4 and CASP5, which had significant positive correlations (Figure 6.D, P<0.001). Thus, the present study further explored the regulatory mechanism of SERPINB1 and body inflammation based on KEGG2, with reference to the Necroptosis process[17]and NOD-like receptor signaling pathway[18], and hypothesized that SERPINB1 mainly may be involved in regulating the inflammatory response of the organism through the NLRC4/CASP1-inflammatory effects pathway (Figure 7).

As a chronic disease, obesity has been increasingly found to be commonly associated with many common human diseases[19]. As one of the diseases with high morbidity and mortality, sepsis was also concerned about the association between obesity and its existence at an early stage, and some of the studies mentioned that obese patients have a higher risk of developing the disease compared with non-obese patients, suggesting that obesity may be one of the risk factors for sepsis. In another part of the study, it was suggested that obese patients seem to have a better prognostic benefit, which seems to be contrary to the former, so the concept of "Obesity Paradox" appeared in the sight of all researchers.

MR studies are second only to randomized controlled trials in terms of level of evidence, and the present study used MR to investigate the causal association of obesity-related indicators (BMI, HC and WC) with sepsis. The results showed significant causal associations between BMI, HC and WC and sepsis, and that BMI, HC and WC are risk factors for sepsis and promote sepsis. It should be noted that the present study proved that obesity is a risk factor for sepsis, but the role of obesity in the whole course of sepsis is affected by many factors, and its correlation with the prognosis of patients was not included in the scope of this study. The role of obesity after sepsis and its mechanism need to be proved by further research, and the present study will not be investigated in depth.

The process of sepsis development is accompanied by apoptotic abnormalities, so the study here also explored the role of anoikis in the process based on the sepsis mRNA-Seq data from the GEO database. The ARDEGs screened were mainly enriched for the functions of negative regulation of cytokine production and negative regulation of interleukin-1 production. Whereas cytokines normally exert their antimicrobial effects locally, the release of large amounts of cytokines can lead to systemic symptoms, such as fever, muscle pain, and in severe cases, death, which is an important part of the disease progression in sepsis. Negative regulation of cytokines in the local antimicrobial process is a process of immune suppression, which is detrimental to disease regression. In contrast, negatively regulated inflammatory factors favor the organism during periods of intense systemic inflammation, and it seems that their function possesses a bidirectionality, and this bidirectionality section may be divided by the severity of sepsis, playing different holistic roles at different stages depending on the disease[20]. The enrichment result of ARDEGs involves the regulation of cytokines, inflammatory mediators, etc., and there is a complex and close regulatory relationship with the intrinsic mechanism of sepsis. As for the pathway, it mainly involves disease-related pathways such as arrhythmogenic right ventricular cardiomyopathy, which does not seem to be closely related to sepsis, but considering the heart as an important organ involved in sepsis, whether the mechanism of damage to the heart by rapidly progressing sepsis and cardiomyopathy have a similarity value remains to be further investigated.

Three core ARDEGs (SERPINB1, MERTK and CEACAM8) were screened by three algorithms, all of which were significantly up-regulated in sepsis and showed good diagnostic efficacy, suggesting that SERPINB1, MERTK and CEACAM8 are diagnostic targets for sepsis. The risk prediction model based on the 3 core ARDEGs showed good predictive efficacy and has good potential for clinical application. Further clinical studies can be conducted to detect the three core ARDEGs in patients with conditions that allow for the early detection of sepsis, in order to stifle the progression of the disease as early as possible, which is of great significance to the prognosis of patients with sepsis.

In the immunoassay of sepsis, significant differences in the infiltration levels of some immune cells were found, for example, the levels of immune cells Macrophages M0, Macrophages M2, Mast cells activated, and Neutrophils were elevated in the sepsis group. These cells are mainly involved in the process of cellular immunity, and the increased level of their infiltration in sepsis suggests that the body's immunity is strengthened; In contrast, the proportion of T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs), NK cells resting, and Dendritic cells resting decreased. The down-regulation of the proportion of resting cells also suggests the activation of immune killer cells and the enhancement of the immune response of the body. This is only an intuitive result of the body's immunity, different immune cells have their own roles in the process of sepsis, and there is a general regulatory relationship between immune cells, the mechanism of which is extremely complex. The level of immune cell infiltration can predict the immune status of the patient and the effect of the current treatment to a certain extent, and at the same time, it has a certain predictive effect on the regression of the patient's disease in the long term. In addition, it should be stated that the immune status of patients with mild sepsis and severe sepsis may also differ by grouping sepsis, which was not investigated in detail in the present study and needs to be followed up further.

During the exploration of the functions and pathways of the 3 core ARDEGs (SERPINB1, MERTK and CEACAM8), it was found that all three were closely associated with apoptosis in different cells. Among them, SERPINB1 showed significant correlation with inflammatory response initiator: CASP1/CASP4/CASP5, and SERPINB1 was involved in the regulation of inflammatory complex, which greatly attracted the attention of this study. So we explored the way of SERPINB1 involved in the regulation of inflammation based on KEGG2, and the results suggested that SERPINB1 may be involved in the regulation of inflammatory response in sepsis through the NLRC4/CASP1-inflammatory effects pathway.

Finally, it is important to state that this study has shortcomings; despite the strict adherence to statistical requirements, the data included in this study were derived from public database data, and the analysis was irresistibly influenced by the results of the original data. Regarding the possible involvement of SERPINB1 in the regulation of inflammatory responses in sepsis through the NLRC4/CASP1- inflammatory effects pathway is currently only at the level of theoretical speculation, and has not been further validated due to the lack of experimental funding, so it is important to look at the results of this study objectively.

Obesity (BMI, HC, WC) is causally associated with sepsis and is a risk factor for sepsis. Core ARDEGs SERPINB1, MERTK and CEACAM8 are potential diagnostic targets for sepsis. And the risk model based on SERPINB1, MERTK and CEACAM showed good potential for clinical application. SERPINB1 may be involved in the regulation of inflammatory responses in sepsis through the NLRC4/CASP1- inflammatory effects signaling pathway.

Ethics approval and consent to participate

Not applicable to this study.

Consent for publication

Not applicable to this study.

Consent to participate

Not applicable to this study.

Data Availability

The data used in this study were obtained from the GEO (https://www. ncbi.nlm.nih.gov/geo/) database and UK Biobank (https: //gwas.mrcieu. ac.uk), both of which are available in publicly available databases. This study complies with its data use and publication rules.

Conflicts of Interest

We declare that there is no conflict of interest regarding the publication of this paper.

Acknowledgement

Not applicable to this study.

Funding Statement

No funding support for this study.

Authors’ contributions

ZW and FK contributed equally. ZW and CB participated in the conception and design of the study.FK and ZW organized the database and statistical analysis.CB, ZX and ZX divided the work and participated in the picture drawing. ZW wrote the frst draft of the manuscript. CB, ZX, and ZX participated in the revision of the manuscript. All authors read and agreed to the fnal manuscript and authorship arrangement. All authors read and approved the fnal manuscript.

Agnello L, Ciaccio M (2023) Biomarkers of Sepsis. DIAGNOSTICS 13(3)
Pierrakos C, Velissaris D, Bisdorff M, Marshall JC, Vincent JL (2020) Biomarkers of sepsis: time for a reappraisal. CRIT CARE 24(1):287
Wang K, Xie S, Xiao K, Yan P, He W, Xie L (2018) Biomarkers of Sepsis-Induced Acute Kidney Injury. BIOMED RES INT 2018:6937947
Iba T, Connors JM, Nagaoka I, Levy JH (2021) Recent advances in the research and management of sepsis-associated DIC. INT J HEMATOL 113(1):24–33
Rello J, Valenzuela-Sanchez F, Ruiz-Rodriguez M, Moyano S (2017) Sepsis: A Review of Advances in Management. ADV THER 34(11):2393–2411
Napolitano LM (2018) Sepsis 2018: Definitions and Guideline Changes. SURG INFECT 19(2):117–125
Salomao R, Ferreira BL, Salomao MC, Santos SS, Azevedo L, Brunialti M (2019) Sepsis: evolving concepts and challenges. BRAZ J MED BIOL RES 52(4):e8595
Srzic I, Nesek AV, Tunjic PD (2022) SEPSIS DEFINITION: WHAT'S NEW IN THE TREATMENT GUIDELINES. ACTA CLIN CROAT 61(Suppl 1):67–72
Frydrych LM, Bian G, O'Lone DE, Ward PA, Delano MJ (2018) Obesity and type 2 diabetes mellitus drive immune dysfunction, infection development, and sepsis mortality. J Leukoc BIOL 104(3):525–534
Amundson DE, Djurkovic S, Matwiyoff GN (2010) The obesity paradox. CRIT CARE CLIN 26(4):583–596
Yeo HJ, Kim TH, Jang JH, Jeon K, Oh DK, Park MH, Lim CM, Kim K, Cho WH (2023) Obesity Paradox and Functional Outcomes in Sepsis: A Multicenter Prospective Study. CRIT CARE MED 51(6):742–752
Jagan N, Morrow LE, Walters RW, Plambeck RW, Wallen TJ, Patel TM, Malesker MA (2020) Sepsis and the Obesity Paradox: Size Matters in More Than One Way. CRIT CARE MED 48(9):e776–e782
Lee DH (2023) Obesity Paradox in Sepsis: Role of Adipose Tissue in Storing Mitochondrial Toxins. CRIT CARE MED 51(8):e172–e174
Robinson J, Swift-Scanlan T, Salyer J (2020) Obesity and 1-Year Mortality in Adults After Sepsis: A Systematic Review. BIOL RES NURS 22(1):103–113
Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, Laurin C, Burgess S, Bowden J, Langdon R et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. ELIFE 7
Mayr FB, Yende S, Angus DC (2014) Epidemiology of severe sepsis. VIRULENCE 5(1):4–11
Coll RC, O'Neill L, Schroder K (2016) Questions and controversies in innate immune research: what is the physiological role of NLRP3? CELL DEATH DISCOV 2:16019
Lage SL, Longo C, Branco LM, Da CT, Buzzo CL, Bortoluci KR (2014) Emerging Concepts about NAIP/NLRC4 Inflammasomes. FRONT IMMUNOL 5:309
Conway B, Rene A (2004) Obesity as a disease: no lightweight matter. OBES REV 5(3):145–151
Slifka MK, Whitton JL (2000) Clinical implications of dysregulated cytokine production. J MOL MED 78(2):74–80

No competing interests reported.

Exploration of the causal relationship between obesity and sepsis and investigation of the mechanism of anoikis in sepsis

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1