Abstract
Background
In older adults without clinical cardiovascular disease, coronary artery calcium (CAC) is associated with other subclinical vascular diseases, which, in turn, predict physical dysfunction. However, the association between CAC and physical function is unstudied.
Methods
In 387 older community-dwellers from the Cardiovascular Health Study without clinical cardiovascular diseases (mean age ± standard deviation = 78.7 ± 3.7, 35% men, 22% African Americans), CAC was measured using electron beam tomography, and physical performance was assessed by usual pace gait speed, chair stand, and tandem stand. Differences in physical performance across CAC quartiles were investigated in the whole cohort and by gender. Associations with gait speed (m/s) were assessed in multivariable models using both the continuous form of CAC score (log(CAC)) and quartiles of CAC, adjusting for demographics and comorbidities.
Results
No differences in physical performance were observed across CAC quartiles in the whole group. In gender-stratified analyses, a significant association was shown among women, who had progressively lower gait speed across CAC quartiles: Those with CAC > 220 walked more than 0.1 m/s slower than those with CAC < 35 (age-adjusted ptrend =.017). After multivariable adjustment, the association remained statistically significant for women in both linear (log(CAC) and gait speed, p =.025) and logistic models: Each of the top three CAC quartiles (35–220, 221–659, and ≥660) had a more than twofold odds of walking slower than 1 m/s, compared to the lowest CAC quartile (< 35; p =.021).
Conclusions
In this sample of older community-dwellers without overt cardiovascular disease, CAC was inversely related to gait speed in women, but not in men.
Keywords: Coronary artery calcium, Physical function, Gait, Aging, Elderly
In the Cardiovascular Health Study (CHS) a lower extent of subclinical cardiovascular disease (CVD), measured with different noninvasive methods—such as ankle–arm blood pressure, carotid ultrasound, and electrocardiogram (ECG)—predicted the likelihood of maintaining intact health, physical function, and cognition (1). However, the relationships between CVD and physical function in healthy older adults, and in particular how specific disease characteristics affect different aspects of mobility, have been poorly investigated.
Coronary artery calcium (CAC) disclosed by electron beam tomography (EBT), is a noninvasive measure of calcified atherosclerotic plaque, which correlates with underlying atherosclerosis (2). CAC seems more sensitive in capturing low levels of subclinical vascular disease than other noninvasive measures of atherosclerosis, such as ankle–arm index (AAI) or carotid intima-media thickness (3), which are associated with a decline of physical performance in the elderly population (4,5). An increased CAC score is also associated with a higher prevalence of subclinical cerebrovascular disease (6), which contributes to physical dysfunction (7). In a cross-sectional investigation, the level of self-reported physical activity was inversely related to the degree of CAC in asymptomatic middle-aged persons with a high cardiovascular risk profile (8). Interestingly, a recent study demonstrated that, only in older women under estrogen therapy, CAC was inversely related to bone mineral density (9), which in turn is associated with reduced physical performance in postmenopausal women (10). Yet, the association between CAC and physical performance in the elderly population is unstudied.
In a subgroup of older participants in the CHS, the extent of subclinical coronary artery disease (CAD) varied widely (3,11). More than 12% of participants without a history of CAD had a CAC score > 1000, and 5% had CAC > 2000. These levels are 3–10 times higher than the CAC scores associated with an increased likelihood of silent myocardial ischemia disclosed with invasive techniques or stress tests (12,13).
We questioned whether, among older CHS participants with no history of CVD, the extent of CAC was associated with measured physical performance. If this were the case, we hypothesized that heart or brain disease, lower extremity arterial disease, or estrogen therapy could be involved in this relationship.
Methods
Population
The CHS design and methods have been detailed elsewhere (14,15). Between 1998 and 2000, 614 of 727 older community-dwellers participants from the Pittsburgh cohort underwent EBT to detect CAC (12,16). The Institutional Review Board of the University of Pittsburgh approved the protocol; participants gave written informed consent. Of the 113 nonparticipants, 69 (61%) were either too ill or could not travel, 18 (16%) had died after the last visit, and 26 (23%) refused. Physical function had been assessed in 1998–1999 (see “Physical Performance Assessment” section below) (17). Of the 614 participants with EBT, 410 were free of prevalent CVD at the time of the scan. CVD was defined as myocardial infarction, angina, congestive heart failure, stroke or transient ischemic attack, coronary artery bypass surgery or percutaneous transluminal angioplasty, carotid surgery, peripheral vascular bypass surgery, angioplasty, or physician-diagnosed intermittent claudication (18).
The present analysis included 387 participants of the 410 with EBT and without prevalent CVD, who performed at least the 15-foot walk (376 during the annual clinic visit and 11 in a home visit). Comparing these 387 (with EBT) and the CHS participants from all the study sites who were free of CVD and had the 1998–1999 visit but not the EBT (N = 1467), gender distribution was similar. Men who underwent EBT were slightly younger than men who did not undergo EBT (mean age ± standard deviation (SD) 79.6 ± 4.3 vs 78.6 ± 3.9, p =.013). A higher proportion of African Americans was found in the EBT group than in the group without EBT (22% vs 14%), and African Americans were slightly younger than their Caucasian counterparts in each group. Weight and chronic disease prevalence were similar between those with and without EBT in both genders. However, among the EBT group, a higher proportion of women reported having smoked (55% vs 33%), whereas no difference was found for men. Indicators of physical function were not entirely consistent across tests: Women who underwent EBT had similar gait speeds and could stand longer in tandem compared to those without EBT, but took longer to complete five chair stands. Men with EBT walked faster and could stand longer in tandem, but had similar chair stand times. The prevalence of those who had a gait speed ≥1 m/s and of those who were able to complete five chair stands did not differ between the two groups. The prevalence of those who could hold the tandem stand test for 10 seconds was slightly higher in men with EBT than in men without EBT, but was similar between women (data not shown).
CAC Measurement
CAC was assessed using an Imatron C-150 scanner (Imatron, Inc., Pittsburgh, PA) and the Agatston scoring method (19) as previously described (20). For the present analyses, CAC score was examined in quartiles (CAC score 0–34, 35–220, 221–659, or ≥660 for increasing quartiles) and as log(CAC score). We also tested sex-specific quartiles.
Physical Performance Assessment
Gait speed was calculated from the number of seconds it took a participant to walk a 15-foot course at usual pace starting from a standstill. Chair stand results were recorded as the number of seconds taken to perform five consecutive stands from a seated position on a 45-cm-tall chair with arms folded across the chest. The standing balance test measured the time a participant could stand in a tandem position (heel-to-toe) up to a maximum of 10 seconds.
Covariates
Demographics and comorbidities previously found to be associated with either CAC (12) or physical function (21) in large community-dwelling cohorts were considered as possible confounders. These included body mass index (BMI; weight in kilograms/height in meters squared), hypertension (systolic blood pressure ≥140/90 or self-reported hypertension on treatment), diabetes (fasting glucose >126 mg/dL [7.0 mmol/L] or self-reported diabetes on treatment), cigarette smoking (ever [past and current use] vs never, as there were few current smokers), depressive symptoms (10-item Centers for Epidemiologic Studies Depression [CES-D] scale score), chronic obstructive pulmonary disease (COPD; self-reported emphysema, asthma, or bronchitis), arthritis, osteoporosis, and hip and femur fractures in the previous year (self-report). Lower extremity arterial disease was defined as AAI < 0.9. A brain magnetic resonance imaging (MRI) scan, performed in 1998, was available for 285 participants. The MRI protocol has been described elsewhere (7). The severity of the white matter hyperintensities and the extent of the ventricular enlargement were graded on a visual scale (0–9, lower to higher severity). Based on previous studies (7,22), MRI variables were recoded as follows: white matter grade ≥3 versus <3, ventricular grade ≥4 versus <4, and brain infarcts ≥1 versus 0. Left ventricular mass (LVM) was estimated by 12-lead resting ECG (23). Data about estrogen replacement therapy were available for all 252 women.
Statistical Analysis
Statistical differences between participants with EBT and those without were assessed using chi-square tests (dichotomous variables) and Student t tests (continuous variables). To assess age-adjusted differences in sample characteristics across CAC score quartiles by gender, we used general linear models for continuous-form measures and logistic regression models for dichotomous-form measures. The outcome variables gait speed, chair stands, and tandem balance tests were used in continuous form and dichotomized at 1 m/s, the ability to complete five chair stands and the ability to hold balance for 10 seconds, respectively. Mantel–Haenszel chi-square tests of trend (dichotomous variables) and analysis of variance (ANOVA; continuous variables) were used to assess differences in physical performance measures across CAC score quartiles. The age-adjusted association between CAC quartiles and physical function measures was tested using general linear models (continuous-form measures) and logistic regression (dichotomous-form measures). These analyses were performed in the whole sample and in gender strata, because an interaction between gender and gait speed proved significantly associated with CAC (p =.005). Then, in a linear regression multivariable model with gait speed as an outcome, age and race were forced to enter, whereas log(CAC score) and other covariates were allowed to enter using a forward stepwise procedure (significance level of p <.05). In a confirmatory logistic regression model, gait speed ≥1 versus <1 m/s was the outcome, and CAC quartiles were an explanatory variable (with CAC 0–34 as the reference category). This model was adjusted for the same covariates used in the linear regression.
Brain MRI variables, LVM, and AAI were individually included in further multivariable models (both linear and logistic regression) to test their impact on the strength of the association between CAC and gait speed. Because of more technological and expensive assessments (in particular brain MRI), some of these last models included a reduced sample size. Therefore, in sensitivity analyses, we also re-examined all the quoted models including only the participants with complete data for these assessments. Statistical significance was defined as p <.05. All analyses were conducted with SPSS 14.0 (Chicago, IL).
Results
Physical function was measured in 387 adults (65.1% women, 21.7% African American) without a history of CVD at the time of the EBT scan. Mean age was 78.7 ± 3.8 (SD) years (range 71–96), and did not differ between genders. CAC scores ranged from 0 to 4151. Compared with women, more men had higher CAC scores (Figure 1): Median CAC was 167 among women and 381 among men (p <.001). The prevalence of African Americans decreased with increasing CAC quartiles (Table 1). In women only, higher CAC was associated with older age and depressive symptoms, whereas prevalence of smoking history, prevalence of arthritis, and a higher white matter grade were associated with CAC only in men (Table 1). In both genders, those with higher CAC were more likely to have brain infarcts on MRI (Table 1).
Figure 1.
Participants per quartiles of coronary artery calcium (CAC) score, by gender
Table 1.
Characteristics of the Sample, by CAC Quartiles and Gender.
| Baseline Variables | CAC 0–34 N = 97 | CAC 35–220 N = 97 | CAC 221–659 N = 96 | CAC ≥660 N = 97 | Age-Adjusted p Value |
|---|---|---|---|---|---|
| Demographics | |||||
| Men | 23 (23.7) | 26 (26.8) | 38 (39.6) | 48 (49.5) | <.001 |
| Age | |||||
| Women | 77.2 ± 0.4 | 79.2 ± 0.4 | 79.1 ± 0.5 | 80.4 ± 0.5 | <.001 |
| Men | 77.4 ± 1.0 | 77.8 ± 0.5 | 78.9 ± 0.7 | 79.3 ± 0.5 | .138 |
| African Americans | |||||
| Women | 27 (36.5) | 13 (18.3) | 9 (15.5) | 4 (8.2) | .004 |
| Men | 12 (52.2) | 6 (23.1) | 7 (18.4) | 6 (12.5) | .006 |
| Health habits | |||||
| Body mass index, kg/m2 | |||||
| Women | 27.8 ± 0.5 | 25.9 ± 0.5 | 25.5 ± 0.7 | 25.3 ± 0.6 | .154 |
| Men | 27.0 ± 0.9 | 25.5 ± 0.8 | 25.6 ± 0.6 | 25.6 ± 0.4 | .361 |
| Ever smoked | |||||
| Women | 40 (54.1) | 34 (48.6) | 28 (49.1) | 31 (64.6) | .279 |
| Men | 13 (56.5) | 14 (53.8) | 26 (68.4) | 38 (79.2) | .022 |
| Chronic health conditions | |||||
| Hypertension | |||||
| Women | 31 (41.9) | 38 (53.5) | 26 (44.8) | 24 (49.0) | .480 |
| Men | 10 (45.5) | 9 (34.6) | 15 (39.5) | 19 (39.6) | .989 |
| Diabetes | |||||
| Women | 7 (9.5) | 6 (8.5) | 6 (10.3) | 5 (10.2) | .599 |
| Men | 4 (17.4) | 4 (15.4) | 3 (7.9) | 4 (8.3) | .237 |
| Depression (CES-D score) | |||||
| Women | 4.1 ± 0.5 | 5.7 ± 0.5 | 5.8 ± 0.6 | 6.1 ± 0.7 | .027 |
| Men | 4.8 ± 1.0 | 3.4 ± 0.5 | 4.5 ± 0.6 | 3.9 ± 0.6 | .524 |
| Arthritis | |||||
| Women | 39 (54.2) | 39 (57.4) | 32 (56.1) | 24 (53.3) | .950 |
| Men | 3 (13.0) | 8 (33.3) | 14 (37.8) | 19 (40.4) | .033 |
| COPD | |||||
| Women | 7 (9.9) | 4 (5.9) | 4 (7.0) | 3 (6.4) | .816 |
| Men | 2 (9.1) | 1 (4.0) | 1 (2.6) | 6 (12.5) | .315 |
| White matter grade ≥ 3 | |||||
| Women | 19 (33.9) | 15 (27.8) | 13 (32.5) | 18 (50.0) | .281 |
| Men | 2 (11.1) | 3 (16.7) | 9 (33.3) | 14 (38.9) | .023 |
| Ventricular grade ≥ 4 | |||||
| Women | 22 (39.3) | 21 (38.9) | 18 (45.0) | 17 (47.2) | .909 |
| Men | 7 (38.9) | 13 (72.2) | 18 (66.7) | 21 (58.3) | .989 |
| Brain infarcts, ≥1 | |||||
| Women | 8 (14.3) | 12 (22.2) | 10 (25.0) | 13 (36.1) | .039 |
| Men | 2 (11.1) | 2 (11.1) | 9 (33.3) | 11 (30.6) | .050 |
| Left ventricular mass, g | |||||
| Women | 142.4 ± 2.6 | 136.5 ± 2.8 | 137.9 ± 3.0 | 137.2 ± 5.0 | .476 |
| Men | 178.5 ± 5.9 | 160.6 ± 4.6 | 171.4 ± 3.9 | 174.1 ± 3.9 | .831 |
Considering the whole sample, none of the three performance-based measures of physical function (gait speed, chair stand, and standing balance) varied significantly across quartiles of CAC. The interaction term between gender and gait speed proved significantly associated with CAC (p =.005). Gender-stratified analyses showed a significant trend toward lower values of gait speed and time holding tandem stand with higher CAC among women, but not among men (Table 2). After adjustment for age, the association between tandem stand and CAC did not hold true. In women, age-adjusted gait speed declined progressively from 0.95 m/s among those in the first quartile, to 0.79 m/s in the fourth quartile, and the proportion of those who were able to walk with a speed ≥1.0 m/s decreased significantly across quartiles of CAC. The log(CAC score) remained significantly associated with reduced gait speed after multivariable adjustment (Table 3). These results were confirmed by the logistic regression model: Compared to women with a CAC score between 0 and 34 units, those in the three higher CAC score quartiles had a two- to threefold increased odds of walking slower than 1 m/s (multivariable-adjusted odds ratios [ORs]: OR 2.66, 95% confidence interval [CI], 1.20–5.89, p =.016 for the second quartile; OR 3.26, 95% CI, 1.36–7.76, p =.008 for the third quartile; OR 2.44, 95% CI, 1.06–6.28, p =.048 for the fourth quartile, compared to the first one; p value for differences across the four groups =.021). As expected, in men the association between CAC and gait speed remained nonsignificant in multivariable models of both linear regression (with gait speed as an outcome, unstandardized β = 0.006 (standard error [SE] 0.012), p =.651) and logistic regression.
Table 2.
Physical Performance in Each Quartile of CAC, for Participants Without Clinical
| CAC Score in Quartiles | CAC 0–34 | CAC 35–220 | CAC 221–659 | CAC > 660 | p Value for Trend | Age-Adjusted p Value |
|---|---|---|---|---|---|---|
| Women | N = 74 | N = 71 | N = 58 | N = 49 | ||
| Gait (N = 252) | ||||||
| Gait speed, 15-foot walk, m/s | 0.95 ± 0.03 | 0.87 ± 0.03 | 0.82 ± 0.03 | 0.79 ± 0.04 | .001 | .017 |
| Gait speed ≥ 1.0 m/s, from 15-footwalk | 33 (44.6) | 15 (21.1) | 11 (19.0) | 9 (18.4) | .001 | .012 |
| Chair stands (N = 225) | ||||||
| Mean time in completers, s | 16.92 ± 0.69 | 16.9 ± 0.74 | 15.43 ± 0.59 | 16.32 ± 0.82 | .114 | .418 |
| Able to complete five attempts | 63 (94.0) | 58 (93.5) | 49 (96.1) | 39 (86.7) | .266 | .436 |
| Standing balance (N = 242) | ||||||
| Time holding tandem stand, s | 8.70 ± 0.38 | 7.99 ± 0.37 | 7.91 ± 0.39 | 7.26 ± 0.47 | .036 | .071 |
| Able to hold tandem stand 10 s | 53 (73.6) | 47 (69.1) | 34 (60.7) | 26 (56.5) | .059 | .177 |
| Men | N = 23 | N = 26 | N = 38 | N = 48 | ||
| Gait (N = 135) | ||||||
| Gait speed, 15-foot walk, m/s | 1.01 ± 0.06 | 0.94 ± 0.05 | 0.93 ± 0.04 | 1.00 ± 0.04 | .816 | .891 |
| Gait speed ≥ 1.0 m/s, from 15-footwalk | 11 (47.8) | 12 (46.2) | 11 (28.9) | 20 (41.7) | .482 | .960 |
| Chair stands (N = 128) | ||||||
| Mean time in completers, s | 14.13 ± 0.76 | 14.80 ± 0.80 | 14.18 ± 0.73 | 13.77 ± 0.62 | .615 | .592 |
| Able to complete five attempts | 22 (100) | 25 (100) | 33 (94.3) | 44 (95.7) | .211 | .291 |
| Standing balance (N = 133) | ||||||
| Time holding tandem stand, s | 9.65 ± 0.45 | 9.17 ± 0.46 | 8.24 ± 0.37 | 8.97 ± 0.32 | .113 | .130 |
| Able to hold tandem stand 10 s | 21 (91.3) | 18 (75.0) | 27 (71.1) | 39 (81.3) | .483 | .762 |
Notes: Data are N (%) or age-adjusted mean ± standard error of the mean.
CAC = coronary artery calcium; CVD = cardiovascular disease.
Table 3.
Linear Regression Models Predicting Gait Speed (m/s) Among Women Without
| Model 1 | Model 2 | Model 3 | Model 4 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Baseline | Unstandardized Coefficients |
Standardized Coefficients |
Unstandardized Coefficients |
Standardized Coefficients |
Unstandardized Coefficients |
Standardized Coefficients |
Unstandardized Coefficients |
Standardized Coefficients |
||||||||
| Variables | β | SEM | β |
p Value |
β | SEM | β |
p Value |
β | SEM | β |
p Value |
β | SEM | β |
p Value |
| Age | −0.021 | 0.004 | −0.296 | <.001 | −0.021 | 0.005 | −0.288 | <.001 | −0.022 | 0.005 | −0.307 | <.001 | −.020 | .005 | −.290 | <.001 |
| African American | −0.099 | 0.039 | −0.158 | .013 | −0.061 | 0.044 | −0.098 | .178 | −0.104 | 0.042 | −0.165 | .008 | −.097 | .040 | −.154 | .017 |
| Log (CAC score) | −0.019 | 0.007 | −0.168 | .008 | −0.022 | 0.008 | −0.190 | .025 | −0.018 | 0.008 | −0.162 | .011 | −.017 | .007 | −.149 | .021 |
| Arthritis | −0.106 | 0.031 | −0.201 | .001 | −0.107 | 0.037 | −0.202 | .004 | −0.096 | 0.033 | −0.182 | .001 | −.104 | .032 | −.198 | .001 |
| Ever smoked | −0.066 | 0.031 | −0.125 | .038 | −0.069 | 0.037 | −0.130 | .071 | −0.074 | 0.033 | −0.141 | .033 | −.075 | .032 | −.144 | .020 |
Notes: Model 1 also adjusted for diabetes, hypertension, body mass index, chronic obstructive pulmonary disease, osteoporosis, and/or fractures in the previous year, and depressive symptoms. Model 2 = Model 1 + white matter hyperintensity grade ≥3 or ventricular enlargement grade ≥4 or brain infarcts ≥1. Model 3 = Model 1 + left ventricular mass. Model 4 = Model 1 + ankle–arm index < 0.9.
CVD = cardiovascular disease; SEM = standard error of the mean; CAC = coronary artery calcium.
In women, the association between CAC and gait speed continued to be statistically significant after adjustment for baseline AAI, white matter grade, ventricular enlargement grade, brain infarcts, and LVM (Table 3). Similar results were achieved in the logistic regression model, where gait speed <1 versus ≥1 was used as the outcome variable, and quartiles of CAC as an explanatory variable (data not shown). In sensitivity analyses including only participants with brain MRI, results of all the models did not substantially change (data not shown). Thirteen percent of women were on estrogen replacement therapy. Further adjustment for estrogen use did not modify the association between CAC and gait speed, using either the linear or the logistic regression analysis. Considering sex-specific quartiles did not modify the results (data not shown).
Discussion
In our sample, women (but not men) had lower gait speeds across increasing quartiles of CAC. The women with CAC >660, compared to those whose CAC was <35, walked, on average, 0.16 m/s slower. This order of values, apparently small, is thought to express substantial differences in physical performance in older adults (24). Accordingly, having a CAC score >34 was associated with a more than twofold increased odds of walking slower than 1 m/s, a threshold shown to predict persistent lower extremity limitation, hospitalization, and death in older adults (25). The nonlinear increase of the OR for the three top quartiles of CAC might suggest a threshold effect at very low levels of disease. The strength of the association between increasing levels of CAC and poorer gait speed did not change after adjustment for demographics, health habits, and chronic health conditions, as well as for lower extremity arterial disease, LVM and subclinical MRI abnormalities.
To our best knowledge, this is the first study that has explored the relationship between CAC and measured physical function in older adults. In relatively healthy older populations, physical performance decline was associated with vascular risk factors (22,26), which are also associated with CAC in middle-aged and older people (12,27,28). Only a few studies have investigated the relationship between quantitative noninvasive measures of atherosclerosis, which are considered an intermediate phenotype linking risk factors to disease, and physical performance. In community-dwelling elders free of overt CVD and neurological disease, carotid plaques and common carotid artery intima-media thickness were associated with slower gait speed (5), and in older adults without clinical intermittent claudication, the AAI proved to be associated with abnormal walking performance (29). Although CAC is strongly associated with other measures of subclinical atherosclerosis, it represents a more sensitive measure to detect low levels of vascular disease in older adults (3). So, we were able to show that also very low levels of disease are associated with physical dysfunction.
Our data are consistent with a cross-sectional observation made in middle-aged asymptomatic individuals referred by physicians for having at least two cardiovascular risk factors, where self-reported physical activity was inversely correlated with CAC levels (9). Unfortunately, we could not test the effect of physical activity, which was not measured at the time of CAC scan.
Based on previous observations, we hypothesized that vascular organ (heart, brain, or lower extremities) abnormalities could mediate the association between CAC and physical performance. In middle-aged and older adults without clinical CVD, CAC was associated with impaired regional LV function (30). One study has observed that persons with high CAC but without coronary artery narrowing had ECG ischemic responses to exercise and exercise tolerance similar to persons with arterial narrowing (31). A more recent study showed that CAC was associated with impaired myocardial perfusion after adenosine (32). These studies raise the question as to whether older adults with higher CAC may have subtle LV dysfunction and self-limit walking speed in response. An earlier article about the CHS cohort showed that older adults without CHF but with global LV dysfunction on ECG were more likely to be frail, which was characterized in part by slow gait and low activity(33). Additional studies are needed to test whether CAC is simply a better marker of disease burden overall or if there are specific effects of CAC on cardiac function to explain these results. CAC has also been associated with subclinical brain damage (7), which in turn contributes toward physical performance impairment (8). In our study, we failed to demonstrate any effect of subclinical heart, brain, or lower extremity vascular abnormalities upon the association between CAC and impaired gait. However, the cross-sectional nature of the study and the limited size of the sample, which in addition, included relatively healthy people, may suggest cautions about a definitive exclusion of such possibility. Moreover, more specific markers of LV function (such as ECG measures) were not available at the time that the CAC scan was performed. A recent study conducted on more than 350 middle-aged and older participants in the population-based Rancho Bernardo Study found an inverse correlation between CAC and bone mineral density only in women who were taking hormone replacement therapy (10), and bone mineral density is in turn associated with reduced physical performance in postmenopausal women (11). In our study, the association between CAC and physical function was unchanged after the adjustment for self-reported osteoporosis and fractures in the previous year, and we were not able to show any effect of estrogen replacement therapy.
Sex differences in the association between CAD and physical function remain controversial. Methodological issues, such as the smaller sample size of men, or a selective survival because of men’s greater cardiovascular mortality (34) seem unlikely to explain the complete lack of association in this sex. On one hand, women, compared to men, have a higher prevalence of frailty (35) and disability (36) in the general population. On the other hand, more specific factors might be responsible for these sex differences. For instance, a greater positive coronary artery remodeling in men than in women has been demonstrated (37). A preferential association of clinical CVD (either ischemic heart disease or stroke) with reduced physical performance in women has been repeatedly demonstrated (38,39). More work is needed to determine whether these differences in clinical outcomes extend to disease measured at a subclinical level.
Some study limitations have to be acknowledged. First, the cross-sectional design limits any cause–effect speculation. Second, possible selection bias could be suggested by the differences between CHS participants with and without an EBT scan.
The strengths of our study rest on the inclusion of community-dwelling participants, the use of reliable quantitative measures of both CAC and physical performance, and the broad range of chronic comorbidities assessed in the CHS. Also the selection of a relatively healthy sample, free of overt CVD, represents an important design feature, which allows the evaluation of the impact of subclinical vascular disease, and has implications for prevention of disability.
Summary
This study provides clues to the identification of the contributors to physical performance decline in apparently healthy older adults. In general, our observation adds to the evidence supporting the association between cardiovascular risk factors and subclinical vascular disease on one hand, and physical performance reduction on the other hand, even if we were not able to better explain this association. The measure of CAC in particular, because of its sensitivity, allowed us to show that also a small extent of vascular disease is associated with a reduction in physical performance in older women. Our results may also have clinical implications. They suggest that, besides the prediction of future coronary events, CAC is associated, at least in women, with substantial impairment of physical function, which, in turn, has been demonstrated as an independent predictor of death and disability in the elderly population (40,41). The lower extent of coronary calcium in the women of our sample, compared with men, and the fact that altered mobility was associated with CAC scores >34, which is considered relatively low in the variability of this indicator in older populations, suggest that the functional impairment detected in this group is related to a very early stage of the atherosclerotic disease: This observation may reinforce the potential for prevention of CVD to reduce disability in older adults.
Acknowledgments
This work was supported by the National Heart, Lung, and Blood Institute, R01-AG-023629, and contract numbers N01-AG-6-2106, N01-AG-6-2102, and N01-AG-6-2103. A full list of participating CHS investigators and institutions can be found at http://www.chs-nhlbi.org. Dr. Inzitari is a Research Scholar at the Claude D. Pepper Older Americans Independence Center of the University of Pittsburgh (P30 AG024827), and his work was supported in part by an educational grant from the “Gianandrea Pugi” Foundation (Florence, Italy).
Footnotes
Decision Editor: Luigi Ferrucci, MD, PhD
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