ldeal Cardiovascular Health Metrics and Coronary Artery Calcification in Northern Chinese Population：A Cross-sectional Study*

LUO Tai Yang， LIU Xiao Hui， DAI Tian Yi， LIU Xin Min， ZHANG Qian， and DONG Jian Zeng

Department of Cardiology， Beijing An Zhen Hospital， Capital Medical University， Beijing Institute of Heart， Lung and Blood Vascular Diseases， Beijing 100029， China

?

ldeal Cardiovascular Health Metrics and Coronary Artery Calcification in Northern Chinese Population：A Cross-sectional Study*

LUO Tai Yang， LIU Xiao Hui， DAI Tian Yi， LIU Xin Min， ZHANG Qian， and DONG Jian Zeng#

Department of Cardiology， Beijing An Zhen Hospital， Capital Medical University， Beijing Institute of Heart， Lung and Blood Vascular Diseases， Beijing 100029， China

Objective Coronary artery calcification （CAC） is a well-established risk predictor of coronary heart

disease events and is recognized as an indicator of subclinical atherosclerosis.

Methods A cross-sectional study consisting of 2999 participants aged ≥40 years from the Jidong community of Tangshan City， an industrial and modern city of China， was conducted between 2013 and 2014 to examine the association between the ideal cardiovascular health （CVH） metrics and CAC. The ideal CVH metrics were determined based on the definition of the American Heart Association （AHA）. The participants were then grouped into 4 categories according to the quartiles of their CVH metric scores as follows： first quartile （0-2）， second quartile （3）， third quartile （4）， and fourth quartile （5-7）. CAC was assessed by using high-pitch dual-source CT， and patients were identified based on thresholds of 0， 10， 100， or 400 Agatston units， as per common practice.

Results The prevalence of subclinical atherosclerosis was 15.92%， 13.85%， 6.76%， and 1.93%， determined by using the CAC scores at thresholds of 0， 10， 100， and 400 Agatston units， respectively. Compared with the group in the first quartile， the other three CVH groups had a lower odds ratio of CAC＞0 after adjusting for age， sex， income level， education level， and alcohol use in the logistic regression analysis. The odds ratios in these groups were 0.86 ［95% confidence interval （CI）， 0.63-1.17； P＜0.05］， 0.75（95% CI， 0.55-1.02； P＜0.05）， and 0.49 （95% CI， 0.35-0.69； P＜0.05）， respectively. These associations of CAC with the CVH metrics were consistent when different CAC cutoff scores were used （0， 10， 100， or 400）.

Conclusion The participants with more-ideal cardiovascular metrics had a lower prevalence of subclinical atherosclerosis determined according to CAC score. Maintaining an ideal cardiovascular health may be valuable in the prevention of atherosclerosis in the general population.

Ideal cardiovascular health； Coronary artery calcification； Atherosclerosis

Biomed Environ Sci， 2016； 29（7）： 475-483 doi： 10.3967/bes2016.063 ISSN： 0895-3988

www.besjournal.com （full text） CN： 11-2816/Q Copyright ?2016 by China CDC

INTRODUCTION

C oronary heart disease （CHD） is the single largest cause of death in developed countries and is one of the leading causes of disease burden in developing countries， accounting for 7.3 million deaths and 58 million disability-adjusted life years （DALYs） worldwide in 2011［1-2］. Inappropriate lifestyle factors increase the risk of CHD［3-5］， including smoking， poor quality diet，physical inactivity， excessive alcohol consumption，and obesity， which are major preventable causes of CHD and mortality［3-5］. Epidemiological studies have showed the correlation between healthy lifestyle factors and reduced risks of myocardial infarction （MI） and CHD mortality［6-9］.

Coronary artery calcification （CAC）， a marker of subclinical atherosclerosis， is well established as a risk predictor of CHD events［10-12］and all-cause mortality［13-14］in asymptomatic adults， and provides incremental prognostic information beyond that of traditional risk factors［14-15］. Previous studies showed that normal body mass index （BMI）［16］， lipids［17-19］，blood pressure［20］， fasting blood glucose［21］， active physical activity［22］， and nonsmoking status were correlated with lower CAC scores［23］. Noninvasive imaging detects CAC in minute amounts and thus is a valuable indicator of preclinical diseases in their early stages. However， limited evidence is available about the association between ideal cardiovascular health behavioral factors and subclinical atherosclerosis assessed based on CAC score，especially in China.

The American Heart Association （AHA） defined seven behaviors and risk factors （smoking status，BMI， physical activity， healthy dietary score， total cholesterol level， blood pressure， and fasting blood glucose level） as health metrics and created three stages for each metric to reflect poor， intermediate，and ideal cardiovascular health status［24］. Identifying health behaviors and risk factors that are correlated with the maintenance of subjects' health is an important strategy for the prevention of CHD.

We hypothesized that ideal cardiovascular metrics would be a protective factor of subclinical cardiovascular disease （assessed based on CAC score）［25］. Therefore， we conducted a cross-sectional study to investigate the association between ideal cardiovascular metrics and CAC in a Chinese population.

METHODS

Study Design and Participants

In the present investigation， we conducted a cross-sectional analysis of baseline data of the target population. This is a community-based， ongoing observational study aimed to investigate the progression of atherosclerosis in Chinese adults［26］. Briefly， from July 2013 to August 2014， 9078 subjects aged ≥18 years who were residents in a community in Jidong were recruited. Jidong is located in Tangshan City， which is a large and littoral modern city located in the southeast of Beijing. All data were handled and managed by using the Ruichi Precision Medical Record System （RPMRS）， which was developed to standardize， integrate， manage， and analyze precision medical data.

The study included 2999 participants aged ≥40 years who had complete information on results of examinations for coronary artery calcification and peripheral arterial atherosclerosis. We excluded 204 participants with a history of stroke， myocardial infarction， heart failure， and cancer. A total of 2795 participants （1401 men and 1394 women） remained in the last analysis. During baseline survey， physical examinations and surveys were conducted by trained medical professionals from the Jidong Oilfield Corporation Medical Center. The study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval for the research protocol was obtained and written informed consents were approved by the ethics committee of Jidong Oilfield Corporation Medical Center prior to the study initiation. Written informed consents were obtained from all the participants.

Assessment of Cardiovascular Health Metrics

According to the guidelines by the American Heart Association， we defined the seven CVH metrics in three levels as follows： ‘ideal，' ‘intermediate，' and ‘poor，'［24］. Based on the score for healthy-diet behaviors， the dietary intake metric was classified as ideal （4 or 5 components）， intermediate （2 or 3 components）， or poor （0 or 1 component）. The smoking metric was classified as ideal （never- or quit-smoking for ＞12 months）， intermediate （former-smoking for ≤12 months）， or poor （current smoking）. Physical activity was classified as ideal（≥150 min/week of moderate intensity or ≥75 min/week of vigorous intensity）， intermediate （1-149 min/week of moderate intensity or 1-74 min/week of vigorous intensity）， or poor （none）. BMI was classified as ideal （＜25 kg/m2）， intermediate （25-29.9 kg/m2）， or poor （≥30 kg/m2）. Blood pressure was classified as ideal ［systolic blood pressure （SBP） of ＜120 mmHg， diastolic blood pressure （DBP） of ＜80 mmHg， and untreated］， intermediate （SBP of 120-139 mmHg， DBP of 80-90 mmHg， and treated to goal）， or poor （SBP of ≥140 mmHg or DBP of ≥90 mmHg）. Fasting blood glucose level was classified as ideal （＜100 mg/dL and untreated），intermediate （100-125 mg/dL and treated to goal），or poor （0-125 dL）. Total cholesterol status was classified as ideal （＜200 mg/dL and untreated），intermediate （200-239 mg/dL or treated to goal）， or poor （≥240 mg/dL）.

BMI was defined based on measured height （accurate to 0.1 cm） and weight （accurate to 0.1 kg），and calculated as the2body weight （kg） divided by the square of height （m）. Blood pressure was measured by using a mercury sphygmomanometer with a cuff of appropriate size. Two SBP and DBP readings were taken at 5 min intervals， aer the parcipants had rested in a chair for at least 5 min. The average of the two readings was used for the current data analyses. If the two measurements differed by more than 5 mmHg， an addional reading was taken， and the average of the three readings was used.

Blood samples were drawn by trained phlebotomists from the subjects aer overnight fasng. The venous blood samples in tubes containing trisodium ethylenediaminetetraacec acid were immediately stored at 4 °C aer antecubital venipuncture. Blood samples were then centrifuged for 10 min at 3000 rotaons per minute at 25 °C. Aer separaon， plasma samples were used within 4 h. All biochemical indexes， including total cholesterol and fasng blood glucose levels，were measured by using an autoanalyzer （AU400，Olympus， Japan） at the laboratory of the Jidong Oilfield Corporaon Medical Center.

CAC was assessed by using high-pitch dual-source CT （Siemens， Germany）. CAC scores were calculated by using the equaon introduced by Agatston et al.［28］， which uses the weighted method， mulplying the calcific volume by a range of coefficients that are dependent on peak density ［coefficient 2， 201-300 Hounsfield units （HU）；coefficient 3， 301-400 HU； coefficient 4， ＞400 HU］. The same assessment soware was used， and the same chief technician and supervising physician directed the high-pitch dual-source CT for the duraon of this study. No defined diagnosc criteria for CAC have been established； thus， we used several commonly used thresholds in clinical pracce to idefy paents with high CAC （0， 10， 100， and 400 Agatston units）［29-31］. To demonstrate that our findings were not sensive to which threshold is used， we present results based on the analyses for all 4 cutoff points. The proporon of individuals in each of these CAC score groups was calculated for each CVH metric level.

To assess the cumulative impact of ideal CVH metrics on coronary artery calcium （CAC）， the total score of the ideal CVH metrics for each individual was calculated as the sum of the scores in the seven CVH metrics. In the logistic regression models for estimating the association between the total score and coronary artery calcium （CAC）， the summary ideal CVH metrics were entered in the models as quartiles， with the lowest quartile as the reference. All the statistical tests were two-sided with the significant level set at 0.05. All the analyses were performed by using SAS 9.4 （SAS Institute， Cary，North Carolina， USA）.

RESULTS

The characteristics of the participants regarding the quartile of the ideal CVH metric score are described in Table 1. Those with a relatively higher ideal CVH metric scores were more likely to be women， younger， have higher educational level and income， and have less alcohol intake. Significant linear trends of each factor and behavior were detected across the ideal CVH metric score categories， with the exception of age and income. The participants with ideal CVH metric scores in a higher quartile had lower baseline BMI values， total cholesterol levels， fasting plasma glucose levels，blood pressures， and CAC scores （P＜0.001）. With respect to healthy behaviors， those with ideal CVH metric scores in a higher quartile had higher baseline physical activity levels and healthy diets than those with lower ideal CVH metric （first quartile） scores and prevalence of smoking （P＜0.001）.

The most common CVH metric score per individual was 4 （26.64%）. Among the study participants， 0.64% had zero ideal CVH metrics，whereas only 8.85% had 6 ideal CVH metrics， and 2.35% had 7 ideal CVH metrics （Figure S1）. The prevalence rates of subclinical atherosclerosis were 15.92%， 13.85%， 6.76%， and 1.93%， determined based on CAC score by using thresholds of 0， 10， 100，and 400 Agatston units， respectively （Table 2）. The prevalence of subclinical atherosclerosis differed in the first， second， third， and fourth quartiles of CVH（P for trend ＜0.001）.

Table 3 shows the association between each component of the ideal CVH metrics and the prevalence of CAC. After adjusting for age， sex，education level， income level， and alcohol use， we found that the ideal fasting blood glucose and smoking metrics were significantly correlated with the low risk of having a higher CAC score. In addition，the trends were consistent among different comparisons that used different CAC cutoff scores. The ideal total cholesterol metric was also significantly correlated with the low risk of having a higher CAC score when using 0 and 10 as CAC cutoffs. However， statistical significance was lost when 100 and 400 were used as CAC cutoff values.

Table 4 shows the odds ratios （ORs） for CAC presence after adjusting for age and sex， education level， income level， and alcohol intake. For the participants with ideal CVH metric scores in the highest quartile， the ORs of CAC scores ＞0 were less than half of those for the participants with ideal CVH metric scores in the first quartile ［OR 0.49， 95% confidence interval （CI）， 0.35-0.69］. In the highest quartile group， similar patterns were evident in the participants with CAC scores of ≥10 （OR 0.50， 95% CI，0.35-0.71） and ≥100 （OR 0.42， 95% CI， 0.26-0.69）. When a CAC score of ≥400 was the considered outcome， a significant reduction in the odds of developing CAC was also observed in those with ideal CVH metric scores in the highest quartile （OR 0.33， 95% CI， 0.14-0.78）.

DISCUSSION

After the AHA defined a set of ideal cardiovascular health metrics to measure the progress toward the 2020 Impact Goal， several studies have attempted to estimate the prevalence of ideal cardiovascular health metrics in the United States［5，32-33］. These studies reported that the prevalence of all the seven ideal cardiovascular health metrics in the US adult population was nearly 1%， which indicated that only few adults achieved ideal cardiovascular health. Studies from China also showed that only 0.1%-0.5% of participants met all the seven ideal cardiovascular health behaviors and factors［34-36］. Our study reported that 58 participants （2.35%） had ideal levels of all the seven health metrics in a Chinese population and was the first to demonstrate that participants with higher ideal cardiovascular health metric scores had a lower prevalence of subclinical atherosclerosis as estimatedbased on CAC scores in the Chinese population.

Table 1. Baseline Characteristics of Participants for All 7 Ideal Cardiovascular Health Metrics Grouped by the Number of Metrics in the Ideal Range

Table 2. ThePrevalence of Subclinical Atherosclerosis Determined by CAC Using Different Thresholds，Representing as Percentage （95% CI）

Table 3. Odds Ratios （95% CI） for Coronary Artery Calcification for Different Categories of Health Metrics*

Table 4. Associations of Coronary Artery Calcification with Number of Ideal Cardiovascular Health Metrics （CVH）， Adjusted OR （95% CI）*

Our study showed that the ideal smoking metric was associated with a lower risk of having CAC. This is consistent with results from other studies in populations other than the Chinese population. A study consisting of 32，481 individuals aged 30 to 90 years found that the ideal smoking metric was negatively correlated with CAC［18］. Likewise， a US cross-sectional study with 9341 asymptomatic participants found that CAC was associated with poor smoking metrics［17］. Health behaviors of smokers may play an important role in mediating the relationship between subclinical atherosclerosis and risk of cardiovascular events.

Other cardiovascular health behaviors， including ideal diet intake， and being physically active were reported to be associated with a reduced likelihood of having subclinical atherosclerosis assessed based on CAC score［22，37］. However， Taylor et al.［38］found no significant relationship between physical activity，particularly high-intensity physical activity， diets， and CAC severity. Consistently with the results of the study by Taylor et al.［38］， our study results showed that ideal diet and physical activity were not associated with lower CAC scores. The difference among the three studies might have arisen from the difference in ethnicity of the study participants. Our study included mainly Han-ethnic Chinese， who probably had different physique and lifestyles from the other ethnic populations.

We also analyzed the association between CAC and the biomarker indicators of ideal cardiovascular health. The results of this study repeated the negative correlations of blood pressure［20］，diabetes［21］， and BMI［16］with CAC in a Chinese population. Different from the previously reported finding that total cholesterol level was not associated with CAC［19］， our finding indicated that the ideal total cholesterol metric was also significantly correlated with the low risk of having a higher CAC score when 0 and 10 were used as CAC cutoff scores. However， statistical significance was lost when 100 and 400 were used as CAC cutoff scores. In addition， consistent to several reports［39-40］，we also found an association between fasting blood glucose level and CAC. The Rancho Bernardo Study showed that fasting blood glucose level （＞100 mg/dL）was an independent predictor of CAC progression in participants aged ＜65 years［39］. Another study reported that fasting blood glucose level in the upper normal range appears to be associated with the presence of CAC in apparently non-diabetic Brazilian men［40］. This association suggested that all levels of dysglycemia， even those well below the current American Diabetic Association diagnostic levels for diabetes mellitus， are associated with increased CHD risk.

We further investigated the cumulative impact of ideal CVH metrics on CAC. Remarkably， the increased total scores of the ideal CVH metrics were associated with low CAC scores， consistent with the results of previous studies that evaluated the association between individual health risk factors or health behaviors and CAC［41］. The negative association between the total scores of the ideal CVH metrics and CAC was found at the clinical CAC cutoff scores of ＞0 versus 0， ≥10 versus ＜10， ≥100 versus ＜100， and ≥400 versus ＜400. A previous study also showed that more favorable CVH categories were associated with lower risk of CAC［41］. Identifying and reducing the earliest damage before developing risk factors in generally healthy individuals would seem to provide a clear advantage of preventing the progression of coronary atherosclerosis.

Although our study included a large sample size and adjusted for various potential confounders，several limitations should be noted. First， dietary intake was defined based on a questionnaire survey modified from the established food frequency questionnaire， which might have influenced the magnitude of this association. Second， we did not take into account the statins used to treat atherosclerosis. Cholesterol-lowering statin medications play a central role in the development of endothelial inflammation and decreased atherosclerosis［42-44］. In this study， the CVH categories （blood pressure， fasting blood glucose level， and total cholesterol level） that included medical factors such as blood pressure were classified as ideal （SBP of ＜120 mmHg， DBP of ＜80 mmHg， and untreated）， intermediate （SBP of 120-139 mmHg， DBP of 80-90 mmHg， and treated to goal）， or poor （SBP of ≥140 mmHg or DBP of ≥90 mmHg）. Thus， whether taking statins into account would affect the magnitude but not the direction of the association is unclear. Third， this was a cross-sectional study， which limited our ability to conclude a cause-and-effect relationship between the ideal cardiovascular metrics and CAC. The causal relationship between the ideal cardiovascular metrics and CAC needs be justified in a further study.

CONCLUSION

In brief， we showed that the ideal cardiovascular metrics were associated with a lower prevalence of subclinical atherosclerosis determined based on CAC score in our Chinese population. Maintaining ideal cardiovascular health may be valuable in the prevention of atherosclerosis in the general population.

ACKNOWLEDGMENTS

We appreciate all the participants and their relatives in the study and the members of the survey teams from the Jidong community.

CONTRIBUTORS

LUO Tai Yang and DONG Jian Zeng wrote the manuscript and interpreted the data. DAI Tian Yi and LUO Tai Yang analyzed the data. LIU Xiao Hui and DONG Jian Zeng critically reviewed the manuscript. LIU Xin Min reviewed/edited the manuscript. ZHANG Qian contributed to the discussion and reviewed/edited the manuscript.

Conflict of Interest Disclosures None.

1. WHO， The World Health Report 2002. Reducing Risks，Promoting Healthy Life. Geneva： World Health Organization；2002.

2. Gaziano TA， Bitton A， Anand S， et al. Growing Epidemic of Coronary Heart Disease in Low- and Middle-Income Countries. Curr Probl Cardiol， 2010； 35， 72-115.

3. Suellentrop K， Morrow B， Williams L， et al. Monitoring progress toward achieving Maternal and Infant Healthy People 2010 objectives--19 states， Pregnancy Risk Assessment Monitoring System （PRAMS）， 2000-2003. MMWR Surveill Summ， 2006； 55，1-11.

4. Danaei G， Ding EL， Mozaffarian D， et al. The preventable causes of death in the United States： comparative risk assessment of dietary， lifestyle， and metabolic risk factors. PLoS Med， 2009； 6，e1000058.

5. Folsom AR， Yatsuya H， Nettleton JA， et al. Community prevalence of ideal cardiovascular health， by the American Heart Association definition， and relationship with cardiovascular disease incidence. J Am Coll Cardiol， 2011； 57，1690-6.

6. Petersen KE， Johnsen NF， Olsen A， et al. The combined impact of adherence to five lifestyle factors on all-cause， cancer and cardiovascular mortality： a prospective cohort study among Danish men and women. Br J Nutr， 2015； 113， 849-58.

7. Stampfer MJ， Hu FB， Manson JE， et al. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med， 2000； 343， 16-22.

8. Chiuve SE， McCullough ML， Sacks FM， et al. Healthy lifestyle factors in the primary prevention of coronary heart disease among men： benefits among users and nonusers of lipid-lowering and antihypertensive medications. Circulation，2006； 114， 160-7.

9. Khaw KT， Wareham N， Bingham S， et al. Combined impact of health behaviours and mortality in men and women： the EPIC-Norfolk prospective population study. PLoS Med， 2008； 5，e12.

10.Arad Y， Spadaro LA， Goodman K， et al. Prediction of coronary events with electron beam computed tomography. J Am Coll Cardiol， 2000； 36， 1253-60.

11.Kondos GT， Hoff JA， Sevrukov A， et al. Electron-beam tomography coronary artery calcium and cardiac events： a 37-month follow-up of 5635 initially asymptomatic low- to intermediate-risk adults. Circulation， 2003； 107， 2571-6.

12.Raggi P， Cooil B， Callister TQ. Use of electron beam tomography data to develop models for prediction of hard coronary events. Am Heart J， 2001； 141， 375-82.

13.Raggi P， Shaw LJ， Berman DS， et al. Gender-based differences in the prognostic value of coronary calcification. J Womens Health （Larchmt）， 2004； 13， 273-83.

14.Shaw LJ， Raggi P， Schisterman E， et al. Prognostic value of cardiac risk factors and coronary artery calcium screening for all-cause mortality. Radiology， 2003； 228， 826-33.

15.Greenland P， LaBree L， Azen SP， et al. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA， 2004； 291， 210-5.

16.Rutter MK， Massaro JM， Hoffmann U， et al. Fasting glucose，obesity， and coronary artery calcification in community-based people without diabetes. Diabetes Care， 2012； 35， 1944-50.

17.Pletcher MJ， Tice JA， Pignone M， et al. What does my patient's coronary artery calcium score mean？ Combining information from the coronary artery calcium score with information from conventional risk factors to estimate coronary heart disease risk. BMC Med， 2004； 2， 31.

18.Hoff JA， Daviglus ML， Chomka EV， et al. Conventional coronary artery disease risk factors and coronary artery calcium detected by electron beam tomography in 30，908 healthy individuals. Ann Epidemiol， 2003； 13， 163-9.

19.Pletcher MJ， Bibbins-Domingo K， Liu K， et al. Nonoptimal lipids commonly present in young adults and coronary calcium later in life： the CARDIA （Coronary Artery Risk Development in Young Adults） study. Ann Intern Med， 2010； 153， 137-46.

20.Pletcher MJ， Bibbins-Domingo K， Lewis CE， et al. Prehypertension during young adulthood and coronary calcium later in life. Ann Intern Med， 2008； 149， 91-9.

21.Meigs JB， Larson MG， D'Agostino RB， et al. Coronary artery calcification in type 2 diabetes and insulin resistance： the framingham offspring study. Diabetes Care， 2002； 25， 1313-9.

22.Desai MY， Nasir K， Rumberger JA， et al. Relation of degree of physical activity to coronary artery calcium score in asymptomatic individuals with multiple metabolic risk factors. Am J Cardiol， 2004； 94， 729-32.

23.Michos ED， Rice KM， Szklo M， et al. Factors associated with low levels of subclinical vascular disease in older adults：multi-ethnic study of atherosclerosis. Preventive cardiology，2009； 12， 72-9.

24.Lloyd-Jones DM， Hong Y， Labarthe D， et al. Defining and setting national goals for cardiovascular health promotion and disease reduction： the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation， 2010； 121，586-613.

25.Pletcher MJ， Tice JA， Pignone M， et al. Using the coronary artery calcium score to predict coronary heart disease events：a systematic review and meta-analysis. Arch Intern Med， 2004；164， 1285-92.

26.Song Q， Liu X， Wang A， et al. Associations between non-traditional lipid measures and risk for type 2 diabetes mellitus in a Chinese community population： a cross-sectional study. Lipids Health Dis， 2016； 15， 70.

27.Zhuang M， Yuan Z， Lin L， et al. Reproducibility and relative validity of a food frequency questionnaire developed for adults in Taizhou， China. PloS One， 2012； 7， e48341.

28.Cheng YJ， Church TS， Kimball TE， et al. Comparison of coronary artery calcium detected by electron beam tomography in patients with to those without symptomatic coronary heart disease. Am J Cardiol， 2003； 92， 498-503.

29.Zhang ZQ. 64-Slice Spiral Computed Tomography in Cardiovascular Imaging. Beijing： People's Medical Publishing House， 2008； 51-2.

30.Pletcher MJ， Tice JA， Pignone M， et al. Using the coronary artery calcium score to predict coronary heart disease events：a systematic review and meta-analysis. Arch Intern Med， 2004；164， 1285-92.

31.Polonsky TS， McClelland RL， Jorgensen NW， et al. Coronary artery calcium score and risk classification for coronary heart disease prediction. JAMA， 2010； 303， 1610-6.

32.Bambs C， Kip KE， Dinga A， et al. Low prevalence of "ideal cardiovascular health" in a community-based population： the heart strategies concentrating on risk evaluation （Heart SCORE）study. Circulation， 2011； 123， 850-7.

33.Ford ES， Greenlund KJ， Hong Y. Ideal cardiovascular health and mortality from all causes and diseases of the circulatory system among adults in the United States. Circulation， 2012； 125，987-95.

34.Zeng Q， Dong SY， Song ZY， et al. Ideal cardiovascular health in Chinese urban population. Int J Cardiol， 2013； 167， 2311-7.

35.Liu Y， Chi HJ， Cui LF， et al. The ideal cardiovascular health metrics associated inversely with mortality from all causes and from cardiovascular diseases among adults in a Northern Chinese industrial city. PloS One， 2014； 9， e89161.

36.Wu S， Huang Z， Yang X， et al. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circ Cardiovasc Qual Outcomes， 2012； 5， 487-93.

37.Nicoll R， Howard JM， Henein MY. A Review of the Effect of Diet on Cardiovascular Calcification. Int J Mol Sci， 2015； 16，8861-83.

38.Taylor AJ， Watkins T， Bell D， et al. Physical activity and the presence and extent of calcified coronary atherosclerosis. Med Sci Sports Exerc， 2002； 34， 228-33.

39.Kramer CK， von Mühlen D， Gross JL， et al. Blood pressure and fasting plasma glucose rather than metabolic syndrome predict coronary artery calcium progression： the Rancho Bernardo Study. Diabetes Care， 2009； 32， 141-6.

40.Nasir K， Santos RD， Tufail K， et al. High-normal fasting blood glucose in non-diabetic range is associated with increased coronary artery calcium burden in asymptomatic men. Atherosclerosis， 2007； 195， e155-60.

41.Saleem Y， DeFina LF， Radford NB， et al. Association of a favorable cardiovascular health profile with the presence of coronary artery calcification. Circ Cardiovascular imaging， 2015；8， e001851.

42.Anderson TJ. New Hope for Lipid-Lowering Beyond Statins：Effect of IMPROVE-IT on Understanding and Implementation of Atherosclerosis Prevention. Can J Cardiol， 2015； 31， 585-7.

43.Feig JE， Feig JL， Kini AS. Statins， atherosclerosis regression and HDL： Insights from within the plaque. Int J Cardiol， 2015； 189，168-71.

44.Kilic U， Gok O， Elibol-Can B， et al. Efficacy of statins on sirtuin 1 and endothelial nitric oxide synthase expression： the role of sirtuin 1 gene variants in human coronary atherosclerosis. Clin Exp Pharmacol Physiol， 2015； 42， 321-30.

April 16， 2016；

*This study was supported by grants from National Natural Science Foundation of China （81400229）， Capital Special Clinical Application Grants （Z141107002514103） and the Recovery Medical Science Foundation.

#Correspondence should be addressed to DONG Jian Zeng， E-mail： jz_dong@126.com， Tel： 86-10-64456865， Fax：86-10-64005361.

Biographical note of the first author： LUO Tai Yang， male， born in 1977， Medical doctor degree， majoring in coronary heart disease and cardiac failture.

Accepted： July 1， 2016