Markers of inflammation predicts long-term mortality in patients with acute coronary syndrome – a longitudinal cohort study

doi:10.21203/rs.3.rs-4973727/v1

Background:

A chronic low-grade inflammation is a well-known risk factor for the development of coronary heart disease (CHD) and future cardiovascular events, and anti-inflammatory therapy can reduce the risk of ischemic cardiovascular disease (CVD) events following a myocardial infarction. It is not known to what extent inflammation at time of an acute event is predictive of long-term events. We explored the hypothesis that routine blood measurements of markers of inflammation in an acute coronary syndrome (ACS) are predictive of future long-term mortality.

Methods:

In a cohort of 5292 consecutive patients admitted to a coronary intensive care unit with suspected ACS over a four-year period in the Carlscrona Heart Attack Prognosis Study (CHAPS), 908 patients aged 30-74 years (644 men, 264 women) had at discharge received the diagnosis of either myocardial infarction (MI) (527) or unstable angina (UA) (381). In this group we performed a longitudinal 10-year follow-up study based on information from the Swedish national registries, using total mortality and cardiac disease mortality as primary outcome measures.

Results:

Long term total mortality and cardiac mortality was significantly associated with higher leucocyte counts (e.g. neutrophiles, monocytes, eosinophiles) and higher levels of inflammatory biomarkers (e.g. C-reactive protein, Serum Amyloid A (SAA), fibrinogen, neutrophile-lymphocyte ratio (NLR) and monocyte-lymphocyte ratio (MLR)), measured at time of hospital admission for ACS. These findings were independent of the ACS diagnosis.

Conclusion:

Our results suggests that degree of inflammation at time of ACS presentation, beyond its established role as major risk factor for development of CHD, has further role in long-term mortality following an ACS. Interestingly, our results suggest that the inflammation at time of the event is a stronger predictor of long term mortality than the outcome of the acute event.

Inflammation

acute coronary syndrome

unstable angina

myocardial infarction

prognosis

mortality

Atherosclerosis is a slowly progressing disease often marked by a long, asymptomatic subclinical phase that can culminate an acute coronary syndrome (ACS), manifested as either myocardial infarction (MI) or unstable angina (UA). ACS is usually a late complication, and often the first manifestation of the underlying atherosclerotic disease. The immediate trigger for an ACS event is typically a rupture (fissure) of a vulnerable atherosclerotic coronary artery plaque or a superficial endothelial erosion, both leading to thrombus formation [1].These pathologies appears to be differently represented across ACS type [2, 3] [4]; ruptured plaques are predominant in ST-elevation myocardial infarction (STEMI), whereas endothelial erosions are more commonly observed in non-ST elevation myocardial infarction (NSTEMI) [4]. It is, however, not known whether such differences would affect long term mortality after an ACS event.”

A chronic low-grade inflammatory process is considered to play a central pathogenic role in the development and progression of atherosclerosis, as well as in the acute thrombotic event in manifested as an ACS [5]. In a previous study based on the Carlscrona Heart Attack Prognosis Study (CHAPS), we demonstrated that blood cell profiles and the degree of inflammation at the time of admission were associated with the outcomes of ACS, differentiating between outcomes such as MI and UA [6]. Our data further suggested that a state of pre-existing low-grade inflammation could predispose individuals to different severities of ACS.

Since our initial research, there have been temporal shifts in the clinical presentation and underlying mechanisms of ACS in most Western countries. Hospitalization and death rates for STEMI have declined, with a relative increase in NSTEMI occurrences and changes in observed plaque characteristics underlying luminal occlusive thrombosis [4]. The rupturing lipid loaded culprit plaques, usually seen with STEMI, have become less dominant and plaques with erosion of the endothelium as initiator of thrombosis more frequent [4]. This can be ascribed to improvements in controlling traditional risk factors, especially dyslipidaemia. Even withexcellent control of this risk factor, other risk elements persist, prompting a continued search for other anti-atherosclerotic therapies, recently reviewed by Peter Libby and Brenden Everett [7]. Recent studies have demonstrated the potential benefits of anti-inflammatory therapies in reducing ischemic events following myocardial infarction [8] [9]. In light of these findings, which highlight the protective effects of such therapies on post-infarction prognosis, we here aimed to explore whether individual differences in inflammatory markers in patients with ACS, as measured at time of the acute event, are associated with long-term prognosis. In a registry based 10-year follow up in CHAPS, we investigated if biomarkers of inflammation, measured at the time of hospital admission, were predictive of long-term mortality (cardiac disease mortality and total mortality).

Study design:

Based on CHAPS we performed an observational cohort sub-study including patients diagnosed with a MI or UA aged 30-74 years at admission.

Patient recruitment

The CHAPS cohort has previously been described in detail [10]. In brief, 5292 consecutive patients admitted to the coronary care unit with acute chest pain (indicative of a possible ACS) at Blekinge Hospital, Karlskrona, between January 26 1992 and January 25 1996 were recruited. Of these, 2992 were between 30-74 years. The diagnosis was set at discharge from the hospital by one of two experienced cardiologists. Based on this diagnosis, patients with MI or UA were identified. In patients admitted more than once during the inclusion period, only the first classifying admittance was included as an ‘event’ (UA or MI) in the analysis. Informed consent was obtained from all included patients before enrolment and the study complies with the Declaration of Helsinki.

Outcome measures

Cardiac disease mortality and total mortality during the follow up period.

Acute coronary syndrome patients

A diagnosis of ACS was confirmed in 908 of the eligible patients aged 30-74 years of age (644 men and 264 women) [10]. Two groups were identified: (i) patients experiencing at least one acute MI during the inclusion period (527) or (ii) patients experiencing no acute MI, but having at least one episode of UA during the inclusion period (381). In 900 of these patients complete data were available to perform longitudinal data analyses.

The diagnostic criteria of acute MI were fulfilled if at least two of the following criteria were met: (i) A history of chest pain of at least 15 min duration, (ii) a rise of cardiac markers (CK-MB and ASAT) to at least twice the upper limit of normality, or (iii) characteristic ECG changes typical for MI typical e.g. change of ST segment and/or of T-waves and/or appearance of new Q-waves. These criteria included both patients with ST-elevation MI (STEMI) and non-ST elevation MI (NSTEMI).

A diagnosis of UA was made when patients fulfilled all of the following criteria: (i) no evidence of MI, (ii) during the preceding 48 h acute chest pain of increased/modified character to any previously experienced, and (iii) angina pectoris diagnosed and medically treated before admission, or alternatively, angina pectoris ascertained by clinical evaluation, including a bicycle exercise test prior to discharge from the hospital [10]. Patients with post-infarction angina and secondary angina were not included.

Patients admitted to the coronary care unit were initially treated with aspirin, and in case of ongoing chest pain, also nitrates and morphine. In patients meeting STEMI criteria, thrombolysis with streptokinase was administrated (193 of 527 patients with MI). If the diagnosis of MI was based on cardiac markers only, thrombolysis was not given. Acute coronary artery intervention was not available at this hospital at the time of the enrolment.

Ethical approval

Carlscrona Heart Attack Prognosis Study (CHAPS) was approved by the Regional Ethical Review Board, Lund, Sweden, (EPN 2009/762 and LU 298-91). All patients gave their informed consent before taking part.

Risk factors

Information on basic characteristics and risk factors were recorded from patient history at admission and/or extracted from earlier medical files, and the information was also verified at discharge from the hospital [10].

Laboratory analyses

Samples for laboratory analysis were collected at hospital admission and transferred to the certified hospital laboratory. Detailed procedures for blood sampling and analytical methods have been described in detail before [6]. Haematological variables (blood cell counts and indices) and plasma fibrinogen were analysed using routine diagnostic methods in fresh samples at time of admission. High sensitivity C reactive protein (hsCRP) and Serum Amyloid A protein (SAA) were analysed in samples that had been stored at -80 degrees ^oC and thawed.

Mortality data

Information on date of death and causes of death was obtained from the Centre for Epidemiology at the Swedish National Board of Health and Welfare for a period from 28^th January 1992 to 10^th May 2017. All registered causes of death were obtained, but only the primary cause of death was used. Using ICD 9- or ICD 10 diagnoses of the primary cause of death a variable was made containing 4 categories to be used in competing-risks survival regression: 1. Cardiac (ICD 9: 402, 404, 410, 411, 412, 413, 414, 427, 428, ICD 10: I48, I50, I11, I13, I20, I21, I23, I24, I25 ), 2. Cerebrovascular (ICD 9: 430, 431, 432, 433, 434, 436, 437, ICD 10: I60, I61, I62, I63, I64, I65, I66, I67), 3. Venous tromboembolism (ICD 9: 415, 451, ICD 10: I26, I80, I81) and 4. Other reasons.

Statistical methods

STATA version 16 (Stata Corporation, Texas, USA) was used for all data analyses. Standard methods were used for descriptive statistics. All-cause mortality was analysed for short- (0-28 days) and long-term mortality (29 days -10 years). The division of short- and long-term mortality was based on the AHA case definitions for acute coronary heart disease in epidemiology and clinical research studies [11]. When analysing short term all-cause mortality Cox regression was used. When analysing long term mortality both Cox regression was used for analysing all-cause mortality and competing-risks survival regression was used to study cardiac disease specific mortality over the same time period. Associations with specific biomarkers were analysed with diagnosis (UA/MI), age and sex in the model and presented by hazard ratio (HR) with 95% confidence intervals (CI) and p-values.

Age was entered into the regressions as a continuous variable. Plasma levels of hs-CRP were dichotomized at 2 mg/L to categorize individuals into high and low risk groups. This cut off is based on the JUPITER study, which selected individuals at high vascular risk because of an enhanced inflammatory response as indicated by hsCRP levels ≥2 mg/L [12]. For other biomarkers to categorise into risk groups, these were divided into tertiles, using tertile 1 as reference in the analyses. The neutrophile to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) was calculated by dividing neutrophiles and monocyte cell counts (10⁹/L), respectively, with lymphocyte cell counts (10⁹/L). Tests for trend was performed by entering tertiles as a linear variable into the regression, and the results presented as p values. In the different multivariate analyses performed, subjects with missing data for any included marker were automatically excluded, leaving about 70% left in the regression for the full model.

Patient and Public Involvement.

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

The study population comprised 908 patients with ACS; 527 were diagnosed with myocardial infarction (MI) and 381 with unstable angina (UA) (see Tables 1a - 1c for descriptive data). Due to lack of follow-up data, 8 patients were excluded. Within the first 28 days, there were 48 deaths (33 men, 15 women), of which 43 were due to cardiac disease. Of these, 42 occurred in the MI group, indicating that the cause of death was related to cardiac complications of the acute MI. Between 29 days and 10 years, there were a total of 306 deaths, 200 of which were due to cardiac causes (125 in MI, 75 in UA) and 106 due to other causes (65 in MI, 41 in UA). From 10 years to 25 years, there were 144 cardiac deaths (79 in MI, 65 in UA) and 175 deaths due to other causes (88 MI, 87 UA).

Table 1a: Clinical characteristics of patients at inclusion

Data taken from Odeberg et. al. [10]. Hypertension and diabetes were defined as a physician's diagnosis prior to hospital admittance. Missing data serum cholesterol (n=102), hypertension (n=34), diabetes (n=34), smoking (n=43).

Table 1b: Cause of death at follow up in relation to diagnosis at inclusion

We first analyzed how ACS diagnosis (MI vs. UA) was associated with short-term (0-28 days) and long-term (29 days - 10 years) mortality after an ACS. A diagnosis of MI over UA was associated with both short-term and long-term total mortality (Table 2). When analyzed separately as cardiac disease mortality, a diagnosis of MI over UA was significantly associated with long-term cardiac disease mortality (HR 1.41 [1.02-1.94]). Short-term total mortality was primarily due to cardiac disease mortality (33 out of 38 deaths, Table 1b). Sex was not associated with either short-term (HR 0.69 [0.30-1.60]) or long-term (HR 1.26 [0.88-1.78]) total mortality. Age was associated with long-term (HR 1.24 [1.17-1.32]) but not short-term (HR 2.08 [0.84-5.15]) total mortality. When analyzing mortality beyond 10 years to the end of the follow-up period (25 years), we found no significant difference between MI and UA with respect to cardiac disease mortality (HR 1.225 [0.975-1.54]) or total mortality (HR 1.10 [0.94-1.28]).

Table 2. Short term (<29 days) and long term (29 days – 10 years) follow up mortality in relation to acute coronary syndrome diagnosis

HR 95% CI

0-28 days

Total mortality (MI vs UA) 5.38 1.84-15.74

29 days – 10 yrs

Total mortality (MI vs UA) 1.27 1.01-1.61

Cardiac mortality (MI vs UA) 1.41 1.02-1.94

MI, myocardial infarction; UA, unstable angina. The diagnosis was set at point of discharge from hospital following acute coronary syndrome event. *Total mortality

Table 1c: Categorized blood biomarkers of inflammation at inclusion

Men (n=644) Women (n=264)

Range n (%) n (%)

hsCRP mg/L ≥2 341 (59.3) 141 (63.5)

Fibrino (g/L) Tert 1 1.5-3.3 233 (40.5) 77 (33.8)

Tert 2 3.4-4.0 159 (27.7) 73 (32.0)

Tert 3 4.1-10.0 183 (31.8) 78 (34.2)

SAA (mg/L) Tert 1 0.111-3.25 209 (36.3) 57 (25.7)

Tert 2 3.26-7.44 191 (33.2) 75 (33.8)

Tert 3 7.45-1570 175 (30.4) 90 (40.5)

Leuco (10⁹/L) Tert 1 2.49-7.39 196 (32.6) 85 (35.4)

Tert 2 7.4-9.8 198 (32.9) 84 (35.0)

Tert 3 9.82-80.9 207 (34.4) 71 (29.6)

Neutro (10⁹/L) Tert 1 0.14-4.79 191 (32.3) 84 (36.5)

Tert 2 4.81-7.04 196 (33.1) 78 (33.9)

Tert 3 7.05-20.06 205 (34.6) 68 (29.6)

Baso (10⁹/L) Tert 1 0-0.039 229 (40.7) 95 (43.0)

Tert 2 0.04-0.059 174 (30.9) 60 (27.1)

Tert 3 0.06-0.33 160 (28.4) 66 (29.9)

Eosino (10⁹/L) Tert 1 0-0.06 155 (27.2) 84 (36.8)

Tert 2 0.07-0.14 186 (32.6) 77 (33.8)

Tert 3 0.15-9.12 229 (40.2) 67 (29.4)

Lympho(10⁹/L) Tert 1 0.16-1.32 206 (34.8) 71 (30.9)

Tert 2 1.33-1.88 191 (32.3) 80 (34.8)

Tert 3 1.89-75.33 195 (32.9) 79 (34.3)

Mono (10⁹/L) Tert 1 0.04-0.4 167 (28.4) 116 (50.7)

Tert 2 0.41-0.56 212 (36.0) 59 (25.8)

Tert 3 0.57-1.60 210 (35.7) 54 (23.6)

T-cyt (10⁹/L) Tert 1 85-198 228 (38.1) 54 (2.6)

Tert 2 199-247 182 (30.4) 94 (39.3)

Tert 3 248-680 188 (31.4) 91 (38.1)

T-mcv (fL) Tert 1 6.5-8.8 221 (39.4) 69 (31.4)

Tert 2 8.9-9.4 167 (29.8) 78 (35.5)

Tert 3 9.5-46.0 173 (30.8) 73 (33.2)

NLR () Tert 1 0.075-2.73 187 (31.6) 84 (36.5)

Tert 2 2.74—4.87 205 (34.7) 82 (35.7)

Tert 3 4.87-48.3 199 (33.7) 64 (27.8)

MLR () Tert 1 0.216-0.892 166 (28.2) 111 (48.5)

Tert 2 0.892-1.477 201 (34.2) 68 (29.7)

Tert 3 1.481-7.142 221 (37.6) 50 (21.8)

Data are means (m) and standard deviations (SD), or numbers (n) and proportions (%). Tert – tertile; hsCRP - high sensitivity CRP; Fibrino - fibrinogen; SAA - Serum Amyloid A; Leuco -total leukocyte cell count; Neutro-neutrophil cell count; Baso-Basophil cell count; Eosino-eosinophil cell count; Lympho - lymphocyte cell count; Mono - monocyte cell count; T-cyt -thrombocyte cell count; T-mcv - thrombocyte median cell volume; NLR - neturophile to lymphocyte ratio; MLR – monocyte to lymphocyte ratio. Plasma levels of hsCRP were dichotomized at 2 mg/L, while other biomarkers were divided in tertiles for categorical comparisons. Missing data hsCRP (n=111), fibrinogen (n=105), s-amyloid A (n=111), leukocytes (n=67), neutrophils (n=86), basophils (n=124), eosinophils (n=110), lymphocytes (n=86), monocytes (n=90), thrombocyte cell count (n=71), T-mcv (n=127), NLR (n=87), MLR (n=91). Data taken from Odeberg et. al. [6].

Associations between diagnosis at inclusion and mortality following an ACS were estimated using cox regression and expressed as hazard ratios (HR) with 95% confidence intervals (95% CI), adjusted for differences in sex and age.

We next analyzed whether inflammatory variables measured in samples taken at admission for an ACS were associated with mortality at follow-up. A statistically significant association was found for blood markers of inflammation with short-term total mortality when analyzed as categorized variables, adjusting for sex and age along with the ACS outcome of the recent acute event (Table 3a). Significant associations were found with the third tertile of serum amyloid A (SAA), leukocytes, neutrophils, and basophils, using tertile 1 as the reference, and for basophils also for tertile 2 (Table 3a). There was a non-significant trend for association with a higher neutrophil to lymphocyte ratio (NLR) but not monocyte to lymphocyte ratio (MLR) (Table 3a). Long-term total mortality, adjusted for differences in age and sex, was significantly associated with hsCRP ≥2 mg/L and higher tertiles of SAA, total leukocytes, neutrophils, basophils, monocytes, and both NLR and MLR (Table 3b).

Table 3a. Short term (<29 days) total mortality in relation to categorized inflammatory markers at acute coronary syndrome event (adjusted for age sex and ACS outcome)

HR 95% CI

hsCRP >2 mg/L 1.66 0.46-5.94

Fibrino Tert 1 1.00 p for trend 0.403

Tert 2 4.23 1.08-16.63

Tert 3 1.90 0.49-7.32

SAA Tert 1 1.0 p for trend 0.045

Tert 2 7.99 0.75-84.85

Tert 3 8.95 1.03-77.69

Leuco Tert 1 1.00 p for trend 0.019

Tert 2 3.02 0.49-7.32

Tert 3 6.70 1.19-37.60

Neutro Tert 1 1.00 p for trend 0.009

Tert 2 3.78 0.66-21.54

Tert 3 9.00 1.69-47.83

Baso Tert 1 1.00 p for trend 0.025

Tert 2 5.44 1.19-24.89

Tert 3 4.68 1.24-17.62

Eosino Tert 1 1.00 p for trend 0.105

Tert 2 0.69 0.18-2.65

Tert 3 2.47 0.72-8.43

Lympho Tert 1 1.00 p for trend 0.149

Tert 2 2.59 0.76-8.84

Tert 3 2.14 0.75-6.16

Mono Tert 1 1.00 p for trend 0.344

Tert 2 1.08 0.26-4.42

Tert 3 1.71 0.52-5.68

T-cyt Tert 1 1.00 p for trend 0.115

Tert 2 1.66 0.48-5.72

Tert 3 2.77 0.77-9.94

T-mcv Tert 1 1.00 p for trend 0.972

Tert 2 0.26 0.06-1.19

Tert 3 1.28 0.37-4.41

NLR Tert 1 1.00 p for trend 0.17

Tert 2 21.5 2.14-215.34

Tert 3 7.97 0.92-69.03

MLR Tert 1 1.00 p for trend 0.80

Tert 2 1.96 0.56-6.96

Tert 3 0.96 0.28-3.32

Associations between inflammatory markers and short term mortality following an ACS were estimated using binary logistic regression and expressed as hazard ratios (HR) with 95% confidence intervals (95% CI), adjusted for differences in sex, age and diagnosis at inclusion. Tert – tertile; hsCRP - high sensitivity CRP; Fibrino-fibrinogen; SAA - Serum Amyloid A; Leuco-total leukocyte cell count; Neutro-neutrophil cell count; Eosino-eosinophil cell count; Baso-Basophil cell count; Lympho-lymphocyte cell count; Mono-monocyte cell count; T-cyt thrombocyte cell count; T-mcv thrombocyte median cell volume; NLR - neutrophile to lymphocyte ratio; MLR – monocyte to lymphocyte ratio. Plasma levels of hsCRP were dichotomized at 2 mg/L, while other biomarkers were divided in tertiles for categorical comparisons using tertile 1 as reference. The tertiles were then entered into the regression as a linear variable to test for trend.

Table 3b. Long term (29 days - 10 years) total mortality in relation to categorized inflammatory markers measured at acute coronary syndrome event (adjusted for age and sex)

HR 95% CI

hsCRP >2 mg/L 1.82 1.39-2.38

Fibrino Tert 1 1.00 p for trend <0.001

Tert 2 1.29 0.93-1.79

Tert 3 2.61 1.86-3.66

SAA Tert 1 1.0 p for trend <0.001

Tert 2 1.56 1.12-2.15

Tert 3 2.35 1.71-3.21

Leuco Tert 1 1.00 p for trend <0.001

Tert 2 1.37 1.02-1.85

Tert 3 2.15 1.61-2.89

Neutro Tert 1 1.00 p for trend 0.001

Tert 2 1.37 1.01-1.85

Tert 3 1.95 1.46-2.62

Baso Tert 1 1.00 p for trend 0.007

Tert 2 1.61 1.20-2.15

Tert 3 1.69 1.26-2.27

Eosino Tert 1 1.00 p for trend 0.366

Tert 2 0.94 0.7-1.27

Tert 3 1.15 0.95-1.53

Lympho Tert 1 1.00 p for trend 0.91

Tert 2 1.01 0.76-1.34

Tert 3 0.98 0.73-1.32

Monocyt Tert 1 1.00 p for trend <0.001

Tert 2 1.29 0.95-1.75

Tert 3 1.75 1.30-2.36

T-cyt Tert 1 1.00 p for trend 0.038

Tert 2 0.98 0.73-1.32

Tert 3 1.34 1.02—1.78

T-mcv Tert 1 1.00 p for trend 0.078

Tert 2 0.73 0.54-0.97

Tert 3 0.78 0.58-1.05

NLR Tert 1 1.00 p for trend 0.001

Tert 2 1.35 0.99-1.82

Tert 3 1.64 1.21-2.22

MLR Tert 1 1.00 p for trend 0.002

Tert 2 1.19 0.87-1.63

Tert 3 1.59 1.18-2.14

Associations between inflammatory markers and long term mortality following an ACS were estimated using cox regression and expressed as hazard ratios (HR) with 95% confidence intervals (95% CI), adjusted for differences in sex and age. Tert – tertile; hsCRP - high sensitivity CRP; Fibrino-fibrinogen; SAA - Serum Amyloid A; Leuco-total leukocyte cell count; Neutro-neutrophil cell count; Eosino-eosinophil cell count; Baso-Basophil cell count; Lympho-lymphocyte cell count; Mono-monocyte cell count; T-cyt - thrombocyte cell count; T-mcv - thrombocyte median cell volume; NLR - neturophile to lymphocyte ratio; MLR – monocyte to lymphocyte ratio. Plasma levels of hsCRP were dichotomized at 2 mg/L, while other biomarkers were divided in tertiles for categorical comparisons using tertile 1 as reference. The tertiles were then entered into the regression as a linear variable to test for trend.

When analyzed separately with respect to cardiac disease mortality, significant associations were found between long term mortality and higher levels of plasma biomarkers of inflammation (hsCRP, SAA, fibrinogen) and with all white blood cell types analyzed except for lymphocytes (Table 4). Significant associations were also found with biomarker indices NLR and MLR (Table 4).

Table 4. Long term (29 days – 10 years) Cardiac disease mortality in relation to categorised inflammatory markers measured at the acute coronary syndrome event (adjusted for age and sex)

Risk factors HR 95% CI

hsCRP >2 mg/L 1.87 1.30-2.70

Fibrino Tert 1 1.00 p for trend <0.001

Tert 2 1.26 0.80-1.98

Tert 3 2.38 1.58-3.57

S-amyloid Tert 1 1.0 p for trend 0.001

Tert 2 1.54 0.99-2.40

Tert 3 2.08 1.36-3.19

Leuco Tert 1 1.00 p for trend <0.001

Tert 2 1.18 0.78-1.80

Tert 3 2.31 1.56-3.32

Neutro Tert 1 1.00 p for trend <0.001

Tert 2 1.28 0.84-1.97

Tert 3 2.28 1.54-3.40

Eosino Tert 1 1.00 p for trend 0.038

Tert 2 1.21 0.79-1.83

Tert 3 1.54 1.02-2.33

Baso Tert 1 1.00 p for trend 0.003

Tert 2 1.24 0.82-1.87

Tert 3 1.79 1.22-2.61

Lymfocyt Tert 1 1.00 p for trend 0.674

Tert 2 0.92 0.62-1.35

Tert 3 1.10 0.75-1.63

Monocyt Tert 1 1.00 p for trend <0.001

Tert 2 1.52 0.98-2.36

Tert 3 2.49 1.64-3.78

T-cyt Tert 1 1.00 p for trend 0.92

Tert 2 0.90 0.61-1.33

Tert 3 1.02 0.69-1.51

T-mcv Tert 1 1.00 p for trend 0.545

Tert 2 0.87 0.58-1.29

Tert 3 0.89 0.59-1.34

NLR Tert 1 1.00 p for trend 0.016

Tert 2 1.15 0.76-1.76

Tert 3 1.61 1.09-2.40

MLR Tert 1 1.00 p for trend 0.004

Tert 2 1.04 0.66-1.63

Tert 3 1.77 1.19-2.64

Associations between risk factors and long term (29 days – 10 years) mortality following an ACS were estimated using binary logistic regression and expressed as hazard ratios (HR) with 95% confidence intervals (95% CI), adjusting for differences in sex and age.

Tert – tertile; hsCRP - high sensitivity CRP; Fibrino-fibrinogen; Leuco-total leukocyte cell count; Neutro-neutrophil cell count; Eosino-eosinophil cell count; Baso-Basophil cell count; Lympho-lymphocyte cell count; Mono-monocyte cell count; T-cyt thrombocyte cell count; T-mcv thrombocyte median cell volume. Plasma levels of hsCRP were dichotomized at 2 mg/L, while other biomarkers were divided in tertiles for categorical comparisons using tertile 1 as reference. The tertiles were then entered into the regression as a linear variable to test for trend.

Adjusting also for the outcome of the original acute event, these associations remained significant for both total and cardiac disease mortality (Supplementary Tables S1 and S2, respectively (see Additional File 1)). Finally, to distinguish an inflammatory response to myocardial tissue necrosis from a possible pre-existing inflammation, we analyzed the levels of the inflammatory variables in relation to the duration from the onset of chest pain until blood sampling, as previously described [6]. Adjusting also for the duration of symptoms before sampling (>240 min) did not influence our findings for either total mortality (Supplementary Table S3 (see Additional File 1)) or cardiovascular mortality (Supplementary Table S4 (see Additional File 1)).

Principal findings

In this study, with a 10-year follow-up using registry data, we found that plasma biomarkers of inflammation (hsCRP, SAA, fibrinogen) measured at the acute event were associated with long-term mortality, independent of the original ACS diagnosis. Additionally, blood cell profiles (e.g., neutrophils, monocytes) at the acute event predicted both short- and long-term mortality. We also found that recently defined inflammatory composite biomarkers (e.g., NLR, MLR) were associated with long-term mortality. Our results suggest that standard blood biomarkers of inflammation measured at the acute event have prognostic value for future risk.

Results in relation to previous studies.

Chronic inflammation is a well-known driver of atherosclerosis progression and risk of future acute CVD events [5]. Previously, based on the CHAPS study, we showed that prothrombotic gene variants predisposed individuals to MI over UA as outcomes of ACS in a fibrinogen concentration-dependent manner [13]. A more pronounced state of inflammation at admission conferred an increased risk towards MI rather than UA [6]. We observed significant differences in blood cell profiles at the time of the event between patients diagnosed with MI and UA, suggesting important differences in the underlying pathology of ACS between these two groups [6].Our current findings that blood cell profiles and levels of plasma biomarkers of inflammation measured at admission are also associated with individual long-term prognosis (cardiac disease mortality and total mortality), independent of the original ACS classification (UA or MI), suggest that the inflammatory activity reflected by these markers, existing prior to the acute event [6], persists throughout the follow-up period. This supports the potential of targeting inflammation post-ACS as an intervention point. This is consistent with the findings of the Jupiter study [12], which demonstrated that in apparently healthy individuals without hyperlipidemia but with elevated baseline hsCRP levels, the statin rosuvastatin significantly reduced the incidence of major cardiovascular events, coinciding with a reduction in hsCRP levels[12]. For individuals in the placebo group with elevated baseline hsCRP levels, these levels remained stable throughout the observation period [14]. Those findings suggested a residual risk associated with inflammation beyond cholesterol-related risks. In the Canakinumab Anti-inflammatory Thrombosis Outcome Study (CANTOS), the inflammatory hypothesis of atherothrombosis was tested by blocking the IL-1 inflammatory signaling pathway with the human monoclonal anti-IL-1 antibody canakinumab [9]. The results showed that treatment reduced the risk of recurrent atherosclerotic events after a myocardial infarction [9], and that this reduction was proportional to the reduction in inflammatory biomarkers e.g. hsCRP [15]. In a sub-study of CANTOS, it was demonstrated that the incidence rates of atherosclerotic events and all-cause mortality decreased in parallel with a reduction in IL-6 levels [16]. This indicates the IL-1-to-IL-6-to CRP signalling pathway to constitute a major target for vascular protection in secondary protection, which has been elaborated in recent reviews [17, 18]. The mechanisms are likely to be multifaceted, including the stimulating effect of IL-6 on bone marrow production of neutrophils. In a recent randomized, double-blind, placebo-control study (the COLCOT study), colchicine, which has been used as a remedy for the inflammation in gout since centuries, was shown to reduce the percentage of ischemic cardiovascular events among patient with a recent myocardial infarction [8]. It was also shown that earlier initiation of therapy <3 days significantly reduced cardiovascular death and/or recurrent event compared to initiation within 4-7 days [19]. The results in COLCOT have been particularly attributed to the inhibiting effect on tubulin polymerization and microtubule formation in phagocytes like neutrophils and macrophages [20]. Together with our findings of a strong significant association between long term CHD mortality and neutrophil counts, this suggest a direct role of neutrophiles in future CVD risk. Our results are also in line with findings in the STABILITY (Stabilization of Atherosclerotic plaque by initiation of Darapladib Therapy) trial [21]. In this long-term prospective study on patients with optimally treated stable CAD, white blood cell counts carried independent prognostic information on the risk of future cardiovascular events [21].

Overall, we find for both short and long term mortality a trend for increased risk with higher concentration of inflammatory markers or blood cell counts, where the highest risk is observed for the third tertile, with the exception of fibrinogen, where the second tertile is associated with short term mortality, but not the third. This is consistent with our previous observation in CHAPS that common structural variants (polymorphisms) in the platelet fibrinogen receptor Gp2b/3a and coagulation factor XIII were associated with MI over UA in the second tertile, but not the third when analysed with an effect model based on functional in vitro studies [13].

Strengths and limitations.

A strength of the current study is the near complete follow up mortality data for the ACS cohort (900 out of 908 patients). Due to strict registration procedures at a national level based on the unique Swedish personal identification number, this registry is regarded as complete. Furthermore, the study was based in one centre with the same two cardiologists assessing and categorising all patients, using consistent criteria.

The patients were recruited before the introduction of PCI, CABG, and modern antithrombotic drugs in the standard management of ACS. These interventions would otherwise influence the outcome and diagnosis in the acute event. The absence of them at that time made it possible for us to identify progression to MI or UA as distinct outcome groups within the cohort. The results from our previous work based on the CHAPS cohort, where we have studied genetic and clinical risk factors and routine plasma biomarkers in relation to ACS outcome [6, 10, 13], support that the classification of ACS patients in CHAPS into these two groups represent distinct outcome entities. By using these outcomes as proxies for underlying pathology, we were able to explore hypotheses that differences in underlying pathological mechanisms in MI and UA affect long term mortality.

Importantly, although we find the outcome of ACS to MI over UA to be significantly associated with long-term mortality, mainly explained by cardiac disease mortality, the associations of inflammatory variables with long term mortality are independent of ACS outcome. This suggest that low grade inflammation at time of event is associated with long term mortality independent of possible differences in underlying pathology of UA and MI.

There are limitations of the study that should be acknowledged. Biochemical analyses of fibrinogen and blood cells were performed over a period of four years, however the hospital routine laboratory used standardised and certified methods, providing consistency over time. Analyses of hsCRP and SAA were performed using frozen samples stored at -80^oC for 15 years; quality assurance work at the laboratory has shown that storage of samples at -80^oC did not influence determined hsCRP and SAA levels, and high sensitivity C-reactive protein (Hs-CRP) remains highly stable in long-term archived human serum [22]. Outcomes are strong and consistent with the age and sex adjusted model leaving 90% left in the regression. Furthermore, the overall patterns show a high internal consistency. As refined criteria and more sensitive and specific biomarkers are implemented the definition of MI continues to evolve. The increase of NSTEMI over UA seen today compared to when CHAPS was conducted, can be partly explained by the use of high sensitivity myocardial damage markers like troponin, which is much more sensitive to detect an MI as compared to older markers like CK/CK-MB (4). It is therefore likely that some of the UA cases in our study would now been diagnosed as NSTEMI, using these recent criteria required for MI diagnosis [23]. However, while modern treatment strategies has significantly reduced coronary artery disease mortality [24], in more than half of patients suffering an acute event, this is the first indication of the underlying atherosclerotic disease, i.e. these are naïve with respect to CAD preventive treatment and thus comparable to the CHAPS cohort. Also, as CHAPS is a single centre study, and treatments and risk factor profiles have partly developed since the study was performed, the results would therefore not necessarily be generalised to a broader modern population.

In conclusion, while chronic low-grade inflammation is well established as a major risk factor for development of CHD and risk of future events, our study indicates that the level of inflammation present at time of acute event carries consequences for long term prognosis. Available routinebiochemical markers that offer prognostic information in samples taken already at presentation of an ACS, independent of underlying pathology and acute diagnosis (UA or MI) offers an opportunity for early risk stratification, which could be of clinical value in terms of more advanced secondary prevention approach in addition to what is in clinical practice of today. The association of inflammatory blood cell profiles with long term prognosis raise several mechanistic hypotheses that warrant further investigation, such as the role of neutrophiles in predisposing to future CVD events. Establishing such mechanisms at the cellular level could lead to optimisation of pharmacological treatment for patients that have suffered an acute event.

ACS; Acute Coronary Syndrome

MI; Myocardial Infarction

UA; Unstable Angina

CHD: Coronary Heart Disease

PCI; Percutaneous Coronary Intervention

CABG; Coronary Arterial Bypass Graft Surgery

hsCRP; High sensitivity C-Reactive Protein

SAA; Serum Amyloid A

OR; Odds Ratio

HR; Hazards Ratio

CI; Confidence Interval

Ethics approval and consent to participate: Carlscrona Heart Attack Prognosis Study (CHAPS) was approved by the Regional Ethical Review Board, Lund, Sweden (EPN 2009/762 and LU 298-91). All participants gave their informed consent before taking part.

Consent for publication: N/A

Availability of data and materials: The data used in this study include registry-based information, as well as laboratory data collected both during routine clinical management and through subsequent research activities conducted by the study team. Due to the sensitive nature of these data and to protect participant confidentiality, they cannot be made publicly available. However, de-identified data may be available from the authors upon reasonable request, contingent upon approval by the relevant ethical review boards and compliance with institutional and regulatory guidelines. Requests for data access should include a detailed proposal and will be evaluated on a case-by-case basis to ensure adherence to ethical standards and privacy regulations.

Conflict of interest: No conflict of interests to declare. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Funding: This work was supported by Stockholm County Council (SLL) grant number: FoUI-949280; the Royal Institute of Technology (KTH) grant number: Faculty Grant; the Swedish agency of innovation (VINNOVA) grant number: 2011-02497;The Operational Healthcare Committee, Region Västra Götaland, grant number: N/A. The Carlscrona Heart Attack Prognosis Study (CHAPS) has been supported by Landstinget Blekinge (Blekinge County Council) grant number: N/A, and the Ingemanssonska Foundation grant number: N/A. The funding agencies had no role in the design of the study; in the analysis and interpretation of the data; in the writing of the report; or in the decision to submit the paper for publication.

Author Contributions: HO, LR, and MF designed and initiated the original CHAPS cohort study, and MF conducted inclusion and collected information of all patients in the CHAPS cohort. JO, LR, UL, AH, HO, IV conceived and designed the current study. MP and IV collected the laboratory data and compiled the datafile. AH and UL collected follow-up information from registries and compiled the follow up data file. AH and UL performed the statistical analyses and compiled the results. JO, UL, AH, HO, LR, MR, MF interpreted the results. JO, UL, HO drafted the paper. AH, LR, MR, IV contributed intellectual content and critical revision. All authors approved the final manuscript.

Acknowledgements: Senior cardiology consultant Dr. Per-Ola Bengtsson for evaluating and categorising patients included in the study.

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Additional File 1 (supplementary tables).
Supplementary table S1. Long term (29 days – 10 years) total mortality in relation to categorized inflammatory markers measured at acute coronary syndrome event (adjusted for differences in sex and age and ACS diagnosis).
Supplementary table S2. Long term (29 days – 10 years) Cardiac disease mortality in relation to categorised inflammatory markers measured at the acute coronary syndrome event (adjusted for differences in sex and age and ACS diagnosis).
Supplementary table S3. Long term (29 days – 10 years) total mortality in relation to categorized inflammatory markers measured at acute coronary syndrome event (adjusted for duration of symptoms, ACS diagnosis, age and sex).
Supplementary table S4. Long term (29 days – 10 years) cardiac disease mortality in relation to categorized inflammatory markers measured at acute coronary syndrome event (adjusted for duration of symptoms, ACS diagnosis, age and sex).

No competing interests reported.

AdditionalFile1.docx

Markers of inflammation predicts long-term mortality in patients with acute coronary syndrome – a longitudinal cohort study

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Abstract

INTRODUCTION

MATERIALS AND METHODS

RESULTS

DISCUSSION

Conclusions and possible clinical implications

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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