The association between body mass index (BMI) in young adulthood and long-term risk of atrial fibrillation (AF) has not yet been examined for men. We conducted a population-based 36-year cohort study to examine the BMI-associated risk of AF in 12,850 young men who had BMI measured at their examination of fitness for military service. AF was identified from the Danish National Registry of Patients, covering all Danish hospitals since 1977. We began follow-up on the twenty-second birthday of each subject and continued until the occurrence of AF, emigration, death, or December 31, 2012. We used Cox regression to compute hazard ratios (HRs) with 95% confidence intervals (CIs), adjusting for education and height. The cohort contributed a total of 375,888 person-years of follow-up and the median follow-up time was 26 years (mean 29 years). The incidence of AF per 100,000 person-years was 53 for men of normal weight (BMI: 18.5 to 24.9 kg/m 2 ), 54 for underweight men (BMI <18.5 kg/m 2 ), 106 for overweight men (BMI: 25.0 to 24.9 kg/m 2 ), and 144 for obese men (BMI ≥30 kg/m 2 ). With normal weight as the reference group, the adjusted HR for AF was 0.99 (95% CI 0.52 to 1.87) for underweight men, 2.08 (95% CI 1.48 to 2.92) for overweight men, and 2.87 (95% CI 1.46 to 5.62) for obese men. The adjusted HR associating 1 unit increase in BMI with AF was 1.12 (95% CI 1.07 to 1.16). In conclusion, overweight and obese young men had more than twice the risk of AF compared with young men of normal weight.
Atrial fibrillation (AF) is the most common rhythm disorder observed in clinical practice. The number of Americans affected by AF is estimated to double from 2.5 million currently to 5 million by 2050. AF more than doubles in prevalence with each advancing decade of life. The extent to which obesity is a risk factor for AF in the youngest age groups, for whom the absolute risk of AF is very low, is unclear. Most previous studies have examined the association between obesity and AF only in older cohorts with a mean age >55 years. Strikingly, 1 recent study examining the risks of obesity in young fertile women showed a twofold-increased risk of AF. Whether this risk increase also applies to young obese men remains unclear. We followed a cohort of 22-year-old men for up to 36 years to examine the impact of obesity on long-term risk of AF.
Methods
The Danish National Health Service provides universal tax-supported health care, guaranteeing unfettered access to general practitioners and hospitals and partial reimbursement for prescribed medications. Very few cardiologists work outside the public hospital system in Denmark. Most patients with AF are therefore diagnosed during a hospital admission or a visit to a hospital outpatient clinic. Individual-level linkage of all Danish registries is possible using unique personal identifiers, and complete data on vital status and migration are available from the Danish Civil Registration System.
Nearly all Danish men are required to register with the Draft Board and undergo an examination of fitness for military service on reaching 18 years of age or shortly thereafter (median age 19 years). During the medical examination, the young men complete a health questionnaire, in which they report chronic diseases that could preclude military service, for example, spinal osteochondrosis, epilepsy, or asthma. The Draft Board verifies such reports with health-care providers, and men deemed ineligible for military service are exempted at this point (approximately 15%). We used a conscription research database to identify all persons in the 1955 (n = 6,502) and 1965 (n = 6,358) birth cohorts, who later appeared before the Draft Board in a Military Conscription District in Northern Denmark with approximately 700,000 inhabitants. Using height and weight measurements at the time of examination, we computed baseline body mass index (BMI) and defined underweight (BMI <18.5 kg/m 2 ), normal weight (BMI: 18.5 to 24.9 kg/m 2 ), overweight (BMI: 25.0 to 29.9 kg/m 2 ), and obesity (BMI ≥30 kg/m 2 ).
We used the Danish National Registry of Patients (DNRP) to identify all inpatient or outpatient diagnoses of AF. The DNRP contains information on patients discharged from all Danish nonpsychiatric hospitals since 1977 and on all hospital emergency room and outpatient specialty clinic visits since 1995. From the DNRP, we also obtained information on sleep apnea, diabetes mellitus, angina pectoris, myocardial infarction, heart failure, and hypertension as potential mediators of the association between BMI and AF. We searched the Aarhus University Prescription Database, covering the study region, for any use of oral antidiabetic or antihypertensive medication from January 1, 1989, onward. Antihypertensive medication use was defined as redemption of prescriptions for at least 2 different antihypertensive agents (angiotensin-converting enzyme inhibitors or angiotensin II receptor antagonists, β blockers, calcium channel blockers, diuretics, or other antihypertensive drugs). To increase specificity, hypertension identified only on the basis of drug use was restricted to examinees without diagnoses of heart failure, myocardial infarction, or angina pectoris.
The conscription database also provided information on years of education at the time of examination. Based on quartiles, we categorized the length of education as short, moderate, long, and very long. Quartiles of height were also computed because short stature, independent of BMI, has been associated with a decreased risk of AF.
We first used descriptive statistics to characterize the study population using categories of BMI, years of education, and height. To ensure that the DNRP (established in 1977) captured all events, we began follow-up on the twenty-second birthday of each subject. We excluded all men who were censored between their examination date and their twenty-second birthday (32 men died and 18 emigrated). Follow-up continued until the first occurrence of AF, death, emigration, or December 31, 2012, whichever came first.
Considering death a competing risk, we illustrated graphically the cumulative incidence function. We used Cox proportional hazards regression, with age as the underlying time scale, to compute hazard ratios (HRs) with 95% confidence intervals (CIs). BMI was analyzed both as a categorical (quartiles) and as a continuous (per 1 unit increase) variable. For the categorical variable, the proportional hazards assumption was assessed using log-log plots and Schoenfeld’s test and was found valid. We assessed the scale of the continuous BMI variable using fractional polynomials and found no evidence of nonlinearity in the log hazard. We repeated all regression analyses adjusting for years of education and height.
To assess the degree to which an association between BMI and AF was mediated through other important risk factors for AF, we performed a subanalysis including the potential mediators as categorical variables in the model. If the BMI-AF association was mediated through these risk factors, we would expect the effect estimate to move toward unity when they were included in the regression model. The study was approved by the Danish Data Protection Agency (2011-41-5807). All analyses were conducted using Stata software, version 12.1 (StataCorp LP, College Station, Texas).
Results
The characteristics of the study population are presented in Table 1 . We identified 12,850 men from the 1955 and 1965 birth cohorts in Northern Denmark who were examined by their Draft Boards and who had complete data on BMI. BMI ranged from a minimum of 14.4 kg/m 2 to a maximum of 42.7 kg/m 2 . The median BMI was 21.8 kg/m 2 (interquartile range 20.4 to 23.5 kg/m 2 ). In the cohort, 617 men (5%) were underweight, 10,639 (83%) were of normal weight, 1,368 (11%) were overweight, and 226 (2%) were obese. There was no substantial difference in height quartiles among BMI categories.
| Characteristic | BMI | Total | |||
|---|---|---|---|---|---|
| Underweight (%) | Normal Weight (%) | Overweight (%) | Obese (%) | ||
| Education (yrs) | |||||
| Short | 170 (28) | 2,783 (26) | 378 (28) | 64 (28) | 3,399 (26) |
| Moderate | 193 (31) | 2,703 (25) | 302 (22) | 43 (19) | 3,244 (25) |
| Long | 175 (28) | 3,923 (37) | 588 (43) | 109 (48) | 4,798 (37) |
| Very long | 79 (13) | 1,230 (12) | 100 (7) | 10 (4) | 1,419 (11) |
| Height | |||||
| Short stature | 185 (30) | 2,929 (28) | 408 (30) | 68 (30) | 3,590 (28) |
| Below average | 127 (21) | 2,621 (25) | 342 (25) | 49 (22) | 3,139 (24) |
| Above average | 121 (20) | 2,419 (23) | 303 (22) | 48 (21) | 2,891 (22) |
| Tall stature | 184 (30) | 2,670 (25) | 315 (23) | 61 (27) | 3,230 (25) |
| Total | 617 (100) | 10,639 (100) | 1,368 (100) | 226 (100) | 12,850 (100) |
The cohort contributed a total of 375,888 person-years of follow-up, with a median follow-up time of 26 years (mean 29 years). Maximum follow-up time in the cohort was 36 years (specifically, 35 years and 364 days). The cumulative incidence of AF throughout the follow-up period is presented in Figure 1 . In all regression analyses, adjustment for education length and height did not change the HRs substantially ( Figure 2 ).
With normal weight as the reference group, the adjusted HR for AF was 0.99 (95% CI 0.52 to 1.87) for underweight men, 2.08 (95% CI 1.48 to 2.92) for overweight men, and 2.87 (95% CI 1.46 to 5.62) for obese men. The adjusted HR associating 1 unit increase in BMI with AF was 1.12 (95% CI 1.07 to 1.16).
The subanalysis ( Table 2 ) revealed that there was no substantial mediation of the obesity-AF association in our cohort through sleep apnea, diabetes mellitus, angina pectoris, and myocardial infarction. A greater degree of mediation was seen through heart failure (HR 2.60, 95% CI 1.33 to 5.11) and hypertension (HR 2.33, 95% CI 1.18 to 4.60). The HR associating obesity with AF remained more than twofold increased when adjusted additionally for both heart failure and hypertension in the regression analysis (HR 2.34, 95% CI 1.18 to 4.62).
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