Abstract
Importance
Myocardial scarring leads to cardiac dysfunction and poor prognosis. The prevalence of and factors associated with unrecognized myocardial infarction and scar have not been previously defined using current methods in a multi-ethnic US population.
Objective
To determine prevalence of and factors associated with myocardial scar in middle and older aged individuals in the United States (U.S).
Design, Setting, and Participants
Multi-Ethnic Study of Atherosclerosis (MESA) is a population based cohort in the U.S. MESA participants were 45-84 years old and free of clinical cardiovascular disease (CVD) at baseline in 2000-2002. In the 10th year examination of MESA (2010-2012), 1840 participants underwent cardiac magnetic resonance imaging (CMR) with gadolinium to detect myocardial scar. CVD risk factors and coronary artery calcium scores were measured at baseline and year 10. Logistic regression models were used to estimate adjusted odds ratios for myocardial scar.
Exposures
Cardiovascular risk factors, coronary artery calcium, left ventricle size and function, carotid intima media thickness
Main Outcome Measure
Myocardial scar detected by CMR.
Results
Of 1840 participants (mean age 68±9 yrs, 52% male), 146 had myocardial scars (7.9%). Most myocardial scars (114/146, 78%) were undetected by electrocardiogram or by clinical adjudication. In adjusted models, age, male gender, body mass index, hypertension, and current smoking at baseline were associated with myocardial scar at year 10 [OR (95% CI): 1.6 (1.4, 1.9) per 8.9 years, p<0.001; 5.8 (3.6, 9.2) men vs. women, p<0.001; 1.3 (1.1, 1.6) per 4.8 kg/m2, p=0.005, 1.6 (1.1, 2.3) for hypertension present, p=0.009; 2.0 (1.2, 3.3) current vs. never smokers, p=0.006, respectively]. Age, gender and ethnicity adjusted CAC score at baseline was also associated with myocardial scar at year 10 [CAC categories of 1-99, 100-399 and ≥ 400 vs. CAC =0: OR (95% CI): 2.4 (1.5, 3.9), 3.0 (1.7, 5.1), 3.3 (1.7, 6.1), respectively, p≤0.001]. CAC score significantly added to the association of myocardial scar with age, gender, ethnicity and traditional CVD risk factors (c-statistic: 0.81 vs. 0.79, with vs. without CAC, respectively, p=0.012).
Conclusions and Relevance
The prevalence of myocardial scars in a US community based multiethnic cohort was 7.9%, of which 78% were unrecognized by electrocardiography or clinical evaluation. Further studies are needed to understand the clinical consequences of these undetected scars and the utility of their identification.
Introduction
Ischemic heart disease is an important public health concern, but a considerable proportion of myocardial infarctions are clinically unrecognized. Given the aging of the US population, it is important to understand the prevalence, risk factors, and prognosis of unrecognized myocardial infarction.1,2 Previous population-based studies in the United States, using electrocardiography (ECG) criteria reported approximately 20% of myocardial infarctions are “silent”. 2,3 More recently, clinical 4-6 and population studies1,7,8 have demonstrated that cardiac magnetic resonance (CMR) imaging with late gadolinium enhancement (LGE) has greater sensitivity than does ECG for detecting myocardial scar. CMR can identify myocardial scar related to myocardial infarction as well as other nonischemic etiologies. Clinical trials now accept unrecognized myocardial scars detected by CMR as endpoints,9 as they often lead to major adverse cardiac events. 1,5
Population studies in Iceland (n=936 participants 1) and Sweden (n=248 participants 7) have documented that the prevalence of myocardial scar detected by CMR is significantly higher than is detected by clinical assessment, serum biomarkers and ECG. Equivalent studies are needed in the United States and may stimulate a greater appreciation of the burden of subclinical disease. Given the high prevalence of ischemic heart disease in the United States and throughout the world, 10 it is important to accurately evaluate the burden and correlates of myocardial scar in the population.
The Multi-Ethnic Study of Atherosclerosis (MESA) recruited individuals from four different ethnicities from six communities in the United States. The purpose of this study was to determine the prevalence of myocardial scar using CMR and to determine the association between cardiovascular disease (CVD) risk factors and myocardial scar in a large population based study.
Methods
Study Sample
The MESA study design has been previously described.11 In brief, 6814 men and women who identified themselves as white, African-American, Hispanic, or Chinese and were 45-84 years old and free of clinically apparent cardiovascular disease were recruited from 2000-2002 from 6 U.S. communities: Baltimore City and Baltimore County, Maryland; Chicago, Illinois; Forsyth County, North Carolina; Los Angeles County, California; Northern Manhattan and the Bronx, New York; and St Paul, Minnesota. Consenting participants (n=3045) underwent CMR from April 2010 until February 2012 (visit 5 of the cohort). Institutional review boards at each center approved the study protocol, and all participants gave written informed consent. Study participants who agreed to participate and who had estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73 m2 (≥ 60 mL/min/1.73 m2 for the site at Northwestern University) and no known allergies to gadolinium (n=1,840) also underwent late gadolinium enhanced (LGE) CMR 15 minutes after administration of 0.15 mmol/kg dose of gadolinium based contrast agent (Magnevist, Bayer Healthcare Pharmaceuticals, Montville, NJ).
Cardiovascular Risk Factors
MESA participants underwent an extensive evaluation including clinical history, physical examination, laboratory tests and anthropometric measurements. All participants were free of clinically apparent cardiovascular disease at enrollment. Standard questionnaires were used to obtain information about participant demographics, medical history including smoking history, current medications including lipid lowering, hypoglycemic and anti-hypertensive medications, and physician diagnosis of hypertension and diabetes. Race/ ethnicity was assessed as one of the overall aims of the MESA study to provide insights about interactions between ethnicity, risk factors, and subclinical and clinical cardiovascular disease. Ethnicity was self-identified in fixed categories as white, black, Hispanic or Chinese.
Centrally trained clinical teams blinded to participant outcome collected information on cardiovascular risk factors. Hypertension was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, or self-reported hypertension with use of anti-hypertensive medications.12 Diabetes was defined based on the use of hypoglycemic drugs or insulin, or fasting blood glucose ≥ 126 mg/dl.13 Serum creatinine measurements, estimated glomerular filtrate rate (eGFR) calculation, micro and macroalbuminuria classifications have been described previously.14,15
ECG studies at year 10 were centrally read and classified using Minnesota code.16 Silent MIs by ECG were identified based on major Q wave abnormalities representing old myocardial infarctions among participants at year 10. Carotid artery intima media thickness (IMT) measurements in MESA have been described previously.17
CAC Score Measurements
The methodology for acquisition and interpretation of the CAC score has been reported previously. 18 Briefly, CAC score was assessed by either a cardiac gated electron beam CT or a multi-detector CT at baseline and at year 10 in six centers. All images were interpreted at the MESA CT reading center and Agatston score was calculated.
Assessment of Clinical Myocardial Infarction (MI) Events
Details of CVD event ascertainment have been published.19 For this report, clinical myocardial infarction (MI) events were cumulative from the beginning of MESA to the Year 10 exam date. Briefly, definite or probable MI required finding participants' hospital records documenting either abnormal cardiac biomarkers (2 times upper limits of normal) regardless of pain or ECG findings; evolving Q waves regardless of pain or biomarker findings; or a combination of chest pain, and ST-T evolution or new LBBB, and biomarker levels 1-2 times upper limits of normal.
CMR Imaging and Image Analysis
CMR was performed using 1.5T scanners (Avanto and Espree, Siemens Medical Systems, Erlangen, Germany, and Signa HD, GE, Milwaukee, WI, USA) with a 6-channel anterior phased array coil. CMR protocol was uniform in all centers and all studies were centrally evaluated by readers blinded to all other study data. Left ventricular (LV) mass, volumes, and functional parameters were determined by a cine steady state free precession sequence using CIM software (version 6.2, Auckland MRI Research Group, University of Auckland, New Zealand).
Myocardial scar was defined as focal LGE either in two adjacent short axis slices or in one short axis and a long axis image at a corresponding location using QMass (version 7.2; Medis, Leiden, the Netherlands). Myocardial scars that involved subendocardium in a coronary artery distribution were defined as “typical” scar. Myocardial scars predominantly affecting midwall or subepicardium without subendocardial involvement in a non-coronary artery distribution were defined as “atypical” scar.
Statistical Analysis
Participant demographics and characteristics at the MESA Year 10 exam are presented as means (standard deviation) or n (%). Log transformation was applied to variables with skewed distribution. The missing data approach was complete-case analysis, which uses only subjects who have all variables observed. Logistic regression models were used to model the log odds of myocardial scar using CAC both continuously and categorically in separate models. For the continuous models, CAC was log-transformed after adding 1 (due to values of 0 CAC score). After log-transformation there was no evidence of non-linearity in the relationship between log (CAC+1) and the log-odds of myocardial scar. For the categorical CAC models, the groups were 0, 1-99, 100-399, and >=400 Agatston units. For each version of CAC, four sets of models were fit based on a priori defined sets of covariates both for cross-sectional and longitudinal associations. Model 1 adjusted for age, gender, and race/ethnicity. Model 2 further adjusted for systolic blood pressure, hypertension medication, total cholesterol, HDL cholesterol, lipid medication, smoking status, diabetes, GFR, and household income>$50k. Model 3 additionally added BMI, LV mass, LV end-diastolic volume, and LV ejection fraction. Model 4 additionally added the average of the left and right distal mean common carotid artery intimal media thickness. To evaluate the improvement in prediction by adding CAC to the model with traditional risk factors, the area under the curve (AUC) was calculated before and after adding CAC to the adjusted model. Both continuous CAC (log (CAC+1)) and categorical CAC were evaluated in a series of models.
Both cross-sectional and longitudinal models were constructed to evaluate the association of age and gender adjusted CAC score with the presence of CMR defined scar in comparison to clinically adjudicated myocardial infarction. These models were additionally adjusted for 10 year Framingham Global Risk score and 10 year ACC/AHA risk score (composite risk scores were used due to the small number of clinically adjudicated myocardial infarctions).20,21
SAS 9.2 (SAS Institute Inc., Cary, NC) and R 2.13.0 (R Foundation for Statistical Computing, Vienna, Austria) was used for the analysis. The statistical testing was 2-sided. P values of ≤ 0.05 were considered statistically significant and are presented for descriptive purposes.
Results
Study Population
During the 10th year of MESA, 3045 of 4716 participants underwent CMR examination (Figure 1). The mean age of participants with CMR was 69 years, 47% were male, 40% were white, 10% were Chinese-American, 25% were African-American and 20% were Hispanic. Of these, 1840 (60%) also completed the LGE CMR examination. Of participants who did not receive LGE CMR, 34.7% declined administration of iv gadolinium, 24.3% were excluded due to low renal function, 4.1% had creatinine measurements more than 30 days ago, 2.6% were allergic to gadolinium, 1.6% did not have iv access, and 32.7% had an unknown reason. Compared to participants without an LGE CMR (n=2876), participants with LGE CMR examination (n=1840) were younger (71.2±9.6 vs. 67.9±8.8 years, p<0.001), more likely to be male (43.2% vs. 52.7%, p<0.001), more likely to be white (38.0% vs. 45.3%, p<0.001), less likely to use anti-hypertensive medication (59.0% vs.50.0%, p<0.001), less likely to have diabetes (22.4% vs. 16.5%, p<0.001) and had a lower Framingham risk score (16.5±9.5% vs. 15.5±9.1%, p<0.01).
Figure 1. Study enrollment and participation.
CMR=cardiac magnetic resonance imaging; eGFR=estimated glomerular filtration rate (The Modification of Diet and Renal Disease [MDRD] GFR prediction equation is used to estimate GFR); IV=intravenous; LGE=late gadolinium enhancement; CAC score=coronary artery calcium score by computed tomography.
Renal dysfunction: estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73 m2 (≥ 60 mL/min/1.73 m2 for the site at Northwestern University)
Recognized Myocardial Scar: Myocardial infarction/scars detected electrocardiogram or by clinical evaluation.
Typical Myocardial Scar: CMR identified myocardial scars that involved subendocardium in a coronary artery distribution
Atypical Myocardial Scar: CMR identified myocardial scars predominantly affecting midwall or subepicardium, typically without subendocardial involvement and in a non-coronary artery distribution
The demographic and clinical characteristics of the 1840 participants who underwent CMR are shown in Tables 1, 2 and eTable 1. Compared to participants without CMR myocardial scar at year 10, those with myocardial scar were older, more likely to be male, more likely to be white, more likely to be hypertensive, had slightly lower total and HDL cholesterol levels, and had a higher prevalence of current cigarette smoking. The baseline and year 10 demographic and clinical characteristics of the participants with CAC score are shown in eTables 2a and 2b by CAC score categories, respectively.
Table 1. Clinical Characteristics of MESA Participants at Baseline (2000-2002) by Presence/Absence of Myocardial Scar at year 10.
| No Scar (N=1694) Mean+/-SD or N (%) | Any Scar (N=146) Mean+/-SD or N (%) | p-value | |
|---|---|---|---|
| AGE (yrs) | 58.1 ± 8.8 | 62.5 ± 9.4 | <0.001 |
| GENDER: | |||
| FEMALE | 858 (50.6%) | 22 (15.1%) | <0.001 |
| MALE | 836 (49.4%) | 124 (84.9%) | |
| RACE/ETHNICITY: | |||
| WHITE | 758 (44.8%) | 76 (52.1%) | 0.03 |
| CHINESE | 165 (9.7%) | 5 (3.4%) | |
| BLACK | 415 (24.5%) | 43 (29.5%) | |
| HISPANIC | 356 (21.0%) | 22 (15.1%) | |
| INCOME <$50K: | |||
| No | 828 (50.0%) | 69 (48.9%) | 0.80 |
| Yes | 827 (50.0%) | 72 (51.1%) | |
| BMI (kg/m2) | 28.0 ± 4.9 | 28.6 ± 4.5 | 0.15 |
| HYPERTENSION: | |||
| No | 1138 (67.2%) | 76 (52.1%) | 0.001 |
| Yes | 556 (32.8%) | 70 (48.0%) | |
| HYPERTENSION MEDS: | |||
| No | 1236 (73.0%) | 87 (59.6%) | 0.001 |
| Yes | 457 (27.0%) | 59 (40.4) | |
| SYSTOLIC BP (mm Hg) | 121.5 ± 19.1 | 128.8 ± 21.0 | <0.001 |
| DIASTOLIC BP (mm Hg) | 71.8 ± 10.1 | 76.4 ± 10.0 | <0.001 |
| TOTAL CHOL. (mg/dl) | 194.4 ± 35.1 | 189.5 ± 30.9 | 0.10 |
| HDL CHOL. (mg/dl) | 50.6 ± 14.6 | 46.0 ± 12.4 | <0.001 |
| LIPID MEDICATION: | |||
| No | 1443 (85.2%) | 119 (81.5%) | 0.23 |
| Yes | 250 (14.8%) | 27 (18.5%) | |
| DIABETES: | |||
| No | 1568 (92.6%) | 127 (87.0%) | 0.02 |
| Yes | 126 (7.4%) | 19 (13.0%) | |
| SMOKING STATUS: | |||
| Never | 843 (49.9%) | 62 (42.8%) | 0.02 |
| Former smoker | 646 (38.2%) | 54 (37.2%) | |
| Current smoker | 202 (11.9%) | 29 (20.0%) | |
| GFR (mL/min/1.73 m2) | 84.0 ± 15.5 | 84.0 ± 16.3 | 0.96 |
| LV MASS (g) | 145.2 ± 36.1 | 176.2 ± 42.0 | <0.001 |
| LV EDV (mL) | 129.3 ± 29.9 | 139.9 ± 33.1 | <0.001 |
| LV SV (mL) | 88.4 ± 19.4 | 91.5 ± 22.0 | 0.07 |
| LV EF (%) | 68.9 ± 6.7 | 65.8 ± 7.4 | <0.001 |
| Common CIMT (mm) | 0.7 ± 0.2 | 0.8 ± 0.2 | <0.001 |
| 10 yr Framingham Global Risk (%) | 11.4 ± 8.4 | 18.7 ± 8.9 | <0.001 |
| 10 yr ACC/AHA Risk (%) | 8.6 ± 8.9 | 16.1 ± 12.2 | <0.001 |
| CAC (Agatston Units): | |||
| 0 | 1006 (59.4%) | 37 (25.3%) | <0.001 |
| 1-99 | 420 (24.8%) | 50 (34.3%) | |
| 100-399 | 179 (10.6%) | 34 (23.3%) | |
| >=400 | 89 (5.3%) | 25 (17.1%) |
Income represents total household annual income. BMI= body mass index. Hypertension was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, or self-reported hypertension with use of anti-hypertensive medications. CHOL=cholesterol. Diabetes was defined based on the use of hypoglycemic drugs or insulin, or fasting blood glucose ≥ 126 mg/dl. GFR=glomerular filtration rate (MDRD equation). LV=Left ventricle, EDV=end diastolic volume, SV=stroke volume, CIMT=carotid intimal media thickness. P-values from ANOVA test or Chi-square test as appropriate across myocardial scar group.
Table 2. Cross Sectional Clinical Characteristics of MESA Participants at year 10 (2010-2012) by Presence/Absence of Myocardial Scar.
| No Scar (N=1694) Mean+/-SD or N (%) | Any Scar (N=146) Mean+/-SD or N (%) | p-value | |
|---|---|---|---|
| AGE (yrs) | 67.5 ± 8.7 | 71.9 ± 9.3 | <0.001 |
| GENDER: | |||
| FEMALE | 858 (50.6%) | 22 (15.1%) | <0.001 |
| MALE | 836 (49.4%) | 124 (84.9%) | |
| RACE/ETHNICITY: | |||
| WHITE | 758 (44.8%) | 76 (52.1%) | 0.01 |
| CHINESE | 165 (9.7%) | 5 (3.4%) | |
| BLACK | 415 (24.5%) | 43 (29.5%) | |
| HISPANIC | 356 (21.0%) | 22 (15.1%) | |
| INCOME <$50K: | |||
| No | 819 (49.7%) | 67 (47.2%) | 0.56 |
| Yes | 829 (50.3%) | 75 (52.8%) | |
| BMI (kg/m2) | 28.4 ± 5.1 | 28.7 ± 4.8 | 0.49 |
| HYPERTENSION: | |||
| No | 791 (46.7%) | 44 (30.1%) | <0.001 |
| Yes | 903 (53.3%) | 102 (69.9%) | |
| HYPERTENSION MEDS: | |||
| No | 1236 (73.0%) | 87 (59.6%) | 0.001 |
| Yes | 457 (27.0%) | 59 (40.4) | |
| SYSTOLIC BP (mm Hg) | 122.0 ± 19.3 | 124.7 ± 17.9 | 0.101 |
| DIASTOLIC BP (mm Hg) | 68.7 ± 9.7 | 70.1 ± 9.6 | 0.096 |
| TOTAL CHOL. (mg/dl) | 194.4 ± 35.1 | 189.5 ± 30.9 | 0.10 |
| TOTAL CHOL. (mg/dl) | 183.5 ± 36.4 | 171.8 ± 36.9 | <0.001 |
| HDL CHOL. (mg/dl) | 54.9 ± 16.3 | 51.4 ± 15.2 | 0.01 |
| LIPID MEDICATION: | |||
| No | 1061 (62.6%) | 78 (53.4%) | 0.03 |
| Yes | 633 (37.4%) | 68 (46.6%) | |
| DIABETES: | |||
| No | 1424 (84.1%) | 114 (78.1%) | 0.06 |
| Yes | 270 (15.9%) | 32 (21.9%) | |
| SMOKING STATUS: | |||
| Never | 739 (43.8%) | 50 (34.5%) | 0.003 |
| Former smoker | 819 (48.5%) | 73 (50.3%) | |
| Current smoker | 130 (7.7%) | 22 (15.2%) | |
| GFR (mL/min/1.73 m2) | 84.8 ± 18.6 | 81.7 ± 17.2 | 0.06 |
| LV MASS (g) | 123.9 ± 31.9 | 155.7 ± 37.5 | <0.001 |
| LV EDV (mL) | 122.5 ± 30.4 | 138.6 ± 40.2 | <0.001 |
| LV SV (mL) | 75.2 ± 18.1 | 77.0 ± 21.5 | 0.25 |
| LV EF (%) | 61.9 ± 6.7 | 56.6 ± 9.3 | <0.001 |
| Common CIMT (mm) | 0.8 ± 0.2 | 0.9 ± 0.2 | <0.001 |
| 10 yr Framingham Global Risk (%) | 15.0 ± 9.0 | 21.7 ± 8.1 | <0.001 |
| 10 yr ACC/AHA Risk (%) | 16.4 ± 15.0 | 24.1 ± 15.4 | <0.001 |
| CAC (Agatston Units): | |||
| 0 | 467 (35.0%) | 5 (4.7%) | <0.001 |
| 1-99 | 408 (30.6%) | 27 (25.2%) | |
| 100-399 | 234 (17.5%) | 30 (28.0%) | |
| >=400 | 225 (16.9%) | 45 (42.1%) |
Income represents total household annual income. BMI= body mass index. Hypertension was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, or self-reported hypertension with use of anti-hypertensive medications. CHOL=cholesterol. Diabetes was defined based on the use of hypoglycemic drugs or insulin, or fasting blood glucose ≥ 126 mg/dl. GFR=glomerular filtration rate (MDRD equation). LV=Left ventricle, EDV=end diastolic volume, SV=stroke volume, CIMT=carotid intimal media thickness. P-values from ANOVA test or Chi-square test as appropriate across myocardial scar group.
Participants with myocardial scar also had higher common carotid intima media thickness (IMT), and higher Framingham risk, ACC/AHA risk and CAC scores compared to those without myocardial scar at baseline and at 10 year follow up (Tables 1 and 2).
Prevalence and factors associated with CMR defined myocardial scar
The overall prevalence of myocardial scar by CMR was 7.9 % (146/1840 participants) (Figure 1). The prevalence of previously unrecognized myocardial scar was 6.2% (114/1840), whereas 1.7% (32/1840) had clinically recognized MI. Thus, the majority (78%, 114/146) of myocardial scars were unrecognized by clinical or ECG adjudication. Among unrecognized myocardial scars, 38% were typical (43/114) and 62% were atypical (71/114) scars. Among recognized myocardial scars, 84% were typical (27/32) and 16% were atypical (5/32). Men had a higher prevalence of myocardial scar compared to women (12.9% vs. 2.5%, respectively; difference 10.4%, 95% CI: 0.10 (0.08, 0.13), p<0.001).
The total number of participants with LGE CMR and concurrent CAC score was 1441. The relative proportions of myocardial scars in the CT cohort were similar to that described above: the prevalence of unrecognized myocardial scar was 5.8% (84/1441), whereas 1.6% (23/1441) had clinically recognized MI (Figure 1). Thus for participants with concurrent LGE CMR and CAC score, the majority (78%, 84/107) of myocardial scars were unrecognized by clinical adjudication or Year 10 ECG. Only ten of 107 participants with scar (9.3%) had evidence of ECG defined silent MI at Year 10.
Table 3 shows minimally adjusted logistic regression models to assess the longitudinal association of individual risk factors at baseline with the presence of myocardial scar at year 10. Greater age and male gender were associated with higher odds of having CMR myocardial scar [OR per SD (95%CI): 1.61 (1.36, 1.91) per 8.9 years, p<0.001; OR (95%CI): 5.76 (3.61, 9.17) for males, p<0.001, respectively). Hypertension, body mass index and current smoking were also associated with having myocardial scar [OR (95%CI):1.61 (1.12, 2.30) for hypertension present, p=0.009; OR per SD (95%CI) 1.32 (1.09, 1.61) per 4.8 kg/m2, p=0.005; and OR (95%CI): 2.00 (1.22, 3.28) current vs. never smokers, p=0.006, respectively]. In a multivariable model including all of these variables, these associations remained significant with slight changes in the magnitude of odds ratios. Framingham risk score and ACC/AHA risk score were associated positively with myocardial scar [0R per SD (95%CI): 1.48 (1.17, 1.86) per 8.7% risk, p=0.001 and 1.44 (1.18, 1.77) per 9.4% risk, p<0.001 respectively]. Both typical and atypical myocardial scars were associated with age and gender [OR (95% CI) per SD: 1.79 (1.41, 2.28) per 8.9 years, p<0.001 and 1.46 (1.17, 1.83) for males, p=0.001, respectively, eTable 3]. Hypertension was significantly associated with typical myocardial scar [OR (95% CI): 1.66 (1.01, 2.74) for hypertension present, p=0.04] but not with atypical myocardial scar [OR (95% CI): 1.54 (0.95, 2.50), p=0.08, eTable 3]. Calcium score at baseline was associated with typical myocardial scar [OR (95% CI): 6.05 (2.90, 12.61) for CAC>0, p<0.001] but not atypical myocardial scar [OR (95% CI): 1.51 (0.90, 2.53), for CAC>0, p=0.12] (eTable 3).
Table 3. Longitudinal Association of Individual Cardiovascular Disease Risk Factors at Baseline (N=1840) with the Presence of Myocardial Scar (N=146) at Year 10 after Adjusting for Age and Gender*.
| Cardiovascular Disease Risk Factor | Any Myocardial Scar OR (95% CI) | p-value | |
|---|---|---|---|
| AGE (per SD=8.9)** | 1.61 [1.36,1.91] | <0.001 | |
| GENDER: | |||
| FEMALE | Ref | <0.001 | |
| MALE | 5.76 [3.61,9.17] | ||
| RACE/ETHNICITY: | |||
| WHITE | Ref | ||
| CHINESE | 0.31 [0.12,0.78] | 0.01 | |
| BLACK | 1.08 [0.72,1.62] | 0.73 | |
| HISPANIC | 0.57 [0.34,0.94] | 0.03 | |
| INCOME >$50K | 0.97 [0.68,1.39] | 0.87 | |
| BMI (per SD=4.8 kg/m2) | 1.32 [1.09,1.61] | 0.005 | |
| HYPERTENSION | |||
| no | ref | ||
| yes | 1.61 [1.12,2.30] | 0.009 | |
| TOTAL CHOL (per SD=34.8mg/dl) | 1.03 [0.86,1.24] | 0.71 | |
| HDL CHOL (per SD=14.5 mg/dl) | 0.93 [0.75,1.15] | 0.49 | |
| LIPID MEDICATION: | |||
| No | Ref | 0.81 | |
| Yes | 1.06 [0.67,1.67] | 0.81 | |
| DIABETES | |||
| No | Ref | 0.13 | |
| Yes | 1.51 [0.88,2.59] | 0.13 | |
| SMOKING STATUS: | |||
| Never | Ref | 0.21 | |
| Former smoker | 0.78 [0.52,1.15] | ||
| Current smoker | 2.00 [1.22,3.28] | 0.006 | |
| GFR (per SD=15.6 mL/min/1.73m2) | 1.06 [0.89,1.27] | 0.51 | |
| LV MASS (per SD=37.5) | 1.81 [1.49,2.19] | <0.001 | |
| LV EDV (per SD=30.3) | 1.24 [1.03,1.49] | 0.02 | |
| LV SV (per SD=19.4) | 1.08 [0.90,1.29] | 0.40 | |
| LV EF (per SD=4.8) | 0.76 [0.64,0.90] | 0.002 | |
| Common CIMT (per SD=0.2) | 1.24 [1.03,1.50] | 0.02 | |
| Framingham Global Risk (per SD=8.7%) *** | 1.48 [1.17,1.86] | 0.001 | |
| ACC/AHA Risk (per SD=9.4%) *** | 1.44 [1.18,1.77] | <0.001 | |
| CAC > 0 (Agatston Units): | 2.61 [1.73,3.95] | <0.001 | |
Separate models, each included one cardiovascular disease risk factor with age and gender.
Age is adjusted only for gender and gender is only adjusted for age.
Framingham risk score and ACC/AHA risk score are not adjusted for age and gender.
Income represents total household annual income. BMI= body mass index. Hypertension was defined as systolic blood pressure (SBP) ≥140 mm Hg, diastolic blood pressure (DBP) ≥90 mm Hg, or self-reported hypertension with use of anti-hypertensive medications. CHOL=cholesterol. Diabetes was defined based on the use of hypoglycemic drugs or insulin, or fasting blood glucose ≥ 126 mg/dl. GFR=glomerular filtration rate (MDRD equation). P-values from ANOVA test or Chi-square test across myocardial scar group.
Relationship of CAC score to myocardial scar
Table 4 shows the cross-sectional relationship of CMR defined myocardial scar and CAC score. The prevalence of myocardial scar increased in relationship to CAC score: CAC = 0, 1.1%, CAC = 1-99, 6.2%, CAC = 100-399, 11.4%; ≥ 400, 16.7%, as did the corresponding odds ratios adjusted for age, gender and race/ethnicity [OR (95%CI): 1 = ref, 4.5 (1.7, 11.9), 7.5 (2.8, 20.0) and 8.4 (3.1, 22.7), respectively]. Further stepwise adjustments for CVD risk factors (Model 2), LV parameters and BMI (model 3) and carotid artery intimal media thickness (Model 4) showed similar association of myocardial scar among CAC score categories with slight changes in the magnitude of odds ratios (Table 4, receiver operating curve, eFigure 1a).
Table 4. Cross-sectional Association of CAC Score at year 10 (n=1441) with the Presence of Myocardial Scar (n=107) at year 10.
| Models | CAC=0 (n=472) | CAC 1-99 (n=435) | CAC 100-399 (n=264) | CAC>=400 (n=270) | Log(CAC+1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #
with scars/ Total* |
OR (95% CI) |
#
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | |
| Model 1 | 5/472 | 1 (ref) | 27/435 | 4.5 (1.7,11.9) | 0.003 | 30/264 | 7.5 (2.8,20.0) | <0.001 | 45/270 | 8.4 (3.1,22.7) | <0.001 | 107/1441 | 1.3 (1.2,1.4) | <0.001 |
| Model 2 | 5/461 | 1 (ref) | 27/426 | 4.1 (1.5,10.9) | 0.005 | 28/248 | 6.5 (2.4,17.8) | <0.001 | 44/261 | 7.1 (2.6,19.7) | <0.001 | 104/1396 | 1.3 (1.1,1.4) | <0.001 |
| Model 3 | 5/460 | 1 (ref) | 27/426 | 4.0 (1.5,10.8) | 0.007 | 28/428 | 6.8 (2.4,19.0) | <0.001 | 44/261 | 6.4 (2.3,17.9) | <0.001 | 104/1395 | 1.2 (1.1,1.4) | <0.001 |
| Model 4 | 4/445 | 1 (ref) | 27/411 | 4.9 (1.7, 14.8) | 0.004 | 27/236 | 8.5 (2.7,23.9) | <0.001 | 42/247 | 7.7 (2.5,23.9) | <0.001 | 100/1339 | 1.3 (1.1,1.4) | <0.001 |
All adjusted variables were from year 10 (MESA exam 5).
Numerators and denominators include only those subjects who have all observed variables.
Log (CAC+1): CAC was log-transformed after adding 1 (due to values of 0 CAC score) for the continuous models.
Model 1: adjusted for age, gender and race/ethnicity;
Model 2: variables in Model 1 plus systolic blood pressure, hypertension medication, total cholesterol, HDL cholesterol, lipid medication, smoking status, diabetes, GFR, household income>$50k;
Model 3: variables in Model 2 plus BMI, LV mass, LV end-diastolic volume, LV ejection fraction;
Model 4: variables in model 3 plus average of left and right distal mean common carotid artery intimal media thickness
Table 5 shows the longitudinal relationship of CMR defined myocardial scar and baseline CAC score. The prevalence of myocardial scar increased in relationship to CAC score: CAC = 0, 3.5%, CAC = 1-99, 10.6%, CAC = 100-399, 16%; ≥ 400, 21.9%, as did the corresponding odds ratios adjusted for age, gender and race/ethnicity [OR (95%CI): 1 = ref, 2.4 (1.5, 3.9), 3.0 (1.7, 5.1) and 3.3 (1.7, 6.1), respectively]. Further stepwise adjustments for CVD risk factors (Model 2), LV parameters and BMI (model 3) and carotid artery intimal media thickness (Model 4) showed similar association of myocardial scar among CAC score categories with slight changes in the magnitude of odds ratios (Table 5, receiver operating curve, eFigure 1b). Considering only unrecognized myocardial scar (eTable 4a), the odds ratios for CAC association with myocardial scar were of similar magnitude (eTable 4b) compared to odds ratios considering all myocardial scars (Table 5). CAC score added significantly to the association of myocardial scar over the variables in models 1-3 (c-statistic: 0.78 vs. 0.76, p=0.003 in model 1; 0.81 vs. 0.79 (eFigure 1b), p=0.01 in model 2; 0.82 vs. 0.80, p=0.013 in model 3, respectively) but not in model 4 (C-statistic: 0.823 vs. 0.81, p=0.08).
Table 5. Longitudinal Association of CAC Score at Baseline (n=1840) with the Presence of Myocardial Scar (n=146) at Year 10.
| Models | CAC=0 (n=1043) | CAC 1-99 (n=470) | CAC 100-399 (n=213) | CAC>=400 (n=114) | Log(CAC+1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #
with scars/ Total* |
OR (95% CI) |
#
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | #
with scars/ Total |
OR (95% CI) |
p | |
| Model 1 | 37/1043 | 1 (ref) | 50/470 | 2.4 (1.5,3.9) | <0.001 | 34/213 | 3.0 (1.7,5.1) | <0.001 | 25/114 | 3.3 (1.7,6.1) | <0.001 | 146/1840 | 1.2 (1.1,1.3) | <0.001 |
| Model 2 | 36/1010 | 1 (ref) | 47/460 | 2.2 (1.4,3.5) | 0.001 | 34/209 | 2.6 (1.5,4.6) | 0.001 | 24/112 | 3.1 (1.6,5.9) | 0.001 | 141/1791 | 1.2 (1.1,1.3) | <0.001 |
| Model 3 | 35/1000 | 1 (ref) | 47/451 | 2.2 (1.3,3.6) | 0.002 | 34/207 | 2.5 (1.4,4.5) | 0.001 | 24/109 | 3.2 (1.6,6.2) | 0.001 | 140/1767 | 1.2 (1.1,1.3) | <0.001 |
| Model 4 | 24/766 | 1 (ref) | 35/335 | 2.3 (1.3,4.1) | 0.004 | 23/156 | 2.2 (1.1,4.3) | 0.029 | 16/82 | 2.2 (1.0,4.8) | 0.05 | 98/1339 | 1.1 (1.0,1.3) | 0.009 |
All covariates taken from baseline (Exam 1).
Numerators and denominators include only those subjects who have all observed variables.
Log (CAC+1): CAC was log-transformed after adding 1 (due to values of 0 CAC score) for the continuous models.
Model 1: adjusted for age, gender and race/ ethnicity;
Model 2: variables in Model 1 plus systolic blood pressure, hypertension medication, total cholesterol, HDL cholesterol, lipid medication, smoking status, diabetes, GFR, household income>$50k;
Model 3: variables in Model 2 plus BMI, LV mass, LV end-diastolic volume, LV ejection fraction;
Model 4: variables in model 3 plus average of left and right distal mean common carotid artery intimal media thickness
Clinically adjudicated MI and CMR scar in relationship to cardiovascular risk and CAC score
We compared the association of CAC score with CMR defined scar versus the association of CAC score with clinically adjudicated myocardial infarction (Table 6). In cross-sectional analysis at year 10, age and gender adjusted CAC score showed similar association with CMR myocardial scar vs. clinically adjudicated MI (OR 95%CI, 1.28 (1.15,1.42), p<0.001 vs. OR 1.34 (1.05,1.70), p=0.02, respectively). For baseline CAC score, similar associations were noted for longitudinal association of CMR myocardial scar vs. clinically adjudicated MI (OR 1.22 (1.13, 1.32), p<0.001 vs. OR 1.35 (1.14, 1.61), p=0.001, respectively). The addition of Framingham risk score or the ACC/AHA risk score to the CAC score did not substantially change these odds ratios (Table 6).
Table 6. The Association of CAC Score with the Presence of Myocardial Scar and Clinical Myocardial Infarction at Year 10.
| Myocardial Scar by CMR | Clinically Adjudicated MI | ||||
|---|---|---|---|---|---|
| Covariates at year 10 | N=107 | N=20 | |||
| OR(95% CI) | p | OR(95% CI) | p | ||
| age+gender | 1.28 (1.15,1.42) | <0.001 | 1.34 (1.05,1.70) | 0.02 | |
| Log(CAC+1) at Year 10 | age, gender, FRS | 1.27 (1.15,1.41) | <0.001 | 1.35 (1.06,1.73) | 0.02 |
| age, gender, AHA | 1.28 (1.15, 1.42) | <0.001 | 1.36 (1.06, 1.73) | 0.01 | |
|
| |||||
| Covariates at baseline | Myocardial Scar by CMR N=146 | Clinically Adjudicated MI N=29 | |||
|
| |||||
| age+gender | 1.22 (1.13,1.32) | <0.001 | 1.35 (1.14,1.61) | 0.001 | |
| Log(CAC+1) at baseline | age, gender, FRS | 1.21 (1.12,1.31) | <0.001 | 1.35 (1.13,1.60) | 0.001 |
| age, gender, AHA | 1.21 (1.12, 1.31) | <0.001 | 1.36 (1.14, 1.61) | <0.001 | |
Log (CAC+1): CAC was log-transformed after adding 1 (due to values of 0 CAC score) for the continuous models.
Associations for exam 5 CAC include all scars/MI events that have both CAC and scar measurements at Year 10. Associations for baseline CAC include all scars/MI events that have both baseline CAC and Year 10 scar measured. FRS = 10 year Framingham global cardiovascular disease risk score, AHA = 10 year American College of Cardiology/American Heart Association risk score. CAC = Agatston calcium score.
Discussion
The most significant long term outcome of coronary atherosclerosis is myocardial infarction. In patients who survive myocardial infarction, normal contractile tissue is replaced by noncontractile fibrosis/ scar. Cardiovascular magnetic resonance is considered a standard of reference for defining the presence of myocardial scar. The MESA cohort is an ideal population to study the long term sequela of cardiovascular risk factors and coronary atherosclerosis on the myocardium. In this U.S. based cohort of men and women (mean age, 69 years), the prevalence of CMR defined myocardial scar was 7.9%; 78% of CMR identified myocardial scars were unrecognized by clinical adjudication or by ECG. Age and gender adjusted calcium score was associated with CMR defined myocardial scar (OR 1.2, p< 0.001) without further improvement of the statistical model with the addition of the ACC/AHA or Framingham risk score (OR 1.21 and 1.21, respectively, p< 0.001 for both).
In MESA, the prevalence of clinically recognized scar versus myocardial scar recognized only by CMR was 1.7% and 6.2%, respectively. The clinical significance of unrecognized myocardial scar remains to be defined, although prior myocardial scar has been noted pathologically in greater than 70% of patients with sudden cardiac death but without prior known coronary artery disease.22 In the ICELAND study1 of 936 elderly participants, the prevalence of recognized and unrecognized myocardial scar by CMR (ischemic pattern) was higher than in MESA, at 9.7% and 17%, respectively. Unrecognized myocardial scar in ICELAND was associated 8% absolute risk increase in mortality. Of 248 participants at age 70 in the PIVUS study7 in Uppsala, Sweden, the prevalence of recognized and unrecognized myocardial scar by CMR was 4.4% and 19.8%, respectively. The lower prevalence of myocardial scar in MESA compared to the ICELAND and PIVUS studies may be due to differences in the prevalence of cardiovascular risk factors which were lower in MESA (e.g. MESA excluded individuals with baseline clinical cardiovascular disease). The prevalence of hypertension, diabetes and smoking was higher in ICELAND (67%, 36% 60%, respectively) than in MESA (50%, 17% and 8%, respectively). In PIVUS, 73% of participants were hypertensive and 12.5% were diabetic. Moreover, the ICELAND cohort average age was 76 versus 69 and 70 in the MESA and PIVUS cohorts. An additional difference is the multi-ethnic population composition of the MESA compared with the predominant northern European ancestry of both ICELAND and PIVUS studies.
To our knowledge, this study represents the first U.S. population–based evaluation of myocardial scar by CMR and its relationship cardiovascular risk factors. CAC score is an important measure of subclinical atherosclerotic burden and is an independent predictor of coronary heart disease and cardiovascular disease.23-25 CAC score has been shown to enhance traditional risk factor based prediction models 23,26 and individuals with a greater number and degree of risk factors are more likely to have higher CAC scores.27 In MESA, CAC score and CVD risk factors were similar between individuals with CMR defined scar compared to clinically overt myocardial infarction events. The current study also demonstrates that the CAC score was associated with subclinical myocardial damage.
Of individual risk factors, age, male gender, CAC score, BMI, current smoking and use of anti-hypertensive medications at baseline were associated with higher odds of myocardial scar. In addition, Chinese and Hispanic ethnicity had lower odds of myocardial scar than whites and blacks. As expected, Framingham and ACC/AHA risk scores were also associated with myocardial scar. On the other hand, established risk factors including serum lipid levels and diabetes showed no significant association with myocardial scar perhaps due to confounding introduced by concurrent medication use. Our results are consistent with previous studies showing age and male gender as risk factors of myocardial scar by CMR. In the ICELAND study 1, diabetes was also a risk factor for unrecognized myocardial MI, perhaps due to a higher prevalence of diabetes in that study compared to MESA.
CMR defined scar has been well-validated in histological studies and is considered a standard of reference in to define the presence and extent of infarction. The ICELAND study showed that participants with unrecognized MI had median CAC scores that were intermediate between those without scar and those with scar and recognized MI, respectively.1 Barbier et al.28 studied noncoronary atherosclerosis and cardiovascular risk factors in the PIVUS study. They found more frequent vascular disease in participants with scar and recognized MI. Christiansen et al.29 reported that 30% of patients with acute chest pain and elevated troponin levels had a previously unrecognized CMR detected ischemic myocardial scar with no or minimal coronary artery disease at coronary angiography.
In general, CMR defined scar represents replacement of contractile myocardium by noncontractile, fibrotic tissue but the etiology of myocardial scar is not specific. Myocardial infarction shows late gadolinium enhancement that is subendocardial or transmural in a coronary territory (typical scar). Atypical scar may instead involve the epicardium or mid myocardial wall and does not correspond to any single coronary territory. Atypical myocardial scars are routinely recognized by CMR and are a novel area of investigation.30 Nonischemic cardiomyopathies, such as hypertrophic cardiomyopathy, sarcoidosis and amyloidosis amongst others, exhibit atypical myocardial scars, but none of the participants in our study population had CMR or clinical characteristics suggestive of these relatively rare conditions. In MESA, atypical and typical myocardial scar had approximately equal prevalence. Participants with atypical scars were more likely to be older, male and obese and were less likely to be of Chinese ethnicity (eTable 2). In addition, unrecognized myocardial scars were more likely to be atypical and recognized myocardial scars were more likely to be typical.
There are several limitations of the current study. The MESA study may not be representative of a general population in the community due to its healthier characteristics. Overall, the MESA cohort had moderate use of anti-hypertensive (29%) and lipid lowering medication (15%) at baseline; medication use increased to 51% and 38%, respectively, at year 10. MESA participants who underwent CMR examination had better renal function than the full cohort. Typical and atypical CMR patterns have been defined by animal studies and patients with clinically overt disease; these scar patterns may represent an over-simplification of scar etiology in asymptomatic individuals and are of unknown clinical significance. CMR is relatively sensitive for detection of myocardial scar, although a minimum scar size of at least 1 gram of tissue is generally accepted as the lower limit of detection. The changes in c-statistics that we observed were also small. The prevalence of myocardial scar is low resulting in small sample sizes and limited power for comparisons by scar subtype.
Conclusions
The prevalence of myocardial scars in a US community based multiethnic cohort was 7.9%, of which 78% were unrecognized by electrocardiography or clinical evaluation. Further studies are needed to understand the clinical consequences of these undetected scars.
Supplementary Material
Acknowledgments
This research was supported by contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute, by grants UL1-TR-000040 and UL1-TR-001079 from NCRR, and by a grant from Bayer Healthcare for the use of gadolinium contrast agent. The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
This publication was developed under a STAR research assistance agreement, No. RD831697 (MESA Air), awarded by the U.S Environmental Protection Agency. It has not been formally reviewed by the EPA. The views expressed in this document are solely those of the authors and the EPA does not endorse any products or commercial services mentioned in this publication.
NIH/ NHLBI approved the design and conduct of the study; there was no other role of the study sponsors for data collection, management, analysis, or interpretation of the data; there was no role of the study sponsors for preparation or review. NHLBI formally reviewed the submitted manuscript; there was no role of the study sponsors for the decision to submit the manuscript for publication.
The authors acknowledge the contributions of Neal Jorgensen, M.S., University of Washington, Department of Biostatistics, for statistical analysis and support.
Drs. Bluemke and McClelland had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. M. Guo and R McClelland conducted and are responsible for the data analysis.
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