Abstract

Studies on the role of inflammation in cardiovascular disease focus on surrogate markers like plasma levels of C-reactive protein or interleukins that are affected by several factors. In this study we employ an approach in which the inflammatory mRNA profile of leucocytes is measured directly in a multigene system. We investigated the mRNA profile for 35 inflammatory markers in blood samples in a case-control study including 524 men with a history of myocardial infarction and 628 control subjects. Compared with controls, patients showed mRNA profiles with increased levels of most inflammatory mRNAs. The 2 most prominent mRNA risk indicators encoded the secreted protein macrophage migration inhibitory factor (crude odds ratio [OR], 3.4 for the highest quartile versus the lowest quartile (95% confidence interval [CI95], 2.3-4.9), and the intracellular regulator proteinase inhibitor 9 (OR, 2.5 for the highest versus the lowest quartile (CI95, 1.8-3.5), both showing an increase in odds ratio with increasing quartiles. Leucocytes in the blood of patients with myocardial infarction are more active in transcription of inflammatory genes, as evidenced by mRNA profiling. These data support the hypothesis that an inflammatory response involving leucocytes plays a role in the pathogenesis of myocardial infarction.

Introduction

Inflammation plays a key role in the pathophysiology of atherosclerosis and in the development of acute coronary events.1  Activated leucocytes, cytokines, and chemokines are prominent features of an atherosclerotic plaque. Moreover, plasma levels of markers of inflammation such as cell adhesion molecules, cytokines, proatherogenic enzymes, and C-reactive protein (CRP) were found to predict cardiovascular events in a variety of clinical settings.2  However surrogate markers like CRP are far removed from the actual disease process since they reflect how the liver reacts to disease in the vasculature. Therefore, the nature of a chronic systemic inflammatory state and the role of circulating leucocytes in maintaining such a state remain unclear.

The inflammatory state of leucocytes may be the byproduct of the local inflammation in the vessel wall, or it may reflect an active or latent infection that is in part responsible for the atherosclerotic process. This is supported by observations of increased neopterin and procalcitonin levels in patients with cardiovascular disease.3-6  Whatever the mechanism, some inflammatory mediators may directly influence the atherosclerotic process in several ways. Interleukin 6, for example, lowers high density lipoprotein and alters lipoprotein metabolism,7  and CRP may facilitate low-density lipoprotein uptake by macrophages.8 

Plasma protein levels do not fully reflect the inflammatory signature of leucocytes in whole blood. Tissue leucocytes and endothelial cells may also contribute to the plasma levels of inflammatory markers, and many inflammatory mediators are also produced by other cell-types. To overcome these obstacles in assessing the inflammatory status of circulating leucocytes, we have developed a sensitive quantitative assay that is capable of measuring a panel of mRNA levels in large series of whole blood samples in a single reaction. The panel was composed of target genes encoding cytokines, chemokines, their receptors (as representatives of the soluble mediators of the inflammatory response), genes encoding nuclear factor κB (NFκB) pathway components (as representatives of the main intracellular signal transduction route of inflammation), tissue factor (as the inducible component of the clotting system), and genes encoding several intracellular components involved in the link between NFκB and apoptosis, a link that is considered important for the survival of immune cells. We have applied this novel high throughput technology in a large population-based case-control study on myocardial infarction to assess whether inflammatory mRNA in circulating cells is increased in patients with myocardial infarction compared to control subjects.

Patients, materials, and methods

Patients and control subjects

Patients were men consecutively diagnosed with a first myocardial infarction before the age of 70 years between January 1990 and January 1996. Two of the following 3 characteristics had to be identifiable in the discharge record or hospital charts to confirm acute myocardial infarction: typical chest pain, electrocardiographical changes indicative of evolving myocardial infarction, or a transient rise in cardiac enzymes to more than twice the normal upper limit. Control subjects were men without a history of myocardial infarction who had a minor orthopaedic intervention between January 1990 and May 1996 and who had received prophylactic anticoagulation treatment after this intervention. Control subjects were identified via the Leiden Anticoagulation Clinic, which serves the same region as the hospitals where the patients were recruited and which was responsible for monitoring prophylactic anticoagulation for several weeks or months after the surgery. They had not used anticoagulants for at least 6 months prior to inclusion and control subjects were frequency matched to the patients on 10-year age groups. To ensure that inflammatory reactions surrounding the cardiac event or orthopaedic intervention had subsided, subjects were included in this study at least 6 months after the date of the event, or index date. Median time between index date and blood collection was 2.8 years (range, 0.6 years to 6.3 years) for cases and control subjects alike. Details of the population-based case-control Study of Myocardial Infarctions Leiden (SMILE), which included 1206 men, are described elsewhere.9 

Medication use and history of diabetes prior to the index date were ascertained by interview with control subjects and retrieved from discharge letters for patients. At time of the blood draw they were assessed by using a structured interview. A person was classified as hypertensive or hypercholesterolemic when he was prescribed specific medications for these conditions. The study was approved by the review committee of the Leiden University Medical Center and the subjects gave written informed consent in accordance with institutional guidelines.

RNA analysis

Morning fasting–citrated blood samples were drawn from the antecubital vein. Immediately thereafter, aliquots of 100 μL were added to 900 μL lysis buffer.10  The samples were stored at –70°C until further use. RNA was isolated using a silica-based method10  and analyzed by multiplex ligation–dependent probe amplification (MLPA) using a kit developed in collaboration with MRC-Holland (Amsterdam, The Netherlands) for the simultaneous detection of 38 messenger RNA molecules.11  This MLPA profiling method is insensitive to the total amount of mRNA that is included in the reaction; therefore, the profile is independent of the total white blood cell (WBC) count. All samples were tested with the same batch of reagents, and a negative and lipopolysaccharide-stimulated control sample were included on each plate. The final polymerase chain reaction (PCR) fragments amplified with carboxyfluorescein-labeled primers were separated by capillary electrophoresis on a 16-capillary ABI-Prism 3100 Genetic Analyzer (Applied Biosystems, Nieuwerkerk aan de IJssel, The Netherlands). Peak area and height were processed using GeneScan analysis software (Applied Biosystems). The levels of mRNA for each gene were expressed as a normalized ratio of the peak area divided by the peak area of a control gene, resulting in the relative abundance of mRNAs of the genes of interest. Our probe set is listed in Table 1 and contains probes for mRNAs of 35 inflammation- and apoptosis-related proteins and 3 control genes, B2M, CDKN1A, and PARN. Expression levels of β2 microglobulin (B2M) were above the upper limit of detection in all samples, whereas CDKN1A and PARN mRNA levels were detectable in all 1152 samples and in 1135 samples, respectively. Therefore, areas were normalized to PARN and to CDKN1A resulting in similar expression profiles. We have opted to present the results relative to CDKN1A because of its insensitivity to in vitro lipopolysaccharide stimulation of an inflammatory response11  (C.A.S., unpublished data, November, 2002).

Table 1.

Alphabetical listing of the mRNAs and median values of cases and control subjects and their range (normalized to CDKN1A)


 

 

Patients
 
 
Controls
 
 
 
Gene symbol
 
Descriptive name
 
Median
 
Range
 
Median
 
Range
 
P
 
B2M  Beta-2-microglobulin   NA*  NA*  NA*  NA*  NA  
BMI1  BMI-1 oncogene homolog   0.84   0.09-2.18   0.76   0.00-2.41   < .001  
CCL3  Chemokine (C-C motif) ligand 3   0.14   0.00-0.85   0.13   0.00-1.16   < .001  
CCL4  Chemokine (C-C motif) ligand 4   1.30   0.23-*  1.26   0.23-*  .57  
CDKN1A  Cyclin-dependent kinase inhibitor 1A   NA   NA   NA   NA   NA  
GSTP1  Glutathione S-transferase   0.31   0.00-1.36   0.29   0.00-1.05   < .001  
IFNG  Interferon, gamma   0.16   0.00-0.80   0.15   0.00-1.78   .21  
IL10  Interleukin 10   0.00   0.00-0.73   0.00   0.00-0.45   .006  
IL12A  Interleukin 12, subunit p35   3.04   0.16-*  2.76   0.16-*  < .001  
IL12B  Interleukin 12, subunit p40   0.00   0.00-0.33   0.00   0.00-1.78   .06  
IL13  Interleukin 13   0.00   0.00-0.26   0.00   0.0-0.08   .96  
IL15 (1)  Interleukin 15, transcript variants 1 and 3   0.48   0.13-2.59   0.46   0.16-2.52   .014  
IL15 (2)  Interleukin 15, transcript variant 2   0.00   0.00-0.26   0.00   0.00-0.08   .003  
IL18  Interleukin 18   0.12   0.00-1.45   0.11   0.00-0.97   .009  
IL1A  Interleukin 1, alpha   0.00   0.00-0.39   0.00   0.00-0.31   < .001  
IL1B  Interleukin 1, beta   0.99   0.24-*  0.87   0.14-*  < .001  
IL1RN  Interleukin 1 receptor antagonist   1.90   0.54-*  1.71   0.47-*  < .001  
IL2  Interleukin 2   0.07   0.00-0.52   0.03   0.00-1.19   < .001  
IL4 (1)  Interleukin 4, transcript variant 1   0.00   0.00-0.31   0.00   0.00-0.22   .79  
IL4 (2)  Interleukin 4, transcript variants 1 and 2   0.03   0.00-0.46   0.00   0.00-0.51   < .001  
IL6  Interleukin 6   0.00   0.00-0.24   0.00   0.00-0.28   .11  
IL8  Interleukin 8   1.38   0.00-*  1.30   0.00-*  .06  
LTA  Lymphotoxin alpha (Tumor necrosis factor, beta)   0.25   0.00-1.08   0.23   0.00-0.82   < .001  
MCP-1  Monocyte chemotactic protein, 1   0.00   0.00-8.22   0.14   0.00-0.70   .31  
MCP-2  Monocyte chemotactic protein, 2   0.00   0.00-10.36   0.00   0.00-0.66   .07  
MIF  Macrophage migration inhibitory factor   0.91   0.00-3.95   0.75   0.00-3.98   < .001  
MYC  v-myc oncogene homolog   1.41   0.43-*  1.25   0.28-*  < .001  
NFKB1  nuclear factor kappa-B, subunit 1   1.12   0.43-*  1.02   0.19-*  < .001  
NFKB2  nuclear factor kappa-B, subunit 2   0.00   0.00-0.29   0.00   0.00-0.20   .049  
NFKBIA  nuclear factor kappa-B inhibitor, alpha   2.98   0.65-*  2.66   0.55-*  < .001  
PARN  Polyadenylate-specific ribonuclease   2.27   0.71-*  2.34   0.67-*  .41  
PDE4B  Phosphodiesterase 4B, cAMP-specific   2.23   0.54-*  2.12   0.58-*  < .001  
PDGFB  Platelet-derived growth factor, beta polypeptide   0.15   0.00-0.65   0.13   0.00-0.75   .002  
PI9  proteinase inhibitor 9, ovalbumin type   1.81   0.24-*  1.58   0.00-*  < .001  
PTP4A2  Protein-tyrosine phosphatase, type 4A, 2   NA*  NA*  NA*  NA*  NA  
PTPN1  Protein-tyrosine phosphatase, nonreceptor-type, 1   0.24   0.00-0.63   0.21   0.00-0.79   < .001  
TF  Tissue factor   0.00   0.00-0.13   0.00   0.00-0.07   .54  
THBS1  Thrombospondin 1   0.29   0.00-1.47   0.30   0.00-1.40   .75  
TNF  Tumor necrosis factor, alpha   0.00   0.00-0.32   0.00   0.00-0.21   .014  
TNFRSF1A
 
Tumor necrosis factor receptor superfamily, 1A
 
2.77
 
1.02-*
 
2.57
 
0.77-*
 
< .001
 

 

 

Patients
 
 
Controls
 
 
 
Gene symbol
 
Descriptive name
 
Median
 
Range
 
Median
 
Range
 
P
 
B2M  Beta-2-microglobulin   NA*  NA*  NA*  NA*  NA  
BMI1  BMI-1 oncogene homolog   0.84   0.09-2.18   0.76   0.00-2.41   < .001  
CCL3  Chemokine (C-C motif) ligand 3   0.14   0.00-0.85   0.13   0.00-1.16   < .001  
CCL4  Chemokine (C-C motif) ligand 4   1.30   0.23-*  1.26   0.23-*  .57  
CDKN1A  Cyclin-dependent kinase inhibitor 1A   NA   NA   NA   NA   NA  
GSTP1  Glutathione S-transferase   0.31   0.00-1.36   0.29   0.00-1.05   < .001  
IFNG  Interferon, gamma   0.16   0.00-0.80   0.15   0.00-1.78   .21  
IL10  Interleukin 10   0.00   0.00-0.73   0.00   0.00-0.45   .006  
IL12A  Interleukin 12, subunit p35   3.04   0.16-*  2.76   0.16-*  < .001  
IL12B  Interleukin 12, subunit p40   0.00   0.00-0.33   0.00   0.00-1.78   .06  
IL13  Interleukin 13   0.00   0.00-0.26   0.00   0.0-0.08   .96  
IL15 (1)  Interleukin 15, transcript variants 1 and 3   0.48   0.13-2.59   0.46   0.16-2.52   .014  
IL15 (2)  Interleukin 15, transcript variant 2   0.00   0.00-0.26   0.00   0.00-0.08   .003  
IL18  Interleukin 18   0.12   0.00-1.45   0.11   0.00-0.97   .009  
IL1A  Interleukin 1, alpha   0.00   0.00-0.39   0.00   0.00-0.31   < .001  
IL1B  Interleukin 1, beta   0.99   0.24-*  0.87   0.14-*  < .001  
IL1RN  Interleukin 1 receptor antagonist   1.90   0.54-*  1.71   0.47-*  < .001  
IL2  Interleukin 2   0.07   0.00-0.52   0.03   0.00-1.19   < .001  
IL4 (1)  Interleukin 4, transcript variant 1   0.00   0.00-0.31   0.00   0.00-0.22   .79  
IL4 (2)  Interleukin 4, transcript variants 1 and 2   0.03   0.00-0.46   0.00   0.00-0.51   < .001  
IL6  Interleukin 6   0.00   0.00-0.24   0.00   0.00-0.28   .11  
IL8  Interleukin 8   1.38   0.00-*  1.30   0.00-*  .06  
LTA  Lymphotoxin alpha (Tumor necrosis factor, beta)   0.25   0.00-1.08   0.23   0.00-0.82   < .001  
MCP-1  Monocyte chemotactic protein, 1   0.00   0.00-8.22   0.14   0.00-0.70   .31  
MCP-2  Monocyte chemotactic protein, 2   0.00   0.00-10.36   0.00   0.00-0.66   .07  
MIF  Macrophage migration inhibitory factor   0.91   0.00-3.95   0.75   0.00-3.98   < .001  
MYC  v-myc oncogene homolog   1.41   0.43-*  1.25   0.28-*  < .001  
NFKB1  nuclear factor kappa-B, subunit 1   1.12   0.43-*  1.02   0.19-*  < .001  
NFKB2  nuclear factor kappa-B, subunit 2   0.00   0.00-0.29   0.00   0.00-0.20   .049  
NFKBIA  nuclear factor kappa-B inhibitor, alpha   2.98   0.65-*  2.66   0.55-*  < .001  
PARN  Polyadenylate-specific ribonuclease   2.27   0.71-*  2.34   0.67-*  .41  
PDE4B  Phosphodiesterase 4B, cAMP-specific   2.23   0.54-*  2.12   0.58-*  < .001  
PDGFB  Platelet-derived growth factor, beta polypeptide   0.15   0.00-0.65   0.13   0.00-0.75   .002  
PI9  proteinase inhibitor 9, ovalbumin type   1.81   0.24-*  1.58   0.00-*  < .001  
PTP4A2  Protein-tyrosine phosphatase, type 4A, 2   NA*  NA*  NA*  NA*  NA  
PTPN1  Protein-tyrosine phosphatase, nonreceptor-type, 1   0.24   0.00-0.63   0.21   0.00-0.79   < .001  
TF  Tissue factor   0.00   0.00-0.13   0.00   0.00-0.07   .54  
THBS1  Thrombospondin 1   0.29   0.00-1.47   0.30   0.00-1.40   .75  
TNF  Tumor necrosis factor, alpha   0.00   0.00-0.32   0.00   0.00-0.21   .014  
TNFRSF1A
 
Tumor necrosis factor receptor superfamily, 1A
 
2.77
 
1.02-*
 
2.57
 
0.77-*
 
< .001
 

P values are according to the (2-tailed) Mann-Whitney test.

*

mRNA levels above the detection limit.

Statistics

Because this mRNA profiling technique was not available when the SMILE study was initiated and the samples were collected, this study was not preceded by a sample size calculation.

The relative mRNA levels of cases and control subjects were compared using a Mann-Whitney test. Persons with values above the upper detection limit were assumed to have the highest levels, whereas persons with values below the detection limit were assumed to have the lowest mRNA level for that marker. Odds ratios (ORs) were calculated as an estimate of the relative risk for myocardial infarction. Quartiles were defined on the basis of the mRNA distribution among control subjects. The lowest quartile was used as a reference category for calculating odds ratios. 95% confidence levels (CI95) were calculated according to the method by Woolf12  or were derived from the standard errors calculated by the logistic model. For variables with more than a quarter of the readings below the lower limit of detection, odds ratios were calculated for persons with detectable levels compared with those with nondetectable levels. About three-fourths of the samples had readings for PTP4A2 above the upper detection limit; therefore, the relative risk of myocardial infarction was estimated by calculating the odds ratio for persons with levels above the upper detection limit compared with those within the detection limits. Odds ratios were adjusted for traditional cardiovascular risk factors such as age, smoking, hypertension, hypercholesterolemia, diabetes, body mass index (BMI), alcohol use, and quartile of CRP by using unconditional logistic regression. The odds ratios were further adjusted for time between the index date and blood sampling. Adjusted odds ratios were also calculated limited to samples drawn at least 2 years after the index date. Because 185 patients stopped smoking in the interval between the time of their myocardial infarction and blood withdrawal, and smoking may influence some of the inflammatory markers, odds ratios were also calculated restricted to men who were nonsmokers both before and after the index date. Odds ratios were also adjusted for use of medication at time of blood sampling. Separate restriction analyses to include only men who were not on lipid-lowering therapy, antihypertensives, and aspirin at time of myocardial infarction and at time of blood sampling were also performed.

Results

It was possible to isolate RNA and perform MLPA analysis on 524 cases and 628 control subjects that fit the criteria required for this study. Mean age was 56.4 years (range, 32.3-70.0 years) and 57.4 years (range, 27.2-74.8 years) for patients and controls, respectively. Out of the patients, 62% were smokers compared with 33% of control subjects (Table 2).

Table 2.

Characteristics of patients and control subjects



 

Patients, n = 524
 

Controls, n = 628
 
Age, y, mean (range)   56.4 (32.3-70.0)   57.4 (27.2-74.8)  
Current smokers, no. (%)   323 (61.6)   209 (33.3)  
Alcohol users, no. (%)   420 (80.2)   543 (86.5)  
Obesity no. (%)*  90 (17.2)   104 (16.6)  
BMI, kg/m2, mean (range)*  27.1 (17.3-45.8)   26.9 (17.1-40.6)  
Diabetes, no. (%)   23 (4.4)   21 (3.3)  
Hypertension, no. (%)†  97 (18.5)   101 (16.1)  
Hypercholesterolemia, no. (%)†
 
12 (2.3)
 
10 (1.6)
 


 

Patients, n = 524
 

Controls, n = 628
 
Age, y, mean (range)   56.4 (32.3-70.0)   57.4 (27.2-74.8)  
Current smokers, no. (%)   323 (61.6)   209 (33.3)  
Alcohol users, no. (%)   420 (80.2)   543 (86.5)  
Obesity no. (%)*  90 (17.2)   104 (16.6)  
BMI, kg/m2, mean (range)*  27.1 (17.3-45.8)   26.9 (17.1-40.6)  
Diabetes, no. (%)   23 (4.4)   21 (3.3)  
Hypertension, no. (%)†  97 (18.5)   101 (16.1)  
Hypercholesterolemia, no. (%)†
 
12 (2.3)
 
10 (1.6)
 
*

A person was defined as obese if his BMI exceeded 30 kg/m2. Data on height and weight were not available for 2 people.

A person was classified as hypertensive or hypercholesterolemic if he was taking prescription drugs for these conditions.

Men with a history of myocardial infarction had higher median leukocyte levels (normalized to CDKN1A) of most inflammatory mRNAs compared with control subjects (Table 1). Analysis based on mRNA levels after stratification in quartiles resulted in elevated odds ratios (Table 3) that were not influenced much by adjustment for traditional risk factors and plasma levels of CRP. The adjusted odds ratios increased 2- to 3-fold for the highest versus lowest quartiles of MIF, PI9, CCL3, PTPN1, and BMI1. PDE4B, GSTP1, NFKB1, IL12A, PDGFB, MYC, IL1B, IL8, NFKB1A, and IL15(1) had odds ratios between 1.5 and 1.9 after adjustment, while LTA, IFNG, IL1RN, TNFRSF1A, IL18, and CCL4 had adjusted odds ratios of 1.3 or 1.4. THBS1 and PARN were not associated with myocardial infarction (adjusted OR [ORadj] 0.9 and 1.0, respectively). The full data in quartiles are shown in Table 4 for those markers that show increasing odds ratios with increasing levels of mRNA.

Table 3.

Odds ratios for highest versus lowest quartile


Marker
 

Patients N = 524
 

Crude OR
 

OR adjusted for age and smoking
 

Adjusted OR*
 

OR for nonsmokers only, age corrected N = 617
 

OR adjusted for traditional risk factors and for time since the event
 

Adjusted OR*for samples collected more than 2 years after the event N = 852
 

OR adjusted for traditional risk factors and for use of medication at time of sample collection
 
MIF  193   3.4 (2.3-4.9)   3.1 (2.1-4.5)   3.0 (2.0-4.4)   5.2 (2.9-9.5)   3.5 (2.3-5.2)   2.0 (1.2-3.1)   3.2 (2.1-4.9)  
PI9  195   2.5 (1.8-3.5)   2.5 (1.8-3.6)   2.6 (1.8-3.8)   3.1 (1.8-5.3)   2.9 (2.0-4.2)   2.0 (1.3-3.0)   2.7 (1.8-4.1)  
CCL3  183   2.2 (1.5-3.0)   2.2 (1.5-3.1)   2.3 (1.6-3.3)   2.8 (1.7-4.6)   2.3 (1.6-3.3)   2.5 (1.6-3.8)   2.4 (1.6-3.5)  
PTPN1  170   2.3 (1.6-3.3)   2.2 (1.5-3.2)   2.2 (1.5-3.2)   3.6 (2.1-6.2)   2.5 (1.7-3.7)   2.4 (1.5-3.8)   2.2 (1.4-3.2)  
BMI1  159   1.9 (1.3-2.7)   1.8 (1.3-2.6)   2.0 (1.4-2.9)   1.7 (1.0-2.8)   2.1 (1.4-3.0)   1.9 (1.3-3.0)   2.0 (1.3-3.0)  
PDE4B  162   1.9 (1.3-2.6)   1.9 (1.3-2.7)   1.9 (1.3-2.8)   2.7 (1.6-4.6)   2.2 (1.5-3.2)   1.9 (1.2-2.9)   1.9 (1.3-2.8)  
GSTP1†  162   1.9 (1.3-2.6)   1.7 (1.2-2.4)   1.8 (1.2-2.5)   1.7 (1.0-2.8)   1.6 (1.1-2.4)   1.9 (1.2-2.9)   1.9 (1.2-2.8)  
NFKB1  182   2.0 (1.4-2.8)   1.7 (1.2-2.5)   1.7 (1.2-2.4)   2.0 (1.2-3.3)   1.8 (1.2-2.6)   1.5 (1.0-2.3)   1.4 (1.0-2.1)  
IL12A  165   1.8 (1.3-2.5)   1.7 (1.2-2.5)   1.7 (1.2-2.4)   2.2 (1.3-3.6)   1.7 (1.2-2.4)   1.9 (1.3-3.0)   1.6 (1.1-2.4)  
PDGFB  156   1.6 (1.1-2.2)   1.6 (1.1-2.3)   1.7 (1.2-2.4)   1.3 (0.8-2.2)   1.8 (1.2-2.6)   1.5 (0.9-2.3)   1.7 (1.2-2.6)  
MYC  182   2.0 (1.4-2.8)   1.5 (1.1-2.2)   1.6 (1.1-2.3)   1.6 (1.0-2.6)   1.5 (1.1-2.2)   1.1 (0.7-1.7)   1.4 (0.9-2.0)  
IL1B  193   2.0 (1.5-2.9)   1.8 (1.2-2.5)   1.6 (1.1-2.3)   2.7 (1.6-4.5)   1.6 (1.1-2.2)   1.7 (1.1-2.6)   1.7 (1.2-2.5)  
IL8  152   1.6 (1.1-2.2)   1.5 (1.1-2.1)   1.6 (1.1-2.3)   2.0 (1.2-3.3)   1.7 (1.2-2.4)   1.2 (0.8-1.9)   1.6 (1.1-2.4)  
NFKBIA  174   1.7 (1.2-2.3)   1.6 (1.1-2.2)   1.5 (1.0-2.1)   1.9 (1.2-3.1)   1.4 (1.0-2.0)   1.3 (0.9-2.0)   1.6 (1.1-2.3)  
IL15(1)  140   1.4 (1.0-2.0)   1.5 (1.0-2.1)   1.5 (1.0-2.2)   1.5 (0.9-2.6)   1.5 (1.0-2.1)   1.4 (0.9-2.1)   1.3 (0.8-1.9)  
LTA  163   1.7 (1.2-2.3)   1.3 (0.9-1.9)   1.4 (1.0-2.0)   1.4 (0.8-2.3)   1.3 (0.9-2.0)   1.4 (0.9-2.1)   1.4 (1.0-2.1)  
IFNG  146   1.3 (0.9-1.8)   1.4 (1.0-2.0)   1.4 (1.0-2.1)   1.5 (0.9-2.4)   1.4 (1.0-2.0)   1.4 (0.9-2.2)   1.4 (1.0-2.1)  
IL1RN  160   1.7 (1.2-2.3)   1.4 (1.0-2.0)   1.3 (0.9-1.9)   1.4 (0.8-2.3)   1.3 (0.9-1.9)   1.3 (0.8-1.9)   1.1 (0.8-1.7)  
TNFRSFIA  153   1.4 (1.0-2.0)   1.4 (1.0-1.9)   1.3 (0.9-1.8)   1.9 (1.2-3.1)   1.3 (0.9-1.8)   2.0 (1.3-3.0)   1.2 (0.8-1.8)  
IL18  134   1.3 (0.9-1.8)   1.3 (0.9-1.8)   1.3 (0.9-1.9)   1.5 (0.9-2.4)   1.3 (0.9-1.9)   2.0 (1.3-3.2)   1.3 (0.9-2.0)  
CCL4  135   1.0 (0.7-1.4)   1.2 (0.9-1.8)   1.3 (0.9-1.9)   0.9 (0.6-1.5)   1.3 (0.9-1.8)   1.5 (1.0-2.3)   1.5 (1.0-2.2)  
THBSI  126   0.9 (0.6-1.2)   0.9 (0.7-1.3)   0.9 (0.6-1.3)   0.7 (0.5-1.2)   1.0 (0.7-1.4)   1.2 (0.8-1.8)   0.9 (0.6-1.4)  
PARN
 
117
 
0.8 (0.6-1.2)
 
1.0 (0.7-1.4)
 
1.0 (0.7-1.5)
 
0.7 (0.4-1.1)
 
1.0 (0.7-1.4)
 
1.3 (0.8-1.9)
 
1.0 (0.7-1.5)
 

Marker
 

Patients N = 524
 

Crude OR
 

OR adjusted for age and smoking
 

Adjusted OR*
 

OR for nonsmokers only, age corrected N = 617
 

OR adjusted for traditional risk factors and for time since the event
 

Adjusted OR*for samples collected more than 2 years after the event N = 852
 

OR adjusted for traditional risk factors and for use of medication at time of sample collection
 
MIF  193   3.4 (2.3-4.9)   3.1 (2.1-4.5)   3.0 (2.0-4.4)   5.2 (2.9-9.5)   3.5 (2.3-5.2)   2.0 (1.2-3.1)   3.2 (2.1-4.9)  
PI9  195   2.5 (1.8-3.5)   2.5 (1.8-3.6)   2.6 (1.8-3.8)   3.1 (1.8-5.3)   2.9 (2.0-4.2)   2.0 (1.3-3.0)   2.7 (1.8-4.1)  
CCL3  183   2.2 (1.5-3.0)   2.2 (1.5-3.1)   2.3 (1.6-3.3)   2.8 (1.7-4.6)   2.3 (1.6-3.3)   2.5 (1.6-3.8)   2.4 (1.6-3.5)  
PTPN1  170   2.3 (1.6-3.3)   2.2 (1.5-3.2)   2.2 (1.5-3.2)   3.6 (2.1-6.2)   2.5 (1.7-3.7)   2.4 (1.5-3.8)   2.2 (1.4-3.2)  
BMI1  159   1.9 (1.3-2.7)   1.8 (1.3-2.6)   2.0 (1.4-2.9)   1.7 (1.0-2.8)   2.1 (1.4-3.0)   1.9 (1.3-3.0)   2.0 (1.3-3.0)  
PDE4B  162   1.9 (1.3-2.6)   1.9 (1.3-2.7)   1.9 (1.3-2.8)   2.7 (1.6-4.6)   2.2 (1.5-3.2)   1.9 (1.2-2.9)   1.9 (1.3-2.8)  
GSTP1†  162   1.9 (1.3-2.6)   1.7 (1.2-2.4)   1.8 (1.2-2.5)   1.7 (1.0-2.8)   1.6 (1.1-2.4)   1.9 (1.2-2.9)   1.9 (1.2-2.8)  
NFKB1  182   2.0 (1.4-2.8)   1.7 (1.2-2.5)   1.7 (1.2-2.4)   2.0 (1.2-3.3)   1.8 (1.2-2.6)   1.5 (1.0-2.3)   1.4 (1.0-2.1)  
IL12A  165   1.8 (1.3-2.5)   1.7 (1.2-2.5)   1.7 (1.2-2.4)   2.2 (1.3-3.6)   1.7 (1.2-2.4)   1.9 (1.3-3.0)   1.6 (1.1-2.4)  
PDGFB  156   1.6 (1.1-2.2)   1.6 (1.1-2.3)   1.7 (1.2-2.4)   1.3 (0.8-2.2)   1.8 (1.2-2.6)   1.5 (0.9-2.3)   1.7 (1.2-2.6)  
MYC  182   2.0 (1.4-2.8)   1.5 (1.1-2.2)   1.6 (1.1-2.3)   1.6 (1.0-2.6)   1.5 (1.1-2.2)   1.1 (0.7-1.7)   1.4 (0.9-2.0)  
IL1B  193   2.0 (1.5-2.9)   1.8 (1.2-2.5)   1.6 (1.1-2.3)   2.7 (1.6-4.5)   1.6 (1.1-2.2)   1.7 (1.1-2.6)   1.7 (1.2-2.5)  
IL8  152   1.6 (1.1-2.2)   1.5 (1.1-2.1)   1.6 (1.1-2.3)   2.0 (1.2-3.3)   1.7 (1.2-2.4)   1.2 (0.8-1.9)   1.6 (1.1-2.4)  
NFKBIA  174   1.7 (1.2-2.3)   1.6 (1.1-2.2)   1.5 (1.0-2.1)   1.9 (1.2-3.1)   1.4 (1.0-2.0)   1.3 (0.9-2.0)   1.6 (1.1-2.3)  
IL15(1)  140   1.4 (1.0-2.0)   1.5 (1.0-2.1)   1.5 (1.0-2.2)   1.5 (0.9-2.6)   1.5 (1.0-2.1)   1.4 (0.9-2.1)   1.3 (0.8-1.9)  
LTA  163   1.7 (1.2-2.3)   1.3 (0.9-1.9)   1.4 (1.0-2.0)   1.4 (0.8-2.3)   1.3 (0.9-2.0)   1.4 (0.9-2.1)   1.4 (1.0-2.1)  
IFNG  146   1.3 (0.9-1.8)   1.4 (1.0-2.0)   1.4 (1.0-2.1)   1.5 (0.9-2.4)   1.4 (1.0-2.0)   1.4 (0.9-2.2)   1.4 (1.0-2.1)  
IL1RN  160   1.7 (1.2-2.3)   1.4 (1.0-2.0)   1.3 (0.9-1.9)   1.4 (0.8-2.3)   1.3 (0.9-1.9)   1.3 (0.8-1.9)   1.1 (0.8-1.7)  
TNFRSFIA  153   1.4 (1.0-2.0)   1.4 (1.0-1.9)   1.3 (0.9-1.8)   1.9 (1.2-3.1)   1.3 (0.9-1.8)   2.0 (1.3-3.0)   1.2 (0.8-1.8)  
IL18  134   1.3 (0.9-1.8)   1.3 (0.9-1.8)   1.3 (0.9-1.9)   1.5 (0.9-2.4)   1.3 (0.9-1.9)   2.0 (1.3-3.2)   1.3 (0.9-2.0)  
CCL4  135   1.0 (0.7-1.4)   1.2 (0.9-1.8)   1.3 (0.9-1.9)   0.9 (0.6-1.5)   1.3 (0.9-1.8)   1.5 (1.0-2.3)   1.5 (1.0-2.2)  
THBSI  126   0.9 (0.6-1.2)   0.9 (0.7-1.3)   0.9 (0.6-1.3)   0.7 (0.5-1.2)   1.0 (0.7-1.4)   1.2 (0.8-1.8)   0.9 (0.6-1.4)  
PARN
 
117
 
0.8 (0.6-1.2)
 
1.0 (0.7-1.4)
 
1.0 (0.7-1.5)
 
0.7 (0.4-1.1)
 
1.0 (0.7-1.4)
 
1.3 (0.8-1.9)
 
1.0 (0.7-1.5)
 

Number of cases in the highest quartiles are shown in column 2. Results of a stratified analysis on men who were nonsmokers both at the index data and the sampling date are shown in column 6 after adjustment for age. Subsequent columns show odds ratios adjusted for traditional risk factors and for time since the event (column 7), adjusted odds ratios using only samples collected more than 2 years after the event (column 8), and adjustment for use of medication at time of sample collection (last column). 95% confidence intervals are shown in parentheses.

*

Adjusted for age, smoking, use of medication for hypertension and hypercholesterolemia, diabetes, BMI, alcohol habit, and quartile of CRP.

Out of 518 cases and 597 controls.

The number of controls in each quartile is 157 except for GSTP1. These numbers apply when restrictions are not used.

Table 4.

Odds ratios with increasing quartiles of mRNA levels for a selection of markers


Inflammatory marker
 

Quartile
 

Patients N = 524
 

Controls N = 628
 

Crude OR
 

Adjusted OR*
 
MIF  1   57   157   1   1  
  2   131   157   2.3 (1.6-3.4)   2.1 (1.4-3.2)  
  3   143   157   2.5 (1.7-3.7)   2.4 (1.6-3.6)  
  4   193   157   3.4 (2.3-4.9)   3.0 (2.0-4.4)  
PI9  1   78   157   1   1  
  2   106   157   1.4 (1.0-2.0)   1.4 (1.0-2.1)  
  3   145   157   1.9 (1.3-2.6)   1.8 (1.2-2.6)  
  4   195   157   2.5 (1.8-3.5)   2.6 (1.8-3.8)  
PTPN1  1   73   157   1   1  
  2   122   157   1.7 (1.2-2.4)   1.7 (1.1-2.4)  
  3   159   157   2.2 (1.5-3.2)   2.0 (1.4-2.9)  
  4   170   157   2.3 (1.6-3.3)   2.2 (1.5-3.2)  
PDGFB  1   98   157   1   1  
  2   124   157   1.3 (0.9-1.8)   1.3 (0.9-1.9)  
  3   146   157   1.5 (1.1-2.1)   1.5 (1.0-2.1)  
  4   156   157   1.6 (1.1-2.2)   1.7 (1.2-2.4)  
IL12A  1   92   157   1   1  
  2   126   157   1.4 (1.0-1.9)   1.3 (0.9-1.9)  
  3   141   157   1.5 (1.1-2.2)   1.4 (1.0-2.1)  
  4   165   157   1.8 (1.3-2.5)   1.7 (1.2-2.4)  
NFKB1  1   90   157   1   1  
  2   108   157   1.2 (0.8-1.7)   1.2 (0.8-1.7)  
  3   144   157   1.6 (1.1-2.3)   1.4 (1.0-2.1)  
  4   182   157   2.0 (1.4-2.8)   1.7 (1.2-2.4)  
MYC  1   91   157   1   1  
  2   96   157   1.0 (0.7-1.5)   1.0 (0.7-1.4)  
  3   155   157   1.7 (1.2-2.4)   1.5 (1.0-2.1)  
  4   182   157   2.0 (1.4-2.8)   1.6 (1.1-2.3)  
LTA  1   97   157   1   1  
  2   107   157   1.1 (0.8-1.6)   1.0 (0.7-1.5)  
  3   157   157   1.6 (1.2-2.3)   1.3 (0.9-1.9)  

 
4
 
163
 
157
 
1.7 (1.2-2.3)
 
1.4 (1.0-2.0)
 

Inflammatory marker
 

Quartile
 

Patients N = 524
 

Controls N = 628
 

Crude OR
 

Adjusted OR*
 
MIF  1   57   157   1   1  
  2   131   157   2.3 (1.6-3.4)   2.1 (1.4-3.2)  
  3   143   157   2.5 (1.7-3.7)   2.4 (1.6-3.6)  
  4   193   157   3.4 (2.3-4.9)   3.0 (2.0-4.4)  
PI9  1   78   157   1   1  
  2   106   157   1.4 (1.0-2.0)   1.4 (1.0-2.1)  
  3   145   157   1.9 (1.3-2.6)   1.8 (1.2-2.6)  
  4   195   157   2.5 (1.8-3.5)   2.6 (1.8-3.8)  
PTPN1  1   73   157   1   1  
  2   122   157   1.7 (1.2-2.4)   1.7 (1.1-2.4)  
  3   159   157   2.2 (1.5-3.2)   2.0 (1.4-2.9)  
  4   170   157   2.3 (1.6-3.3)   2.2 (1.5-3.2)  
PDGFB  1   98   157   1   1  
  2   124   157   1.3 (0.9-1.8)   1.3 (0.9-1.9)  
  3   146   157   1.5 (1.1-2.1)   1.5 (1.0-2.1)  
  4   156   157   1.6 (1.1-2.2)   1.7 (1.2-2.4)  
IL12A  1   92   157   1   1  
  2   126   157   1.4 (1.0-1.9)   1.3 (0.9-1.9)  
  3   141   157   1.5 (1.1-2.2)   1.4 (1.0-2.1)  
  4   165   157   1.8 (1.3-2.5)   1.7 (1.2-2.4)  
NFKB1  1   90   157   1   1  
  2   108   157   1.2 (0.8-1.7)   1.2 (0.8-1.7)  
  3   144   157   1.6 (1.1-2.3)   1.4 (1.0-2.1)  
  4   182   157   2.0 (1.4-2.8)   1.7 (1.2-2.4)  
MYC  1   91   157   1   1  
  2   96   157   1.0 (0.7-1.5)   1.0 (0.7-1.4)  
  3   155   157   1.7 (1.2-2.4)   1.5 (1.0-2.1)  
  4   182   157   2.0 (1.4-2.8)   1.6 (1.1-2.3)  
LTA  1   97   157   1   1  
  2   107   157   1.1 (0.8-1.6)   1.0 (0.7-1.5)  
  3   157   157   1.6 (1.2-2.3)   1.3 (0.9-1.9)  

 
4
 
163
 
157
 
1.7 (1.2-2.3)
 
1.4 (1.0-2.0)
 

95% confidence intervals are shown in parentheses. The 1st quartile is always the reference category.

*

Adjusted for age, smoking, use of medication for hypertension or hypercholesterolemia, diabetes, BMI, alcohol habit, and quartile of CRP.

Odds ratios for markers with more than a quarter of the readings below detection levels are shown in Table 5. In this comparison of detectable versus undetectable levels, IL15(2), MCP-2, and IL2 were associated with an approximately 2-fold increase in odds ratio. IL12B had an adjusted odds ratio of 1.6 and IL6, TNF, IL4(2), and IL10 had a somewhat lower adjusted odds ratio of 1.4. Too few samples had detectable levels of IL13 and TF mRNA (9 and 15 samples, respectively). MCP-1 and NFKB2 had an odds ratio of 1.2 (CI95, 0.9-1.6 for both) and IL4 was not associated with myocardial infarction. IL1A was peculiar because it gave an odds ratio below 1, indicating a protective effect (OR, 0.6; CI95, 0.5-0.8). Most of the readings for PTP4A2 were above the upper detection limit. The ORadj for persons with levels above the upper detection limit compared with those with levels within the detection limits was 1.7 (CI95, 1.2-2.3).

Table 5.

Odds ratios for markers with readings above or below the detection limits


Marker
 

Patients N = 524
 

Controls N = 628
 

Crude OR
 

OR adjusted for age and smoking
 

Adjusted OR*
 

OR for nonsmokers only, age corrected N = 617
 

OR adjusted for traditional risk factors and for time since the event
 

Adjusted OR*samples collected more than 2 years after the event N = 852
 

OR adjusted for traditional risk factors and for use of medication at time of sample collection
 
Detectable versus nondetectable levels of relative mRNA           
IL15(2)  42   25   2.1 (1.3-3.5)   2.2 (1.3-3.7)   2.2 (1.3-3.7)   2.7 (1.4-5.5)   2.1 (1.2-3.6)   2.4 (1.4-4.4)   2.3 (1.2-4.1)  
MCP-2  31   23   1.7 (1.0-2.9)   1.7 (1.0-3.1)   1.8 (1.0-3.2)   2.1 (1.0-4.5)   2.1 (1.1-3.7)   2.0 (1.1-3.8)   2.0 (1.0-3.8)  
IL2  334   316   1.7 (1.4-2.2)   1.7 (1.3-2.1)   1.7 (1.3-2.2)   2.0 (1.4-2.8)   1.9 (1.5-2.5)   1.2 (0.9-1.6)   1.9 (1.5-2.6)  
IL12B  57   48   1.5 (1.0-2.2)   1.5 (1.0-2.4)   1.6 (1.1-2.5)   1.5 (0.8-2.6)   1.7 (1.1-2.6)   1.5 (0.9-2.5)   1.6 (1.0-2.6)  
IL6  49   43   1.4 (0.9-2.1)   1.4 (0.9-2.1)   1.4 (0.9-2.3)   1.4 (0.7-2.6)   1.4 (0.9-2.2)   1.8 (1.0-3.0)   1.9 (1.1-3.2)  
TNF  76   62   1.5 (1.1-2.2)   1.5 (1.0-2.1)   1.4 (1.0-2.1)   1.7 (1.0-2.9)   1.5 (1.0-2.2)   1.6 (1.0-2.4)   1.4 (0.9-2.1)  
IL4(2)  266   248   1.6 (1.3-2.0)   1.5 (1.2-1.9)   1.4 (1.1-1.8)   1.7 (1.2-2.4)   1.5 (1.1-1.9)   1.4 (1.0-1.9)   1.4 (1.1-1.8)  
IL10  58   42   1.7 (1.1-2.6)   1.5 (1.0-2.3)   1.4 (0.9-2.1)   2.3 (1.2-4.4)   1.4 (0.9-2.1)   1.3 (0.8-2.2)   1.2 (0.7-1.9)  
IL13  4   5   1.0 (0.3-3.6)   1.0 (0.2-3.9)   1.2 (0.3-5.0)   0.5 (0.1-4.9)   1.2 (0.3-5.0)   0.4 (0.0-4.0)   2.1 (0.4-10.1)  
TF  8   7   1.4 (0.5-3.8)   1.2 (0.4-3.4)   1.2 (0.4-3.7)   0.4 (0.0-3.7)   1.2 (0.4-3.6)   1.1 (0.3-3.8)   1.3 (0.4-4.4)  
MCP-1  120   128   1.2 (0.9-1.5)   1.1 (0.8-1.4)   1.2 (0.9-1.6)   1.0 (0.6-1.5)   1.1 (0.8-1.5)   1.3 (0.9-1.9)   1.2 (0.8-1.6)  
NFKB2  152   152   1.3 (1.0-1.7)   1.2 (0.9-1.6)   1.2 (0.9-1.6)   1.4 (0.9-2.0)   1.2 (0.9-1.6)   1.4 (1.0-1.9)   1.3 (0.9-1.8)  
IL4(1)  110   138   1.0 (0.7-1.3)   0.9 (0.7-1.2)   0.9 (0.7-1.2)   0.9 (0.6-1.4)   1.0 (0.7-1.3)   0.8 (0.6-1.2)   0.9 (0.6-1.2)  
IL1A
 
189
 
291
 
0.7 (0.5-0.8)
 
0.6 (0.5-0.8)
 
0.6 (0.5-0.8)
 
0.5 (0.4-0.7)
 
0.7 (0.5-0.9)
 
0.5 (0.4-0.7)
 
0.6 (0.4-0.8)
 
Marker with levels above the upper detection limit versus levels within the detection limits           
PTP4A2
 
428†
 
437†
 
1.8 (1.3-2.3)
 
1.7 (1.2-2.2)
 
1.7 (1.2-2.3)
 
1.8 (1.2-2.7)‡
 
1.8 (1.3-2.5)
 
2.1 (1.4-3.1)#
 
1.9 (1.4-2.6)
 

Marker
 

Patients N = 524
 

Controls N = 628
 

Crude OR
 

OR adjusted for age and smoking
 

Adjusted OR*
 

OR for nonsmokers only, age corrected N = 617
 

OR adjusted for traditional risk factors and for time since the event
 

Adjusted OR*samples collected more than 2 years after the event N = 852
 

OR adjusted for traditional risk factors and for use of medication at time of sample collection
 
Detectable versus nondetectable levels of relative mRNA           
IL15(2)  42   25   2.1 (1.3-3.5)   2.2 (1.3-3.7)   2.2 (1.3-3.7)   2.7 (1.4-5.5)   2.1 (1.2-3.6)   2.4 (1.4-4.4)   2.3 (1.2-4.1)  
MCP-2  31   23   1.7 (1.0-2.9)   1.7 (1.0-3.1)   1.8 (1.0-3.2)   2.1 (1.0-4.5)   2.1 (1.1-3.7)   2.0 (1.1-3.8)   2.0 (1.0-3.8)  
IL2  334   316   1.7 (1.4-2.2)   1.7 (1.3-2.1)   1.7 (1.3-2.2)   2.0 (1.4-2.8)   1.9 (1.5-2.5)   1.2 (0.9-1.6)   1.9 (1.5-2.6)  
IL12B  57   48   1.5 (1.0-2.2)   1.5 (1.0-2.4)   1.6 (1.1-2.5)   1.5 (0.8-2.6)   1.7 (1.1-2.6)   1.5 (0.9-2.5)   1.6 (1.0-2.6)  
IL6  49   43   1.4 (0.9-2.1)   1.4 (0.9-2.1)   1.4 (0.9-2.3)   1.4 (0.7-2.6)   1.4 (0.9-2.2)   1.8 (1.0-3.0)   1.9 (1.1-3.2)  
TNF  76   62   1.5 (1.1-2.2)   1.5 (1.0-2.1)   1.4 (1.0-2.1)   1.7 (1.0-2.9)   1.5 (1.0-2.2)   1.6 (1.0-2.4)   1.4 (0.9-2.1)  
IL4(2)  266   248   1.6 (1.3-2.0)   1.5 (1.2-1.9)   1.4 (1.1-1.8)   1.7 (1.2-2.4)   1.5 (1.1-1.9)   1.4 (1.0-1.9)   1.4 (1.1-1.8)  
IL10  58   42   1.7 (1.1-2.6)   1.5 (1.0-2.3)   1.4 (0.9-2.1)   2.3 (1.2-4.4)   1.4 (0.9-2.1)   1.3 (0.8-2.2)   1.2 (0.7-1.9)  
IL13  4   5   1.0 (0.3-3.6)   1.0 (0.2-3.9)   1.2 (0.3-5.0)   0.5 (0.1-4.9)   1.2 (0.3-5.0)   0.4 (0.0-4.0)   2.1 (0.4-10.1)  
TF  8   7   1.4 (0.5-3.8)   1.2 (0.4-3.4)   1.2 (0.4-3.7)   0.4 (0.0-3.7)   1.2 (0.4-3.6)   1.1 (0.3-3.8)   1.3 (0.4-4.4)  
MCP-1  120   128   1.2 (0.9-1.5)   1.1 (0.8-1.4)   1.2 (0.9-1.6)   1.0 (0.6-1.5)   1.1 (0.8-1.5)   1.3 (0.9-1.9)   1.2 (0.8-1.6)  
NFKB2  152   152   1.3 (1.0-1.7)   1.2 (0.9-1.6)   1.2 (0.9-1.6)   1.4 (0.9-2.0)   1.2 (0.9-1.6)   1.4 (1.0-1.9)   1.3 (0.9-1.8)  
IL4(1)  110   138   1.0 (0.7-1.3)   0.9 (0.7-1.2)   0.9 (0.7-1.2)   0.9 (0.6-1.4)   1.0 (0.7-1.3)   0.8 (0.6-1.2)   0.9 (0.6-1.2)  
IL1A
 
189
 
291
 
0.7 (0.5-0.8)
 
0.6 (0.5-0.8)
 
0.6 (0.5-0.8)
 
0.5 (0.4-0.7)
 
0.7 (0.5-0.9)
 
0.5 (0.4-0.7)
 
0.6 (0.4-0.8)
 
Marker with levels above the upper detection limit versus levels within the detection limits           
PTP4A2
 
428†
 
437†
 
1.8 (1.3-2.3)
 
1.7 (1.2-2.2)
 
1.7 (1.2-2.3)
 
1.8 (1.2-2.7)‡
 
1.8 (1.3-2.5)
 
2.1 (1.4-3.1)#
 
1.9 (1.4-2.6)
 

Results of a stratified analysis on men who were nonsmokers at the index date and sampling date are shown in column 7. Subsequent columns show odds ratios adjusted for traditional risk factors and for time since the event (column 8), adjusted odds ratios using only samples collected more than 2 years after the event (column 9), and adjustment for use of medication at time of sample collection (last column). 95% confidence intervals are shown in parentheses.

*

Adjusted for age, smoking, use of medication for hypertension or hypercholesterolemia, diabetes, BMI, alcohol habit, and quartile of CRP.

Out of 518 cases and 599 controls.

Out of 592 samples.

#

Out of 841 samples.

Few of the crude odds ratios were changed by adjustment for cardiovascular risk factors, and only 7 mRNA markers (LTA, MIF, IL1B, IL1RN, MYC, NFKB1, and CCL4) showed differences between the crude and adjusted odds ratios greater than 0.2. Multivariate analysis indicated that smoking accounted for most of these differences (Tables 3 and 5). A stratified analysis showed that the inflammatory markers have elevated odds ratios even in nonsmokers (Tables 3 and 5), and, in most cases, the odds ratios were higher in men who did not smoke on and after the index date even after adjustment for traditional risk factors. This was particularly striking for the highest compared with the lowest quartile of mRNA levels of MIF with an age-adjusted odds ratio of 5.2 (95CI, 2.9-9.5) in nonsmokers.

Analysis excluding persons with levels of CRP above 10 mg/L did not materially change the odds ratios (data not shown). Adjustment for use of medication at time of blood sampling in the logistic regression model had little effect on the odds ratios. Odds ratios remained elevated even after restriction to subjects who never used aspirin or medications for hypercholesterolemia or hypertension. Adjustment for traditional risk factors had little effect on these odds ratios. Time since the index date did not change the adjusted odds ratios for the highest quartiles, except for those of MIF, PI9, and PTPN1, which increased further to 3.5 (95CI, 2.3-5.2), 2.9 (95CI, 2.0-4.2), and 2.5 (95CI, 1.7-3.7), respectively (Tables 3 and 5). The odds ratios of these markers were elevated even when cases who had a myocardial infarction less than 2 years before sample collection were excluded from the analysis (MIF, 2.0 [1.2-3.1]; PI9, 2.0 [1.3-3.0]; and PTPN1, 2.4 [1.5-3.8]). Besides the odds ratios of MIF and PI9, in this analysis, only the odds ratios of MYC, IL8, and IL2 decrease somewhat (Tables 3 and 5).

Discussion

Patients with a history of myocardial infarction have a different mRNA signature for inflammatory markers than healthy control subjects, with higher expression of circulating inflammatory RNA. More specifically, increased mRNA levels of MIF, PI9, CCL3, PTPN1, BMI1, and IL15(2) were found to be associated with myocardial infarction, with odds ratios in the range of 2 to 3 for patients compared with control subjects. Elevated mRNA levels of PDGFB, IL12A, IL12B, MYC, NFKB1, GSTP1, IL18, IL15(2), IL1B, IL1RN, IL8, NFKBIA, PDE4B, MCP-2, and IL2 all yielded odds ratios for myocardial infarction varying between 1.5 and 2.

The highest odds ratio was observed for patients with high mRNA levels of macrophage migration inhibitory factor (MIF). Recently, this protein has been associated with an array of autoimmune and inflammatory diseases including severe sepsis,13  arthritis,14  bronchial asthma,15  and acute respiratory distress syndrome.16,17 MIF counteracts the immunosuppressive effects of glucocorticoids,18  and prolongs the inflammatory response by inhibiting apoptosis of macrophages.19  Its plasma levels increase up to 5-fold in the acute phase of myocardial infarction, but decrease to levels similar to those in healthy persons within 3 weeks. The rapid increase is thought to be due to the production or release of MIF by necrotic tissue in the heart, cellular infiltrate at local sites, and peripheral blood mononuclear cells.20,21  These studies were performed on a small number of patients and controls, and aimed at the inflammatory response during and shortly after the myocardial event. The major source of MIF may differ in the acute and subacute stages of myocardial infarction.21  Our study shows that expression of MIF in circulating cells is higher in men with myocardial infarction even in the stable state long after the event.

PI9 was also prominently associated with myocardial infarction. This protein protects cells from apoptosis due to granzyme B released by cytotoxic lymphocytes to kill abnormal cells.22  It also inhibits the conversion of the inactive precursors of IL1B and IL18 into the active forms. Its involvement in atherosclerosis has been suggested by its altered expression in atherosclerotic lesions.23  The present finding of increased expression in the circulation of men with a history of myocardial infarction adds evidence to this and suggests that it may exert an influence outside the plaque itself.

The absence of a noticeable effect of adjustment or of restrictions to people on particular groups of medication shows that these associations are not brought about by traditional risk factors or by medication use. The only cardiovascular risk factor that had an effect on some of the odds ratios (LTA, MIF, IL1B, IL1RN, MYC, NFKB1, and CCL4) was smoking. This finding is not surprising since it is generally accepted that smoking enhances vascular inflammation, and some systemic inflammatory markers, including CRP, are higher in smokers than former or never smokers.24,25  The results of the stratified analysis on nonsmokers show that the inflammatory markers have an effect themselves, and that smoking masked some of this effect.

Adjustment for levels of CRP did not change the odds ratios. This underlines that in cardiovascular disease inflammation plays a role in 3 compartments. The first is in the inflamed vascular wall where many inflammatory cells are known to accumulate. The second is in the liver where acute phase reactants such as CRP are synthesized, most likely in response to cytokines that are produced elsewhere in the body. The results from our study indicate that there is a third inflammatory compartment in circulating leucocytes. Apparently, the relationship between events in leucocytes on the one hand and acute-phase protein production in the liver on the other, if there is any, is not a simple one.

In quantitative or semiquantitative mRNA profiling methods accuracy is generally poor. As described in our original methods paper reproducibility of the MLPA using independent duplicate samples was satisfactory: interassay correlation between 3 representative data sets of independent samples was 0.96 and intra-assay variation between 4 independent samples was 0.97.11  With respect to the reproducibility of the presented profiling results we would like to add the following. Based on the repeat measurements of the lipopolysaccharide samples in the present study we find coefficients of variation for individual mRNA species of around 0.25. Second, we recently measured a second mRNA profile, centered on the expression of members of the Toll-like receptor family of proteins, in the SMILE samples (S.B.W., P.R.H., unpublished results, April 2004). PI9, MIF, and PARN were also included in the second study, and we were able to replicate the findings reported here.

Our study evaluated mRNA-based profiles in a case-control design. The inflammatory profile that we have observed may reflect a chronic inflammatory state in circulating blood cells that is predictive for myocardial infarction. Alternatively, it may be a result of the myocardial infarction itself either directly, since several of the mRNA markers that we assessed may also increase during the acute phase of a myocardial infarction (TNFA,26 IL627 ; IL1RN and IL1028 ; MIF20,21 ), or indirectly through decreased left ventricular function in myocardial infarction patients,29  a variable that was not assessed in the SMILE study. Support for the hypothesis that we may indeed be dealing with causative risk factors comes from the observation that the increased odds ratios were observed in blood samples drawn at least 6 months (median, 2.8 years) after the index date, and from the observation that time since the event did not decrease the odds ratios. In any case, an inflammatory state of cells in the circulation may well have implications on the outcome and progress of inflammation-related diseases.

After the human genome effort, gene expression profiling is rapidly developing into a powerful tool. It has shown promising results in identifying patterns of aberrant expression in cancer patients, and in determining subtypes of leukemias.30  These studies have been based on microarray techniques that, due to their cost, have limited analysis to typically tens of samples. To study more complex diseases a substantially larger number of samples need to be analyzed to get reproducible and meaningful results. The present RNA profiling study utilizes more than 1000 samples to study myocardial infarction, the largest number of samples for such a study to date. This greatly reduces noise and effects of factors not directly related to the disease and it also decreases the chances of false positives, which is a major problem in microarray studies.

In conclusion, this study presents the direct measurement of molecular signatures from more than 30 inflammatory genes in almost 1200 individuals. These large-scale mRNA measurements give direct insight into the inflammatory status of circulating leucocytes without the limitations of the more common measurements of CRP and cytokine/chemokine levels in blood or other smaller RNA studies. Leucocytes in the blood of patients with myocardial infarction are more active in transcription of inflammatory genes. Our results therefore represent a further step in demonstrating that inflammation in activated leukocytes is a hallmark in the etiology of cardiovascular disease.

Prepublished online as Blood First Edition Paper, November 2, 2004; DOI 10.1182/blood-2004-08-3283.

Supported by the Netherlands Heart Foundation (grant no. 92.345) and the EU Fifth Framework Improving Human Potential Program (S.B.W.).

There is no conflict of interest to report, and all costs of the study were provided by nonprofit organizations. None of the authors has an interest, directly or indirectly, in the companies from which the reagents for these studies were acquired.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 U.S.C. section 1734.

S.B.W. and C.A.S. were responsible for setting up the RNA assays. S.B.W. performed all laboratory analyses. F.R.R. and C.J.M.D. were responsible for setting up the SMILE study and for collecting all patient data and blood samples. S.B.W. performed all statistical data analyses, and F.R.R. and C.J.M.D. oversaw these. P.H.R. and F.R.R. initiated the study and carry overall responsibility for the laboratory work and for the writing of the paper. P.H.R. and F.R.R. also take responsibility for the integrity of the work as a whole, from inception to published article.

We would like to thank Hella Aberson for her help in developing and establishing the MLPA technique, Ank Ververs-Schreijer for data management, and Thea Visser-Oppelaar for technical help in the SMILE study. A special thanks goes to all the men who consented to participate in this study.

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