Search tips
Search criteria 


Logo of amjepidLink to Publisher's site
Am J Epidemiol. 2010 March 1; 171(5): 540–549.
Published online 2010 January 28. doi:  10.1093/aje/kwp428
PMCID: PMC2842214

Markers of Atherosclerosis and Inflammation for Prediction of Coronary Heart Disease in Older Adults


Although both inflammatory and atherosclerosis markers have been associated with coronary heart disease (CHD) risk, data directly comparing their predictive value are limited. The authors compared the value of 2 atherosclerosis markers (ankle-arm index (AAI) and aortic pulse wave velocity (aPWV)) and 3 inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)) in predicting CHD events. Among 2,191 adults aged 70–79 years at baseline (1997–1998) from the Health, Aging, and Body Composition Study cohort, the authors examined adjudicated incident myocardial infarction or CHD death (“hard” events) and “hard” events plus hospitalization for angina or coronary revascularization (total CHD events). During 8 years of follow-up between 1997–1998 and June 2007, 351 participants developed total CHD events (197 “hard” events). IL-6 (highest quartile vs. lowest: hazard ratio = 1.82, 95% confidence interval: 1.33, 2.49; P-trend < 0.001) and AAI (AAI ≤ 0.9 vs. AAI 1.01–1.30: hazard ratio = 1.57, 95% confidence interval: 1.14, 2.18) predicted CHD events above traditional risk factors and modestly improved global measures of predictive accuracy. CRP, TNF-α, and aPWV had weaker associations. IL-6 and AAI accurately reclassified 6.6% and 3.3% of participants, respectively (P’s ≤ 0.05). Results were similar for “hard” CHD, with higher reclassification rates for AAI. IL-6 and AAI are associated with future CHD events beyond traditional risk factors and modestly improve risk prediction in older adults.

Keywords: atherosclerosis, cohort studies, coronary disease, inflammation

Several markers of inflammation and of atherosclerosis have been shown to be predictors of coronary heart disease (CHD) (13). However, several recent studies have found that serum biomarkers, even when combined, added only moderately to traditional risk factors (4, 5), and their incremental value over traditional risk factors is controversial (6, 7). Few investigators have performed direct comparisons of markers of both inflammation and atherosclerosis in CHD risk prediction (8), and none have used the novel statistical method of net reclassification, which allows calculation of the proportions of adults with more accurate risk stratification by novel markers as compared with traditional risk factors (9, 10). We hypothesized that these markers, resulting from different mechanisms underlying CHD, might have incremental value when used in combination with traditional risk factors in identifying high-risk older adults. In a population-based cohort of older adults, we compared the incremental value of 2 markers of atherosclerosis (ankle-arm index (AAI) and aortic pulse wave velocity (aPWV)—a marker of arterial stiffness) and 3 markers of inflammation (C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)) in predicting CHD events over traditional risk factors. Actual risk prediction with traditional risk factors performs less well in the growing population of older persons (11, 12) compared with middle-aged adults (4, 5), but it remains unclear which novel risk markers should be further examined to improve CHD risk prediction in clinical practice.


Study population

Participants were part of the Health, Aging, and Body Composition Study, a population-based cohort study of 3,075 community-dwelling men and women aged 70–79 years at entry in 1997–1998. Participants were identified from a random sample of white and black Medicare-eligible adults living in designated zip-code areas surrounding Pittsburgh, Pennsylvania, and Memphis, Tennessee. Details on the eligibility criteria have been previously published (13). All participants gave written informed consent, and relevant institutional review boards approved the protocol.

Among the 3,075 participants, we excluded 841 who had overt cardiovascular disease at baseline, defined as a diagnosis of CHD (angina, prior myocardial infarction, angioplasty of coronary arteries, or coronary artery surgery), stroke or transient ischemic attack, peripheral arterial revascularization, carotid artery disease, or heart failure or having a pacemaker (14), based on algorithms mirroring those of the Cardiovascular Health Study. We also excluded 23 participants with missing data for all 5 markers of inflammation or atherosclerosis and 20 participants with missing data on any of the traditional risk factors. The final sample for our analyses comprised 2,191 participants.


Markers of atherosclerosis and of inflammation at baseline.

We examined all markers of atherosclerosis and of inflammation for which data were available at baseline in this cohort and that had reported associations with CHD (1, 3), including reported associations in this cohort (1517). For AAI, blood pressures were measured in the right arm and both ankles (posterior tibial artery) with standard cuffs and a pencil Doppler flow probe (Parks Medical Electronics, Inc., Aloha, Oregon) (16). The systolic blood pressure of the ankle was divided by the systolic blood pressure of the arm to create the AAI. Measures were repeated, and the results were averaged. The lower of the values between the 2 legs was used, and AAI ≤ 0.90 was considered evidence of occlusive disease. For assessment of aPWV, a marker of arterial stiffness, simultaneous Doppler flow signals obtained from the right carotid and right femoral arteries with transcutaneous Doppler flow probes were measured (16). Three separate measurements were recorded for each participant, and results from all acceptable runs were averaged for the final aPWV value. Stiffer vessels are identified by a faster aPWV. Replicate measures of aPWV in 14 participants revealed intraclass correlations of 0.88 between sonographers and 0.84 between readers (16).

Measures of IL-6 and CRP levels were obtained from frozen stored serum samples and measures of TNF-α levels were obtained from frozen plasma samples, collected after an overnight fast (15). Cytokines were measured in duplicate using a high-sensitivity enzyme-linked immunosorbent assay kit from R&D Systems (R&D Systems, Inc., Minneapolis, Minnesota) at the Health, Aging, and Body Composition Core Laboratory (University of Vermont). Blind duplicate analyses (n = 150) for IL-6, CRP, and TNF-α showed interassay coefficients of variation of 10.3%, 8.0%, and 15.8%, respectively (15).

Cardiovascular events.

We assessed incident CHD events and mortality among participants without overt cardiovascular disease at baseline (14). Using algorithms mirroring those of the Cardiovascular Health Study (14), diagnoses and causes of death were adjudicated until 2006–2007 on the basis of interviews, reviews of all hospital records, death certificates, and other documents by a panel of clinicians blinded to the results for subclinical CHD markers. CHD events were classified as either nonfatal myocardial infarction or coronary death (defined as “hard” events according to the current Framingham Risk Score (18)) and as “hard” events plus hospitalization for angina or revascularization (coronary angioplasty or surgery) (total CHD events).


Participants reported their histories of smoking and were classified as never, current, or former smokers. Hypertension was defined as self-reported hypertension and the use of antihypertensive medication or as measured blood pressure ≥140 mm Hg (systolic pressure) and/or ≥90 mm Hg (diastolic pressure). Diabetes was defined as self-reported diagnosis of diabetes and/or the use of any hypoglycemic medication (14). Fasting total and high density lipoprotein cholesterol were measured as previously described (15).

Statistical analyses

We used Cox proportional hazards models to assess the utility of adding subclinical CHD markers to traditional risk factors for the prediction of future CHD events (4, 5). Primary analyses were adjusted for traditional risk factors included in the current Framingham Risk Score (18), as well as diabetes, a strong independent CHD risk factor (19). We also examined associations in a further adjusted model including other potential risk factors (i.e., creatinine levels) or confounders. To allow for nonlinear effects, we estimated the effects of the subclinical CHD measures by quartile and for clinically defined categories of CRP (<1, 1–3, or >3 mg/L (20)) and AAI (<0.90, 0.91–1.00, 1.01–1.30, 1.31–1.40, or >1.40 (3, 21)). For interactions, we hypothesized that the relations between markers and CHD might differ by race or gender. We verified the proportional hazards assumption using graphical methods and Schoenfeld tests (all P’s > 0.20, except P = 0.14 for AAI, but graphical methods did not suggest nonproportionality over time for AAI).

As was recently recommended for assessment of novel markers (22), we examined several statistical measures. To assess improvements in discrimination, we used Harrell's C index (23), an adaptation of the C statistic or area under the receiver operating characteristic curve to the Cox model. Similarly to previous studies (4, 5), we compared C indices for traditional risk factors with and without each marker. Because the sample included too few events for split-sample validation, we instead adjusted the C index for optimism using bootstrap resampling (24) with 1,000 replications (23). To ensure that the comparisons across markers were unconfounded by extraneous differences, we estimated all C indices using the same subset of 1,515 participants with complete data (mainly because of 422 missing aPWV values).

As measures of overall model fit (10, 24), we examined likelihood ratio test χ2 statistics, Akaike's Information Criterion (10), and the Bayes Information Criterion (24). To assess model calibration, we used Parzen and Lipsitz's adaptation (25) of the Hosmer-Lemeshow test to the Cox model.

We computed net reclassification rates (9, 10) for markers that both had strong relations with CHD events beyond traditional risk factors and improved global measures of predictive accuracy above traditional risk factors. To avoid extrapolation beyond the range of our data, we used the Cox models to estimate 7.5-year rather than 10-year risks. We also assessed net reclassification in the intermediate risk categories (10%–20% 10-year risk) of most clinical interest (26)—that is, 7.5%–15% risk for a 7.5-year time frame. We examined reclassification among the 1,985 participants with no missing AAI and IL-6 data, to avoid confounding by differences in the set of included observations.

Statistical analyses were conducted using Stata 9.2 (Stata Corporation, College Station, Texas) and R (R Project for Statistical Computing, Vienna, Austria).


At baseline, the mean age of study participants was 73.5 years; 55% were women, and 41% were black (Table 1). The mean 10-year Framingham Risk Score was 16.6%, and most participants were in the intermediate CHD risk group. In gender-stratified and age-adjusted analyses, all inflammatory markers were correlated (r = 0.13–0.46; all P’s < 0.001), AAI and aPWV were weakly correlated (r = −0.07 in men, r = −0.14 in women), and no correlation was found between inflammatory and atherosclerotic markers (except for aPWV with IL-6 (r = 0.13) and TNF-α (r = 0.10) in women; both P’s < 0.01).

Table 1.
Characteristics of Participants at Baseline (n = 2,191), Health, Aging, and Body Composition Study, 1997–1998

During a median follow-up period of 8.2 years (maximum, 10.2 years) between 1997–1998 and June 2007, 351 participants developed total CHD events (197 “hard” events). Event rates were more frequent at higher IL-6, TNF-α, and aPWV levels and at both extremes of the AAI distribution (Table 2). In multivariate analyses, IL-6, AAI, and aPWV predicted CHD events beyond traditional risk factors. There was borderline-significant evidence for increased CHD risk for CRP > 3.0 mg/L and increasing quartiles of TNF-α, given multiple comparisons. Estimates per log unit increase in the continuous marker values or further adjustment for other potential risk factors and confounders yielded similar results. Participants in the highest quartile of IL-6 combined with low (≤0.90; n = 71) or high (>1.30; n = 28) AAI were at particularly increased risk, with hazard ratios of 2.32 (95% confidence interval (CI): 1.41, 3.79) and 2.67 (95% CI: 1.23, 5.79), respectively. The relation between different markers and CHD events did not differ by race or gender (P’s for interaction > 0.05), except that TNF-α was less strongly associated with CHD events among blacks (P values for interaction were 0.03 for total CHD events and 0.06 for “hard” CHD, to be interpreted with caution in the context of multiple comparisons). Results were similar for “hard” CHD events (see Web Table 1, which is posted on the Journal’s Web site (, except that associations with aPWV were no longer statistically significant.

Table 2.
Hazard Ratios for Incidence of Total Coronary Heart Disease Events (n = 351 Events) in Older Adults During a Median Follow-up Period of 8.2 Years (Maximum, 10.2 Years), According to Measures of Subclinical Cardiovascular Disease (n = 2,191), Health, Aging ...

Table 3 summarizes comparisons of discrimination, model fit, and calibration. In general, the 3 models adding IL-6, AAI, or both to traditional risk factors scored highest in terms of the C index and likelihood ratio χ2 and near the minimum in terms of Akaike's Information Criterion. The AAI and the IL-6 + AAI models were more heavily penalized for calibration and by the Bayes Information Criterion, reflecting their additional parameters. Patterns were similar for hard CHD events (Web Table 2 ( Sensitivity analyses that did not exclude persons missing any subclinical CHD data yielded similar results for C indices. At baseline, the 1,515 participants with all subclinical CHD measures were generally similar to the remaining 676, except that those with complete data were slightly older (73.6 years (standard deviation, 2.8) vs. 73.3 years (standard deviation, 2.9); P = 0.02), more likely to be white (63% vs. 37%; P < 0.001), and more likely to have higher education (P < 0.001). Sensitivity analyses including the 416 participants missing aPWV values yielded similar results for the other subclinical CHD markers.

Table 3.
Discrimination, Model Fit, and Calibration Estimates for Total Coronary Heart Disease Events (n = 235 Events) in Older Adults During a Median Follow-up Period of 8.2 Years (Maximum, 10.2 Years), According to Traditional Cardiovascular Disease Risk Factors, ...

The IL-6, AAI, and IL-6 + AAI models, all 3 of which had strong relations with CHD events beyond traditional risk factors and improved global measures of predictive accuracy, correctly reclassified 6.6% (95% CI: 1.2, 11.9), 3.3% (95% CI: 0.04, 6.5), and 5.0% (95% CI: −0.6, 10.6) of participants, respectively, over traditional risk factors (Table 4). IL-6, AAI, or both correctly reclassified 15.6% (95% CI: 9.1, 22.1), 7.0% (95% CI: 2.9, 11.2), and 12.0% (95% CI: 5.4, 18.6) of intermediate-risk participants (all P’s < 0.001). For “hard” CHD events, reclassification rates were similar for IL-6 (for all participants/intermediate-risk participants, 4.4%/11.0%) but higher for AAI (7.9%/19.3%) and IL-6 + AAI (7.7%/19.8%). For IL-6, net reclassification of events was towards lower risk, with good calibration, suggesting that the lower estimated risk fitted the data well. The net reclassification index with IL-6 + AAI was not statistically different from simpler models with only IL-6 (P = 0.43) or AAI (P = 0.55). Table 5 and Table 6 show the predicted 7.5-year CHD risks from Cox regression models for categories of Framingham Risk Score stratified by IL-6 and AAI, respectively.

Table 4.
Predictive Risks and Reclassification of Total Coronary Heart Disease Events Using a Multivariate Risk Prediction Model With and Without Inclusion of IL-6, AAI, or IL-6 + AAI (n = 1,985), Health, Aging, and Body Composition Study, 1997–2007a
Table 5.
Risk of Coronary Heart Disease Over a 7.5-Year Period According to Framingham Risk Score and Quartile of Interleukin-6 Level (n = 1,985), Health, Aging, and Body Composition Study, 1997–2007
Table 6.
Risk of Coronary Heart Disease Over a 7.5-Year Period According to Framingham Risk Score and Category of AAI (n = 1,985), Health, Aging, and Body Composition Study, 1997–2007


In this population-based study of older adults, IL-6 and AAI were associated with increased risk of incident CHD beyond traditional risk factors. IL-6 or AAI more accurately reclassified 3%–7% of participants into persons who did and did not develop an event as compared with traditional risk factors (7%–16% for the intermediate-risk group). A similar magnitude of net reclassification has been previously found for CRP in middle-aged women (26). Elevated CRP, TNF-α, and aPWV levels were associated with a pattern of increased CHD risk but added less to risk prediction above traditional risk factors than IL-6 and AAI. AAI was a stronger predictor of CHD than was aPWV, particularly for “hard” CHD events; to our knowledge, these 2 markers have not been directly compared previously (27).

Our findings are consistent with previous studies on the association between inflammatory markers and CHD in older adults (15, 28) and on single markers of atherosclerosis (3, 17, 29), but our results add information on the direct comparisons of both markers of inflammation and markers of atherosclerosis. In older adults, we found improved CHD risk prediction with IL-6 and AAI, in contrast to the use of biomarkers in middle-aged adults (4, 5). In the Atherosclerosis Risk in Communities Study, Folsom et al. (5) found that CRP and other novel risk factors did not significantly increase C index. In the Framingham Heart Study, middle-aged adults with high multimarker risk scores had increased risk of cardiovascular events (for the highest quintile vs. the lowest 2 quintiles, adjusted hazard ratio = 1.84, 95% CI: 1.11, 3.05) but only a small improvement in C index over traditional risk factors (4). Reclassification of risk was not examined in those 2 studies. In contrast, a study in white men aged 69–74 years found that addition of 4 biomarkers to traditional risk factors significantly increased C index values for cardiovascular death (0.748 vs. 0.688 in 661 participants without cardiovascular disease) (30), but the investigators did not examine risk prediction for CHD events. These potential differences might be related to the age of the studied populations, since lower predictive value (as measured by C index) and weaker associations of traditional risk factors with CHD have been reported in older adults (11, 12) as compared with middle-aged adults (4, 5), leading to a wider opportunity for improvement of risk prediction in older adults.

Few researchers have performed direct comparisons of the predictive value of both markers of atherosclerosis and markers of inflammation (8). In the Cardiovascular Health Study, associations between AAI and cardiovascular events remained after adjustment for CRP (3), as well as associations between CRP and CHD events after adjustment for several subclinical cardiovascular disease measurements (for CRP > 3 mg/L vs. CRP < 1 mg/L, the adjusted hazard ratio was 1.37 (95% CI: 1.06, 1.78)) (31). However, the authors did not directly compare the predictive values of both of these sets of markers. In the same Cardiovascular Health Study cohort, elevated CRP, elevated carotid intima-media thickness, and plaques all increased the adjusted risk for cardiovascular events, with modest improvement in C index, but elevated CRP remained a predictor beyond traditional risk factors only in participants with carotid atherosclerosis on ultrasound (8).

The net risk reclassification with IL-6 and AAI over traditional risk factors was similar to that reported with CRP in middle-aged women (5.7% overall, 15% for the intermediate-risk group) (26). The net increase in C index was modest, but the use of the C index for assessment of novel markers is controversial (9, 32, 33). The C index is particularly suited to retrospective case-control studies (24), and it may have little direct clinical relevance for assessing the role of novel markers to better stratify risk and target preventive therapies (32, 33). Other parameters of model fit were also improved by the addition of IL-6 or AAI, with the exception of the Bayes Information Criterion for AAI, which might be more sensitive to the increased complexity of these models.

Our study had several strengths and limitations. These data were drawn from a well-characterized population-based cohort of older adults and contained a high number of CHD events over 8 years of follow-up, and the CHD events were formally adjudicated. Blood sampling for cytokine concentrations was performed at a single point in time, which is a limitation of most published observational cohort studies (1, 4, 31). Use of a single blood sample for determination of cytokine concentration could be influenced by circadian rhythm (15), which would tend to weaken the observed associations because of added variability. Because of the multiple testing, borderline-significant P values, particularly P values between 0.05 and 0.01 for the 5 markers compared in Table 2, should be interpreted with caution and in the light of other findings and biologic plausibility.

Clinically, traditional risk factors have lower discrimination in older adults than in younger adults and poorly predict CHD in older adults. In our data, the C index for traditional risk factors was 0.631 (Table 3). C indices for the Framingham Risk Score were 0.63 in men and 0.66 in women in the Cardiovascular Health Study (11), as compared with 0.79 and 0.83, respectively, in the Framingham Heart Study (11). Some traditional risk factors have weaker associations with CHD risk; total and low density lipoprotein cholesterol were strong cardiovascular risk factors in middle-aged adults but not in older adults in our analysis and previous analyses (34). The addition of IL-6 or AAI to traditional risk factors modestly improved prediction of the true cardiovascular risk in older adults, with 3%–7% of all participants and 7%–16% of the intermediate-risk group being reclassified into more accurate risk categories. It has been suggested that novel markers could be particularly useful in refining risk prediction in intermediate-risk adults (26) to identify persons at increased risk who would benefit from preventive therapies, such as statins or aspirin. This might allow more efficient allocation of resources for prevention, which should be confirmed by cost-effectiveness analyses. Combination of measures of inflammation and atherosclerosis might be useful for identification of very-high-risk older adults, such as those in the highest quartile of IL-6 with an AAI less than or equal to 0.90 or an AAI greater than 1.30 (high AAI values reflect decreased arterial compliance, usually attributed to arterial calcification (3)); however, these combined categories had low numbers of participants (n = 71 and n = 28, respectively), and net reclassification was significantly improved in intermediate-risk participants but not in overall participants. Currently, there are no standardized IL-6 assays available for clinical use. However, our data suggest that IL-6 might be a better risk discriminator than CRP in older adults. The hepatic synthesis of CRP is largely under the regulation of IL-6; CRP might represent a “distal” secondary phenomenon to a more “proximal” primary excess production of IL-6 in the pathophysiologic chain of events (35). If our results for the association between IL-6 and CHD events are confirmed in other studies, investigators in future studies should validate standardized IL-6 assays for use in clinical practice, similar to CRP (20).

In summary, our study suggests that IL-6 and AAI are associated with future CHD events and help improve cardiovascular risk prediction beyond traditional risk factors in the elderly, with modestly improved risk reclassification. These findings are particularly important, since actual risk prediction with traditional risk factors is less accurate in older persons (11, 12) as compared with middle-aged adults (4, 5). This study suggests that new risk scores that include IL-6 or AAI might improve CHD prediction and risk stratification in the elderly.

Supplementary Material

[Web Tables 1 & 2]


Author affiliations: Department of Ambulatory Care and Community Medicine, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland (Nicolas Rodondi, Jacques Cornuz); University Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland (Pedro Marques-Vidal); Center for Cardiovascular and Metabolic Research (CardioMet), University of Lausanne, Lausanne, Switzerland (Pedro Marques-Vidal); Division of Cardiology, School of Medicine, Emory University, Atlanta, Georgia (Javed Butler); Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania (Kim Sutton-Tyrrell, Anne B. Newman); Department of Preventive Medicine, College of Medicine, University of Tennessee, Memphis, Tennessee (Suzanne Satterfield); Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Maryland (Tamara Harris); Intramural Research Program, National Institute on Aging, Baltimore, Maryland (Luigi Ferrucci); Department of Epidemiology and Biostatistics, School of Medicine, University of California, San Francisco, San Francisco, California (Douglas C. Bauer, Eric Vittinghoff); and Division of General Internal Medicine, Department of Medicine, School of Medicine, University of California, San Francisco, San Francisco, California (Douglas C. Bauer).

This research was supported by grants N01-AG-6-2101, N01-AG-6-2103, N01-AG-6-2106, N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106 from the National Institute on Aging, US National Institutes of Health, and in part by the Intramural Research Program of the National Institute on Aging. The National Institute on Aging funded the Health, Aging, and Body Composition study, reviewed the manuscript, and approved its publication. This research was partially supported by grant 3200B0-116097 to Dr. Nicolas Rodondi from the Swiss National Science Foundation.

Conflict of interest: none declared.



ankle-arm index
aortic pulse wave velocity
coronary heart disease
confidence interval
C-reactive protein
tumor necrosis factor-α


1. Ridker PM, Hennekens CH, Buring JE, et al. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342(12):836–843. [PubMed]
2. Ridker PM, Rifai N, Stampfer MJ, et al. Plasma concentration of interleukin-6 and the risk of future myocardial infarction among apparently healthy men. Circulation. 2000;101(15):1767–1772. [PubMed]
3. O'Hare AM, Katz R, Shlipak MG, et al. Mortality and cardiovascular risk across the ankle-arm index spectrum: results from the Cardiovascular Health Study. Circulation. 2006;113(3):388–393. [PubMed]
4. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355(25):2631–2639. [PubMed]
5. Folsom AR, Chambless LE, Ballantyne CM, et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the Atherosclerosis Risk in Communities Study. Arch Intern Med. 2006;166(13):1368–1373. [PubMed]
6. Redberg RF, Vogel RA, Criqui MH, et al. 34th Bethesda Conference: Task Force #3—what is the spectrum of current and emerging techniques for the noninvasive measurement of atherosclerosis? J Am Coll Cardiol. 2003;41(11):1886–1898. [PubMed]
7. Smith SC, Jr, Greenland P, Grundy SM. Prevention conference V. Beyond secondary prevention: identifying the high-risk patient for primary prevention: executive summary. Circulation. 2000;101(1):111–116. [PubMed]
8. Cao JJ, Arnold AM, Manolio TA, et al. Association of carotid artery intima-media thickness, plaques, and C-reactive protein with future cardiovascular disease and all-cause mortality: the Cardiovascular Health Study. Circulation. 2007;116(1):32–38. [PubMed]
9. Pencina MJ, D'Agostino RB, Sr, D'Agostino RB, Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–172. [PubMed]
10. Ingelsson E, Schaefer EJ, Contois JH, et al. Clinical utility of different lipid measures for prediction of coronary heart disease in men and women. JAMA. 2007;298(7):776–785. [PubMed]
11. D'Agostino RB, Sr, Grundy S, Sullivan LM, et al. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180–187. [PubMed]
12. 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(2):210–215. [PubMed]
13. Yaffe K, Kanaya A, Lindquist K, et al. The metabolic syndrome, inflammation, and risk of cognitive decline. JAMA. 2004;292(18):2237–2242. [PubMed]
14. Rodondi N, Newman AB, Vittinghoff E, et al. Subclinical hypothyroidism and the risk of heart failure, other cardiovascular events, and death. Arch Intern Med. 2005;165(21):2460–2466. [PubMed]
15. Cesari M, Penninx BW, Newman AB, et al. Inflammatory markers and onset of cardiovascular events: results from the Health ABC Study. Circulation. 2003;108(19):2317–2322. [PubMed]
16. Sutton-Tyrrell K, Najjar SS, Boudreau RM, et al. Elevated aortic pulse wave velocity, a marker of arterial stiffness, predicts cardiovascular events in well-functioning older adults. Circulation. 2005;111(25):3384–3390. [PubMed]
17. Sutton-Tyrrell K, Venkitachalam L, Kanaya AM, et al. Relationship of ankle blood pressures to cardiovascular events in older adults. Stroke. 2008;39(3):863–869. [PubMed]
18. National Cholesterol Education Program. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) JAMA. 2001;285(19):2486–2497. [PubMed]
19. Lee CD, Folsom AR, Pankow JS, et al. Cardiovascular events in diabetic and nondiabetic adults with or without history of myocardial infarction. Circulation. 2004;109(7):855–860. [PubMed]
20. Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation. 2003;107(3):499–511. [PubMed]
21. Resnick HE, Lindsay RS, McDermott MM, et al. Relationship of high and low ankle brachial index to all-cause and cardiovascular disease mortality: the Strong Heart Study. Circulation. 2004;109(6):733–739. [PubMed]
22. Hlatky MA, Greenland P, Arnett DK, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–2416. [PMC free article] [PubMed]
23. Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–387. [PubMed]
24. Cook NR, Buring JE, Ridker PM. The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med. 2006;145(1):21–29. [PubMed]
25. Parzen M, Lipsitz SR. A global goodness-of-fit statistic for Cox regression models. Biometrics. 1999;55(2):580–584. [PubMed]
26. Cook NR. Comments on ‘Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond’ by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929) Stat Med. 2008;27(2):191–195. [PubMed]
27. Simon A, Mijiti W, Gariepy J, et al. Current possibilities for detecting high risk of cardiovascular disease. Int J Cardiol. 2006;110(2):146–152. [PubMed]
28. Koukkunen H, Penttilä K, Kemppainen A, et al. C-reactive protein, fibrinogen, interleukin-6 and tumour necrosis factor-alpha in the prognostic classification of unstable angina pectoris. Ann Med. 2001;33(1):37–47. [PubMed]
29. Fowkes FG, Murray GD, Butcher I, et al. Ankle brachial index combined with Framingham Risk Score to predict cardiovascular events and mortality: a meta-analysis. JAMA. 2008;300(2):197–208. [PMC free article] [PubMed]
30. Zethelius B, Berglund L, Sundström J, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med. 2008;358(20):2107–2116. [PubMed]
31. Cushman M, Arnold AM, Psaty BM, et al. C-reactive protein and the 10-year incidence of coronary heart disease in older men and women: the Cardiovascular Health Study. Circulation. 2005;112(1):25–31. [PubMed]
32. Janes H, Pepe MS, Gu W. Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med. 2008;149(10):751–760. [PMC free article] [PubMed]
33. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928–935. [PubMed]
34. Psaty BM, Anderson M, Kronmal RA, et al. The association between lipid levels and the risks of incident myocardial infarction, stroke, and total mortality: the Cardiovascular Health Study. J Am Geriatr Soc. 2004;52(10):1639–1647. [PubMed]
35. Yudkin JS, Kumari M, Humphries SE, et al. Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link? Atherosclerosis. 2000;148(2):209–214. [PubMed]

Articles from American Journal of Epidemiology are provided here courtesy of Oxford University Press