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Prognostic implications of systemic immune-inflammation index in myocardial infarction patients with and without diabetes: insights from the NOAFCAMI-SH registry

Cardiovascular Diabetology volume 23, Article number: 41 (2024) Cite this article

Abstract

Background

It is well-known that systemic inflammation plays a crucial role in the pathogenesis and prognosis of acute myocardial infarction (AMI). The systemic immune-inflammation index (SII, platelet × neutrophil/lymphocyte ratio) is a novel index that is used for the characterization of the severity of systemic inflammation. Recent studies have identified the high SII level as an independent predictor of poor outcomes in patients with AMI. We aimed to investigate the prognostic implications of SII in AMI patients with and without diabetes mellitus (DM).

Methods

We included 2111 patients with AMI from February 2014 to March 2018. Multivariable Cox regression analyses were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause death and cardiovascular (CV) death. Multiple imputation was used for missing covariates.

Results

Of 2111 patients (mean age: 65.2 ± 12.2 years, 77.5% were males) analyzed, 789 (37.4%) had DM. Generalized additive model analyses showed that as the SII increased, the C-reactive protein and peak TnT elevated while the LVEF declined, and these associations were similar in patients with and without DM. During a median of 2.5 years of follow-up, 210 all-cause deaths and 154 CV deaths occurred. When treating the SII as a continuous variable, a higher log-transformed SII was significantly associated with increased all-cause mortality (HR: 1.57, 95%CI: 1.02–2.43) and CV mortality (HR: 1.85, 95%CI 1.12–3.05), and such an association was also significant in the diabetics (HRs and 95%CIs for all-cause death and CV death were 2.90 [1.40–6.01] and 3.28 [1.43–7.57], respectively) while not significant in the nondiabetics (Pinteraction for all-cause death and CV death were 0.019 and 0.049, respectively). Additionally, compared to patients with the lowest tertiles of SII, those with the highest tertiles of SII possessed significantly higher all-cause mortality (HR: 1.82, 95%CI 1.19–2.79) and CV mortality (HR: 1.82, 95%CI 1.19–2.79) after multivariable adjustment, and this relationship remained pronounced in the diabetics (HRs and 95%CIs for all-cause death and CV death were 2.00 [1.13–3.55] and 2.09 [1.10–3.98], respectively) but was not observed in the nondiabetics (HRs and 95%CIs for all-cause death and CV death were 1.21 [0.75–1.97] and 1.60 [0.89–2.90], respectively). Our restricted cubic splines analyses indicated a pronounced linear association between SII and mortality only in diabetics.

Conclusions

In AMI patients with DM, high SII is an independent predictor of poor survival and may be helpful for patient’s risk stratification.

Introduction

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide and accounts for more than 1/3 of all deaths in developed countries [1]. Evolving evidence has suggested that the blood cells such as macrophages, neutrophils, monocytes, and platelets are involved in systemic inflammation and are associated with the pathogenesis of atheroprogression and plaque invulnerability [2, 3]. Upon activation, immune cells will produce and secrete a large number of proinflammatory cytokines including interleukin-8 (IL-8), IL-6, and IL-1β, which have been linked to the outcomes of AMI patients [4,5,6]. Nevertheless, previous research exploring the clinical usefulness of anti-inflammatory therapies in AMI has obtained controversial results [7], some studies support the utility of anti-inflammatory drugs [8, 9], while others are against it [10, 11]. It is possible that the application of adequate inflammatory biomarkers for the post-MI systemic inflammation evaluation may be helpful in the decision-making of anti-inflammatory treatment.

Until now, the predictive performance of several inflammatory biomarkers, for example, the high-sensitivity C-reactive protein (hs-CRP) and IL-6, has been studied in AMI individuals [12]. More recently, a novel index, the systemic immune-inflammation index (SII), is proposed by Hu et al. to facilitate the risk stratification of patients with hepatocellular carcinoma. The SII is calculated using neutrophil, lymphocyte, and platelet counts (SII = platelet count × neutrophil/lymphocyte ratio), which considers an individual’s inflammatory and immune status simultaneously [13]. Since then, it has been mentioned that SII may be associated with poor outcomes in patients with various cardiovascular diseases [14, 15].

Diabetes mellitus (DM) and AMI are highly prevalent, aggravate each other, and own shared risk factors, of which inflammation plays a crucial role in the pathogenesis and prognosis of both conditions [16, 17]. Recent studies have reported that in patients with DM, the CV benefits of antidiabetic agents are not solely dependent on their hypoglycemic effects, but are also partially due to their anti-inflammation properties [18, 19]. Although the prognostic value of SII has been studied in patients with AMI, the association between SII and clinical outcomes in AMI patients with DM remains unclear.

Accordingly, using data from the New-Onset Atrial Fibrillation Complicating Acute Myocardial Infarction in ShangHai (NOAFCAMI-SH) registry, we intend to investigate the prognostic implications of SII in long-term mortality in AMI patients with and without DM.

Materials and methods

Study design and population

This investigation is a retrospective sub-analysis of the NOAFCAMI-SH registry (ClinicalTrials.gov, NCT03533543). The detailed study designs have been previously reported elsewhere [20, 21]. In brief, a total of 2399 patients with AMI who did not have a medical record of AF and were hospitalized in the coronary care unit of Shanghai Tenth People’s Hospital between February 2014 and March 2018 were included in the NOAFCAMI-SH registry. Information about patients’ demographics, concomitant diseases, admission characteristics, laboratory tests, echocardiographic and angiographic data, as well as medications was comprehensively collected and stored in an electronic database.

For the present analysis, patients meeting the following criteria were excluded: (1) patients who died during hospitalization or lost to follow-up, (2) severe inflammation (leukocyte counts ≥ 20 × 109 cells/L or C-reactive protein ≥ 200 mg/L), (3) severe chronic kidney disease (CKD) with an estimated glomerular filtration rate < 15 mL/min/1.73 m2, (4) medical history of hematological diseases (thrombocythemia and granulocytopenia) and autoimmune diseases. Hence, 2111 AMI patients with complete follow-up were included in the final analysis (Fig. 1). The study process was in accordance with the Declaration of Helsinki and was approved by Shanghai Tenth People’s Hospital Ethics Review Committee (approval number: SHSY-IEC-KY-4.1/18–199/01). Due to the anonymous nature of the data, the requirement for informed consent was waived.

Fig. 1
figure 1

Flowchart of patient inclusion and exclusion for this study. AMI, acute myocardial infarction

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Data collection

The patient’s baseline characteristics, which included demographics, comorbidities, laboratory data, medication usage, and echocardiographic and angiographic information were retrieved from the NOAFCAMI-SH registry database. Demographics included age, sex, and smoking status. Comorbidities included hypertension, diabetes, hyperlipidemia, chronic kidney disease (CKD), heart failure (HF), myocardial infarction (MI) percutaneous coronary intervention (PCI), and stroke/transient ischemic attack (TIA). Venous blood samples were obtained from the cubital vein within 24 h after the index AMI admission. Laboratory data included complete blood count, C-reactive protein (CRP), serum creatinine, and peak level of TnT. The complete blood count including leukocyte, neutrophil, lymphocyte, and platelet counts was examined using the CELL-DYN 3700 (Abbott Laboratories, Illinois, United States) in a central laboratory. An echocardiogram examination was performed by sonologists within 7 days after admission to evaluate the following parameters: left ventricular ejection fraction (LVEF), left atrial diameter, left ventricular end-diastolic diameter (LVEDD), and left ventricular end-systolic diameter (LVESD). Medications included the use of aspirin, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/ angiotensin receptor–neprilysin inhibitor (ACEI/ARB/ARNI), β-blocker, and statin. All data were collected by trained independent investigators.

Definitions

The diagnosis of AMI was established based on the contemporary guidelines as an ST-segment elevation MI (STEMI) or non-ST-segment elevation MI (NSTEMI) [22]. DM was defined as a self-reported history of DM, or patients with fasting glucose > 126 mg/dL or HbA1c > 6.5%, or those taking insulin or other antidiabetic treatments. Hypertension was defined as a self-reported history of hypertension, an SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, or the use of antihypertensive medications. CKD was defined as patients with an eGFR < 60 ml/min/1.73 m2, which was estimated using the Chronic Kidney Disease Epidemiology Collaboration equation. The SII was defined as follows: total peripheral platelets count × neutrophil-to-lymphocyte ratio/109 cells/L. The analyzed population was divided into three groups according to the tertiles of SII (Tertile 1: SII < 613.57; Tertile 2: 613.57 ≤ SII < 1136.56; Tertile 3: SII ≥ 1136.56).

Outcomes and follow-up

The primary outcome was all-cause death and the secondary outcome was cardiovascular death (CV death). All deaths without a validated non-cardiovascular cause would be considered CV death. All patients were followed from the index discharge to the date of occurrence of an outcome of interest, or last follow-up (April 2019), whichever came first. Hence, the adjudication of clinical outcomes cannot be influenced by the COVID-19 pandemic. For patients who died during hospitalization, mortality data were obtained from medical records. For those dying in the community, mortality data were collected by telephone interviews with the next-of-kin. Survival status was adjudicated by trained physicians who were blinded to the patient’s clinical and laboratory data.

Statistical analysis

We used means ± SD to present continuous variables with a normal distribution and median (interquartile range) for those with a skewed distribution. The ANOVA and rank sum tests were used to explore differences in measurement data with normal and skewed distribution between the SII tertiles, respectively. Categorical variables were presented by SII tertiles and percentages. We used Fisher’s exact test for dichotomous variables.

A generalized additive model (GAM) and smoothing curve fitting were used to assess the potential relationship between log-transformed SII and inflammation and myocardial injury indicators (CRP, peak TnT, and LVEF levels). SII truncation was performed on all patients outside the 99th percentile to avoid the effect of extreme values.

For long-term survival analyses, the cumulative mortality was plotted using the Kaplan–Meier method, and the differences across three groups were compared using the log-rank test. The incidence rate was calculated using the total number of deaths during the observational period divided by person-years at risk. The association of SII (either as a continuous variable [transformed to a logarithmic scale] or a categorical variable treating the lowest tertiles of SII as reference) with mortality was evaluated using the multivariable Cox proportional hazards regression analysis in the whole, diabetic, and non-diabetic cohorts, respectively. Covariates avoiding collinearity in multivariable models were selected based on the P-value (< 0.05) in univariable analysis (Additional file 1: Table S1) or clinical plausibility, including age, sex, current smoker, comorbidities (hypertension, diabetes, dyslipidemia, CKD, HF, and MI), STEMI, Killip > I, primary PCI, peak TnT, CRP, serum creatinine, LVEF, as well as medications (aspirin, ACEI/ARB/ARNI, β-blocker). Multiple imputation via chained equations was used to impute missing values in CRP (N = 102, 4.8%), serum creatinine (N = 26, 1.2%), and LVEF (N = 97, 4.6%). We assumed that data were missing at random and modeled missing data with the predictive mean matching method. Pooled analyses across the 20 imputed datasets followed Rubin’s rules (Additional file 1: Methods) [23]. To investigate the single impacts of leukocyte subtypes on patient’s prognosis, we further explored whether there were associations between lymphocyte, neutrophil, platelet counts, and mortality. We also performed three sensitivity analyses to examine the robustness of our findings: (1) repeating the multivariable analyses in a complete-case dataset by removing covariates with missing data; and further accounting for the impacts of (2) oral anticoagulants and diuretics, as well as (3) antidiabetic agents in the multivariable models.

The potential nonlinear correlations between SII level and long-term mortality were assessed on a continuous scale with the restricted cubic spline (RCS) method. Three equally spaced knots were set at 10th, 50th, and 90th percentiles. The hazard ratios (HR) and 95% confidence intervals (CI) were calculated after adjustment for the aforementioned covariates. We conducted all statistical analyses using R v4.0.3 software (‘mice’, ‘mgcv’, ‘rms’ packages) and Stata v17.0 software. A two-sided p-value < 0.05 was considered statistically significant.

Results

The baseline characteristics divided by SII tertiles were displayed in Table 1. Among the analyzed participants, 1637 (77.5%) were males; the mean age was 65.2 years (SD: 12.2 years). Pronounced differences in the medical history of CKD and PCI, admission characteristics (STEMI, heart rate, and Killip class), laboratory indices (CRP, peak TnT, creatinine, as well as leukocyte, neutrophil, lymphocyte, and platelet counts), usage of primary PCI and medications (aspirin, ACEI/ARB/ARNI, β-blocker, diuretics, and insulin), and LVEF level were observed across the SII tertiles. Additional file 1: Tables S2 and S3 summarized the baseline characteristics of AMI patients with and without DM, respectively. The characteristics including CRP, peak TnT, complete blood counts, and LVEF level remained significantly different between SII tertiles in both cohorts.

Table 1 Baseline characteristics of AMI patients by tertiles of systemic-immune inflammation index
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The GAM model was applied to explore the associations between SII and CRP, peak TnT, and LVEF levels. The estimated smooth effect curves were shown in Fig. 2. As log-transformed SII increased, the CRP and peak TnT levels elevated while the LVEF level declined (Figs. 2A–C). Qualitatively similar visual patterns were observed for the diabetic and non-diabetic cohorts (Figs. 2D-F). Notably, the association between log-transformed SII and CRP appeared to be stronger among individuals with DM than among those without DM.

Fig. 2
figure 2

Associations of systemic immune-inflammation index (SII) with inflammation, myocardial injury, and cardiac function assessed with GAM. The generalized additive models (GAM) show a nearly linear association between the log-transformed SII level and CRP, peak TnT, and LVEF levels in the overall cohort, and in the diabetic and nondiabetic cohorts, respectively. The 95% confidence intervals are in shading. The navyblue dots indicate the whole cohort, and the green and red dots indicate the diabetic and nondiabetic cohorts, respectively. CRP C-reactive protein, LVEF left ventricular ejection fraction

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Over a median of 2.5 years (maximum 5.1 years) of follow-up, 210 all-cause deaths and 154 CV deaths were identified. Cumulative hazard curves of all-cause death and CV death across SII tertiles were illustrated in Fig. 3. Patients in the SII tertile 3 group suffered higher all-cause mortality and CV mortality compared to those in the SII tertiles 1 and 2 groups (all p values of log-rank test < 0.05). In the fully adjusted Cox regression models, when treating the lowest tertiles of SII as the reference, the highest tertiles of SII demonstrated significantly positive associations with all-cause death (HR: 1.54, 95%CI 1.07–2.21, P for trend = 0.016) and CV death (HR: 1.82, 95%CI 1.19–2.79, P for trend = 0.004) among the whole population, especially for those with DM (HRs and 95%CIs for all-cause death and CV death were 2.00 [1.13–3.55] and 2.09 [1.10–3.98], respectively), whereas an unpronounced result was obtained in the non-diabetic cohort (HRs and 95%CIs for all-cause death and CV death were 1.21 [0.75–1.97] and 1.60 [0.89–2.90], respectively). Similar trends were found when introducing the SII as a continuous variable into the multivariable models (Table 2). On average, the HR was increased by 57% (HR: 1.57, 95%CI 1.02–2.43) and 1.9-fold (HR: 2.90, 95%CI 1.40–6.01) for all-cause death; and by 85% (HR: 1.85, 95%CI 1.12–3.05) and 2.28-fold (HR: 3.28, 95%CI 1.43–7.57) for CV death in the whole and diabetic cohorts, respectively. When repeating the main analysis in individuals without missing data, sensitivity analyses showed slightly elevated correlations between SII and all-cause death and CV death (Additional file 1: Table S4). Moreover, when further accounting for the impacts of oral anticoagulants and diuretics (Additional file 1: Table S5), as well as the antidiabetic agents (Additional file 1: Table S6) on the association between SII and long-term mortality, the results of which were similar to that in the main analysis.

Fig. 3
figure 3

Primary and secondary endpoints. Long-term all-cause mortality and CV mortality in the whole cohort (A, D), and in the diabetic (B, E), and nondiabetic (C, F) cohorts according to the tertiles of SII level, respectively. CV cardiovascular

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Table 2 Association between tertiles of systemic immune-inflammation index and death in patients with and without diabetes
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As shown in Fig. 4, dose–response relationships between log-transformed SII and all-cause death and CV death were plotted using RCS. Significant linear associations between SII and all-cause mortality (Poverall = 0.020, Pnonlinearity = 0.658) and CV mortality (Poverall = 0.025, Pnonlinearity = 0.887) were only found in the diabetic cohort (Fig. 4B, E), while no evident associations between SII and death were observed in patients with DM (all Poverall > 0.05).

Fig. 4
figure 4

Hazard ratios (HRs) for all-cause death and CV death by SII on a continuous scale in the whole cohort, and in the diabetic and nondiabetic cohorts. HRs with 95%CIs for all-cause death and CV death, accounting for SII on a continuous scale, are from Cox regression restricted cubic splines (RCS). Orange areas show the distribution of SII levels. The solid red line in each figure indicates the HR, and the dashed red lines indicate the 95%CI. Covariates in the multivariable RCS model included: age, sex, current smoker, comorbidities (hypertension, diabetes, dyslipidemia, CKD, heart failure, myocardial infarction), STEMI, Killip > I, primary PCI, peak TnT, CRP, serum creatinine, LVEF, as well as medications (aspirin, ACEI/ARB/ARNI, β-blocker). CKD chronic kidney disease, PCI percutaneous coronary intervention

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Furthermore, when investigating the associations of leukocyte subtypes with death, we demonstrated that higher log-transformed neutrophil counts were significantly associated with increased all-cause mortality (HR: 4.59, 95%CI 1.26–16.70, P = 0.024) and CV mortality (HR: 5.37, 95%CI 1.22–23.68, P = 0.031) in the diabetic cohort after accounting for confounders (Additional file 1: Table S7). A greater log-transformed platelet count was independently associated with increased all-cause mortality (HR: 3.24, 95%CI 1.16–9.02, P = 0.026) in the whole population and a marginally elevated mortality was observed in the diabetics (HR: 4.36, 95%CI 0.94–20.15, P = 0.063) (Additional file 1: Table S8). However, lymphocyte counts were not associated with poor survival (Additional file 1: Table S9).

Discussions

The principal findings of this study were as follows: (1) there was a positive association between SII level and myocardial injury and cardiac dysfunction after AMI; (2) patients with a greater SII level experienced higher risk of all-cause and CV deaths compared to those with lower SII; (3) high SII remained an independent predictor of long-term mortality in the diabetic cohort after multivariable adjustment, and a linear association between SII and poor survival was further uncovered in the RCS analyses.

Preceding studies have identified chronic inflammation as an important risk factor for several diseases such as cancer, DM, and atherosclerotic disease [24]. The SII was a novel biomarker that was used for the characterization of systemic inflammation, which is evaluated using neutrophil, lymphocyte, and platelet counts [13]. It is well-known that the immune cells are involved in cardiac injury and repair [25], and numerous studies have suggested the SII as an independent predictor of adverse CV outcomes after AMI, for example, mortality, arrhythmia, and stent thrombosis [26,27,28]. From the mechanism perspective, activated neutrophils release a variety of proteolytic enzymes such as myeloperoxidase and elastase, thus leading to myocardial injury [29]. By contrast, lymphocytes represent a regulated inflammatory process that suppresses the exorbitant immune response and limits myocardial damage. Upon activation, platelets will either release a number of proinflammatory chemokines and cytokines that contribute to thrombosis or interact with other leukocytes to exacerbate atherosclerosis and plaque instability which is often related to detrimental CV outcomes [30].

In a retrospective cohort study of 314 elderly patients with NSTEMI, Orhan et al. demonstrated that a greater level of SII was significantly associated with increased in-hospital mortality and long-term mortality after adjusting for age, DM, hypertension, HF, and Charlson comorbidity index [28]. Additionally, in the Li et al., the SII was validated as an independent predictor of the composite of all-cause death, non-fatal ischemic stroke, and nonfatal MI in 1701 ACS patients undergoing PCI. Moreover, the addition of SII on top of the GRACE risk score significantly improves the latter one's predictive value [31]. In line with prior studies, we found that a higher SII level remained an independent risk factor of long-term mortality after multivariable adjustment (Table 2). The positive association of SII with impaired LVEF and extensive myocardial necrosis uncovered by our GAM analyses could partially explain the adverse prognostic impact of high SII, given the well-known detrimental effects of decreased LVEF and elevated TnT levels. On the other hand, as an emerging index of prothrombotic activity, a high SII level may indicate the presence of excessive thrombotic burden, which has also been considered an important risk factor of the no-reflow phenomenon and poor survival following AMI [32].

Another interesting finding that we have shown, to our knowledge for the first time, is that the SII was an independent risk factor of poor survival only in patients with diabetes but not in those without diabetes. Our RCS analyses further indicated a linear correlation between SII and long-term mortality in diabetics, which may suggest the clinical utility of anti-inflammatory therapies in this high-risk population. Reduced inflammation response in AMI patients with DM such as treating hyperglycemia will slow down the adverse cardiac remodeling and also reduce the CV outcomes [33]. Emerging evidence has indicated that certain glucose-lowering agents such as metformin and sodium-glucose cotransporter 2 inhibitors could provide survival benefits to patients with AMI partly due to their anti-inflammatory properties [18, 19]. Whether it is possible to prescribe appropriate hypoglycemic therapies based on the SII level to improve the prognosis of AMI patients with diabetes remains to be determined.

Despite the known impacts of leukocytes and platelets on the prognosis of AMI individuals, we found that the significant association between SII and mortality was mainly mediated by neutrophils, particularly in the diabetics (Additional file 1: Table S4). The exact mechanisms cannot be determined in this analysis, while we postulated that the differences in the functional status of neutrophils in diabetics and nondiabetics may be one of the possible explanations. Under diabetic conditions, neutrophils are more prone to producing superoxide and inflammatory cytokines, which results in tissue injuries [34]. On the other hand, the diabetic microenvironment favors the form of neutrophils extracellular traps (NETs) [35]. As shown in the Menegazzo et al., plasma from type 2 DM patients included more NETosis products, such as elastase, oligonucleotides, and double-strand DNA, when compared to nondiabetic individuals [36]. The NETs are pivotal scaffolds in pathologic thrombi and fuel cardiovascular, inflammatory, and thrombotic diseases. It has been well established that NETs are key in promoting DM-related complications [37, 38]. Taken together, our results suggest that a therapeutic strategy targeting neutrophils or NETs may be reasonable in improving the outcomes of AMI patients with DM. Further studies are highly desirable to address this issue.

The present study also has several limitations. First, this was a single-center retrospective cohort study, it was difficult to eliminate underlying selection bias and some unmeasured confounders. Second, due to a lack of data on previous medication usage, such as the use of steroids and antibiotics, which may influence the assessment of SII. However, our study excluded patients with severe inflammation, hematological diseases, or autoimmune diseases, and adjusted for a majority of confounders that may influence blood cell counts, including smoking status, diabetes, and aspirin. Third, as several other inflammatory markers such as monocyte chemoattractant protein-1 (MCP-1) [39] and IL-6 [40] that have been associated with poor CV outcomes in patients with AMI were not available in the NOAFCAMI-SH database, their impacts on the association between SII and clinical outcomes after AMI remained to be elucidated. Fourth, although we excluded patients with severe inflammation and accounted for the effect of CRP that is generally elevated in the setting of acute infection in the multivariable analyses, we still cannot eliminate the impact of acute infection on our results. Finally, we did not investigate the association of dynamic changes in the SII level with long-term mortality, which needs to be further addressed.

Conclusions

In summary, we demonstrate that high SII is positively associated with myocardial injury and cardiac dysfunction, and is also an independent risk factor for long-term mortality in AMI patients, particularly in diabetics. Given the detrimental prognostic impacts of extensive inflammation response after AMI, further studies are warranted to determine whether the SII could be helpful in the risk stratification of AMI patients with DM as well as in the clinical decision-making of anti-inflammatory therapy utility.

Availability of data and materials

The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.

Abbreviations

AMI:

Acute myocardial infarction

CKD:

Chronic kidney disease

DM:

Diabetes mellitus

GAM:

Generalized additive model

HF:

Heart failure

LVEF:

Left ventricular ejection fraction

NET:

Neutrophils extracellular trap

RCS:

Restricted cubic spline

SII:

Systemic immune-inflammation index

References

  1. Yusuf S, Reddy S, Ounpuu S, Anand S. Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation. 2001;104(22):2746–53.

    Article  CAS  PubMed  Google Scholar 

  2. Prabhu SD, Frangogiannis NG. The biological basis for cardiac repair after myocardial infarction: from inflammation to fibrosis. Circ Res. 2016;119(1):91–112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Murphy AJ, Tall AR. Disordered haematopoiesis and athero-thrombosis. Eur Heart J. 2016;37(14):1113–21.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shetelig C, Limalanathan S, Hoffmann P, et al. Association of IL-8 with infarct size and clinical outcomes in patients with STEMI. J Am Coll Cardiol. 2018;72(2):187–98.

    Article  CAS  PubMed  Google Scholar 

  5. Henein MY, Vancheri S, Longo G, Vancheri F. The role of inflammation in cardiovascular disease. Int J Mol Sci. 2022;23(21):12906.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Shahrivari M, Wise E, Resende M, et al. Peripheral blood cytokine levels after acute myocardial infarction: IL-1β- and IL-6-related impairment of bone marrow function. Circ Res. 2017;120(12):1947–57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Huang S, Frangogiannis NG. Anti-inflammatory therapies in myocardial infarction: failures, hopes and challenges. Br J Pharmacol. 2018;175(9):1377–400.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tardif JC, Kouz S, Waters DD, et al. Efficacy and safety of low-dose colchicine after myocardial infarction. N Engl J Med. 2019;381(26):2497–505.

    Article  CAS  PubMed  Google Scholar 

  9. Ridker PM, Everett BM, Thuren T, et al. Antiinflammatory therapy with canakinumab for atherosclerotic disease. N Engl J Med. 2017;377(12):1119–31.

    Article  CAS  PubMed  Google Scholar 

  10. Morton AC, Rothman AM, Greenwood JP, et al. The effect of interleukin-1 receptor antagonist therapy on markers of inflammation in non-ST elevation acute coronary syndromes: the MRC-ILA Heart Study. Eur Heart J. 2015;36(6):377–84.

    Article  CAS  PubMed  Google Scholar 

  11. Hudson MP, Armstrong PW, Ruzyllo W, et al. Effects of selective matrix metalloproteinase inhibitor (PG-116800) to prevent ventricular remodeling after myocardial infarction: results of the PREMIER (Prevention of Myocardial Infarction Early Remodeling) trial. J Am Coll Cardiol. 2006;48(1):15–20.

    Article  CAS  PubMed  Google Scholar 

  12. Ridker PM. From C-reactive protein to interleukin-6 to interleukin-1: moving upstream to identify novel targets for atheroprotection. Circ Res. 2016;118(1):145–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hu B, Yang XR, Xu Y, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. 2014;20(23):6212–22.

    Article  CAS  PubMed  Google Scholar 

  14. Wang Z, Qin Z, Yuan R, et al. Systemic immune-inflammation index as a prognostic marker for advanced chronic heart failure with renal dysfunction. ESC Heart Fail. 2023;10(1):478–91.

    Article  PubMed  Google Scholar 

  15. Xu M, Chen R, Liu L, et al. Systemic immune-inflammation index and incident cardiovascular diseases among middle-aged and elderly Chinese adults: The Dongfeng-Tongji cohort study. Atherosclerosis. 2021;323:20–9.

    Article  CAS  PubMed  Google Scholar 

  16. Pradhan AD, Ridker PM. Do atherosclerosis and type 2 diabetes share a common inflammatory basis? Eur Heart J. 2002;23(11):831–4.

    Article  CAS  PubMed  Google Scholar 

  17. Malmberg K, Yusuf S, Gerstein HC, et al. Impact of diabetes on long-term prognosis in patients with unstable angina and non-Q-wave myocardial infarction: results of the OASIS (Organization to Assess Strategies for Ischemic Syndromes) Registry. Circulation. 2000;102(9):1014–9.

    Article  CAS  PubMed  Google Scholar 

  18. Fei Q, Ma H, Zou J, et al. Metformin protects against ischaemic myocardial injury by alleviating autophagy-ROS-NLRP3-mediated inflammatory response in macrophages. J Mol Cell Cardiol. 2020;145:1–13.

    Article  CAS  PubMed  Google Scholar 

  19. Paolisso P, Bergamaschi L, Santulli G, et al. Infarct size, inflammatory burden, and admission hyperglycemia in diabetic patients with acute myocardial infarction treated with SGLT2-inhibitors: a multicenter international registry. Cardiovasc Diabetol. 2022;21(1):77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Luo J, Xu S, Li H, et al. Long-term impact of the burden of new-onset atrial fibrillation in patients with acute myocardial infarction: results from the NOAFCAMI-SH registry. Europace. 2021;23(2):196–204.

    Article  PubMed  Google Scholar 

  21. Luo J, Xu S, Li H, et al. Long-term impact of new-onset atrial fibrillation complicating acute myocardial infarction on heart failure. ESC Heart Fail. 2020;7(5):2762–72.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Thygesen K, Alpert JS, Jaffe AS, et al. Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol. 2018;72(18):2231–64.

    Article  PubMed  Google Scholar 

  23. Rubin D. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987.

    Book  Google Scholar 

  24. Fullerton JN, Gilroy DW. Resolution of inflammation: a new therapeutic frontier. Nat Rev Drug Discov. 2016;15(8):551–67.

    Article  CAS  PubMed  Google Scholar 

  25. Rurik JG, Aghajanian H, Epstein JA. Immune cells and immunotherapy for cardiac injury and repair. Circ Res. 2021;128(11):1766–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Aksakal E, Aksu U, Birdal O, et al. Role of systemic immune-inflammatory index in predicting the development of in-hospital malignant ventricular arrhythmia in patients with ST-elevated myocardial infarction. Angiology. 2022;74:881.

    Article  PubMed  Google Scholar 

  27. Dolu AK, Karayiğit O, Ozkan C, Çelik MC, Kalçık M. Relationship between intracoronary thrombus burden and systemic immune-inflammation index in patients with ST-segment elevation myocardial infarction. Acta Cardiol. 2023;78(1):72–9.

    Article  CAS  PubMed  Google Scholar 

  28. Orhan AL, Şaylık F, Çiçek V, et al. Evaluating the systemic immune-inflammation index for in-hospital and long-term mortality in elderly non-ST-elevation myocardial infarction patients. Aging Clin Exp Res. 2022;34(7):1687–95.

    Article  PubMed  Google Scholar 

  29. Zhang N, Aiyasiding X, Li WJ, Liao HH, Tang QZ. Neutrophil degranulation and myocardial infarction. Cell Commun Signal. 2022;20(1):50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Totani L, Evangelista V. Platelet-leukocyte interactions in cardiovascular disease and beyond. Arterioscler Thromb Vasc Biol. 2010;30(12):2357–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Li Q, Ma X, Shao Q, et al. Prognostic impact of multiple lymphocyte-based inflammatory indices in acute coronary syndrome patients. Front Cardiovasc Med. 2022;9:811790.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Biccirè FG, Farcomeni A, Gaudio C, et al. D-dimer for risk stratification and antithrombotic treatment management in acute coronary syndrome patients: a systematic review and metanalysis. Thromb J. 2021;19(1):102.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Marfella R, Di Filippo C, Portoghese M, et al. Tight glycemic control reduces heart inflammation and remodeling during acute myocardial infarction in hyperglycemic patients. J Am Coll Cardiol. 2009;53(16):1425–36.

    Article  CAS  PubMed  Google Scholar 

  34. Karima M, Kantarci A, Ohira T, et al. Enhanced superoxide release and elevated protein kinase C activity in neutrophils from diabetic patients: association with periodontitis. J Leukoc Biol. 2005;78(4):862–70.

    Article  CAS  PubMed  Google Scholar 

  35. Wong SL, Demers M, Martinod K, et al. Diabetes primes neutrophils to undergo NETosis, which impairs wound healing. Nat Med. 2015;21(7):815–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Menegazzo L, Ciciliot S, Poncina N, et al. NETosis is induced by high glucose and associated with type 2 diabetes. Acta Diabetol. 2015;52(3):497–503.

    Article  CAS  PubMed  Google Scholar 

  37. Njeim R, Azar WS, Fares AH, et al. NETosis contributes to the pathogenesis of diabetes and its complications. J Mol Endocrinol. 2020;65(4):R65-r76.

    Article  CAS  PubMed  Google Scholar 

  38. Josefs T, Barrett TJ, Brown EJ, et al. Neutrophil extracellular traps promote macrophage inflammation and impair atherosclerosis resolution in diabetic mice. JCI Insight. 2020;5(7).

  39. de Lemos JA, Morrow DA, Sabatine MS, et al. Association between plasma levels of monocyte chemoattractant protein-1 and long-term clinical outcomes in patients with acute coronary syndromes. Circulation. 2003;107(5):690–5.

    Article  PubMed  Google Scholar 

  40. Gabriel AS, Martinsson A, Wretlind B, Ahnve S. IL-6 levels in acute and post-myocardial infarction: their relation to CRP levels, infarction size, left ventricular systolic function, and heart failure. Eur J Intern Med. 2004;15(8):523–8.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all patients and study participants in the NOAFCAMI-SH registry.

Funding

This work was supported by the Natural Science Foundation of Shanghai (18ZR1429700), the National Natural Science Foundation of China (82200318), and the Climbing Program of Shanghai Tenth People’s Hospital (2021SYPDRC049, 2021SYPDRC035).

Author information

Authors and Affiliations

  1. Department of Cardiology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, 301 Middle Yanchang Road, Jingan District, Shanghai, 200072, China

    Jiachen Luo, Xiaoming Qin, Xingxu Zhang, Yiwei Zhang, Fang Yuan, Wentao Shi, Baoxin Liu & Yidong Wei

Authors
  1. Jiachen Luo
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  2. Xiaoming Qin
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  3. Xingxu Zhang
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  4. Yiwei Zhang
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  5. Fang Yuan
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  6. Wentao Shi
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  7. Baoxin Liu
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  8. Yidong Wei
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Consortia

the NOAFCAMI-SH Registry Investigators

Contributions

JCL contributed to the conception and design of the work, analyzed the data, acquisition of the funding, and wrote the manuscript; XMQ, XXZ, YWZ, YF, and WTS contributed to the acquisition of the data, analyzed the data, and critically revised the manuscript; BXL contributed to the acquisition and interpretation of the data, acquisition of the funding, and critically revised the manuscript. YDW contributed to the conception and design of the work, interpretation of the data, acquisition of the funding and critically revised the manuscript; All authors have approved the final version of the manuscript for publication. YDW has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Corresponding author

Correspondence to Yidong Wei.

Ethics declarations

Ethics approval and consent to participate

The study protocol has been approved by Shanghai Tenth People’s Hospital Ethics Review Committee (SHSY-IEC-KY-4.1/18-199/01). Due to the anonymous nature of the data, the requirement for informed consent was waived.

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Not applicable.

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The authors declare that they have no competing interests.0 bcv

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Supplementary Information

Additional file 1: Table S1.

Univariable analysis for the all-cause death in the whole cohort. Table S2. Baseline characteristics of patients with diabetes by tertiles of systemic-immune inflammation index. Table S3. Baseline characteristics of patients without diabetes by tertiles of systemic-immune inflammation index. Table S4. Sensitivity analysis: Association between tertiles of the systemic immune-inflammation index and death in a complete dataset (N = 1895). Table S5. Sensitivity analysis: Association between tertiles of the systemic immune-inflammation index and death in patients with and without diabetes after further accounting for oral anticoagulants and diuretics. Table S6. Sensitivity analysis: Association between tertiles of the systemic immune-inflammation index and death in the overall and diabetic cohorts after further accounting for anti-diabetic agents. Table S7. Association between neutrophil counts and clinical outcomes. Table S8. Association between lymphocyte counts and clinical outcomes. Table S9. Association between platelet counts and clinical outcomes

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Luo, J., Qin, X., Zhang, X. et al. Prognostic implications of systemic immune-inflammation index in myocardial infarction patients with and without diabetes: insights from the NOAFCAMI-SH registry. Cardiovasc Diabetol 23, 41 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12933-024-02129-x

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Keywords

Cardiovascular Diabetology

ISSN: 1475-2840