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Figure 1.
Chronic Renal Insufficiency Cohort Study Participants by Coronary Artery Calcification Score and Estimated Glomerular Filtration Rate
Chronic Renal Insufficiency Cohort Study Participants by Coronary Artery Calcification Score and Estimated Glomerular Filtration Rate

A score of 0 indicates no coronary artery calcification; greater than 0 to 100, moderate calcification; greater than 100, severe calcification.

Figure 2.
Kaplan-Meier Cumulative Event Rate of Cardiovascular Disease, Myocardial Infarction, Heart Failure, and All-Cause Mortality According to Coronary Artery Calcification Score Among Chronic Renal Insufficiency Cohort Participants Without a History of Cardiovascular Disease
Kaplan-Meier Cumulative Event Rate of Cardiovascular Disease, Myocardial Infarction, Heart Failure, and All-Cause Mortality According to Coronary Artery Calcification Score Among Chronic Renal Insufficiency Cohort Participants Without a History of Cardiovascular Disease

A score of 0 indicates no coronary artery calcification; greater than 0 to 100, moderate calcification; greater than 100, severe calcification. Cardiovascular disease included myocardial infarction, heart failure, and stroke.

Figure 3.
Multivariable-Adjusted Hazard Ratios of Cardiovascular Disease by ACC/AHA Atherosclerotic Cardiovascular Disease Risk Score and CAC Score Among Chronic Renal Insufficiency Cohort Participants Without a History of Cardiovascular Disease
Multivariable-Adjusted Hazard Ratios of Cardiovascular Disease by ACC/AHA Atherosclerotic Cardiovascular Disease Risk Score and CAC Score Among Chronic Renal Insufficiency Cohort Participants Without a History of Cardiovascular Disease

Error bars indicate 95% CIs; ACC, American College of Cardiology; AHA, American Heart Association; CAC, coronary artery calcification. A CAC score of 0 indicates no calcification; greater than 0 to 100, moderate calcification; greater than 100, severe calcification.

Table 1.  
Baseline Characteristics of Chronic Renal Insufficiency Cohort Study Participants Without a History of Cardiovascular Disease by Coronary Artery Calcification Score Category
Baseline Characteristics of Chronic Renal Insufficiency Cohort Study Participants Without a History of Cardiovascular Disease by Coronary Artery Calcification Score Category
Table 2.  
Multivariable-Adjusted Hazard Ratios for Cardiovascular Disease and Death Associated With Coronary Artery Calcification Among Chronic Renal Insufficiency Cohort Study Participants Without a History of Cardiovascular Disease
Multivariable-Adjusted Hazard Ratios for Cardiovascular Disease and Death Associated With Coronary Artery Calcification Among Chronic Renal Insufficiency Cohort Study Participants Without a History of Cardiovascular Disease
1.
Gansevoort  RT, Correa-Rotter  R, Hemmelgarn  BR,  et al.  Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention.  Lancet. 2013;382(9889):339-352.PubMedGoogle ScholarCrossref
2.
Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305.PubMedGoogle ScholarCrossref
3.
Matsushita  K, van der Velde  M, Astor  BC,  et al; Chronic Kidney Disease Prognosis Consortium.  Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.  Lancet. 2010;375(9731):2073-2081.PubMedGoogle ScholarCrossref
4.
Hemmelgarn  BR, Manns  BJ, Lloyd  A,  et al; Alberta Kidney Disease Network.  Relation between kidney function, proteinuria, and adverse outcomes.  JAMA. 2010;303(5):423-429.PubMedGoogle ScholarCrossref
5.
He  J, Reilly  M, Yang  W,  et al; CRIC Investigators.  Risk factors for coronary artery calcium among patients with chronic kidney disease (from the Chronic Renal Insufficiency Cohort Study).  Am J Cardiol. 2012;110(12):1735-1741.PubMedGoogle ScholarCrossref
6.
Kestenbaum  BR, Adeney  KL, de Boer  IH, Ix  JH, Shlipak  MG, Siscovick  DS.  Incidence and progression of coronary calcification in chronic kidney disease: the Multi-Ethnic Study of Atherosclerosis.  Kidney Int. 2009;76(9):991-998.PubMedGoogle ScholarCrossref
7.
Russo  D, Palmiero  G, De Blasio  AP, Balletta  MM, Andreucci  VE.  Coronary artery calcification in patients with CRF not undergoing dialysis.  Am J Kidney Dis. 2004;44:1024-1030.Google ScholarCrossref
8.
Greenland  P, Bonow  RO, Brundage  BH,  et al; American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography); Society of Atherosclerosis Imaging and Prevention; Society of Cardiovascular Computed Tomography.  ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography).  Circulation. 2007;115:402-426.PubMedGoogle ScholarCrossref
9.
Peters  SA, Bakker  M, den Ruijter  HM, Bots  ML.  Added value of CAC in risk stratification for cardiovascular events: a systematic review.  Eur J Clin Invest. 2012;42(1):110-116.PubMedGoogle ScholarCrossref
10.
Bashir  A, Moody  WE, Edwards  NC, Ferro  CJ, Townend  JN, Steeds  RP.  Coronary artery calcium assessment in CKD: utility in cardiovascular disease risk assessment and treatment?  Am J Kidney Dis. 2015;65(6):937-948.PubMedGoogle ScholarCrossref
11.
Matsushita  K, Sang  Y, Ballew  SH,  et al.  Subclinical atherosclerosis measures for cardiovascular prediction in CKD.  J Am Soc Nephrol. 2015;26(2):439-447.PubMedGoogle ScholarCrossref
12.
Goff  DC  Jr, Lloyd-Jones  DM, Bennett  G,  et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  Circulation. 2014;129(25)(suppl 2):S49-S73.PubMedGoogle ScholarCrossref
13.
Lash  JP, Go  AS, Appel  LJ,  et al; Chronic Renal Insufficiency Cohort (CRIC) Study Group.  Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.  Clin J Am Soc Nephrol. 2009;4(8):1302-1311.PubMedGoogle ScholarCrossref
14.
Pickering  TG, Hall  JE, Appel  LJ,  et al; Council on High Blood Pressure Research Professional and Public Education Subcommittee, American Heart Association.  Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee.  J Clin Hypertens (Greenwich). 2005;7(2):102-109.PubMedGoogle ScholarCrossref
15.
Anderson  AH, Yang  W, Hsu  CY,  et al; CRIC Study Investigators.  Estimating GFR among participants in the Chronic Renal Insufficiency Cohort (CRIC) study.  Am J Kidney Dis. 2012;60(2):250-261.PubMedGoogle ScholarCrossref
16.
Agatston  AS, Janowitz  WR, Hildner  FJ, Zusmer  NR, Viamonte  M  Jr, Detrano  R.  Quantification of coronary artery calcium using ultrafast computed tomography.  J Am Coll Cardiol. 1990;15(4):827-832.PubMedGoogle ScholarCrossref
17.
Kaplan  EL, Meier  P.  Nonparametric estimation from incomplete observations.  J Am Stat Assoc. 1958;53(282):457-481.Google ScholarCrossref
18.
Cox  DR.  Regression models and life tables.  J Royal Stat Soc Series B. 1972;34:187-188, 220.Google Scholar
19.
Pencina  MJ, D’Agostino  RB.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.  Stat Med. 2004;23(13):2109-2123.PubMedGoogle ScholarCrossref
20.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21.PubMedGoogle ScholarCrossref
21.
Wilkieson  TJ, Rahman  MO, Gangji  AS,  et al.  Coronary artery calcification, cardiovascular events, and death: a prospective cohort study of incident patients on hemodialysis.  Can J Kidney Health Dis. 2015;2:29.PubMedGoogle ScholarCrossref
22.
Polonsky  TS, McClelland  RL, Jorgensen  NW,  et al.  Coronary artery calcium score and risk classification for coronary heart disease prediction.  JAMA. 2010;303(16):1610-1616.PubMedGoogle ScholarCrossref
23.
Cho  I, Min  HS, Chun  EJ,  et al.  Coronary atherosclerosis detected by coronary CT angiography in asymptomatic subjects with early chronic kidney disease.  Atherosclerosis. 2010;208(2):406-411.PubMedGoogle ScholarCrossref
24.
Joosen  IA, Schiphof  F, Versteylen  MO,  et al.  Relation between mild to moderate chronic kidney disease and coronary artery disease determined with coronary CT angiography.  PLoS One. 2012;7(10):e47267.PubMedGoogle ScholarCrossref
25.
Winther  S, Svensson  M, Jørgensen  HS,  et al.  Diagnostic performance of coronary CT angiography and myocardial perfusion imaging in kidney transplantation candidates.  JACC Cardiovasc Imaging. 2015;8(5):553-562.PubMedGoogle ScholarCrossref
26.
Russo  D, Corrao  S, Battaglia  Y,  et al.  Progression of coronary artery calcification and cardiac events in patients with chronic renal disease not receiving dialysis.  Kidney Int. 2011;80(1):112-118.PubMedGoogle ScholarCrossref
27.
Baber  U, de Lemos  JA, Khera  A,  et al.  Non-traditional risk factors predict coronary calcification in chronic kidney disease in a population-based cohort.  Kidney Int. 2008;73(5):615-621.PubMedGoogle ScholarCrossref
28.
Jono  S, McKee  MD, Murry  CE,  et al.  Phosphate regulation of vascular smooth muscle cell calcification.  Circ Res. 2000;87(7):E10-E17.PubMedGoogle ScholarCrossref
29.
Steitz  SA, Speer  MY, Curinga  G,  et al.  Smooth muscle cell phenotypic transition associated with calcification: upregulation of Cbfa1 and downregulation of smooth muscle lineage markers.  Circ Res. 2001;89(12):1147-1154.PubMedGoogle ScholarCrossref
30.
Goodman  WG, London  G, Amann  K,  et al; Vascular Calcification Work Group.  Vascular calcification in chronic kidney disease.  Am J Kidney Dis. 2004;43(3):572-579.PubMedGoogle ScholarCrossref
31.
Shroff  RC, Shanahan  CM.  The vascular biology of calcification.  Semin Dial. 2007;20(2):103-109.PubMedGoogle ScholarCrossref
32.
Wade  AN, Reilly  MP.  Coronary calcification in chronic kidney disease: morphology, mechanisms and mortality.  Clin J Am Soc Nephrol. 2009;4(12):1883-1885.PubMedGoogle ScholarCrossref
33.
Proudfoot  D, Shanahan  CM.  Biology of calcification in vascular cells: intima versus media.  Herz. 2001;26(4):245-251.PubMedGoogle ScholarCrossref
34.
Demer  LL, Tintut  Y.  Vascular calcification: pathobiology of a multifaceted disease.  Circulation. 2008;117(22):2938-2948.PubMedGoogle ScholarCrossref
35.
London  GM, Guérin  AP, Marchais  SJ, Métivier  F, Pannier  B, Adda  H.  Arterial media calcification in end-stage renal disease: impact on all-cause and cardiovascular mortality.  Nephrol Dial Transplant. 2003;18(9):1731-1740.PubMedGoogle ScholarCrossref
Original Investigation
June 2017

Coronary Artery Calcification and Risk of Cardiovascular Disease and Death Among Patients With Chronic Kidney Disease

Author Affiliations
  • 1Department of Medicine, School of Medicine, Tulane University, New Orleans, Louisiana
  • 2Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana
  • 3LA BioMed at Harbor-UCLA Medical Center, Los Angeles, California
  • 4Department of Medicine, Division of Cardiology, Columbia University, New York, New York
  • 5Department of Biostatistics and Epidemiology, School of Medicine, University of Pennsylvania, Philadelphia
  • 6Joslyn Diabetic Center, Harvard Medical School, Boston, Massachusetts
  • 7Department of Medicine, University Hospitals of Case Western Reserve University, Cleveland, Ohio
  • 8Department of Medicine, School of Medicine, University of Pennsylvania, Philadelphia
  • 9Department of Medicine, School of Medicine, George Washington University, Washington, DC
  • 10Department of Medicine, University of Illinois Hospital and Health Sciences System, Chicago
  • 11Department of Medicine, School of Medicine, Wake Forest University, Winston-Salem, North Carolina
  • 12Department of Medicine, School of Medicine, Duke University, Durham, North Carolina
JAMA Cardiol. 2017;2(6):635-643. doi:10.1001/jamacardio.2017.0363
Key Points

Question  Does coronary artery calcification (CAC) predict cardiovascular disease risk among patients with chronic kidney disease (CKD)?

Findings  In this prospective cohort study, 1 SD log higher in CAC score was significantly associated with a 40% higher risk of cardiovascular disease, a 44% higher risk of myocardial infarction, and a 39% higher risk of heart failure after adjusting for important risk factors. Inclusion of CAC score led to a significant increase in the C statistic for predicting cardiovascular disease over use of established and novel risk factors among patients with CKD.

Meaning  Use of the CAC score improves risk prediction for cardiovascular disease, myocardial infarction, and heart failure over use of established and novel risk factors among patients with CKD.

Abstract

Importance  Coronary artery calcification (CAC) is highly prevalent in dialysis-naive patients with chronic kidney disease (CKD). However, there are sparse data on the association of CAC with subsequent risk of cardiovascular disease and all-cause mortality in this population.

Objective  To study the prospective association of CAC with risk of cardiovascular disease and all-cause mortality among dialysis-naive patients with CKD.

Design, Setting, and Participants  The prospective Chronic Renal Insufficiency Cohort study recruited adults with an estimated glomerular filtration rate of 20 to 70 mL/min/1.73 m2 from 7 clinical centers in the United States. There were 1541 participants without cardiovascular disease at baseline who had CAC scores.

Exposures  Coronary artery calcification was assessed using electron-beam or multidetector computed tomography.

Main Outcomes and Measures  Incidence of cardiovascular disease (including myocardial infarction, heart failure, and stroke) and all-cause mortality were reported every 6 months and confirmed by medical record adjudication.

Results  During an average follow-up of 5.9 years in 1541 participants aged 21 to 74 years, there were 188 cardiovascular disease events (60 cases of myocardial infarction, 120 heart failures, and 27 strokes; patients may have had >1 event) and 137 all-cause deaths. In Cox proportional hazards models adjusted for age, sex, race, clinical site, education level, physical activity, total cholesterol level, high-density lipoprotein cholesterol level, systolic blood pressure, use of antihypertensive treatment, current cigarette smoking, diabetes status, body mass index, C-reactive protein level, hemoglobin A1c level, phosphorus level, troponin T level, log N-terminal pro–B-type natriuretic peptide level, fibroblast growth factor 23 level, estimated glomerular filtration rate, and proteinuria, the hazard ratios associated with per 1 SD log of CAC were 1.40 (95% CI, 1.16-1.69; P < .001) for cardiovascular disease, 1.44 (95% CI, 1.02-2.02; P = .04) for myocardial infarction, 1.39 (95% CI, 1.10-1.76; P = .006) for heart failure, and 1.19 (95% CI, 0.94-1.51; P = .15) for all-cause mortality. In addition, inclusion of CAC score led to an increase in the C statistic of 0.02 (95% CI, 0-0.09; P < .001) for predicting cardiovascular disease over use of all the above-mentioned established and novel cardiovascular disease risk factors.

Conclusions and Relevance  Coronary artery calcification is independently and significantly related to the risks of cardiovascular disease, myocardial infarction, and heart failure in patients with CKD. In addition, CAC improves risk prediction for cardiovascular disease, myocardial infarction, and heart failure over use of established and novel cardiovascular disease risk factors among patients with CKD; however, the changes in the C statistic are small.

Introduction

Cardiovascular disease is the major cause of premature death in patients with chronic kidney disease (CKD).1,2 Large prospective cohort studies have documented significant associations of worse kidney function and proteinuria with cardiovascular disease and mortality that are independent of traditional risk factors.2-4 Coronary artery calcification (CAC) is highly prevalent and more severe in patients with CKD compared with those without CKD.5-7 Coronary artery calcification independently predicts the risk of coronary heart disease and total cardiovascular disease more than traditional risk factors in the general population.8,9

However, there are sparse data available from dialysis-naive patients with CKD and it is uncertain whether CAC score confers an elevated risk of cardiovascular disease morbidity and mortality in such patients.10 In a subgroup analysis of 1284 participants with CKD from the Multi-Ethnic Study of Atherosclerosis, CAC score, carotid intima-media thickness, and ankle-brachial index improved cardiovascular disease risk prediction, and the greatest improvement in risk prediction was observed with CAC score.11 However, the following cardiovascular disease risk factors that are especially important for patients with CKD were not adjusted for in that analysis11: inflammatory biomarkers, hemoglobin A1c level, phosphorus level, N-terminal pro–B-type natriuretic peptide (NT-proBNP) level, fibroblast growth factor 23 level, kidney function, and proteinuria.

The objectives of the Chronic Renal Insufficiency Cohort (CRIC) study were to investigate the prospective association of CAC with risk of cardiovascular disease and all-cause mortality in dialysis-naive patients with CKD after adjusting for important risk factors for cardiovascular disease, and to assess whether inclusion of CAC improves cardiovascular disease risk prediction over use of the American College of Cardiology (ACC) and the American Heart Association (AHA) atherosclerotic cardiovascular disease risk factors and other novel risk factors.12

Methods
Study Participants

The CRIC study included a racially and ethnically diverse group of men and women aged 21 to 74 years with mild to moderate CKD (age-based estimated glomerular filtration rate [eGFR] entry criteria of 20-70 mL/min/1.73 m2). A total of 3939 patients were recruited between May 2003 and August 2008 from 7 clinical centers in the United States.13 Patients were identified through searches of laboratory databases, medical records, and referrals from health care providers. Patients with cirrhosis, human immunodeficiency virus infection, polycystic kidney disease, or renal cell carcinoma; those receiving dialysis or recipients of a kidney transplant; or those taking immunosuppressant drugs were excluded. In addition, patients with a history of coronary artery revascularization were excluded from electron-beam or multidetector computed tomography (CT). Of these CRIC study participants, 1142 patients were randomly selected from all study sites and stratified by age, sex, race/ethnicity, diabetes status, and eGFR level for electron-beam or multidetector CT without any oversampling.

In addition, electron-beam or multidetector CT was performed in all eligible participants from 3 clinical centers for an ancillary study. Data on CAC were available for a total of 2069 patients.5 Of the 2069 patients, there were 528 who reported a history of coronary artery disease, heart failure, atrial fibrillation, stroke, or peripheral vascular disease at baseline and were excluded, leaving 1541 for this analysis.

The CRIC study was approved by the institutional review boards from each participating institution. Written informed consent was obtained from all participants. The CRIC study also conformed to the Health Insurance Portability and Accountability Act guidelines.

Baseline Measurement

All CRIC study data were collected by trained study staff during the baseline and annual clinical visits. All data collection procedures and equipment were standardized across the 7 study sites. Baseline information on demographic characteristics, lifestyle risk factors, history of cardiovascular disease, and use of medications was obtained by standard questionnaire. Race/ethnicity was self-reported and used as a covariable in the analysis. Cigarette smoking was defined by lifetime smoking of more than 100 cigarettes and alcohol consumption as drinking 1 or more alcoholic beverages each week during the previous year.

Physical activity was calculated as total metabolic equivalents per week. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared and was used as an index for obesity. Three seated blood pressure measurements using an aneroid sphygmomanometer after at least 5 minutes of quiet rest were obtained by trained and certified staff who followed a standard protocol, with the averages of 3 measurements used for the analysis.14 Hypertension was defined as a systolic blood pressure of 140 mm Hg or greater, a diastolic blood pressure of 90 mm Hg or greater, or current use of an antihypertensive medication.

Levels of glucose, cholesterol, triglycerides, glycated hemoglobin A1c, phosphorus, calcium, total parathyroid hormone, and alkaline phosphatase were measured using standard laboratory methods. Levels of high-sensitivity C-reactive protein (CRP) and cystatin C were measured using the particle-enhanced immunonephelometry method. High-sensitivity troponin T level was measured using the Roche Elecsys immunoassay (Roche Diagnostics). N-terminal pro–B-type natriuretic peptide level was measured using the Elecsys 2010 analyzer (Roche Diagnostics). Fibroblast growth factor 23 level was measured using a second-generation C-terminal assay (Immutopics). Creatinine level was measured using an enzymatic method on an Ortho Vitros 950 and standardized to isotope-dilution mass spectrometry traceable values. Estimated glomerular filtration rate was calculated using the estimated equation derived from the CRIC cohort.15

Diabetes was defined as a fasting plasma glucose level of 126 mg/dL or greater (to convert glucose to mmol/L, multiply by 0.0555), a nonfasting plasma glucose level of 200 mg/dL or greater, or self-reported use of any antidiabetes medication. A 24-hour urine specimen was collected and urinary protein concentration was determined using the turbidometric method with benzethonium chloride. All laboratory measurements were conducted at the CRIC study central laboratory at the University of Pennsylvania with stringent quality control.

As a part of the CRIC protocol, a subcohort of participants underwent CAC measurement with either electron-beam or multidetector CT at 1 year. Trained and certified technologists scanned all participants twice using phantoms of known physical calcium concentrations. A cardiologist read all CT scans at a central reading center (Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center).

The total Agatston score, which is a pseudo-continuous variable derived from plaque densities and their areas in all coronary arteries, was computed.16 The average Agatston score from the 2 scans was used in all the analyses. Based on the distribution of Agatston scores, the CRIC participants were divided into CAC score categories of 0 (no CAC), greater than 0 to 100 (moderate CAC), or greater than 100 (severe CAC). In addition, CAC per 1 SD log was calculated and used as a continuous variable for the analysis.

Assessment of Outcomes

Patients were followed up annually via clinic visits and interim telephone contact at 6 months. Cardiovascular disease (including myocardial infarction, heart failure, and stroke) outcomes were assessed using a standard Medical Event Questionnaire during all follow-up contacts. If patients were hospitalized or stayed overnight in an emergency department due to a cardiovascular disease event, including those who later died, their medical records were requested for cardiovascular event verification. Two physicians adjudicated each cardiovascular event, classifying each as either probable or definite.

Heart failure was identified by hospital admission for new or worsening heart failure signs and symptoms and diminished cardiac output. Myocardial infarction was defined by characteristic changes in levels of troponin T and creatine kinase myocardial band, symptoms of myocardial ischemia, changes in electrocardiogram results, new fixed-perfusion abnormalities, or a combination of these. Stroke was defined as rapid onset of a neurological deficit, headache, or other nonvascular cause, by presence of a clinically relevant lesion on brain imaging for longer than 24 hours, or death within 24 hours.

All events classified as probable or definite during adjudication were included in the analyses. Due to lack of cause-specific information regarding out-of-hospital deaths, these events were not included in the analysis. Follow-up was censored at the time of study outcome onset, death, withdrawal, loss to follow-up, or the end of the follow-up period, whichever occurred first.

Statistical Methods

Baseline characteristics of participants were summarized as mean (SD) for continuous variables, percentage for categorical variables, or median (interquartile range) for variables with skewed distribution by CAC categories. Statistical significance was tested using analysis of variance for continuous variables and χ2 tests for categorical variables. Logarithmic transformations were performed for severely skewed variables to stabilize variances and normalize distributions.

Cumulative rates of cardiovascular disease (included myocardial infarction, heart failure, and stroke), myocardial infarction, heart failure, and all-cause mortality were calculated using the Kaplan-Meier method by CAC score categories and compared using the log-rank test with the null hypothesis that the cumulative incidence was the same across the CAC score categories.17 Hazard ratios for the associations of CAC categories or log CAC scores with cardiovascular disease, myocardial infarction, heart failure, and death were estimated using Cox proportional hazards models with follow-up used as the time scale.18 The assumption of proportionality was tested using Schoenfeld residuals and interaction terms with time for each exposure variable and covariate. No substantial deviations from proportionality were observed.

For each outcome and exposure (categorical and log CAC scores), 3 models were fitted. Model 1 adjusted for age, sex, race, and clinical sites; model 2 adjusted for the factors in model 1 and the ACC/AHA atherosclerotic cardiovascular disease risk factors (age, sex, race, clinical site, total cholesterol level, high-density lipoprotein cholesterol level, systolic blood pressure, use of antihypertensive treatment, current cigarette smoking, and diabetes status); and model 3 adjusted for factors in models 1 and 2 plus education level, BMI, physical activity, high-sensitivity CRP level, hemoglobin A1c level, phosphorus level, high-sensitivity troponin T level, NT-proBNP level, fibroblast growth factor 23 level, eGFR, and 24-hour urinary protein level using backward elimination. Complete case analysis was used for the main findings. Multiple imputation for missing covariate values was performed in a sensitivity analysis, and no substantial differences were observed.

To assess the added value of CAC in prediction models, multivariable Cox proportional hazards models were compared with and without CAC score. The C statistic, the C statistic difference with or without CAC score, continuous net reclassification improvement, and integrated discrimination improvement were evaluated using methods accounting for censoring.19,20 The C statistic measures concordance between model-based risk estimates and observed events (equivalent to the area under the receiver operating characteristic curve).

The net reclassification improvement and integrated discrimination improvement are statistics proposed as measures of the incremental prognostic effect that a new biomarker will have when added to an existing prediction model. Net reclassification improvement is interpreted as the proportion of patients reclassified to a more appropriate risk category when a new biomarker is added to the prediction model. Integrated discrimination improvement is defined as an improvement in discrimination slopes (ie, the difference in means of model-based risks for events minus nonevents) if adding a new biomarker to the prediction model. Bootstrapping methods were used to obtain 95% CIs. All analyses were conducted using SAS version 9.1 (SAS Institute Inc). All P values were 2-sided and statistical significance was defined as P < .05.

Results

Among 1541 CRIC participants without a self-reported history of cardiovascular disease, 302 (19.6%) had an eGFR of less than 30 mL/min/1.73 m2, 467 (30.3%) had an eGFR of 30 to 44 mL/min/1.73 m2, 419 (27.2%) had an eGFR of 45 to 59 mL/min/1.73 m2, and 353 (22.9%) had an eGFR of 60 mL/min/1.73 m2 or greater (eFigure 1 in the Supplement). Of 451 participants who had a CAC score of greater than 100, 109 (24.2%) had an eGFR of less than 30 mL/min/1.73 m2, 158 (35.0%) had an eGFR of 30 to 44 mL/min/1.73 m2, 131 (29.0%) had an eGFR of 45 to 59 mL/min/1.73 m2, and 53 (11.8%) had an eGFR of 60 mL/min/1.73 m2 (Figure 1).

Baseline characteristics of participants according to CAC score categories appear in Table 1. Compared with those without CAC, participants with moderate or severe CAC were more likely to be older and male, less likely to be physically active, and more likely to have a history of hypertension and diabetes, and use of antihypertensive and lipid-lowering medications. On average, participants with CAC had higher levels of BMI, systolic blood pressure, hemoglobin A1c, phosphorus, high-sensitivity troponin T, NT-proBNP, fibroblast growth factor 23, and urinary protein, but lower levels of total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and eGFR.

During an average follow-up of 5.9 years, there were 188 cardiovascular disease events (60 cases of myocardial infarction, 120 heart failures, and 27 strokes; patients may have had >1 event) and 137 all-cause deaths. Kaplan-Meier cumulative incidence plots showed that moderate and severe CAC were associated with higher cumulative incidence of cardiovascular disease, myocardial infarction, heart failure, and all-cause mortality (Figure 2). After adjustment for age, sex, race, and clinic sites, moderate and severe CAC were significantly associated with higher risk of cardiovascular disease, myocardial infarction, heart failure, and all-cause mortality (Table 2). After additional adjustment for ACC/AHA atherosclerotic cardiovascular disease risk factors (total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, antihypertensive treatment, current cigarette smoking, and diabetes), the associations were slightly diminished but remained highly significant for severe CAC.

After further adjustment for important cardiovascular disease risk factors in patients with CKD, including education level, BMI, physical activity, high-sensitivity CRP, hemoglobin A1c level, phosphorus level, high-sensitivity troponin T level, NT-proBNP level, fibroblast growth factor 23 level, eGFR, and proteinuria, severe CAC (score >100) was significantly associated with heart failure and composite cardiovascular disease. Furthermore, log CAC score was significantly and independently associated with higher risk of cardiovascular disease, myocardial infarction, and heart failure in multiple adjusted models (Table 2).

In addition, among 528 participants with a history of cardiovascular disease, CAC was significantly and independently associated with cardiovascular disease and all-cause mortality. For 1 SD higher level of log CAC scores, the hazard ratio was 1.29 (95% CI, 1.05-1.58; P = .02) for cardiovascular disease, 2.10 (95% CI, 1.34-3.29; P = .001) for myocardial infarction, and 1.63 (95% CI, 1.29-2.07; P < .001) for all-cause mortality after adjusting for the above-mentioned established and novel cardiovascular disease risk factors (eTable 1 in the Supplement). In the pooled analyses including all participants, the associations of CAC with cardiovascular disease and all-cause mortality were even stronger (eFigure 2 and eTable 2 in the Supplement). For 1 SD higher level of log CAC scores, the hazard ratio was 1.38 (95% CI, 1.20-1.59; P < .001) for cardiovascular disease, 1.69 (95% CI, 1.28-2.23; P < .001) for myocardial infarction, 1.42 (95% CI, 1.19-1.69; P < .001) for heart failure, and 1.35 (95% CI, 1.15-1.60; P < .001) for all-cause mortality after adjusting for multiple established and novel risk factors.

Inclusion of CAC score improved risk predictions for cardiovascular disease, myocardial infarction, heart failure, and all-cause mortality over use of the ACC/AHA atherosclerotic cardiovascular disease risk predictors (eTable 3 in the Supplement). Furthermore, the C statistics were significantly improved for prediction of cardiovascular disease, myocardial infarction, and heart failure but not for all-cause mortality when CAC score was added to the full prediction model, which included the ACC/AHA atherosclerotic cardiovascular disease risk predictors and novel risk factors. The change in C statistic was 0.02 (95% CI, 0-0.09; P < .001) for cardiovascular disease, 0.02 (95% CI, 0-0.09; P = .03) for myocardial infarction, and 0.01 (95% CI, 0-0.09; P = .009) for heart failure.

During 5 years of follow-up, the C statistic for continuous net reclassification improvement in CAC was 0.47 (95% CI, 0.14-0.77) for cardiovascular disease, 0.55 (95% CI, 0.14-0.78) for myocardial infarction, and 0.36 (95% CI, 0.14-0.77) for heart failure; all 3 were significant and reflect moderately strong improvements. There were similar results for integrated discrimination improvement with a C statistic of 0.02 (95% CI, 0.01-0.06) for cardiovascular disease, 0.01 (95% CI, 0.01-0.06) for myocardial infarction, and 0.01 (95% CI, 0.01-0.06) for heart failure; all 3 were significant improvements compared with the full prediction model.

Cardiovascular disease incidence and all-cause mortality significantly increased with ACC/AHA atherosclerotic cardiovascular disease risk scores (eFigure 3 in the Supplement). Within each ACC/AHA atherosclerotic cardiovascular disease risk score category (ie, <5.0%, 5.0%-7.5% and >7.5%), cardiovascular disease risk significantly increased at higher CAC levels after adjusting for sex, race/ethnicity, and clinic sites (Figure 3).

Discussion

The findings from the CRIC study indicate that CAC is strongly and significantly associated with risk of subsequent cardiovascular disease, myocardial infarction, heart failure, and all-cause mortality in patients with CKD. These relationships are graded and independent of the ACC/AHA atherosclerotic cardiovascular disease risk factors, as well as education level, BMI, physical activity, high-sensitivity CRP, hemoglobin A1c level, phosphorus level, high-sensitivity troponin T level, NT-proBNP level, fibroblast growth factor 23 level, eGFR, and proteinuria. Furthermore, CAC scores improve risk prediction of future cardiovascular disease, myocardial infarction, and heart failure over use of the ACC/AHA atherosclerotic cardiovascular disease risk factors and other important novel cardiovascular disease risk factors in patients with CKD; however, the changes in the C statistic are small.

These findings have important clinical and public health implications because cardiovascular disease is the major cause of premature death in patients with CKD.1-4 Coronary artery calcification is accelerated in patients with CKD due to 2 distinct pathological processes that result in medial (arteriosclerosis) and intimal (atherosclerosis) deposition.21 Although there are data indicating that very high CAC scores may be associated with increased risk of death in patients undergoing hemodialysis, the average CAC scores in most patients are elevated at a level at which discriminatory power may be reduced.22 Furthermore, there are fewer data available for dialysis-naive patients with CKD, for whom it is uncertain whether CAC score confers an elevated risk of cardiovascular disease events and all-cause mortality.10

Our study indicated that CAC was strongly and independently associated with the incidence of cardiovascular disease events and all-cause mortality among dialysis-naive patients with CKD. Plaque assessment by coronary CT angiography has shown some promise in predicting cardiovascular disease outcomes among patients with CKD.23-25 In our study, inclusion of CAC score in the prediction models led to a significant increase in the C statistic and positive net reclassification improvement and integrated discrimination improvement over use of ACC/AHA atherosclerotic cardiovascular disease risk factors and other novel risk factors. These findings suggest that CAC could be used for risk stratification and prediction among patients with CKD.

Although it is well documented that CAC predicts the risk of cardiovascular disease, myocardial infarction, and heart failure in the general population,8,26 few prospective cohort studies have examined these associations in patients with CKD.11,27 Russo et al26 reported that a CAC score greater than 100 was associated with an 8.4-fold (95% CI, 2.3- to 30-fold; P = .001) higher risk of cardiovascular disease events (cardiac death and myocardial infarction) compared with those with a CAC score of 100 or less among 181 dialysis-naive patients with CKD. In 1284 patients with CKD in stages 1, 2, or 3 from the Multi-Ethnic Study of Atherosclerosis, a 1 SD higher CAC score was associated with a hazard ratio of 1.69 (95% CI, 1.45-1.97) for cardiovascular disease after adjusting for the Framingham risk predictors.11

The CRIC study is the largest prospective cohort study among dialysis-naive patients with CKD to examine the association between baseline CAC score and subsequent risk of cardiovascular disease, myocardial infarction, and heart failure. In addition, there was adjustment for ACC/AHA atherosclerotic cardiovascular disease risk predictors and important novel cardiovascular disease risk factors among patients with CKD. Our study supports an independent association between CAC scores and the risk of cardiovascular disease, myocardial infarction, and heart failure.

The underlying pathological process of CAC in patients with CKD may be somewhat different compared with patients without CKD.21,27 Previous studies suggested that serum levels of calcium and phosphorus were associated with risk of CAC independent of traditional risk factors in patients with CKD.5,27 High intracellular phosphorus promotes the mineralization of vascular smooth muscle cells and medial artery calcification.28,29 Medial artery calcification is more prevalent in patients with CKD30,31 even though the atherosclerotic intimal calcification associated with cellular necrosis, inflammation, lipid deposition, and vascular occlusion is also present in patients with CKD.32 Medial artery calcification causes arterial stiffness, left ventricular hypertrophy, a decrease in coronary artery perfusion, and impairment of collateral circulation formation, which leads to adverse outcomes.33,34 Both intimal and medial artery calcification were associated with total and cardiovascular disease mortality in patients with CKD receiving dialysis.35

The findings from the CRIC study suggest that CAC score is associated with excess risk of cardiovascular disease, myocardial infarction, and heart failure independent of atherosclerotic and mineral and bone metabolism factors. Our findings warrant further investigation on novel mechanisms of CAC related to cardiovascular disease in patients with CKD. In addition, clinical trials of the prevention and treatment of CAC by maintaining normal phosphorus homeostasis on the risk of cardiovascular disease are warranted.

Limitations

Several limitations of this study should be noted. First, we are unable to differentiate intima vs media calcification due to limitations in the methods used to measure CAC in this study. Second, there are relatively small numbers of events within each CAC score category for clinical outcomes, potentially contributing to the statistical insignificance of the association of CAC score categories with cardiovascular disease. However, continuous CAC score was significantly associated with cardiovascular disease.

In addition, cause-specific mortality data from out-of-hospital deaths are not currently available. Therefore, out-of-hospital cardiovascular disease deaths were not included in this analysis, which could reduce the statistical power. A majority of participants (56%) were taking statins during the study, which would weaken the observed association.

Conclusions

Coronary artery calcification is independently and significantly related to the risks of cardiovascular disease, myocardial infarction, and heart failure in patients with CKD. In addition, CAC improves risk prediction for cardiovascular disease, myocardial infarction, and heart failure over use of established and novel cardiovascular disease risk factors among patients with CKD; however, the changes in the C statistic are small.

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Article Information

Corresponding Author: Jing Chen, MD, MSc, School of Medicine, Tulane University, 1430 Tulane Ave, SL-45, New Orleans, LA 70112 (jchen@tulane.edu).

Accepted for Publication: December 3, 2016.

Published Online: March 22, 2017. doi:10.1001/jamacardio.2017.0363

Author Contributions: Dr Chen had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Chen, Rahman, Wright, Wolf, He.

Acquisition, analysis, or interpretation of data: Chen, Budoff, Reilly, Yang, Rosas, Rahman, Zhang, Roy, Lustigova, Nessel, Raj, Porter, Soliman, Wright.

Drafting of the manuscript: Chen, Yang, Wright, Wolf, He.

Critical revision of the manuscript for important intellectual content: Budoff, Reilly, Yang, Rosas, Rahman, Zhang, Roy, Lustigova, Nessel, Raj, Porter, Soliman, Wright, He.

Statistical analysis: Chen, Yang, Zhang, Roy, He.

Obtained funding: Rahman, Wright, He.

Administrative, technical, or material support: Rosas, Nessel, Porter, Wright, Wolf.

Study supervision: Budoff, Rahman, Lustigova, Nessel, Wright, He.

Conflict of Interest Disclosures: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Budoff reported receiving grants from the National Institutes of Health and General Electric. Dr Rahman reported receiving grants from the National Institutes of Health. Ms Nessel reported receiving funding from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr Wolf reported serving as a consultant to Amgen, Ardelyx, Diasorin, Lilly, Pfizer, Ultragenyx, Amag, Keryx, Sanofi, Incyte, and ZS; receiving grants from the National Institutes of Health and Shire; receiving payment for lectures from Sanofi; having an institutional patent pending; and owning stock in Keryx. No other disclosures were reported.

Funding/Support: Funding for the Chronic Renal Insufficiency Cohort (CRIC) study was obtained under a cooperative agreement from the National Institute of Diabetes and Digestive and Kidney Diseases (grants U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). This study also was supported in part by clinical and translational science awards from the National Institutes of Health and the National Center for Advancing Translational Sciences (UL1TR000003 awarded to the Perelman School of Medicine, University of Pennsylvania; UL1TR-000424 awarded to Johns Hopkins University; GCRC M01 RR-16500 awarded to the University of Maryland; UL1TR000439 awarded to the Clinical and Translational Science Collaborative of Cleveland; UL1TR000433 awarded to the Michigan Institute for Clinical and Health Research; UL1RR029879 awarded to the University of Illinois, Chicago; P20 GM109036 awarded to the Center of Biomedical Research Excellence, Tulane University; and UCSF-CTSI UL1 RR-024131 awarded to Kaiser Permanente).

Role of the Funder/Sponsor: John Kusek, PhD (program director at the National Institute of Diabetes and Digestive and Kidney Diseases), contributed to the design and conduct of the study. Otherwise, the funding source had no role in the collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The CRIC investigators were Lawrence J. Appel, MD, MPH, Johns Hopkins University, Baltimore, Maryland; Harold I. Feldman, MD, MSCE, Perelman School of Medicine, University of Pennsylvania, Philadelphia; Alan S. Go, MD, Kaiser Permanente Division of Research, Oakland, California; Jiang He, MD, PhD, Tulane School of Public Health and Tropical Medicine, New Orleans, Louisiana; John W. Kusek, PhD, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland; James P. Lash, MD, University of Illinois, Chicago; Akinlolu Ojo, MD, PhD, University of Michigan, Ann Arbor; Mahboob Rahman, MD, Case Western Reserve University School of Medicine, Cleveland, Ohio; Raymond R. Townsend, MD, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

Additional Contributions: We thank Katherine Obst, BS (Tulane University, Department of Epidemiology), for editorial assistance. Ms Obst was compensated for her contribution.

References
1.
Gansevoort  RT, Correa-Rotter  R, Hemmelgarn  BR,  et al.  Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention.  Lancet. 2013;382(9889):339-352.PubMedGoogle ScholarCrossref
2.
Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305.PubMedGoogle ScholarCrossref
3.
Matsushita  K, van der Velde  M, Astor  BC,  et al; Chronic Kidney Disease Prognosis Consortium.  Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.  Lancet. 2010;375(9731):2073-2081.PubMedGoogle ScholarCrossref
4.
Hemmelgarn  BR, Manns  BJ, Lloyd  A,  et al; Alberta Kidney Disease Network.  Relation between kidney function, proteinuria, and adverse outcomes.  JAMA. 2010;303(5):423-429.PubMedGoogle ScholarCrossref
5.
He  J, Reilly  M, Yang  W,  et al; CRIC Investigators.  Risk factors for coronary artery calcium among patients with chronic kidney disease (from the Chronic Renal Insufficiency Cohort Study).  Am J Cardiol. 2012;110(12):1735-1741.PubMedGoogle ScholarCrossref
6.
Kestenbaum  BR, Adeney  KL, de Boer  IH, Ix  JH, Shlipak  MG, Siscovick  DS.  Incidence and progression of coronary calcification in chronic kidney disease: the Multi-Ethnic Study of Atherosclerosis.  Kidney Int. 2009;76(9):991-998.PubMedGoogle ScholarCrossref
7.
Russo  D, Palmiero  G, De Blasio  AP, Balletta  MM, Andreucci  VE.  Coronary artery calcification in patients with CRF not undergoing dialysis.  Am J Kidney Dis. 2004;44:1024-1030.Google ScholarCrossref
8.
Greenland  P, Bonow  RO, Brundage  BH,  et al; American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography); Society of Atherosclerosis Imaging and Prevention; Society of Cardiovascular Computed Tomography.  ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain: a report of the American College of Cardiology Foundation Clinical Expert Consensus Task Force (ACCF/AHA Writing Committee to Update the 2000 Expert Consensus Document on Electron Beam Computed Tomography).  Circulation. 2007;115:402-426.PubMedGoogle ScholarCrossref
9.
Peters  SA, Bakker  M, den Ruijter  HM, Bots  ML.  Added value of CAC in risk stratification for cardiovascular events: a systematic review.  Eur J Clin Invest. 2012;42(1):110-116.PubMedGoogle ScholarCrossref
10.
Bashir  A, Moody  WE, Edwards  NC, Ferro  CJ, Townend  JN, Steeds  RP.  Coronary artery calcium assessment in CKD: utility in cardiovascular disease risk assessment and treatment?  Am J Kidney Dis. 2015;65(6):937-948.PubMedGoogle ScholarCrossref
11.
Matsushita  K, Sang  Y, Ballew  SH,  et al.  Subclinical atherosclerosis measures for cardiovascular prediction in CKD.  J Am Soc Nephrol. 2015;26(2):439-447.PubMedGoogle ScholarCrossref
12.
Goff  DC  Jr, Lloyd-Jones  DM, Bennett  G,  et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  Circulation. 2014;129(25)(suppl 2):S49-S73.PubMedGoogle ScholarCrossref
13.
Lash  JP, Go  AS, Appel  LJ,  et al; Chronic Renal Insufficiency Cohort (CRIC) Study Group.  Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.  Clin J Am Soc Nephrol. 2009;4(8):1302-1311.PubMedGoogle ScholarCrossref
14.
Pickering  TG, Hall  JE, Appel  LJ,  et al; Council on High Blood Pressure Research Professional and Public Education Subcommittee, American Heart Association.  Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on High Blood Pressure Research Professional and Public Education Subcommittee.  J Clin Hypertens (Greenwich). 2005;7(2):102-109.PubMedGoogle ScholarCrossref
15.
Anderson  AH, Yang  W, Hsu  CY,  et al; CRIC Study Investigators.  Estimating GFR among participants in the Chronic Renal Insufficiency Cohort (CRIC) study.  Am J Kidney Dis. 2012;60(2):250-261.PubMedGoogle ScholarCrossref
16.
Agatston  AS, Janowitz  WR, Hildner  FJ, Zusmer  NR, Viamonte  M  Jr, Detrano  R.  Quantification of coronary artery calcium using ultrafast computed tomography.  J Am Coll Cardiol. 1990;15(4):827-832.PubMedGoogle ScholarCrossref
17.
Kaplan  EL, Meier  P.  Nonparametric estimation from incomplete observations.  J Am Stat Assoc. 1958;53(282):457-481.Google ScholarCrossref
18.
Cox  DR.  Regression models and life tables.  J Royal Stat Soc Series B. 1972;34:187-188, 220.Google Scholar
19.
Pencina  MJ, D’Agostino  RB.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.  Stat Med. 2004;23(13):2109-2123.PubMedGoogle ScholarCrossref
20.
Pencina  MJ, D’Agostino  RB  Sr, Steyerberg  EW.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21.PubMedGoogle ScholarCrossref
21.
Wilkieson  TJ, Rahman  MO, Gangji  AS,  et al.  Coronary artery calcification, cardiovascular events, and death: a prospective cohort study of incident patients on hemodialysis.  Can J Kidney Health Dis. 2015;2:29.PubMedGoogle ScholarCrossref
22.
Polonsky  TS, McClelland  RL, Jorgensen  NW,  et al.  Coronary artery calcium score and risk classification for coronary heart disease prediction.  JAMA. 2010;303(16):1610-1616.PubMedGoogle ScholarCrossref
23.
Cho  I, Min  HS, Chun  EJ,  et al.  Coronary atherosclerosis detected by coronary CT angiography in asymptomatic subjects with early chronic kidney disease.  Atherosclerosis. 2010;208(2):406-411.PubMedGoogle ScholarCrossref
24.
Joosen  IA, Schiphof  F, Versteylen  MO,  et al.  Relation between mild to moderate chronic kidney disease and coronary artery disease determined with coronary CT angiography.  PLoS One. 2012;7(10):e47267.PubMedGoogle ScholarCrossref
25.
Winther  S, Svensson  M, Jørgensen  HS,  et al.  Diagnostic performance of coronary CT angiography and myocardial perfusion imaging in kidney transplantation candidates.  JACC Cardiovasc Imaging. 2015;8(5):553-562.PubMedGoogle ScholarCrossref
26.
Russo  D, Corrao  S, Battaglia  Y,  et al.  Progression of coronary artery calcification and cardiac events in patients with chronic renal disease not receiving dialysis.  Kidney Int. 2011;80(1):112-118.PubMedGoogle ScholarCrossref
27.
Baber  U, de Lemos  JA, Khera  A,  et al.  Non-traditional risk factors predict coronary calcification in chronic kidney disease in a population-based cohort.  Kidney Int. 2008;73(5):615-621.PubMedGoogle ScholarCrossref
28.
Jono  S, McKee  MD, Murry  CE,  et al.  Phosphate regulation of vascular smooth muscle cell calcification.  Circ Res. 2000;87(7):E10-E17.PubMedGoogle ScholarCrossref
29.
Steitz  SA, Speer  MY, Curinga  G,  et al.  Smooth muscle cell phenotypic transition associated with calcification: upregulation of Cbfa1 and downregulation of smooth muscle lineage markers.  Circ Res. 2001;89(12):1147-1154.PubMedGoogle ScholarCrossref
30.
Goodman  WG, London  G, Amann  K,  et al; Vascular Calcification Work Group.  Vascular calcification in chronic kidney disease.  Am J Kidney Dis. 2004;43(3):572-579.PubMedGoogle ScholarCrossref
31.
Shroff  RC, Shanahan  CM.  The vascular biology of calcification.  Semin Dial. 2007;20(2):103-109.PubMedGoogle ScholarCrossref
32.
Wade  AN, Reilly  MP.  Coronary calcification in chronic kidney disease: morphology, mechanisms and mortality.  Clin J Am Soc Nephrol. 2009;4(12):1883-1885.PubMedGoogle ScholarCrossref
33.
Proudfoot  D, Shanahan  CM.  Biology of calcification in vascular cells: intima versus media.  Herz. 2001;26(4):245-251.PubMedGoogle ScholarCrossref
34.
Demer  LL, Tintut  Y.  Vascular calcification: pathobiology of a multifaceted disease.  Circulation. 2008;117(22):2938-2948.PubMedGoogle ScholarCrossref
35.
London  GM, Guérin  AP, Marchais  SJ, Métivier  F, Pannier  B, Adda  H.  Arterial media calcification in end-stage renal disease: impact on all-cause and cardiovascular mortality.  Nephrol Dial Transplant. 2003;18(9):1731-1740.PubMedGoogle ScholarCrossref
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