Our knowledge of the association between abdominal obesity (AO) and the risk of atrial fibrillation (AF) after adjusting for body mass index (BMI) is limited. We included 11,617 Black and White participants (mean age 63.0 ± 8.4 years) from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) national cohort study who were free of AF at baseline. A multivariable logistic regression model was used to estimate the odds ratio (OR) with 95% confidence interval (CI) of incident AF associated with AO. We also evaluated the association between waist circumference (WC) and incident AF. Over a median follow-up of 9.4 years, 999 participants developed AF. AO was associated with an increased risk of AF in a multivariable model adjusted for sociodemographic, lifestyle, and cardiovascular risk factors (OR 1.43, 95% CI 1.24 to 1.65, p <0.001). The association was attenuated after adjusting for BMI (OR 1.13, 95% CI 0.95 to 1.35, p = 0.16). There was no evidence of interaction between AO and incident AF by age category (age >65 vs age ≤65), gender, race, obesity, or BMI category. Conversely, a 10cm increase in WC was associated with a higher incidence of AF after controlling for BMI (OR 1.18 95% CI 1.09 to 1.29, p <0.001), in both nonobese (OR 1.14, 95% CI 1.03 to 1.28, p = 0.02) and obese (OR 1.26, 95% CI 1.11 to 1.42, p <0.001) people. In conclusion, there was an association between AO and incident AF, but the association was weakened after adjusting for BMI. There was a significant association between WC and incident AF, after taking other AF risk factors and BMI into account. WC is a potentially modifiable risk factor for AF, and further research is warranted to explore the effect of decreasing WC on the population AF burden.
Previous studies demonstrated that subjects with obesity have a higher risk of atrial fibrillation (AF) compared with nonobese counterparts. , Several anthropometric measures, including body mass index (BMI), waist circumference (WC), and waist-to-hip ratio, have been used to identify obese subjects. Obesity defined by BMI is widely used, but increasing evidence shows that it can miss a significant portion of subjects with increased cardiovascular risk. , Studies have suggested that abdominally defined obesity (AO) has a stronger association with cardiometabolic disease than BMI-defined obesity. Previous studies attempting to elucidate the association of AO and incident AF were limited by relatively short follow-up, lack of proper adjustment for BMI, and a homogenous ethnic population. , Therefore, we propose to examine the association between AO and incident AF in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) population. We also assess the association between WC and incident AF, stratified by the presence of BMI-defined obesity.
Details of the study design and methodology of REGARDS have been published previously. The REGARDS study is a prospective cohort study designed to identify contributors to the Black-White and regional disparities in stroke mortality. Between January 2003 and October 2007, 30,239 participants aged 45 years or older were recruited from the continental United States. Written informed consent was obtained from all participants and the study was approved by the institutional review boards of all participating universities.
Of the 30,239 participants initially enrolled, 15,521 participants were still active in the study and completed a follow-up examination conducted between 2013 and 2016, approximately 10 years after the first in-home visit. Of those, we excluded 3,904 participants with baseline AF or missing data on AF, BMI, WC, or other covariates. The final sample included 11,617 participants with complete data at baseline and follow-up visits.
Incident AF was identified by a resting electrocardiogram obtained during the home visit and a self-reported history of AF diagnosis by a physician during the computer-assisted telephone interview system surveys. Self-reported data were used for the following variables: age, gender, race, education (college graduate and above, some college, high school graduate, less than high school), income (less than $20k per year, $20k–$34k per year, $35k–$74k per year, $75k and above per year, refused to answer), region of residence (belt, buckle, nonbelt), smoking status (current, never, past), physical activity (none, 1 to 3 times per week, 4 or more times per week), alcohol intake (current, never, past), and history of stroke. History of coronary heart disease was ascertained by a self-reported history of myocardial infarction, coronary artery bypass grafting, angioplasty or stenting, or electrocardiographic evidence of previous myocardial infarction. AO was defined as WC >102 cm in men and >88 cm in women. Underweight, normal weight, overweight, and obesity were defined as BMI of <18.5, 18.5 to 24.9, 25 to 29.9, and ≥30 kg/m 2 , respectively.
We performed descriptive statistics for demographic, socioeconomic, lifestyle, anthropometric, and medical history variables at the baseline assessment according to the presence of AO. Frequencies and percentages were used to describe categorical variables. Means and standard deviations were used to describe continuous variables. We tested for differences in distributions of variables between exposure groups using the chi-square test for categorical variables and analysis of variance test for continuous variables.
Multivariable logistic regression analyses were performed to estimate the odds ratio (OR) with 95% confidence interval (CI) of incident AF associated with AO. Variables were selected based on previously published AF risk factors. , Multivariable models were fit sequentially as follows: Model 1: Sociodemographic: age (as a continuous variable), gender, race, education, household income, and region; Model 2: variables in Model 1 plus lifestyle factors (smoking, physical activity, alcohol intake); Model 3: variables in Model 2 plus cardiovascular disease (CVD) risk factors (hypertension, dyslipidemia, diabetes mellitus, history of coronary artery disease, history of stroke); Model 4: variables in Model 3 plus BMI (as a continuous variable).
Effect modification by age group >65 versus ≤65, race, gender, BMI-defined obesity, and BMI category was evaluated by stratified analysis and comparison of models with and without interaction terms using the likelihood ratio test. Potential effect modifiers were selected based on previous studies assessing the association between AO and incident AF.
We also evaluated the association between WC as a continuous variable and incident AF. We conducted a multivariable logistic regression analysis to estimate the OR with 95% CI of incident AF associated with WC, with the same multivariable models used for the association between AO and incident AF. We tested whether the association between WC and incident AF differed across BMI-defined obesity status by introducing WC BMI-defined obesity into the logistic regression model. A stratified analysis based on BMI-defined obesity was performed as well. All statistical analyses were performed using R Statistical Software version 4.0.0 (Foundation for Statistical Computing, Vienna, Austria) at α = 0.05 significance level.
A total of 11,617 participants were included in this analysis. Table 1 lists the baseline characteristics of the study participants stratified by AO. Compared with those without AO, participants with AO were more likely to be Black, have lower education levels, and have lower income. There was a higher prevalence of hypertension, hyperlipidemia, diabetes mellitus, and a history of CVD observed in those with AO compared with those without.
|Variable||Overall||Abdominal obesity||p value|
|(n=11617)||No (n=6248)||Yes (n=5369)|
|<18.5||96 (0.8%)||92 (1.5%)||4 (0.1%)|
|18.5–24.9||2699 (23.2%)||2564 (41.0%)||135 (2.5%)|
|25–29.9||4474 (38.5%)||3018 (48.3%)||1456 (27.1%)|
|≥ 30||4348 (37.4%)||574 (9.2%)||3774 (70.3%)|
|BMI (kg/m 2 , mean (SD))||29.18 (5.87%)||25.68 (3.35%)||33.25 (5.55%)||<0.001|
|Waist circumference (cm, mean (SD))||94.99 (14.95%)||85.69 (10.12%)||105.82 (12.11%)||<0.001|
|Age (years, mean (SD))||62.99 (8.35%)||63.13 (8.63%)||62.83 (8.00%)||0.053|
|White||7452 (64.1%)||4447 (71.2%)||3005 (56.0%)||<0.001|
|Male||5208 (44.8%)||3370 (53.9%)||1838 (34.2%)||<0.001|
|College graduate and above||5008 (43.1%)||3011 (48.2%)||1997 (37.2%)|
|Some college||3071 (26.4%)||1598 (25.6%)||1473 (27.4%)|
|High school graduate||2698 (23.2%)||1294 (20.7%)||1404 (26.2%)|
|Less than high school||840 (7.2%)||345 (5.5%)||495 (9.2%)|
|< $20k||1363 (11.7%)||518 (8.4%)||829 (15.5%)|
|$20k–$34.9k||2530 (21.8%)||1210 (19.4%)||1320 (24.6%)|
|$35k–$74.9k||3985 (34.3%)||2227 (35.6%)||1758 (32.7%)|
|≥ $75k||2533 (21.8%)||1600 (25.6%)||933 (17.4%)|
|Refused||1206 (10.4%)||677 (10.8%)||529 (9.9%)|
|Belt||3905 (33.6%)||2069 (33.1%)||1836 (34.2%)|
|Buckle||2539 (21.9%)||1359 (21.8%)||1180 (22.0%)|
|Non-belt||5173 (44.5%)||2820 (45.1%)||2353 (43.8%)|
|Current||1271 (10.9%)||725 (11.6)||546 (10.2%)|
|Never||5735 (49.4%)||3125 (50.0)||2610 (48.6%)|
|Past||4611 (39.7%)||2398 (38.4%)||2213 (41.2%)|
|None||3361 (28.9%)||1472 (23.6%)||1889 (35.2%)|
|1 to 3 times per week||4590 (39.5%)||2491 (39.9%)||2099 (39.1%)|
|4 or more per week||3666 (31.6%)||2285 (36.6%)||1381 (25.7%)|
|Current||6710 (57.8%)||3920 (62.7%)||2790 (52.0%)|
|Never||3196 (27.5%)||1507 (24.1%)||1689 (31.5%)|
|Past||1711 (14.7%)||821 (13.1%)||890 (16.6%)|
|Hypertension||6118 (52.7%)||2649 (42.4%)||3469 (64.6%)||<0.001|
|Hyperlipidemia||3714 (32.0%)||1799 (28.8%)||1915 (35.7%)||<0.001|
|Diabetes mellitus||1892 (16.3%)||530 (8.5%)||1362 (25.4%)||<0.001|
|Prior CHD||1359 (11.7%)||678 (10.9%)||681 (12.7%)||0.002|
|Prior stroke||390 (3.4%)||170 (2.7%)||220 (4.1%)||<0.001|