Inflammatory and neutrophil extracellular trap markers to predict cardiac events after ST-segment elevation myocardial infarction

Abstract

Background and aims

Inflammation plays a pivotal role in the pathophysiology of ST-elevation myocardial infarction (STEMI). This involves neutrophil activation and the local release of pro-inflammatory mediators. The formation of neutrophil extracellular traps (NETs) in coronary thrombosis has been linked to poor short-term prognosis following STEMI, but the usefulness of specific circulating NET components as prognostic markers is unclear. We aimed to evaluate the NET-specific marker nucleosomal citrullinated histone H3 (H3Cit-DNA) and other classical inflammatory markers to predict adverse events after STEMI.

Methods

This is a single-center retrospective cohort study of patients with STEMI undergoing primary percutaneous coronary intervention (PCI) from 2015 to 2019. We analyzed the association between serum H3Cit-DNA levels, double-stranded DNA, and classical inflammatory markers –such us interleukin (IL) 6 and 1β, TNF-α, and C-reactive protein (CRP)– on admission and the occurrence of major cardiovascular events (MACE), including death, reinfarction, urgent revascularization, or heart failure, after STEMI.

Results

A total of 487 patients were studied, of which 380 were men [78%]; mean [SD] age of patients was 63 [13] years, and median [95%CI] follow-up was 5.4 [5.2-5.5] years. Median [IQR] H3Cit level was 179.30 [105.30-281.47] ng/ml. No relevant association was found between H3Cit-DNA levels and 30-day mortality (OR, 1.03 [95%CI, 0.71-1.50], p = 0.861) or MACE (0.98 [0.72-1.32], p = 0.879), Killip class (0.95 [0.74-1.21], p = 0.664), or left ventricular ejection fraction (ref.cat. > 50%; < 35%, RRR 1.01 [95%CI, 0.74-1.38], p = 0.952; 35-50%, 1.26 [1.07-1.48], p = 0.005]. Adding CRP and IL-6 levels as covariates to a model based on classical risk factors significantly improved the prediction of MACE at 30 days after STEMI (IDI 0.13; NRI 0.32, p < 0.05).

Conclusions

Circulating levels of the NET marker H3Cit-DNA at the time of primary PCI were not predictive of cardiovascular events following STEMI. In contrast, the classical inflammatory markers CRP and interleukin-6 significantly enhanced the discriminative capacity of a clinical 30-day risk prediction model. These findings suggest that measuring circulating NET-specific markers may have limited utility in assessing the inflammatory state during the early stages of STEMI.

Introduction

Inflammation plays a pivotal role in the pathophysiology of atherosclerosis. In ST-elevation myocardial infarction (STEMI), the initial inflammatory response is typically triggered by plaque rupture and coronary occlusion at the culprit lesion site. This process encompasses the activation of polymorphonuclear neutrophils (PMN), the recruitment of monocytes, and the local release of pro-inflammatory mediators, such as interleukins (IL) [1].

The acute cytokine surge enhances neutrophil activation, promotes the release of granule enzymes such as myeloperoxidase (MPO) and catalase, and triggers an oxidative response [2]. During this phase of post-STEMI inflammation, processes that worsen the detrimental effects of inflammation –particularly involving key cytokines (e.g., IL-1 and IL-6) and PMN-induced neutrophil extracellular trap (NET) formation– are especially active, further amplifying their harmful impact [3].

Local inflammation evolves into a systemic response characterized by elevated inflammatory biomarkers, making early pro-inflammatory markers valuable tools for risk stratification after STEMI. Among the biomarkers of inflammation in atherosclerotic cardiovascular disease, C-reactive protein (CRP) is the most extensively studied. CRP, an acute-phase protein, is predominantly produced by hepatocytes under the influence of cytokines such as IL-6 and tumor necrosis factor-α [4]. Elevated CRP levels are associated with an increased risk of atherosclerotic cardiovascular events. Some but not all studies have found that serum СRР predicts the risk of a recurrent in-hospital cardiac event [5] or 30-day or long-term mortality after STEMI [6,7].

Interleukin-1β amplifies inflammation by promoting its own expression in various cell types and stimulating the production of IL-6 [8]. Blocking IL-1β and IL-6 receptors with specific antibodies has been shown to reduce circulating CRP levels [9,10]. Elevated IL-6 levels are associated with an increased risk of major adverse cardiovascular events (MACE) following myocardial infarction [11].

On the other hand, emerging experimental and clinical evidence indicates that the formation of NETs in coronary thrombosis exerts harmful effects. NETs are modified chromatin structures released by neutrophils in response to specific stimuli, bound to cytoplasmic and granular proteins. Although NETs play a protective role against pathogens, dysregulated NETs exhibit strong proinflammatory and prothrombotic properties. In patients with STEMI, the presence of NETs in coronary thrombi is associated with larger infarcts and worse short-term outcomes [12–14]. The NET-specific circulating marker, citrullinated histone H3 DNA complex (H3Cit-DNA), plays a central role in NET formation. A standardized detection method is now available [15], although its clinical applicability in this context remains uncertain.

The primary objective of this study was to assess the utility of peripheral H3Cit-DNA levels and classical inflammatory markers in predicting adverse outcomes after STEMI.

Patients and methods

We conducted a retrospective cohort study of patients with STEMI who underwent primary percutaneous coronary intervention (PPCI) between January 1, 2015 and January 5, 2019 in a tertiary academic hospital in Madrid. This project adhered to the Declaration of Helsinki and was approved by the Ethics Committee of our institution (Project ID 07.18). Data collection was based on a review of the participants’ medical records, and it was guaranteed that all persons involved in the study would respect the confidentiality of all patient information. Patients were identified by a numerical code to ensure the confidentiality of their personal data. The equivalence between the code and the patient was known only to the project coordinator. Data access date for research purposes was March 1, 2022.

Patients

All patients with a final diagnosis of STEMI who presented to the hospital within 12 hours of symptom onset were consecutively included in the study. Patients who underwent more than one PPCI during the study period were included only once. All patients were of legal age and signed an informed consent form.

STEMI was defined as ST-segment elevation > 0.2 mV in 2 or more contiguous chest leads, > 0.1 mV in 2 or more limb leads, or new left bundle branch block on the electrocardiogram, combined with symptoms of myocardial ischemia and troponin I levels > 99th percentile. Diabetes mellitus was defined by previous diagnosis or random plasma glucose > 200 mg/dL on admission. Hypertension and dyslipidemia were defined by active drug treatment on admission or previous diagnosis. Previous ischemic heart disease was determined by history of myocardial infarction (MI), PCI, or coronary artery bypass grafting.

Epidemiologic, clinical, and angiographic data were collected from electronic medical records and coronary angiograms. Glycoprotein IIb/IIIa (GPIIbIIIa) inhibitors were administered at the discretion of the interventional cardiologist; their use was modified during the study period according to scientific evidence and guideline recommendations [16,17]. Patients were followed up routinely in the cardiology office or by a pre-consented telephone interview.

Serum samples

Blood samples were collected immediately after PPCI and centrifuged at 3,500 rpm for 5 minutes. The separated serum was stored in aliquots at -80 °C in the biobank until analysis.

Analysis of H3Cit-DNA.

The H3Cit-DNA ELISA is described in detail here [15]. Briefly, calibration standards were prepared from H3R2,8,17 Cit dNucs (EpiCypher #16-1362) in a twofold dilution series at 1000, 500, 250, 125, 62.5, 31.3, 15.6 and 0 ng/mL in standard diluent (50 mmol/L Tris-HCl pH 7.5, 300 mmol/L NaCl, 0.01% [w/v] BSA, 0.01% [v/v] Tween-20). Uncoated high binding clear 96-well plates (Thermofisher #3855) were coated with an anti-H3R8Cit monoclonal capture antibody (Abcam ab232939). A horseradish peroxidase-conjugated anti-DNA antibody (Roche #11774425001, reconstituted according to the manufacturer’s instructions) was used as the detection antibody.

Detection of circulating double-stranded cell-free DNA (dsDNA).

The Quant-it PicoGreen dsDNA Kit (Invitrogen) and the Fluoroskan Ascent FL Kit with 485 nm (excitation) and 538 nm (emission) wavelength filters were used for dsDNA quantification.

Determination of serum levels of inflammatory markers.

The cytokines IL-6, TNFα, and IL-1β were determined by ELISA (Elabscience, China). The optical density (OD) was measured at 450 nm. The detection ranges were 15.63-1000 pg/mL for TNFα, 12.5-800 pg/mL for IL-6, and 31.25-2000 pg/mL for IL-1β. C-reactive protein was quantified by latex-enhanced immunoturbidimetric assay. The lower detection limit was 0.05 mg/dL. The main resources used in the analyses are listed online (S1 Table).

Statistical analysis

Descriptive analysis was performed using mean and standard deviation or median and 25th and 75th percentiles, as appropriate. H3Cit-DNA was log2 transformed to obtain a log-normal distribution. Absolute and relative frequencies were used to describe categorical variables. Median follow-up was determined using the reverse Kaplan-Meier estimator. Survival functions were estimated by the Kaplan-Meier method.

Univariate analysis of the association between inflammatory markers and four clinical outcomes was performed using binary logistic regression (Killip classification 0/I vs II/III, 30-day MACE, and 30-day mortality after STEMI) or multinomial logistic regression (left ventricular ejection fraction [LVEF] < 35%; 35-50%; > 50%). In the logistic regression analysis, all inflammatory marker values were included, whether above or below the detection limit of the corresponding technique.

A prediction model for MACE at 30 days was developed using the following approach. First, all inflammatory markers were evaluated in a multivariable logistic regression model using an automated backward strategy based on p-values <0.05 to retain variables in the model. We refer to this as the ‘inflammatory marker model’. Second, we created another logistic model based on the classical risk factors: age, sex, smoking, hypertension, dyslipidemia, diabetes, previous ischemic heart disease, Killip class at admission, culprit artery of the infarction, affected arterial segment, number of coronary vessels with significant disease, TIMI classification after PPCI, and LVEF at discharge. Again, using an automated regression strategy based on p-values < 0.05, a “clinical model” was obtained. Finally, using a backward parsimonious strategy, a “clinical-inflammatory model” was developed by merging the two previous models. To assess whether the “clinical-inflammatory model” had a better discriminatory ability than the “clinical model”, the area under the receiver operating characteristic (AUC) was estimated for both models, as well as the reclassification ability by integrated discrimination improvement (IDI) and net reclassification improvement (NRI). We compared the models using a likelihood-ratio test.

A nomogram was developed for simplicity and applicability. Bootstrapping internal validation of the clinical-inflammatory model was performed with 500 resamples. Calibration was assessed using a calibration plot to provide calibration in the large (CITL) as well as slope. Discrimination was assessed using the c-statistic, which is equivalent to the area under the receiver operating characteristic (ROC) curve. The significance level was set at 0.05. Stata v. 18 was used for statistical analysis.

Results

During the study period, 621 urgent coronary angiograms were performed, and the final diagnosis was STEMI in 563 cases. Patients who underwent more than one PPCI during the study period (n = 21) were included only once, and 24 patients admitted more than 12 hours after symptom onset were excluded. In 31 of the 518 eligible patients, the serum sample was insufficient, lost, or not collected at the time of PPCI. We performed a sensitivity analysis in this subgroup, and they are comparable to the remaining 487 patients in terms of baseline variables. Therefore, 487 patients were included in the study. A flow chart of the patients is shown in Fig 1.

[Figure omitted. See PDF.]

IL-6, interleukin 6; IL-1β, interleukin 1 beta; LBBB, left bundle branch block, MINOCA, myocardial infarction and non‐obstructed coronary arteries; PPCI, primary percutaneous coronary intervention; SCD, sudden cardiac death; STEMI, ST-elevation myocardial infarction; TNFα, tumor necrosis factor alpha.

The mean age of the patients was 63 (SD 13) years and 78% were male. A history of coronary artery disease was recorded in 14% of patients. Before admission, 13 patients (2.7%) were on oral anticoagulation, 98 (20%) were on single or dual antiplatelet therapy, and 160 (33%) were taking statins. Prior to PPCI, a loading dose of acetylsalicylic acid and a second antiplatelet agent were administered: clopidogrel (in 60.8% of patients), ticagrelor (23.0%), or prasugrel (14.5%). Fifty-three percent of patients received a gpIIb/IIIa inhibitor (eptifibatide) during and/or after the procedure. The majority (92%) were revascularized with at least one stent, which was a drug-eluting stent in 76% of cases. Table 1 summarizes baseline patient characteristics and serum levels of H3Cit-DNA and other inflammatory markers measured at the time of PPCI.

[Figure omitted. See PDF.]

Median (95% CI) follow-up was 5.4 (5.2-5.5) years. The proportion of patients with MACE after STEMI was 6.4% (31 patients/n = 485) at 1 month, 14.4% (70/485) at 1 year and 17.9% (87/485) at 2 years. Mortality was 4.1% (20/487) at 1 month, 6.6% (32/487) at 1 year, and 7.8% (38/487) at 2 years. The adverse events recorded during the first month after STEMI included 20 deaths, 5 cases of re-STEMI, and 6 episodes of heart failure requiring hospitalization. During this period, all deaths were cardiac-related, and no patients were lost to follow-up.

Table 2 shows the univariable analysis of the association between inflammatory markers and the four clinical endpoints (Killip classification, LVEF and MACE, and 30-day mortality after STEMI). Serum H3Cit-DNA levels (median [IQR] 179.30 [105.30-281.47] ng/mL) were not associated with any outcomes, except for a mild to moderate reduction in LVEF (35–50%) compared to normal LVEF (reference category: > 50%). The relative risk ratios (RRR) were as follows: < 35%, 1.01 (95% CI, 0.74–1.38; p = 0.952); 35–50%, 1.26 (95% CI, 1.07–1.48; p = 0.005). CRP, IL-6, TNFα, and IL-1β were detectable in 80%, 41%, 12%, and 8% of patients, respectively (Table 1). C-reactive protein and IL-6 levels were significantly associated with all four clinical endpoints in univariable analysis. Both dsDNA and TNFα were significantly associated with 30-day mortality, Killip class, and 30-day MACE.

[Figure omitted. See PDF.]

The “inflammatory marker model” incorporated CRP and IL-6 to predict 30-day MACE. The initial variables included in the clinical model were age, sex, smoking status, hypertension, dyslipidemia, diabetes, previous ischemic heart disease (IHD), Killip class on admission, TIMI flow after PCI, number of diseased vessels, the culprit coronary artery and segment affected, and LVEF at discharge. After applying a backward selection strategy, the final clinical model for 30-day MACE included previous IHD, Killip class, final TIMI flow, and hypertension. The “clinical-inflammatory model” was developed by combining the final clinical and inflammatory marker models. Results of the 30-day analysis are presented in Table 3.

[Figure omitted. See PDF.]

At 30 days, the addition of CRP and IL-6 significantly enhanced the clinical model’s performance (likelihood-ratio test p < 0.0001; AUC 0.866 vs. 0.884; NRI 31.7% and IDI 13%). The nomogram for the 30-day MACE prediction model is shown in Fig 2.

[Figure omitted. See PDF.]

In terms of internal validation, the CITL and slopes after bootstrapping were 0.005 (95%CI -0.471; 0.546) and 0.906 (95%CI 0.601; 1.186) for the 30-day MACE model. The optimism-adjusted c-statistic was 0.873 (95%CI 0.798; 0.943). Calibration plot of 30-day MACE prediction model shown as supplemental data (S1 Fig).

Discussion

This study evaluated the short-term prognostic value of classical inflammatory markers and the NET-specific marker H3Cit-DNA within 30 days after STEMI. To our surprise, we found no association between H3Cit-DNA and adverse outcomes after STEMI. Additionally, H3Cit-DNA levels were not associated with intermediate variables, such as admission Killip class or discharge LVEF. Instead, C-reactive protein and interleukin 6 significantly enhanced the discriminative ability of a 30-day STEMI clinical risk prediction model.

NETs in coronary thrombi from patients with STEMI [12,18–21] have been directly related to infarct size and inversely related to ST-segment resolution [12]. More recently, NET formation in coronary thrombi has also been associated with patient outcome after STEMI [13]. Moreover, Hofbauer et al [14] found a remarkable local increase of monocyte chemoattractant protein (MCP)-1 and NET markers [dsDNA and H3Cit] in the culprit artery of the infarct and described a mutual induction of NETs and MCP-1, which in turn was associated with recurrent cardiovascular events [22] and all-cause mortality after myocardial infarction[23].

Our group demonstrated a high NET burden in coronary thrombi of a small series of patients with COVID-19 [24], suggesting that NETs play a pivotal role in coronary thrombosis in severe SARS-CoV-2 infection, similar to what has been demonstrated in other organs. Thus, this condition may be paradigmatic for an inflammatory state predisposing to STEMI.

Therefore, finding a reliable circulating NET marker in patients with STEMI seems to be a noteworthy goal and has been pursued by several investigators [25–27]. There is evidence of an association between post-infarction dsDNA levels and the occurrence of adverse events after STEMI, but dsDNA is a non-specific marker of NETosis, which is also associated with cell destruction. In contrast, this association is not clear for NET-specific markers. In the largest series (259 patients), Langseth et al [25] found a direct association between dsDNA levels and the occurrence of adverse clinical events one year after STEMI; such an association was not observed for two NET-specific markers, MPO–DNA complex and H3Cit. In this study, serum samples were collected at a median of 18 hours following PCI.

In a smaller series [26] involving 83 patients with similar characteristics, dsDNA, MPO-DNA complex, and NETs-related tissue factor were analyzed in patients undergoing PCI within the first 12 hours after STEMI. Plasma samples were collected from the infarct-related artery (IRA) and the radial artery during PCI. All three markers were significantly higher in samples obtained from the IRA compared to peripheral arteries. Conversely, no significant difference was observed in TNFα levels between coronary and peripheral blood. Only coronary dsDNA was independently associated with the development of in-hospital MACE.

Until recently, determination of H3Cit in peripheral blood has been unreliable. Thålin et al [15] have shown that the detection of enzymatically citrullinated H3 proteins is hampered by large enzyme-dependent batch variability as well as their instability in plasma, and that most commercially available antibodies against intrapeptidyl citrulline have poor specificity for the described target. These investigators have recently developed a new assay using highly specific monoclonal antibodies and semisynthetic nucleosomes containing citrulline instead of arginine at histone H3, arginine residues 2, 8 and 17 (H3R2,8,17Cit) as calibration standards. Validation of the assay demonstrated its ability to accurately and reliably quantify H3Cit-DNA levels in human plasma.

The lack of association between circulating H3Cit levels and outcomes after STEMI in previous studies may be due to the aforementioned technical issues. Similarly, a recent study [28] raised concerns about the detection of circulating MPO-DNA complexes, noting that the specificity of commercial ELISA kits for NET detection is “highly questionable.”

Recently, Benkhoff et al. [29] studied 361 patients with STEMI and reported an association between H3Cit-DNA levels in plasma samples collected 24 hours after presentation and MACE at 12 months. H3Cit-DNA levels were also linked to early mortality (within 30 days). This study employed the standardized method of Thålin et al. [15]. A key distinction compared to our study, which may explain these findings, is the cohort’s very high cardiovascular risk profile prior to STEMI –64% of patients had hypertension and 40% had diabetes– and a remarkably high early mortality rate of approximately 20%. Regarding H3Cit-DNA kinetics post-STEMI, Benkhoff [29] observed that levels remained relatively stable during the first few days, suggesting a persistent thromboinflammatory state. Another study [30] reported that NET markers increased significantly 30 minutes after PCI, decreased at 24 hours, but remained elevated compared to the control group.

The present study assessed inflammatory markers for their ability to predict MACE in the short term following myocardial infarction. In addition to H3Cit-DNA, the analyzed markers included dsDNA, CRP, IL-6, TNFα, and IL-1β. There is clinical evidence supporting the prognostic value of CRP and IL-6 in acute coronary syndrome; however, the relevance of IL-1β and TNF-α as prognostic markers in this context remains uncertain [3]. Our findings confirm the prognostic value of CPR and IL-6 after STEMI. In our series, CRP and IL-6 levels were above the detection threshold in 80% and 41% of patients, respectively; whereas TNFα and IL-1β were detectable in only 12% and 8% of patients. The prognostic capacity of these markers likely reflects their expression in peripheral blood post-STEMI. The low detectability of TNFα and IL-1β highlights their limited utility as prognostic indicators in this setting.

At 30 days, a model combining three clinical parameters –age, Killip class, and TIMI flow after PPCI– with CRP and IL-6 levels demonstrated excellent prognostic discriminative ability. The inclusion of CRP and IL-6 significantly enhanced the predictive performance of the clinical model by the likelihood-ratio test. Additionally, the net reclassification improvement (NRI) of 31.7% indicates that a significant proportion of individuals had their risk categories more accurately classified. The integrated discrimination improvement (IDI) of 13% further highlights the model’s enhanced ability to distinguish between individuals with and without the outcome. These findings underscore the clinical utility of incorporating CRP and IL-6 into the model, emphasizing their potential to improve risk stratification and guide decision-making.

C-reactive protein is a widely used marker of inflammation. The widespread use of IL-6 during the SARS-CoV-2 pandemic may facilitate its incorporation into clinical practice to select, together with CRP, patients with STEMI who may benefit from inflammation-targeted therapies in the future.

This study has several limitations. Firstly, H3Cit is part of the PAD4-dependent NET formation pathway [31] and we would thus not quantify PAD4-independent NET formation. However, it should be noted that H3Cit is a central player in neutrophil nuclear chromatin release and its circulating levels are useful for predicting mortality in cancer patients [32]. Second, the H3Cit-DNA ELISA was developed and optimized for quantification in citrated plasma and, although it has previously been applied on serum samples [33], we cannot exclude suboptimal performance in this medium. Lastly, this is a single-center retrospective cohort study with a limited sample size and a low rate of MACE, and external testing on larger series would be necessary to confirm our results.

Conclusions

Circulating levels of the NET marker H3Cit-DNA were not associated with cardiovascular events after STEMI. Instead, C-reactive protein and interleukin-6 were associated with MACE and significantly improved the predictive ability of a clinical 30-day risk prediction model after STEMI.

Supporting information

S1 Fig.

Calibration plots of the prediction model at 1 month.

https://doi.org/10.1371/journal.pone.0319759.s001

(TIF)

S1 Table. Key resources.

https://doi.org/10.1371/journal.pone.0319759.s002

(DOCX)

Acknowledgments

The authors thank the donors and the Biobank Hospital Universitario Puerta de Hierro-Majadahonda/Instituto de Investigación Sanitaria Puerta de Hierro-Segovia de Arana (PT17/0015/0020 in the Spanish National Biobanks Network) for the human samples used in this study. The authors are indebted to the nursing staff of the Hemodynamic laboratory, who collected the blood samples, and to the cardiologists on duty at the Coronary Care Unit, who participated in the collection of the patients’ clinical information.

References

1. 1. Maier W, Altwegg LA, Corti R, Gay S, Hersberger M, Maly FE, et al. Inflammatory markers at the site of ruptured plaque in acute myocardial infarction: locally increased interleukin-6 and serum amyloid A but decreased C-reactive protein. Circulation. 2005;111(11):1355–61. pmid:15753219

* View Article

* PubMed/NCBI

* Google Scholar

2. 2. Neumann F-J, Ott I, Gawaz M, Richardt G, Holzapfel H, Jochum M, et al. Cardiac release of cytokines and inflammatory responses in acute myocardial infarction. Circulation. 1995;92(4):748–55.

* View Article

* Google Scholar

3. 3. Matter MA, Paneni F, Libby P, Frantz S, Stähli BE, Templin C, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103. pmid:37587550

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Kushner I. The phenomenon of the acute phase response. Ann N Y Acad Sci. 1982;389:39–48. pmid:7046585

* View Article

* PubMed/NCBI

* Google Scholar

5. 5. Tomoda H, Aoki N. Prognostic value of C-reactive protein levels within six hours after the onset of acute myocardial infarction. Am Heart J. 2000;140(2):324–8. pmid:10925350

* View Article

* PubMed/NCBI

* Google Scholar

6. 6. Mega JL, Morrow DA, De Lemos JA, Sabatine MS, Murphy SA, Rifai N, et al. B-type natriuretic peptide at presentation and prognosis in patients with ST-segment elevation myocardial infarction: an ENTIRE-TIMI-23 substudy. J Am Coll Cardiol. 2004;44(2):335–9. pmid:15261928

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. Makrygiannis SS, Ampartzidou OS, Zairis MN, Patsourakos NG, Pitsavos C, Tousoulis D, et al. Prognostic usefulness of serial C-reactive protein measurements in ST-elevation acute myocardial infarction. Am J Cardiol. 2013;111(1):26–30. pmid:23040593

* View Article

* PubMed/NCBI

* Google Scholar

8. 8. Libby P. Interleukin-1 Beta as a Target for Atherosclerosis Therapy: Biological Basis of CANTOS and Beyond. J Am Coll Cardiol. 2017;70:2278-2289. https://doi.org/10.1016/j.jacc.2017.09.028.

* View Article

* Google Scholar

9. 9. Abbate A, Trankle CR, Buckley LF, Lipinski MJ, Appleton D, Kadariya D, et al. Interleukin-1 Blockade Inhibits the Acute Inflammatory Response in Patients With ST-Segment-Elevation Myocardial Infarction. J Am Heart Assoc. 2020;9(5):e014941. pmid:32122219

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Broch K, Anstensrud AK, Woxholt S, Sharma K, Tøllefsen IM, Bendz B, et al. Randomized Trial of Interleukin-6 Receptor Inhibition in Patients With Acute ST-Segment Elevation Myocardial Infarction. J Am Coll Cardiol. 2021;77:1845-1855.

* View Article

* Google Scholar

11. 11. Fanola CL, Morrow DA, Cannon CP, Jarolim P, Lukas MA, Bode C, et al. Interleukin-6 and the risk of adverse outcomes in patients after an acute coronary syndrome: observations from the SOLID-TIMI 52 (stabilization of plaque using darapladib-thrombolysis in myocardial infarction 52) trial. J Am Heart Assoc. 2017;6(10):e005637. pmid:29066436

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Mangold A, Alias S, Scherz T, Hofbauer T, Jakowitsch J, Panzenböck A, et al. Coronary neutrophil extracellular trap burden and deoxyribonuclease activity in ST-elevation acute coronary syndrome are predictors of ST-segment resolution and infarct size. Circ Res. 2015;116(7):1182–92. pmid:25547404

* View Article

* PubMed/NCBI

* Google Scholar

13. 13. Blasco A, Coronado M-J, Vela P, Martín P, Solano J, Ramil E, et al. Prognostic implications of neutrophil extracellular traps in coronary thrombi of patients with St-elevation myocardial infarction. Thromb Haemost. 2021;122(08):1415–28.

* View Article

* Google Scholar

14. 14. Hofbauer TM, Ondracek AS, Mangold A, Scherz T, Nechvile J, Seidl V, et al. Neutrophil extracellular traps induce MCP-1 at the culprit site in ST-segment elevation myocardial infarction. Front Cell Dev Biol. 2020;8:564169. pmid:33240874

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Thålin C, Aguilera K, Hall NW, Marunde MR, Burg JM, Rosell A, et al. Quantification of citrullinated histones: development of an improved assay to reliably quantify nucleosomal H3Cit in human plasma. J Thromb Haemost. 2020;18(10):2732–43. pmid:32654410

* View Article

* PubMed/NCBI

* Google Scholar

16. 16. Ibánez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Rev Esp Cardiol (Engl Ed). 2017;70(12):1082. pmid:29198432

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Terkelsen CJ, Pinto DS, Thiele H, Clemmensen P, Nikus K, Lassen JF, et al. 2012 ESC STEMI guidelines and reperfusion therapy: evidence base ignored, threatening optimal patient management. Heart. 2013;99(16):1154–6. pmid:23781110

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Maugeri N, Campana L, Gavina M, Covino C, De Metrio M, Panciroli C, et al. Activated platelets present high mobility group box 1 to neutrophils, inducing autophagy and promoting the extrusion of neutrophil extracellular traps. J Thromb Haemost. 2014;12(12):2074–88. pmid:25163512

* View Article

* PubMed/NCBI

* Google Scholar

19. 19. Riegger J, Byrne RA, Joner M, Chandraratne S, Gershlick AH, Ten Berg JM, et al. Histopathological evaluation of thrombus in patients presenting with stent thrombosis. A multicenter European study: a report of the prevention of late stent thrombosis by an interdisciplinary global European effort consortium. Eur Heart J. 2016;37(19):1538–49. pmid:26761950

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Stakos DA, Kambas K, Konstantinidis T, Mitroulis I, Apostolidou E, Arelaki S, et al. Expression of functional tissue factor by neutrophil extracellular traps in culprit artery of acute myocardial infarction. Eur Heart J. 2015;36(22):1405–14. pmid:25660055

* View Article

* PubMed/NCBI

* Google Scholar

21. 21. Novotny J, Chandraratne S, Weinberger T, Philippi V, Stark K, Ehrlich A, et al. Histological comparison of arterial thrombi in mice and men and the influence of Cl-amidine on thrombus formation. PLoS One. 2018;13(1):e0190728. pmid:29293656

* View Article

* PubMed/NCBI

* Google Scholar

22. 22. Blanco-Colio LM, Méndez-Barbero N, Pello Lázaro AM, Aceña Á, Tarín N, Cristóbal C, et al. MCP-1 predicts recurrent cardiovascular events in patients with persistent inflammation. J Clin Med. 2021;10(5):1137. pmid:33803115

* View Article

* PubMed/NCBI

* Google Scholar

23. 23. de Lemos JA, Morrow DA, Blazing MA, Jarolim P, Wiviott SD, Sabatine MS, et al. Serial measurement of monocyte chemoattractant protein-1 after acute coronary syndromes: results from the A to Z trial. J Am Coll Cardiol. 2007;50(22):2117–24. pmid:18036447

* View Article

* PubMed/NCBI

* Google Scholar

24. 24. Blasco A, Coronado M-J, Hernández-Terciado F, Martín P, Royuela A, Ramil E, et al. Assessment of neutrophil extracellular traps in coronary thrombus of a case series of patients with COVID-19 and myocardial infarction. JAMA Cardiol. 2021;6(4):469–74. pmid:33372956

* View Article

* PubMed/NCBI

* Google Scholar

25. 25. Langseth MS, Helseth R, Ritschel V, Hansen CH, Andersen GØ, Eritsland J, et al. Double-Stranded DNA and NETs components in relation to clinical outcome after ST-elevation myocardial infarction. Sci Rep. 2020;10(1):5007. pmid:32193509

* View Article

* PubMed/NCBI

* Google Scholar

26. 26. Liu J, Yang D, Wang X, Zhu Z, Wang T, Ma A, et al. Neutrophil extracellular traps and dsDNA predict outcomes among patients with ST-elevation myocardial infarction. Sci Rep. 2019;9(1):11599. pmid:31406121

* View Article

* PubMed/NCBI

* Google Scholar

27. 27. Wang X, Yang D, Liu J, Fan X, Ma A, Liu P. Prognostic value of culprit artery double-stranded DNA in ST-segment elevated myocardial infarction. Sci Rep. 2018;8(1):9294. pmid:29915211

* View Article

* PubMed/NCBI

* Google Scholar

28. 28. Hayden H, Ibrahim N, Klopf J, Zagrapan B, Mauracher L-M, Hell L, et al. ELISA detection of MPO-DNA complexes in human plasma is error-prone and yields limited information on neutrophil extracellular traps formed in vivo. PLoS One. 2021;16(4):e0250265. pmid:33886636

* View Article

* PubMed/NCBI

* Google Scholar

29. 29. Benkhoff M, Alde K, Ehreiser V, Dahlmanns J, Metzen D, Haurand JM, et al. Thromboinflammation is associated with clinical outcome after ST-elevation myocardial infarction. Blood Adv. 2024;8(21):5581–9. pmid:39226457

* View Article

* PubMed/NCBI

* Google Scholar

30. 30. Ferré-Vallverdú M, Latorre AM, Fuset MP, Sánchez E, Madrid I, Ten F, et al. Neutrophil extracellular traps (NETs) in patients with STEMI. Association with percutaneous coronary intervention and antithrombotic treatments. Thromb Res. 2022;213:78–83. pmid:35306431

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Liu X, Arfman T, Wichapong K, Reutelingsperger CPM, Voorberg J, Nicolaes GAF. PAD4 takes charge during neutrophil activation: Impact of PAD4 mediated NET formation on immune-mediated disease. J Thromb Haemost. 2021;19(7):1607–17. pmid:33773016

* View Article

* PubMed/NCBI

* Google Scholar

32. 32. Thålin C, Lundström S, Seignez C, Daleskog M, Lundström A, Henriksson P, et al. Citrullinated histone H3 as a novel prognostic blood marker in patients with advanced cancer. PLoS One. 2018;13(1):e0191231. pmid:29324871

* View Article

* PubMed/NCBI

* Google Scholar

33. 33. Blasco A, Rosell A, Castejón R, Coronado MJ, Royuela A, Ramil E, et al. Analysis of NETs (neutrophil extracellular traps) in coronary thrombus and peripheral blood of patients with ST-segment elevation myocardial infarction. Thromb Res. 2024;235:18–21. pmid:38281441

* View Article

* PubMed/NCBI

* Google Scholar

Citation: Blasco A, Rosell A, Castejón R, Royuela A, Thålin C, Ramil E, et al. (2025) Inflammatory and neutrophil extracellular trap markers to predict cardiac events after ST-segment elevation myocardial infarction. PLoS ONE 20(4): e0319759. https://doi.org/10.1371/journal.pone.0319759

About the Authors:

Ana Blasco

Roles: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing

E-mail: [email protected], [email protected]

Affiliations: Cardiology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain, Research Ethics Committee, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain

ORICD: https://orcid.org/0000-0001-9067-6773

Axel Rosell

Roles: Investigation, Resources, Validation, Writing – original draft, Writing – review & editing

Affiliations: Hematology and Regenerative Medicine, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden, Department of Clinical Sciences, Karolinska Institutet Danderyd Hospital, Stockholm, Sweden

Raquel Castejón

Roles: Investigation, Resources, Validation, Writing – original draft

Affiliation: Internal Medicine Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

Ana Royuela

Roles: Formal analysis, Methodology, Writing – original draft

Affiliations: Biostatistics Unit, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain, Center for Biomedical Research in Epidemiology and Public Health Network (CIBERESP), Madrid, Spain

Charlotte Thålin

Roles: Investigation, Resources, Validation, Writing – original draft

Affiliation: Department of Clinical Sciences, Karolinska Institutet Danderyd Hospital, Stockholm, Sweden

Elvira Ramil

Roles: Investigation, Resources, Validation

Affiliation: Sequencing and Molecular Biology Unit, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain

Silvia Elorza

Roles: Investigation, Resources, Validation

Affiliation: Clinical Biochemistry Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

María-José Coronado

Roles: Funding acquisition, Visualization

Affiliation: Confocal Microscopy Unit, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain

Paloma Martín

Roles: Conceptualization

Affiliations: Molecular Pathology Laboratory, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain, Center for Biomedical Research Network (CIBERONC), Madrid, Spain

Javier Vázquez

Roles: Investigation

Affiliation: Cardiology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

Carolina González-Andrés

Roles: Investigation

Affiliation: Cardiology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

Juan M. Escudier

Roles: Investigation

Affiliation: Cardiology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

Javier Ortega

Roles: Investigation

Affiliation: Cardiology Department, Hospital Universitario Puerta de Hierro-Majadahonda, Madrid, Spain

Carmen Bellas

Roles: Conceptualization, Funding acquisition, Writing – review & editing

Affiliations: Molecular Pathology Laboratory, Instituto de Investigación Puerta de Hierro-Segovia de Arana, Madrid, Spain, Center for Biomedical Research Network (CIBERONC), Madrid, Spain

References

1. Maier W, Altwegg LA, Corti R, Gay S, Hersberger M, Maly FE, et al. Inflammatory markers at the site of ruptured plaque in acute myocardial infarction: locally increased interleukin-6 and serum amyloid A but decreased C-reactive protein. Circulation. 2005;111(11):1355–61. pmid:15753219

2. Neumann F-J, Ott I, Gawaz M, Richardt G, Holzapfel H, Jochum M, et al. Cardiac release of cytokines and inflammatory responses in acute myocardial infarction. Circulation. 1995;92(4):748–55.

3. Matter MA, Paneni F, Libby P, Frantz S, Stähli BE, Templin C, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103. pmid:37587550

4. Kushner I. The phenomenon of the acute phase response. Ann N Y Acad Sci. 1982;389:39–48. pmid:7046585

5. Tomoda H, Aoki N. Prognostic value of C-reactive protein levels within six hours after the onset of acute myocardial infarction. Am Heart J. 2000;140(2):324–8. pmid:10925350

6. Mega JL, Morrow DA, De Lemos JA, Sabatine MS, Murphy SA, Rifai N, et al. B-type natriuretic peptide at presentation and prognosis in patients with ST-segment elevation myocardial infarction: an ENTIRE-TIMI-23 substudy. J Am Coll Cardiol. 2004;44(2):335–9. pmid:15261928

7. Makrygiannis SS, Ampartzidou OS, Zairis MN, Patsourakos NG, Pitsavos C, Tousoulis D, et al. Prognostic usefulness of serial C-reactive protein measurements in ST-elevation acute myocardial infarction. Am J Cardiol. 2013;111(1):26–30. pmid:23040593

8. Libby P. Interleukin-1 Beta as a Target for Atherosclerosis Therapy: Biological Basis of CANTOS and Beyond. J Am Coll Cardiol. 2017;70:2278-2289. https://doi.org/10.1016/j.jacc.2017.09.028.

9. Abbate A, Trankle CR, Buckley LF, Lipinski MJ, Appleton D, Kadariya D, et al. Interleukin-1 Blockade Inhibits the Acute Inflammatory Response in Patients With ST-Segment-Elevation Myocardial Infarction. J Am Heart Assoc. 2020;9(5):e014941. pmid:32122219

10. Broch K, Anstensrud AK, Woxholt S, Sharma K, Tøllefsen IM, Bendz B, et al. Randomized Trial of Interleukin-6 Receptor Inhibition in Patients With Acute ST-Segment Elevation Myocardial Infarction. J Am Coll Cardiol. 2021;77:1845-1855.

11. Fanola CL, Morrow DA, Cannon CP, Jarolim P, Lukas MA, Bode C, et al. Interleukin-6 and the risk of adverse outcomes in patients after an acute coronary syndrome: observations from the SOLID-TIMI 52 (stabilization of plaque using darapladib-thrombolysis in myocardial infarction 52) trial. J Am Heart Assoc. 2017;6(10):e005637. pmid:29066436

12. Mangold A, Alias S, Scherz T, Hofbauer T, Jakowitsch J, Panzenböck A, et al. Coronary neutrophil extracellular trap burden and deoxyribonuclease activity in ST-elevation acute coronary syndrome are predictors of ST-segment resolution and infarct size. Circ Res. 2015;116(7):1182–92. pmid:25547404

13. Blasco A, Coronado M-J, Vela P, Martín P, Solano J, Ramil E, et al. Prognostic implications of neutrophil extracellular traps in coronary thrombi of patients with St-elevation myocardial infarction. Thromb Haemost. 2021;122(08):1415–28.

14. Hofbauer TM, Ondracek AS, Mangold A, Scherz T, Nechvile J, Seidl V, et al. Neutrophil extracellular traps induce MCP-1 at the culprit site in ST-segment elevation myocardial infarction. Front Cell Dev Biol. 2020;8:564169. pmid:33240874

15. Thålin C, Aguilera K, Hall NW, Marunde MR, Burg JM, Rosell A, et al. Quantification of citrullinated histones: development of an improved assay to reliably quantify nucleosomal H3Cit in human plasma. J Thromb Haemost. 2020;18(10):2732–43. pmid:32654410

16. Ibánez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Rev Esp Cardiol (Engl Ed). 2017;70(12):1082. pmid:29198432

17. Terkelsen CJ, Pinto DS, Thiele H, Clemmensen P, Nikus K, Lassen JF, et al. 2012 ESC STEMI guidelines and reperfusion therapy: evidence base ignored, threatening optimal patient management. Heart. 2013;99(16):1154–6. pmid:23781110

18. Maugeri N, Campana L, Gavina M, Covino C, De Metrio M, Panciroli C, et al. Activated platelets present high mobility group box 1 to neutrophils, inducing autophagy and promoting the extrusion of neutrophil extracellular traps. J Thromb Haemost. 2014;12(12):2074–88. pmid:25163512

19. Riegger J, Byrne RA, Joner M, Chandraratne S, Gershlick AH, Ten Berg JM, et al. Histopathological evaluation of thrombus in patients presenting with stent thrombosis. A multicenter European study: a report of the prevention of late stent thrombosis by an interdisciplinary global European effort consortium. Eur Heart J. 2016;37(19):1538–49. pmid:26761950

20. Stakos DA, Kambas K, Konstantinidis T, Mitroulis I, Apostolidou E, Arelaki S, et al. Expression of functional tissue factor by neutrophil extracellular traps in culprit artery of acute myocardial infarction. Eur Heart J. 2015;36(22):1405–14. pmid:25660055

21. Novotny J, Chandraratne S, Weinberger T, Philippi V, Stark K, Ehrlich A, et al. Histological comparison of arterial thrombi in mice and men and the influence of Cl-amidine on thrombus formation. PLoS One. 2018;13(1):e0190728. pmid:29293656

22. Blanco-Colio LM, Méndez-Barbero N, Pello Lázaro AM, Aceña Á, Tarín N, Cristóbal C, et al. MCP-1 predicts recurrent cardiovascular events in patients with persistent inflammation. J Clin Med. 2021;10(5):1137. pmid:33803115

23. de Lemos JA, Morrow DA, Blazing MA, Jarolim P, Wiviott SD, Sabatine MS, et al. Serial measurement of monocyte chemoattractant protein-1 after acute coronary syndromes: results from the A to Z trial. J Am Coll Cardiol. 2007;50(22):2117–24. pmid:18036447

24. Blasco A, Coronado M-J, Hernández-Terciado F, Martín P, Royuela A, Ramil E, et al. Assessment of neutrophil extracellular traps in coronary thrombus of a case series of patients with COVID-19 and myocardial infarction. JAMA Cardiol. 2021;6(4):469–74. pmid:33372956

25. Langseth MS, Helseth R, Ritschel V, Hansen CH, Andersen GØ, Eritsland J, et al. Double-Stranded DNA and NETs components in relation to clinical outcome after ST-elevation myocardial infarction. Sci Rep. 2020;10(1):5007. pmid:32193509

26. Liu J, Yang D, Wang X, Zhu Z, Wang T, Ma A, et al. Neutrophil extracellular traps and dsDNA predict outcomes among patients with ST-elevation myocardial infarction. Sci Rep. 2019;9(1):11599. pmid:31406121

27. Wang X, Yang D, Liu J, Fan X, Ma A, Liu P. Prognostic value of culprit artery double-stranded DNA in ST-segment elevated myocardial infarction. Sci Rep. 2018;8(1):9294. pmid:29915211

28. Hayden H, Ibrahim N, Klopf J, Zagrapan B, Mauracher L-M, Hell L, et al. ELISA detection of MPO-DNA complexes in human plasma is error-prone and yields limited information on neutrophil extracellular traps formed in vivo. PLoS One. 2021;16(4):e0250265. pmid:33886636

29. Benkhoff M, Alde K, Ehreiser V, Dahlmanns J, Metzen D, Haurand JM, et al. Thromboinflammation is associated with clinical outcome after ST-elevation myocardial infarction. Blood Adv. 2024;8(21):5581–9. pmid:39226457

30. Ferré-Vallverdú M, Latorre AM, Fuset MP, Sánchez E, Madrid I, Ten F, et al. Neutrophil extracellular traps (NETs) in patients with STEMI. Association with percutaneous coronary intervention and antithrombotic treatments. Thromb Res. 2022;213:78–83. pmid:35306431

31. Liu X, Arfman T, Wichapong K, Reutelingsperger CPM, Voorberg J, Nicolaes GAF. PAD4 takes charge during neutrophil activation: Impact of PAD4 mediated NET formation on immune-mediated disease. J Thromb Haemost. 2021;19(7):1607–17. pmid:33773016

32. Thålin C, Lundström S, Seignez C, Daleskog M, Lundström A, Henriksson P, et al. Citrullinated histone H3 as a novel prognostic blood marker in patients with advanced cancer. PLoS One. 2018;13(1):e0191231. pmid:29324871

33. Blasco A, Rosell A, Castejón R, Coronado MJ, Royuela A, Ramil E, et al. Analysis of NETs (neutrophil extracellular traps) in coronary thrombus and peripheral blood of patients with ST-segment elevation myocardial infarction. Thromb Res. 2024;235:18–21. pmid:38281441