Abstract
Background
Studies have suggested a relationship between higher baseline serum uric acid (sUA) levels and an elevated risk of subsequent ischemic cardiovascular outcomes among acute coronary syndrome (ACS) patients; this relationship may be modified by a clinical history of gout and has not been studied in large patient cohorts. We sought to understand the effect of sUA and gout on ACS outcomes.
Methods
Using PLATO and TRACER data on 27,959 ACS patients, we evaluated baseline sUA levels in relation to a composite of cardiovascular death, myocardial infarction (MI), or stroke. We assessed interaction terms to determine if a baseline clinical diagnosis of gout modified this putative relationship; 46% (n = 12,882) had sUA levels elevated >6.0 mg/dL.
Results
Patients with elevated levels were more often male with a history of prior MI, diabetes, and heart failure compared with those with sUA <6.0 mg/dL. The unadjusted risk of the composite endpoint increased with corresponding elevations in sUA levels (per 1 mg/dL increase) (HR = 1.23 [95% CI: 1.20–1.26]) above the statistical inflection point of 5.0 mg/dL. After adjustment, the association between sUA level and the composite outcome remained significant(HR = 1.07 [95% CI: 1.04–1.10]), and baseline gout did not modify this relationship.
Conclusions
In patients with ACS, increasing levels of sUA are associated with an elevated risk of cardiovascular events, regardless of a clinical diagnosis of gout. Further investigation is warranted to determine the mechanism behind this relationship and to delineate whether sUA is an appropriate therapeutic target to reduce cardiovascular risk.
Uric acid, the end-product of the purine metabolism, has long been known to be associated with cardiovascular disease (CVD).1 Many studies have demonstrated an association between hyperuricemia and obesity,2 hypertension,3 diabetes,4 and CVD itself,5 yet considerable controversy remains as to whether serum uric acid (sUA) level is an independent predictor of CVD or simply a marker for incident CVD.6 The nature of this sUA/CVD relationship in patients who have experienced acute coronary syndrome (ACS), as well as the role of gout in this relationship, is unclear.7
Several recent clinical trials of therapeutic interventions in ACS patients provide a unique opportunity to explore the association between sUA level, a clinical diagnosis of gout, and incident cardiovascular events. The PLATO and TRACER trials were both large, contemporary, multi-national trials that evaluated the efficacy of ticagrelor and vorapaxar, respectively, in hospitalized ACS patients.8,9 Most of these patients had baseline sUA measurements and clinical history data with respect to gout, and all had rigorous endpoint ascertainment.
The objective of our study was to assess the relationship of sUA level at ACS presentation with subsequent cardiovascular events and to determine whether a clinical diagnosis of gout influences this relationship. We hypothesized that a higher baseline sUA level in ACS patients would be associated with an elevated rate of subsequent ischemic events, and this relationship would be influenced by whether these patients had a diagnosis of gout at baseline or during follow-up.
Methods
Study design and population
This analysis was performed using data from the PLATO and TRACER trials, both of which have been previously described.8–11 Briefly, PLATO was a multicenter trial that randomized 18,624 patients hospitalized with ACS (with or without ST-segment elevation) to ticagrelor or clopidogrel for the prevention of secondary cardiovascular events (median follow-up of 355 days).8 In TRACER, 12,944 patients hospitalized with ACS without ST-segment elevation were randomized to treatment with vorapaxar, a thrombin receptor antagonist, or placebo (median follow-up of 476 days).9 For purposes of our secondary analysis of the PLATO and TRACER trials, patients from both trials were excluded if their baseline sUA level was missing (n = 3609, 11%).
Study definitions and endpoints
Elevated sUA level, or hyperuricemia, was defined using the established clinical cut-point of 6 mg/dL.12 A history of gout at baseline was defined as a self-reported medical history of gout (this information was collected in PLATO, but not in TRACER), use of gout medication at baseline (including allopurinol, febuxostat, colbenemid, colchicine, and probenecid), or detection of uric acid crystals on baseline urinalysis. Post-baseline gout was defined as gout or gouty arthritis listed as an adverse event, post-baseline medication for gout (same as those listed above), or detection of uric acid crystals in post-baseline urinalysis. Since gout episodes are self-limited and the associated inflammation is expected to resolve within 14 days, events meeting the post-baseline gout definition criteria were regarded as separate episodes if they occurred more than 14 days apart.13
The primary composite endpoint for this analysis was time to first occurrence of cardiovascular death, spontaneous myocardial infarction (MI), or stroke. Individual secondary endpoints included time to cardiovascular death, time to all-cause death, time to first subsequent spontaneous MI, and time to first subsequent stroke. All endpoints were adjudicated by an independent clinical events committee according to trial-specific definitions.8,9
Statistical analyses
Baseline patient characteristics were presented by uric acid level using the clinically relevant cut-point of 6.0 mg/dL, with continuous variables summarized by median (and quartiles), and categorical data frequencies and percentages. For each primary and secondary endpoint, event counts and Kaplan–Meier estimates of 1-year rates were presented by sUA level. The association between continuous sUA, gout diagnosis, and its interaction with endpoints was analyzed using Cox proportional hazard models. Risk relationships were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). χ2 statistics were used to test the significance of these relationships. The assumption of proportional hazards was tested using Schoenfeld residuals. The linearity assumption for sUA was tested, and linear splines were used, as appropriate. Specifically, a linear spline cut-point of 5 mg/dL (as opposed to 6 mg/dL) was used for modeling the relationship of endpoints with sUA, due to a lack of association between values less than 5 mg/dL and the composite endpoint; this empirical observation of an inflection point in the relationship between sUA and outcomes influenced our statistical analyses, such that all subsequent modeling examined risk associated with sUA levels above 5 mg/dL. Nevertheless, we clearly recognize that this statistical inflection point is distinct from the clinical cut-point of 6.0 mg/dL, which is the current clinical definition of hyperuricemia.
For each endpoint, we followed three sequential stages of modeling. In the first stage of modeling, we examined the main effect of baseline uric acid, initially in unadjusted models, and then after adjustment, for covariates selected from existing adjustment models developed for TRACER and PLATO. In the second stage of modeling, baseline gout and the interaction of baseline gout with uric acid were added to the unadjusted and adjusted models on baseline uric acid alone. In the final stage of modeling, the diagnosis of gout history at baseline was expanded to include implicit post-baseline evidence of the disease; this definition of gout was added to the initial models (unadjusted and adjusted) as a time-dependent covariate, along with its interaction with sUA. Chronic and short-term (14-day) risks associated with a post-baseline gout diagnosis were first assessed separately to allow for potential differences in these types of risk following post-baseline evidence of disease.
Adjustment covariates included: age, sex, race, region, clinical factors (i.e., weight, tobacco use, diabetes, hypertension, angina history, atrial fibrillation, prior stroke, prior transient ischemic attack, prior MI, prior percutaneous coronary intervention, prior coronary artery bypass grafting, peripheral arterial disease, congestive heart failure, baseline aspirin use, baseline beta-blocker use, and baseline lipid-lowering therapy), and presentation characteristics (i.e., heart rate, systolic blood pressure, diastolic blood pressure, Killip class, estimated glomerular filtration rate [via the Modification of Diet in Renal Disease Study, MDRD, equation], hemoglobin, positive troponin or creatine kinase MB, total cholesterol, triglycerides, time from onset of symptoms to randomization, and type of ACS). We did not adjust for the use of gout medications at baseline or during follow-up, since gout medication use was part of the gout history definition or post-baseline gout episode.
Statistical analyses were performed by the coordinating center (Duke Clinical Research Institute, Durham, NC) using SAS software version 9.4 (SAS Institute Inc., Cary, NC). This analysis was supported by funding from AstraZeneca.
Results
Baseline characteristics of patient population
Of the 27,959 patients included in this analysis (82% of the PLATO population and 98% of the TRACER population), 46% (n = 12,882) had an elevated sUA level above the clinical cut-point of 6.0 mg/dL, and 4.7% (n = 1305) had a history of gout at baseline (Figure 1). Patients with sUA ≥6.0 were more often male, non-smokers, and had a history of hypertension, diabetes, hyperlipidemia, coronary artery disease, MI, heart failure, peripheral artery disease, and renal disease compared with those with sUA <6.0 (Table 1). Patients with an elevated sUA level at baseline were also more likely to have a history of gout compared with those with sUA < 6.0 mg/dL (6% vs. 3%, respectively; Figure 1 and Supplemental Figure 1).
Figure 1.
Distribution of baseline sUA by baseline gout status. Displayed is the distribution of baseline sUA, according to baseline gout status. sUA, serum uric acid.
Table 1.
Baseline characteristics by baseline serum uric acid level*
| Patient characteristics | Uric acid <6.0 mg/dL (n = 15,077) | Uric acid ≥6.0 mg/dL (n = 12,882) | Total (n = 27,959) |
|---|---|---|---|
|
| |||
| Trial | |||
| PLATO | 8865/15077 (58.8%) | 6391/12882 (49.6%) | 15,256/27959 (54.6%) |
| TRACER | 6212/15077 (41.2%) | 6491/12882 (50.4%) | 12,703/27959 (45.4%) |
| Age | 62 (55, 70) | 64 (57, 72) | 63 (56, 71) |
| Female sex | 5252 (34.8%) | 2639 (20.5%) | 7891 (28.2%) |
| Non-white race | 1499 (10.0%) | 1537 (12.0%) | 3036 (10.9%) |
| WHO region | |||
| WHO Africa region | 124 (0.8%) | 193 (1.5%) | 317 (1.1%) |
| WHO European region | 10,266 (68.1%) | 8127 (63.1%) | 18,393 (65.8%) |
| WHO region of the Americas | 3276 (21.7%) | 3265 (25.3%) | 6541 (23.4%) |
| WHO South-East Asia region | 361 (2.4%) | 308 (2.4%) | 669 (2.4%) |
| WHO Western Pacific region | 1050 (7.0%) | 989 (7.7%) | 2039 (7.3%) |
| BMI, kg/m^2 | 27 (24, 30) | 28 (26, 32) | 28 (25, 31) |
| Weight, kg | 77 (68, 87) | 84 (73, 95) | 80 (70, 90) |
| Clinical history | |||
| Tobacco use | 5273 (35.0%) | 3637 (28.2%) | 8910 (31.9%) |
| Hypertension | 9384 (62.2%) | 9521 (73.9%) | 18,905 (67.6%) |
| Diabetes | 4045 (26.8%) | 3796 (29.5%) | 7841 (28.0%) |
| Hypercholesterolemia | 7622 (50.6%) | 7340 (57.0%) | 14,962 (53.5%) |
| Prior angina | 6862 (45.5%) | 6640 (51.6%) | 13,502 (48.3%) |
| Prior MI | 3188 (21.1%) | 3655 (28.4%) | 6843 (24.5%) |
| Prior PCI | 2392 (15.9%) | 2662 (20.7%) | 5054 (18.1%) |
| Prior CABG | 1008 (6.7%) | 1395 (10.8%) | 2403 (8.6%) |
| Prior atrial fibrillation | 433 (6.7%) | 527 (7.9%) | 960 (7.3%) |
| Prior stroke | 525 (3.5%) | 604 (4.7%) | 1129 (4.0%) |
| Prior TIA | 350 (2.3%) | 369 (2.9%) | 719 (2.6%) |
| CHF | 702 (4.7%) | 1351 (10.5%) | 2053 (7.3%) |
| Peripheral arterial disease | 850 (5.6%) | 1030 (8.0%) | 1880 (6.7%) |
| Presentation characteristics | |||
| Heart rate, bpm | 71 (63, 80) | 71 (63, 81) | 71 (63, 80) |
| Systolic BP, mmHg | 130 (120, 147) | 130 (120, 148) | 130 (120, 148) |
| Diastolic BP, mmHg | 79 (70, 85) | 78 (69, 85) | 79 (70, 85) |
| Killip class | |||
| I | 14,319 (95.1%) | 11,673 (90.8%) | 25,992 (93.1%) |
| II | 662 (4.4%) | 986 (7.7%) | 1648 (5.9%) |
| III | 70 (0.5%) | 179 (1.4%) | 249 (0.9%) |
| IV | 13 (0.1%) | 18 (0.1%) | 31 (0.1%) |
| Type of ACS | |||
| STEMI | 3690 (41.6%) | 2463 (38.5%) | 6153 (40.3%) |
| NSTEMI/unstable angina | 5175 (58.4%) | 3928 (61.5%) | 9103 (59.7%) |
| Hours from symptom onset to randomization | 16.1 (6.8, 23.7) | 18.2 (8.3, 26.8) | 17.1 (7.4, 24.8) |
| Laboratory data on presentation | |||
| Baseline sUA (mg/dL) | 4.9 (4.3, 5.4) | 7.0 (6.4, 7.9) | 5.8 (4.8, 6.9) |
| Baseline hemoglobin (g/dL) | 13.9 (12.7, 14.8) | 14.0 (12.9, 15.0) | 13.9 (12.7, 14.8) |
| Baseline eGFR (via MDRD) | 85.6 (72.4, 101.2) | 74.2 (58.2, 88.8) | 81.3 (65.5, 97.1) |
| Troponin or CKMB elevated at baseline | 13,207 (87.8%) | 11,476 (89.4%) | 24,683 (88.6%) |
| Baseline total cholesterol (mg/dL) | 193.3 (162.4, 224.3) | 189.5 (158.5, 224.3) | 189.5 (158.5, 224.3) |
| Baseline triglycerides (mg/dL) | 124.0 (88.6, 177.1) | 150.6 (106.3, 212.6) | 132.9 (97.4, 194.9) |
| Baseline HDL-C (mg/dL) | 46.0 (38.0, 56.1) | 42.2 (35.0, 51.0) | 44.1 (36.1, 54.0) |
| Baseline LDL-C (mg/dL) | 115.2 (88.4, 144.4) | 111.7 (84.1, 141.8) | 113.8 (86.4, 143.2) |
| Medications at randomization | |||
| Baseline aspirin use | 14,437 (95.8%) | 12,352 (95.9%) | 26,789 (95.8%) |
| Baseline beta-blocker use | 10,927 (72.5%) | 9652 (74.9%) | 20,579 (73.6%) |
| Baseline lipid-lowering therapy | 12,442 (82.5%) | 10,639 (82.6%) | 23,081 (82.6%) |
| Baseline colchicine use | 26 (0.2%) | 67 (0.5%) | 93 (0.3%) |
| Baseline allopurinol use | 311 (2.1%) | 389 (3.0%) | 700 (2.5%) |
ACS, Acute coronary syndrome; BMI, body mass index; CABG, coronary artery bypass grafting; CHF, congestive heart failure; CKMB, creatine kinase MB; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MDRD, Modification of Diet in Renal Disease; MI, myocardial infarction; NSTEMI, non–ST-segment elevation myocardial infarction; PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction; sUA, serum uric acid; TIA, transient ischemic attack; WHO, World Health Organization.
Continuous variables presented as median (Q1, Q3); discrete variables presented as N (%).
Composite endpoint frequency
Over a median follow-up period of 365 days, a total of 2556 patients (Kaplan–Meier rate at 1 year: 8.9% [n = 2338; excludes 218 events which occurred after 1 year]) experienced the primary composite endpoint; of these, 1430 (56%) had an elevated sUA level and 165 (6.5%) had a history of gout at baseline. The frequency of the composite endpoint components are shown in Table 2 by baseline sUA level.
Table 2.
Endpoint frequencies at 1 year, with %KM rate estimates, by baseline sUA level
| Endpoints | Uric acid <6.0 mg/dL (n = 15,077) | Uric Acid ≥6.0 mg/dL (n = 12,882) | Total (n = 27,959) |
|---|---|---|---|
|
| |||
| CV death/MI/stroke | 1027 (7.2) | 1311 (10.8) | 2338 (8.9) |
| CV death | 402 (2.8) | 586 (4.9) | 988 (3.8) |
| All-cause death | 502 (3.5) | 723 (6.0) | 1225 (4.7) |
| MI | 577 (4.1) | 719 (6.1) | 1296 (5.0) |
| Stroke | 142 (1.0) | 197 (1.6) | 339 (1.3) |
CV, Cardiovascular; KM, Kaplan–Meier; All other abbreviations can be found in Table 1.
Relationship between baseline sUA level and CV outcomes
The risk of the primary composite endpoint of cardiovascular death, MI, or stroke increased with each 1 mg/dL rise in baseline sUA level above the inflection point of 5 mg/dL (unadjusted HR 1.23 [95% CI 1.20–1.26]; P < .0001; Table 3, Figure 2). The univariate association with increasing baseline sUA level was also significant for each of the secondary endpoints. After adjustment for confounding factors, the significant association with the primary composite endpoint remained, though it was slightly attenuated (adjusted HR 1.07 [95% CI 1.04–1.10]; P < .0001), and the same was true for each of the secondary endpoints (Table 3).
Table 3.
Association between baseline sUA level and the primary and secondary endpoints
| Unadjusted* |
Adjusted* |
|||
|---|---|---|---|---|
| Endpoints | HR (95% CI) | P-value | HR (95% CI) | P |
|
| ||||
| CV death/MI/stroke | 1.23 (1.20,1.26) | <.0001 | 1.07 (1.04,1.10) | <.0001 |
| CV death | 1.32 (1.28,1.37) | <.0001 | 1.09 (1.05,1.14) | <.0001 |
| All-cause death | 1.32 (1.29,1.36) | <.0001 | 1.10 (1.06,1.14) | <.0001 |
| MI | 1.21 (1.17,1.24) | <.0001 | 1.06 (1.02,1.10) | .0043 |
| Stroke | 1.17 (1.10,1.25) | <.0001 | 1.09 (1.00,1.18) | .0486 |
Figure 2.
sUA level and occurrence of primary composite endpoint.Displayed is the association between baseline sUA level and 1-year occurrence of the primary composite endpoint. The black line represents the unadjusted HR per 1 mg/dL increase in sUA above 5 mg/dL (HR 1.23 [95% CI 1.20–1.26]). CI, Confidence interval; CV, cardiovascular; HR, hazard ratio; MI, myocardial infarction; sUA, serum uric acid.
Relationship between baseline sUA level, gout, and CV outcomes
The unadjusted relationship between sUA level in ACS patients and the primary endpoint was modified by the presence of a gout diagnosis at baseline such that the risk of a composite CV outcome was higher in those without gout compared with those with gout (HR 1.24 [95% CI 1.21–1.27) vs. HR 1.13 [95% CI 1.05–1.25]; interaction P = .033). After adjustment for clinical factors, the association between sUA level and the primary outcome remained significant (HR 1.07 [95% CI: 1.04–1.10]; P < .0001); however, the presence of baseline gout no longer modified this relationship (interaction P = .696).
A history of gout did not modify the unadjusted association of baseline sUA level with any of the secondary endpoints with the exception of spontaneous MI, for which the risk was higher in those without than in those with gout (HR 1.22 [95% CI 1.18–1.26] vs. HR 1.06 [95% CI 0.95–1.18]; interaction P = .015). After adjustment, this interaction was no longer statistically significant (P = .407), yet the relationship between sUA level and spontaneous MI remained (HR 1.06 [95% CI 1.02–1.10]; P = .004). The relationship between sUA and each secondary outcome remained significant after adjustment for gout and other clinical factors except for stroke (adjusted HR 1.09 [95% CI 1.00–1.18]; P = .053). A history of gout at baseline was not independently associated with any of the primary or secondary endpoints after adjustment for possible confounding factors.
There were 925 (3%) patients with an episode of gout post-baseline. Analysis of the relationship between a gout diagnosis and the primary and secondary endpoints revealed that there was no difference if a post-baseline gout episode was modeled as having chronic risk versus short-term (14-day) risk. Therefore, in order to explore whether the development of gout after ACS affected the relationship between baseline sUA level and CV outcomes, gout was modeled as a single time-dependent covariate, including a gout diagnosis at baseline or post-baseline. The diagnosis of gout either at baseline or after the index ACS hospitalization modified the unadjusted relationship between baseline sUA level and the primary composite endpoint (HR 1.24 [95% CI 1.21–1.27] in those without gout vs. HR 1.10 [95% CI 1.04–1.20] in those with gout; interaction P = .0054), as well as between sUA and spontaneous MI alone (HR 1.22 [95% CI 1.18–1.26] in those without gout vs. HR 1.08 [95% CI 0.98–1.18] in those with gout; interaction P = .0018). Nonetheless, after adjustment, these interactions were no longer significant, while associations between sUA level and outcomes remained significant for primary and all secondary endpoints except for stroke (Table 4).
Table 4.
Association between baseline sUA level, gout diagnosis at any point (modeled as time-dependent covariate), and the primary and secondary endpoints
| Adjusted* |
|||
|---|---|---|---|
| Event | Effect | HR (95% CI) | P |
|
| |||
| CV death/MI/stroke | Continuous baseline sUA (>5) | 1.07 (1.04–1.10) | <.0001 |
| Baseline gout | 1.02 (0.87,1.18) | .8398 | |
| Interaction | – | .8134 | |
| CV death | Continuous baseline sUA (>5) | 1.09 (1.05,1.14) | <.0001 |
| Baseline gout | 0.97 (0.76,1.22) | .7757 | |
| Interaction | – | .6368 | |
| All-cause death | Continuous baseline sUA (>5) | 1.10 (1.06,1.14) | <.0001 |
| Baseline gout | 1.03 (0.84,1.26) | .0860 | |
| Interaction | – | .2432 | |
| MI | Continuous baseline sUA (>5) | 1.06 (1.02,1.10) | .0036 |
| Baseline gout | 1.05 (0.86,1.27) | .6608 | |
| Interaction | – | .5849 | |
| Stroke | Continuous baseline sUA (>5) | 1.08 (1.00,1.18) | .0603 |
| Baseline gout | 1.28 (0.87,1.89) | .2049 | |
| Interaction | – | .2221 | |
All models (adjusted and unadjusted) include stratification by clinical trial. Conditional HRs are presented only when interaction is significant at α = .05. For models with non-significant interaction terms, main effect P values and estimates presented are after removal of interactions from models.
Discussion
The role of uric acid in the development of cardiovascular disease continues to be controversial. Our data add to the growing evidence that sUA might be an independent marker of adverse cardiovascular outcomes. Specifically, in our analysis of 27,959 ACS patients from the PLATO and TRACER trials, an increasing level of sUA beyond 5 mg/dL at the time of ACS admission conferred a higher risk of cardiovascular death, spontaneous MI, or stroke over a median follow-up of 365 days. This association remained after adjustment for several confounding clinical factors and was not influenced by a clinical diagnosis of gout either at baseline or after the ACS event.
These data are consistent with several recent studies which support the role of sUA as a possible independent predictor of cardiovascular disease across multiple patient populations. Several large population studies have found evidence of an association between sUA level and cardiovascular outcomes.5,7,14,15 For example, data from the National Health and Nutrition Examination Survey revealed that in a representative sample of the United States population, elevated sUA levels were associated with cardiovascular mortality in both women and men over an average of 16.4 years of follow-up.16
Substantial data also support a predictive relationship between sUA level and cardiovascular outcomes in those with known coronary artery disease.17–20 There is evidence that sUA can predict future cardiac events, particularly in those with acute coronary syndrome.21–23 For example, the GISSI-Prevenzione trial enrolled patients with a recent MI; investigators found that increasing levels of sUA were associated with an elevated incidence of cardiovascular events. Furthermore, GISSI-Prevenzione investigators discovered that adding sUA to standard cardiovascular risk models significantly increased the discriminatory capacity of those models.24 Similarly, in a German study of 5124 ACS patients who underwent percutaneous coronary intervention, every 1 mg/dL increase in uric acid level was associated with a 12% increase in the adjusted risk of 1-year mortality.25 Our data are consistent with results of both of these trials, given that we found there was a 7% adjusted increase in cardiovascular events for each 1 mg/dL increase in sUA level above 5 mg/dL.
Despite the consistencies among the studies mentioned above, not all studies have shown an independent association between sUA level and cardiovascular events,26–28 and the role of gout in this relationship remains controversial. Choi and Curhan used prospective data to show that gout is independently related to all-cause and cardiovascular mortality in men.29 In a recent large study of 61,527 Taiwanese subjects, gout (but not hyperuricemia) was linked to increased all-cause and CV mortality rates30; similar results have been shown in other studies, as well.31 In contrast, two other recent large studies found that both hyperuricemia and gout were independently related to cardiovascular outcomes.32,33 Notably, each of the studies mentioned above was cross-sectional, rather than focused on patients with recent ACS. Studies on the relationship between gout and cardiovascular outcomes after ACS are few, limited by a small sample size, and were performed using cohorts from the 1970s, which was before the advent of modern ACS therapy. 34 Our data extend prior knowledge by showing that neither a history of gout nor the development of gout after ACS appears to influence the significant independent relationship between sUA level and cardiovascular outcomes after ACS. Perhaps a modifying relationship with gout was not seen because of the vasoprotective effect of certain gout therapies, such as allopurinol.35 Regardless, the finding that sUA is associated with elevated cardiovascular risk irrespective of the presence of gout, is relevant to a broad population, including patients with hyperuricemia but without clinical gout, as well as patients with ACS and renal disease.
Of potential clinical utility is our finding that the inflection point of the association between sUA and cardiovascular events in this population occurred at 5 mg/dL rather than the established clinical cut-point of 6 mg/dL. The clinical threshold for hyperuricemia has long been debated, and the cut-point of 6 mg/dL is based on studies showing an increased future risk of gout higher than this sUA level.36,37 Nevertheless, the best threshold to predict future cardiovascular risk is still unknown; our data suggest that the established threshold of 6 mg/dL should perhaps be revisited in this context.
Whether sUA has a causative role in the development of cardiovascular disease has yet to be determined. In vitro studies have shown that uric acid promotes endothelial dysfunction and proliferation,38 generates intermediate reactive oxidative species,39 and leads to inflammation.40 Studies of uric acid-lowering therapies on left ventricular performance in heart failure have shown promising results,41,42 but relatively few studies have examined the effects of such therapy in patients with known coronary artery disease.35 In a recent case–control study of 2277 MI patients and 4849 matched controls, allopurinol use was associated with a 20% decreased risk of MI after adjustment43; while provocative, such results needs to be replicated prospectively in a randomized-controlled trial before sUA can be considered a therapeutic target to lower cardiovascular risk. The on-going ALL-HEART study will help to address this issue by randomizing more than 5000 patients with ischemic heart disease to allopurinol therapy versus usual care and assessing cardiovascular events; results from this trial are expected after the year 2019.44 At the very least, our data suggests that patients with elevated sUA after ACS are at increased risk for future events and might be candidates for more aggressive secondary prevention measures.
Study strengths and limitations
Our study has strengths that should be noted: A large sample size, multi-national contemporary population, long-term follow-up, and rigorous endpoint ascertainment all lend validity to our findings. Our study also has several limitations. First, we performed a retrospective analysis in which we adjusted for multiple potential confounding variables, yet our results could be limited by residual confounding. Second, in this combined cohort, we were unable to adjust for other biomarkers of large prognostic importance like cardiac dysfunction and inflammatory activity which might be associated both with sUA level and outcomes. Third, we were unable to adjust for all known biomarkers that are associated with cardiovascular outcomes (such as high-sensitivity C-reactive protein), and for all medications that can potentially modify sUA level (i.e., angiotensin-converting enzyme inhibitors and diuretics); however, we were able to adjust for beta-blocker, statin, and aspirin use—all of which are known to affect sUA levels.12 Notably, we could not adjust for gout medications (including allopurinol, febuxostat, colbenemid, colchicine, and probenecid), since use of these medications formed part of our definition of a history of gout, or of a post-baseline gout episode. Although further exploration of the relationship of ticagrelor (which can modestly increase sUA level)45 with gout and CV outcomes was beyond the scope of this analysis, such an association clearly merits further prospective study. Finally, our study at least partially relied on a self-reported history of gout and gout medication use instead of a physician-confirmed diagnosis.
Conclusion
Our data suggest that increasing sUA is associated with a higher risk of cardiovascular events in patients with ACS, and that this relationship is independent of clinical risk factors including a clinical diagnosis of gout. Whether sUA should be a therapeutic target to lower CVD risk will require further investigation.
Supplementary Material
Acknowledgments
The authors would like to thank Erin Hanley, MS, for her editorial contributions to this manuscript. Ms. Hanley did not receive compensation for her assistance, apart from her employment at the institution where this study was conducted.
Source of funding:
This study was funded by Astra Zeneca.
Footnotes
Conflict of interest disclosures
NJP reports having ownership interest in Freedom Health, Inc.; Physician Partners, LLC; RXAdvance, LLC; Florida Medical Associates, LLC; CNH reports receiving a research grant from Gilead Sciences, Inc.; all other authors report no relevant disclosures.
Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.ahj.2017.02.023.
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