Abstract
Aims
Obesity is a major risk factor for atrial fibrillation (AF). Compared with stable weight, gaining weight was associated with a higher risk of incident AF in observational studies. The results, however, are conflicting regarding weight loss and risk of AF. This study aimed to assess the association between 5-year weight changes and risk of incident AF.
Methods and results
The study was based on participants from the Danish Diet, Cancer, and Health Cohort. Body mass index (BMI) was assessed at a baseline examination and at a second examination 5 years later. Diagnoses of AF and co-morbidities were retrieved from the Danish National Patient Registry. In total, 43 758 participants without prior AF were included. The median age was 61 years and 54% were female. During a median follow-up of 15.7 years, 5312 individuals had incident AF (incidence rate 8.6/1000 person-years). Compared with stable weight, weight gain between 2.5 and 5 BMI units (kg/m2) was associated with a higher risk of AF [hazard ratio (HR) 1.24, 95% confidence interval (CI) 1.09–1.41]. Weight gain of 5 or more BMI units (kg/m2) was associated with a HR of 1.95 (95% CI 1.48–2.56) of incident AF. However, there was no statistically significant association between weight loss and risk of AF.
Conclusion
Five-year weight gain was associated with greater risk of AF compared with stable weight in the Danish Diet, Cancer, and Health Cohort. There was no statistically significant association between weight loss and risk of AF.
Keywords: Atrial fibrillation, Obesity, Lifestyle modification, Epidemiology, Cohort study
Graphical Abstract
Graphical Abstract.
See the editorial comment for this articles ‘Are body mass index changes related to incident atrial fibrillation?—Results of the Danish Diet, Cancer, and Health Cohort’, by B.J. Medina-Inojosa and F. Lopez-Jimenez, https://doi.org/10.1093/eurjpc/zwad333; ‘A novel non-invasive estimate of biological age: can an echocardiogram measure the patient's age?’, by J.A. Naser et al., https://doi.org/10.1093/eurjpc/zwad307.
Introduction
The World Health Organization estimated that between 1975 and 2016 the prevalence of obesity tripled, from 4.7% to 13.1%.1 Overweight and obesity are causal risk factors for a wide range of cardiovascular diseases, including atrial fibrillation (AF).2 Both observational studies and Mendelian randomization studies indicate a higher risk of AF with higher body mass index (BMI).2 The association between obesity and AF may be partly mediated by risk factors such as hypertension, heart failure, obstructive sleep apnoea, and diabetes leading to ischaemic heart disease.3 However, there are also several direct pathophysiological mechanisms of obesity predisposing to AF, including haemodynamic changes, left atrial remodelling, and inflammation.3 In randomized controlled trials, weight management and weight loss in patients with AF resulted in reduced AF burden.4,5 In observational studies with consecutive weight measures, weight increases were associated with a higher risk of incident AF compared with stable weight.6,7 However, results were conflicting regarding the association between weight loss and risk of AF.8–10 In this study, we aimed to assess the association between 5-year weight loss, weight gain, and risk of incident AF in the Danish Diet, Cancer, and Health Cohort.
Methods
Study population
The study was based on the Danish cohort study Diet, Cancer, and Health, which has previously been described in detail.11 Briefly, between 1993 and 1997, a total of 160 725 females and males born in Denmark and living in the greater Copenhagen or Aarhus areas, aged 50–64 years, with no diagnosis of cancer, were invited and 57 053 individuals accepted participation. At enrolment, participants attended a clinic for measurements and completed questionnaires. Because of processing delays, 585 were later excluded because of a cancer diagnosis prior to enrolment, which had not been registered in the Danish Cancer Registry at the time of invitation. Three withdrew their consent leaving 56 465 participants. Approximately 5 years after the first examination, the participants were invited to participate in a follow-up examination, which did not include a centre visit but was conducted via mail. Out of the original participants, 11 592 did not participate in the second examination. For this study, we excluded participants with missing data (n = 79). A participant flowchart is shown in Supplementary material online, Figure S1. The Diet, Cancer, and Health study was approved by the relevant scientific Ethic Committees and the Data Protection Agency, and all participants gave written informed consent at enrolment.
Assessment of changes in body mass index and covariates
At the baseline examination, weight and height of each participant were measured at the study centres by trained professionals. At the second examination, weight was self-reported. Body mass index was calculated as [weight (kg)]/[height (m2)]. Participants were categorized into four BMI groups: <18.5 kg/m2 (underweight), 18.5–24.9 kg/m2 (normal weight), 25–29.9 kg/m2 (overweight), and ≥30 kg/m2 (obese) based on the World Health Organization’s classification.12 We divided BMI changes between first and second examinations into five groups: weight loss of 5 or more kg/m2, weight loss between 2.5 and 5 kg/m2, stable weight (weight loss or weight gain smaller than 2.5 kg/m2), weight gain between 2.5 and 5 kg/m2, and weight gain of 5 or more kg/m2 based on categories used in a previous Norwegian study.9 At the first and second examinations, a detailed food frequency questionnaire was filled in. In the food frequency questionnaire, alcohol intake was reported as the average units of beer, wine, and spirits consumed over the preceding year and converted into grams of alcohol per day in total. The number of drinks per week was calculated with a definition of a standard unit containing 10 g of alcohol. At the research examinations, participants filled in questionnaires about smoking status (never, prior, or current) and several co-morbidities including hypertension and diabetes mellitus.
Outcomes
We identified all participants with a diagnosis of AF and/or atrial flutter in the Danish National Patient Register, which includes discharge diagnoses from in-hospital patients, emergency rooms, and outpatient visits.13 In a previous validation study, the positive predictive value of AF and/or atrial flutter in the Danish National Patient Register was 95%.14 For covariates, we identified all patients with a diagnosis of heart failure, ischaemic stroke, diabetes mellitus, myocardial infarction, and hypertension in the Danish National Patient Register until the time of each research examination. Overall, the validity of cardiovascular diagnoses in the register has been reported to be high.14 The International Classification of Diseases-10 codes for the outcome and each covariate can be seen in Supplementary material online, Table S1. For hypertension and diabetes mellitus, we combined information from the Danish National Patient Register and self-reported information since these conditions are often diagnosed and managed by a general practitioner and thus not registered in the Danish National Patient Register. Information on vital status was obtained from the National Central Person Register.
Statistical analyses
Covariates from the time of the second research examination were included in all analyses. Baseline characteristics were described according to changes in BMI from first to second examination as classified above. In addition, we described characteristics according to BMI categories at the second research examination (underweight, normal weight, overweight, and obese). Finally, we described study participant characteristics at enrolment according to participation and non-participation in the second research examination. Descriptive statistics were presented using medians and 25th–75th percentiles or frequency counts and per cent, as appropriate.
The participants were followed from the time of the second research examination until the date of AF diagnosis, death, emigration, or end of follow-up in September 2017, whichever came first.
We estimated the hazard ratios (HRs) of AF using Cox proportional hazards models. Assumptions of independent entry were checked and found valid. For each model, the proportional hazard assumption was checked by plotting the observed and fitted curves and by log–log curves.
To verify the underlying assumption of a higher risk of AF with higher BMI, we examined the association between BMI at the second research examination and risk of AF. Thus, we assessed the incidence rates of AF per 1000 person-years among all BMI groups (underweight, normal weight, overweight, and obese). In addition, we examined HRs of AF among participants in the following BMI groups (underweight, overweight, and obese) and compared with participants in the normal weight BMI group. The models were adjusted for age, sex, educational level, alcohol intake, smoking status, hypertension, heart failure, stroke, diabetes mellitus, and myocardial infarction at time of the second examination. We performed the analyses for the entire population and stratified by sex. Hazard ratios according to continuous BMI at the second research examination were visualized using restricted cubic splines.
For assessment of associations between 5-year changes in weight and risk of AF, we assessed the incidence rates of AF per 1000 person-years according to changes in BMI. We estimated the HRs for AF among participants in the BMI changes categories previously described. Participants with stable weight were used for reference. The models were adjusted for the variables mentioned above and BMI at the first examination. The analyses of association between BMI changes and risk of AF were performed for the entire population and stratified by sex. Hazard ratios according to continuous changes in BMI were visualized using cubic splines both for the entire cohort and stratified by sex.
For the association between 5-year changes in BMI and risk of AF, we performed several sensitivity analyses. To reduce bias from weight changes due to illness, we omitted all participants who were diagnosed with AF within the first year of follow-up. We omitted participants who were underweight at the first examination and performed the analyses only in participants who were overweight or obese at the first examination. We also restricted the analysis to participants who were obese at the first examination. We conducted an analysis adjusting for death as a competing risk using the Fine and Gray model. Finally, we restricted follow-up to 5 years in sensitivity analyses.
Two-sided P-values below 0.05 were considered statistically significant. Analyses were performed using the Stata program software version 17.0.
Results
For the analyses, 43 758 participants were included. During a median follow-up of 15.7 years, 5312 participants were diagnosed with incident AF, 156 migrated, and 9017 individuals died. The incidence rate of AF was 8.6 per 1000 person-years overall.
Study population characteristics
From the first to the second research examination, 39 076 (89%) participants had a stable weight, 2627 (6%) had a weight loss of more than 2.5 kg/m2, whereas 2055 (4.6%) had weight gain of more than 2.5 kg/m2. Table 1 shows the study population characteristics according to changes in BMI. There was a higher proportion of females and a lower median alcohol intake among participants who gained and lost weight compared with individuals with a stable weight. Current smoking was more prevalent among participants who lost weight compared with stable weight and weight gain. Co-morbidities were generally less frequent in participants with a stable weight compared with individuals with a weight loss or a weight gain. Supplementary material online, Table S2 displays the study population characteristics according to BMI at the second research examination. Female sex and current smoking were more prevalent in individuals who were underweight, whereas hypertension, diabetes mellitus, and myocardial infarction were more prevalent among participants with obesity. The 11 592 individuals who did not participate in the second research examination were more frequently female, had lower education, were more frequently currently smoking, and had more co-morbidities at the first research examination compared with individuals who participated in the second research examination (see Supplementary material online, Table S3). However, BMI did not differ substantially at the first research examination between participants and non-participants.
Table 1.
Study population characteristics according to changes in body mass index from first to second examination
| Change in body mass index (kg/m2) | ||||||
|---|---|---|---|---|---|---|
| Weight loss (n = 2627, 6%) | Stable weight (n = 39 076, 89%) | Weight gain (n = 2055, 4.6%) | ||||
| Total (n = 43 758) | <−5 (n = 365) | ≥−5 to <−2.5 (n = 2262) | ≥−2.5 to <2.5 (n = 39 076) | ≥2.5 to <5 (n = 1802) | ≥5 (n = 253) | |
| Age, years | 61 (58–66) | 62 (58–66) | 62 (58–66) | 62 (58–66) | 61 (58–65) | 61 (58–64) |
| Females | 23 397 (54%) | 259 (71%) | 1426 (63%) | 20 490 (52%) | 1048 (58%) | 174 (69%) |
| School education < 8 years | 13 625 (31%) | 157 (43%) | 795 (35%) | 11 846 (30%) | 704 (39%) | 123 (49%) |
| Body mass index, kg/m2 | 25 (23–28) | 26 (23–30) | 25 (23–28) | 25 (23–28) | 29 (27–32) | 34 (31–39) |
| Waist circumference, cm | 93 (85–100) | 96 (86–105) | 93 (84–101) | 92 (85–100) | 102 (93–110) | 108 (98–120) |
| Alcohol intake, drinks/week | 10 (4–22) | 4 (1–11) | 8 (3–17) | 10 (5–23) | 8 (2–22) | 5 (1–14) |
| Current smoking | 12 228 (28%) | 130 (36%) | 763 (34%) | 10 794 (28%) | 479 (27%) | 62 (25%) |
| Prior smoking | 15 408 (35%) | 100 (27%) | 704 (31%) | 13 665 (35%) | 825 (46%) | 114 (45%) |
| Hypertension | 7504 (17%) | 121 (33%) | 551 (24%) | 6373 (16%) | 383 (21%) | 76 (30%) |
| Diabetes mellitus | 1245 (3%) | 49 (13%) | 152 (7%) | 952 (2%) | 74 (4%) | 18 (7%) |
| Heart failure | 302 (1%) | 9 (3%) | 35 (2%) | 232 (1%) | 18 (1%) | 8 (3%) |
| Myocardial infarction | 1336 (3%) | 18 (5%) | 105 (5%) | 1126 (3%) | 72 (4%) | 15 (6%) |
| Stroke | 761 (2%) | 11 (3%) | 59 (3%) | 630 (2%) | 57 (3%) | 4 (2%) |
Values are n (%) or median (25th–75th percentile).
Body mass index at second research examination and risk of atrial fibrillation
Supplementary material online, Table S4 shows the incidence rates of AF per 1000 person-years and HRs according to BMI at the second research examination for the total population and separately for females and males. For the total population, compared with normal weight, underweight [HR 1.69, 95% confidence interval (CI) 1.28–2.23], overweight (HR 1.18, 95% CI 1.11–1.26), and obesity (HR 1.76, 95% CI 1.63–1.90) were associated with a higher risk of AF. The associations were similar for females. However, for males, there was no statistically significant association between underweight and AF risk (HR 0.81, 95% CI 0.30–2.17). Supplementary material online, Figure S2 shows restricted cubic splines of HRs of AF by continuous BMI at the second research examination.
Five-year changes in body mass index and risk of atrial fibrillation
Absolute and relative risks of AF according to changes in BMI from the first to the second research examination are shown in Table 2. The incidence of AF per 1000 person-years was 8.4 (95% CI 8.1–8.6) among participants with stable weight. Among participants with a weight loss of 5 or more BMI units (kg/m2), the incidence rate per 1000 person-years was 12.4 (95% CI 9.6–16.1) while it was 15.4 (95% CI 11.7–20.2) among participants with a weight gain of 5 or more BMI units (kg/m2). Compared with participants with a stable weight, individuals with a weight gain between 2.5 and 5 BMI units (kg/m2) had a higher risk of AF (HR 1.24, 95% CI 1.09–1.41). Among participants with a weight gain of 5 or more BMI units (kg/m2), the risk of AF was also higher compared with stable weight (HR 1.95, 95% CI 1.48–2.56). However, there was no statistically significant association between weight loss of 2.5–5 BMI units (kg/m2) (HR 1.00, 95% CI 0.88–1.12) or 5 or more BMI units (kg/m2) (HR 1.04, 95% CI 0.79–1.36) and risk of AF.
Table 2.
Absolute and relative risks of atrial fibrillation according to 5-year changes in body mass index
| Change in body mass index (kg/m2) | |||||
|---|---|---|---|---|---|
| Weight loss | Stable weight | Weight gain | |||
| Atrial fibrillation | <−5 (n = 365) | ≥−5 to <−2.5 (n = 2262) | ≥−2.5 to <2.5 (n = 39 076) | ≥2.5 to <5 (n = 1802) | ≥5 (n = 253) |
| Total | |||||
| Incidence rate/1000 person-years (95% CI) | 12.4 (9.6–16.1) | 10.0 (9.0–11.2) | 8.4 (8.1–8.6) | 10.1 (9.0–11.5) | 15.4 (11.7–20.2) |
| Hazard ratiosa (95% CI) | 1.04 (0.79–1.36) | 1.00 (0.88–1.12) | Reference | 1.24 (1.09–1.41) | 1.95 (1.48–2.56) |
| Females | |||||
| Incidence rate/1000 person-years (95% CI) | 11.1 (8.1–15.3) | 8.0 (6.8–9.3) | 6.1 (5.9–6.4) | 8.2 (6.9–9.8) | 12.1 (8.5–17.4) |
| Hazard ratiosb (95% CI) | 1.18 (0.84–1.66) | 1.04 (0.88–1.23) | Reference | 1.34 (1.11–1.61) | 1.92 (1.34–2.76) |
| Males | |||||
| Incidence rate/1000 person-years (95% CI) | 16.1 (10.2–25.2) | 14.0 (11.9–16.5) | 11.0 (10.6–11.4) | 13.0 (11.0–15.5) | 24.2 (15.9–36.8) |
| Hazard ratiosb (95% CI) | 0.94 (0.59–1.48) | 0.95 (0.80–1.13) | Reference | 1.15 (0.96–1.38) | 2.06 (1.35–3.14) |
aThe model was adjusted for age, sex, educational level, BMI at first examination, alcohol intake, smoking status, hypertension, heart failure, stroke, diabetes mellitus, and myocardial infarction at time of second examination.
bThe model was adjusted for the variables mentioned above, excluding sex.
In sex-stratified analyses, the association between weight gain of 5 or more BMI units (kg/m2) and higher AF risk was present for both females and males. A weight gain between 2.5 and 5 BMI units (kg/m2) was associated with a statistically significant higher risk of AF in females (HR 1.34, 95% CI 1.11–1.61) but not in males (HR 1.15, 95% CI 0.96–1.38). Figure 1 shows restricted cubic splines of HRs of AF by changes in BMI from the first to the second research examination for the total population and stratified by sex. In sensitivity analyses (see Supplementary material online, Table S5), omitting participants with AF diagnosed within the first year after the second research examination did not substantively alter the results. When restricting follow-up to 5 years, there were no statistically significant associations between changes in BMI and risk of AF. The results were similar when considering competing risk of death and omitting participants who were underweight at first examination. Restricting to participants who were overweight or obese at the first examination did not alter the results either. We restricted the analysis to participants who were obese at the first examination and found a signal of a lower risk of AF among those who lost weight from the first to the second examination compared with those who had a stable weight (see Supplementary material online, Table S5). However, the results were not statistically significant.
Figure 1.
Restricted cubic splines showing hazard ratios of atrial fibrillation by changes in body mass index from first to second examination for the total population and stratified by sex. The solid lines represent hazard ratio estimates by continuous change in body mass index with a change of 0 kg/m2 as reference in the total population, in females, and in males. The dotted lines represent the 95% confidence interval. The bars show the density distribution of body mass index changes. The models were adjusted for age, sex, educational level, body mass index at first examination, alcohol intake, smoking status, hypertension, heart failure, stroke, diabetes mellitus, and myocardial infarction at time of second examination. Sex was not included in the adjustment models when stratifying by sex.
Discussion
In this cohort study, we observed that individuals who gained weight over a 5-year period had higher rates of AF compared with stable weight during long-term follow-up. However, we did not find statistically significant associations between weight loss and risk of AF.
Several other observational studies found, in concordance with the results of this study, that overweight, obesity, and weight gain were associated with higher risk of AF.6–9 Similarly, other studies failed to find statistically significant associations between reduction in weight and risk of AF,6,7 which was also confirmed in a meta-analysis of ten studies, including 108 996 individuals.15 In the Atherosclerosis Risk in Communities and Trøndelag Health studies, however, the authors reported higher risks of AF in individuals who lost weight.8,9 The lack of association between weight loss and AF in several observational studies including this study, and even higher risk with weight loss in other observational studies, may reflect the nature of weight loss. The lack of associations between weight loss and AF risk may be due to unmeasured confounding. A range of acute and chronic disorders, associated with AF risk, can lead to unintentional weight loss, and in observational studies, it is often not possible to determine whether weight loss was intentional or unintentional. We were not able to determine the nature of weight loss; thus, weight loss could be unintentional. In our study, participants who lost weight had higher proportions of co-morbidities compared with participants with stable weight, which could be indicative of an unintentional weight loss. Another potential explanation is that structural cardiac changes caused by obesity may to some extent be irreversible leading to a maintained risk of AF despite weight loss.3 However, a cohort study from a medical centre in Israel, including 18 290 individuals, identified that each 1 kg/m2 weight loss was associated with a 7% lower risk of AF compared with stable weight.10 In the Israeli study, the participants were slightly younger at baseline (mean age 49 years) compared with our and other studies (mean/median age 51–66 years).6–9 Since younger individuals have overall lower risks of co-morbidities, one could speculate a lower proportion of unintentional weight loss in this study, thus, decreasing the risk of unmeasured confounding in the association between weight loss and risk of AF.
A potential effect of weight loss on risk of incident AF is supported by several observational studies showing that bariatric surgery, resulting in significant weight loss, was associated with a lower risk of incident AF compared with no surgery.16–18 In meta-analyses of trials in which patients were randomized to semaglutide, a glucagon-like peptide-1 analogue used for treatment of diabetes and obesity, treatment resulted in a lower risk of incident AF.19,20 However, in the Look Action for Health in Diabetes (AHEAD) randomized trial, participants were randomized into intensive lifestyle intervention including caloric restriction and physical activity, resulting in initial weight loss.21 In a substudy from Look AHEAD, there was no effect of lifestyle intervention on the risk of incident AF during a follow-up of 9 years.22 Importantly, the weight difference between the intervention and control group was attenuated during the study period due to regaining of weight in the intervention group.21
While the effects of weight loss for primary prevention of AF are unclear, there are several studies supporting weight loss for secondary AF prevention. A randomized study of 248 patients with AF found that weight loss reduced AF symptoms and burden.5 In the Long-Term Effect of Goal-Directed Weight Management in an Atrial Fibrillation Cohort: A Long-Term Follow-up Study (LEGACY), the authors found supporting results.23 In another study from the LEGACY cohort, weight loss was also associated with a lower risk of progression from paroxysmal to persistent AF.24 In addition, a cohort study offering risk factor and weight management to patients with AF, found that weight loss was associated with greater success after AF ablation.25 Thus, the 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice recommend weight loss and risk factor management for reduction of AF incidence, progression, recurrence, and symptoms (Class IIa, Level B).26
Several limitations should be considered when interpreting the results of this study. The study was observational; thus, we cannot assess causality or exclude unmeasured confounding. Importantly, since we were not able to assess the reason for weight changes, unintentional weight changes due to illnesses not adjusted for could be a source of confounding. Furthermore, we assessed BMI at two time points, but we were not able to assess weight changes before, in-between, or after the two research examinations. In the HUNT study, a high degree of intrapersonal weight variability was associated with higher risk of AF compared with a low degree of variability.9 Another potential confounder between BMI and risk of AF is sleep apnoea,27 which we were not able to adjust for in this study. Weight was measured at the first research examination, while it was self-reported at the second research examination. In case of systematic non-differential under-reporting of weight at the second research examination, this could potentially bias the results. The cohort consisted of participants who were middle-aged at entry; thus, generalizability to other ages is unclear. Finally, AF was assessed from diagnosis codes through the Danish National Patient Registry and not ECG monitoring, which could lead to an underestimation of AF incidence, and we were not able to assess the type of AF (i.e. paroxysmal and persistent).
Conclusions
A 5-year weight gain was associated with a greater risk of AF compared with stable weight in the Danish Diet, Cancer, and Health Cohort. However, there was no statistically significant association between weight loss and risk of AF. These results suggest that weight management should be recommended for primary prevention of AF.
Supplementary material
Supplementary material is available at European Journal of Preventive Cardiology.
Supplementary Material
Acknowledgements
We thank the participants of the Danish Diet, Cancer, and Health Study for their time and cooperation.
Contributor Information
Tanja Charlotte Frederiksen, Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark; Department of Clinical Medicine, Health, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark.
Morten Krogh Christiansen, Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark.
Emelia J Benjamin, Department of Medicine, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA.
Kim Overvad, Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
Anja Olsen, Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark; Danish Cancer Society Research Center, København Ø, Denmark.
Christina Catherine Dahm, Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
Henrik Kjærulf Jensen, Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark; Department of Clinical Medicine, Health, Aarhus University, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark.
Author contributions
T.C.F., C.C.D., M.K.C., K.O., E.B.J., and H.K.J. contributed to the conception or design of the work. T.C.F., K.O., A.O., and C.C.D. contributed to the acquisition, analysis, or interpretation of data for the work. T.C.F. drafted the manuscript. C.C.D., M.K.C., A.O., K.O., E.J.B., and H.K.J. critically revised the manuscript. All gave final approval and agreed to be accountable for all aspects of work ensuring integrity and accuracy.
Funding
This study was supported by Aarhus University, Helsefonden (20-B-0206), and Sundhed, Region Midt (A3116) to T.C.F.; National Heart, Lung, and Blood Institute (R01HL092577) and American Heart Association (AHA_18SFRN34110082) to E.J.B.; Novo Nordisk Foundation (NNF18OC0031258 and NNF20OC0065151) to H.K.J; and Danish Cancer Society for funding of the cohort.
Data availability
The data are available from Diet, Cancer, and Health Steering Committee at the Danish Cancer Society (dchdata@cancer.dk).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data are available from Diet, Cancer, and Health Steering Committee at the Danish Cancer Society (dchdata@cancer.dk).