Author + information
- Received July 17, 2017
- Revision received September 8, 2017
- Accepted September 11, 2017
- Published online November 13, 2017.
- Juan Miguel Fernández-Alvira, PhDa,
- Valentín Fuster, MD, PhDa,b,∗ (, )
- Stuart Pocock, PhDa,c,
- Javier Sanz, MDa,b,
- Leticia Fernández-Friera, MD, PhDa,d,e,
- Martín Laclaustra, MD, PhDa,f,
- Rodrigo Fernández-Jiménez, MDa,b,e,
- José Mendiguren, MDg,
- Antonio Fernández-Ortiz, MD, PhDa,h,k,
- Borja Ibáñez, MD, PhDa,e,j and
- Héctor Bueno, MD, PhDa,i,k,∗∗ ()
- aCentro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
- bIcahn School of Medicine at Mount Sinai, New York, New York
- cLondon School of Hygiene and Tropical Medicine, London, United Kingdom
- dDepartamento de Cardiología, Hospital Universitario HM Montepríncipe, Centro Integral de Enfermedades Cardiovasculares (CIEC), Madrid, Spain
- eCentro de Investigación Biomédica en Red (CIBER) de Enfermedades Cardiovasculares, Spain
- fAragon Institute for Health Research, Translational Research Unit, Hospital Universitario Miguel Servet, Zaragoza, Spain
- gBanco de Santander, Madrid, Spain
- hDepartamento de Cardiología, Hospital Clínico San Carlos, Madrid, Spain
- iInstituto de Investigación i+12, Cardiology Department, Hospital Universitario 12 de Octubre, Madrid, Spain
- jDepartamento de Cardiología, Instituto de Investigación Sanitaria (IIS)-Fundación Jiménez Díaz Hospital, Madrid, Spain
- kFacultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
- ↵∗Address for correspondence:
Dr. Valentín Fuster, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro, 3, 28029–Madrid, Spain.
- ↵∗∗Dr. Héctor Bueno, Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Melchor Fernández Almagro, 3, 28029–Madrid, Spain.
Background The ideal cardiovascular health score (ICHS) is recommended for use in primary prevention. Simpler tools not requiring laboratory tests, such as the Fuster-BEWAT (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) score (FBS), are also available.
Objectives The purpose of this study was to compare the effectiveness of ICHS and FBS in predicting the presence and extent of subclinical atherosclerosis.
Methods A total of 3,983 participants 40 to 54 years of age were enrolled in the PESA (Progression of Early Subclinical Atherosclerosis) cohort. Subclinical atherosclerosis was measured in right and left carotids, abdominal aorta, right and left iliofemoral arteries, and coronary arteries. Subjects were classified as having poor, intermediate, or ideal cardiovascular health based on the number of favorable ICHS or FBS.
Results With poor ICHS and FBS as references, individuals with ideal ICHS and FBS showed lower adjusted odds of having atherosclerotic plaques (ICHS odds ratio [OR]: 0.41; 95% confidence interval [CI]: 0.31 to 0.55 vs. FBS OR: 0.49; 95% CI: 0.36 to 0.66), coronary artery calcium (CACS) ≥1 (CACS OR: 0.41; 95% CI: 0.28 to 0.60 vs. CACS OR: 0.53; 95% CI: 0.38 to 0.74), higher number of affected territories (OR: 0.32; 95% CI: 0.26 to 0.41 vs. OR: 0.39; 95% CI: 0.31 to 0.50), and higher CACS level (OR: 0.40; 95% CI: 0.28 to 0.58 vs. OR: 0.52; 95% CI: 0.38 to 0.72). Similar levels of significantly discriminating accuracy were found for ICHS and FBS with respect to the presence of plaques (C-statistic: 0.694; 95% CI: 0.678 to 0.711 vs. 0.692; 95% CI: 0.676 to 0.709, respectively) and for CACS ≥1 (C-statistic: 0.782; 95% CI: 0.765 to 0.800 vs. 0.780; 95% CI: 0.762 to 0.798, respectively).
Conclusions Both scores predict the presence and extent of subclinical atherosclerosis with similar accuracy, highlighting the value of the FBS as a simpler and more affordable score for evaluating the risk of subclinical disease.
- cardiovascular risk
- Fuster-BEWAT score
- ideal cardiovascular health
- predictive tools
- subclinical atherosclerosis
Cardiovascular disease (CVD) remains the first cause of mortality and morbidity worldwide (1,2). In 2010, the American Heart Association proposed a new paradigm by shifting the classic focus on reducing the prevalence of CVD to a national goal of improving CV health in the population by measuring the ideal cardiovascular health score (ICHS) (3). The ICHS metrics focus on a number of lifestyle factors (smoking, body weight, physical activity, and diet) and 3 established risk factors (blood cholesterol, blood glucose, and blood pressure). Since 2010, extensive research has explored the prevalence of ideal CV health in different populations showing very low prevalence of ideal ICHS metrics overall (4) and its association with lower CVD and all-cause mortality (5).
The association between ICHS metrics and subclinical disease is a key area of interest to explore the pathways underlying the associations between ICHS and CV risk (6). Among several markers of subclinical CVD, the coronary artery calcification score (CACS) has been identified as one of the most robust markers of subclinical disease and predictor of future CVD events (7). Higher ICHS metrics are associated with lower CACS (8–11). The association among ICHS and other markers of subclinical disease, such as the carotid intima-media thickness (12,13), carotid plaque area (13), and pulse wave velocity as a measurement of arterial stiffness (14,15) has been investigated, but their predictive value is limited (16,17). To the best of our knowledge, the association between ICHS metrics and extensive subclinical atherosclerosis measured by the presence of atherosclerotic plaques in several arterial regions has not been studied yet.
In addition to ICHS, other screening tools, such as the Fuster-BEWAT (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) score (FBS) (18) have been recently developed for use in lifestyle-based CVD prevention. FBS collects clinical information on lifestyle and risk factors including smoking, physical activity, diet (fruit and vegetable consumption), body weight, and blood pressure, but contrary to ICHS, it does not require laboratory results, making it easier and more suitable for use. Nevertheless, whether FBS is useful for predicting subclinical atherosclerosis and whether its discriminating accuracy is similar to ICHS are unknown.
This study, first, explored the association between ICHS and FBS metrics and the presence and extent of subclinical atherosclerosis measured by 2-dimensional (2D)-carotid, aortic, and iliofemoral vascular ultrasonography and CACS by computed tomography (CT) and, second, compared the accuracy of both scores for predicting subclinical atherosclerosis.
Study design and population
The PESA (Progression of Early Subclinical Atherosclerosis) study rationale and design have been described elsewhere (19). Briefly, PESA is a prospective cohort study of 4,184 asymptomatic employees of Banco Santander in Madrid (Spain), 40 to 54 years of age, and free of CVD; the study was designed to assess the prevalence and determinants of subclinical atherosclerosis. Participants underwent a complete clinical evaluation, blood and urine analysis, lifestyle questionnaire analysis, accelerometry assessment of physical activity, electrocardiography, and assessment of subclinical atherosclerosis by noninvasive vascular imaging tests, including 2D vascular ultrasonography and CT for CACS measurement. Complete data for the actual analysis were available for 3,983 participants (95.2%). The Ethics Committee of Instituto de Salud Carlos III in Madrid, Spain, approved the study protocol, and written informed consent was obtained from each participant prior to enrollment.
Assessment of participants’ characteristics: Lifestyle and CVD risk factors
Conventional risk factors, such as smoking habits, and a diagnosis of hypertension, diabetes, or dyslipidemia, or family history of CVD were previously defined (20) and were collected as part of each participant’s medical history. Blood pressure was measured at rest by using an automatic oscillometric sphygmomanometer (Omron Hem-907, Omron Healthcare, Kyoto, Japan). Anthropometric measurements were obtained following a standardized procedure. Body mass index was calculated as body mass in divided by the participant’s height squared (kg/m2). Blood and urine samples were collected after >8 h of fasting. Dietary intake was assessed by using a computerized questionnaire (Dietary History-Enrica) (21), previously validated (22), conducted by trained dieticians, designed to record habitual food intake over the previous year. Physical activity was assessed by triaxial accelerometry (ActiTrainer accelerometers; Actigraph, Pensacola, Florida) during 7 consecutive days, including sleeping time. Moderate and vigorous levels of physical activity were defined according to Troiano cutoff points (23).
Imaging studies included 2D vascular ultrasonography of carotid arteries, infrarenal aortas, and iliofemoral arteries and CACS by CT scan. Vascular ultrasonography was performed using an iU22 ultrasound station (Philips Healthcare, Bothell, Washington), with adapted scanning protocols (20). Plaques were defined as any focal protrusion of more than 0.5 mm or more than 50% thicker than the surrounding intima-media (24). CACS were estimated by using the Agatston method by noncontrast electrocardiography-gated prospective acquisition, using a 16-slice Brilliance CT scanner (Philips Healthcare, Andover, Massachusetts) (20) and graded as <1, 1 to <100, 100 to <400, or ≥400 (25). The PESA Core Imaging Laboratory at the Centro Nacional de Investigaciones Cardiovasculares Carlos III analyzed all imaging recordings.
Definition of subclinical atherosclerosis
Subclinical atherosclerosis at each vascular site was defined as the presence of any atherosclerotic plaque in the carotid, aortic, or iliofemoral territory or having CACS of ≥1. The number of vascular sites affected (right carotid, left carotid, abdominal aorta, right iliofemoral artery, left iliofemoral and coronary arteries) was used for defining the multiterritorial extent of subclinical atherosclerosis and classified as disease free (no vascular sites affected) or as having focal (1 site), intermediate (2 to 3 sites), or generalized (4 to 6 sites) atherosclerosis (20).
Cardiovascular health metrics
The 7 ICHS behaviors and risk factors (exercise, body mass index, diet, smoking status, blood pressure, serum cholesterol, and fasting glucose) were classified according to American Heart Association definitions (3) as poor, intermediate, or ideal (Table 1). Each component was then dichotomized as being ideal versus nonideal, and subjects were classified as having poor, intermediate, or ideal CV health based on the total number of ideal metrics (0 to 2 poor, 3 to 5 intermediate, 6 to 7 ideal) (2).
The 5 FBS components, blood pressure (B), exercise (E), weight (W), alimentation (A), and tobacco (T), were divided into 4 categories ranging from 0 to 3 according to the previously published description (Table 2) (18). Each component was dichotomized as ideal (3) or nonideal (0 to 2), and subjects were classified as having poor, intermediate, or ideal CV health based on the total number of ideal components (0 to 1 poor, 2 to 3 intermediate, 4 to 5 ideal).
All statistical analyses were performed using SPSS software version 20.0 (IBM, Armonk, New York). Subclinical atherosclerosis was dichotomized as presence of plaque versus no plaque. CACS was dichotomized as <1 and ≥1 Agatston unit. Distribution of each individual ICHS (classified as poor, intermediate, ideal) and FBS (classified as 0, 1, 2, 3) metrics are presented for the total sample and stratified by sex, as well as by distribution of the number of ideal metrics for each score.
Associations between individual metrics in the ICHS and FBS and the presence of subclinical atherosclerosis defined as having plaque or CACS ≥1 were examined by use of logistic regression models. The associations between the ICHS and FBS categorized as poor, intermediate, and ideal and the presence of subclinical atherosclerosis were also examined by logistic regression models. Ordinal regression models were fitted to explore the association between ICHS and FBS and the multiterritorial extent of subclinical atherosclerosis and the amount of CACS (divided into <1, 1 to <100, 100 to <400, and ≥400 Agatston units). All regression models were adjusted for age, sex, family CVD history, and educational level. The C-statistic or area under the receiver operating characteristic (ROC) curve (AUC) and 95% confidence interval (CI) were calculated for each model as a measure of the discriminatory power of each score.
The mean age of the 3,983 participants was 45.8 ± 4.3 years (62.8% men). The 10-year Framingham risk score was 5.8 ± 4.3 for the total sample (men: 7.7 ± 4.3; women: 2.8 ± 2.0), and the 30-year Framingham risk score was 17.7 ± 11.7 overall (men: 22.8 ± 11.3; women: 9.1 ± 6.0).
Overall, only 3.2% of subjects met all 7 ideal ICHS metrics, whereas 6.5% of subjects met all 5 ideal FBS metrics (Tables 1 and 2). Most of the sample (71.7%) met between 3 and 5 ideal ICHS metrics (intermediate CV health). Likewise, 61.2% of the sample met between 2 and 3 ideal FBS components (intermediate CV health). The overall prevalence of a favorable ICHS (at least 6 ideal metrics) or favorable FBS (at least 4 ideal metrics) was 17.8% and 31.0%, respectively. Women presented a significantly higher number of ideal metrics in both scores and significantly higher proportion of ideal levels in all metrics, except for fruit and vegetable consumption and smoking status.
Among health behaviors, ideal dietary metrics had the lowest prevalence (18.8% for ICHS and 25.5% for FBS), whereas ideal physical activity levels were highly prevalent (93.0% for ICHS and 93.4% for FBS). The prevalence of ideal blood pressure was 59.7% for ICHS and 61.5% for FBS, and ideal body mass index prevalence was 41.5% for both scores. The prevalence of ideal total cholesterol and plasma glucose, assessed only for ICHS, was 47.0% and 86.8%, respectively. Nonsmokers represented 68.9% and 72.0% of the total sample according to the ICHS and FBS, respectively.
Subclinical atherosclerosis and association with ICHS and FBS results
The presence of at least 1 atherosclerotic plaque could be identified in 2,377 subjects (59.7%), more frequently in men than in women, and more often in the iliofemoral bed than in other arterial beds (Online Table 1). Although involvement of multiple territories was found in 1,619 individuals (40.7%), calcium calcification with Agatston score ≥1 was observed in 700 participants (17.6%).
Overall, there was a strong inverse association between ICHS and FBS and subclinical atherosclerosis. Compared with participants categorized as having poor ICHS (0 to 2 ideal factors) or poor FBS (0 to 1 ideal factor), adjusted odds ratios (ORs) for plaque presence and for CACS ≥1 were significantly lower among subjects classified as having intermediate and ideal scores (Table 3). The association between the individual components of both scores and subclinical atherosclerosis is shown in Online Table 2. Both scores were also associated with the extent of subclinical atherosclerosis and with the degree of coronary calcification (Central Illustration, Table 4, Online Figure 1).
The AUC analysis showed similar levels of discriminating accuracy of ICHS (C-statistic: 0.694; 95% confidence interval [CI]: 0.678 to 0.711) and FBS (C-statistic: 0.692; 95% CI: 0.676 to 0.709) in identifying the presence of plaques as well as for identifying CACS ≥1 (C-statistic: 0.782; 95% CI: 0.765 to 0.800, vs. C-statistic: 0.780; 95% CI: 0.762 to 0.798, respectively) (Table 3, Figure 1). By using ordinal regression models, the ORs for a greater extent of subclinical atherosclerosis, measured by the number of diseased vascular sites (none, focal, intermediate, or generalized) and CACS level (<1, 1 to <100, 100 to <400, or ≥400) were significantly lower among subjects with ideal ICHS or ideal FBS, taking the poorest score as a reference (p < 0.001 for all comparisons). The AUC analysis showed equivalent discriminating accuracy levels for both models in predicting generalized subclinical atherosclerosis: ICHS C-statistic of 0.779 (95% CI: 0.759 to 0.795); a FBS C-statistic of 0.773 (95% CI: 0.752 to 0.795); and very similar values in predicting CACS ≥400 level, with ICHS C-statistic of 0.881 (95% CI: 0.836 to 0.925) and an FBS C-statistic of 0.861 (95% CI: 0.816 to 0.907) (Table 4, Online Figure 1).
Better profiles of CV health behaviors and risk factors, reflected by higher ICHS and FBS metrics, are strongly associated with a lower prevalence and a lower extent of subclinical atherosclerosis in healthy individuals. This is good evidence of the impact of lifestyle and risk factors on the early phase of the disease. Both scores showed good and comparable predictive values for all outcomes measured in the PESA cohort, including the presence of any atherosclerotic plaque, presence and amount of calcium in coronary arteries, and number of affected arterial sites.
Our study shows an inverse relationship between ideal CV risk score metrics and the presence of subclinical atherosclerosis, evaluated by 2 different indices. To our knowledge, this is the first study to show this relationship with multiterritorial disease in a large cohort of healthy individuals. Previous studies evaluating ICHS metrics and subclinical atherosclerosis used coronary calcium as the marker. Robbins et al. (9) and Bensenor et al. (11) showed a strong inverse relationship between ICHS metrics and prevalence of coronary artery calcium in adults. Saleem et al. (10) also found that middle-aged men and women with a favorable ICHS have a lower prevalence and severity of subclinical atherosclerosis as estimated by CACS. Ahmed et al. (26) found that regular exercise, adherence to a Mediterranean-style diet, smoking avoidance, and maintenance of normal weight were associated with lower CAC incidence and progression, and significantly lower all-cause mortality over 7.6 years in participants 44 to 84 years of age from the MESA (Multi-Ethnic Study of Atherosclerosis) study. It has been shown, however, that the absence of coronary artery calcium does not necessarily mean that individuals are disease free (20). The high prevalence of atherosclerotic plaques (59.7%) compared with the prevalence of CACS ≥1 (17.6%) in our study suggests that coronary artery calcium represents a more advanced stage of disease. Other studies also concluded that exploring several territories allows overcoming the potential problem of not detecting lesions when only a single territory is taken into account (27). In fact, the overall prevalence of subclinical atherosclerosis found in our cohort is high.
The majority of participants (approximately 80%) with poor ICHS and FBS in our study presented at least 1 affected site. However, subclinical atherosclerosis was also present in approximately one-half of the population with ideal ICHS and FBS metrics. The follow-up data in the PESA cohort will allow investigating whether participants showing ideal ICHS and FBS metrics have less progression of subclinical atherosclerosis and/or lower incidence of clinical events over time and whether remaining in ICHS and FBS categories (ideal, intermediate, poor) leads to different rates of worsening subclinical atherosclerosis and transition to clinical atherosclerosis, including CV events and mortality.
Although the ICHS and the FBS share 5 metrics (blood pressure, physical activity, dietary metric, body weight, and tobacco consumption), the ICHS also includes cholesterol and fasting glucose levels. Because the FBS does not require laboratory analyses to be derived and given the comparable predictive value of both scores, the FBS may be considered a practical and affordable option with which to foster primary CV prevention in settings where easy laboratory data are not available. This may not be considered an advantage in high-resource environments, where routine screening for risk factors by laboratory analysis are recommended (28,29) but may be particularly relevant in low resource areas, such as in developing countries, where the burden of CVD is growing faster. It also may be used for educational purposes in nonmedical environments (i.e., schools) and for personal self-monitoring as a tool to improve self-CV care.
It must be acknowledged that the benefit of predicting subclinical atherosclerosis is still not well defined. Although subclinical atherosclerosis precedes clinical cardiovascular disease, it needs be proven that if, for a given level of cardiovascular risk, people with subclinical noncoronary atherosclerosis are at higher risk of later clinical atherosclerosis than people without subclinical atherosclerosis and whether they may benefit from more intensive primary prevention strategies at an earlier stage.
A number of other limitations should be considered when interpreting this study. First, the results are based on cross-sectional data from the PESA cohort at baseline and therefore cannot establish causality. However, the ongoing PESA follow-up will study the association between CV health and the progression of subclinical atherosclerosis and subsequent CV events. Second, the PESA cohort consists of middle-aged participants, predominantly middle- to high-income Caucasian workers not randomly selected from the general population, and therefore, the generalizability of the results is limited. In comparison with previous reports of the distribution of ICHS metrics, our sample presents a higher prevalence of good ICHS metrics (28,29). Despite these limitations, the use of contemporary imaging technology, the systematic and extensive collection of behavioral and risk factors, and the overall high-quality data profile obtained from a relatively young cohort may help provide useful insights into the early stages of subclinical atherosclerosis and its association with risk and its association with CV risk and behavioral patterns and will help understanding their influence on monitoring atherosclerosis progression.
The use of scores assessing CV health behaviors and risk factors is useful for predicting the presence of subclinical atherosclerosis in healthy adults at low short-term CV risk. Although the ICHS and the FBS showed similar predictive values for detecting subclinical disease, the FBS is simpler and does not need laboratory results. Therefore, it may be considered the first option in settings where access to laboratory analysis is limited.
COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: The ICHS, which incorporates 7 metrics (exercise, body mass index, diet, smoking status, blood pressure, and blood cholesterol and fasting glucose levels), has validated predictive value for cardiovascular events. The Fuster-BEWAT score uses 5 metrics (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) and does not require laboratory tests. Both scores exhibit comparable predictive values for detection of subclinical atherosclerosis in ostensibly healthy individuals.
TRANSLATIONAL OUTLOOK: The FBS is an easy, painless, inexpensive tool that could be implemented in resource-constrained health care settings to identify individuals with a high likelihood of subclinical atherosclerosis at whom preventive management strategies can be directed.
The PESA study was co-funded by Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) and Banco Santander. Funding was also provided by Institute of Health Carlos III (PI15/02019) and European Regional Development Fund. CNIC is supported by the Ministry of Economy, Industry and Competitiveness and Pro CNIC Foundation; and is a Severo Ochoa Center of Excellence (SEV-2015-0505). This work is part of a project that received funding from the European Union Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant 707642 and American Heart Association grant 14SFRN20490315. Dr. Bueno has received research funding from Instituto de Salud Carlos III (PIE16/00021), AstraZeneca, Bristol-Myers Squibb, Janssen, and Novartis; is a consultant for Abbott, AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, and Novartis; and has received speakers fees and travel and attendance support from AstraZeneca, Bayer, Bristol-Myers Squibb-Pfizer, Ferrer, Novartis, Servier, and Medscape. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Matthew Budoff, MD, served as Guest Editor for this paper.
- Abbreviations and Acronyms
- coronary artery calcium score
- computed tomography
- cardiovascular disease
- Fuster-BEWAT (blood pressure [B], exercise [E], weight [W], alimentation [A], and tobacco [T]) score
- ideal cardiovascular health
- Received July 17, 2017.
- Revision received September 8, 2017.
- Accepted September 11, 2017.
- 2017 The Authors
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