Low-grade inflammation in the prognosis of patients undergoing coronary artery bypass grafting: the value of neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF15)

Low-grade inflammation in the prognosis of patients undergoing coronary artery bypass grafting: the value of neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF15) | 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 Research Article Low-grade inflammation in the prognosis of patients undergoing coronary artery bypass grafting: the value of neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF15) Alla A. Garganeeva, Elena A. Kuzheleva, Olga V. Tukish, Alexey N. Repin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3397585/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Predicting major adverse cardiovascular events (MACEs) after coronary interventions is an urgent and important task. Subclinical inflammation markers are becoming increasingly investigated for this purpose. Aim To determine the role of the neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF-15) in predicting MACE in patients after coronary artery bypass grafting (CABG). Methods This prospective observational study included 80 patients with coronary artery disease who underwent CABG and were followed up for at least 1 year. In a prospective follow-up, a combined endpoint (MACE) had 27.5% (a total of 22 events). Results The values of GDF-15 and NLR were comparable in groups with and without MACE. A ROC analysis showed a low AUC for NLR (AUC = 0.566 (p = 0.363)) and GDF-15 (AUC = 0.621 (p = 0.096)). The value of the product GDF-15*NLR was determined. The median was 3108.05 (2069; 4145) for patients who did not have MACE and 4108.8 (2779.4; 5890.5) for patients with MACE (p = 0.010). This association remained after the introduction of amendments to sex, age, diabetes, and left ventricular ejection fraction. Conclusions The value of the product indicators NLR and GDF-15 is associated with the development of adverse cardiovascular events in patients after CABG. CABG inflammation neutrophil-to-lymphocyte ratio GDF15 MACE Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Coronary artery disease (CAD) is one of the main causes of the development of chronic heart failure (CHF) and cardiovascular mortality [ 1 ]. The pathological effect of CAD on heart function is realized in various ways. First, it is the development of myocardial infarction (IM) with the rapid loss of a section of viable myocardium and constructional dysfunction of the left ventricle (LV). However, even in the absence of MI, chronic coronary failure has a significant effect on the progression of heart dysfunction. Frequent episodes of transient ischemia lead to the development of a state of “stunning” cardiomyocytes, and persistent reduction in coronary blood flow causes myocardial hibernation, which inevitably ends in cardiomyocyte necrosis in the case of ongoing ischemia. There are three strategic approaches to treating patients with CAD: only optimal drug therapy or its combination with surgical (coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI)) revascularization of the myocardium. At the same time, the results of large randomized clinical trials (RCTs) comparing these treatment strategies are often controversial and largely depend on the design of the research conducted. For example, in the STICH (Surgical Treatment for Ischemic Heart Failure) study, 244 cases out of 602 (41%) were recorded in the drug therapy group, and 218 cases out of 610 (36%) were recorded in the CABG group (p = 0.12) during the observation. At the same time, cardiovascular mortality was higher in the drug therapy group only: 201 patients (33%) in the drug therapy group and 168 (28%) in the CABG group (p = 0.05). The development of a combined endpoint, death from any cause or hospitalization for cardiovascular diseases, was also higher in the drug therapy group (411 patients (68%) vs. 351 (58%) in the CABG group (p < 0.001)) [ 2 ]. At the same time, according to the subanalysis of the present study, the successful revascularization of the myocardium led to the improvement of systolic heart function in some patients. This effect depended on the initial value of LV EF and was also associated with its postoperative dynamics [ 3 ]. In an EXCEL study on the safety and effectiveness of everolimus-eluting stents compared to bypass surgery in patients with left main CAD, the major adverse cardiovascular event (MACE) development rate (death, stroke or MI) was approximately 15% in both groups for 3 years of observation (with only 5–7% of patients in the study cohort having stagnant CHF) [ 4 ]. In the recently presented study REVIVED (REVIVED-BCIS2; NCT01920048), including patients with LV EF 35% or less, extensive coronary artery disease amenable to PCI, and demonstrable myocardial viability, patients were randomized for PCI followed by optimal medical therapy (PCI group) or only optimal medical therapy group. A total of 347 people were included in the PCI group, and 353 were included in the optimal medical therapy group. Over a median of 41 months of observation, the average rate of death from any cause or hospitalization for heart failure was 37.2% in the PCI group and 38.0% in the optimal medical therapy group (p = 0.96) [ 5 ]. At the same time, the value of LV EF was the same in both groups after 6 and 12 months. Therefore, PCI revascularization, according to this study, has no advantages in the studied cohort compared to optimal medical therapy. Predicting MACE after high-tech interventions is an urgent and important task for the scientific and medical community. Subclinical inflammation markers are becoming increasingly investigated for this purpose. Today, inflammation is recognized as a universal way for the body to adapt to changing environmental conditions. The modern concept of immunology of cardiovascular homeostasis is based on the so-called “hazard hypothesis” (“danger hypothesis”), proposed by P. Matzinger in 1994 [ 6 ]. According to this theory, there are hazard signals, which have been called molecular patterns associated with damage (DAMPs - damage-associated molecular pattern), that initiate the immune response in the absence of exogenous infectious pathogens [ 7 , 8 ]. Thus, acute or chronic myocardial cell damage is associated with the release of cell degradation products, which are DAMPs [ 9 ]. In turn, the release of proinflammatory cytokines causes the activation of residual tissue macrophages and leads to the recruitment of different populations of circulating immune cells in the heart under the influence of specific chemokine molecules [ 10 ]. Despite the active study of the pathogenesis of low-grade inflammation, it remains to date not fully understood. Low-grade inflammation has been shown to have a negative role in cardiovascular disease [ 11 ]. The development and progression of atherosclerosis and chronic heart failure (CHF) is accompanied by the activation of immune processes [ 12 – 14 ]. However, the lack of success in developing new anti-inflammatory treatments for heart failure may be due to our poor understanding of complex inflammatory systems in chronic cardiovascular disease (CVD). [ 14 ]. Treatment of clinically pronounced coronary artery disease (CAD) with obstructive arteries with myocardial revascularization by coronary artery bypass graft surgery (CABG) is a modern and effective method. However, the prognosis of patients after CABG can be significantly different from the regression of symptoms of coronary and heart failure to cardiovascular death [ 15 ]. During artificial blood circulation, proinflammatory mediators trigger the activation of leukocytes, vascular endothelial cells and platelets. This is manifested by a systemic inflammatory reaction that leads to organ dysfunction affecting the heart, brain, lungs and kidneys and may affect the prognosis of patients. According to the literature, the neutrophil-to-lymphocyte ratio (NLR) predicts the development of postpericardiotomy syndrome (PPS) [ 16 ], atrial fibrillation (AF) [ 17 , 18 ], acute kidney injury (AKI) [ 19 , 20 ], saphenous vein graft failure [ 21 , 22 ], ischemic stroke (IS) [ 23 ] and even early postoperative mortality [ 24 – 26 ] after CABG. Therefore, NLR is a promising marker of prognosis for patients after CABG. Growth differentiation factor 15 (GDF-15) is a member of the transforming growth factor b (TGF-b) cytokine superfamily. Circulating GDF-15 concentrations are increased across a wide spectrum of cardiovascular diseases, including acute and chronic CAD, congestive heart failure, and IS. Growth differentiation factor 15 is also upregulated by other cardiovascular events triggering oxidative stress, including pressure overload and atherosclerosis. Moreover, increased circulating GDF-15 concentrations have been linked to an enhanced risk of future adverse cardiovascular events. An increase in the GDF-15 concentration in CABG may help in predicting the development of postoperative AF [ 27 ] and AKI [ 28 , 29 ] In addition, individual studies have shown that GDF-15 determination can help predict cardiovascular mortality in cardiac patients [ 30 ]. Therefore, the aim of our study was to determine the role of NLP and GDF-15 in predicting MACE in patients with CABG. 2. Materials and Methods The prospective observational study included 80 patients with CAD who underwent CABG in the present hospitalization (2019–2020 inclusion years). The research protocol adhered to the principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (Protocol № 188 of 18.09.2019). Before any procedure, all patients signed a form of voluntary informed consent. The diagnosis of CHF was based on the current clinical guidelines. Multivascular stenosing atherosclerosis of coronary arteries was diagnosed using invasive coronary angiography on the angiographic complex “Cardio-scop-V” and the ACOM. PC of Siemens (Germany) based on clinical indications. Echocardiography was performed with the Philips HD15 Ultrasound system. In addition to clinical laboratory examination (general blood test with leukocyte counts, biochemical blood analysis with estimated glomerular filtration rate (eGFR) according to the formula Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)). The GDF-15 concentration was studied by immunoassay using the “Human GDF-15/MIC1 ELISA” (“BioVendor”, Czech Republic). The result was determined to be in pg/ml. The research was carried out using the equipment of the Center for Collective Use "Medical Genomics" of the Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia. Blood was collected one day before CABG from the cubital vein in the morning on an empty stomach. Further preparation of blood samples for analysis included centrifuging, serum separation, and freezing at -80°C. The analysis was conducted after one blood serum thawing. The noninclusion criteria were refusal to participate in the study, myocardial infarction (MI), stroke during the last 6 months, implanted cardiac rhythm management devices, the need for additional cardiac surgery, except CAGB, the presence of advanced kidney disease (eGFR < 30 ml/min/1.73 m2), and severe related diseases (oncological diseases in the active stage, infiltrating heart diseases, autoimmune diseases, acute infections and exacerbation of chronic somatic diseases). Patients were excluded from the study in cases of death during hospitalization. Patients were monitored for 12 months. The medication prescribed after surgery was fully consistent with current clinical guidelines and was not significantly different in patients with and without developmental adverse cardiovascular events. In prospective follow-up, a combined endpoint (MACE) was recorded, including death from cardiovascular diseases, decompensation of heart failure or the need for intravenous diuretic therapy or doubling the dose of a diuretic, an acute ischemic event requiring unplanned revascularization, stroke, unplanned hospitalization for coronary artery disease and/or CHF (a total of 22 events (27.5%)). The combined endpoint (MACE) included death from cardiovascular disease, heart failure decompensation or the need for intravenous diuretic therapy or doubling the dose of a diuretic, an acute ischemic event requiring unplanned revascularization, stroke, unplanned hospitalization for coronary artery disease and/or CHF. In a prospective follow-up, 27.5% had a combined endpoint (a total of 22 events). Data were analyzed with the use of the programs “IBM SPSS 21” and MS “Excel”. Continuous variables are presented as the median and interquartile range (Me (Q25; Q75), considering the nonnormal distribution parameters. Categorical data are presented in absolute and relative values: n (%). Continuous variables in the independent samples were analyzed using the Mann‒Whitney test. Spearman’s rank coefficient of correlation was used to estimate the correlation relationships. The statistical significance of differences for categorical variables was determined using the χ2 Pearson criterion and two-sided Fisher’s exact test. The influence of the studied factors on the development of MACE was analyzed using logistic regression analysis and receiver operating characteristic (ROC) analysis (calculated area under the curve (AUC)). A value of p < 0.05 was considered statistically significant. 3. Results The main characteristics of the study cohort are presented in Table 1 . Table 1 Clinical characteristics of patients prior to CABG in groups depending on MACE registration Indicator MACE – (n = 58) MACE + (n = 22) р Sex male/female, n (%) 51 (87,9)/7 (12,1) 20 (90,9)/2 (9,1) 1.000 Suffered/in history MI, n (%) 33 (56,9) 17 (77,3) 0,123 DM type 2, n (%) 13 (22,4) 7 (31,8) 0,386 Obesity, n (%) 23 (39,7) 7 (31,8) 0,518 Stroke in history, n (%) 2 (3,4) 1 (4,5) 1,000 PCI in history, n (%) 10 (17,2) 5 (22,7) 0,749 PAD, n (%) 5 (8,6) 1 (4,5) 1,000 CAS (≥ 40%), n (%) 16 (27,6) 4 (18,2) 0,386 NYHA classes of CHF I, n (%) II, n (%) III, n (%) 6 (10,3) 30 (51,7) 22 (37,9) 2 (9,1) 11 (50) 9 (40,9) 0,965 Smoking, n (%) 34 (58,6) 12 (54,5) 0,742 HFpEF, n (%) 32 (55,2) 9 (40,9) 0,287 HFmrEF, n (%) 8 (13,8) 2 (9,1) HFrEF, n (%) 18 (31) 11 (50) AF, n (%) 16 (27,6) 3 (13,6) 0,247 CKD grade II-III, n (%) 16 (27,6) 10 (45,5) 0,128 Median age, years, Me (Q25; Q75) 63(58,7;68) 61(57,7;65) 0,295 LVEF, %, Me (Q25; Q75) 54,5(37;64) 41(27;63) 0,156 eGFR, ml/min/1.73m 2 , Me (Q25; Q75) 72,5(59;80) 68(55,7;77) 0,316 CPB, min, Me (Q25; Q75) 91 (75,8;115,2) 101,5(77,3;128,9) 0,419 ACC, min, Me (Q25; Q75) 53,7 (43,9;70,2) 55,4(49,8;83,5) 0,196 Note - MI - myocardial infarction, DM - diabetes mellitus, PCI - percutaneous coronary intervention, PAD - lower extremity peripheral artery disease, CAS - carotid atherosclerosis, NYHA - New York Heart Association, CHF- chronic heart failure, HFpEF - heart failure with preserved ejection fraction, HFmrEF - heart failure with mildly reduced ejection fraction, HFrEF - heart failure with reduced ejection fraction, AF – atrial fibrillation, CKD - chronic kidney disease, LVEF - left ventricular ejection fraction, eGFR - estimated glomerular filtration rate, CPB - cardiopulmonary bypass time, ACC - aortic cross-clamp time, Me (Q25; Q75) - median and interquartile range, p - statistical significance according to Wald's test. The groups of patients, both with and without MACE throughout the year, did not differ according to the main clinical-anamnestic parameters: 90% of patients were male, and the average age was 63. More than half of the patients had heart failure with preserved ejection fraction (HFpEF) or heart failure with mildly reduced ejection fraction (HFmrEF) of left ventricular and New York Heart Association (NYHA) class II. Cardiopulmonary bypass (CPB) time during CABG, as well as the aortic cross-clamp time during CABG, was also comparable in the study groups. There were no significant differences in the content of the main laboratory markers of inflammation in the blood between groups (Table 2 ). Table 2 The significance of key laboratory indicators Indicator, Me (Q25; Q75) MACE – (n = 58) MACE + (n = 22) р WBC, 10^9/l 6,9(6,1;8,27) 7,1(6,1;8,7) 0,575 Platelets, 10^9/l 208 (175;248) 208(166;243,5) 0,901 ESR, mm/h 9 (5; 14) 10 (3;15,25) 0,927 Fibrinogen, g/l 3,44(3,1;4) 3,14(2,9;3,69) 0,080 Lymphocytes, 10^9/l 2,6(2,2;3,1) 2,5(1,9;3,1) 0,670 Neutrophils, 10^9/l 3,34(2,6;4,4) 3,8 (2,7;5,2) 0,431 NLR, pg/ml 1,33(1,03;1,67) 1,38(1,09;1,97) 0,362 GDF-15, pg/ml 2235(1632,5;2907) 2328(2145;3172) 0,096 CRP, mg/l 4,45(2,17;8,9) 4,8(2,52;9,03) 0,419 Note - WBC - white blood cells, ESR - erythrocyte sedimentation rate, NLR - neutrophil-to-lymphocyte ratio, GDF-15 – growth differentiation factor 15, CRP - C - reactive protein, Me (Q25; Q75) - median and interquartile range, p - statistical significance according to Wald's test. Thus, the values of both indicators analyzed in this paper – GDF-15 and NLR – were comparable in groups with and without the development of MACE. A ROC analysis was carried out to identify the statistical associations between GDF-15 and NLR and the development of MACE. Both indicators showed a low AUC: for NLR, AUC = 0.566 (p = 0.363), and for GDF-15, AUC = 0.621 (p = 0.096). This confirms the absence of these markers' association with MACE in the research cohort (Fig. 1 , 2 ). To analyze the relationship between GDF-15 and the NLR, a scatter diagram was constructed (Fig. 3 ). Spearman’s rank coefficient of correlation was r = -0.185, p = 0.101, so there was no significant linear correlation between the two. The visual approximation of the nature of the scattering diagram to the hyperbolic curve that is mathematically described by the function y = k/x is interesting. It was assumed that with the increase in k, equal to the product of the concentration of GDF-15 and NLR, the likelihood of MACE increases. To test this hypothesis, the value of the product GDF-15*NLR was determined. The median was 3108.05 (2069; 4145) for patients who did not have MACE and 4108.8 (2779.4; 5890.5) for patients with MACE. The differences were statistically significant: p = 0.010. ROC analysis was performed to determine the statistical association of GDF15*NLR with the development of MACE. The area under the curve was AUC = 0.687 at the level of statistical significance (p = 0.010) (Fig. 4 ). Logistic regression analysis was carried out to determine the independent predictive value of the product NLR*GDF-15. Corrections were made for sex, age, diabetes mellitus, and LVEF. The results of the logistic regression analysis are presented in Table 3 . Table 3 Logistic regression results confirming the independent predictive value of GDF-15*NLR for MACE Indicator B (regression coefficient) Wald Significance Test p Sex(1 - male) -,082 ,008 ,928 Age years -,038 ,929 ,335 DM type2(1 - yes) -,474 ,582 ,446 LVEF (%) -,022 1,417 ,234 GDF15*NLR ,000 4,328 ,037 Constant 1,553 ,335 ,563 Note – p - statistical significance according to Wald's test, DM – diabetes, LVEF - left ventricular ejection fraction, GDF15*NLR - the value of the product indicators NLR and GDF-15 According to the analysis, NLR*GDF15 was an independent predictor of MACEs for 12 months after CABG (p = 0.037). According to ROC analysis, the cutoff point for this indicator is 3216, which allows predicting the development of MACE with a sensitivity of 68% and a specificity of 52%. This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn. 4. Discussion Low-grade inflammation plays an important role in the development and progression of coronary and heart failure [ 11 ]. It has been demonstrated in experimental and clinical studies that atherosclerosis is an inflammatory disease [31–33. ]. Exposure to cardiovascular risk factors leads to loss of protective endothelium and accumulation of low-density lipoproteins in the subendothelial space of the vascular wall, which causes a low inflammatory response [ 34 ]. At the same time, an increase in the concentration of certain inflammatory biomarkers, including immune cell populations and inflammasomes, allows the prediction of the progression of CAD regardless of traditional risk factors [ 35 – 37 ]. In our study, the content of highly sensitive C-reactive protein (CRP), as well as traditional inflammatory parameters of general blood analysis (leukocytes, erythrocyte sedimentation rate (ESR)), was not associated with the development of MACE. This was probably because the study included patients with planned cardiac surgery, which excluded patients with inflammation. Recently, the literature has focused on inflammatory index reflecting low-grade systemic inflammation, such as the NLR, platelet-to-lymphocyte ratio (PLR), and neutrophil*platelet/lymphocyte ratio, which is a systemic immune-inflammation index (SII) [ 38 – 39 ]. The neutrophil-to-lymphocyte ratio is one of the foremost considered, widely available indicators reflecting the processes of systemic inflammation. It is now widely used in almost all branches of medicine as an easily accessible and informative marker of the immune response to communicable and noncommunicable agents. The pathogenetic meaning of this marker is explained by the fact that the NLR reflects a dynamic relationship between congenital (neutrophils) and adaptive (lymphocytes) cell immune responses during the development and progression of disease. The normal range of NLR values is 1–2, and values above 3.0 and below 0.7 in adults are pathological. A slight increase in NLR between 2.3 and 3.0 may be an early sign of disease, including atherosclerotic genesis [ 40 ]. According to the literature, broadly accessible markers that demonstrate associations with the closest prognosis in patients after CABG. For example, an increase in NLR above 8.34 in patients after CABG was associated with PPS [ 16 ], and an increase in NLR above 2.13 in any period (before or after CABG) was associated with a high risk of developing or relapsing AF [ 18 ]. In addition, the NLR value above 2.675 prior to the CABG operation was associated with the insolvency of the subcutaneous vein transplant, but this study [ 22 ] failed to achieve the required level of statistical significance (p = 0.075). In another study [ 24 ], increases in NLR > 6.4 and 31.8 in the first hour and day after surgery, respectively, were closely related to mortality. In patients with chronic total occlusion of coronary arteries, the NLR and its dynamics after PCI were associated with the development of MACE for 9–12 months of observation [ 41 ]. However, we have not been able to find such data in the literature for patients who have undergone CABG with artificial circulation. Thus, its role in predicting delayed outcomes in patients after CABG has not been determined to date. In our cohort, the NLR value was not associated with the development of MACE at the prospective one-year observation (AUC = 0,565; р=0,368). This may be partly because NLR is affected by many conditions, including age, the presence of chronic diseases, including DM, obesity, psychiatric diagnosis, cancer, anemia and stress [ 42 ]. Growth differentiation factor 15, or macrophage inhibitory cytokine-1, acts as a marker of inflammation and plays a role in cardiovascular pathogenesis, metabolic disorders and neurodegenerative processes. Levels of GDF-15 in serum also increase with aging and in response to cell stress and mitochondrial dysfunction [ 43 ]. Recently, the role of GDF-15 in aging and metabolic disorders has been actively discussed by the scientific community. Notably, the GDF-15 receptor, its underlying signaling pathways and biological effects are poorly understood [ 44 ]. Some studies have demonstrated the role of GDF-15 in predicting adverse outcomes in CHF, both with a reduced fraction of the left ventricle ejection [ 45 ] and with a preserved fraction of the left ventricle ejection [ 46 ]. At the same time, the dynamics of GDF-15 concentration in patients with decompensation of heart failure reflected the increased risk of repeated hospitalization and death in the RELAX-AHF study [ 47 ]. However, GDF-15 remains a relatively poorly studied marker for CABG patients. There is conflicting evidence in the literature regarding the association of GDF-15 with the development of adverse cardiovascular events in CABG patients. Bouchot O. et al. [ 27 ] showed that low GDF-15 values are associated with the development of postoperative AF. However, other studies have demonstrated that high GDF-15 values are unfavorable and associated with the development of AKI [ 43 ]. In addition, a study by Matthias Heringlake et al. [ 30 ] showed that the plasma GDF-15 level prior to CABG is an independent predictor of postoperative mortality and morbidity in cardiac patients. This is an important addition to known scales for risk stratification in these patients. In our cohort, there was also no statistically significant association with the development of MACEs during the one-year observation period (AUC = 0,621; р=0,096). However, given the wide variety of immune processes and the lack of evidence for a correlation between GDF-15 and NLR, we performed a correlation analysis between GDF-15 and NLR. No significant linear correlation was found (r= -0,184, p = 0.102). At the same time, a visual examination of the scattering diagram (Fig. 3 ) led us to conclude that there was a nonlinear relationship between these parameters. This relationship, according to the distribution of points on the scattering graph, could be a hyperbola. In this case, the product of NLR and GDF-15 would have a separate predictive value. The literature does not describe such data, so we propose a scientific hypothesis. According to the ROC analysis, the AUC for predicting MACEs was 0.648 (p = 0.011). The cutoff point for the NLR*GDF-15 product was found to be 3216. It predicts the development of MACE with a sensitivity of 68% and a specificity of 52%. It is crucial to note that the statistically significant association of the product NLR*GDF-15 with the development of MACE in patients who underwent CABG remained after the introduction of amendments to sex, age, presence of DM, and LVEF. Therefore, this indicator was an independent predictor of adverse cardiovascular events. The results are new and have not been previously obtained in other studies. This could be the basis for new scientific hypotheses and new large-scale studies. Study limitations: the main limitation is the small sample size, which is partially offset by the high homogeneity of the group, excluding any additional cardiac surgery, severe associated pathology, including inflammatory etiology. More studies are needed to confirm these results. 5. Conclusion The value of the product indicators NLR and GDF-15 is associated with the development of adverse cardiovascular events in patients after CABG. Declarations Author Contributions: Conceptualization, G.A. and R.A.; methodology, K.E.; formal analysis, K.E. and T.O.; investigation, K.E. and T.O.; data curation, G.A.; writing—original draft preparation, K.E. and T.O.; writing—review and editing, K.E. and T.O.; supervision, R.A. and G.A.; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the state task, FSR № 122020300045-5 (03.02.2022). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Local Ethics Committee of Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia (Protocol № 188 of 18.09.2019). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data available in a publicly accessible repository The data presented in this study are openly available in FigShare at doi: 10.6084/m9.figshare.23898078, reference number https://figshare.com/s/9b37a1d407c9d7908e17. Conflicts of Interest: The authors declare no conflicts of interest. Supplementary Materials: The following are available online at https://docs.google.com/spreadsheets/d/1zD_iIqSUfRF0RvWITtrGHHE4ehHBWUby/edit?usp=sharing&ouid=108698147447942789821&rtpof=true&sd=true . References Knuuti J, Wijns W, Saraste A, ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41(3):407–477. 10.1093/eurheartj/ehz425 . Erratum in: Eur Heart J. 2020;41(44):4242. PMID: 31504439. Velazquez EJ, et al. Coronary-artery bypass surgery in patients with left ventricular dysfunction. 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Comparing the effectiveness of neutrophil-lymphocyte ratio as a mortality predictor on middle and advanced age coronary artery bypass graft patients. North Clin Istanb. 2014;1(2):95–100. PMID: 28058310; PMCID: PMC5175070.]. Bouchot O, Guenancia C, Kahli A, Pujos C, Malapert G, Vergely C, Laurent G. Low Circulating Levels of Growth Differentiation Factor-15 Before Coronary Artery Bypass Surgery May Predict Postoperative Atrial Fibrillation. J Cardiothorac Vasc Anesth. 2015;29(5):1131–9. 10.1053/j.jvca.2015.01.023 . Epub 2015 Jan 9. PMID: 25990268. Kahli A, Guenancia C, Zeller M, Grosjean S, Stamboul K, Rochette L, Girard C, Vergely C. Growth differentiation factor-15 (GDF-15) levels are associated with cardiac and renal injury in patients undergoing coronary artery bypass grafting with cardiopulmonary bypass. PLoS ONE. 2014;9(8):e105759. 10.1371/journal.pone.0105759 . PMID: 25171167; PMCID: PMC4149498. Guenancia C, Kahli A, Laurent G, Hachet O, Malapert G, Grosjean S, Girard C, Vergely C, Bouchot O. Preoperative growth differentiation factor 15 as a novel biomarker of acute kidney injury after cardiac bypass surgery. Int J Cardiol. 2015;197:66–71. Epub 2015 Jun 17. PMID: 26113476. Heringlake M, Charitos EI, Gatz N, Käbler JH, Beilharz A, Holz D, Schön J, Paarmann H, Petersen M, Hanke T. Growth differentiation factor 15: a novel risk marker adjunct to the EuroSCORE for risk stratification in cardiac surgery patients. J Am Coll Cardiol. 2013;61(6): 672 – 81. 10.1016/j.jacc.2012.09.059 . PMID: 23391200. Libby P, Hansson GK. From Focal Lipid Storage to Systemic Inflammation: JACC Review Topic of the Week. J Am Coll Cardiol. 2019;74(12):1594–607. 10.1016/j.jacc.2019.07.061 . PMID: 31537270; PMCID: PMC6910128. Libby P. The changing landscape of atherosclerosis. Nature. 2021;592(7855):524–533. 10.1038/s41586-021-03392-8 . Epub 2021 Apr 21. PMID: 33883728. Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7(1):131. 10.1038/s41392-022-00955-7 . PMID: 35459215; PMCID: PMC9033871. Tabas I, García-Cardeña G, Owens GK. Recent insights into the cellular biology of atherosclerosis. J Cell Biol. 2015;209(1):13–22. 10.1083/jcb.201412052 . PMID: 25869663; PMCID: PMC4395483. Ridker PM. A Test in Context: High-Sensitivity C-Reactive Protein. J Am Coll Cardiol. 2016;67(6):712–723. doi: 10.1016/j.jacc.2015.11.037. PMID: 26868696. Kercheva M, Ryabov V, Gombozhapova A, Rebenkova M, Kzhyshkowska J. Macrophages of the Heart-Kidney Axis: Their Dynamics and Correlations with Clinical Data and Outcomes in Patients with Myocardial Infarction. J Pers Med. 2022;12(2):127. 10.3390/jpm12020127 . PMID: 35207615; PMCID: PMC8879726. Olsen MB, Gregersen I, Sandanger Ø, Yang K, Sokolova M, Halvorsen BE, Gullestad L, Broch K, Aukrust P, Louwe MC. Targeting the Inflammasome in Cardiovascular Disease. JACC Basic Transl Sci. 2021;7(1):84–98. PMID: 35128212; PMCID: PMC8807732. Liu Y, Ye T, Chen L, Jin T, Sheng Y, Wu G, Zong G. Systemic immune-inflammation index predicts the severity of coronary stenosis in patients with coronary heart disease. Coron Artery Dis. 2021;32(8):715–720. 10.1097/MCA.0000000000001037 . PMID: 33826540. Karadeniz FÖ, Karadeniz Y, Altuntaş E. Systemic immune-inflammation index, and neutrophilto-lymphocyte and platelet-to-lymphocyte ratios can predict clinical outcomes in patients with acute coronary syndrome. Cardiovasc J Afr. 2023;34:1–7. 10.5830/CVJA-2023-011 . Epub ahead of print. PMID: 37145864. Zahorec R. Neutrophil-to-lymphocyte ratio, past, present and future perspectives. Bratisl Lek Listy. 2021;122(7):474–488. doi: 10.4149/BLL_2021_078. PMID: 34161115. Li C, Zhang F, Shen Y, Xu R, Chen Z, Dai Y, Lu H, Chang S, Qian J, Wang X, Ge J. Impact of Neutrophil to Lymphocyte Ratio (NLR) Index and Its Periprocedural Change (NLR∆) for Percutaneous Coronary Intervention in Patients With Chronic Total Occlusion. Angiology. 2017;68(7):640–6. Epub 2016 May 19. PMID: 27207843. Zahorec R, Rochette L, Dogon G, Zeller M, Cottin Y, Vergely C. GDF15 and Cardiac Cells: Current Concepts and New Insights. Int J Mol Sci. 2021;22(16):8889. 10.3390/ijms22168889 . PMID: 34445593; PMCID: PMC8396208./BLL_2021_078. PMID: 34161115. Rochette L, Dogon G, Zeller M, Cottin Y, Vergely C. GDF15 and Cardiac Cells: Current Concepts and New Insights. Int J Mol Sci. 2021;22(16):8889. 10.3390/ijms22168889 . PMID: 34445593; PMCID: PMC8396208. Meijers WC, Bayes-Genis A, Mebazaa A, Bauersachs J, Cleland JGF, Coats AJS, Januzzi JL, Maisel AS, McDonald K, Mueller T, Richards AM, Seferovic P, Mueller C, de Boer RA. Circulating heart failure biomarkers beyond natriuretic peptides: review from the Biomarker Study Group of the Heart Failure Association (HFA), European Society of Cardiology (ESC). Eur J Heart Fail. 2021;23(10):1610–32. 10.1002/ejhf.2346 . Epub 2021 Oct 10. PMID: 34498368; PMCID: PMC9292239. Kempf T, von Haehling S, Peter T, Allhoff T, Cicoira M, Doehner W, Ponikowski P, Filippatos GS, Rozentryt P, Drexler H, Anker SD, Wollert KC. Prognostic utility of growth differentiation factor-15 in patients with chronic heart failure. J Am Coll Cardiol. 2007;50(11):1054–60. 10.1016/j.jacc.2007.04.091 . Epub 2007 Aug 24. PMID: 17825714. Izumiya Y, Hanatani S, Kimura Y, Takashio S, Yamamoto E, Kusaka H, Tokitsu T, Rokutanda T, Araki S, Tsujita K, Tanaka T, Yamamuro M, Kojima S, Tayama S, Kaikita K, Hokimoto S, Ogawa H. Growth differentiation factor-15 is a useful prognostic marker in patients with heart failure with preserved ejection fraction. Can J Cardiol. 2014;30(3):338–44. Epub 2013 Dec 18. PMID: 2448491. Cotter G, Voors AA, Prescott MF, Felker GM, Filippatos G, Greenberg BH, Pang PS, Ponikowski P, Milo O, Hua TA, Qian M, Severin TM, Teerlink JR, Metra M, Davison BA. Growth differentiation factor 15 (GDF-15) in patients admitted for acute heart failure: results from the RELAX-AHF study. Eur J Heart Fail. 2015;17(11):1133-43. 10.1002/ejhf.331 . Epub 2015 Sep 3. PMID: 26333529. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-3397585","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":238052598,"identity":"91c0c6df-ea08-49d5-8645-9ef9b997eb4c","order_by":0,"name":"Alla A. Garganeeva","email":"","orcid":"","institution":"Tomsk National Research Medical Center, Russian Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alla","middleName":"A.","lastName":"Garganeeva","suffix":""},{"id":238052599,"identity":"6987a62f-ac12-4b65-baa5-ca054bf3e4f2","order_by":1,"name":"Elena A. Kuzheleva","email":"","orcid":"","institution":"Tomsk National Research Medical Center, Russian Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elena","middleName":"A.","lastName":"Kuzheleva","suffix":""},{"id":238052600,"identity":"219f4ff1-7609-427e-b99c-ad5c631e0e50","order_by":2,"name":"Olga V. Tukish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACdjCZwMMgwXwAyGAmQgtIzQGwFrYE0rQwMEjwGBCnhZ+Z/eLnj21pMvKze75J/NxhLWdwgPeZBD4tks08xRIH23J4DO6c3SbZeybdWLKB3QyvFoPDPAlALRU8BhK52yR42w4n9jOwsRHSkvwDpEV+Rs4zyb9th+vbCGthPwZ2GMONHDZpoC0J/IS0AP3CZnHmXBqPwY00Y2vZtnTDmc1szBb4tPCztz++UVGWbC8/I/nhzbdt1vIGx9sYb+DTwsAAjg4wYIG4h3DUsD+AsZg/EFQ8CkbBKBgFIxIAAKFqQ2rwZ/gKAAAAAElFTkSuQmCC","orcid":"","institution":"Tomsk National Research Medical Center, Russian Academy of Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Olga","middleName":"V.","lastName":"Tukish","suffix":""},{"id":238052601,"identity":"5c170665-cd3a-4228-b4ba-3240e8ace994","order_by":3,"name":"Alexey N. Repin","email":"","orcid":"","institution":"Tomsk National Research Medical Center, Russian Academy of Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"N.","lastName":"Repin","suffix":""}],"badges":[],"createdAt":"2023-09-29 08:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3397585/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3397585/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44383432,"identity":"2036dc81-b115-461d-809e-e9ee706176c0","added_by":"auto","created_at":"2023-10-10 18:39:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210465,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of NLR and development of MACE: AUC = 0.566 (p = 0.363)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3397585/v1/86e73822dfdc110f89f944f3.jpeg"},{"id":44382349,"identity":"d104dbab-98ba-4ed5-afca-16457ef30161","added_by":"auto","created_at":"2023-10-10 18:31:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":206382,"visible":true,"origin":"","legend":"\u003cp\u003eROC-analysis GDF-15 and development MACE: AUC=0,621 (р=0,096).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3397585/v1/99e0be83a22522b9772b38bf.jpeg"},{"id":44382348,"identity":"40417bba-4ec7-42d1-8804-3eff7ed09eb9","added_by":"auto","created_at":"2023-10-10 18:31:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17071,"visible":true,"origin":"","legend":"\u003cp\u003eThe scatter diagram for NLR and GDF15: no significant linear correlation between indicators (r = -0,185, p = 0.101).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3397585/v1/39f56bedd44d307cd3c08f2e.png"},{"id":44384152,"identity":"1ec611c2-4d34-4584-a0b5-90fc99bbecb1","added_by":"auto","created_at":"2023-10-10 18:47:25","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":190095,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis product NLR*GDF-15 and development MACE: AUC= 0,687; р = 0,010\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3397585/v1/31519ec099d46a4a66761d1c.jpeg"},{"id":45502695,"identity":"f116c9cc-5897-49d5-9949-c766425d572a","added_by":"auto","created_at":"2023-10-31 03:38:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":528639,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3397585/v1/06c1848c-5c4e-4e1c-9975-25d1e8fa73dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Low-grade inflammation in the prognosis of patients undergoing coronary artery bypass grafting: the value of neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF15)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCoronary artery disease (CAD) is one of the main causes of the development of chronic heart failure (CHF) and cardiovascular mortality\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe pathological effect of CAD on heart function is realized in various ways. First, it is the development of myocardial infarction (IM) with the rapid loss of a section of viable myocardium and constructional dysfunction of the left ventricle (LV). However, even in the absence of MI, chronic coronary failure has a significant effect on the progression of heart dysfunction. Frequent episodes of transient ischemia lead to the development of a state of \u0026ldquo;stunning\u0026rdquo; cardiomyocytes, and persistent reduction in coronary blood flow causes myocardial hibernation, which inevitably ends in cardiomyocyte necrosis in the case of ongoing ischemia.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThere are three strategic approaches to treating patients with CAD: only optimal drug therapy or its combination with surgical (coronary artery bypass graft surgery (CABG) or percutaneous coronary intervention (PCI)) revascularization of the myocardium. At the same time, the results of large randomized clinical trials (RCTs) comparing these treatment strategies are often controversial and largely depend on the design of the research conducted.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFor example, in the STICH (Surgical Treatment for Ischemic Heart Failure) study, 244 cases out of 602 (41%) were recorded in the drug therapy group, and 218 cases out of 610 (36%) were recorded in the CABG group (p\u0026thinsp;=\u0026thinsp;0.12) during the observation. At the same time, cardiovascular mortality was higher in the drug therapy group only: 201 patients (33%) in the drug therapy group and 168 (28%) in the CABG group (p\u0026thinsp;=\u0026thinsp;0.05). The development of a combined endpoint, death from any cause or hospitalization for cardiovascular diseases, was also higher in the drug therapy group (411 patients (68%) vs. 351 (58%) in the CABG group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001))\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAt the same time, according to the subanalysis of the present study, the successful revascularization of the myocardium led to the improvement of systolic heart function in some patients. This effect depended on the initial value of LV EF and was also associated with its postoperative dynamics\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn an EXCEL study on the safety and effectiveness of everolimus-eluting stents compared to bypass surgery in patients with left main CAD, the major adverse cardiovascular event (MACE) development rate (death, stroke or MI) was approximately 15% in both groups for 3 years of observation (with only 5\u0026ndash;7% of patients in the study cohort having stagnant CHF)\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIn the recently presented study REVIVED (REVIVED-BCIS2; NCT01920048), including patients with LV EF 35% or less, extensive coronary artery disease amenable to PCI, and demonstrable myocardial viability, patients were randomized for PCI followed by optimal medical therapy (PCI group) or only optimal medical therapy group. A total of 347 people were included in the PCI group, and 353 were included in the optimal medical therapy group. Over a median of 41 months of observation, the average rate of death from any cause or hospitalization for heart failure was 37.2% in the PCI group and 38.0% in the optimal medical therapy group (p\u0026thinsp;=\u0026thinsp;0.96)\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAt the same time, the value of LV EF was the same in both groups after 6 and 12 months. Therefore, PCI revascularization, according to this study, has no advantages in the studied cohort compared to optimal medical therapy.\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ePredicting MACE after high-tech interventions is an urgent and important task for the scientific and medical community. Subclinical inflammation markers are becoming increasingly investigated for this purpose.\u003c/p\u003e\n \u003cp\u003eToday, inflammation is recognized as a universal way for the body to adapt to changing environmental conditions. The modern concept of immunology of cardiovascular homeostasis is based on the so-called \u0026ldquo;hazard hypothesis\u0026rdquo; (\u0026ldquo;danger hypothesis\u0026rdquo;), proposed by P. Matzinger in 1994 [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to this theory, there are hazard signals, which have been called molecular patterns associated with damage (DAMPs - damage-associated molecular pattern), that initiate the immune response in the absence of exogenous infectious pathogens [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, acute or chronic myocardial cell damage is associated with the release of cell degradation products, which are DAMPs [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. In turn, the release of proinflammatory cytokines causes the activation of residual tissue macrophages and leads to the recruitment of different populations of circulating immune cells in the heart under the influence of specific chemokine molecules [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite the active study of the pathogenesis of low-grade inflammation, it remains to date not fully understood.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eLow-grade inflammation has been shown to have a negative role in cardiovascular disease [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The development and progression of atherosclerosis and chronic heart failure (CHF) is accompanied by the activation of immune processes [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the lack of success in developing new anti-inflammatory treatments for heart failure may be due to our poor understanding of complex inflammatory systems in chronic cardiovascular disease (CVD). [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTreatment of clinically pronounced coronary artery disease (CAD) with obstructive arteries with myocardial revascularization by coronary artery bypass graft surgery (CABG) is a modern and effective method. However, the prognosis of patients after CABG can be significantly different from the regression of symptoms of coronary and heart failure to cardiovascular death [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. During artificial blood circulation, proinflammatory mediators trigger the activation of leukocytes, vascular endothelial cells and platelets. This is manifested by a systemic inflammatory reaction that leads to organ dysfunction affecting the heart, brain, lungs and kidneys and may affect the prognosis of patients.\u003c/p\u003e\n\u003cp\u003eAccording to the literature, the neutrophil-to-lymphocyte ratio (NLR) predicts the development of postpericardiotomy syndrome (PPS) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], atrial fibrillation (AF) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], acute kidney injury (AKI) [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], saphenous vein graft failure [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e], ischemic stroke (IS) [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and even early postoperative mortality [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] after CABG. Therefore, NLR is a promising marker of prognosis for patients after CABG.\u003c/p\u003e\n\u003cp\u003eGrowth differentiation factor 15 (GDF-15) is a member of the transforming growth factor b (TGF-b) cytokine superfamily. Circulating GDF-15 concentrations are increased across a wide spectrum of cardiovascular diseases, including acute and chronic CAD, congestive heart failure, and IS. Growth differentiation factor 15 is also upregulated by other cardiovascular events triggering oxidative stress, including pressure overload and atherosclerosis. Moreover, increased circulating GDF-15 concentrations have been linked to an enhanced risk of future adverse cardiovascular events.\u003c/p\u003e\n\u003cp\u003eAn increase in the GDF-15 concentration in CABG may help in predicting the development of postoperative AF [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] and AKI [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn addition, individual studies have shown that GDF-15 determination can help predict cardiovascular mortality in cardiac patients [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTherefore, the aim of our study was to determine the role of NLP and GDF-15 in predicting MACE in patients with CABG.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe prospective observational study included 80 patients with CAD who underwent CABG in the present hospitalization (2019\u0026ndash;2020 inclusion years). The research protocol adhered to the principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (Protocol № 188 of 18.09.2019). Before any procedure, all patients signed a form of voluntary informed consent.\u003c/p\u003e \u003cp\u003e The diagnosis of CHF was based on the current clinical guidelines. Multivascular stenosing atherosclerosis of coronary arteries was diagnosed using invasive coronary angiography on the angiographic complex \u0026ldquo;Cardio-scop-V\u0026rdquo; and the ACOM. PC of Siemens (Germany) based on clinical indications. Echocardiography was performed with the Philips HD15 Ultrasound system.\u003c/p\u003e \u003cp\u003eIn addition to clinical laboratory examination (general blood test with leukocyte counts, biochemical blood analysis with estimated glomerular filtration rate (eGFR) according to the formula Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)). The GDF-15 concentration was studied by immunoassay using the \u0026ldquo;Human GDF-15/MIC1 ELISA\u0026rdquo; (\u0026ldquo;BioVendor\u0026rdquo;, Czech Republic). The result was determined to be in pg/ml. The research was carried out using the equipment of the Center for Collective Use \"Medical Genomics\" of the Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia.\u003c/p\u003e \u003cp\u003eBlood was collected one day before CABG from the cubital vein in the morning on an empty stomach. Further preparation of blood samples for analysis included centrifuging, serum separation, and freezing at -80\u0026deg;C. The analysis was conducted after one blood serum thawing.\u003c/p\u003e \u003cp\u003eThe noninclusion criteria were refusal to participate in the study, myocardial infarction (MI), stroke during the last 6 months, implanted cardiac rhythm management devices, the need for additional cardiac surgery, except CAGB, the presence of advanced kidney disease (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;30 ml/min/1.73 m2), and severe related diseases (oncological diseases in the active stage, infiltrating heart diseases, autoimmune diseases, acute infections and exacerbation of chronic somatic diseases). Patients were excluded from the study in cases of death during hospitalization.\u003c/p\u003e \u003cp\u003ePatients were monitored for 12 months. The medication prescribed after surgery was fully consistent with current clinical guidelines and was not significantly different in patients with and without developmental adverse cardiovascular events.\u003c/p\u003e \u003cp\u003eIn prospective follow-up, a combined endpoint (MACE) was recorded, including death from cardiovascular diseases, decompensation of heart failure or the need for intravenous diuretic therapy or doubling the dose of a diuretic, an acute ischemic event requiring unplanned revascularization, stroke, unplanned hospitalization for coronary artery disease and/or CHF (a total of 22 events (27.5%)).\u003c/p\u003e \u003cp\u003eThe combined endpoint (MACE) included death from cardiovascular disease, heart failure decompensation or the need for intravenous diuretic therapy or doubling the dose of a diuretic, an acute ischemic event requiring unplanned revascularization, stroke, unplanned hospitalization for coronary artery disease and/or CHF. In a prospective follow-up, 27.5% had a combined endpoint (a total of 22 events).\u003c/p\u003e \u003cp\u003eData were analyzed with the use of the programs \u0026ldquo;IBM SPSS 21\u0026rdquo; and MS \u0026ldquo;Excel\u0026rdquo;. Continuous variables are presented as the median and interquartile range (Me (Q25; Q75), considering the nonnormal distribution parameters. Categorical data are presented in absolute and relative values: n (%). Continuous variables in the independent samples were analyzed using the Mann‒Whitney test. Spearman\u0026rsquo;s rank coefficient of correlation was used to estimate the correlation relationships. The statistical significance of differences for categorical variables was determined using the χ2 Pearson criterion and two-sided Fisher\u0026rsquo;s exact test. The influence of the studied factors on the development of MACE was analyzed using logistic regression analysis and receiver operating characteristic (ROC) analysis (calculated area under the curve (AUC)). A value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe main characteristics of the study cohort are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eClinical characteristics of patients prior to CABG in groups depending on MACE registration\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\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACE \u0026ndash;\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACE +\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eр\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex male/female, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (87,9)/7 (12,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (90,9)/2 (9,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuffered/in history MI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (56,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (77,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM type 2, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (22,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (31,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (39,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (31,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke in history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI in history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (17,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (22,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (8,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAS (\u0026ge;\u0026thinsp;40%), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (27,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (18,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA classes of CHF\u003c/p\u003e \u003cp\u003eI, n (%)\u003c/p\u003e \u003cp\u003eII, n (%)\u003c/p\u003e \u003cp\u003eIII, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10,3)\u003c/p\u003e \u003cp\u003e30 (51,7)\u003c/p\u003e \u003cp\u003e22 (37,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (9,1)\u003c/p\u003e \u003cp\u003e11 (50)\u003c/p\u003e \u003cp\u003e9 (40,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (58,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (54,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,742\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFpEF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (55,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (40,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0,287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFmrEF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (13,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (9,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFrEF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026nbsp;(50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (27,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (13,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD grade II-III, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (27,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (45,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age, years, Me (Q25; Q75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(58,7;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(57,7;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF, %, Me (Q25; Q75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54,5(37;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(27;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e, Me (Q25; Q75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72,5(59;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(55,7;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPB, min, Me (Q25; Q75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (75,8;115,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101,5(77,3;128,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC, min, Me (Q25; Q75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53,7 (43,9;70,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,4(49,8;83,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote - MI - myocardial infarction, DM - diabetes mellitus, PCI - percutaneous coronary intervention, PAD - lower extremity peripheral artery disease, CAS - carotid atherosclerosis, NYHA - New York Heart Association, CHF- chronic heart failure, HFpEF - heart failure with preserved ejection fraction, HFmrEF - heart failure with mildly reduced ejection fraction, HFrEF - heart failure with reduced ejection fraction, AF \u0026ndash; atrial fibrillation, CKD - chronic kidney disease, LVEF - left ventricular ejection fraction, eGFR - estimated glomerular filtration rate, CPB - cardiopulmonary bypass time, ACC - aortic cross-clamp time, Me (Q25; Q75) - median and interquartile range, p - statistical significance according to Wald's test.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe groups of patients, both with and without MACE throughout the year, did not differ according to the main clinical-anamnestic parameters: 90% of patients were male, and the average age was 63. More than half of the patients had heart failure with preserved ejection fraction (HFpEF) or heart failure with mildly reduced ejection fraction (HFmrEF) of left ventricular and New York Heart Association (NYHA) class II. Cardiopulmonary bypass (CPB) time during CABG, as well as the aortic cross-clamp time during CABG, was also comparable in the study groups.\u003c/p\u003e \u003cp\u003eThere were no significant differences in the content of the main laboratory markers of inflammation in the blood between groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eThe significance of key laboratory indicators\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\u003eIndicator, Me (Q25; Q75)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMACE \u0026ndash;\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMACE +\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eр\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, 10^9/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,9(6,1;8,27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,1(6,1;8,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, 10^9/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (175;248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208(166;243,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR, mm/h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (5; 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3;15,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen, g/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,44(3,1;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,14(2,9;3,69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes, 10^9/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,6(2,2;3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,5(1,9;3,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils, 10^9/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,34(2,6;4,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,8 (2,7;5,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,33(1,03;1,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,38(1,09;1,97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15, pg/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2235(1632,5;2907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2328(2145;3172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,45(2,17;8,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,8(2,52;9,03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote - WBC - white blood cells, ESR - erythrocyte sedimentation rate, NLR - neutrophil-to-lymphocyte ratio, GDF-15 \u0026ndash; growth differentiation factor 15, CRP - C - reactive protein, Me (Q25; Q75) - median and interquartile range, p - statistical significance according to Wald's test.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThus, the values of both indicators analyzed in this paper \u0026ndash; GDF-15 and NLR \u0026ndash; were comparable in groups with and without the development of MACE. A ROC analysis was carried out to identify the statistical associations between GDF-15 and NLR and the development of MACE. Both indicators showed a low AUC: for NLR, AUC\u0026thinsp;=\u0026thinsp;0.566 (p\u0026thinsp;=\u0026thinsp;0.363), and for GDF-15, AUC\u0026thinsp;=\u0026thinsp;0.621 (p\u0026thinsp;=\u0026thinsp;0.096). This confirms the absence of these markers' association with MACE in the research cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo analyze the relationship between GDF-15 and the NLR, a scatter diagram was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Spearman\u0026rsquo;s rank coefficient of correlation was r = -0.185, p\u0026thinsp;=\u0026thinsp;0.101, so there was no significant linear correlation between the two.\u003c/p\u003e \u003cp\u003eThe visual approximation of the nature of the scattering diagram to the hyperbolic curve that is mathematically described by the function y\u0026thinsp;=\u0026thinsp;k/x is interesting.\u003c/p\u003e \u003cp\u003eIt was assumed that with the increase in k, equal to the product of the concentration of GDF-15 and NLR, the likelihood of MACE increases. To test this hypothesis, the value of the product GDF-15*NLR was determined. The median was 3108.05 (2069; 4145) for patients who did not have MACE and 4108.8 (2779.4; 5890.5) for patients with MACE. The differences were statistically significant: p\u0026thinsp;=\u0026thinsp;0.010.\u003c/p\u003e \u003cp\u003eROC analysis was performed to determine the statistical association of GDF15*NLR with the development of MACE. The area under the curve was AUC\u0026thinsp;=\u0026thinsp;0.687 at the level of statistical significance (p\u0026thinsp;=\u0026thinsp;0.010) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLogistic regression analysis was carried out to determine the independent predictive value of the product NLR*GDF-15. Corrections were made for sex, age, diabetes mellitus, and LVEF. The results of the logistic regression analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression results confirming the independent predictive value of GDF-15*NLR for MACE\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\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB (regression coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald Significance Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(1 - male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM type2(1 - yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF15*NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote \u0026ndash; p - statistical significance according to Wald's test, DM \u0026ndash; diabetes, LVEF - left ventricular ejection fraction, GDF15*NLR - the value of the product indicators NLR and GDF-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the analysis, NLR*GDF15 was an independent predictor of MACEs for 12 months after CABG (p\u0026thinsp;=\u0026thinsp;0.037). According to ROC analysis, the cutoff point for this indicator is 3216, which allows predicting the development of MACE with a sensitivity of 68% and a specificity of 52%.\u003c/p\u003e \u003cp\u003eThis section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation as well as the experimental conclusions that can be drawn.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLow-grade inflammation plays an important role in the development and progression of coronary and heart failure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It has been demonstrated in experimental and clinical studies that atherosclerosis is an inflammatory disease [31\u0026ndash;33. ].\u003c/p\u003e \u003cp\u003eExposure to cardiovascular risk factors leads to loss of protective endothelium and accumulation of low-density lipoproteins in the subendothelial space of the vascular wall, which causes a low inflammatory response [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. At the same time, an increase in the concentration of certain inflammatory biomarkers, including immune cell populations and inflammasomes, allows the prediction of the progression of CAD regardless of traditional risk factors [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In our study, the content of highly sensitive C-reactive protein (CRP), as well as traditional inflammatory parameters of general blood analysis (leukocytes, erythrocyte sedimentation rate (ESR)), was not associated with the development of MACE. This was probably because the study included patients with planned cardiac surgery, which excluded patients with inflammation. Recently, the literature has focused on inflammatory index reflecting low-grade systemic inflammation, such as the NLR, platelet-to-lymphocyte ratio (PLR), and neutrophil*platelet/lymphocyte ratio, which is a systemic immune-inflammation index (SII) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe neutrophil-to-lymphocyte ratio is one of the foremost considered, widely available indicators reflecting the processes of systemic inflammation. It is now widely used in almost all branches of medicine as an easily accessible and informative marker of the immune response to communicable and noncommunicable agents. The pathogenetic meaning of this marker is explained by the fact that the NLR reflects a dynamic relationship between congenital (neutrophils) and adaptive (lymphocytes) cell immune responses during the development and progression of disease. The normal range of NLR values is 1\u0026ndash;2, and values above 3.0 and below 0.7 in adults are pathological. A slight increase in NLR between 2.3 and 3.0 may be an early sign of disease, including atherosclerotic genesis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the literature, broadly accessible markers that demonstrate associations with the closest prognosis in patients after CABG. For example, an increase in NLR above 8.34 in patients after CABG was associated with PPS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and an increase in NLR above 2.13 in any period (before or after CABG) was associated with a high risk of developing or relapsing AF [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, the NLR value above 2.675 prior to the CABG operation was associated with the insolvency of the subcutaneous vein transplant, but this study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] failed to achieve the required level of statistical significance (p\u0026thinsp;=\u0026thinsp;0.075). In another study [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], increases in NLR\u0026thinsp;\u0026gt;\u0026thinsp;6.4 and 31.8 in the first hour and day after surgery, respectively, were closely related to mortality.\u003c/p\u003e \u003cp\u003eIn patients with chronic total occlusion of coronary arteries, the NLR and its dynamics after PCI were associated with the development of MACE for 9\u0026ndash;12 months of observation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. However, we have not been able to find such data in the literature for patients who have undergone CABG with artificial circulation.\u003c/p\u003e \u003cp\u003eThus, its role in predicting delayed outcomes in patients after CABG has not been determined to date. In our cohort, the NLR value was not associated with the development of MACE at the prospective one-year observation (AUC\u0026thinsp;=\u0026thinsp;0,565; р=0,368). This may be partly because NLR is affected by many conditions, including age, the presence of chronic diseases, including DM, obesity, psychiatric diagnosis, cancer, anemia and stress [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrowth differentiation factor 15, or macrophage inhibitory cytokine-1, acts as a marker of inflammation and plays a role in cardiovascular pathogenesis, metabolic disorders and neurodegenerative processes. Levels of GDF-15 in serum also increase with aging and in response to cell stress and mitochondrial dysfunction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Recently, the role of GDF-15 in aging and metabolic disorders has been actively discussed by the scientific community. Notably, the GDF-15 receptor, its underlying signaling pathways and biological effects are poorly understood [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSome studies have demonstrated the role of GDF-15 in predicting adverse outcomes in CHF, both with a reduced fraction of the left ventricle ejection [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and with a preserved fraction of the left ventricle ejection [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. At the same time, the dynamics of GDF-15 concentration in patients with decompensation of heart failure reflected the increased risk of repeated hospitalization and death in the RELAX-AHF study [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, GDF-15 remains a relatively poorly studied marker for CABG patients. There is conflicting evidence in the literature regarding the association of GDF-15 with the development of adverse cardiovascular events in CABG patients. Bouchot O. et al. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] showed that low GDF-15 values are associated with the development of postoperative AF. However, other studies have demonstrated that high GDF-15 values are unfavorable and associated with the development of AKI [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, a study by Matthias Heringlake et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] showed that the plasma GDF-15 level prior to CABG is an independent predictor of postoperative mortality and morbidity in cardiac patients. This is an important addition to known scales for risk stratification in these patients.\u003c/p\u003e \u003cp\u003eIn our cohort, there was also no statistically significant association with the development of MACEs during the one-year observation period (AUC\u0026thinsp;=\u0026thinsp;0,621; р=0,096).\u003c/p\u003e \u003cp\u003eHowever, given the wide variety of immune processes and the lack of evidence for a correlation between GDF-15 and NLR, we performed a correlation analysis between GDF-15 and NLR. No significant linear correlation was found (r= -0,184, p\u0026thinsp;=\u0026thinsp;0.102). At the same time, a visual examination of the scattering diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) led us to conclude that there was a nonlinear relationship between these parameters. This relationship, according to the distribution of points on the scattering graph, could be a hyperbola. In this case, the product of NLR and GDF-15 would have a separate predictive value. The literature does not describe such data, so we propose a scientific hypothesis. According to the ROC analysis, the AUC for predicting MACEs was 0.648 (p\u0026thinsp;=\u0026thinsp;0.011). The cutoff point for the NLR*GDF-15 product was found to be 3216. It predicts the development of MACE with a sensitivity of 68% and a specificity of 52%.\u003c/p\u003e \u003cp\u003eIt is crucial to note that the statistically significant association of the product NLR*GDF-15 with the development of MACE in patients who underwent CABG remained after the introduction of amendments to sex, age, presence of DM, and LVEF. Therefore, this indicator was an independent predictor of adverse cardiovascular events. The results are new and have not been previously obtained in other studies. This could be the basis for new scientific hypotheses and new large-scale studies.\u003c/p\u003e \u003cp\u003eStudy limitations: the main limitation is the small sample size, which is partially offset by the high homogeneity of the group, excluding any additional cardiac surgery, severe associated pathology, including inflammatory etiology. More studies are needed to confirm these results.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe value of the product indicators NLR and GDF-15 is associated with the development of adverse cardiovascular events in patients after CABG.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, G.A. and R.A.; methodology, K.E.; formal analysis, K.E. and T.O.; investigation, K.E. and T.O.; data curation, G.A.; writing\u0026mdash;original draft preparation, K.E. and T.O.; writing\u0026mdash;review and editing, K.E. and T.O.; supervision, R.A. and G.A.; funding acquisition, G.A. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the state task, FSR № 122020300045-5 (03.02.2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki and approved by the\u0026nbsp;Local Ethics Committee\u0026nbsp;of Cardiology Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia\u0026nbsp;(Protocol № 188 of 18.09.2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Data available in a publicly accessible repository\u003cbr\u003e\u0026nbsp;The data presented in this study are openly available in FigShare at doi: 10.6084/m9.figshare.23898078, reference number https://figshare.com/s/9b37a1d407c9d7908e17.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u003c/strong\u003e The following are available online at https://docs.google.com/spreadsheets/d/1zD_iIqSUfRF0RvWITtrGHHE4ehHBWUby/edit?usp=sharing\u0026amp;ouid=108698147447942789821\u0026amp;rtpof=true\u0026amp;sd=true\u003cdel cite=\"mailto:Ольга%20В.%20Тукиш\" datetime=\"2023-10-05T09:44\"\u003e\u0026nbsp;\u003c/del\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKnuuti J, Wijns W, Saraste A, ESC Scientific Document Group. 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. 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PMID: 26333529.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CABG, inflammation, neutrophil-to-lymphocyte ratio, GDF15, MACE","lastPublishedDoi":"10.21203/rs.3.rs-3397585/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3397585/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePredicting major adverse cardiovascular events (MACEs) after coronary interventions is an urgent and important task. Subclinical inflammation markers are becoming increasingly investigated for this purpose.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eTo determine the role of the neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF-15) in predicting MACE in patients after coronary artery bypass grafting (CABG).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective observational study included 80 patients with coronary artery disease who underwent CABG and were followed up for at least 1 year. In a prospective follow-up, a combined endpoint (MACE) had 27.5% (a total of 22 events).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe values of GDF-15 and NLR were comparable in groups with and without MACE. A ROC analysis showed a low AUC for NLR (AUC\u0026thinsp;=\u0026thinsp;0.566 (p\u0026thinsp;=\u0026thinsp;0.363)) and GDF-15 (AUC\u0026thinsp;=\u0026thinsp;0.621 (p\u0026thinsp;=\u0026thinsp;0.096)). The value of the product GDF-15*NLR was determined. The median was 3108.05 (2069; 4145) for patients who did not have MACE and 4108.8 (2779.4; 5890.5) for patients with MACE (p\u0026thinsp;=\u0026thinsp;0.010). This association remained after the introduction of amendments to sex, age, diabetes, and left ventricular ejection fraction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe value of the product indicators NLR and GDF-15 is associated with the development of adverse cardiovascular events in patients after CABG.\u003c/p\u003e","manuscriptTitle":"Low-grade inflammation in the prognosis of patients undergoing coronary artery bypass grafting: the value of neutrophil-to-lymphocyte ratio (NLR) and growth differentiation factor 15 (GDF15)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-10 18:31:21","doi":"10.21203/rs.3.rs-3397585/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39a1fc0b-b5b2-405c-aa19-d7e8e61485bf","owner":[],"postedDate":"October 10th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-31T03:29:37+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-10 18:31:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3397585","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3397585","identity":"rs-3397585","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}