https://orcid.org/0000-0001-8953-7549
Figures
Abstract
Background
Increasing evidences indicate that systemic inflammation plays a significant role of adverse prognosis in patients with coronary heart disease (CHD). The inflammatory burden index (IBI) is a novel biomarker that reflects systemic inflammation. The aim of this study was to investigate the association between IBI and prognosis of CHD patients.
Methods
In this retrospective analysis, data from 2453 CHD patients enrolled from December 2017 to December 2022.IBI was defined as neutrophil/lymphocyte*C-reactive protein, with patients categorized into four groups based on quartiles of baseline IBI levels. The primary outcome was adverse cardiovascular and cerebrovascular events (MACCEs) occurring during hospitalization, which included repeat revascularization, new-onset atrial fibrillation (NOAF), stroke, and all-cause in-hospital mortality. Multivariate logistic regression models and restricted cubic spline (RCS) analysis were used to investigate the association between IBI and prognosis of CHD patients.
Results
High levels of IBI were associated with higher risk of MACCEs, especially when IBI ≥ 45.68, patients exhibited a higher risk of MACCEs (P < 0.05). After adjusting for baseline confounders, multivariate logistic regression analysis demonstrated that baseline IBI was an independent predictor for NOAF (Odds Ratio (OR): 2.05; 95% confidence interval (CI): 1.30–3.24; P = 0.002) and contrast-induced nephropathy (CIN) (OR: 1.95; 95%CI: 1.16–3.28; P = 0.012) in CHD patients, mainly driven by the highest quartile. In addition, RCS confirmed a linear relationship between IBI and NOAF (P for non-linear = 0.425) and a nonlinear relationship with CIN (P for non-linear = 0.032).
Citation: Lu W, Liao X, Jiang Y, Luo B, Chen L, Lin Y (2025) Association between inflammatory burden index and prognosis in patients with coronary heart disease: A retrospective study. PLoS One 20(7): e0325287. https://doi.org/10.1371/journal.pone.0325287
Editor: Chiara Lazzeri, Azienda Ospedaliero Universitaria Careggi, ITALY
Received: December 14, 2024; Accepted: May 11, 2025; Published: July 7, 2025
Copyright: © 2025 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data could not be shared publicly due to the restrictions of the Ethics Committee of Fujian Medical University Union Hospital and the policies of the patient privacy Agency. Investigators who met the criteria for confidential data access could obtain data from the Ethics Committee of Fujian MedicalUniversity Union Hospital (xhyyllwyh@163.com).
Funding: This study was supported by The fifth batch of hospital key discipline construction projects in the form of a grant awarded to YJL (No.2022YYZDXK01) and The fifth batch of hospital key discipline construction projects in the form of a salary for YJL. The specific roles of this author are articulated in the ‘author contributions’ section. And this study was supported by Fujian Clinical Medical Research Center for Cardiac Macrovascular Diseases in the form of a grant awarded to LWC (No.2021YGPT002) and Fujian Clinical Medical Research Center for Cardiac Macrovascular Diseases in the form of a salary for LWC. The specific roles of this author are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Coronary heart disease (CHD) is the most common cardiovascular disease worldwide, with a rising trend in both morbidity and mortality, and tend to be younger. It is reported that up to 9.1 million people die from CHD every year, accounting for 49.2% of all cardiovascular disease deaths and 16.3% of all-cause mortality, which brings a heavy burden to the patient’s family and society [1–3].Although interventional and pharmacologic therapies have substantially improved survival rates among CHD patients, the occurrence of adverse cardiovascular and cerebrovascular events (MACCEs) still has a significant negative impact on the prognosis and quality of life of patients [4–6]. Therefore, there is an urgent need for simple and effective biomarkers to facilitate early risk assessment and timely treatment.
The pathogenesis of CHD is complex and related to multiple factors, among which coronary atherosclerosis (AS) is its primary pathophysiological basis [7]. In recent years, evidence indicates that inflammatory responses and inflammatory factors play a pivotal role in promoting the formation, development and rupture of AS plaques [8]. Inflammatory factors promote the activation of endothelial cells, increase vascular permeability, and promote monocyte migration to the vascular wall, where they transform into macrophages and foam cells, ultimately contributing to plaque formation [9]. In addition, inflammation not only plays a contributory role in the development and progression of CHD, but also affects the process of repair and ventricular remodeling after myocardial ischemia in patients [10]. It is worth noting that the development of AS and the injury of endothelial function will promote inflammation in the body, forming a vicious cycle and affecting the prognosis of CHD patients [11]. Studies have shown that inflammation is closely related to the occurrence of MACCEs such as stroke and cardiovascular death, among patients with coronary artery disease(CAD), with systemic anti-inflammatory therapy shown to reduce the risk of MACCEs effectively [12–14]. Therefore, inflammatory biomarkers have significant clinical value in the diagnosis, treatment and prognosis of CHD [15–17].
Recently, Xie et al. [18] combined neutrophil to lymphocyte ratio (NLR) and C-reactive protein (CRP) to develop a novel systemic inflammatory marker named inflammatory burden index (IBI). Multiple studies have suggested that IBI is a reliable prognostic marker for different tumors and can effectively predict cachexia, mortality and complications of patients [19–21].Moreover, Song et al. [22] and Du et al. [23] demonstrated that the increase of IBI was strongly associated with an increased risk of adverse outcomes and the development of complications in patients with cerebrovascular diseases, and Du et al. found that IBI had the highest predictive accuracy and reclassification indexes. These evidences suggest that IBI is a potential predictor of poor prognosis in patients. However, studies specifically examining the relationship between IBI and cardiovascular disease are limited. Whether IBI has an effect on adverse prognosis in CHD patients remains unknown. Therefore, we conducted a retrospective study to explore the correlation between IBI and the prognosis of patients with CHD.
Methods
Study population
The study population consisted of patients diagnosed with CHD [24] at the Fujian Medical University Union Hospital from December 2017 to December 2022. The first time we accessed the data was on August 20, 2023. Exclusion criteria were as follows: (1) age < 18 years, (2) coronary artery bypass grafting (CABG) and other urgent surgeries, (3) severe hepatic and renal insufficiency, (4) patients suffering from systemic inflammatory diseases, malignant tumors, hematological diseases, autoimmune diseases, and severe infectious diseases, and (5) patients with incomplete medical records. This study adhered to the principles of the Declaration of Helsinki and received approval from the Ethics Committee of Fujian Medical University Union Hospital (Ethics approval No. 2023KY032). Obtaining informed consent was waived for this study because it was a retrospective study.
Data collection
Data were collected through the hospital electronic medical record system, including general clinical data, laboratory tests, medicine care, and clinical outcomes. General clinical data included age, gender, smoking, drinking, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), left ventricular ejection fraction (LVEF), heart rate (HR), hypertension, diabetes mellitus (DM), Hyperlipidemia, chronic kidney disease (CKD), prior myocardial infarction (MI), prior percutaneous coronary intervention (PCI), prior CABG, types of CHD. Laboratory tests collected white blood cell count, red blood cell count, neutrophil, lymphocyte, platelet count, hemoglobin count, fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-L), high-density lipoprotein cholesterol (HDL-L), fibrinogen, serum creatinine (Scr), and blood urea nitrogen (BUN). Medical treatment included PCI and pharmacologic therapy, including statins, dual antiplatelet therapy dual (DAPT), diuretics, β-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor enkephalinase inhibitors (ACEI/ARB/ARNI). Fasting venous blood was collected for the first time in the morning after admission to all patients, and the blood index level was determined.
Outcome measures
The primary outcome was MACCEs occurring during hospitalization, which included repeat revascularization, new-onset atrial fibrillation (NOAF), stroke, and all-cause in-hospital mortality. All-cause hospital mortality was defined as death from any cause during hospitalization. Repeat revascularization was defined as a second stent placement in addition to the first stent during hospitalization and excluded patients who underwent CABG procedures. New-onset atrial fibrillation was defined as no history of atrial fibrillation, which was documented using routine electrocardiograms, ambulatory electrocardiograms, and electrocardiographic monitoring equipment during the outpatient or hospitalization period. Secondary outcomes were contrast-induced nephropathy (CIN) and 1-year all-cause readmission rate. CIN was defined as an increase in serum creatinine concentration of 25% or 44.2 μmol/L from baseline within 48–72 hours of exposure to a contrast agent, with the exclusion of other factors that could have contributed to acute kidney injury. The outcomes were collected primarily through an electronic medical record system. Patients included in this study were followed up for 1 year.
Statistical analysis
Statistical analyses were performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA) and R 4.4.1 software (R Statistical Calculation Foundation, Vienna, Austria), and a two-sided P < 0.05 was considered to be statistically significant differences.
Categorical variables were expressed as frequencies and percentage, and were analyzed using the chi-square test. Continuous variables were assessed for normal distribution, and variables conforming to normal distribution were expressed as the mean with standard deviation (SD), and were analyzed using the independent samples t-test. While non-normally distributed variables were expressed as median (IQR), and were analyzed using the Mann-Whitney U-test. The analysis proceeded in three stages. First, the inflammatory biomarker index (IBI) was divided into quartiles for subgroup comparison. Next, potential risk factors for clinical outcomes were identified through univariate logistic regression. Finally, independent predictors of clinical outcomes were determined using multivariate logistic regression models. Multivariate Cox regression models were used to assess all-cause readmission rate. Restricted cubic spline (RCS) analyses were used to explore the dose-response relationship between IBI and clinical outcomes. The optimal model was selected by Akaike information criterion (AIC). Variables with more than 20% missing values were not included in the analysis. Multiple interpolation was used to supplement variables with less than 20% missing values.
Results
Patient characteristics
A total of 2453 patients were finally enrolled in this study [1], and the flow chart of patient inclusion is shown in Fig 1. The mean age of the 2453 patients was 64.49 ± 10.59, and 78.4% were male. Patients were divided into four groups according to the IBI quartile for baseline characteristics analysis (Table 1). As the IBI quartiles increased, the incidence of CKD in CHD patients increased, but was accompanied by lower SBP and LVEF, and a decreased history of MI and PCI. Interestingly, the incidence of hyperlipidemia peaked in the third quartile.
Abbreviations: CHD, coronary heart disease; CABG, coronary artery bypass surgery; IBI, inflammatory burden index.
Compared to the first three groups, patients with the highest IBI level had the highest levels of white blood cells, lymphocytes, platelets, FBG, TC, LDL-L, fibrinogen, Scr and BUN, but the highest levels of TG in second quartile. In addition, we observed that the levels of red blood cells, neutrophils, hemoglobin, and HDL-L decreased as the IBI quartile increased.
In terms of medicine care, patients in the highest quartile were treated with DAPT more frequently than patients in the lowest quartile array. However, diuretics, statins, β-blockers, and ACEI/ARB/ARNI were used most frequently in the lowest quartile.
Association between IBI and CHD patient outcomes
In hospital outcomes for CHD patients, the risk of MACCEs increased with the increase of IBI quartile, including the incidence of NOAF (P = 0.003) and all-cause in-hospital mortality (P < 0.001) was statistically significant. However, no difference was observed between repeated revascularization (P = 0.814) and stroke (P = 0.922) in the four groups.
In addition, we observed a significant increase in CIN risk in the highest quartile (14.7%, P < 0.001), but there was no difference in all-cause readmission rates among the four groups (P = 0.105) (Table 2).
Multivariate logistic regression models were used to further explore the association between IBI and outcome indicators (Table 3). Model I was an unadjusted model, and as IBI increased, so did the risk of MACCEs (mainly NOAF and all-cause in-hospital mortality). Model II adjusted for sex and age, and the results were consistent with Model I. Model III adjusted for statistically significant differences in the baseline analysis (P < 0.05) and included sex, age, SBP, BMI, hyperlipidemia, LVEF, HR, CKD, prior MI, prior PCI, types of CHD, red blood cells, lymphocytes, neutrophils, platelets, hemoglobin, FBG, TC, LDL-L, HDL-L, fibrinogen, Scr, BUN, diuretics, DAPT, β-blockers, statins, ACEI/ARB/ARNI. It is worth noting that leukocytes and total cholesterol were not included in the analysis due to covariance (variance in stimulation factor (VIF)>5). The results further confirmed that increased IBI levels were independently associated with increased MACCEs (P < 0.05). Specifically, high IBI was an independent risk factor for NOAF in CHD patients (OR: 2.05; 95%CI: 1.30–3.24; P = 0.002). Although we observed that IBI levels were significantly associated with all-cause hospital mortality in a previous analysis (P < 0.001), this result was not further confirmed after adjusting for baseline confounders (OR: 5.37; 95%CI: 0.53–54.90; P = 0.156). However, no difference (P > 0.05) was observed between repeat revascularization (P = 0.739) and stroke (P = 0.936) in the different IBI groups.
Furthermore, in this study, IBI levels were significantly associated with the risk of CIN after adjusting for baseline confounders (OR: 1.95; 95% CI: 1.16–3.28; P = 0.012). The OR were 0.68, 1.02 and 1.95 for the second quartile, the third quartile and the fourth quartile compared to the first quartile. There was no statistically significant difference in all-cause readmission rates among IBI groups (P = 0.237) (Table 3).
RCS analysis was used to assessed the association between IBI and NOAF, IBI and CIN. We observed a linear relationship between continuous IBI and NOAF (P for linear = 0.425) (Fig 2), but a nonlinear relationship between continuous IBI and CIN risk (P for linear = 0.0318) (Fig 3). The risk of CIN increased with increasing levels of IBI.
Abbreviations: NOAF, new-onset atrial fibrillation; OR, odds ratio; CI, confidence interval; IBI, inflammatory burden index.
Abbreviations: CIN, contrast-Induced nephropathy; OR, odds ratio; CI, confidence interval; IBI, inflammatory burden index.
Discussion
To the best of our knowledge, this is the first study to investigate IBI and prognosis in patients with CHD. The results of the study showed that higher levels of IBI were associated with an increased risk of NOAF and CIN compared to lower levels of IBI, which were further confirmed after adjusting for potential confounders. In addition, we investigated the dose-response relationship between IBI and NOAF and CIN in CHD patients, and the RCS confirmed a linear relationship between IBI and NOAF and a nonlinear relationship with CIN. Therefore, IBI may be a reliable independent risk factor for assessing prognosis in CHD patients.
Atherosclerosis (AS) is the primary pathophysiological basis of CHD, and the stability of AS plaques is crucial in the occurrence and development of CHD [11]. In recent decades, research data have shown that the balance between pro-inflammatory and anti-inflammatory responses in the immune system plays an important role in plaque stability and disease progression [7,25]. There is a complex pathophysiological relationship between AS, inflammation and CHD, with inflammatory responses and inflammatory factors are present throughout the whole process of the occurrence and development of CHD [26]. Therefore, inflammatory markers appear to be the most promising as a strong predictor of prognosis in patients with CHD. As a novel marker of systemic inflammation, IBI has been confirmed to be closely related to patient prognosis [22]. However, the mechanism related to the prognosis of patients with IBI and CHD remains unclear. Previous studies have shown that neutrophils secrete a large number of inflammatory mediators, anaerobic free radicals and chemotactic agents, which are focused on the site of plaque erosion, leading to endothelial cell damage and activation of macrophages, which initiates AS and promotes plaque progression [27]. The process of AS leads to apoptosis of lymphocytes co-existing in plaques, which aggravate plaque load and increases the risk of poor prognosis [28]. Neutrophils and lymphocytes combine information on innate and adaptive immunity and are effective biomarkers of prognosis in CHD patients, providing new insights into inflammatory AS [29–31]. In addition, CRP not only serves as a biomarker for AS but also contributes to thrombosis risk by impairing endothelial function, promoting endothelial dysfunction, and inducing the release of circulating endothelial cells and endothelial microparticles [32,33]. A large number of studies have demonstrated that abnormal neutrophils, lymphocytes and CRP are associated with an increased risk of poor prognosis in patients with CAD [34–37]. IBI combines the advantages of different inflammatory markers, and abnormal IBI may indicate more severe endothelial cell damage and plaque load. Therefore, IBI may have potential prognostic value for the prognosis of CHD patients.
Previous studies have shown that the level of systemic inflammation is strongly associated with MACCEs in CHD patients [38]. Consistently, in our study, we found that higher levels of IBI were associated with a higher incidence of MACCEs in CHD patients. An observational study noted that systemic inflammatory biomarkers were independent predictors of MACCEs in CHD patients undergoing non-cardiac surgery [36]. Wada et al. observed that a high NLR was associated with poor prognosis in patients with CAD [39]. A cohort study suggested that with the use of high-sensitivity C-reactive protein (hs-CRP) alone, the risk of poor prognosis in patients may be incorrectly assessed, and that the risk of MACCEs was increased only when NLR was increased in concert with hs-CRP [40]. IBI was proposed by Xie et al. to reflect systemic inflammatory status and is a promising prognostic biomarker [18]. Recently, more and more studies have focused on the correlation between IBI and prognosis in different populations [22,23,41]. Our findings also support this conclusion, with elevated IBI reflecting an enhanced inflammatory response in patients with CHD. All these evidences suggest that IBI is a reliable inflammatory biomarker for predicting the prognosis of CHD patients.
Our study found a positive correlation between baseline IBI and NOAF in CHD patients, driven by the highest quartile of IBI, and a linear and increasing relationship between the IBI and NOAF. Similar to our findings, Nortamo et al. found that hs-CRP was closely associated to the occurrence of NOAF in CAD [42]. Ali et al. confirmed that the systemic immune inflammatory index is a predictor of NOAF after ST-segment elevation MI [43]. Inflammatory markers that are prevalent in CHD patients, such as leukocytes and CRP, are thought to be closely associated with electrophysiologic stability of the heart [36,37,44]. Inflammatory mediators increase the risk of atrial fibrillation (AF) by remodeling cardiac tissue and neurophysiology, leading to structural changes in the cardiac muscle and the conductive system, altering the electrophysiological properties of the atria [45,46]. Notably, AF also induces AS and promotes thrombosis, further promoting or exacerbating CHD [47]. Therefore, it can be hypothesized that controlling the inflammatory response may help reduce the risk of NOAF in CHD patients. In addition, our study showed that IBI was associated with all-cause in-hospital mortality, but there was no significant correlation between IBI and all-cause in-hospital mortality after adjusting for baseline confounders. This contradicts the results of existing studies [23] which may be caused by differences in inflammatory response, duration of inflammation and follow-up time of outcomes in different disease populations. Therefore, long-term large-sample studies are needed for further exploration in the future.
In recent years, with the extensive development of interventional surgery in clinical practice, the incidence of CIN caused by the sharp increase in the use of contrast agents also shows a corresponding increasing trend, which seriously affects the prognosis of patients [48]. Factors related to CIN include renal medullary ischemia and hypoxia, oxidative stress, endothelial dysfunction and inflammatory response, etc. however, the formation mechanism of CIN is complex and still completely unclear [49]. Currently, most studies agree that inflammatory response plays a crucial role in the onset and progression of CIN [48–50]. Some inflammatory markers, such as NLR, CRP and red blood cell distribution width, have been proposed to be closely related to the occurrence of CIN after PCI [51]. Butt et al. Found that NLR had good accuracy in predicting CIN in CAD [52]. Consistently, after adjusting for confounders, we observed that high levels of IBI were an independent risk factor for CIN in CHD patients, and RCS results further revealed a nonlinear relationship between IBI and CIN risk. This provides further support for the predictive value of inflammatory markers in CIN. Previous animal experiments have found inflammatory factors increase significantly after contrast agent administration, causing kidney tissue damage and thus inducing acute kidney injury. In addition, nephroenzymes and antithrombin can improve kidney injury by inhibiting inflammatory response [53,54]. We also noticed that low IBI was accompanied by high CIN, which may be associated with lymphocytosis and can exacerbate renal injury by releasing pro-inflammatory cytokines, activating NLRP3 inflammatory vesicles, cytotoxicity, and immune cell infiltration further exacerbating inflammation [55,56]. Therefore, the systemic inflammation reflected by IBI may, to some extent, reflect the kidney injury and help prevent CIN. Therefore, early identification of high-risk patients can help provide clinical potential intervention strategies to reduce the incidence of CIN and improve patient prognosis.
Limitations
In this study, we not only evaluated the prognostic value of IBI in CHD patients, but also explored the dose-response relationship between IBI and prognosis to provide strategies for future prevention and improvement of CHD prognosis. However, our study has several limitations. First, this study was a retrospective, single-center study. There may be deficiencies in patient selection, study design and generalization. Secondly, the level of IBI changes dynamically, and the correlation between the dynamic changes of IBI and prognosis was not explored in this study. Therefore, future multi-center, prospective studies are needed for validation.
Conclusion
We applied the inflammatory biomarker index (IBI) to patients with coronary heart disease (CHD) for the first time and confirmed that it is a valid marker for predicting prognosis in this population. IBI was independently associated with the risk of NOAF and CIN in CHD patients. Additionally, a nonlinear relationship was observed between IBI and CIN risk in these patients. Therefore, IBI may be a novel inflammatory marker to predict the prognosis of CHD patients.
References
- 1. Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347–913. pmid:38264914
- 2. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. pmid:33309175
- 3. Mensah GA, Roth GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risk Factors: 2020 and Beyond. J Am Coll Cardiol. 2019;74:2529–32.
- 4. Yang J, Zhou Y, Zhang T, Lin X, Ma X, Wang Z, et al. Fasting blood glucose and HbA1c correlate with severity of coronary artery disease in elective PCI patients with HbA1c 5.7% to 6.4. Angiology. 2020;71(2):167–74. pmid:31749367
- 5. Reardon CA, Lingaraju A, Schoenfelt KQ, Zhou G, Cui C, Jacobs-El H, et al. Obesity and insulin resistance promote atherosclerosis through an IFNγ-regulated macrophage protein network. Cell Rep. 2018;23(10):3021–30. pmid:29874587
- 6. Jernberg T, Hasvold P, Henriksson M, Hjelm H, Thuresson M, Janzon M. Cardiovascular risk in post-myocardial infarction patients: nationwide real world data demonstrate the importance of a long-term perspective. Eur Heart J. 2015;36(19):1163–70. pmid:25586123
- 7. Ketelhuth DFJ, Hansson GK. Modulation of autoimmunity and atherosclerosis - common targets and promising translational approaches against disease. Circ J. 2015;79(5):924–33. pmid:25766275
- 8. Libby P, Loscalzo J, Ridker PM, Farkouh ME, Hsue PY, Fuster V, et al. Inflammation, immunity, and infection in atherothrombosis: JACC review topic of the week. J Am Coll Cardiol. 2018;72(17):2071–81. pmid:30336831
- 9. Wolf D, Ley K. Immunity and inflammation in atherosclerosis. Circ Res. 2019;124(2):315–27. pmid:30653442
- 10. Prabhu SD, Frangogiannis NG. The biological basis for cardiac repair after myocardial infarction: from inflammation to fibrosis. Circ Res. 2016;119(1):91–112. pmid:27340270
- 11. Raggi P, Genest J, Giles JT, Rayner KJ, Dwivedi G, Beanlands RS, et al. Role of inflammation in the pathogenesis of atherosclerosis and therapeutic interventions. Atherosclerosis. 2018;276:98–108. pmid:30055326
- 12. Libby P, Ridker PM, Hansson GK, Leducq Transatlantic Network on Atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129–38. pmid:19942084
- 13. Libby P. Inflammation in atherosclerosis-no longer a theory. Clin Chem. 2021;67(1):131–42. pmid:33393629
- 14. Ajala ON, Everett BM. Targeting inflammation to reduce residual cardiovascular risk. Curr Atheroscler Rep. 2020;22(11):66. pmid:32880743
- 15. Amezcua-Castillo E, González-Pacheco H, Sáenz-San Martín A, Méndez-Ocampo P, Gutierrez-Moctezuma I, Massó F, et al. C-reactive protein: the quintessential marker of systemic inflammation in coronary artery disease-advancing toward precision medicine. Biomedicines. 2023;11(9):2444. pmid:37760885
- 16. Salzinger B, Lundwall K, Evans M, Mörtberg J, Wallén H, Jernberg T, et al. Associations between inflammatory and angiogenic proteomic biomarkers, and cardiovascular events and mortality in relation to kidney function. Clin Kidney J. 2024;17(3):sfae050. pmid:38524235
- 17. Algoet M, Janssens S, Himmelreich U, Gsell W, Pusovnik M, Van den Eynde J, et al. Myocardial ischemia-reperfusion injury and the influence of inflammation. Trends Cardiovasc Med. 2023;33(6):357–66. pmid:35181472
- 18. Xie H, Ruan G, Ge Y, Zhang Q, Zhang H, Lin S, et al. Inflammatory burden as a prognostic biomarker for cancer. Clin Nutr. 2022;41(6):1236–43. pmid:35504166
- 19. Yin C, Okugawa Y, Kitajima T, Shimura T, Ma R, Kawamura M, et al. Clinical significance of the preoperative inflammatory burden index in esophageal cancer. Oncology. 2024;102(7):556–64. pmid:38142688
- 20. Pelc Z, Sędłak K, Mlak R, Leśniewska M, Mielniczek K, Rola P, et al. Prognostic value of inflammatory burden index in advanced gastric cancer patients undergoing multimodal treatment. Cancers (Basel). 2024;16(4):828. pmid:38398218
- 21. Ding P, Wu H, Liu P, Sun C, Yang P, Tian Y, et al. The inflammatory burden index: a promising prognostic predictor in patients with locally advanced gastric cancer. Clin Nutr. 2023;42(2):247–8. pmid:36653262
- 22. Song Z, Lin F, Chen Y, Li T, Li R, Lu J, et al. inflammatory burden index: association between novel systemic inflammatory biomarkers and prognosis as well as in-hospital complications of patients with aneurysmal subarachnoid hemorrhage. J Inflamm Res. 2023;16:3911–21. pmid:37692059
- 23. Du M, Xu L, Zhang X, Huang X, Cao H, Qiu F, et al. Association between inflammatory burden index and unfavorable prognosis after endovascular thrombectomy in acute ischemic stroke. J Inflamm Res. 2023;16:3009–17. pmid:37489151
- 24.
American Medical Association. The Complete Official Codebook: ICD-10-CM. Eden Prairie, MN: Optum360, LLC.; 2016.
- 25. Forteza MJ, Berg M, Edsfeldt A, Sun J, Baumgartner R, Kareinen I, et al. Pyruvate dehydrogenase kinase regulates vascular inflammation in atherosclerosis and increases cardiovascular risk. Cardiovasc Res. 2023;119(7):1524–36. pmid:36866436
- 26. Kong P, Cui Z-Y, Huang X-F, Zhang D-D, Guo R-J, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7(1):131. pmid:35459215
- 27. Shirakawa K, Sano M. Neutrophils and neutrophil extracellular traps in cardiovascular disease: an overview and potential therapeutic approaches. Biomedicines. 2022;10(8):1850. pmid:36009397
- 28. Hofmann U, Frantz S. Role of lymphocytes in myocardial injury, healing, and remodeling after myocardial infarction. Circ Res. 2015;116(2):354–67. pmid:25593279
- 29. Adamstein NH, MacFadyen JG, Rose LM, Glynn RJ, Dey AK, Libby P, et al. The neutrophil-lymphocyte ratio and incident atherosclerotic events: analyses from five contemporary randomized trials. Eur Heart J. 2021;42(9):896–903. pmid:33417682
- 30. Tudurachi B-S, Anghel L, Tudurachi A, Sascău RA, Stătescu C. Assessment of inflammatory hematological ratios (NLR, PLR, MLR, LMR and monocyte/hdl-cholesterol ratio) in acute myocardial infarction and particularities in young patients. Int J Mol Sci. 2023;24(18):14378. pmid:37762680
- 31. Gaggini M, Minichilli F, Gorini F, Turco SD, Landi P, Pingitore A, et al. FIB-4 index and neutrophil-to-lymphocyte-ratio as death predictor in coronary artery disease patients. Biomedicines. 2022;11(1):76. pmid:36672584
- 32. Devaraj S, Kumaresan PR, Jialal I. C-reactive protein induces release of both endothelial microparticles and circulating endothelial cells in vitro and in vivo: further evidence of endothelial dysfunction. Clin Chem. 2011;57(12):1757–61. pmid:21980169
- 33. Konijnenberg LSF, Damman P, Duncker DJ, Kloner RA, Nijveldt R, van Geuns R-JM, et al. Pathophysiology and diagnosis of coronary microvascular dysfunction in ST-elevation myocardial infarction. Cardiovasc Res. 2020;116(4):787–805. pmid:31710673
- 34. Chen Y, Chen S, Han Y, Xu Q, Zhao X. Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are important indicators for predicting in-hospital death in elderly AMI patients. J Inflamm Res. 2023;16:2051–61. pmid:37215380
- 35. Liu G-Q, Zhang W-J, Shangguan J-H, Zhu X-D, Wang W, Guo Q-Q, et al. Association of derived neutrophil-to-lymphocyte ratio with prognosis of coronary heart disease after PCI. Front Cardiovasc Med. 2021;8:705862. pmid:34604350
- 36. Larmann J, Handke J, Scholz AS, Dehne S, Arens C, Gillmann H-J, et al. Preoperative neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are associated with major adverse cardiovascular and cerebrovascular events in coronary heart disease patients undergoing non-cardiac surgery. BMC Cardiovasc Disord. 2020;20(1):230. pmid:32423376
- 37. Yu L, Sun J, Liu X. Serum C reactive protein and procalcitonin are valuable predictors of coronary heart disease and poor prognosis in the elderly. Am J Transl Res. 2023;15(6):4188–95. pmid:37434846
- 38. Fan W, Wei C, Liu Y, Sun Q, Tian Y, Wang X, et al. The prognostic value of hematologic inflammatory markers in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Clin Appl Thromb Hemost. 2022;28. pmid:36567485
- 39. Wada H, Dohi T, Miyauchi K, Nishio R, Takeuchi M, Takahashi N, et al. Neutrophil to lymphocyte ratio and long-term cardiovascular outcomes in coronary artery disease patients with low high-sensitivity C-reactive protein level. Int Heart J. 2020;61(3):447–53. pmid:32418963
- 40. He J, Song C, Zhang R, Yuan S, Li J, Dou K. Discordance between neutrophil to lymphocyte ratio and high sensitivity C-reactive protein to predict clinical events in patients with stable coronary artery disease: a large-scale cohort study. J Inflamm Res. 2023;16:5439–50. pmid:38026249
- 41. He C, Wu D, Wei X, Li Y, Liao Y, Yang D. Association between inflammatory burden index and all-cause mortality in the general population aged over 45 years: Data from NHANES 2005-2017. Nutr Metab Cardiovasc Dis. 2024;34(1):64–74. pmid:38016891
- 42. Nortamo S, Ukkola O, Lepojärvi S, Kenttä T, Kiviniemi A, Junttila J, et al. Association of sST2 and hs-CRP levels with new-onset atrial fibrillation in coronary artery disease. Int J Cardiol. 2017;248:173–8. pmid:28942872
- 43. Bağcı A, Aksoy F. Systemic immune-inflammation index predicts new-onset atrial fibrillation after ST elevation myocardial infarction. Biomark Med. 2021;15(10):731–9. pmid:34155910
- 44. Korantzopoulos P, Letsas KP, Tse G, Fragakis N, Goudis CA, Liu T. Inflammation and atrial fibrillation: a comprehensive review. J Arrhythm. 2018;34(4):394–401. pmid:30167010
- 45. Heijman J, Voigt N, Nattel S, Dobrev D. Cellular and molecular electrophysiology of atrial fibrillation initiation, maintenance, and progression. Circ Res. 2014;114(9):1483–99. pmid:24763466
- 46. Lau DH, Nattel S, Kalman JM, Sanders P. Modifiable risk factors and atrial fibrillation. Circulation. 2017;136(6):583–96. pmid:28784826
- 47. Liang F, Wang Y. Coronary heart disease and atrial fibrillation: a vicious cycle. Am J Physiol Heart Circ Physiol. 2021;320(1):H1–12. pmid:33185113
- 48. Zhang F, Lu Z, Wang F. Advances in the pathogenesis and prevention of contrast-induced nephropathy. Life Sci. 2020;259:118379. pmid:32890604
- 49. Azzalini L, Spagnoli V, Ly HQ. Contrast-induced nephropathy: from pathophysiology to preventive strategies. Can J Cardiol. 2016;32(2):247–55. pmid:26277092
- 50. Wang Z, Wang Q, Gong X. Unveiling the mysteries of contrast-induced acute kidney injury: new horizons in pathogenesis and prevention. Toxics. 2024;12(8):620. pmid:39195722
- 51. Mahapatro A, Nobakht S, Mukesh S, Daryagasht AA, Korsapati AR, Jain SM, et al. Evaluating biomarkers for contrast-induced nephropathy following coronary interventions: an umbrella review on meta-analyses. Eur J Med Res. 2024;29(1):210. pmid:38561791
- 52. Butt K, D’Souza J, Yuan C, Jayakumaran J, Nguyen M, Butt HI, et al. Correlation of the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) with contrast-induced nephropathy in patients with acute coronary syndrome undergoing percutaneous coronary interventions. Cureus. 2020;12(12):e11879. pmid:33415032
- 53. Wang F, Yin J, Lu Z, Zhang G, Li J, Xing T, et al. Limb ischemic preconditioning protects against contrast-induced nephropathy via renalase. EBioMedicine. 2016;9:356–65. pmid:27333047
- 54. Lu Z, Cheng D, Yin J, Wu R, Zhang G, Zhao Q, et al. Antithrombin III protects against contrast-induced nephropathy. EBioMedicine. 2017;17:101–7. pmid:28219627
- 55. Lee K, Jang HR, Rabb H. Lymphocytes and innate immune cells in acute kidney injury and repair. Nat Rev Nephrol. 2024;20(12):789–805. pmid:39095505
- 56. Cao C, Yao Y, Zeng R. Lymphocytes: versatile participants in acute kidney injury and progression to chronic kidney disease. Front Physiol. 2021;12:729084. pmid:34616308