Atrial fibrillation (AF) is common in patients with life-threatening cancer and those undergoing active cancer treatment. However, data from subjects with a history of non–life-threatening cancer and those who do not require active cancer treatment are lacking. A total of 15,428 (mean age 66 ± 8.9 years; 47% women; 45% blacks) participants from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study with baseline data on previous cancer diagnosis and AF were included. Participants with life-threatening cancer and active cancer treatment within 2 years of study enrollment were excluded. History of cancer was identified using computer-assisted telephone interviews. AF cases were identified from baseline electrocardiogram data and by a self-reported history of a previous diagnosis. Logistic regression was used to examine the cross-sectional association between cancer diagnosis and AF. A total of 2,248 (15%) participants had a diagnosis of cancer and 1,295 (8.4%) had AF. In a multivariable logistic regression model adjusted for sociodemographic characteristics (age, gender, race, education, income, and region of residence) and cardiovascular risk factors (systolic blood pressure, high-density lipoprotein cholesterol, total cholesterol, C-reactive protein, body mass index, smoking, diabetes, antihypertensive and lipid-lowering agents, left ventricular hypertrophy, and cardiovascular disease), those with cancer were more likely to have prevalent AF than those without cancer (odds ratio 1.19, 95% confidence interval 1.02 to 1.38). Subgroup analyses by age, sex, race, cardiovascular disease, and C-reactive protein yielded similar results. In conclusion, AF was more prevalent in participants with a history of non–life-threatening cancer and those who did not require active cancer treatment in REGARDS.
The development of atrial fibrillation (AF) after cancer surgery is well known. Several studies have suggested that the association between cancer and AF is not limited to the postoperative period. Case–control studies have reported associations of colorectal and breast cancers with AF, and registry data from Denmark have reported similar associations with cancers of the colon, lung, kidney, and ovary. Notably, these reports from nonsurgical populations focused on subjects with recent cancer diagnoses or those who were admitted to the hospital for cancer treatment. However, data from subjects with a history of non–life-threatening cancer and those who do not require active cancer treatment are lacking. An association between AF and cancer from this population would support that cancer represents a co-morbid state predisposing to AF. Therefore, the purpose of this study was to examine the association between cancer (non–life-threatening cancer or requiring active treatment) and AF using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study.
Methods
Details of REGARDS have been published previously. Briefly, this prospective cohort study was designed to identify causes of regional and racial disparities in stroke mortality. The study population oversampled blacks and residents of the stroke belt (North Carolina, South Carolina, Georgia, Alabama, Mississippi, Tennessee, Arkansas, and Louisiana). From January 2003 to October 2007, a total of 30,239 participants were recruited from a commercially available list of residents using postal mailings and telephone data. Demographic information and medical histories were obtained using a computer-assisted telephone interview (CATI) system that was conducted by trained interviewers. Additionally, a brief in-home physical examination was performed approximately 3 to 4 weeks after the telephone interview. During the in-home visit, trained staff collected information regarding medications, blood and urine samples, and a resting electrocardiogram. This analysis examined the cross-sectional association between cancer and AF. Participants were excluded if they were missing the following at baseline: cancer data, AF data, or baseline covariate data.
Cancer diagnosis was determined by a positive response to the following question during the CATI: “Have you ever been diagnosed with cancer?” Patients with life-threatening cancers or those who were receiving or received active cancer treatment within 2 years of study enrollment were excluded from participation in REGARDS. We assumed that participants with an affirmative response to the aforementioned question had survived cancer, no longer required treatment, or had cancers with indolent courses (non–life-threatening).
AF was identified at baseline by the scheduled electrocardiogram and also from a self-reported history of a physician diagnosis during the CATI surveys. The electrocardiograms were read and coded at a central reading center (Epidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston-Salem, North Carolina) by analysts who were blind to other REGARDS data. Self-reported AF was defined as an affirmative response to the following question: “Has a physician or a health professional ever told you that you had atrial fibrillation?”
Participant characteristics collected during the initial REGARDS in-home visit were used in this analysis. Age, sex, race, income, education, and smoking status were self-reported. Annual household income was dichotomized at <$20,000 or ≥$20,000. Similarly, education was categorized into “high school or less” or “some college or more.” Smoking was defined as ever (e.g., current and former) or never smoker. Fasting blood samples were obtained and assayed for total cholesterol, high-density lipoprotein (HDL) cholesterol, glucose, creatinine, C-reactive protein (CRP), and urine albumin-to-creatinine ratio (ACR). Diabetes was defined as a fasting glucose level ≥126 mg/dl (or a nonfasting glucose, ≥200 mg/dl in those failing to fast) or self-reported diabetes medication use. Regular aspirin use and antihypertensive and lipid-lowering medication use were defined by the self-reported current use of these medications during the CATI surveys. Body mass index was computed as the weight in kilograms divided by the square of the height in meters. After the participant rested for 5 minutes in a seated position, blood pressure was measured using a sphygmomanometer. Two values were obtained following a standardized protocol and averaged. Using baseline electrocardiogram data, left ventricular hypertrophy was defined by the Sokolow–Lyon criteria. Cardiovascular disease was defined as a history of coronary heart disease or stroke. Coronary heart disease was confirmed by self-reported history of myocardial infarction, coronary artery bypass grafting, coronary angioplasty, or stenting or if evidence of previous myocardial infarction was present on the baseline electrocardiogram. Previous stroke was ascertained by participant self-reported history.
Categorical variables were reported as frequency and percentage, whereas continuous variables were reported as mean ± standard deviation. Statistical significance of differences for categorical variables was tested using the chi-square method and the Wilcoxon rank sum procedure for continuous variables. Logistic regression was used to compute odds ratios (OR) and 95% confidence intervals (CI) for the association between cancer diagnosis and AF. Multivariable models were adjusted as follows: model 1 adjusted for age, sex, race, education, income, and geographic region; model 2 included covariates in model 1 with the addition of systolic blood pressure, HDL cholesterol, total cholesterol, log(CRP), body mass index, smoking, diabetes, aspirin, antihypertensive medications, lipid-lowering therapies, left ventricular hypertrophy, and cardiovascular disease; and model 3 included model 2 covariates plus serum creatinine and log(ACR). Additionally, subgroup analyses were performed to evaluate effect modification by age (dichotomized at 65 years), sex, race, cardiovascular disease, and CRP (dichotomized at the median value) by including interaction terms in the model. Statistical significance for all comparisons including interactions was defined as p <0.05. SAS version 9.3 (SAS Institute Inc., Cary, NC) was used for all analyses.
Results
Of the 30,239 participants from the original REGARDS cohort, 56 were excluded for data anomalies and 12,105 participants had missing data regarding previous cancer. Of those who remained, 362 participants with missing AF data and 2,288 participants with either missing baseline characteristics or missing medication data also were excluded. A total of 15,428 (mean age 66 ± 8.9 years; 47% women; 45% blacks) participants were included in the final analysis.
A total of 2,248 (15%) participants reported a cancer diagnosis and 1,295 (8.4%) had evidence of AF. Those with a history of cancer (n = 249, 11%) were more likely to have AF than those without cancer (n = 1,046, 7.9%; p <0.0001). The prevalence of AF in those with and without cancer by age, sex, race, cardiovascular disease, and CRP is shown in Figure 1 .
Baseline characteristics by cancer diagnosis are presented in Table 1 . Participants with a cancer diagnosis were more likely to be older, male, white, and to report a history of lower educational attainment, smoking, aspirin use, and cardiovascular disease than those without a cancer diagnosis. Those with cancer also were more likely to have less values for body mass index, total cholesterol, and HDL cholesterol and to have greater values for serum creatinine and log(ACR) compared with subjects without a cancer diagnosis.
| Characteristic | Prior Cancer (n=2,248) | No Prior Cancer (n=13,180) | P-value ∗ |
|---|---|---|---|
| Age, mean ± SD (years) | 70 ± 8.6 | 65 ± 8.7 | <0.0001 |
| Male | 1,322 (59%) | 6,856 (52%) | <0.0001 |
| Black | 680 (30%) | 5,601 (43%) | <0.0001 |
| Region | |||
| Stroke buckle | 331 (15%) | 2,047 (16%) | |
| Stroke belt | 806 (36%) | 4,628 (35%) | |
| Non-belt | 1,111 (49%) | 6,505 (49%) | 0.57 |
| Education, high school or less | 815 (36%) | 5,280 (40%) | 0.0006 |
| Annual income, <$20,000 | 397 (18%) | 2,508 (19%) | 0.13 |
| Body mass index, mean ± SD (kg/m 2 ) | 28 ± 5.5 | 29 ± 6.0 | <0.0001 |
| Ever smoker | 1,354 (60%) | 7,549 (57%) | 0.0088 |
| Diabetes mellitus | 460 (20%) | 2,912 (22%) | 0.084 |
| Systolic blood pressure, mean ± SD (mm Hg) | 129 ± 16 | 129 ± 17 | 0.72 |
| Total cholesterol, mean ± SD (mg/dL) | 187 ± 39 | 192 ± 40 | <0.0001 |
| HDL-cholesterol, mean ± SD (mg/dL) | 50 ± 16 | 52 ± 16 | 0.0006 |
| Aspirin use | 1,097 (49%) | 5,976 (45%) | 0.0024 |
| Antihypertensive medication use | 1,233 (55%) | 7,014 (53%) | 0.15 |
| Lipid-lowering medication use | 768 (34%) | 4,311 (33%) | 0.17 |
| Log(CRP), mean ± SD (mg/L) | 0.79 ± 1.2 | 0.81 ± 1.2 | 0.17 |
| Left ventricular hypertrophy | 242 (11%) | 1,428 (11%) | 0.92 |
| Cardiovascular disease | 636 (28%) | 2,993 (23%) | <0.0001 |
| Serum creatinine, mean ± SD (mg/dL) | 0.99 ± 0.59 | 0.93 ± 0.48 | <0.0001 |
| Log(ACR), mean ± SD (mg/g) | 2.5 ± 1.3 | 2.4 ± 1.3 | <0.0001 |
∗ Statistical significance for categorical variables tested using the chi-square method and for continuous variables the Wilcoxon-rank sum was used.
Those with a history of cancer were more likely to have AF at baseline than those without a diagnosis (unadjusted OR 1.45, 95% CI 1.25 to 1.67). After adjustment for demographic characteristics, cardiovascular risk factors, and potential confounders, cancer was significantly associated with AF ( Table 2 ). Similar results were observed when the analysis was stratified by age, sex, race, cardiovascular disease, and CRP ( Table 3 ).
| AF cases | Model 1 ∗ OR (95%CI) | P-value | Model 2 † OR (95%CI) | P-value | Model 3 ‡ OR (95%CI) | P-value | |
|---|---|---|---|---|---|---|---|
| No Cancer | 1,046/13,180 | 1.0 | – | 1.0 | – | 1.0 | – |
| Cancer | 249/2,248 | 1.23 (1.06, 1.43) | 0.0072 | 1.19 (1.02, 1.38) | 0.028 | 1.18 (1.01, 1.38) | 0.036 |
∗ Adjusted for age, sex, race, education, income, and geographic region.
† Adjusted for Model 1 covariates plus systolic blood pressure, HDL-cholesterol, total cholesterol, log(CRP), body mass index, smoking, diabetes, antihypertensive medications, lipid-lowering therapies, left ventricular hypertrophy, and prior history of cardiovascular disease.
‡ Adjusted for Model 2 covariates plus serum creatinine and log(ACR).
| Variable | Model 1 ∗ OR (95%CI) | P-value | Model 2 † OR (95%CI) | P-value | Model 3 ‡ OR (95%CI) | P-value | Interaction § P-value |
|---|---|---|---|---|---|---|---|
| Age (years) | |||||||
| <65 | 1.10 (0.80, 1.52) | 0.57 | 1.05 (0.75, 1.45) | 0.79 | 1.04 (0.75, 1.45) | 0.80 | 0.11 |
| ≥65 | 1.34 (1.13, 1.59) | 0.0007 | 1.27 (1.07, 1.51) | 0.0066 | 1.26 (1.06, 1.50) | 0.011 | |
| Sex | |||||||
| Female | 1.44 (1.15, 1.80) | 0.0015 | 1.34 (1.07, 1.69) | 0.011 | 1.37 (1.08, 1.72) | 0.0084 | 0.37 |
| Male | 1.08 (0.88, 1.32) | 0.46 | 1.06 (0.87, 1.31) | 0.56 | 1.04 (0.84, 1.28) | 0.72 | |
| Race | |||||||
| Black | 1.49 (1.13, 1.97) | 0.0045 | 1.41 (1.06, 1.86) | 0.018 | 1.34 (1.003, 1.80) | 0.048 | 0.59 |
| White | 1.12 (0.94, 1.34) | 0.21 | 1.09 (0.91, 1.31) | 0.36 | 1.10 (0.92, 1.32) | 0.31 | |
| Cardiovascular disease | |||||||
| No | 1.22 (0.99, 1.50) | 0.055 | 1.19 (0.97, 1.47) | 0.092 | 1.19 (0.97, 1.47) | 0.10 | 0.79 |
| Yes | 1.21 (0.96, 1.51) | 0.11 | 1.17 (0.93, 1.46) | 0.19 | 1.15 (0.91, 1.45) | 0.24 | |
| Log(CRP) (mg/L) | |||||||
| <0.80 | 1.26 (1.01, 1.57) | 0.042 | 1.23 (0.98, 1.53) | 0.075 | 1.21 (0.96, 1.51) | 0.11 | 0.69 |
| ≥0.80 | 1.19 (0.97, 1.46) | 0.097 | 1.15 (0.94, 1.42) | 0.18 | 1.15 (0.93, 1.42) | 0.19 |
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