Abstract
Objective
It is recognized that higher height and weight are associated with higher risk of atrial fibrillation or flutter (AF) but it is unclear whether risk of AF is related to body fat, body fat location, or lean body mass.
Design and Methods
We studied the Danish population-based prospective cohort Diet, Cancer and Health conducted among 55 273 men and women 50-64 years of age at recruitment. We investigated the associations between bioelectrical impedance derived measures of body composition and combinations of anthropometric measures of body fat distribution and risk of an incident record of AF in the Danish Registry of Patients.
Results
During follow-up (median 13.5 years) AF developed in 1 669 men and 912 women. Higher body fat at any measured location was associated with higher risk of AF. The adjusted hazard ratio (HR) per 1 sex-specific standard deviation (SD) increment in body fat mass was 1.29 (95% confidence interval [CI], 1.24-1.33). Higher lean body mass was also associated with a higher risk of AF. The adjusted HR for 1 sex-specific SD increment was 1.40 (95% CI, 1.35-1.45).
Conclusion
Higher body fat and higher lean body mass were both associated with higher risk of AF.
Keywords: anthropometry, atrial fibrillation, bioelectrical impedance, body fat mass, epidemiology, lean body mass
Introduction
Atrial fibrillation (AF) is a public health problem because the arrhythmia affects millions of people and predisposes to heart failure, dementia, stroke and death.1-3 It is widely recognized that higher birth weight,4 body height,5-10 weight and body mass index6-8, 11-15 confer increased risk of AF. Also higher waist16-18 and hip circumference18 have recently been introduced as risk factors for AF. Body mass index as well as waist and hip circumference do not fully account for either body fat, or for lean body mass (which is calculated by subtracting body fat mass from total body mass). It is therefore not clear, whether risk of AF is related to body fat, fat distribution or lean body mass.
We examined the association between classical anthropometric measures, bioimpedance derived estimates of total fat mass, body fat percentage, lean body mass and risk of AF, here defined as an incident diagnosis of either atrial fibrillation or flutter.
Methods
The Danish Diet, Cancer, and Health Study
The Danish Diet, Cancer, and Health Study is a prospective cohort.19, 20 From December 1993 through May 1997, 80 996 men and 79 729 women age 50 to 64 years were invited to participate in the study. Eligible cohort members were born in Denmark, living in the Copenhagen or Aarhus areas, and with no previous cancer diagnosis recorded in the Danish Cancer Registry. We excluded individuals for whom data were missing and participants with a prevalent AF diagnosis in the Danish National Registry of Patients (established in 1976).
Exposure information
Extensive anthropometric measurements including bioelectrical impedance, were collected at the enrolment into the study at two study clinics in Aarhus and Copenhagen by trained laboratory technicians. Weight was measured by a digital scale weight (Soehnle, Murrhardt, Germany) and recorded to the nearest 100 g. Dimensions were measured to the nearest half centimeter. Height was measured with the participants standing without shoes. Waist circumference was measured at the narrowest part between the lower rib and the iliac crest (the natural waist) or, in case of an indeterminable waist narrowing, halfway between the lower rib and the iliac crest. Hip circumference was measured over the widest part of the buttocks.
Non-fasting measurements of bioelectrical impedance were obtained using a BIA 101-F device (Akern/RJL, Florence, Italy) with the participant lying relaxed. Legs were approximately 45° apart and arms were 30° from the torso. Sensing electrodes were placed over the wrist and over the ankle and current electrodes over the metacarpals or metatarsals. Reliability and validity of the impedance method has previously been investigated in a Danish population, age 35–65 years, using a four-compartment model with potassium counting and dilutometry as reference.21 Sex-specific equations developed in that study were used to estimate fat-free mass.21
Covariates
Systolic and diastolic blood pressures were measured by an automatic blood pressure device (Takeda UH 751, Tokyo, Japan). Non-fasting total serum cholesterol was measured according to national guidelines.22
All participants filled in a questionnaire about medical diseases including myocardial infarction, angina, stroke, hypertension, hypercholesterolemia, diabetes, and drug treatment related to those conditions. Participants also completed a questionnaire about smoking habits, alcohol intake, physical activity, health, and duration of education.23, 24 The daily intake of specific foods and nutrients was calculated from a detailed semiquantitative food-frequency questionnaire. Study participants were asked to complete a questionnaire about type of alcohol: light beer, ordinary beer, strong beer, wine, fortified wine, and spirits, and frequency of consumption (never, less than once per month, once per month, 2-3 times per month, once per week, 2-4 times per week, 5-6 times per week, once per day, 2-3 times per day, 4-5 times per day, 6-7 times per day, and ≥ 8 times per day). The study participants completed a questionnaire about physical activities during working hours and during leisure time.25
The baseline data were linked to the Danish Cancer Registry and other population-based registries, including the Danish National Registry of Patients, and the Danish Civil Registration System, using the civil registry number, which is a unique number given since 1968 to everyone having an address in Denmark
The Danish Civil Registration System
Since 1968 the Civil Registration System has held electronic records of all changes in status including change of address, date of emigration, and date of death for the Danish population.26
The Danish National Registry of Patients
The Danish National Registry of Patients was established in 1976, and records 99.4% of all non-psychiatric hospital admissions in Denmark.27 Data include the civil registry number, dates of admission and discharge, surgical procedures performed, and one or several diagnoses per discharge. Until 1993 these were classified according to the Danish version of the International Classification of Diseases, 8th Revision (ICD-8), and thereafter according to the national version of ICD-10. Outpatient hospital clinic diagnoses of AF or flutter were included from January 1, 1995. The discharging physician coded all diagnoses for each patient discharged. A change in ICD-codes from ICD-8 to ICD-10 occurred in Denmark at the beginning of 1994: AF and atrial flutter were coded separately in ICD-8 (codes 427.93 and 427.94), but in ICD-10 AF and flutter have the same ICD code (I48). ICD codes used were for hypertension (400–404, 410.09, 411.09, 412.09, 413.09, 414.09, 435.09, 437.00,437.01, 437.08, 437.09, 438.09, I10–I15), diabetes (249, 250, E10–E14), ischemic heart disease (410–414, I20–I25), congestive heart failure (425.99, 427.09, 427.10, 427.11, 427.19, 427.99, 428.99, I50), and mitral and/or aortic valve disease (394–396, I05, I06, I08, I34, I35). There was no attempt to separate primary from secondary diagnoses.
Follow-up
The outcome of interest was AF, considered as present if either atrial fibrillation or flutter were diagnosed in hospitals or in outpatient clinics and reported to the Danish National Registry of Patients. AF only reported from emergency rooms was not included as an outcome of interest because the validity of emergency room diagnoses is in general not very high. Validation studies have shown that a hospital diagnosis including outpatient diagnoses of AF among participants in the Diet, Cancer and Health study has a high positive predictive value.28, 29
Approvals
The Diet, Cancer and Health Study was approved by the Regional Ethics Committees in Copenhagen and Aarhus, and by The Danish Data Protection Agency. Written informed consent was obtained from all participants.
Statistical methods
For descriptive statistics we report medians with 10th and 90th percentiles and percentages for discrete variables. Data were analyzed in Cox's proportional hazards regression models with delayed entry and age as the underlying time variable.30 The observation time was ended by a hospital diagnosis of AF, and observation time was censored by death, emigration or end of follow-up, December 31, 2009.
Exposures of primary interest were measures of anthropometry or body composition as derived from bioelectrical impedance measures of body composition. For comparability in a public health perspective possible associations were considered using observed sex-specific standard deviation as the scale unit. Data were examined for possible threshold effects (deviation from linear relation between exposure and risk of AF) by spline regression analysis using four knots.
All results were reported as linear function hazard ratios with 95% confidence intervals. Multivariable models were adjusted for baseline-registered smoking status, educational level, and physical activity. Fruit and vegetable intake, alcohol consumption, and total energy intake were modeled applying 4-knotted restricted cubic splines on sex-specific deviation from the mean. Adjustment also was made for diagnoses of hypertension, diabetes mellitus, hypercholesterolemia, ischemic heart disease, congestive heart failure, and valvular heart disease, which were included as time-dependent variables using information from the Danish National Patient Registry from 1977 to end of follow-up. Hypertension, diabetes, and hypercholesterolemia registry data were diagnosed if either reported in the registry or self-reported by the participants at baseline of either the condition/diagnosis or the relevant treatment. In women we also adjusted for hormone replacement therapy and menopausal status.
The proportionality assumptions of the models were evaluated by use of Schoenfeld residuals and graphically by log-minus-log plots. All analyses were performed using Stata version 12.0, College Station, Texas, USA.
Results
In total 56 447 participants accepted the invitation to participate in the Diet, Cancer and Health Study. After exclusions due to AF at or prior to baseline (n=378) and missing information (n=796) 55 273 participants were included with complete data for the present cohort study on anthropometry and risk of AF. Median age was 56.1 years. During a median follow-up of 13.5 years (range 0.02-16.1 years) AF occurred in 1 669 men and 912 women. The incidence rates of AF were 5.0 per 1000 person-years in men and 2.4 per 1000 person-years in women.
Table 1 shows baseline characteristics of the total cohort and of participants who developed newly-diagnosed AF. Sex-specific baseline characteristics are shown in Supplementary Table 1.
Table 1. Baseline characteristics of total cohort and participants with newly-diagnosed atrial fibrillation.
| Characteristic | Cohort (n=55 273) |
Newly-diagnosed atrial fibrillation (n=2 581) |
|---|---|---|
| Age (years) | 56.1 (51.2-63.2) | 58.6 (51.9-64.0) |
| Men | 47.6 | 64.7 |
| Educational level | ||
| Primary school | 14.8 | 14.9 |
| Higher education 1-2 years | 23.0 | 20.7 |
| Higher education 3-4 years | 40.0 | 38.1 |
| Higher education >4 years | 22.2 | 26.3 |
| Smoking status | ||
| Never smoker | 35.2 | 31.9 |
| Former smoker | 28.8 | 32.2 |
| Current smoker | 36.0 | 35.9 |
| < 15 g per day | 13.1 | 12.1 |
| 15-25 g per day | 16.1 | 17.1 |
| More than 25 g per day | 6.8 | 6.8 |
| Alcohol (g per day) | 13 (2-47) | 16 (2-59) |
| Total energy intake (kj per day) | 9544 (6604-13385) | 9920 (6918-13835) |
| Total fruit intake (g per day) | 169 (42-425) | 159 (39-417) |
| Total vegetables intake (g per day) | 162 (66-312) | 159 (61-306) |
| Physical activity | ||
| < 0.5 h per week | 23.9 | 24.6 |
| ≥ 0.5-3.5 h per week | 40.4 | 39.7 |
| >3.5 h per week | 35.7 | 35.7 |
| Hypertension* | 16.6 | 25.7 |
| Diabetes mellitus* | 2.2 | 3.6 |
| Hypercholesterolemia* | 7.4 | 9.4 |
| Ischemic heart disease* | 3.5 | 7.9 |
| Congestive heart failure* | 0.4 | 1.7 |
| Valvular heart disease* | 0.2 | 1.0 |
Medians with 10th and 90th percentiles in brackets for continuous variables.
Percentages for discrete variables.
For variable definitions please see method section.
Table 2 shows anthropometric characteristics in the total cohort and in those who subsequently developed AF. Supplementary Table 2 shows sex-specific anthropometric characteristics. Those who developed AF had higher baseline mean weight, body mass index, waist and hip circumferences, fat mass and percentage, and lean body mass than the entire cohort.
Table 2. Characteristics of exposure variables in total cohort and participants with newly-diagnosed atrial fibrillation.
| Characteristic | Cohort (n =55 273) |
Participants with newly-diagnosed atrial fibrillation (n =2 581) |
|---|---|---|
| Height (cm) | 170 (8.9) | 173 (8.8) |
| Weight (kg) | 75.6 (14.1) | 82.3 (15.6) |
| BMI (kg/m2) | 26.0 (4.1) | 27.4 (4.6) |
| BMI grouped according to WHO | ||
| Underweight (< 18.5) | 0.8 | 0.5 |
| Normal weight (18.5-<25) | 43.1 | 30.7 |
| Overweight (25-<30) | 41.7 | 45.1 |
| Obese (≥30) | 14.5 | 23.8 |
| Waist circumference (cm) | 88.7 (12.7) | 94.4 (13.3) |
| Hip circumference (cm) | 101 (7.9) | 103 (8.6) |
| Waist-to-hip ratio | 0.88 (0.10) | 0.92 (0.10) |
| Bioelectrical impedance derived measures of body composition | ||
| Fat mass (kg) | 23.5 (8.3) | 25.7 (9.5) |
| Fat percentage | 30.7 (7.4) | 30.7 (7.5) |
| Lean body mass (kg) | 52.1 (9.7) | 56.6 (10.0) |
Means with standard deviation in brackets. Abbreviations: BMI, body mass index; WHO, World Health Organization.
Table 3 shows hazard ratios for the associations between anthropometric measures and bioelectrical impedance derived measures of body composition and incident AF. All anthropometric measures were associated with higher risk of AF in age- and sex-adjusted analyses as well as models adjusted for potential confounding from diet and lifestyle. Higher body fat at any location studied was associated with higher risk of AF. The adjusted hazard ratio (HR) (95% confidence interval), per 1 sex-specific standard deviation (SD) increment in body fat mass was 1.29 (95% CI, 1.24-1.33). Also higher lean body mass was associated with higher risk of AF. The adjusted HR for 1 increment in sex-specific SD was 1.40 (95% CI, 1.35-1.45). The higher risk of AF by higher body fat attenuated, when adjusted for lean body mass. Mutual adjustment of body fat and lean body mass did not affect the results concerning lean body mass. Supplementary Table 3 shows hazard ratios by sex. There was no statistically significant interaction between sex and the different anthropometric measures when using sex-specific standard deviation as the scale unit. The P-values for interaction between sex and body fat mass was 0.96 and for lean body mass 0.89.
Table 3. Cox proportional hazard ratios with 95% confidence interval in brackets for the associations between anthropometric measures, bioelectrical impedance derived measures of body composition and newly-diagnosed atrial fibrillation.
| Hazard ratio (95% confidence interval) per increment of 1 sex-specific standard deviation | ||
|---|---|---|
| Variable | Age- and sex-adjusted | Multivariable adjusted* |
| Anthropometric measures | ||
| Height | 1.25 (1.20-1.30) | 1.29 (1.24-1.34) |
| Height adjusted for weight | 1.11 (1.06-1.15) | 1.16 (1.11-1.21) |
| Weight | 1.41 (1.36-1.45) | 1.36 (1.31-1.40) |
| Weight adjusted for height | 1.36 (1.31-1.41) | 1.29 (1.24-1.34) |
| Body mass index | 1.32 (1.27-1.36) | 1.26 (1.21-1.30) |
| Waist circumference | 1.35 (1.30-1.40) | 1.28 (1.23-1.33) |
| Hip circumference | 1.33 (1.28-1.37) | 1.29 (1.24-1.33) |
| Waist-to-hip ratio | 1.19 (1.14-1.23) | 1.11 (1.07-1.16) |
| Bioelectrical impedance derived measures of body composition | ||
| Fat mass | 1.34 (1.30-1.39) | 1.29 (1.24-1.33) |
| Fat mass adjusted for lean body mass | 1.10 (1.05-1.16) | 1.03 (0.99-1.09) |
| Fat percentage | 1.25 (1.21-1.31) | 1.19 (1.14-1.24) |
| Lean body mass | 1.44 (1.39-1.49) | 1.40 (1.35-1.45) |
| Lean body mass adjusted for height | 1.48 (1.41-1.55) | 1.38 (1.32-1.45) |
| Lean body mass adjusted for fat mass | 1.34 (1.28-1.41) | 1.37 (1.30-1.44) |
Measures of anthropometry and body composition were mutually adjusted, when specified in the Table.
Adjusted for smoking status, fruit and vegetable intake, alcohol consumption, physical activity, total energy intake, educational level, hypertension, diabetes mellitus, hypercholesterolemia, ischemic heart disease, congestive heart failure, and valvular heart disease. All mentioned diagnoses were included as time-dependent variables using information from the Danish National Patient Registry from 1977 to end of follow-up. For hypertension, diabetes, and hypercholesterolemia registry data was supplemented by patients self-report at baseline of either the diagnosis/condition or the relevant treatment. Women also adjusted for hormone replacement therapy and menopausal status.
Figure 1 shows splines of the fully adjusted relation between measures of anthropometry or body composition and risk of AF. There was no significant deviation from the assumption of a log-linear relation between exposures of interest and risk of AF, except for body mass index, body fat mass, and body fat percentage, where we observed a horizontal slope in the range between -2 SD and -1 SD.
Figure 1.
Splines with four knots of age and sex adjusted (broken lines) and fully adjusted (solid lines) relation between measures of anthropometry or body composition and risk of AF. Shaded areas are 95% confidence intervals around fully adjusted splines. For information on fully adjusted model consult method section. P-values for deviation from log-linearity: Height (p = 0.5), weight (p = 0.5), body mass index (p = 0.01), waist circumference (p = 0.4), hip circumference (p = 0.3), waist-to-hip ratio (p = 0.5), body fat mass (p = 0.01), body fat percentage (p < 0.001), lean body mass (p = 0.5).
Exclusion of participants with cardiovascular disease and diabetes at baseline did not appreciably change the measures of association.
Discussion
We found higher risk of AF by higher levels of anthropometric measurements, such as height, weight, body mass index, hip circumference, waist circumference, and also for the bioimpedance derived measures of body fat mass, body fat percentage, and lean body mass. The higher risk of AF by higher body fat diminished, when adjusted for lean body mass. This should be inferred by caution, because body fat correlates to lean body mass. (The correlation coefficient between fat mass and lean body mass was 0.64 in men and 0.63 in women). In addition, a higher level of adiposity will, given a constant level of all other components, increase the weight of bones and muscles leading to a higher lean body mass. However, adjusting lean body mass for height or total body fat did not change the hazard for lean body mass stressing the potential importance of lean body mass as a risk factor for AF.
To place our study in context, multiple studies have previously reported higher risk of AF by higher birth weight,4 body height,5-10 weight and body mass index.6-8, 11-15 Also higher waist16-18 and hip circumference have been associated with higher risk of AF.18 However, to our knowledge no study has previously reported on bioimpedance derived components of adiposity in relation to new-onset AF.
There are multiple plausible biological pathways linking body fat and lean body mass to the occurrence of AF. Height, weight, and body fat predispose to left atrial enlargement, which in turn predisposes to AF.31-33 A large heart (in a large body) is prone to AF for two reasons. First, trigger activity in the form of ectopic beats is more prominent in a large heart, possibly because of more ectopic activity originating from larger amount of atrial tissue in the pulmonary veins, or provoked by a more profound stretching of the pulmonary veins.33 Second, the initiation and perpetuation of AF is easier in a large atrium.32 This is in accordance with findings in animals where AF is seen in large species such as horses or elephants, but not in mice.
Higher body mass index is associated with inflammation reflected in higher concentrations of C-reactive protein.34 A higher risk of AF by higher C-reactive protein has been reported from multiple studies.8, 35, 36 The biological plausibility of inflammation being causally related to AF is supported by a recent study demonstrating that an IL6R (the gene coding for the interleukin-6 receptor) polymorphism is related to AF.37 In addition, obesity is a major risk factor for obstructive sleep apnea; sleep apnea predisposes to AF.38 Finally, obesity predisposes to the intermediate occurrence of hypertension, diabetes, acute myocardial infarction, heart failure and heart valve disease, which in turn increase risk of AF.
Strengths and limitations
Major strengths of our study are the population-based design, the large number of outcomes, and the use of validated ascertainment of multiple measures of body size and covariates. The potential for selection and surveillance biases in our study was diminished by the Danish health care system, which is uniformly organized, non-profit system, free, and has near complete follow-up facilitated by nationwide, population-based registries.
We concede that our study has potential limitations. We acknowledge that we did not adjudicate AF diagnoses. However, validation showed a high positive predictive value of a diagnosis of AF in the Danish National Registry of Patients among participants in Diet, Cancer and Health,28, 29 and in a similar Swedish study.39 We note that obese participants may more frequently seek medical care than their non-obese counterparts, which may increase the probability of being diagnosed with AF. Nonetheless, restriction of analysis of the association between body mass index and risk of AF in the Framingham Heart Study to individuals diagnosed with AF on electrocardiograms at routine follow-up examinations showed that risk of AF by obesity was retained.40 Another potential limitation is that observational data cannot establish causal relations or mechanistic insights.
Although we adjusted for a robust number of covariates we cannot rule out residual confounding from potential confounders not included in the analyses, such as glucose or HgbA1c. However, adjustment for covariates did not change the results substantially.
We included potential intermediate variables in the analyses. We adjusted for smoking status, fruit and vegetable intake, alcohol consumption, physical activity, total energy intake, educational level, hypertension, diabetes mellitus, hypercholesterolemia, ischemic heart disease, congestive heart failure, and valvular heart disease. All these variables could be in the causal pathway determining body composition or in the causal pathway between fat mass or lean body mass and the occurrence of AF. This may imply “overadjustment”. The intermediate variables were strongly influenced by and associated with other risk factors for AF. Adjustment for potential confounders was therefore of highest priority. There was, however, no substantial difference between results of analyses with and without inclusion of intermediate variables.
We could not analyze initial, paroxysmal, persistent and permanent atrial fibrillation and atrial flutter separately, and acknowledge that they may vary in their relations to anthropometric features. In addition, we note the potential for misclassification of AF status at baseline and in follow up because AF is not infrequently clinically unrecognized. Our cohort was of European ancestry; the generalizability of our findings to other races and ethnicities is uncertain.
Implications
Improvements in general welfare often leads to a higher population height and higher lean body mass. Increase in availability and access to calories predisposes to a higher lean and body fat mass in developing countries. In developed countries intake of excess calories contributes to a higher body fat mass. Such changes at the population level may have a large impact on the global incidence of AF.
Conclusion
We confirmed the greater risk of AF by higher height, weight, body mass index, and waist circumference. We also report a greater risk of AF for higher values of the bioimpedance derived measures of body fat mass, body fat percentage, and lean body mass.
Supplementary Material
Body Size and Risk of AF - DDCHCS.
What is already known about this subject?
Higher height and weight are associated with higher risk of atrial fibrillation or flutter (AF).
It is unclear whether risk of AF is related to body fat, body fat location, or lean body mass.
What does this study add?
Higher risk of AF was found by higher levels of anthropometric measurements, such as height, weight, body mass index, hip circumference and waist circumference.
Higher risk of AF was also found for the bioimpedance derived measures of body fat mass, body fat percentage, and lean body mass.
The higher risk of AF by higher body fat diminished, when adjusted for lean body mass.
Acknowledgments
The Danish Cancer Society is thanked for access to data.
The study was supported by the Danish Council for Strategic Research (grant 09-066965). Dr. Benjamin is supported by The National Heart and Lung Institute, USA (1R01HL092577 and 1R01 HL102214).
LF, EJB, MFG, AT, and KO designed the study. LF, EBJ, MFG, AP, AT, and KO contributed to data analysis and wrote the manuscript. LF, MFG, AP, and KO had full access to the data and take responsibility for the integrity of the data and the accuracy of the data analysis.
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