Evidence-based therapies are available to reduce the risk for death from cardiovascular disease, yet many patients go untreated. Novel methods are needed to identify those at highest risk for cardiovascular death. In this study, the biomarkers β 2 -microglobulin, cystatin C, and C-reactive protein were measured at baseline in a cohort of participants who underwent coronary angiography. Adjusted Cox proportional-hazards models were used to determine whether the biomarkers predicted all-cause and cardiovascular mortality. Additionally, improvements in risk reclassification and discrimination were evaluated by calculating the net reclassification improvement, C-index, and integrated discrimination improvement with the addition of the biomarkers to a baseline model of risk factors for cardiovascular disease and death. During a median follow-up period of 5.6 years, there were 78 deaths among 470 participants. All biomarkers independently predicted future all-cause and cardiovascular mortality. A significant improvement in risk reclassification was observed for all-cause (net reclassification improvement 35.8%, p = 0.004) and cardiovascular (net reclassification improvement 61.9%, p = 0.008) mortality compared to the baseline risk factors model. Additionally, there was significantly increased risk discrimination with C-indexes of 0.777 (change in C-index 0.057, 95% confidence interval 0.016 to 0.097) and 0.826 (change in C-index 0.071, 95% confidence interval 0.010 to 0.133) for all-cause and cardiovascular mortality, respectively. Improvements in risk discrimination were further supported using the integrated discrimination improvement index. In conclusion, this study provides evidence that β 2 -microglobulin, cystatin C, and C-reactive protein predict mortality and improve risk reclassification and discrimination for a high-risk cohort of patients who undergo coronary angiography.
The development and refinement of risk stratification tools and prognostication models will continue to significantly influence the treatment and prevention of cardiovascular disease. To date, these efforts have largely aimed to reclassify intermediate-risk patients either upward into a subset for which intervention becomes clearly indicated or downward into a subset for which it is likely that they can safely abstain from treatment. However, it is becoming increasingly clear that patients thought to be at high-risk similarly can be restratified and may particularly benefit from appropriately intensified therapy. Especially with more expensive or invasive cardiovascular therapies, it is important to develop new tools to identify those truly at highest risk and most suitable for intervention and/or more intensive risk factor modification. To that end, we have previously identified a set of biomarkers that are preferentially expressed in patients with peripheral arterial disease, a group of patients at particularly elevated risk for major clinical events such as myocardial infarction and stroke. In the present study, we evaluated whether these biomarkers improve risk modeling in a cohort of patients who underwent coronary angiography.
Methods
The Genetic Determinants of Peripheral Arterial Disease (GenePAD) study consists of patients who underwent an elective, nonemergent coronary angiography for angina, shortness of breath, or abnormal stress test results at Stanford University Medical Center or Mount Sinai Medical Center from January 1, 2004, to March 1, 2008. As previously detailed, a subcohort of patients was selected from the total cohort (n = 1,755) to characterize the role of biomarkers in cardiovascular disease. There were 470 patients with data on all biomarkers and relevant covariates included in the study. All patients provided written informed consent. The GenePAD study was approved by the Stanford University and Mount Sinai School of Medicine committees for the protection of human subjects.
The biomarkers of interest were β 2 -microglobulin, cystatin C, and C-reactive protein. Blood samples were collected on fasting participants while they were being prepared for scheduled coronary angiography. The biomarkers were measured using standard nephelometry using the BNII-Nephelometry system (Dade Behring, Inc., Deerfield, Illinois). The intra- and interassay coefficients of variation were <4.1% and <3.3% for β 2 -microglobulin, <4.4% and <5.7% for cystatin C, and <2.83% and <5.1% for C-reactive protein, respectively.
The outcomes of interest in this analysis were death from any cause and from cardiovascular causes. Cardiovascular deaths were attributed to myocardial infarction, cardiac arrest, stroke, heart failure, or aneurysm rupture. Ascertainment of mortality was achieved through phone or postal communication, medical record review, and the Social Security Death Index. New deaths were identified through March 31, 2012.
At enrollment, participants provided information on all included covariates through a trained nurse or research assistant. Diabetes status was classified as the use of insulin or oral hypoglycemic agents as ascertained by direct medication inventory. Total cholesterol and high-density lipoprotein cholesterol were measured by standard assays using AU5400 Chemistry Immuno-Analyzer (Olympus America Inc., Melville, New York). The glomerular filtration rate was estimated using the Modification of Diet in Renal Disease (MDRD) method. An experienced cardiologist who was blinded to participant details evaluated coronary angiograms. Hemodynamically significant coronary artery disease (CAD) was defined as >60% stenosis.
Cumulative mortality for all-cause and cardiovascular mortality was calculated for each biomarker using the Kaplan-Meier method with the median level for each biomarker as the designated cut-off value between groups. Additionally, participants in the upper 50% for all 3 biomarkers were compared to those in the lower 50% for all 3 biomarkers.
Continuous variables with a right skew (β 2 -microglobulin, cystatin C, and C-reactive protein) were log-transformed to achieve a normal distribution. The association of biomarkers with death from all causes and death from cardiovascular causes was investigated using Cox proportional-hazards regression. Hazard ratios were expressed per 1-SD change of the log biomarker level. Standard deviations were 6.4, 0.98, and 7.0 mg/L for β 2 -microglobulin, cystatin C, and C-reactive protein, respectively. Subgroup analysis was carried out for all-cause mortality according to CAD status. Because of limited numbers of cardiovascular deaths (n = 19), we elected not to undertake subgroup analysis on this outcome.
For all survival analyses, the follow-up time was defined as the period between the enrollment interview and the last confirmed follow-up or date of death. If participants had confirmed death of unknown cause, they were excluded from the cardiovascular mortality analysis (n = 48). Survival analyses were adjusted for age, gender, race, systolic blood pressure, body mass index, total cholesterol, high-density lipoprotein cholesterol, smoking history, use of lipid-lowering and antihypertensive medications, use of insulin or oral hypoglycemic agents, and glomerular filtration rate. All variables were continuous except race (categorical), diabetes status, smoking, and the use or nonuse of lipid-lowering and antihypertensive medications (dichotomous). Proportional-hazards assumptions were evaluated using Schoenfeld’s residuals tests. Calibration was assessed on all models using the Grønnesby-Borgan test to evaluate goodness of fit (p >0.05) by comparing predicted mortalities with observed mortalities, as described for survival analysis.
The net reclassification improvement (NRI), C-index and integrated discrimination improvement (IDI) were evaluated to determine whether the biomarkers significantly improved risk reclassification and discrimination for all-cause and cardiovascular mortality when added to a baseline model. In this diverse population at high-risk for cardiovascular events, we used a baseline model consisting of risk factors for cardiovascular disease and death, including age, gender, race, smoking history, body mass index, systolic blood pressure, use of lipid-lowering or antihypertensive medications, diabetes, total cholesterol, high-density lipoprotein cholesterol, and glomerular filtration rate. Additionally, secondary analyses were conducted using risk variables from the European SCORE risk model to evaluate model improvement against an established risk score. This model was established for cardiovascular mortality and includes age, gender, smoking history, systolic blood pressure, and total cholesterol.
The NRI was used to evaluate the proportion of correct risk reclassification when adding biomarkers to the baseline model. We used the category-free NRI because it has been suggested to be the most objective and reproducible measure of improvement in risk prediction, especially when established a priori risk categories do not exist. Furthermore, we calculated the NRI separately in participants with and without events during follow-up.
The C-index was used to estimate improvements in model discrimination with the addition of the biomarkers. In survival analysis, the C-index is equivalent to the area under the receiver-operating characteristic curve or C-statistic while allowing for censored data, with a 1% increase indicating that the correct order of failure (e.g., mortality) would be correctly predicted in an additional 1 in every 100 pairs of randomly selected patients compared to the baseline model.
Model performance was further evaluated with the addition of the biomarkers using the IDI. The IDI compares 2 models according to the average difference in predicted risk between those who have the event and those who do not. If the new model assigns a higher risk to those who will have a mortality and a lower risk to those who will not, compared to the baseline model, the IDI will be >0. Therefore, the IDI can be interpreted as the average net improvement in the predicted risk for the outcome in the new model compared to the baseline model.
Tests were considered significant if the 2-sided p value was <0.05. All analyses were performed using Stata version 12.0 (StataCorp LP, College Station, Texas). Study data were collected and managed using REDCap electronic data capture tools hosted at Stanford University.
Results
Enrollment characteristics of the 470 patients constituting the study sample are listed in Table 1 . During a median follow-up period of 5.6 years, there were 78 deaths (17%), of which 19 were known to be from cardiovascular causes.
| Characteristic | Value |
|---|---|
| Age (yrs) | 67 ± 10 |
| Women | 226 (48%) |
| Caucasian | 253 (54%) |
| Black | 77 (16%) |
| Hispanic | 58 (12%) |
| Asian | 33 (7%) |
| Other ∗ | 49 (10%) |
| Systolic blood pressure (mm Hg) | 141 ± 22 |
| Body mass index (kg/m 2 ) | 29 ± 6 |
| Lipids (mg/dl) | |
| Total cholesterol | 145 ± 38 |
| High-density lipoprotein cholesterol | 42 ± 13 |
| Ever smoker | 267 (57%) |
| Use of cholesterol-lowering medication | 301 (64%) |
| Use of antihypertensive medication | 391 (83%) |
| Use of insulin or oral hypoglycemic agent | 146 (31%) |
| Glomerular filtration rate (ml/min/1.73 m 2 ) | 79 ± 37 |
| Biomarker levels (mg/L) | |
| β 2 -microglobulin | 1.88 (1.50–2.57) |
| Cystatin C | 0.72 (0.61–0.93) |
| C-reactive protein | 1.60 (0.60–4.30) |
| CAD † | 219 (47%) |
∗ Includes Asian Indian, Pakistani, Middle Eastern, and Pacific Islander.
We observed increased cumulative all-cause mortality ( Figure 1 ) and cardiovascular mortality ( Supplementary Figure 1 ) in patients with levels of β 2 -microglobulin, cystatin C, or C-reactive protein that were greater than the study median. This relation was most pronounced when comparing participants with measurements higher than the median for all biomarkers compared to lower than the median for all biomarkers.
The adjusted hazard ratios for the association of all biomarkers with mortality are listed in Table 2 . Higher levels of the biomarkers β 2 -microglobulin, cystatin C, and C-reactive protein were significantly associated with increased all-cause and cardiovascular mortality during follow-up. The observed associations did not significantly differ according to gender or race (p ≥0.05). We also conducted analyses using fasting glucose as an alternative measure of diabetes status, which yielded statistically similar results (data not shown). Schoenfeld’s residuals tests demonstrated that the proportional-hazards assumption was met for all models. Regression coefficients for the all-cause mortality analysis are listed in Supplementary Table 1 .
| Biomarker | HR | 95% CI | p Value |
|---|---|---|---|
| All-cause mortality | |||
| β 2 -microglobulin | |||
| Overall | 1.80 | 1.38–2.34 | <0.001 |
| CAD only | 1.75 | 1.20–2.56 | 0.004 |
| Non-CAD | 1.96 | 1.24–3.10 | 0.004 |
| Cystatin C | |||
| Overall | 1.74 | 1.31–2.29 | <0.001 |
| CAD only | 1.79 | 1.20–2.65 | 0.004 |
| Non-CAD | 1.61 | 0.98–2.63 | 0.060 |
| C-reactive protein | |||
| Overall | 1.70 | 1.37–2.10 | <0.001 |
| CAD only | 1.67 | 1.28–2.17 | <0.001 |
| Non-CAD | 1.66 | 1.04–2.66 | 0.035 |
| Cardiovascular mortality | |||
| β 2 -microglobulin overall | 2.25 | 1.34–3.77 | 0.002 |
| Cystatin C overall | 2.35 | 1.40–3.93 | 0.001 |
| C-reactive protein overall | 1.96 | 1.24–3.09 | 0.004 |
In subgroup analysis, β 2 -microglobulin, cystatin C, and C-reactive protein were predictive of all-cause mortality in patients with CAD diagnosed at enrollment. Beta-2-microglobulin and C-reactive protein continued to significantly predict mortality risk in patients without CAD, while cystatin C demonstrated a borderline significance in this subgroup.
Assessment of calibration using the Grønnesby-Borgan statistic demonstrated good fit for all models with and without biomarkers (p ≥0.05).
The category-free NRI showed significant improvement in the net proportion of risk reclassification for all models with the addition of β 2 -microglobulin, cystatin C, and C-reactive protein, individually and combined, compared to the baseline risk factors model for all-cause and cardiovascular mortality ( Table 3 ).
| Model | Overall | NRI Mortalities | NRI Nonmortalities | |
|---|---|---|---|---|
| NRI | p Value | |||
| All-cause mortality | ||||
| BRF ∗ | Reference | 1.0 (reference) | Reference | Reference |
| BRF + β 2 -microglobulin | 25.0% | 0.044 | 0.0% | 25.0% |
| BRF + cystatin C | 27.0% | 0.029 | 0.0% | 27.0% |
| BRF + C-reactive protein | 45.0% | <0.001 | 23.1% | 21.9% |
| BRF + all biomarkers | 35.8% | 0.004 | 10.3% | 25.5% |
| Cardiovascular mortality | ||||
| BRF | Reference | 1.0 (reference) | Reference | Reference |
| BRF + β 2 -microglobulin | 54.9% | 0.019 | 26.3% | 28.5% |
| BRF + cystatin C | 72.9% | 0.002 | 47.4% | 25.6% |
| BRF + C-reactive protein | 66.0% | 0.005 | 47.4% | 18.6% |
| BRF + all biomarkers | 61.9% | 0.008 | 36.8% | 25.1% |
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