Obesity
Original Article EPIDEMIOLOGY/GENETICS
Body Fat, Body Fat Distribution, Lean Body Mass and Atrial Fibrillation and Flutter. A Danish Cohort Study Lars Frost1, Emelia J. Benjamin2, Morten Fenger-Grïn3, Asger Pedersen4, Anne Tjïnneland5 and Kim Overvad6,7
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. Methods: This article reports the Danish population-based prospective cohort Diet, Cancer and Health study conducted among 55,273 men and women 50-64 years of age at recruitment. 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 were investigated. 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. Obesity (2014) 22, 1546–1552. doi:10.1002/oby.20706
Introduction
Methods
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 index (6-8,11-15) confer increased risk of AF. Also higher waist (16-18) and hip circumference (18) 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.
The Danish Diet, Cancer, and Health Study
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 AF or flutter.
Exposure information
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 aged 50-64 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).
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
1 Department of Medicine, Silkeborg Hospital & Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark. Correspondence: Lars Frost ([email protected]) 2 Department of Medicine, School of Medicine and Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USA 3 Research Unit for General Practice, Aarhus University, Aarhus, Denmark 4 Hammel Neurorehabilitation and Research Centre, Hammel, Denmark 5 Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark 6 Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark 7 Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aalborg University Hospital, Aalborg, Denmark
Funding agencies: 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). Disclosure: Nothing to report. Author contributions: 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. Additional Supporting Information may be found in the online version of this article. Received: 28 November 2013; Accepted: 14 January 2014; Published online 17 January 2014. doi:10.1002/oby.20706
1546
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
www.obesityjournal.org
Original Article
Obesity
EPIDEMIOLOGY/GENETICS
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).
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 Covariates Systolic and diastolic blood pressures were measured by an automatic blood pressure device (Takeda UH 751, Tokyo, Japan). Nonfasting 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
www.obesityjournal.org
The outcome of interest was AF, considered as present if either AF 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
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
1547
Obesity
Body Size and Risk of AF—DDCHCS Frost et al.
All analyses were performed using Stata version 12.0, College Station, TX, USA.
TABLE 1 Baseline characteristics of total cohort and
participants with newly-diagnosed atrial fibrillation
Characteristic
Cohort (n555 273)
Newly-diagnosed atrial fibrillation (n52 581)
Age (years) 56.1 (51.2-63.2) Men 47.6 Educational level Primary school 14.8 Higher education 1-2 years 23.0 Higher education 3-4 years 40.0 Higher education >4 years 22.2 Smoking status Never smoker 35.2 Former smoker 28.8 Current smoker 36.0 < 15 g per day 13.1 15-25 g per day 16.1 More than 25 g per day 6.8 Alcohol (g per day) 13 (2-47) Total energy intake (kj 9544 (6604-13385) per day) Total fruit intake 169 (42-425) (g per day) Total vegetables 162 (66-312) intake (g per day) Physical activity < 0.5 h per week 23.9 0.5-3.5 h per week 40.4 >3.5 h per week 35.7 Hypertensiona 16.6 Diabetes mellitusa 2.2 Hypercholesterolemiaa 7.4 Ischemic heart diseasea 3.5 Congestive heart failurea 0.4 Valvular heart diseasea 0.2
58.6 (51.9-64.0) 64.7 14.9 20.7 38.1 26.3 31.9 32.2 35.9 12.1 17.1 6.8 16 (2-59) 9920 (6918-13835) 159 (39-417) 159 (61-306)
24.6 39.7 35.7 25.7 3.6 9.4 7.9 1.7 1.0
Medians with 10th and 90th percentiles in brackets for continuous variables. Percentages for discrete variables. a For variable definitions please see method section.
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.
1548
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
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 5 378) and missing information (n 5 796) 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 1,000 person-years in men and 2.4 per 1,000 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 Supporting Information Table 1. Table 2 shows anthropometric characteristics in the total cohort and in those who subsequently developed AF. Supporting Information 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 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
TABLE 2 Characteristics of exposure variables in total cohort and participants with newly-diagnosed atrial fibrillation
Characteristic
Cohort (n 555 273)
Newly-diagnosed atrial fibrillation (n 52 581)
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-
Original Article EPIDEMIOLOGY/GENETICS
Body Fat, Body Fat Distribution, Lean Body Mass and Atrial Fibrillation and Flutter. A Danish Cohort Study Lars Frost1, Emelia J. Benjamin2, Morten Fenger-Grïn3, Asger Pedersen4, Anne Tjïnneland5 and Kim Overvad6,7
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. Methods: This article reports the Danish population-based prospective cohort Diet, Cancer and Health study conducted among 55,273 men and women 50-64 years of age at recruitment. 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 were investigated. 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. Obesity (2014) 22, 1546–1552. doi:10.1002/oby.20706
Introduction
Methods
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 index (6-8,11-15) confer increased risk of AF. Also higher waist (16-18) and hip circumference (18) 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.
The Danish Diet, Cancer, and Health Study
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 AF or flutter.
Exposure information
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 aged 50-64 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).
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
1 Department of Medicine, Silkeborg Hospital & Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark. Correspondence: Lars Frost ([email protected]) 2 Department of Medicine, School of Medicine and Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USA 3 Research Unit for General Practice, Aarhus University, Aarhus, Denmark 4 Hammel Neurorehabilitation and Research Centre, Hammel, Denmark 5 Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark 6 Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark 7 Department of Cardiology, Center for Cardiovascular Research, Aalborg Hospital, Aalborg University Hospital, Aalborg, Denmark
Funding agencies: 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). Disclosure: Nothing to report. Author contributions: 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. Additional Supporting Information may be found in the online version of this article. Received: 28 November 2013; Accepted: 14 January 2014; Published online 17 January 2014. doi:10.1002/oby.20706
1546
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
www.obesityjournal.org
Original Article
Obesity
EPIDEMIOLOGY/GENETICS
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).
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 Covariates Systolic and diastolic blood pressures were measured by an automatic blood pressure device (Takeda UH 751, Tokyo, Japan). Nonfasting 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
www.obesityjournal.org
The outcome of interest was AF, considered as present if either AF 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
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
1547
Obesity
Body Size and Risk of AF—DDCHCS Frost et al.
All analyses were performed using Stata version 12.0, College Station, TX, USA.
TABLE 1 Baseline characteristics of total cohort and
participants with newly-diagnosed atrial fibrillation
Characteristic
Cohort (n555 273)
Newly-diagnosed atrial fibrillation (n52 581)
Age (years) 56.1 (51.2-63.2) Men 47.6 Educational level Primary school 14.8 Higher education 1-2 years 23.0 Higher education 3-4 years 40.0 Higher education >4 years 22.2 Smoking status Never smoker 35.2 Former smoker 28.8 Current smoker 36.0 < 15 g per day 13.1 15-25 g per day 16.1 More than 25 g per day 6.8 Alcohol (g per day) 13 (2-47) Total energy intake (kj 9544 (6604-13385) per day) Total fruit intake 169 (42-425) (g per day) Total vegetables 162 (66-312) intake (g per day) Physical activity < 0.5 h per week 23.9 0.5-3.5 h per week 40.4 >3.5 h per week 35.7 Hypertensiona 16.6 Diabetes mellitusa 2.2 Hypercholesterolemiaa 7.4 Ischemic heart diseasea 3.5 Congestive heart failurea 0.4 Valvular heart diseasea 0.2
58.6 (51.9-64.0) 64.7 14.9 20.7 38.1 26.3 31.9 32.2 35.9 12.1 17.1 6.8 16 (2-59) 9920 (6918-13835) 159 (39-417) 159 (61-306)
24.6 39.7 35.7 25.7 3.6 9.4 7.9 1.7 1.0
Medians with 10th and 90th percentiles in brackets for continuous variables. Percentages for discrete variables. a For variable definitions please see method section.
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.
1548
Obesity | VOLUME 22 | NUMBER 6 | JUNE 2014
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 5 378) and missing information (n 5 796) 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 1,000 person-years in men and 2.4 per 1,000 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 Supporting Information Table 1. Table 2 shows anthropometric characteristics in the total cohort and in those who subsequently developed AF. Supporting Information 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 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
TABLE 2 Characteristics of exposure variables in total cohort and participants with newly-diagnosed atrial fibrillation
Characteristic
Cohort (n 555 273)
Newly-diagnosed atrial fibrillation (n 52 581)
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-
