TNFAIP3, TNIP1, and MyD88 polymorphisms predict septic-shock-related death in patients who underwent major surgery

Journal of Clinical Medicine Article TNFAIP3,TNIP1, and MyD88 Polymorphisms Predict Septic-Shock-Related Death in Patients Who Underwent Major Surgery Maria Ángeles Jiménez-Sousa 1,*, Alejandra Fadrique 2, Pilar Liu 2, Amanda Fernández-Rodríguez 1, Mario Lorenzo-López 2, Esther Gómez-Sánchez 2, Alicia Gómez-Sanz 1, María Heredia-Rodríguez 2, Estefanía Gómez-Pesquera 2, Isidoro Martínez 1, Eduardo Tamayo 2,† and Salvador Resino 1,*,† 1Unidad de Infección Viral e Inmunidad, Centro Nacional de Microbiología, Instituto de Salud Carlos III, 28220 Majadahonda, Spain; [email protected] (A.F.-R.); [email protected] (A.G.-S.); [email protected] (I.M.) 2Departamento de Anestesiología y Reanimación, Hospital Clínico Universitario, 47005 Valladolid, Spain; [email protected] (A.F.); [email protected] (P.L.); mariolor[email protected] (M.L.-L.); [email protected] (E.G.-S.); [email protected] (M.H.-R.); [email protected] (E.G.-P.); [email protected] (E.T.) *Correspondence: [email protected] (M.Á.J.-S.); [email protected] (S.R.); Tel.: +34-918-223-266 (M.Á.J.-S. & S.R.) † These authors contributed equally to this study. Received: 28 January 2019; Accepted: 18 February 2019; Published: 26 February 2019   Abstract: Background: In many immune-related diseases, inflammatory responses and several clinical outcomes are related to increased NFκ B activity. We aimed to evaluate whether SNPs related to the NFκ B signaling pathway are associated with higher susceptibility to infection, septic shock, and septic-shock-related death in European patients who underwent major surgery. Methods: We performed a case-control study on 184 patients with septic shock and 212 with systemic inflammatory response syndrome, and a longitudinal substudy on septic shock patients. Thirty-three SNPs within genes belonging to or regulating the NFκ B signaling pathway were genotyped by Agena Bioscience’s MassARRAY platform. Results: No significant results were found for susceptibility to infection and septic shock in the multivariate analysis after adjusting for multiple comparisons. Regarding septic-shock-related death, patients with TNFAIP3 rs6920220 AA, TNIP1 rs73272842 AA, TNIP1 rs3792783 GG, and TNIP1 rs7708392 CC genotypes had the highest risk of septic-shock-related death in the first 28 and 90 days. Also, the MyD88 rs7744 GG genotype was associated with a higher risk of death during the first 90 days. Haplotype analysis shows us that patients with the TNIP1 GAG haplotype (composed of rs73272842, rs3792783, and rs7708392) had a lower risk of death in the first 28 days and the TNIP1 AGC haplotype was associated with a higher risk of death in the first 90 days. Conclusions: The SNPs in the genes TNFAIP3,TNIP1, and MyD88 were linked to the risk of septic-shock-related death in patients who underwent major surgery. Keywords: TNFAIP3;TNIP1;MyD88; SNPs; septic shock; survival; major surgery 1. Introduction Sepsis is a life-threatening organ dysfunction that results from a dysregulated host response to infection [ 1 ]. Sepsis is the leading cause of admission to intensive care units (ICUs) and death in the critically ill population worldwide [ 2 ]. Septic shock is the most severe stage of sepsis and causes a substantial increase in mortality due to severe cellular and metabolic abnormalities [ 1 ]. The number of patients with sepsis and septic shock is growing worldwide, probably due to the increased number of J. Clin. Med. 2019,8, 283; doi:10.3390/jcm8030283 www.mdpi.com/journal/jcm J. Clin. Med. 2019,8, 283 2 of 15 elderly patients who suffer from more comorbidities [ 3 , 4 ]. The proportion of patients with sepsis who die has decreased during the last two decades [ 3 , 5 ], but sepsis-related mortality remains unacceptably high and constitutes a substantial cost for healthcare systems [ 3 , 6 ]. Identifying predictors of sepsis morbidity and mortality is a priority to provide adequate management of patients [7]. Sepsis is related to excessive inflammation that may result in a dysfunction of the immune response and tissue damage that promote organ dysfunction and even multiorgan failure [ 8 , 9 ]. The role of transcription factor nuclear factor-kappaB (NFκ B) in the development of organ injury and death during sepsis is widely known [ 10 ]. NFκ B also plays a central role in sepsis through its ability to modulate the expression of a large number of genes that control innate immunity, inflammation, cellular stress response, cell proliferation, and survival. As a result, activation of NFκ B promotes the development of inflammation, cell apoptosis, and endothelial damage—three key factors in the development of septic shock [11]. NFκ B is activated by many different stimuli, including microbial pathogens, cytokines, and stress, among others, through canonical or noncanonical cascades [ 11 ]. The canonical pathway is triggered by several receptors such as toll-like receptors (TLRs), NOD-like receptors (NLRs), RIG-I-like receptors (RLRs), and cytokine receptors, among others [ 12 ]. In the NFκ B signaling cascade, many proteins are involved, such as interleukin-1 receptor-associated kinases (IRAKs), toll-interacting protein (TOLLIP), and myeloid differentiation primary response 88 (MyD88), as well as microRNAs, such as the 146a (miR-146a) [ 13 ]. In quiescent cells, NFκ B is inactive due to binding to its inhibitory protein (IkB) in a protein complex located in the cytoplasm. In the septic process, immune stimuli trigger the I κ B polyubiquitination and its subsequent proteasomal degradation, releasing NFκ B, which translocates to the nucleus and activates the expression of multiple target genes [ 11 ]. The regulation of the NFκ B signaling pathway is an essential step for controlling excessive immune response and tissue injury. One of the genes involved in this pathway is the tumor necrosis factor alpha-induced protein 3 (TNFAIP3), which plays a crucial role in the negative regulation of NFκ B signaling by its dual function as both a deubiquitinase and an ubiquitin ligase [ 12 ]. TNFAIP3 may collaborate with other proteins to regulate the NFκ B pathway, such as the TNFAIP3-interacting protein 1 (TNIP1) and TNF receptor-associated factor 6 (TRAF6) [13]. In patients with sepsis, growing evidence suggests that single nucleotide polymorphisms (SNPs) are critical determinants of interindividual differences both in inflammatory responses and in clinical outcomes [ 14 ]. However, there is scarce information about the role of NF-kB-signaling-pathway-related SNPs. Polymorphisms at TLR genes have been the most studied [ 14 ]. Furthermore, there are some previous studies that have found significant associations of SNPs at TRAF6 [ 15 , 16 ], MIR146A [ 17 ], IRAK-1 [ 18 – 20 ], IRAK-4 [ 20 , 21 ], MyD88 [ 20 ], and TOLLIP [ 22 ] genes with sepsis. Finally, several studies have demonstrated the association of both TNFAIP3 and TNIP1 SNPs with multiple chronic inflammatory diseases [ 13 , 23 ], but there has not been any study analyzing their relationship with sepsis. In this study, we aimed to evaluate whether SNPs in several NFκ B-signaling-pathway-related genes are associated with susceptibility to infection, septic shock, and septic-shock-related death in European patients who underwent major abdominal or cardiac surgery. 2. Patients and Methods 2.1. Patients We performed a case-control study on 396 patients who underwent major surgery (cardiac or abdominal) from the Hospital Clínico Universitario of Valladolid (Spain), between April 2008 and November 2012: (a) 184 patients who underwent major surgery and developed an infection (positive culture) and a subsequent septic shock (Septic Shock group); (b) 212 patients, with age and gender similar to the septic shock patients, who underwent major surgery and did not develop sepsis, but who did develop a systemic inflammatory response syndrome (SIRS group - control group), which is a frequent condition after major surgery. Those patients who did not have SIRS or septic J. Clin. Med. 2019,8, 283 3 of 15 shock were excluded. Furthermore, we also analyzed the survival in patients with septic shock, using two censoring points (28 and 90 days). The study was conducted following the ethical requirements established by the Declaration of Helsinki. The Ethics Committee of Instituto de Salud Carlos III (Majadahonda) and Hospital Clínico Universitario (Valladolid) approved the study. All participants provided written informed consent. When a patient was unable to sign, the consent was signed by a family member or legal representative of the patient. 2.2. Clinical Data Patients’ epidemiological and clinical data were collected from medical records. All patients underwent a major surgery, which was defined as a surgical procedure under general anesthesia and respiratory assistance. All heart surgeries involved cardiopulmonary bypass. The indication for emergency surgery included pathologies such as intestinal perforation, aortic dissection, heart disease due to stenosis of the trunk of the left coronary artery, and postoperative bleeding. Sequential Organ Failure Assessment (SOFA score [ 24 ]) and Acute Physiology and Chronic Health Evaluation (APACHE II score [ 25 ]) for assessing the severity of sepsis were calculated within the first 24 h after septic shock diagnosis. In this study, SIRS was considered as a clinical response to a noninfectious insult, since SIRS related to infection was excluded. The SIRS diagnosis was made during the first 24 h postsurgery. Septic shock was defined as a state of acute circulatory failure characterized by persistent arterial hypotension unexplained by other causes other than infection. Hypotension was defined by a systolic blood pressure below 90 mmHg, a mean arterial pressure <60 mmHg, or a reduction in systolic blood pressure of >40 mmHg from baseline, despite adequate volume resuscitation, in the absence of other causes for hypotension. The diagnosis of septic shock was made during the entire follow-up time postsurgery. Inotropic agents were administered early as recommended by the Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock. All patients were treated with noradrenaline, and in some cases, adrenalin and dobutamine were also administered. Both diagnoses (SIRS and septic shock) were established according to the criteria laid down by the SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference (Sepsis-2) [26]. Antibiotic therapy for sepsis was based on our prior experience in identifying the most common bacterial pathogens associated with sepsis in our medical ICU, according to international guidelines [ 27 ]. Antibiotic administration included initial empirical treatment of methicillin-resistant Staphylococcus aureus with linezolid or teicoplanin and treatment of Pseudomonas aeruginosa with at least one of the following antibiotics: imipenem, cefepime, or piperacillin/tazobactam in association with amikacin or ciprofloxacin. 2.3. SNP Selection We selected 33 SNPs via a literature search in PubMed of genes involved in the NFκ B signaling pathway and that were previously related to chronic inflammatory diseases. The selected polymorphisms are located at the following genes: TNFAIP3,IRAK1,IRAK2,IRAK4,MIR146A,MyD88, TLR1,TLR4,TNIP1,TOLLIP, and TRAF6 (Supplemental Table S1). 2.4. DNA Genotyping Total DNA from peripheral blood was extracted using the High Pure PCR Template Preparation kit (Roche Diagnostics GmbH, Mannheim, Germany). Next, DNA samples were genotyped at the Spanish National Genotyping Center (CeGen; http://www.cegen.org) by the Agena Bioscience’s MassARRAY platform (San Diego, CA, USA) using the iPLEX®Gold assay design system. J. Clin. Med. 2019,8, 283 4 of 15 2.5. Outcome Variables Two main outcome variables were analyzed: (1) susceptibility to infection, septic shock, and septic-shock-related death (case-control study); (2) mortality after diagnosis of septic shock (longitudinal substudy). For survival analysis, we used two censoring points: (1) 28-day mortality (early mortality mainly related to infection [ 28 ]), which is used as the primary endpoint for severe sepsis in most clinical trials of new therapeutic approaches; (2) 90-day mortality (late mortality mainly related to causes other than sepsis [ 28 ]), which is the other primary endpoint used to evaluate excess mortality beyond the first 28 days. 2.6. Statistical Analysis For the description of the study population, the differences between groups were calculated by the Mann–Whitney U test for continuous variables and the chi-squared/Fisher’s exact test for categorical variables. The NFκ B-pathway-related SNPs were analyzed for deviation from the Hardy–Weinberg equilibrium (HWE), where p< 0.001 was considered to be statistically significant. For SNPs in the X chromosome, HWE was calculated excluding male genotypes. Regarding the genetic association study, analyses were carried out for dominant, recessive, overdominant, codominant, and additive models, selecting the inheritance model that best fit our data. Firstly, in the case-control study, logistic regression was performed to investigate the association between SNPs involved in the NFκ B signaling pathway and the development of septic shock (Septic Shock group versus SIRS group). Multivariate logistic regression analysis was used to adjust for the main covariates selected by a stepwise method (forward): age, gender, smoking, drinking, comorbidities (obesity, diabetes, hypertension, chronic kidney disease, heart disease, chronic obstructive pulmonary disease (COPD), neoplasia, and liver disease), SOFA score, and type of surgery (emergency or scheduled; cardiac or abdominal). Secondly, in the longitudinal substudy, a survival analysis was used to evaluate mortality in the first 28 and 90 days in septic shock patients (Septic Shock group). Survival probabilities were estimated by the Kaplan–Meier product-limit method, and groups were compared using the log-rank test. In order to exclude spurious associations, multiple testing correction was carried out by the false discovery rate (FDR) with the Benjamini and Hochberg procedure. Only SNPs with a p-value less than 0.1 from the Kaplan–Meier method (after an FDR adjustment) were analyzed in the Cox regression analysis. All multivariate Cox regression tests were adjusted by the most significant covariates, which were selected by a stepwise method (forward), from the following list: age, gender, antibiotic treatment, peritonitis, hypertension, lactate, comorbidities (obesity, diabetes, chronic kidney disease, heart disease, COPD, neoplasia, and liver disease), SOFA score, and type of surgery (emergency or scheduled; cardiac or abdominal). Next, we analyzed the diagnostic performance of SNPs for predicting septic-shock-related death using the area under the receiver-operating characteristic (AUROC) curve. In this analysis, only the five most significant clinical variables for each time point (28 and 90 days) and the most significant SNPs resulting from the Cox regression analysis were used. The following criteria for levels of accuracy were taken into account: >0.90–1 = excellent, >0.80–0.90 = good , >0.70–0.80 = fair, and >0.60–0.70 = poor. Delong test was carried out to compare the two AUROC curves. Additionally, we analyzed the diagnostic accuracy of adding SNPs to the model by calculating sensitivity, specificity, and positive and negative predictive value. Several cut-offs were used: (a) 95% of sensitivity; (b) maximum test sensitivity plus specificity; (c) 95% of specificity. All statistical analyses were performed using the R statistical package version 3.4.3 (R Foundation for Statistical Computing, Vienna, Austria). All p-values were considered significant with values of p< 0.05 (two-tailed). Besides, linkage disequilibrium (LD) was computed by Haploview 4.2 software, and haplotype-based association testing was performed using PLINK software. J. Clin. Med. 2019,8, 283 5 of 15 3. Results 3.1. Clinical Characteristics of the Study Population Table 1shows demographic and clinical characteristics of 396 patients who underwent cardiac or abdominal surgery and developed septic shock (n= 184, case group) or SIRS (n= 212, control group). The Septic Shock group had higher percentages of patients with chronic kidney disease, abdominal surgery, emergency surgery, and higher values of SOFA and APACHE II score, while the SIRS group had higher percentages of patients with heart disease, cancer, and cardiac surgery (p< 0.05). Table 1. Baseline characteristics of patients with systemic inflammatory response syndrome (control group) and patients with septic shock who underwent major surgery. Characteristics SIRS Group Septic Shock Group p-Value * No. patients 212 184 – Gender (male) 136 (64.1%) 121 (65.8%) 0.819 Age (years) 72 (65–78) 73 (63–79) 0.456 Prior or pre-existing conditions Smoker 27 (12.7%) 33 (17.9%) 0.207 Alcoholism 6 (2.8%) 11 (6.0%) 0.203 Obesity 26 (12.3%) 29 (15.8%) 0.400 Diabetes 43 (20.3%) 25 (13.6%) 0.104 Heart disease 123 (58.0%) 83 (45.1%) 0.012 COPD 30 (14.1%) 32 (17.4%) 0.478 Hypertension 127 (59.9%) 102 (55.4%) 0.463 Chronic kidney disease 13 (6.1%) 29 (15.8%) 0.004 Cancer 76 (35.8%) 43 (23.4%) 0.008 Liver disease 3 (1.4%) 7 (3.8%) 0.199 Surgery Cardiac (versus abdominal) 117 (55.2%) 76 (41.3%) 0.006 Emergency (versus scheduled) 19 (9.0%) 116 (63.0%) <0.001 Severity indexes SOFA score 3 (3–4) 9 (7–10) <0.001 APACHE II score 9 (8–10) 16 (13–19) <0.001 Values are expressed as median (percentile 25–percentile 75) and absolute count (percentage). * p-values were calculated by Chi-square test or Fisher’s exact test for categorical variables and Mann–Whitney test for continuous variables. Significant differences are shown in bold. COPD, chronic obstructive pulmonary disease; SIRS, systemic inflammatory response syndrome; SOFA, sequential organ failure assessment; APACHE, acute physiology and chronic health evaluation. The baseline characteristics of the 184 septic shock patients are shown in Table 2. Overall, the median age was 73 years, 65.8% were males, and more than 50% of patients had abdominal or emergency surgeries, and infection by gram-negative bacteria. When the population was stratified by exitus versus nonexitus, the patients who died were older, had higher lactate and procalcitonin values and SOFA and APACHE II scores, more reduced period of time from surgery to septic shock diagnosis, and higher percentages of chronic kidney disease and emergency surgery (p< 0.05). Patients who underwent cardiac surgery or elective surgery had longer period of time from surgery to septic shock diagnosis than patients who underwent abdominal surgery or emergency surgery (Supplemental Table S2). All septic shock patients had an infection that was microbiologically confirmed. Eighty-seven percent had an adequate initial empirical treatment according to the antibiogram data. J. Clin. Med. 2019,8, 283 6 of 15 Table 2. Summary of epidemiological and clinical characteristics of septic shock patients who underwent major surgery. Characteristics All Patients Nonexitus Exitus p-Value * No. patients 184 80 104 – Gender (male) 121 (65.8%) 56 (70.0%) 65 (62.5%) 0.288 Age (years) 73 (63–79) 68 (58–77) 77 (69–81) <0.001 Pre-existing conditions Smoker 33 (17.9%) 14 (17.5%) 19 (18.3%) 0.670 Alcoholism 11 (6.0%) 4 (5.0%) 7 (6.7%) 0.597 Obesity 29 (15.8%) 12 (15.0%) 17 (16.3%) 0.654 Diabetes 25 (13.6%) 11 (13.8%) 14 (13.5%) 0.679 Heart disease 83 (45.1%) 33 (41.3%) 50 (48.1%) 0.419 COPD 32 (17.4%) 14 (17.5%) 18 (17.3%) 0.679 Hypertension 102 (55.4%) 43 (53.8%) 59 (56.7%) 0.606 Chronic kidney disease 29 (15.8%) 4 (5.0%) 25 (24.0%) 0.001 Cancer 43 (23.4%) 14 (17.5%) 29 (27.9%) 0.099 Liver disease 7 (3.8%) 3 (3.8%) 4 (3.8%) 0.679 Surgery Cardiac (versus abdominal) 76 (41.3%) 38 (47.5%) 38 (36.5%) 0.134 Emergency (versus scheduled) 116 (63.0%) 40 (50.0%) 28 (73.1%) 0.001 Severity Time to septic shock (days) 1(0–4) 2 (1–5) 1 (0–4) 0.047 Late septic shock (>4 days) 41 (22.3%) 20 (25.0%) 21 (20.2%) 0.437 Lactate (mg/dL) 26.0 (16.0–42.2) 21.0 (14.0–30.0) 30.0 (18.0–49.0) <0.001 Lactate (>18 mg/dL or 2 mmol/L) 122 (66.3%) 45 (56.3%) 77 (74%) 0.040 White Blood Cell (×103cells/mm3)14.9 (9.4–20.3) 16.1 (10.5–20.6) 13.7 (9.1–20.3) 0.358 C-Reactive protein (mg/L) 241.0 (130.2–307.2) 236.4 (123.8–307.7) 241.5 (133.5–306.9) 0.951 Procalcitonin (ng/mL) 5.0 (1.7–20.2) 3.5 (1.2–17.2) 5.9 (2.0–32.8) 0.033 SOFA score 9 (7–10) 8 (7–10) 9 (8–11) 0.004 APACHE II score 16 (13–19) 15 (12–18) 18 (14–21) <0.001 Exitus At 7 days 23 (12.5%) – 23 (22.1%) – At 28 days 66 (35.9%) – 66 (63.5%) – At 90 days 102 (55.4%) – 102 (98.1%) – Microorganism isolated Gram-positive 94 (51.1%) 40 (50.0%) 54 (51.9%) 0.796 Gram-negative 98 (53.3%) 47 (58.8%) 51 (49.0%) 0.191 Fungus 38 (20.7%) 13 (16.3%) 25 (24.0%) 0.196 Site of infection Catheter bacteraemia 66 (35.9%) 35 (43.8%) 31 (29.8%) 0.051 Surgical site infection 47 (25.5%) 20 (25.0%) 27 (26.0%) 0.882 Urinary tract infection 19 (10.3%) 8 (10.0%) 11 (10.6%) 0.899 Endocarditis 10 (5.4%) 4 (5.0%) 6 (5.8%) 0.820 Peritonitis 83 (45.1%) 30 (37.5%) 53 (51.0%) 0.069 Pneumonia 90 (48.9%) 42 (52.5%) 48 (46.2%) 0.393 Adequate initial empirical treatment 161 (87.5%) 69 (86.3%) 92 (88.5%) 0.653 Values are expressed as median (percentile 25–percentile 75) and absolute count (percentage). * p-values were calculated by Chi-square test or Fisher’s exact test for categorical variables and Mann–Whitney test for continuous variables. Significant differences are shown in bold. Note that patients may have had more than one organism cultured. COPD, chronic obstructive pulmonary disease; SOFA, sequential organ failure assessment; APACHE, acute physiology and chronic health evaluation. 3.2. Characteristics of NF-κB-Signaling-Pathway-Related SNPs Most of the SNPs had low/medium LD among them with a maximum of r 2 = 0.83 (Figure 1). Fourteen out of 33 SNPs were located in an intronic region, nine were in an exonic region, and six in an upstream and four in the downstream region of their respective genes (Supplemental Table S1). All SNPs had a minor allelic frequency higher than 10%, except TNFAIP3 rs2230926; TNIP1 rs17728338, rs6579837, and rs5743867; and TRAF6 rs16928973. Similarly, most SNPs fulfilled the HWE (p> 0.001), J. Clin. Med. 2019,8, 283 7 of 15 except IRAK1 rs1059701, rs1059703; and IRAK4 rs1461567. The genotypic frequencies were similar between the Septic Shock group and the SIRS group (Supplemental Table S1). J. Clin. Med. 2019, 8, x FOR PEER REVIEW 7 of 15 frequencies were similar between the Septic Shock group and the SIRS group (Supplemental Table S1). Figure 1. Pairwise linkage disequilibrium (LD) patterns for polymorphisms involved in the NF-κB pathway. Each diagonal represents a different SNP, with each square representing the coefficient of linkage disequilibrium (D’) or r2 data for a pairwise comparison between two SNPs. 3.3. Association between NF-κB-Signaling-Pathway-Related SNPs and Susceptibility to Infection and Septic Shock Several SNPs were associated with susceptibility to infection and septic shock, one in the univariate analysis (rs6853 at MyD88 gene) and seven with the multivariate model (rs610604, rs6922466, rs7753394, and rs583522 at TNFAIP3 gene; rs6579837, rs73272842, rs3792783 at TNIP1 gene). However, none of them remained significant after adjusting for multiple comparisons (Supplemental Table S3). 3.4. Association between NF-κB-Signaling-Pathway-Related SNPs and Death in Septic Shock Patients Regarding death within the first 28 days, 10 SNPs were significantly associated with death, of which only 4 remained significant after correction for multiple tests: rs6920220 in TNFAIP3 gene (p = 0.007) and rs73272842, rs3792783, and rs7708392 in TNIP1 gene (p = 0.025, p = 0.007, and p = 0.025, respectively). Concerning death within the first 90 days, nine SNPs showed significant associations, but only TNFAIP3 rs6920220 (p = 0.007), MyD88 rs7744 (p = 0.043), and both rs73272842 and rs3792783 in TNIP1 gene (p = 0.033 and 0.007, respectively) remained significant after correction for multiple tests. The survival probabilities are shown in Table 3 (a full description of all 33 SNPs in Supplemental Table S4). Table 4 shows the risk of dying in the first 28 and 90 days after a septic shock diagnosis. The TNFAIP3 rs6920220 AA, TNIP1 rs73272842 AA, TNIP1 rs3792783 GG, and TNIP1 rs7708392 CC genotypes were significantly associated with a higher death risk in the first 28 days (adjusted hazard ratio [aHR] = 8.37 [p = 9.57 × 10−5], aHR = 10.84 [p = 8.89 × 10−5], aHR = 10.06 [p = 2.61 × 10−5], and aHR = 3.58 [p = 0.001], respectively) and 90 days (aHR = 7.56 [p = 1.96 × 10−4], aHR = 5.68 [p = 0.005], aHR = 5.10 [p = 0.004], and aHR = 2.33 [p = 0.025], respectively) than TNFAIP3 rs6920220 GG/GA, and TNIP1 rs73272842 GG/GA, rs3792783 GA/AA, and rs7708392 GG/GC genotypes after correction for Figure 1. Pairwise linkage disequilibrium (LD) patterns for polymorphisms involved in the NFκ B pathway. Each diagonal represents a different SNP, with each square representing the coefficient of linkage disequilibrium (D’) or r2data for a pairwise comparison between two SNPs. 3.3. Association between NFκ B-Signaling-Pathway-Related SNPs and Susceptibility to Infection and Septic Shock Several SNPs were associated with susceptibility to infection and septic shock, one in the univariate analysis (rs6853 at MyD88 gene) and seven with the multivariate model (rs610604, rs6922466, rs7753394, and rs583522 at TNFAIP3 gene; rs6579837, rs73272842, rs3792783 at TNIP1 gene). However, none of them remained significant after adjusting for multiple comparisons (Supplemental Table S3). 3.4. Association between NF-κB-Signaling-Pathway-Related SNPs and Death in Septic Shock Patients Regarding death within the first 28 days, 10 SNPs were significantly associated with death, of which only 4 remained significant after correction for multiple tests: rs6920220 in TNFAIP3 gene ( p= 0.007 ) and rs73272842, rs3792783, and rs7708392 in TNIP1 gene (p= 0.025, p= 0.007, and p= 0.025, respectively). Concerning death within the first 90 days, nine SNPs showed significant associations, but only TNFAIP3 rs6920220 (p= 0.007), MyD88 rs7744 (p= 0.043), and both rs73272842 and rs3792783 in TNIP1 gene (p= 0.033 and 0.007, respectively) remained significant after correction for multiple tests. The survival probabilities are shown in Table 3(a full description of all 33 SNPs in Supplemental Table S4). Table 4shows the risk of dying in the first 28 and 90 days after a septic shock diagnosis. The TNFAIP3 rs6920220 AA, TNIP1 rs73272842 AA, TNIP1 rs3792783 GG, and TNIP1 rs7708392 CC genotypes were significantly associated with a higher death risk in the first 28 days (adjusted hazard ratio [aHR] = 8.37 [p= 9.57 × 10 −5 ], aHR = 10.84 [p= 8.89 × 10 −5 ], aHR = 10.06 [p= 2.61 × 10 −5 ], and aHR = 3.58 [p= 0.001], respectively) and 90 days (aHR = 7.56 [p= 1.96 × 10 −4 ], aHR = 5.68 [ p= 0.005 ], aHR = 5.10 [p= 0.004], and aHR = 2.33 [p= 0.025], respectively) than TNFAIP3 rs6920220 GG/GA, and TNIP1 rs73272842 GG/GA, rs3792783 GA/AA, and rs7708392 GG/GC genotypes after correction for multiple tests. Moreover, MyD88 rs7744 GG genotype was associated with a higher risk of death during the first 90 days (aHR = 4.32 [p= 0.030]). J. Clin. Med. 2019,8, 283 8 of 15 Table 3. Survival probabilities at 28 and 90 days (Kaplan–Meier product-limit method) for SNPs related to the NFκ B signaling pathway in septic shock patients who underwent major abdominal or cardiac surgery. Gene SNPs Genotype n28 Days 90 Days Deaths p*p** Deaths p*p** MyD88 rs7744 AA/AG 178 63 (35.4%) 0.026 0.123 98 (55.1%) 0.005 0.043 GG 4 3 (75.0%) 4 (100%) TNFAIP3 rs6920220 GG/GA 176 61 (34.7%) 2.28 ×10−40.007 97 (55.1%) 2.28 ×10−40.007 AA 7 5 (71.4%) 5 (71.4%) TNIP1 rs73272842 GG/GA 178 62 (34.8%) 0.003 0.025 98 (55.1%) 0.003 0.033 AA 5 4 (80.0%) 4 (80%) rs3792783 AA/AG 177 61 (34.5%) 4.40 ×10−40.007 97 (54.8%) 4.40 ×10−40.007 GG 6 5 (83.3%) 5 (83.3%) rs7708392 GG/GC 169 57 (33.7%) 0.003 0.025 92 (54.4%) 0.013 0.083 CC 14 9 (64.3%) 10 (71.4%) Values are expressed as absolute count and percentage. * p-values were calculated by log-rank tests; ** p-values were corrected for multiple testing using the false discovery rate (FDR) with Benjamini and Hochberg procedure. Significant differences are shown in bold. SNPs, single nucleotide polymorphisms; NFκ B, nuclear factor kappa-light-chain-enhancer of activated B cells; TNFAIP3, TNF alpha-induced protein 3; MyD88, innate immune signal transduction adaptor; TNIP1, TNFAIP3 interacting protein 1. Table 4. Risk of death in septic shock patients who underwent major cardiac or abdominal surgery according to SNPs located in genes related to the NF-κB signaling pathway. Genes SNPs Day 28 Day 90 aHR (95% CI) p*p** aHR (95% CI) p*p** MyD88 rs7744 (GG) 1.64 (0.36; 7.56) 0.525 0.525 4.32 (1.15; 16.23) 0.030 0.030 TNFAIP3 rs6920220 (AA) 8.37 (2.97; 23.55) 5.74 ×10−59.57 ×10−57.56 (2.88; 19.84) 3.92 ×10−51.96 ×10−4 TNIP1 rs73272842 (AA) 10.84 (3.50; 33.55) 3.56 ×10−58.89 ×10−55.68 (1.79; 18.06) 0.003 0.005 rs3792783 (GG) 10.06 (3.73; 27.17) 5.22 ×10−62.61 ×10−55.10 (1.87; 13.87) 0.001 0.004 rs7708392 (CC) 3.58 (1.66; 7.72) 0.001 0.001 2.33 (1.14; 4.76) 0.020 0.025 Values are expressed as hazard ratio and 95% confidence interval. * p-values were calculated by Cox regression tests; ** p-values were corrected for multiple testing using the false discovery rate (FDR) with Benjamini and Hochberg procedure. Statistically significant differences are shown in bold. aHR, adjusted hazard ratio; 95% CI, 95% confidence interval; SNPs, single nucleotide polymorphisms; NFκ B, nuclear factor kappa-light-chain-enhancer of activated B cells; TNFAIP3, TNF alpha-induced protein 3; MyD88, innate immune signal transduction adaptor; TNIP1, TNFAIP3 interacting protein 1. Three major haplotypes for TNIP1 SNPs (composed of rs73272842, rs3792783, and rs7708392) were also related to death in septic shock patients (Table 5). The GAG haplotype was associated with a lower risk of death in the first 28 days (aOR = 0.53 [p= 0.024]). The AGC haplotype was associated with a higher risk of death in the first 90 days after septic shock onset (aOR = 2.09 [p= 0.031]). Table 5. Association between TNIP1 haplotypes and death in septic shock patients. TNIP1 Haplotypes Association Exitus rs73272842 rs3792783 rs7708392 Freq. aOR (95% CI) p-Value 28 days G A G 0.745 0.53 (0.30; 0.92) 0.024 A G C 0.122 1.90 (0.96; 3.79) 0.069 G A C 0.111 1.59 (0.75; 3.37) 0.227 90 days G A G 0.745 0.62 (0.36; 1.05) 0.071 A G C 0.122 2.09 (1.05; 4.17) 0.031 G A C 0.111 1.08 (0.53; 2.20) 0.840 Values are expressed as odds ratio and 95% confidence interval. p-values were calculated by multivariate logistic regression adjusted by the most important clinical and epidemiological characteristics. Only haplotypes with frequency >0.1 are shown. Significant differences are shown in bold. aOR adjusted odds ratio; 95% CI, 95% confidence interval; SNPs, single nucleotide polymorphisms; NFκ B, nuclear factor kappa-light-chain-enhancer of activated B cells; TNIP1, TNFAIP3 (TNF alpha-induced protein 3) interacting protein 1. J. Clin. Med. 2019,8, 283 9 of 15 3.5. Diagnostic Performance of NFκ B-Signaling-Pathway-Related SNPs for Prediction of Septic Shock-Related Death We evaluated the diagnostic accuracy for predicting septic-shock-related death from a multivariate model formed by the five most significant clinical variables and the SNPs selected in the previous analysis (MyD88 rs7744; TNFAIP3 rs6920220; and TNIP1 rs73272842, rs3792783, and rs7708392 SNPs) (Figure 2). The five most significant clinical variables for each time point were: (i) the first 28 days: lactate, peritonitis, heart disease, chronic kidney disease, elective surgery; (ii) the first 90 days: age, lactate, heart disease, chronic kidney disease, elective surgery. The SNPs that remained in both models (28 and 90 days) after stepwise selection were TNFAIP3 rs6920220 and TNIP1 rs3792783. J. Clin. Med. 2019, 8, x FOR PEER REVIEW 9 of 15 Table 5. Association between TNIP1 haplotypes and death in septic shock patients. TNIP1 Haplotypes Association Exitus rs73272842 rs3792783 rs7708392 Freq. aOR (95% CI) p-Value 28 days G A G 0.745 0.53 (0.30; 0.92) 0.024 A G C 0.122 1.90 (0.96; 3.79) 0.069 G A C 0.111 1.59 (0.75; 3.37) 0.227 90 days G A G 0.745 0.62 (0.36; 1.05) 0.071 A G C 0.122 2.09 (1.05; 4.17) 0.031 G A C 0.111 1.08 (0.53; 2.20) 0.840 Values are expressed as odds ratio and 95% confidence interval. p-values were calculated by multivariate logistic regression adjusted by the most important clinical and epidemiological characteristics. Only haplotypes with frequency >0.1 are shown. Significant differences are shown in bold. aOR adjusted odds ratio; 95% CI, 95% confidence interval; SNPs, single nucleotide polymorphisms; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; TNIP1, TNFAIP3 (TNF alpha-induced protein 3) interacting protein 1. 3.5. Diagnostic Performance of NF-κB-Signaling-Pathway-Related SNPs for Prediction of Septic Shock-Related Death We evaluated the diagnostic accuracy for predicting septic-shock-related death from a multivariate model formed by the five most significant clinical variables and the SNPs selected in the previous analysis (MyD88 rs7744; TNFAIP3 rs6920220; and TNIP1 rs73272842, rs3792783, and rs7708392 SNPs) (Figure 2). The five most significant clinical variables for each time point were: i) the first 28 days: lactate, peritonitis, heart disease, chronic kidney disease, elective surgery; ii) the first 90 days: age, lactate, heart disease, chronic kidney disease, elective surgery. The SNPs that remained in both models (28 and 90 days) after stepwise selection were TNFAIP3 rs6920220 and TNIP1 rs3792783. Figure 2. Predictive value of polymorphisms related to the NF-κB pathway in combination with clinical variables. The five most significant clinical variables for each time point were: i) first 28 days: lactate, peritonitis, heart disease, chronic kidney disease, elective surgery; ii) first 90 days: age, lactate, heart disease, chronic kidney disease, elective surgery. The polymorphisms that remained in both models (28 and 90 days) after stepwise were rs6920220 and rs3792783. Seven patients were excluded due to missing data for any of the covariates included in the model. Figure 2. Predictive value of polymorphisms related to the NFκ B pathway in combination with clinical variables. The five most significant clinical variables for each time point were: (i) first 28 days: lactate, peritonitis, heart disease, chronic kidney disease, elective surgery; (ii) first 90 days: age, lactate, heart disease, chronic kidney disease, elective surgery. The polymorphisms that remained in both models (28 and 90 days) after stepwise were rs6920220 and rs3792783. Seven patients were excluded due to missing data for any of the covariates included in the model. The diagnostic performance of Cox regression models with only clinical variables was higher than 0.75, both for the first 28 days and the first 90 days (AUROC = 0.776 and AUROC = 0.772, respectively). When the SNPs were added to the Cox regression models, the diagnostic performance increased significantly only for the first 28 days (AUROC = 0.819; p= 0.033) (Figure 2). Additionally, the model with clinical variables and SNPs had higher sensitivity and specificity values than the model including only clinical variables (Supplemental Table S5). 4. Discussion In this study, we analyzed the impact of SNPs located in genes involved in the NFκ B signaling pathway on the clinical progression of septic shock. Our main findings were: (1) a possible role of eight SNPs located in TNFAIP3,TNIP1, and MyD88 genes on susceptibility to infection and septic shock, but this association was lost after adjusting for multiple comparisons; (2) 5 of the 33 SNPs analyzed were associated with a higher risk of death in septic shock patients (TNFAIP3 [rs6920220], TNIP1 [rs73272842, rs3792783, rs7708392], and MyD88 [rs7744]). To our knowledge, our study is the