The measurement of carotid intima-media thickness (CIMT) is a valid method to quantify levels of atherosclerosis. The present study was conducted to compare the strengths of associations between CIMT and cardiovascular risk factors in two different populations.
The Multi-Ethnic Study of Atherosclerosis (MESA) and the Heinz Nixdorf Recall Study (HNR) are two population-based prospective cohort studies of subclinical cardiovascular disease. All Caucasian subjects aged 45 to 75 years from these cohorts who were free of baseline cardiovascular disease ( n = 2,820 in HNR, n = 2,270 in MESA) were combined. CIMT images were obtained using B-mode sonography at the right and left common carotid artery and measured 1 cm starting from the bulb.
In both studies, age, male sex, and systolic blood pressure showed the strongest association ( P < .0001 for each) for a higher CIMT. The mean of mean far wall CIMT was slightly higher in MESA participants (0.71 vs 0.67 mm). Almost all significant variables were consistent between the two cohorts in both magnitude of association with CIMT and statistical significance, including age, sex, smoking, diabetes, cholesterol levels, and blood pressure. For example, the association with systolic blood pressure was (ΔSD = 0.011; 95% confidence interval, 0.0009 to 0.014) per mm Hg in MESA and (ΔSD = 0.010; 95% confidence interval, 0.005 to 0.021) per mm Hg in HNR. This consistency persisted throughout the traditional (Framingham) risk factors.
A comparison of the associations between traditional cardiovascular risk factors and CIMT across two culturally diverse populations showed remarkable consistency.
There has been some suggestion of heterogeneity between possible variables of higher carotid intima-media thickness (CIMT) across different populations. It is important to determine if this heterogeneity occurs among the traditional risk factors for cardiovascular disease or if it is isolated to nontraditional factors. After all, large cohort studies are extremely expensive and may take many years to yield risk scores (such as the Framingham risk score, replication of which would take ≥10 years of follow-up data). Therefore, it is important to know if the risk factors identified in one population can be meaningfully generalized to a different population. For an end point such as common CIMT, it is helpful to know if the drivers of increased CIMT are similar, so that guidelines found in one population can be meaningfully applied to other populations.
One example of a nontraditional risk factor with possible differences between European and North American populations is alcohol consumption. Epidemiologic data have long suggested a J-shaped or U-shaped association between alcohol consumption and the level of subclinical atherosclerosis. However, some recent studies did not show this relation for CIMT, so that the overall data are still inconsistent, leading to clinical controversy. Additionally, the hypothesized protective value of moderate alcohol consumption in contrast to higher risk for cardiovascular morbidity and mortality in cases of both abstention and heavier consumption of alcohol must be regarded with caution because of systematic errors and bias in observational studies. However, because alcohol is used differently in various countries, a comparison among different populations may help resolve these apparent inconsistencies in the current data.
The quantification of CIMT on the basis of B-mode ultrasonography is regarded as a reliable marker of subclinical atherosclerosis, and it has been shown to be independently associated with cardiovascular risk factor burden as well as the severity and outcome of cardiovascular diseases. CIMT has been used to investigate the association between alcohol consumption and subclinical atherosclerosis. Although alcohol might be the most controversial possible variable for which to investigate an association with increased CIMT, there is also a clear need to understand how other variables of CIMT, in general, may vary among culturally diverse but genetically similar populations.
The Multi-Ethnic Study of Atherosclerosis (MESA) and the Heinz Nixdorf Recall (Risk Factors, Evaluation of Coronary Calcium and Lifestyle Factors) Study (HNR) are two population-based studies, both of which have measured CIMT in the quantification of subclinical atherosclerosis. Different cross-sectional data for each of these studies have been published elsewhere. It was our aim to investigate the association between traditional cardiovascular risk factors in healthy populations in the United States and Germany. As a secondary objective, we wanted to examine alcohol consumption and CIMT, because this is a more complex risk factor that might be more nonlinear than most risk factors. We had initially hypothesized that the United States might have more “binge drinking” and thus potentially show greater cardiovascular risk for the same level of alcohol consumption.
The MESA study recruited 6,814 participants between 2000 and 2002 across six centers in the United States, with participants recruited using locally available resources, including lists of residents, dwellings, telephone exchanges, division of motor vehicle lists, consumer lists, voter registration lists, and census data. Each site recruited an approximately equal number of men and women, according to prespecified age and racial and ethnic proportions. Participants were between 45 and 84 years of age and identified themselves as one of Caucasian, African American, Hispanic, or Chinese. For this comparison of the two study cohorts, only the 2,270 Caucasians aged 45 to 75 years with complete information on risk factors, blood pressure, and lipid-lowering medications and intima-media thickness were included.
The HNR recruited a total of 4,814 Caucasians aged 45 to 75 years from three neighboring cities in Germany between 2000 and 2003 at a single center, with a response rate of 55.8%. Participants were a random sample derived from mandatory citizen registries and provided to the study center. For this comparison of the two study cohorts, we included only members of HNR who were free of clinical cardiovascular disease at baseline, for a total of 2,820 eligible participants. The study was certified and recertified in 2006.
Therefore, between the two cohorts, we included a total of 5,090 male and female participants from both studies in whom ultrasound examinations of the carotid arteries were performed. For both studies, approval was provided by the local institutional review boards, and all study participants gave consent at the time of study enrollment for both MESA and HNR.
The traditional cardiovascular risk factors that are part of the Framingham risk scoring algorithm were measured in both studies (see below). In addition, body mass index was computed on the basis of direct measurements of height and weight. All medication information was based on participants’ self-reports. Standard enzymatic methods were used to measure total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides. Low-density lipoprotein cholesterol was calculated using the Friedewald equation in MESA and measured directly in HNR. Blood samples were obtained after a 12-hour fast in MESA. In HNR, participants fasted for 9.7 ± 4.9 hours (median, 12 hours) before blood sampling, with 34.4% having fasted for 6 hours. In both studies, blood pressure was measured using an oscillographic method with two different systems (Dinamap, GE Healthcare, Milwaukee, WI; HEM-705CP, Omron, Hoofddrop, The Netherlands). The mean values of the second and third of three measurements taken ≥2 min apart were used. Hypertension was defined in both studies as blood pressure >140/90 mm Hg or use of antihypertensive medication. Participants were considered to have diabetes if they were taking antidiabetic medications or had fasting glucose levels >126 mg/dL. Smoking history was categorized as (1) currently smoking; (2) former, defined as not smoking within the past 30 days in MESA and as stopped smoking within the past year or >1 year ago in HNR; and (3) never. The use of lipid-lowering medications was documented. This included statins, fibrates, bile acid sequestrants, and nicotinic acid derivatives.
In the present study, hypercholesterolemia was defined as total cholesterol ≥240 mg/dL. High blood pressure was defined as systolic blood pressure ≥120 mm Hg, diastolic blood pressure ≥80 mm Hg, or active treatment with blood pressure–lowering medication plus self-report of hypertension. The dichotomous version of smoking was defined as ever smoking.
The ankle-brachial index (ABI) was measured in supine participants with systolic blood pressures measured in both arms and legs with appropriately sized cuffs. For both legs (when possible), the systolic blood pressure was measured in each posterior tibial and dorsalis pedis artery. All pressures were detected using a continuous-wave Doppler ultrasound probe. The ABI was calculated as the higher systolic blood pressure in the posterior tibial or dorsalis pedis artery divided by the higher of the arm systolic blood pressures values.
Measurement of CIMT
Both MESA and HNR used comparable methods for the measurement of CIMT, and we used standardization of measures to improve comparability further. CIMT images were obtained using B-mode sonography at the right and left common carotid artery and measured 1 cm starting from the bulb.
For MESA, technicians, trained at Tufts Medical Center, at each MESA study site performed B-mode ultrasonography of the near and far walls of the common carotid artery for both the right and left arteries. A single ultrasound reading center (Department of Radiology, Tufts Medical Center) measured the mean CIMT of the common carotid sites as the mean of the mean CIMT of the near and far walls of the right and left sides. This differs from other MESA CIMT measures in that MESA has often presented the mean of the maximal CIMT. In addition, because HNR measured only the common carotid artery (because of the higher reproducibility of measures made at this site), we considered only the common CIMT for the purposes of comparison between MESA and HNR. Hence, in the present study, we calculated CIMT data for HNR and MESA participants that were measured at the far wall of the common carotid artery.
In the HNR cohort, ultrasound images were obtained using an ultrasound machine (Vivid FiVe; GE Vingmed Ultrasound AS, Horten, Norway) using a linear-array 10-MHz scan head. All images were saved to magneto-optical disks and transferred to an offline workstation (EchoPAC, GE Vingmed Ultrasound AS). Measurement techniques and the protocol of data collection have been described previously. CIMT images were read and measured at one center. Briefly, 10 manual measurements per left and right common carotid artery were conducted for every participant at plaque-free areas directly proximal to the opening into the carotid bulb. Measurements were performed on the far wall of the artery, and mean CIMT was calculated from all measurements on both sides. Plaque formations were defined as CIMT >50% of the adjacent CIMT and excluded from all measurements. All HNR sonographers underwent certification by investigators of the German Study of Health in Pomerania and were regularly monitored by external auditors.
In MESA, participants completed a 120-item food-frequency questionnaire that included information on their drinking habits for the previous year. For alcoholic beverages, nine options were given: “rare or never,” “1–3 per month,” “1 per week,” “2–4 per week,” “5–6 per week,” “1 per day,” “2–3 per day,” “4–5 per day,” and “6 per day.” The percentages of alcohol in wine, beer, and liquor were assumed to be 9.3%, 3.6%, and 14.2%, respectively, as being representative of typical American beverages of these types. Age, sex, and beverage-specific portion sizes were used to convert small, medium, and large servings to grams. These assumptions were used to estimate grams per day for each of beer, wine, and liquor which could then be converted to drinks per day. The food-frequency questionnaire used in MESA is based on a questionnaire that was originally designed for the Insulin Resistance and Atherosclerosis Study.
In HNR, computer-assisted face-to-face interviews were conducted at the study center by trained and certified medical personnel, and yearly questionnaires have been sent to the participants to collect information about medical and socioeconomic factors. Regarding alcohol consumption, the participants were asked how often they ingested the following alcoholic drinks: beer, wine and sparkling wine, and liqueur or hard liqueur. The participants were asked to choose among “hardly ever,” “1–3 times per month,” “1–3 times per week,” “4–6 times per week,” and “daily.” The second question was an addition to the first question and concerned the quantification of alcohol consumption (beer: >2 L, 1–2 L, 0.5–1 L, 0.25–0.5 L, <0.25 L, or hardly ever; wine and sparkling wine: >0.7 L, 0.4–0.7 L, 0.2–0.4 L, 0.1–0.2 L, <0.1 L, or hardly ever; hard liqueur and liqueur: ≥10 glasses, five to nine glasses, three or four glasses, one or two glasses, or hardly ever).
Alcohol use was treated differently than many of the other study variables for two reasons. First, unlike most of the risk factors, there was a solid hypothesis that alcohol consumption might have important nonlinear relations with the outcome (CIMT), this being the classic J or U shape. Second, unlike most risk factors there was not a clear understanding of how alcohol would relate to CIMT on the basis of previous research in the two cohorts. As a result, the analysis protocol for alcohol was much more complex than for the other candidate risk factors.
Because of concerns about the comparability of CIMT measurements done by different ultrasonographers and the lack of any cross-validation between cohorts, we standardized the CIMT measures using a Z -score approach. This composite Z score was created for overall mean CIMT by standardization (subtraction of the mean and division by the standard deviation of each measure). The resulting variable, hereafter referred to as Z -score CIMT, had a mean of 0 and a standard deviation of 1 by design. This standardization was done independently for each cohort. Using the Z -score approach allowed us to compare the strengths of the associations with alcohol in each cohort but did not allow us to directly compare the level of subclinical disease between the cohorts.
We used multivariate linear regression to model the association between Z -score CIMT and the traditional risk factors for cardiovascular disease. We modeled HNR and MESA separately so that we could compare the relative strength of these variables. We then included alcohol use as a covariate and used a lightly adjusted model (age, sex, smoking status, and pack-years) consisting of key confounders to test for associations between the different levels of alcohol use and CIMT. A second set of models used only the MESA cohort, for which we have the ability to isolate former drinkers.
All analyses were conducted using either SAS version 9.2 (SAS Institute Inc., Cary, NC) or Stata version 11 (StataCorp LP, College Station, TX).
The final study population of the present study consisted of 2,270 subjects from MESA and 2,820 from HNR. Baseline demographics and pooled descriptive statistics including measurement data of CIMT are presented in Table 1 . Both studies included a similar number of men ( Table 1 ). Although the mean age was slightly higher in MESA (60.2 ± 8.7 years in MESA vs 58.8 ± 7.6 years in HNR), most of cardiovascular risk factors were worse on average in HNR, except for body mass index and HDL cholesterol ( Table 1 ). Regarding medical treatment, intake of cholesterol medications was more than twofold higher in MESA (17.5% in MESA vs 8.2% in HNR; Table 1 ). The 10-year Framingham risk score was slightly lower in the MESA cohort (10%) than in the HNR cohort (11%). The mean of the mean far-wall CIMT was slightly higher in MESA participants (0.71 vs 0.67 mm), although this difference could be partially or wholly due to ultrasonographer effects. In Table 2 , CIMT is stratified by 10-year hard coronary heart disease Framingham risk score. We note that risk score category was strongly associated with mean CIMT, which is expected given that this risk score is a function of the traditional cardiovascular risk factors.
|Variable||MESA ( n = 2,270)||HNR ( n = 2,820)|
|Common CIMT (mm) ∗||0.709 ± 0.192||0.668 ± 0.128|
|Age (y)||60.2 ± 8.7||58.8 ± 7.6|
|Body mass index (kg/m 2 )||27.9 ± 5.2||27.5 ± 4.4|
|Systolic blood pressure (mm Hg)||121.8 ± 19.6||132.0 ± 20.8|
|Diastolic blood pressure (mm Hg)||70.6 ± 10.0||81.3 ± 10.8|
|Blood pressure medication||30.5%||27.4%|
|Pack-years||15.2 ± 28.2||14.9 ± 24.0|
|Total cholesterol (mg/dL)||196.2 ± 35.2||230.8 ± 37.8|
|HDL cholesterol (mg/dL)||52.1 ± 15.7||59.2 ± 17.4|
|Low-density lipoprotein cholesterol (mg/dL)||117.6 ± 30.0||147.4 ± 36.0|
|Triglycerides (mg/dL)||134.3 ± 93.5||143.7 ± 100.2|
|Glucose (mg/dL)||91.1 ± 21.9||109.0 ± 24.3|
|Alcoholic drinks/week||8.1 ± 16.7||5.3 ± 9.2|
|ABI||1.13 ± 0.114||1.14 ± 0.14|
|Framingham risk score||0.10 ± 0.08||0.11 ± 0.08|
|Framingham risk low (<10% over 10 y)||n = 1,447||n = 1,541|
|Common CIMT (mm) ∗||0.670 ± 0.161||0.627 ± 0.106|
|Age (y)||58 ± 9||56 ± 7|
|ABI||1.13 ± 0.10||1.14 ± 0.13|
|Framingham risk intermediate (≥10% and <20% over 10 y)||n = 574||n = 891|
|Common CIMT (mm) ∗||0.777 ± 0.216||0.668 ± 0.471|
|Age (y)||63 ± 7||61 ± 7|
|ABI||1.13 ± 0.13||1.15 ± 0.14|
|Framingham risk high (≥20% over 10 y)||n = 194||n = 355|
|Common CIMT (mm) ∗||0.833 ± 0.226||0.767 ± 0.142|
|Age (y)||68 ± 5||65 ± 6|
|ABI||1.12 ± 0.134||1.11 ± 0.20|
Regarding Table 3 , an adjusted study-stratified model for common CIMT via Z -score CIMT values for MESA and HNR was used. We observed that the variables associated with higher CIMT were similar in both cohorts, including age, sex, body mass index, smoking, blood pressure, and cholesterol. In both studies, age, male sex, and systolic blood pressure were the strongest variables ( P < .0001 for each). HDL cholesterol and triglycerides (only in HNR) had significant inverse associations with CIMT (HDL: ΔSD = −0.005 [95% confidence interval [CI], −0.0076 to −0.0015] in MESA and ΔSD = −0.0056 [95% CI, −0.008 to −0.003] in HNR; triglycerides: ΔSD = −0.001 [95% CI, −0.006 to 0.004] in MESA and ΔSD = −0.007 [95% CI, −0.010 to −0.002] in HNR) ( Table 3 ). A key differences between the variables for higher CIMT is that the presence of treated diabetes was associated with more CIMT in German (SD = 0.43; 95% CI, 0.23 to 0.62) but not in American (SD = 0.10; 95% CI, −0.11 to 0.32) participants. This difference between cohorts disappeared when diabetes was parameterized as either treatment for diabetes or glucose > 126 mg/dL ( Table 3 ). There was small heterogeneity between the strength of the association between smoking and outcomes between the two cohorts (test for interaction significant at the level P = .0400).
|ΔSD||95% confidence interval||P||ΔSD||95% confidence interval||P|
|Age (per year)||0.035||0.030 to 0.039||<.0001||0.038||0.029 to 0.039||<.0001|
|Age 2||0.0006||0.0004 to 0.011||.0011||−0.0004||−0.0001 to 0.002||.227|
|Male gender||0.20||0.20 to 0.38||<.0001||0.32||0.24 to 0.40||<.0001|
|Body mass index (kg/m 2 )||0.012||0.006 to 0.019||.0038||0.013||0.005 to 0.021||<.0001|
|Systolic blood pressure (mm Hg)||0.011||0.009 to 0.014||<.0001||0.010||0.005 to 0.021||.0020|
|Diastolic blood pressure (mm Hg)||−0.010||−0.016 to −0.004||.0004||−0.009||−0.014 to −0.004||.0005|
|Use of antihypertensive drugs||0.053||−0.033 to 0.140||.2254||0.072||−0.007 to 0.152||.0749|
|Ever smoker||0.027||−0.060 to 0.113||.5464||0.059||−0.023 to 0.142||.1609|
|Pack-years of smoking||0.002||0.000 to 0.004||.0174||0.003||0.001 to 0.005||.0008|
|Total cholesterol (mg/dL)||0.002||0.001 to 0.003||.0025||0.002||0.001 to 0.003||<.0001|
|HDL cholesterol (mg/dL)||−0.004||−0.008 to −0.001||.0043||−0.006||−0.0081 to −0.0032||<.0001|
|Use of lipid-lowering drugs||0.053||−0.122 to 0.154||.3099||0.012||−0.122 to 0.125||.9843|
|Triglycerides (10 mg/dL)||−0.0001||−0.001 to − 0.0004||.6556||−0.0006||−0.001 to − 0.0003||.0012|
|Diabetes (treatment or glucose > 126 mg/dL)||0.133||−0.03 to 0.38||.1178||0.08||−0.03 to 0.19||.1653|