Association of Serum Uric Acid With Incident Atrial Fibrillation (from the Atherosclerosis Risk in Communities [ARIC] Study)

Atrial fibrillation (AF) is one of the most common arrhythmias seen in clinical practice. Current evidence suggests that serum uric acid (SUA) could be a marker of oxidative damage, a factor reported as a part of the mechanisms of AF. The purpose of the present study was to evaluate whether SUA predicted AF in the Atherosclerosis Risk In Communities (ARIC) study. The present analysis included 15,382 AF-free black and white men and women, aged 45 to 64 years, from the ARIC study, a population-based prospective cohort in the United States. SUA was determined using the uricase-peroxidase method at baseline. The primary outcome was the incidence of AF, defined as the occurrence of AF detected using hospital discharge codes, scheduled study electrocardiograms, and/or death certificates during the follow-up period (1987 to 2004). We identified 1,085 cases of incident AF. In Cox proportional hazards models adjusted for age, gender, race, center, education, body mass index, serum glucose, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, alcohol use, prevalent coronary heart disease and heart failure, serum creatinine, diuretics, and P-wave duration on the electrocardiogram (as a measure of left atrial size) at baseline, the hazard ratio of AF associated with a SD increment in SUA was 1.16 (95% confidence interval 1.06 to 1.26). The association of SUA with AF risk differed by race and gender (p for interaction <0.01). In conclusion, elevated SUA is associated with a greater risk of AF, particularly among blacks and women. Additional studies should replicate this association and explore potential mechanisms.

Serum uric acid (SUA) is a byproduct of purine catabolism, the terminal steps of which are catalyzed by xanthine oxidoreductase. SUA is an independent predictor of cardiovascular disease and mortality. The rationale for this association is that SUA levels increase owing to increased purine catabolism resulting from tissue hypoxia and apoptosis, and this excess leads to the formation of reactive oxygen species capable of oxidative stress damage. Therefore, SUA might be an inexpensive marker of the effects of oxidative stress on the heart. We hypothesized that in a population-based study of middle-age men and women from 4 United States communities, those with elevated SUA at baseline would be at an increased risk of the development of future atrial fibrillation (AF) and that this association would be independent of the cardiovascular risk factors.


The Atherosclerosis Risk In Communities (ARIC) study is a longitudinal community-based cohort of 15,792 men and women aged 45 to 64 years at enrollment. The cohort was sampled from 4 United States communities: Forsyth County, North Carolina; Jackson, Mississippi; the northwest suburbs of Minneapolis, Minnesota; and Washington County, Maryland. By design, the Jackson site exclusively recruited blacks, thereby accounting for 90% of blacks in the study. Most of the remaining blacks were from Forsyth County. The sampling procedures and methods used in the ARIC study have previously been described in detail. Baseline visits were conducted from 1987 through 1989.

The participants were followed up subsequently by annual telephone interviews and 3 field center visits every 3 years (the last in 1996 to 1998). For the present study, the participants were excluded for the following reasons: electrocardiogram (ECG)-diagnosed AF at baseline (n = 37), missing SUA measurement at baseline (n = 128), and absent electrocardiographic examination (n = 245). This left 15,382 ARIC participants for analysis.

The participants were asked to fast for ≥12 hours before the morning blood collection. After applying a tourniquet, the blood was drawn from the antecubital vein while the participants were seated. The blood specimens were collected into vacuum tubes containing serum-separator gel (glucose, creatinine, and SUA) and ethylenediaminetetraacetic acid (lipids). The tubes were centrifuged at 3,000 g for 10 minutes at 4°C. After separation, the aliquots were quickly frozen at −70°C until analysis (within a few weeks). Uric acid was measured using the uricase-peroxidase enzymatic method. The reliability coefficient of SUA was 0.91, as assessed by repeated measurements taken ≥1 week apart in 40 subjects (10 per ARIC center), and the within-person variability was 7.2%. SUA was measured at baseline (n = 15,646) and at visit 2 (n = 14,296).

The AF diagnoses were obtained from 3 sources: ECGs at the 4 scheduled study examinations through 1998, hospital discharge records through 2004, and death certificates through 2004. The study participants underwent standard 12-lead electrocardiographic recordings at baseline and at each of the 3 follow-up examinations. All electrocardiographic recordings were done with the MAC PC Personal Cardiographs (Marquette Electronics, Milwaukee, Wisconsin). All electrocardiographic recordings automatically coded as AF were visually rechecked to confirm the diagnosis.

As a part of the standard follow-up procedures in ARIC, a trained abstractor obtained and recorded all “International Classification of Diseases” (ICD), 9th revision, hospital discharge diagnoses codes from each participant’s hospitalizations reported in the annual follow-up interview or from the hospital surveillance. AF was defined as the presence of ICD-9 code 427.31 in the discharge codes. The participants with a diagnosis of atrial flutter (ICD-9 code 427.32) who have not developed AF on subsequent follow-up were not considered to have AF (n = 53), and their follow-up time was continued until censored. Transient AF can occur during cardiac operative procedures. Therefore, we excluded transient AF occurring simultaneously with heart revascularization surgery (ICD-9 code 36.X) or other cardiac surgery involving the heart valves or septa (ICD-9 code 35.X), without evidence of AF in subsequent hospitalizations or study examinations (n = 86 cases), and their follow-up time was continued until censored. The participants were also considered to have AF if the underlying cause of death was AF (ICD-10 code I48 or ICD-9 code 427.3). A validation study of the AF ICD code in a sample of ARIC hospitalizations reported a sensitivity and positive predictive values close to 90%.

The incidence date of AF was defined as the date of the first ECG showing AF, the first hospital discharge with an AF or atrial flutter diagnosis (the latter only if AF was identified later in the same patient), or a death certificate with AF, whichever occurred earlier.

Additional variables were collected at visit 1 (baseline). Information on age, gender, race, educational attainment, alcohol and cigarette use, and physical activity were based on self-report. Other variables such as blood pressure and body mass index were measured. The serum creatinine, glucose, and triglyceride levels were measured. The use of diuretics within the 2 weeks before the baseline interview were identified from medication containers and used as a categorical variable. Metabolic syndrome was defined according to the National Cholesterol Education Program.

Prevalent heart failure was defined as the current use of medications for heart failure at the baseline examination or evidence of manifest heart failure, stage 3, according to the Gothenburg criteria. Prevalent myocardial infarction (MI) was defined as a self-reported history of physician-diagnosed MI, silent MI on ECG, or hospitalized MI at baseline.

The incidence of cardiovascular events was collected during the follow-up. Incident heart failure cases were identified through a review of the death certificates and local hospital discharge lists. Heart failure was defined according to the ICD-9 or ICD-10. Incident heart failure was defined as first heart failure hospitalization (ICD-9 code 428) or any death for which the death certificate included a heart failure code (ICD-9 428 and ICD-10 I50). Nonhospitalized, nonfatal heart failure was not captured. Hospitalized MI was defined according to ARIC standardized criteria.

Follow-up started the date of the first examination and ended at the development of AF, death, loss to follow-up, or December 31, 2004 (whichever occurred earlier). The incidence rates were calculated for each quartile of SUA, dividing the number of incident AF cases by the person-years of follow-up. We calculated the hazard ratio (HR) of developing AF per SD of SUA using Cox proportional hazards models, adjusting for potential confounding factors. We also evaluated the association of the quartiles of SUA with the AF incidence, creating dummy variables and using the lowest quartile of SUA as the reference in Cox proportional hazards models. We conducted a chi-square goodness-of-fit test and adjusted our analysis for age, gender, education, race, center, body mass index, alcohol use, serum glucose, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, prevalent coronary heart disease and heart failure, serum creatinine, diuretics, and P-wave duration on ECG at baseline. To assess the robustness of our analysis, we performed 2 subsidiary analyses. First, we calculated the mean SUA level using the first (baseline) and second (visit 2) measurements and determined the event rates and HRs. Second, SUA was used as a time-varying covariate in the multivariate Cox proportional hazards models. Finally, we included interaction terms to evaluate whether the association between AF and SUA differed by race, gender, or the presence of the metabolic syndrome.

Because SUA might increase the risk of AF through an increased risk of heart failure or MI, we conducted an additional analysis, censoring subjects when they developed heart failure or MI before they developed AF.

Proportional hazard assumptions and assumptions of linearity of the association were examined using scaled Schoenfeld residuals. Analyses were performed using Stata, version 10 (College Station, Texas), and all tests were 2-tailed, with p <0.05 considered as statistically significant.


The selected baseline characteristics of the cohort of 15,382 adults free of AF are listed by quartile of baseline SUA in Table 1 . The participants with the greatest quartile of SUA were more likely to be older, black, and male and had a greater prevalence of cardiovascular risk factors, incident heart failure, and MI compared to the participants with the lowest SUA quartile.

Table 1

Baseline characteristics of 15,382 adults free of atrial fibrillation (AF) by quartile of serum uric acid (SUA)

Characteristic Baseline SUA
Quartile 1 Quartile 2 Quartile 3 Quartile 4 p Value for Trend
SUA cutpoints (mg/dl) <5.0 5.0–5.9 6.0–7.0 >7.0
Subjects (n) 3,880 3,919 3,927 3,656
Age (years) 53.0 ± 5.6 54.1 ± 5.6 54.3 ± 5.6 54.6 ± 5.8 <0.01
Women 87% 63% 39% 26% <0.01
Blacks 19% 23% 22% 30% <0.01
Body mass index (kg/m 2 ) 24.8 ± 4.1 26.8 ± 4.8 28.0 ± 5.0 29.4 ± 5.1 <0.01
Serum creatinine (mg/dl) 0.98 ± 0.20 1.06 ± 0.26 1.14 ± 0.35 1.25 ± 0.67 <0.01
Diuretic use 9% 12% 18% 32% <0.01
Systolic blood pressure (mm Hg) 136.4 ± 17.1 138.8 ± 17.0 141.1 ± 16.7 144.8 ± 17.8 <0.01
Serum glucose (mg/dl) 107.7 ± 44.5 109.8 ± 41.2 111.0 ± 38.1 114.9 ± 39.9 <0.01
Prevalent heart failure 2% 3% 5% 9% <0.01
Prevalent coronary artery disease 1% 3% 5% 8% <0.01
Incident heart failure 7% 10% 12% 17% <0.01
Incident myocardial infarction 6% 7% 9% 13% <0.01

Data are presented as mean ± SD or %.

We identified 1,085 participants with incident AF during a median follow-up of 16.8 years. Incident AF was positively associated with SUA levels, with an AF rate of 3/1,000 person-years in the lowest quartile of SUA versus 8/1,000 person-years in the greatest quartile (p <0.01; Table 2 ). This nearly threefold risk increment persisted when we used the mean SUA of visits 1 and 2 as the main exposure (p <0.01).

Table 2

Incidence rates and hazard ratios (HRs) of atrial fibrillation (AF) by quartile of serum uric acid (SUA)

SUA Quartile At Risk (n) Developing AF (n) Incidence rate of AF/1,000 Person-Years HR (95% CI)
Baseline quartile
Quartile 1 (<5.0 mg/dl) 3,880 186 3 1 (reference)
Quartile 2 (5.0–5.9 mg/dl) 3,919 243 4 1.04 (0.83–1.30)
Quartile 3 (6.0–7.0 mg/dl) 3,927 297 5 1.30 (1.04–1.62)
Quartile 4 (>7.0 mg/dl) 3,656 359 8 1.74 (1.39–2.17)
p Value for Trend <0.01 <0.01
Mean quartiles
Quartile 1 (<5.0 mg/dl) 3,584 158 3 1 (reference)
Quartile 2 (5.0–5.9 mg/dl) 3,461 213 4 1.29 (1.02–1.63)
Quartile 3 (6.0–7.0 mg/dl) 3,390 250 5 1.40 (1.11–1.78)
Quartile 4 (>7.0 mg/dl) 3,448 319 7 2.00 (1.58–2.52)
p Value for Trend <0.01 <0.01

Age, gender, and race adjusted.

Excluding cases of AF between visits 1 and 2 and those who did not attend visit 2.

The adjusted relation between the baseline SUA and incident AF are listed in Table 3 . After simultaneous adjustment for age, gender, race, education, field center, body mass index, alcohol use, serum glucose, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, prevalent heart failure, prevalent coronary heart disease, serum creatinine, P-wave duration on the ECG (as a measure of left atrial size) at baseline, and diuretic use, SUA was significantly associated with the subsequent risk of incident AF (HR 1.16; 95% confidence interval [CI] 1.06 to 1.26 per SD increase). Similar risk associations were seen using the mean value of SUA between visits 1 and 2 and on analysis that included SUA as a time-varying exposure. When stratified by ARIC recruitment center, only the Jackson and Minneapolis sites had a significant association between SUA and AF.

Dec 16, 2016 | Posted by in CARDIOLOGY | Comments Off on Association of Serum Uric Acid With Incident Atrial Fibrillation (from the Atherosclerosis Risk in Communities [ARIC] Study)

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