Coronary artery calcium in the population-based ImaLife study
Xia, Congying
DOI:
10.33612/diss.136415357
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date: 2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Xia, C. (2020). Coronary artery calcium in the population-based ImaLife study: relation to cardiovascular risk factors and cognitive function. University of Groningen. https://doi.org/10.33612/diss.136415357
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
GENERAL INTRODUCTION
The burden of coronary atherosclerosis is a risk factor for major coronary events [1]. Coronary calcification has been found to be strongly correlated to the total amount of coronary atherosclerotic plaques and therefore been considered as a surrogate of atherosclerotic coronary artery disease [2]. Before the 1960s, calci-fications in coronary arteries could only be found by pathologists during autopsy [3]. With the advent of imaging techniques, coronary calcifications can be detected non-invasively. Radiographic detection of coronary calcification can be traced back to the 1960s when fluorography was used [4-6]. As radiographic modalities devel-oped and improved over time, especially with the development of high-speed com-puted tomography (CT) systems, it became possible to rapidly and reliably detect and quantify coronary calcifications [7].
Dr. Arthur Agatston and Dr. Warren Janowitz introduced the first method to quan-tify coronary atherosclerotic burden on ECG-triggered cardiac CT images in 1990 based on electron-beam CT [8]. This score is called the Agatston score, or coro-nary artery calcium (CAC) score. Since then, an increasing number of epidemiolog-ic studies have implemented CAC quantifepidemiolog-ication and evaluated its association with the risk of coronary artery disease (CAD) worldwide [9-18].
CAC acquisition in epidemiologic studies
A reliable and reproducible approach for quantifying CAC is a prerequisite for ap-plying CAC scoring in clinical practice. A challenge of obtaining motion-free coro-nary images for CAC scoring is the constant rapid motion of the heart. High tempo-ral resolution and ECG triggering of the CT system are essential to overcome this challenge. In the 2000s, derived CAC scores were measured in large populations using electron-beam CT (EBCT) [11, 19-22]. Later on, multidetector CT (MDCT) systems were used. CAC scores obtained with MDCT systems have been found to be strongly correlated to scores obtained with EBCT systems, although differences in absolute CAC scores between EBCT and MDCT exist [23-25]. In 2005, a newer CT technique, called dual-source CT was introduced. A dual-source CT scanner contains two x-ray resources and two corresponding detector sets, positioned at around 90° angles to each other. With the third generation of this technique, a temporal resolution of 66 milliseconds is reached, which is higher than achieved with current state-of-the-art single source MDCT systems [26]. Nonetheless, both MDCT and dual-source CT systems have been routinely used in the recent decade in modern epidemiological imaging studies to acquire CAC scores with the use of ECG-triggering [27-30].
The high acquisition speed of dual-source CT in high-pitch mode allows scanning of the entire lungs and heart in less than a second. This may allow to obtain CAC score with minimal cardiac motion artifacts even without the use of ECG-triggering. In other words, it may enable reliable CAC score analyses on a non-ECG triggered chest scan for lung cancer screening. In lung cancer screening, the heart is inher-ently visible on the scan and CAC is commonly observed [31]. It is important to know the accuracy of CAC scores obtained from lung cancer screening chest CT, because reliable CAC scores can then be used for cardiovascular risk evaluation. In this way, combining CAC scoring with lung cancer screening within a single CT scan may increase the effectiveness of CT based screening. However, whether CAC scores obtained with routine chest CT for lung cancer screening are compa-rable to those obtained with standard methods of using ECG-triggered cardiac CT needs to be elucidated.
CAC score in the ImaLife study
Besides reliable and reproducible imaging techniques to obtain CAC scores, an-other crucial requirement to be able to use the CAC score as a risk maker for CAD, are CAC score reference values by age and sex. CAC score distribution by age and sex has been reported based on EBCT and earlier generation MDCT systems. Particularly, in the USA, age- and sex- distributions have been established in ten thousands of asymptomatic participants based on EBCT measurements [19, 20]. However, these participants were self-referred or referred by their physicians to un-dergo CAC examination because of the presence of CVD risk factors and concerns about potential CAD. Therefore, results of these studies may not represent the CAC score distribution in a general population. In contrast, the Multi-Ethnic Study of Atherosclerosis (MESA) in the USA [27] and the Heinz Nixdorf Recall study in Germany [22] included unselected adults populations without history of CAD. In addition, the Rotterdam coronary calcification study in the Netherlands, measured CAC scores using EBCT, but only in elderly [11]. So far, no CAC score reference values have been established in the general Dutch population. Neither have popu-lation-based imaging studies been performed that investigated imaging biomarkers of big three (Big-3) diseases (namely, CAC score for CAD, lung nodules for lung cancer, pulmonary density and bronchial wall thickness for chronic obstructive pul-monary disease) integrally. Therefore, embedded in the population-based Lifelines cohort study in the Northern part of the Netherlands [32], the ImaLife (Imaging in Lifelines) study was designed. The goal of this study is to establish popula-tion-based reference values of Big-3 imaging biomarkers in Dutch adults aged ≥45 years using state-of-the-art third-generation dual-source CT.
Cardiovascular risk and CAC
Assessment of an individual’s risk is the core of primary prevention of CAD. Major well-known causal risk factors for CAD are smoking, high blood pressure, elevated blood cholesterol and high blood glucose [1]. Besides, aging is also an important and inevitable independent risk factor for CAD, even when the abovementioned causal risk factors are under control [33]. Integrated risk scores such as the Sys-tematic COronary Risk Evaluation (SCORE) and the atherosclerotic cardiovascular disease (ASCVD) 10-year risk scores are developed based on the classical risk factors and aim to stratify individuals at low, intermediate and high risk of cardio-vascular disease. However, these integrated scores have moderate overall dis-criminative ability (C-statistics around 0.75) to predict major coronary events [34]. Moreover, classical risk factors based scores fail to identify some individuals who are at high-risk, especially in young adults (aged ≤55 years for men and ≤65 years for women), since classical risk factors are usually absent at a young age [35]. New approaches that can improve cardiovascular risk assessment are needed. A promising approach is to directly measure the burden of atherosclerotic plaques, because increased burden of atherosclerosis is associated with risk of cardiovas-cular events, and therefore may better reflect the risk of CAD [36, 37]. The CAC score can be used to quantify the total burden of coronary atherosclerotic plaque noninvasively [38]. The predictive value of the CAC score for adverse cardiac out-comes has been investigated in prior population-based prospective cohort studies. These studies showed that individuals with higher CAC scores had an increased risk of developing coronary events compared to individuals with CAC score of zero [9-13, 39]. Studies have also found discrepancy in risk categorization based on CAC score versus based on integrated risk scores [40-42]; adding CAC scores to classical risk factors based scores was shown to improve overall risk prediction [43-46].
According to current guidelines, CAC scoring can be considered in individuals who are classified at intermediate risk level according to classical risk factors, in whom preventive intervention is mostly uncertain; in these individuals the CAC score helps to improve the stratification into the correct CAD risk category [47, 48]. Moreover, CAC may also be useful as a surrogate, subclinical outcome for cardio-vascular disease, f.e. to identify new cardiocardio-vascular risk factors. These new risk factors are important for assessment of risk and could also be potential targets for early intervention to delay disease progression or prevent disease onset [49-55]. Of these, skin autofluorescence (SAF) has attracted interest. SAF values reflect tissue advanced glycation end products accumulation which are involved in the patho-physiological process of vascular atherosclerosis [56]. SAF measurement may
have clinical value in CAD risk assessment since it is noninvasive, fast, convenient and inexpensive. There is growing evidence that SAF can predict cardiovascular events not only in conditions of diabetes mellitus or renal failure but also in the gen-eral population [57-59]. However, the relationship of SAF with subclinical athero-sclerosis is less clear. Prior studies that investigated whether SAF was associated with subclinical atherosclerosis determined by CAC scores in selected populations yield inconsistent results [54, 60, 61]. Knowledge of the relationship between SAF and CAC in a general population may help a better understanding of the relative value of using SAF and/or CAC scores as predictors of CAD risk.
CAC and cognitive function
Both CAD and dementia are common in the elderly. Alzheimer’s disease and vascular dementia are the most common subtypes of dementia. Both Alzheimer’s disease and vascular dementia share risk factors with cardiovascular disease, such as aging, smoking, hypertension and hypercholesterolemia. These cardiovascular risk factors may contribute to dementia via pathophysiological pathways includ-ing cerebral atherosclerosis and neurodegeneration [62, 63]. Knowledge about the relationship between CAD and dementia could give an insight into possible measures that could be taken to prevent or halt the process of dementia in CAD patients. CAC score is an imaging surrogate for subclinical CAD, and it reflects the cumulative effect of the lifetime exposure to known and unknown cardiovascular risk factors [64]. CAC scoring may allow to detect individuals who are prone to cognitive decline. Prior studies on the association of CAC and clinical CAD with cognitive decline including dementia showed discrepant results. So far, there is lim-ited evidence to support the statement that CAC is associated with worse cognitive performance [65-67]. A systematic review to summarize prior findings from multiple studies can help to better understand the relationship of CAC and clinical CAD with dementia. Furthermore, more investigation is needed regarding this topic to accrue evidence on the potential value of CAC scoring in this area.
Outline of this thesis
The aim of this thesis is to investigate the CAC score distribution in association with cardiovascular risk factors and cognitive function in a general population. All studies described in this thesis are in the framework of the ImaLife study.
In the first part of this thesis the CAC scoring technique in a population-based study of Big-3 imaging biomarkers is introduced. Chapter 2 describes the study design and rationale of the ImaLife study. In Chapter 3, the feasibility of non-ECG triggered chest CT in CAC acquisition is investigated. In the second part,
asso-ciations between cardiovascular risk factors and CAC are described. Chapter 4 describes the distribution of CAC scores and the relation between classical cardio-vascular risk factors and CAC in a middle-aged Dutch population. In chapter 5, we investigated the association between skin autofluorescence, a marker of advanced glycation end products, and CAC. The third part of this thesis focuses on CAC and cognitive function. Current available evidence on the association of CAC and clin-ical CAD with cognitive function is systematclin-ically reviewed in Chapter 6. In Chap-ter 7, we investigated the association between CAC and cognitive function in the ImaLife population. Finally, in Chapter 8, the general discussion, the main findings of this thesis are summarized. The results of this thesis and their relevance are dis-cussed in a broader perspective, and directions for further research are proposed.
REFERENCE:
1 Grundy SM. Primary prevention of coronary heart disease: integrating risk assessment with intervention. Circu-lation 1999;100:988-98.
2 Sangiorgi G, Rumberger JA, Severson A, et al. Arterial calcifica-tion and not lumen stenosis is highly correlated with atherosclerotic plaque burden in humans: a histologic study of 723 coronary artery segments using nondecalcifying methodology. Journal of the American College of Cardiology 1998;31:126-33.
3 Oliver MF, Samuel E, Morley P, et al. Detection of coronary-artery calcification during life. Lancet (London, England) 1964;1:891-5.
4 Jorgens J, Lieber A. The use of cinefluorography in acquired heart disease. Circulation 1961;24:851-6. 5 Bartel AG, Chen JT, Peter RH, et al. The significance of coronary calcifica-tion detected by fluoroscopy. A report of 360 patients. Circulation 1974;49:1247-53.
6 Eggen DA, Strong JP, McGill HC, Jr. Coronary calcification. Relation-ship to clinically significant coronary lesions and race, sex, and topographic distribution. Circulation 1965;32:948-55. 7 Oudkerk M, Stillman AE, Halli-burton SS, et al. Coronary artery calcium screening: current status and recom-mendations from the European Society of Cardiac Radiology and North Ameri-can Society for Cardiovascular Imaging. European radiology 2008;18:2785-807.
8 Agatston AS, Janowitz WR, Hild-ner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. Journal of the American College of Cardiology 1990;15:827-32. 9 Detrano R, Guerci AD, Carr JJ, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. The New England journal of medicine 2008;358:1336-45.
10 Erbel R, Mohlenkamp S, Moe-bus S, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. Journal of the American College of Cardiology 2010;56:1397-406.
11 Vliegenthart R, Oudkerk M, Song B, et al. Coronary calcification detected by electron-beam computed to-mography and myocardial infarction. The Rotterdam Coronary Calcification Study. European heart journal 2002;23:1596-603.
12 Ferencik M, Pencina KM, Liu T, et al. Coronary Artery Calcium Dis-tribution Is an Independent Predictor of Incident Major Coronary Heart Disease Events: Results From the Framingham Heart Study. Circulation Cardiovascular imaging 2017;10.
13 Carr JJ, Jacobs DR, Jr., Ter-ry JG, et al. Association of CoronaTer-ry Artery Calcium in Adults Aged 32 to 46 Years With Incident Coronary Heart Disease and Death. JAMA cardiology
2017;2:391-9.
14 Lambrechtsen J, Gerke O, Eg-strup K, et al. The relation between coro-nary artery calcification in asymptomatic subjects and both traditional risk factors and living in the city centre: a DanRisk substudy. Journal of internal medicine 2012;271:444-50.
15 Han D, B OH, Gransar H, et al. Prevalence and Distribution of Coronary Artery Calcification in Asymptomatic United States and Korean Adults- Cross-Sectional Propensity-Matched Analysis. Circulation journal : official journal of the Japanese Circulation Soci-ety 2016;80:2349-55.
16 Hisamatsu T, Liu K, Chan C, et al. Coronary Artery Calcium Progres-sion Among the US and Japanese Men. Circulation Cardiovascular imaging 2019;12:e008104.
17 Wang M, Hou ZH, Xu H, et al. Association of Estimated Long-term Ex-posure to Air Pollution and Traffic Prox-imity With a Marker for Coronary Athero-sclerosis in a Nationwide Study in China. JAMA network open 2019;2:e196553. 18 Pereira AC, Gomez LM, Bitten-court MS, et al. Age, Gender, and Race-Based Coronary Artery Calcium Score Percentiles in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Clinical cardiology 2016;39:352-9. 19 Mitchell TL, Pippin JJ, Devers SM, et al. Age- and sex-based nomo-grams from coronary artery calcium scores as determined by electron beam computed tomography. The American journal of cardiology 2001;87:453-6, a6.
20 Nasir K, Raggi P, Rumberger JA, et al. Coronary artery calcium volume scores on electron beam tomography in 12,936 asymptomatic adults. The Amer-ican journal of cardiology 2004;93:1146-9.
21 Rumberger JA, Kaufman L. A rosetta stone for coronary calcium risk stratification: agatston, volume, and mass scores in 11,490 individuals. AJR American journal of roentgenology 2003;181:743-8.
22 Schmermund A, Mohlenkamp S, Berenbein S, et al. Population-based assessment of subclinical coronary atherosclerosis using electron-beam computed tomography. Atherosclerosis 2006;185:177-82.
23 Mao SS, Pal RS, McKay CR, et al. Comparison of coronary artery calcium scores between electron beam computed tomography and 64-multide-tector computed tomographic scanner. Journal of computer assisted tomogra-phy 2009;33:175-8.
24 Nasir K, Budoff MJ, Post WS, et al. Electron beam CT versus helical CT scans for assessing coronary calcifica-tion: current utility and future directions. American heart journal 2003;146:969-77.
25 Detrano RC, Anderson M, Nelson J, et al. Coronary calcium measurements: effect of CT scanner type and calcium measure on rescan reproducibility--MESA study. Radiology 2005;236:477-84.
26 Lewis MA, Pascoal A, Keevil SF, et al. Selecting a CT scanner for
cardiac imaging: the heart of the mat-ter. The British journal of radiology 2016;89:20160376.
27 McClelland RL, Chung H, De-trano R, et al. Distribution of coronary artery calcium by race, gender, and age: results from the Multi-Ethnic Study of Atherosclerosis (MESA). Circulation 2006;113:30-7.
28 Hoffmann U, Massaro JM, Fox CS, et al. Defining normal distributions of coronary artery calcium in women and men (from the Framingham Heart Study). The American journal of cardiolo-gy 2008;102:1136-41, 41.e1.
29 Bergström G, Berglund G, Blomberg A, et al. The Swedish CArdio-Pulmonary BioImage Study: objectives and design. Journal of internal medicine 2015;278:645-59.
30 Vonder M, van der Aalst CM, Vliegenthart R, et al. Coronary Artery Calcium Imaging in the ROBINSCA Trial: Rationale, Design, and Techni-cal Background. Academic radiology 2018;25:118-28.
31 Mets OM, Vliegenthart R, Gon-drie MJ, et al. Lung cancer screening CT-based prediction of cardiovascular events. JACC Cardiovascular imaging 2013;6:899-907.
32 Scholtens S, Smidt N, Swertz MA, et al. Cohort Profile: LifeLines, a three-generation cohort study and bio-bank. International journal of epidemiolo-gy 2015;44:1172-80.
33 Wang JC, Bennett M. Aging and atherosclerosis: mechanisms, functional consequences, and potential
therapeu-tics for cellular senescence. Circulation research 2012;111:245-59.
34 Siontis GC, Tzoulaki I, Siontis KC, et al. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ (Clini-cal research ed) 2012;344:e3318. 35 Akosah KO, Schaper A, Cogbill C, et al. Preventing myocardial infarction in the young adult in the first place: how do the National Cholesterol Education Panel III guidelines perform? Journal of the American College of Cardiology 2003;41:1475-9.
36 Arbab-Zadeh A, Fuster V. The myth of the “vulnerable plaque”: transi-tioning from a focus on individual lesions to atherosclerotic disease burden for coronary artery disease risk assess-ment. Journal of the American College of Cardiology 2015;65:846-55.
37 Libby P. Mechanisms of acute coronary syndromes and their impli-cations for therapy. The New England journal of medicine 2013;368:2004-13. 38 Rumberger JA, Simons DB, Fitzpatrick LA, et al. Coronary artery cal-cium area by electron-beam computed tomography and coronary atherosclerot-ic plaque area. A histopathologatherosclerot-ic correla-tive study. Circulation 1995;92:2157-62. 39 Vliegenthart R, Oudkerk M, Hofman A, et al. Coronary calcification improves cardiovascular risk prediction in the elderly. Circulation 2005;112:572-7.
40 Diederichsen AC, Sand NP, Norgaard B, et al. Discrepancy be-tween coronary artery calcium score
and HeartScore in middle-aged Danes: the DanRisk study. European journal of preventive cardiology 2012;19:558-64. 41 Okwuosa TM, Greenland P, Ning H, et al. Distribution of coronary artery calcium scores by Framingham 10-year risk strata in the MESA (Multi-Ethnic Study of Atherosclerosis) potential implications for coronary risk assess-ment. Journal of the American College of Cardiology 2011;57:1838-45.
42 Okwuosa TM, Greenland P, Ning H, et al. Yield of screening for coronary artery calcium in early middle-age adults based on the 10-year Framingham Risk Score: the CARDIA study. JACC Cardio-vascular imaging 2012;5:923-30. 43 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.
44 Greenland P, LaBree L, Azen SP, et al. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individu-als. Jama 2004;291:210-5.
45 McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Fac-tors: Derivation in the MESA (Multi-Eth-nic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). Journal of the American College of Cardiology 2015;66:1643-53.
46 Kavousi M, Elias-Smale S, Rutten JH, et al. Evaluation of newer risk
markers for coronary heart disease risk classification: a cohort study. Annals of internal medicine 2012;156:438-44. 47 Knuuti J, Wijns W, Saraste A, et al. 2019 ESC Guidelines for the diagno-sis and management of chronic coronary syndromes. European heart journal 2020;41:407-77.
48 Bittner VA. The New 2019 ACC/ AHA Guideline on the Primary Preven-tion of Cardiovascular Disease [pub-lished online ahead of print, 2019 Mar 17] Circulation 2019.
49 Shin S, Kim KJ, Chang HJ, et al. Impact of serum calcium and phosphate on coronary atherosclerosis detected by cardiac computed tomography. Europe-an heart journal 2012;33:2873-81. 50 Kwak SM, Kim JS, Choi Y, et al. Dietary intake of calcium and phospho-rus and serum concentration in relation to the risk of coronary artery calcification in asymptomatic adults. Arteriosclero-sis, thromboArteriosclero-sis, and vascular biology 2014;34:1763-9.
51 Ko BJ, Chang Y, Jung HS, et al. Relationship Between Low Relative Muscle Mass and Coronary Artery Cal-cification in Healthy Adults. Arterioscle-rosis, thrombosis, and vascular biology 2016;36:1016-21.
52 Chang Y, Ryu S, Sung KC, et al. Alcoholic and non-alcoholic fatty liver disease and associations with coronary artery calcification: evidence from the Kangbuk Samsung Health Study. Gut 2019;68:1667-75.
53 Kim ES, Shin JA, Shin JY, et al. Association between low serum free
thyroxine concentrations and coronary artery calcification in healthy euthy-roid subjects. Thyeuthy-roid : official journal of the American Thyroid Association 2012;22:870-6.
54 den Dekker MA, Zwiers M, van den Heuvel ER, et al. Skin autofluores-cence, a non-invasive marker for AGE accumulation, is associated with the degree of atherosclerosis. PloS one 2013;8:e83084.
55 den Harder AM, de Jong PA, de Groot MCH, et al. Commonly available hematological biomarkers are associat-ed with the extent of coronary calcifica-tions. Atherosclerosis 2018;275:166-73. 56 Da Moura Semedo C, Webb M, Waller H, et al. Skin autofluorescence, a non-invasive marker of advanced glyca-tion end products: clinical relevance and limitations. Postgraduate medical journal 2017;93:289-94.
57 Meerwaldt R, Lutgers HL, Links TP, et al. Skin autofluorescence is a strong predictor of cardiac mortality in diabetes. Diabetes care 2007;30:107-12. 58 van Waateringe RP, Fokkens BT, Slagter SN, et al. Skin autofluores-cence predicts incident type 2 diabetes, cardiovascular disease and mortality in the general population. Diabetologia 2019;62:269-80.
59 Cavero-Redondo I, Soriano-Ca-no A, Álvarez-BueSoriano-Ca-no C, et al. Skin Autofluorescence-Indicated Advanced Glycation End Products as Predictors of Cardiovascular and All-Cause Mortality in High-Risk Subjects: A Systematic Review and Meta-analysis. Journal
of the American Heart Association 2018;7:e009833.
60 Hangai M, Takebe N, Honma H, et al. Association of Advanced Glycation End Products with coronary Artery Calci-fication in Japanese Subjects with Type 2 Diabetes as Assessed by Skin Auto-fluorescence. Journal of atherosclerosis and thrombosis 2016;23:1178-87. 61 Wang AY, Wong CK, Yau YY, et al. Skin autofluorescence associates with vascular calcification in chronic kid-ney disease. Arteriosclerosis, thrombo-sis, and vascular biology 2014;34:1784-90.
62 Qiu C, Fratiglioni L. A major role for cardiovascular burden in age-related cognitive decline. Nature reviews Cardi-ology 2015;12:267-77.
63 Casserly I, Topol E. Conver-gence of atherosclerosis and Alzhei-mer’s disease: inflammation, cholesterol, and misfolded proteins. Lancet (London, England) 2004;363:1139-46.
64 Blaha MJ, Silverman MG, Budoff MJ. Is there a role for coronary artery calcium scoring for management of asymptomatic patients at risk for coro-nary artery disease?: Clinical risk scores are not sufficient to define primary prevention treatment strategies among asymptomatic patients. Circulation Cardiovascular imaging 2014;7:398-408; discussion
65 Bos D, Vernooij MW, de Bruijn RF, et al. Atherosclerotic calcification is related to a higher risk of dementia and cognitive decline. Alzheimer’s & dementia : the journal of the Alzheimer’s
Association 2015;11:639-47.e1. 66 Kuller LH, Lopez OL, Mackey RH, et al. Subclinical Cardiovascular Disease and Death, Dementia, and Coronary Heart Disease in Patients 80+ Years. Journal of the American College of Cardiology 2016;67:1013-22.
67 Fujiyoshi A, Jacobs DR, Jr., Fitzpatrick AL, et al. Coronary Artery Calcium and Risk of Dementia in MESA (Multi-Ethnic Study of Atherosclerosis). Circulation Cardiovascular imaging 2017;10.
