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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Prev Cardiol. Author manuscript; available in PMC Sep 2, 2010.
Published in final edited form as:
PMCID: PMC2932469
NIHMSID: NIHMS224288
Factors Associated With Low Levels of Subclinical Vascular Disease in Older Adults: Multi-Ethnic Study of Atherosclerosis
Erin D. Michos, MD, MHS,1 Kenneth M. Rice, PhD,2 Moyses Szklo, MD, DrPH,3 Gregory L. Burke, MD, MS,4 David S. Siscovick, MD, MPH,5 Russell P. Tracy, PhD,6 R. Graham Barr, MD, DrPH,7 Jennifer A. Nettleton, PhD,8 Philip Greenland, MD,9 David R. Jacobs, Jr, PhD,10 and Wendy Post, MD, MS1,3
1Johns Hopkins University, School of Medicine, Division of Cardiology
2University of Washington, Department of Biostatistics
3Johns Hopkins University, Bloomberg School of Public Health, Department of Epidemiology
4Wake Forest University, School of Medicine, Department of Public Health Sciences
5University of Washington, School of Medicine, Cardiovascular Health Research Unit
6University of Vermont, College of Medicine, Department of Pathology - Colchester Research Facility
7Columbia University, Schools of Medicine and Public Health, Departments of Medicine and Epidemiology
8University of Texas School of Public Health at Houston, Division of Epidemiology
9Northwestern University, Feinberg School of Medicine, Department of Preventive Medicine
10University of Minnesota, School of Public Health, Department of Epidemiology, University of Oslo, Department of Nutrition
Address for correspondence: Erin D. Michos, MD, MHS, Division of Cardiology, Johns Hopkins University, Carneige 568, 600 N. Wolfe Street, Baltimore, MD 21287 ; edonnell/at/jhmi.edu
Coronary artery calcium (CAC), carotid intimal medial thickness (cIMT), and reduced ankle brachial indices (ABI) are markers of subclinical vascular disease strongly associated with aging. We identified factors associated with low levels of subclinical vascular disease in 1824 participants ≥70 years in the Multi-Ethnic Study of Atherosclerosis. 452 had low CAC (<25th percentile), 441 had low cIMT (<25th percentile), 1636 had normal ABI (>0.9), and 165 had a combination index indicating favorable values for all three parameters. This combination index was independently associated with younger age [OR=2.5 per 1 SD (95%CI 1.8–3.6)], female gender [OR=3.0(1.9–4.8)], lower BMI [OR=1.6 per 1 SD (1.2–2.0)], absence of hypertension [OR=1.8(1.2–2.6)], absence of dyslipidemia [OR=1.6 (1.04–2.4)], and never smoking [OR=1.7(1.1–2.6)]. No significant associations were observed for C-reactive protein, education, diet, or physical activity. Favorable levels of multiple traditional risk factors, but not several novel risk factors, were associated with subclinical markers of successful cardiovascular aging.
A favorable cardiovascular disease (CVD) risk profile, based on traditional risk factors, is uncommon among adult Americans and is associated with low long-term mortality rates and greater longevity.1 Among middle-aged adults, low levels of major CVD risk factors - particularly lower cholesterol and blood pressure (BP) and non-use of cigarettes - confer marked reduction in the relative risk of both coronary heart disease (CHD) death and all-cause mortality, and appear to increase longevity by up to 9 years.2
Subclinical vascular disease, such as increased carotid intimal medial thickness (cIMT)3 and low ankle-brachial index (ABI)4, predict future CVD events independently of traditional risk factors. In the Cardiovascular Health Study (CHS), low ABI5 and increased cIMT6 in individuals >65 years at baseline were associated with reduced likelihood of successful aging (i.e. remaining free of CVD or other chronic life-threatening diseases) after eight years of follow-up.7
Coronary artery calcification (CAC), a marker of subclinical atherosclerosis, is another well-established predictor of CVD events8 and all-cause mortality9 in asymptomatic adults and provides incremental prognostic information beyond that of traditional risk factors.10 However, the various measures of subclinical vascular disease are only modestly correlated with each other11 which suggests that multiple measures may be needed to characterize overall vascular health.
Few studies other than CHS have evaluated low subclinical vascular disease burden in an exclusively older population. Identifying healthy behaviors that are associated with maintenance of good health in older adults is an important strategy in the prevention of chronic disease. Using the Multi-Ethnic Study of Atherosclerosis (MESA) cohort, we have characterized the risk factor profile of older individuals who have achieved successful cardiovascular aging defined as individuals who have not only survived to ≥70 years free of clinical CVD, but who also had little evidence of subclinical vascular disease by a combination index of three separate parameters (CAC<25th percentile, cIMT<25th percentile, and ABI>0.9) (i.e. individuals who essentially have a vascular age younger than their chronological age).
Participants
The Multi-Ethnic Study of Atherosclerosis (MESA) is a prospective cohort study investigating subclinical CVD in 6814 individuals aged 45–84 years without clinical CVD at baseline recruited between the years 2000 to 2002 from six U.S. communities (Baltimore, MD; Chicago, IL; Forsyth County, NC; Los Angeles County, CA; Manhattan, NY; and St. Paul, MN). Individuals were excluded if they had clinical CVD, including physician-diagnosed myocardial infarction (MI), angina, stroke, transient ischemic attack, heart failure, use of nitroglycerin, current atrial fibrillation, or had undergone a procedure related to CVD.
We limited our analyses to individuals aged ≥70 years at baseline. The present analysis was based on cross-sectional data obtained at the baseline visit. Participants gave informed consent, and the MESA study protocol was approved by the Institutional Review Board at each clinical site. Further details about the study design have been published elsewhere12 and are available online at www.mesa-nhlbi.org.
Risk Factor Assessment
Standardized questionnaires at the baseline examination were used to obtain information about participant demographics, medical history, medication usage, lifestyle characteristics, and family history (FH) of MI. Physical activity was calculated as the total minutes of all moderate and vigorous activity multiplied by their respective metabolic equivalents. Education, as a proxy for socioeconomic status in this older cohort, was categorized as <12th grade vs completed high school or higher.
Blood samples were obtained after a 12-hour fast to measure glucose, total cholesterol, HDL-cholesterol (HDL-c), C-reactive protein (CRP), fibrinogen, homocysteine, and interleukin-6 (IL-6). LDL-cholesterol (LDL-c) was calculated using the Friedewald equation.13 Height, weight, and BP were measured at the baseline examination. Body mass index (BMI) was calculated as weight (kg)/ height (m2). Resting BP was measured three times in the seated position using a Dinamap automated sphygmomanometer, and the average of the 2nd and 3rd readings was used.
Information regarding diet was derived from a food frequency questionnaire,14,15 with modifications to include Chinese culinary practices. We created an a priori `healthy diet score' where servings/day of each fruit, vegetable, non-fried fish, seeds, nuts, and whole grain were summed and servings/day of each high fat dairy, deserts, red meat, high fat processed meats, and sodas were subtracted from this sum.
To minimize the number of variables explored in our multivariable models, many continuous variables were combined with medication usage to form dichotomous variables. Hypertension was defined as systolic BP≥140 mm Hg, and/or diastolic BP≥90 mm Hg, and/or use of anti-hypertensive medications. Diabetes was defined as fasting blood glucose ≥126 mg/dL and/or use of hypoglycemic medications. Dyslipidemia was defined as total cholesterol/HDL-c ratio >5 and/or on cholesterol-lowering medications. Ever-smoking was defined as ≥100 cigarettes in lifetime.
Subclinical Vascular Disease Assessment
Each participant underwent scanning of their coronary arteries by either ECG-gated electron beam (EBCT) or multidetector (MDCT) computed tomography per MESA protocol.16 CAC was quantified using the previously described Agatston method.17 Scans were read centrally at the Los Angeles Biomedical Research Institute at Harbor-UCLA. Each participant was scanned twice, and the average Agatston score was used after phantom attenuation adjustment.
Carotid IMT was measured using high resolution B-mode ultrasound between lumen intima and media-adventitia interfaces of near and far walls of the common carotid artery (1 cm segment proximal to the bifurcation) and the internal carotid artery (including the bifurcation and 1 cm distal to the bifurcation). The maximum cIMTs for each of these two segments were standardized (by subtraction of the MESA population mean and division by its standard deviation), and the mean of the standardized cIMT for the common and internal carotid maxima combined was used in the analyses as a z-max score. The ABI was computed separately for each leg with the numerator being the higher of the posterior tibial or dorsalis pedis systolic pressures and the denominator being the average of the right and left brachial systolic pressures.
For these analyses, low subclinical vascular disease was defined as CAC<25th percentile, cIMT<25th percentile, and normal ABI>0.9. Previous studies have used a maximum cIMT<20% percentile as a marker of healthy aging18; however we chose a priori to use the lowest quartiles for cIMT and CAC in order to include more people to increase power in our multivariable analyses. An ABI>0.9 is a threshold commonly used to suggest the absence of lower extremity arterial disease.7 A combination index defined healthy vascular aging as low subclinical vascular disease by all three parameters (CAC<25th percentile and cIMT<25th percentile and ABI>0.9).
Statistical Methods
In this report, the primary outcome is low subclinical vascular disease burden by the combination index. Baseline characteristics according to vascular status (“healthy” versus not) were assessed via univariate analyses, using t-tests for continuous variables and chi-square tests for categorical variables. Separate multivariable logistic regression analyses were used to determine the cross-sectional associations of risk factors with our primary outcome of the combination index, as well as each separate binary indicator of low subclinical vascular disease. For comparability, computation of all odds ratios (OR) for continuous variables was standardized. The reported coefficients give the estimated increase in odds associated with one standard deviation difference in the respective covariate. Note that OR<1.0 indicates that variable is less likely to be associated with subclinical vascular health.
The authors had full access to the data and take responsibility for its integrity. All authors have read and agree to the manuscript as written.
There were 1824 individuals in MESA who were ≥70 years at the baseline examination. Only 165 participants met the criteria for the low subclinical vascular disease in all three vascular territories by the combination index, while 452 had low CAC (<25th percentile), 441 had low cIMT (<25th percentile), and 1636 had a normal ABI (>0.9) (Table I). The threshold for CAC<25th percentile was specifically <1.6 Agatston units. CAC, ABI, and cIMT were modestly correlated, as previously reported for the whole cohort.11
Table I
Table I
Number of participants categorized by markers of low subclinical disease burden: MESA 2000–2002
The unadjusted baseline characteristics of study participants are outlined in Table II. The 165 participants with low subclinical vascular disease by the combination index were younger, more often female, with lower BMIs, were less likely to have a FH of MI, more often never-smokers, had lower systolic BP, less likely to be on BP or cholesterol medications, had higher HDL-c, lower levels of homocysteine and IL-6, and a trend towards lower CRP levels.
Table II
Table II
Unadjusted baseline characteristics of study participants by absence or presence of combination index, MESA 2000–2002 (mean values ± SD or % distribution)
In multivariable analyses adjusted for all of the risk factors in Table III, we found that younger age [OR 2.5 per 1 SD (95% CI 1.8–3.6)], female gender [OR 3.0 (1.9–4.8)], lower BMI [OR 1.6 per 1 SD (1.2–2.0)], absence of hypertension [OR 1.8 (1.2–2.6)], absence of dyslipidemia [OR 1.6 (1.04–2.4)], and never-smoking [OR 1.7 (1.1–2.6)] were all independently associated with low subclinical vascular disease in all three vascular territories defined by the combination index (p<0.05 for all). Absence of diabetes and FH of MI were not significantly associated with the combination index, although the direction of the multivariable-adjusted ORs were as expected (OR=1.6, p=0.15; OR=1.20, p=0.34, respectively).
TABLE III
TABLE III
Factors Associated with Low Subclinical Vascular Disease Burden: MESA 2000–2002
A few differences in associations between some of the risk factors and the separate outcomes of CAC, cIMT, and ABI were observed. Similarly to the combination index, the factors of younger age, absence of dyslipidemia, and never smoking were also associated with all the separate outcomes of low CAC, low cIMT, and normal ABI (Table III). Some of the factors did not meet the level of statistical significance with certain outcomes, but the direction of the multivariable-adjusted ORs was the same across all measures of low subclinical disease. Examples of this include absence of hypertension which was statistically significantly associated with low CAC and low cIMT, but not normal ABI. Absence of diabetes and lower fibrinogen levels were associated with normal ABI but were not statistically significantly associated with low CAC or low cIMT. Absence of a FH of MI and lower IL-6 levels were statistically significantly associated with low CAC but not cIMT or ABI. Finally, while absence of current alcohol use was inversely associated with a normal ABI, it was not associated with the other measures of vascular health.
On the other hand, some of the factors had qualitatively different associations for certain outcomes (Table III). For example, women and those with non-white race/ethnicity were more likely to have low CAC and cIMT but less likely to have normal ABI. Lower BMI was associated with the combination index and cIMT, but inversely associated with higher ABI.
Other factors such as a healthy diet score, physical activity, education, homocysteine, and CRP were not independently associated with the combination index nor with any of the separate indicators of low subclinical disease in multivariable-adjusted models (Table III). Also in unadjusted analyses (Table II), healthy diet and physical activity were still not associated with the combination index.
Because of concern for overadjustment with the markers of inflammation which may be correlated with each other but still possibly independent negative predictors of vascular health, we also explored supplemental multivariable models where each inflammatory or novel risk factor was evaluated individually, adjusting for clinical covariates but not for the other markers. However there still were no independent associations of fibrinogen, CRP, IL-6, nor homocysteine with the combination index above traditional risk factors. We also performed a supplemental analysis of study participants ≥75 years and found similar results.
We found in this sample of asymptomatic older adults ≥70 years that low subclinical vascular disease by the combination index (lowest quartiles of CAC and cIMT with a normal ABI) was independently associated with traditional risk factors of younger age, female gender, lower BMI, absence of hypertension and dyslipidemia, and never smoking. This confirms prior studies that low levels of multiple traditional risk factors contribute to successful cardiovascular aging. The Framingham Heart Study showed that traditional markers of low-risk status (fewer or no cigarettes smoked, lower BP, favorable lipid profile, absence of a FH of premature CHD, absence of diabetes) were associated with survival beyond 75 years.19
Non-white race was significantly associated with low CAC and cIMT with a non-significant trend for the combination index, whereas non-white race was inversely associated with normal ABI. Other MESA analyses have confirmed these racial/ethnic disparities with African-Americans having less CAC but also lower ABIs that are not explained by differences in coronary risk factors.20,21
We did not find any independent associations of diet, physical activity, or the several novel risk factors that we explored with subclinical vascular health after adjusting for traditional risk factors. However, other studies have shown an association between healthy aging and lifestyle factors such as the Mediterranean diet22, physical activity23, higher education6, and modest wine consumption6. The lack of association of healthy diet and physical activity with low subclinical disease in our study may be because the lifestyle practices of these individuals at age ≥70 years may not reflect their usual diet pattern or physical activity throughout their youth and middle ages. Those with more adverse subclinical profiles may have altered their diet and physical activity in response to knowledge of elevated risk factors.
In addition, studies have found associations between CRP, IL-6, homocysteine, and fibrinogen with CVD events.24,25 However similar to our study, other studies have not found any association of CRP with subclinical vascular disease.26,27 We found that homocysteine and IL-6 were associated with a decreased odds of the healthy combination index in unadjusted analyses, but we did not find any independent association of these markers with vascular health after covariate adjustment. Perhaps factors associated with subclinical disease, especially cross-sectionally, are different from those factors predicting clinical events.
It has been accepted for some time that vascular age is not the same as chronological age (“A man is only as old as his arteries” said Dr. Thomas Sydenham in 1689), and our study reinforces this message. The use of a combination index to identify the absence of subclinical vascular disease in multiple vascular territories is a strength of the present analysis and has precedent from the CHS cohort, which used an index based on ABI, carotid artery stenosis, cIMT, electrocardiographic and echocardiographic abnormalities, and angina;18 however, CAC was not part of their combination index. Because CAC is strongly associated with aging, low CAC scores in older individuals are uncommon, particularly for men,28 but are associated with low CVD event rates.29,30 The close association between CAC and vascular age has been applied to the Framingham Risk Score. It has been proposed that the amount of plaque burden measured by CAC scoring be used to modify the points assigned to chronological age when calculating 10-year CVD risk, so that older individuals with low CAC could potentially be assigned a lower chronological age in risk-prediction models.31 The Framingham Risk Score has been shown to be an imperfect tool in predicting subclinical atherosclerosis risk across multiple arterial beds.32
Although certain factors such as younger age, absence of dyslipidemia, and never smoking were consistently found to be associated with each separate parameter of healthy vasculature in addition to the combination index, there were some differences in the associations between some of the other risk factors and the individual measures of subclinical disease. For example lower BMI was inversely associated with normal ABI, which may reflect residual confounding such as with smoking quantity. Also, the absence of diabetes was not found to have a statistically significant association with low CAC or the combination index, despite its association with cIMT and ABI, although adjusting for CVD risk factors in the multivariable models had slightly attenuated any association with diabetes.
Additionally, a negative FH of MI is strongly associated with the lack of significant CAC even after adjusting for traditional risk factors, and supports the notion that genetic factors play a role as supported by other studies. However, we did not find lack of FH to be associated with low cIMT, although there was a non-significant trend for association of FH with normal ABI and the combination index in the multivariable analysis. This suggests that some factors leading to the development and progression of CAC may be different than those for cIMT. The MESA Family Study is evaluating genetic predictors of subclinical vascular disease, and differences in associations between risk factors and the various measures of subclinical vascular disease will be explored further in future MESA papers using the entire cohort. A recent study from MESA found that while CAC was a stronger predictor for MI and overall CVD events compared to cIMT, cIMT was marginally better at predicting stroke than CAC.33 Thus the predictive ability of subclinical atherosclerosis imaging for CVD events varies by the different vascular beds, and it remains to be seen whether a composite index of multiple vascular beds would be better than single bioimaging tests.
The conclusions about relationships drawn here are probably influenced by several aspects of our study design. All MESA participants were free of clinical CVD; the range of differences between the comparison group and those who aged successfully was only in subclinical measures, not clinical disease. Thus we studied healthy vascular aging in the context of generally fairly healthy aging (i.e. absence of clinical disease). The risk factors studied here might well have distinguished older MESA participants from those excluded because of clinical CVD. It is possible that some risk factors play a more important role for disease development in a younger middle-age cohort but are relatively less important in this cohort surviving to ≥70 years at entry into study free of clinical CVD at baseline. If our comparison group were elderly subjects in the general U.S. population, we would expect to see a much stronger association of the absence of the major CVD risk factors with low subclinical vascular disease.
This study is limited by the fact that these measurements were made cross-sectionally. We do not have information about risk factors in these participants measured earlier in life. Residual confounding, measurement imprecision, and survival bias may also be plausible explanation for some of the results. There may have been real but small effects that we were not able to detect statistically due to limitations of sample size. However, most of our findings were predicted by our a priori hypotheses and the biological bases have been confirmed in other studies.
This study is unique in its evaluation of an asymptomatic multi-ethnic group of older individuals who have not only survived to ≥70 years and are free of clinical CVD, but who also remained free of significant subclinical vascular disease. In summary, by evaluating factors associated with low subclinical vascular disease burden in healthy asymptomatic older adults, our findings are consistent with results from previous longevity cohort studies demonstrating lower levels of multiple traditional risk factors, but perhaps not several novel risk factors, are associated with successful vascular aging. Follow-up of the MESA cohort for future CVD events will allow us to determine which measures of subclinical vascular disease are the best predictors of cardiovascular health in older adults.
Acknowledgements
This research was supported by contracts N01-HC-95159 through N01-HC-95169 (MESA) from the NHLBI, National Institutes of Health. EDM is funded by the PJ Schafer Memorial Fund for Cardiovascular Research at Johns Hopkins University. WP is supported by the Paul Beeson Physician Faculty Scholars in Aging Program and the Johns Hopkins Donald W. Reynolds Cardiovascular Research Center. The authors thank the other investigators, the staff, and the participants of MESA for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
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