(2024) 22:345
Du et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03565-y

BMC Medicine

RESEARCH

Open Access

Adulthood weight changes, body massindex in youth, genetic susceptibility and riskof atrial fibrillation: a population‑based cohort
study
Yufeng Du1,2*, Lu Qi3,4, Yan Borné2 and Emily Sonestedt2,5*

Abstract
Background Epidemiological evidence on weight change and atrial fibrillation (AF) remains limited and inconsistent. Previous studies on body mass index (BMI) in youth and AF rarely considered subsequent BMI. This study aimed
to assess the associations of AF with weight change and BMI in youth, as well as modified effect by genetic susceptibility of AF.
Methods The study included 21,761 individuals (mean age 57.8 years) from the Malmö Diet and Cancer cohort.
Weight information was obtained at three time points, including recalled weight at age 20 years, measured weightat baseline (middle adulthood), and reported weight at 5-year follow-up examination (late middle adulthood).
A weighted genetic risk score of AF was created using 134 variants.
Results During a median follow-up of 23.2 years, a total of 4038 participants developed AF. The association
between weight change from early to middle adulthood and AF risk was modified by sex (Pinteraction = 0.004); weightloss was associated with a lower AF risk in females, but not in males. Conversely, weight gain was positively associated with AF risk in a linear manner in females, whereas increased AF risk appeared only when weight gain exceededa threshold in males. Participants with weight gain of > 5 kg from middle to late middle adulthood had a 19% higher
risk of AF relative to those with stable weight, whereas weight loss showed a null association. Compared to individuals with a lower BMI at age 20 years, those with a BMI above 25 kg/m2 had an increased risk of AF (HR = 1.14; 95% CI:
1.02–1.28), after controlling for baseline BMI; this association was more pronounced in males or those with a lower
genetic risk of AF.
Conclusions Weight gain in middle adulthood was associated with higher AF risk. Weight loss from early to middle adulthood, but not from middle to late middle adulthood, was associated with a lower risk of AF only in females.
Higher BMI in youth was associated with an increased risk of AF, particularly among males or those with a lower
genetic risk of AF.
Keywords Atrial fibrillation, Body mass index, Weight change, Prospective cohort, Genetic predisposition*Correspondence:
Yufeng Du
yufeng.du@med.lu.se
Emily Sonestedt
emily.sonestedt@med.lu.se
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

Du et al. BMC Medicine(2024) 22:345

Background
According to the Global Burden of Disease 2019 study,
the worldwide prevalent cases of atrial fibrillation (AF)is 59.7 million; almost doubled since 1990 [1]. Approximately 25% of people aged over 55 years in Europe andthe USA will develop AF during their later lifetime [2, 3].
Patients with AF are at substantial risk for stroke, heart
failure, and all-cause death [4]. Obesity, which has tripledover the past 40 years and affected 14.0% of the world’s
adults in 2019 [5], ranks the second largest populationattributable factor for the development of AF following
hypertension [6].
The increased risk of AF related to obesity has been
well studied in observational and Mendelian randomization studies [7, 8], with the possible mechanisms involving atrial enlargement, conduction abnormalities, andincreased atrial fibrosis [9, 10]. However, limited andmixed data exists on weight change and risk of AF [11–16]. Due to limited data and high heterogeneity, the mostrecent meta-analysis provided only tentative conclusions
on weight change and AF risk [17], indicating a positive
association between weight gain and AF, while a null
association was found with weight loss [17]. Two subsequent cohort studies in Europe, with a relatively small
sample size [16] or only covering weight change in late
middle adulthood [13], reported a positive association
between weight gain and AF but inconsistent results forweight loss [13, 16].
Regarding BMI in youth, five previous studies in
Europe, utilizing register-based weight data, consistently
showed an elevated risk of AF associated with higher
BMI [18–22]. However, previous evidence was limited
to either males or females only and did not adjust forseveral important confounders, such as physical activity, diet, smoking, and alcohol consumption. In previous studies, the median age of participants at the end offollow-up was below 55 years. However, the incidence of
AF increases sharply after 65 years old, and more than
70% of prevalent cases are aged 65 years or older [23,24]. These studies thus missed the opportunity to explore
the association of BMI in youth with AF occurred laterin life. In addition, in these studies, weight was measured
at a single time point, making it unlikely to account for
the residual confounding from subsequent BMI. Furthermore, whether genetic susceptibility of AF modified theassociation of AF with weight change and BMI in youth
has not been investigated.
We aimed to investigate the association of weightchange from age 20 years to baseline (reflecting weightchange from early to middle adulthood), weight change
from baseline to follow-up examination (reflecting
weight change from middle to late middle adulthood),

Page 2 of 11

and BMI at age 20 years with later-life incident AF in
the Malmö Diet and Cancer (MDC) cohort, a large
population-based prospective cohort with over 23 years
of follow-up. We also sought to assess the potential
modified effect of the genetic risk score (GRS) of AF on
the associations between exposures and AF risk.

Methods
Study population

Details of MDC have been published elsewhere [25, 26].
In brief, MDC is a population-based prospective cohort
conducted in Malmö, Sweden. Of the 74,318 invited
persons, 30,446 individuals aged 45 to 73 years participated in the baseline examination from 1991 to 1996.
Participants visited the study center twice at baseline.
During the first visit, self-administered questionnaires
assessing lifestyle, sociodemographic, and dietary
information were explained and distributed to participants. Physical examinations and blood sample collection were performed by trained nurses. Two weeks
later, the returned questionnaires were reviewed and adiet interview was performed. About 5 years after baseline, all baseline participants were invited to participate
in the follow-up examination. This examination, whichcollected information on lifestyle, diet, and weight,
was conducted between 1997 and 2001. The study wasapproved by the Ethical Committee at the Medical Faculty at Lund University (approval number: LU 51/90,
LU 2009/633, LU 2011/537 and LU 2012/762). All participants provided written informed consent.
A total of 28,098 participants completed the baseline
questionnaire, anthropometric measurements, and dietary assessment. For the analyses of BMI at age 20 years
and weight change from age 20 years to baseline, we
excluded participants with prevalent cancer (n = 1747),
cardiovascular disease (CVD, n = 760), AF (n = 223) atbaseline, or with missing data on weight aged 20 years(n = 3399) or weight at baseline (n = 26) or 5 covariates (n = 182), finally leaving 21,761 participants forthe analyses. For weight change from baseline to follow-up examination, participants who did not attend
the follow-up examination (n = 5049, with 705 participants due to death), or had incident cancer (n = 781),
CVD (n = 221), or AF (n = 173) from baseline to 5-year
follow-up examination, or had missing data on weightat baseline (n = 22), weight at follow-up examination(n = 262), or 4 covariates (n = 82), were excluded, leaving 18,778 participants for this analyses. In the analyses involving genetic data, non-European individuals(n = 899/n = 742) were further excluded. More details
are presented in the flowchart (Additional file 1: Fig.
S1).

Du et al. BMC Medicine(2024) 22:345

Exposure assessment

At baseline examination, weight was measured to thenearest 0.1 kg using a balance-beam scale in wearing lightindoor clothing and no shoes. Height (cm) was measured
in a standing position using a wall-mounted stadiometer. Weight at age 20 years was recalled by participants
at baseline. Weight change from age 20 years to baseline(exposure 1) was calculated as baseline weight minus
weight at age 20 years and was further divided into thefollowing five groups based on previous studies with similar time length for weight change [27, 28]: loss > 2.5 kg,
stable (− 2.5–2.4 kg), gain (2.5–9.9 kg), gain (10–19.9 kg),
gain (≥ 20 kg). We chose to use weight change (rather
than BMI change) as exposure because absolute weightchange can be explained more directly by healthcare
providers and policymakers and is easily understood inweight management programs [29]. BMI at age 20 years(exposure 2) was calculated as the recalled weight (kg)divided by the squared height (m) measured at baseline.
BMI at age 20 years was further categorized into the following groups [29]: < 18.5 kg/m2, 18.5 to 22.4 kg/m2, 22.5
to 24.9 kg/m2, and ≥ 25 kg/m2; overweight and obese
participants were categorized into one group due to thesmall number of obese youth (n = 132).
At the 5-year follow-up examination, participants wereasked to report their current weight. Weight change frombaseline to follow-up examination (exposure 3) was calculated as weight at follow-up examination minus weightat baseline and was further divided into the following fivegroups based on our data distribution: loss > 5 kg, loss(− 5 to − 2.4 kg), stable (− 2.5–2.4 kg), gain (2.5–4.9 kg),
gain (≥ 5 kg). The cutoffs for the extreme weight loss(> 5 kg) and gain (≥ 5 kg) were the 10th and 90th percentiles, respectively. Participants were asked to recall theirweight at age 20 years again at follow-up examination,and this variable was used to assess the reliability of therecalled weight at baseline by calculating the intraclasscorrelation coefficient (ICC).
AF ascertainment

The outcome AF includes AF and/or atrial flutter (AFL)
given their close relationship and the same Swedish ICD10 coding before January 2013 [19, 30]. Prevalent orincident AF cases were ascertained through the Swedish
Hospital Discharge Register and the Swedish Causes of
Death Register [31]. The AF cases were defined according to the following codes of the International Classification of Diseases (ICD): 427.92 (ICD-8, used before1986), 427D (ICD-9, used from 1987 to 1996), and I48(ICD-10, used since 1997) [31]. In a validation study
conducted at the MDC, where AF cases were reviewed
by an arrhythmologist and a physician, the registers

Page 3 of 11

demonstrated high validity in diagnosing AF, with a positive predictive value of 97% [31]. Follow-up time was calculated from the date of completion of the baseline (forexposure 1 and exposure 2) or of the 5-year follow-up
examination (for exposure 3) until the date of the firstdiagnosis of AF, death, emigration, or 31 December 2018,
whichever occurred first. The rate of loss to follow-up
due to emigration in this study was less than 1%.
AF genetic risk score construction

Participants were genotyped using the Illumina GSA v1
genotyping array. The Haplotype Reference Consortiumreference was used for imputation [32]. Quality control
procedures for the genetic data were described in detailelsewhere [33]. Using 134 single nucleotide polymorphisms (SNPs) identified from a recent meta-analysis of
genome-wide association study (GWAS) in Europeans[34], we created a weighted GRS for AF using the following equation: GRS = (β1 × SNP1 + … + β134 × SNP134) × (134/sum of the β coefficients), with SNP representing risk allele count (0, 1, or 2) for each SNP and
β coefficients are weights obtained from GWAS [34].
Low (quintile 1), intermediate (quintiles 2–4), and high(quintile 5) genetic risk categories were created basedon the distribution of GRS. We checked the overlapping
between 134 SNPs of AF and 941 SNPs of BMI identified
in a most recent GWAS in Europeans [35], and no overlapping between the two groups of SNPs was observed.
Assessment of covariates and other variables

Age and sex were collected from the Swedish registry
using a personal identification number. The baseline andfollow-up examination took more than 5 years but thefollow-up ended at a fixed date, so the year of participating in the baseline or follow-up examination (quartiles)
was adjusted in this study. Data on smoking status (current, former, and never), and educational level (elementary, primary and secondary, upper secondary, further
education without a degree, and university degree) werecollected by a self-administered questionnaire at baseline. Data on alcohol consumption was collected froma food frequency questionnaire (FFQ) and food diaryand was further divided into a variable with six categories (zero intake, and sex-specific quintiles for those whoreported drinking). The leisure time physical activity,
based on 17 different leisure-time physical activities, wascategorized into five groups (< 7.5, 7.5–15, 15–25, 25–50,
and > 50 MET-h/week). The dietary information wasassessed using a modified diet history method including
a 7-day food diary, a 168-item semiquantitative FFQ, anda 45–60-min dietary interview. The diet quality index was
created by summing up five dietary factors based on the
Swedish dietary guidelines: fiber (> 2.4 g/MJ), fruit andDu et al. BMC Medicine(2024) 22:345

vegetables (> 400 g/day), fish (> 300 g/week), added sugar(< 10% energy), and red and processed meat (< 500 g/week) [36]. We assigned a value of 1 to each dietary factor
that met the above criteria and 0 otherwise, resulting in a
total score between 0 and 5. Participants were asked torate their health status by the question: how do you feelright now, physically and mentally, with respect to yourhealth and your well-being? The answer was a score ranging from 1 to 7, with a higher score indicating a better
health status; a score of 1 refers to “feel very bad, could
not feel worse” and 7 refers to “feel very well, could not
feel better.”
After participants rested for 10 min, blood pressure
was measured by nurses in the supine position using amercury sphygmomanometer. Hypertension was defined
as systolic blood pressure ≥ 140 mmHg and/or diastolicblood pressure ≥ 90 mmHg or the use of antihypertensive
medication. Diabetes was ascertained by self-reported
diabetes diagnosis or use of diabetes medications orinformation from the registries mentioned above. The
prevalent or incident cases of cancer and CVD wereidentified by the registries. Information on the use of
lipid-lowering medication and prevalent goiter was selfreported. Data on smoking status, alcohol consumption,
leisure-time physical activity, self-rated health score, useof lipid-lowering medication, and prevalence of diabetes,
hypertension, and goiter were updated during a 5-year
follow-up examination.
The variables described below are used in sensitive
analysis. The deaths were identified through the Swedish National Tax Agency, Statistics in Sweden, and the
National Board of Health and Welfare. Sleep data werecollected only during the early and middle periods ofthe MDC baseline (1991 to 1994), involving 17,537 participants. Data were collected on sleep duration (categorized as 7–8 h or other), waking problems at night (none,
small, moderate, very big), and waking up too early problems (none, small, moderate, very big).
Statistical analyses

Baseline characteristics were presented by exposure
categories with means and standard deviations (SD) or
percentages. The Cox proportional hazards regression
models estimate the associations between exposures
and AF risk. The covariates were progressively adjusted
in four models. Sex, age, height, and years of participant recruitment were adjusted in model 1. Model 2
was adjusted for variables in model 1 plus educational
level, leisure time physical activity, alcohol consumption, smoking status, diet quality index, and self-rated
health score. We adjusted for variables in model 2 plus
goiter, diabetes, hypertension, lipid-lowering medication (model 3), and further for BMI at age 20 years

Page 4 of 11(continuous, for exposure 1) or BMI at baseline (continuous, for exposure 2 and exposure 3) in model 4
to observe the associations independent of these twovariables. For exposure 3, covariates obtained at thefollow-up examination were adjusted. All covariates
were adjusted in the forms as described in the Covariates Assessment section unless stated otherwise. The
proportional hazard assumption was tested by the
Schoenfeld test and visual inspection of the Schoenfeld residuals plot. No violation of the assumption
was found. To investigate dose–response associations
between exposures and AF risk, we fitted restrictedcubic spline models with 3 knots. The p-value for nonlinearity was determined from the Wald test. We conducted stratified analyses by sex, age, BMI, smoking
status, drinking status, physical activity, diet quality(< 2/ ≥ 2), self-rated health status (< 6/ ≥ 6), GRS, diabetes, hypertension, goiter, and use of lipid-lowering
medication to assess potential interactions. P for interaction was determined by Wald tests for cross-productterms (spline basis of exposure × stratification variables). Pearson correlation coefficients between BMI andweight changes were calculated.
The following sensitivity analyses were performed:(1) To minimize the chance of reverse causation, mainmodels were repeated after excluding cases of AF occurring within the first 5 years (n = 254 for exposure 1
and exposure 2/n = 472 for exposure 3; the numbers
below correspond to this pattern) or the first 10 years(n = 824/n = 1299) of the follow-up; (2) Fine and Greymodel was used to account for the competing risks
from deaths; (3) given that cancer accounted for onethird of unintentional weight loss cases in older adults,
and mental disorders were also linked to such weightloss [37], we repeated the main analyses after excluding participants with low ratings (1 and 2) for physicaland mental health (n = 575/n = 406), self-reported mental disorders (n = 179/n = 234), or incidents of cancer(n = 1120/n = 1311) within the first 5 years of followup. (4) Since unintentional weight loss can be an earlyprecursor to the occurrence of various life-shortening
diseases and has been related to the increased mortality risk [37, 38], we repeated the main analyses after
excluding deaths occurring within the first 10 years(n = 1526/n = 1967) of the follow-up to minimize the confounding from comorbidity-related unintentional weightloss. (5) We adjusted for the three above-mentioned variables of sleep in a subpopulation with available weightand sleep data (n = 12,440/n = 11,945) because sleep pattern has been reported as a risk factor for AF [39].
Statistical analyses were performed using R version
4.2.1 (R Foundation). Two-sided P < 0.05 was consideredsignificant.

Du et al. BMC Medicine(2024) 22:345

Page 5 of 11

Table 1 Baseline characteristics of study participants according to weight change from age 20 years to baseline
Characteristics

Total

Absolute weight change from age 20 years to baseline<  − 2.5 kg− 2.5 to 2.4 kg

2.5 to 9.9 kg

10.0 to 19.9 kg≥ 20 kg

Participants, n

21,761

1072

2032

5696

8219

4742

Age at baseline, years

57.75 ± 7.68

59.41 ± 7.90

57.98 ± 7.92

57.20 ± 7.73

57.50 ± 7.61

58.38 ± 7.50

Female13,366 (61.4)

684 (63.8)

1213 (59.7)

3586 (63.0)5047 (61.4)

2836 (59.8)

BMI at age 20 years, kg/m2

21.27 ± 2.58

23.97 ± 3.42

22.07 ± 2.42

21.35 ± 2.27

20.97 ± 2.35

20.73 ± 2.69

BMI at baseline, kg/m2

25.69 ± 3.95

21.42 ± 2.89

22.19 ± 2.41

23.57 ± 2.31

25.95 ± 2.53

30.25 ± 3.76

Height, cm

168.63 ± 8.84

167.54 ± 8.81

168.36 ± 8.80

168.21 ± 8.62

168.54 ± 8.75

169.65 ± 9.19

University degree3213 (14.8)

139 (13.0)

336 (16.5)

966 (17.0)

1212 (14.8)

560 (11.8)

Current

6301 (29.0)

524 (48.9)

786 (38.7)

1766 (31.0)

2142 (26.1)

1083 (22.8)

Former7276 (33.4)

242 (22.6)

512 (25.2)

1715 (30.1)

2982 (36.3)

1825 (38.5)

Never8184 (37.6)

306 (28.5)734 (36.1)

2215 (38.9)

3095 (37.7)

1834 (38.7)

1295 (6.0)

96 (9.0)122 (6.0)

255 (4.5)

450 (5.5)372 (7.8)

High leisure-time physical activ- 3649 (16.8)
ity (> 50 MET-h/week)

211 (19.7)

365 (18.0)1023 (18.0)

1364 (16.6)

686 (14.5)

Diabetes

905 (4.2)

46 (4.3)

60 (3.0)144 (2.5)292 (3.6)363 (7.7)

Hypertension

13,025 (59.9)

544 (50.8)1029 (50.6)

2962 (52.0)5002 (60.9)3488 (73.6)

Goiter1263 (5.8)

75 (7.0)105 (5.2)

304 (5.3)

445 (5.4)

334 (7.0)

Lipid-lowering medication

528 (2.4)

11 (1.0)40 (2.0)92 (1.6)

217 (2.6)

168 (3.5)

Self-rated health score5.25 ± 1.31

5.20 ± 1.37

5.42 ± 1.26

5.36 ± 1.25

5.26 ± 1.27

5.04 ± 1.40

Diet quality index

1.90 ± 1.28

1.85 ± 1.32

1.96 ± 1.32

1.93 ± 1.30

1.89 ± 1.26

1.87 ± 1.26

Smoking status

Zero-consumers of alcohol

Variables are presented as mean ± SD or n (%)

Results
Table 1 presents the baseline characteristics across categories of weight change from age 20 years to baseline. The
average weight change was 12.5 kg from age 20 years tobaseline. Compared with participants with stable weight,
those who were in the highest weight gain group (≥ 20 kg)
were more likely to have lower BMI at age 20 years, education levels, alcohol consumption, physical activity, dietary quality index, and self-rated health status. They werealso more likely to be former smokers, to use lipid-lowering medication, and to have a higher prevalence of diabetes, hypertension, and goiter at baseline. We observed
similar distributions for most characteristics according
to weight change from baseline to follow-up examination (Additional file 1: Table S1). Of note, participants
who experienced the highest weight loss tended to havehigher BMI at baseline, physical activity level, and dietary
quality, be current smokers, and have a higher prevalenceof diabetes and hypertension (Additional file 1: Table S1).
Participants who had a higher BMI (≥ 25 kg/m2) at age
20 years tended to be males or former smokers and had
a higher prevalence of hypertension and diabetes at baseline relative to those with a lower BMI (18.5–22.4 kg/m2)(Additional file 1: Table S2). Weight changes were negatively correlated with the BMIs at the onset of calculating change (Additional file 1: Table S3). A total of 17,427

participants recalled their weight at age 20 years both atbaseline and follow-up examination, and ICC was 0.924,
indicating high reliability for this exposure.
Participants were followed for a median of 23.2 years
from baseline and 18.5 years from the follow-up examination, with 4038 and 3547 incident AF cases documented, respectively. The mean age at the diagnosis of
AF was 76.7 years (SD, 8.1 years). Table 2 presents theassociations between exposures and AF risk. In model 4,
the hazard ratio (HR) and 95% CI for participants gaining
5–10 kg and larger than 20 kg weight from age 20 years
to baseline were 1.21 (1.07–1.37) and 1.52 (1.34–1.73),
respectively, when compared to those with a stableweight. Weight loss over 2.5 kg in this stage was not associated with AF risk. Those who gained weight over 5 kgfrom baseline to follow-up examination had an elevated
AF risk relative to those with stable weight (HR = 1.19;
95% CI, 1.06–1.33). Elevated risk was also seen for those
who had a weight loss of over 5 kg in model 3 (HR = 1.25;
95% CI, 1.11–1.40), but this association disappeared after
further adjustment for BMI at baseline. Compared withthose with a lower BMI at age 20 years (18.5 to 22.4 kg/m2), individuals with a higher BMI (≥ 25 kg/m2) had
a 14% increased risk of AF in Model 4. Adjustment forbaseline BMI in Model 4 markedly attenuated the associations observed in Model 3.

Du et al. BMC Medicine(2024) 22:345

Page 6 of 11

Table 2 Hazard ratios (95% CIs) for AF by absolute weight change and BMI at age 20 years
Number ofparticipants

Cases/person years

Model 1Model 2Model 3Model 4Weight change from age 20 years to baseline   <  − 2.5 kg

1072

176/19,818

1.12 (0.93–1.35)

1.09 (0.91–1.31)

1.10 (0.92–1.33)

0.99 (0.82–1.19)  − 2.5 to 2.4 kg

2032

317/40,529

1.00

1.00

1.00

1.00

2.5 to 9.9 kg

5696

895/117,502

1.00 (0.88–1.14)

1.01 (0.89–1.15)

1.00 (0.88–1.13)

1.04 (0.91–1.18)

10 to 19.9 kg

8219

1521/167,571

1.18 (1.05–1.33)

1.18 (1.04–1.33)

1.13 (1.00–1.27)

1.21 (1.07–1.37)

4742

1129/91,618

1.52 (1.34–1.72)

1.50 (1.33–1.71)

1.38 (1.21–1.56)

1.52 (1.34–1.73)  ≥ 20 kg

Weight change from baseline to follow-up examination  <  − 5 kg

1643

373/24,346

1.35 (1.21–1.51)

1.32 (1.18–1.47)

1.25 (1.11–1.40)

1.04 (0.92–1.17)   − 5 to − 2.4 kg

2751

572/42,970

1.09 (0.99–1.20)

1.09 (0.99–1.19)

1.06 (0.96–1.16)

0.98 (0.89–1.08)   − 2.5 to 2.4 kg

9900

1805/163,880

1.00

1.00

1.00

1.00

2.5 to 4.9 kg

2292

400/38,827

1.03 (0.93–1.15)

1.02 (0.92–1.14)

1.03 (0.92–1.15)

1.04 (0.93––1.16)

2192

397/36,149

1.24 (1.11–1.39)

1.18 (1.06–1.32)

1.19 (1.06–1.33)

1.19 (1.06–1.33)  ≥ 5 kg

BMI at age 20 years (kg/m2)   < 18.5

2647

371/55,635

0.94 (0.84–1.05)

0.94 (0.84–1.05)

0.93 (0.83–1.04)

0.99 (0.88–1.10)

18.5 to 22.4

12,9662318/264,506

1.00

1.00

1.00

1.00

22.5 to 24.9

4612

968/88,827

1.09 (1.01–1.17)

1.09 (1.01–1.17)

1.08 (1.00–1.17)

0.97 (0.90–1.05)

1536

381/28,070

1.49 (1.33–1.66)

1.46 (1.31–1.63)

1.41 (1.27–1.58)

1.14 (1.02–1.28)  ≥ 25Model 1 age, sex, height, and year of participant recruitment. Model 2 covariates in model 1 plus physical activity, alcohol, smoking, educational level, diet quality
index, and self-rated health score. Model 3 covariates in model 2 plus goiter, diabetes, hypertension, and lipid-lowering medication. Model 4 covariates in model
3 plus BMI at 20 years old (continuous) for weight change from age 20 years to baseline, and baseline BMI (continuous) for weight change from baseline to 5-year
follow-up and BMI at age 20 years

Fig. 1 Dose–response associations of AF with weight change from age 20 years to baseline (a), weight change from baseline to 5-year follow-up
examination (b), and BMI at age 20 years (c) stratified by sex. The zero (weight change) or median of BMI at age 20 years was used as reference level(HR = 1). Multivariable model adjusted for age, sex, height, year of participants recruitment, physical activity, alcohol, smoking, educational level,
diet quality index, self-rated health score, goiter, diabetes, hypertension, lipid-lowering medication, and BMI at age 20 years (continuous) for weightchange from age 20 years to baseline, and baseline BMI (continuous) for weight change from baseline to 5-year follow-up examination and BMI
at age 20 years

The spline analysis showed significant nonlinear associations of weight change with AF risk (Pnonlinear < 0.05)(Additional file 1: Fig. S2). Association between weightchange from age 20 years to baseline and AF varied
by sex (Pinteraction = 0.004): in females, weight loss was
associated with a lower AF risk while weight gain was
associated with higher risk; in males, AF risk sharply

increased with weight gain over approximately 16.5 kg
but showed no association when below this threshold (Fig. 1). The association was U-shaped for AF andweight change from baseline to follow-up examination and was not modified by sex (Fig. 1 and Additional
file 1: Fig. S2). Sex modified the association between
BMI at age 20 years with risk increase associatedDu et al. BMC Medicine(2024) 22:345

Page 7 of 11

Fig. 2 Dose–response associations of AF with BMI at age 20 years (a, stratified by GRS), and weight change from baseline to 5-year follow-up
examination (b, stratified by self-rated health status; c, stratified by physical activity). The zero (weight change) or median of BMI at age 20 years
was used as reference level (HR = 1). Multivariable model adjusted for age, sex, height, year of participant recruitment, physical activity, alcohol,
smoking, educational level, diet quality index, self-rated health score, goiter, diabetes, hypertension, and lipid-lowering medication, baseline BMI(continuous)with higher BMI was only restricted to males (Pinteraction = 0.047) (Fig. 1).
In stratified analyses by age, BMI, drinking status,
diet quality, diabetes, hypertension, goiter, and use of
lipid-lowering medication, the results were similar ineach stratum (Additional file 1: Table S4). However, amodified effect was observed for self-rated health status, physical activity, and GRS of AF. We presented
the results with significant interactions in Fig. 2. The
increased AF risk associated with higher BMI at age
20 years tended to be restricted to individuals who
had a lower genetic risk of AF (Pinteraction = 0.006). For
weight change from baseline to follow-up examination,
an elevated risk of AF associated with weight loss wasobserved for those who had a lower physical activity
level (Pinteraction = 0.049), and higher AF risk with weightgain appears to be stronger in individuals with better
health status (Pinteraction < 0.001). Smoking showed aborderline interaction with weight change from baseline to follow-up examination (Pinteraction = 0.067) (Additional file 1: Fig. S3).
The results for sensitivity analyses are presented in
Additional file 1: Tables S5–S7. The results were similar after excluding AF cases occurring within the first
5 or 10 years of follow-up, excluding participants witha poor health condition, mental disorder, and incidents
of cancer within the first 5 years of the follow-up, or
deaths occurring within the first 10 years of the followup, although the magnitude of some associations was
slightly changed. In a subpopulation with available
sleep data, additional adjustments for sleep variables
yielded similar results. When using the percentage of
weight change as the exposure, similar results wereobserved (Additional file 1: Fig. S4).

Discussion
In this large population, we found that the association
between weight change from early to middle adulthoodand AF risk varied by sex; weight loss was associated witha lower AF risk only in females, and a positive association with weight gain showed a linear pattern in females
but became significant only when weight gain exceededa threshold in males. Weight gain from middle to late
middle adulthood was associated with higher AF risks,
whereas weight loss showed no significant association.
The genetic susceptibility of AF did not modify findingsof weight change. Having a higher BMI in youth was
associated with an increased risk of AF; this association
was modified by sex and GRS of AF. Main results wererobust in sensitivity analyses and most subgroups.
Two previous studies on weight gain from early to
middle adulthood and AF risk reported similar positive
associations as in our study [11, 16]. The HUNT study,
which included 15,214 individuals, showed that a BMI
gain of more than 5 kg/m2 was associated with a 2.6-foldincrease in the risk of AF [16]. Another Swedish cohort(including 6903 males) found a risk increase of 90% for aweight gain of more than 35% versus those with a stableweight [11]. For weight gain from middle to late middle
adulthood, our findings were supported by two cohorts
in Denmark [13] and Sweden [15]. The Atherosclerosis
Risk in Communities (ARIC) study found an increasein AF risk associated with weight gain in males but not
in females [14], while the Danish cohort [13] and ourstudy demonstrated that this association was not modified by sex. This inconsistency is possibly due to statistical power, as the Danish cohort and our study had alarger number of AF cases than the ARIC study (n > 4000
versus n = 1775). Our study adds new knowledge thatDu et al. BMC Medicine(2024) 22:345

the association between weight change and AF was not
modified by the genetic risk of AF, which suggests that
all individuals, regardless of their genetic susceptibility to
AF, would benefit from avoiding substantial weight gain.
Weight gain from early to late middle adulthood mainlyreflects the accumulation of fat mass, especially visceraladipose tissue [40, 41]. Epicardial fat plays a crucial rolein AF pathogenesis, potentially via promoting fibrosis,
triggering electrical remodeling, and inducing proinflammatory, profibrotic, and proarrhythmic effects [9].
Moreover, the effect of weight gain on AF risk may be
mediated through the increases in cardiometabolic diseases, such as hypertension, diabetes, and obstructive
sleep apnea [42], all of which are involved in the pathogenesis of AF.
In females, we noted an inverse association between
weight loss from early to middle adulthood and AF. However, the HUNT study showed null associations [16]. This
study had a smaller sample size than ours. Additionally,
the benefits of weight loss may be masked by a higher
initial BMI, considering that weight change was inversely
associated with BMI at age 20 years (r =  − 0.23), a finding
also observed in a large US cohort (r =  − 0.24) [28]. However, this adjustment was not performed in the HUNT
study. Our observed null association between weight loss
and AF in males was strengthened by a Swedish cohort
comprising only male participants [11].
Previous studies on weight loss in middle or late middle adulthood produced mixed results, including positive[14], negative [12], and null associations [13]. The Danish
cohort [13], in which baseline BMI was adjusted, showeda similar null association as in our fully adjusted models. The ARIC study [14] observed a positive association
between weight loss and AF risk, which is similar to our
results in models 1–3. This association, however, attenuated markedly and became nonsignificant after controlling for baseline BMI. This adjustment was not performedin the ARIC study. Weight change is negatively correlatedwith baseline BMI (r =  − 0.25) in our study, which suggests that this elevated AF risk was mainly contributedby higher baseline BMI rather than the weight loss itself.
By contrast, a study in Israel, using screened data from atertiary medical center, saw a risk reduction of 12% fora 5-kg weight loss [12]. This inconsistency with ours andthe Danish cohort may result from the younger age of
their participants (49 versus 58/61 years). An explanationis that, in younger participants, who are less susceptible
to chronic conditions causing unintentional weight loss,
the association of AF with weight loss is, therefore, lesslikely to be confounded.
In stratified analysis, an increased risk of AF associated with weight loss from middle to late middle

Page 8 of 11

adulthood tends to be limited to individuals with lowlevels of physical activity or poor health status, or toformer and current smokers. These results collectively support the likelihood that this increased AF risk
among them may be due to confounding from underlying comorbidities related to unintentional weightloss. Nevertheless, in individuals with good health status, assumed to be less prone to comorbidities, weightloss was not associated with lower AF risk. Moreover,in sensitivity analysis, individuals with low ratings fortheir mental and physical health, incidents of cancer
within the first 5 years of follow-up, as well as deaths
occurring within the first 10 years of follow-up, were
excluded to minimize confounding from underlying
comorbidities. Weight loss remained unrelated to lower
AF risk in these analyses. Based on these considerations
and previous evidence [17], another explanation thatobesity-induced changes in cardiac structure and function are less likely to be reversible via weight loss occurring in late middle adulthood seems more plausible[43]. Despite both fat-free and fat body mass have beenshown to be causally related to a higher risk of AF [44],our study added the evidence that weight loss occurringin the later stage of middle adulthood is not associatedwith a reduced risk of AF. More large cohorts covering early and middle adulthood, with multiple weightassessments, are needed to explore more specific stages
when the risk of AF can be reversed by weight loss. The
findings regarding weight change and AF risk may not
be directly applicable to the secondary prevention of
AF; this is due to the observation of the “obesity paradox” phenomenon in AF, suggesting that obesity may
confer benefits for those with prevalent AF [45–47].
Our findings for BMI at age 20 years corroborate previous studies using data from military conscripts ormedical birth registries [18–22]. We extended previous
evidence by accounting for subsequent BMI, suggestingthat higher BMI in youth is a crucial risk factor for AF
irrespective of their subsequent BMI. The mechanisms
underlying this finding may involve obesity-related
unfavorable changes in metabolism and myocardial
structure and function, which have been noted in adolescents [48]. Our observed increased risk related to ahigher BMI was limited to males. This is plausible as
more apparent obesity-related alterations in cardiometabolic risk factors have been noted in boys and youngmen [49]. Another finding was that increased risk inrelation to a higher BMI seems more relevant in individuals with a lower genetic risk of AF; this novel finding warrants confirmation in further studies. It is worth
noting that BMI is less sensitive in identifying excess
adiposity in youth [50]. Using other measures, such as
body fat percentage, may provide new insights.

Du et al. BMC Medicine(2024) 22:345

This study has several strengths. To our knowledge,
this study is the first to account for genetic risk andsubsequent BMI in the analysis of BMI in youth. The
large sample size and long follow-up period of thisstudy ensured the ability to conduct multiple stratified
and sensitivity analyses, assess long-term risk, and capture the periods of high AF incidence. Other strengths
are its population-based prospective design and coverage of two time periods of weight change.
Some limitations of this study also need to be noted.
First, residual confounding is unavoidable due to theobservational nature of our study despite the most identified risk factors were carefully considered. Second,
recall bias was inevitable for exposure weight at age
20 years. However, our data indicated the high reliability of this exposure. A meta-analysis of validation studies revealed a correlation coefficient of 0.82 between
recalled early-life weight and measured weight, with asmall mean difference of 0.87 kg [51]. In addition, thedistribution of BMI at age 20 years in our study is similar to that in a study using measured data from Swedish military conscripts during 1969–1997 (including
1,547,478 men aged 18 years) [20]. The proportions ofbeing normal, overweight, and obese were 93.7%, 5.4%,
and 1.0% in our male participants, and 92.2%, 5.5%, and2.3% in their study, respectively. Third, we use heightmeasured at baseline in calculating BMI at age 20 years,
which could introduce systematic error, especially for
those who were older at baseline, since aging is accompanied by a decrease in height [52]. This error may notalter our findings, given similar associations acrossstrata in the stratified analysis by age. Fourth, we haveno data on intentional and unintentional weight loss,
which is common in observational studies considering
the difficulty in diagnosing unintentional weight loss
[37]. Fifth, because AF cases were identified throughregisters in this study, undiagnosed cases, especiallyasymptomatic ones, may exist, and different patternsof AF (i.e., first-diagnosed AF, paroxysmal AF, and persistent AF), which exhibit distinct impacts on clinicaloutcomes [53], could not be separated. Further, atrial
cardiomyopathy, characterized by electrophysiological, contractile/functional, and structural changes in
the atria and playing a pivotal role in the pathology ofAF [54], was not feasible in this study. Future studies
exploring various patterns of AF and atrial cardiomyopathy could help refine weight management targets
for each specific pattern and aid in the developmentof early prevention strategies for AF. Finally, our study
only included participants living in Sweden, and prevalent cancer and CVD cases were excluded to enhance
the internal validity, but this limits the generalizabilityof our findings to other populations.

Page 9 of 11

Conclusions
Weight gain during middle adulthood contributes to anincreased risk of AF, whereas weight loss, occurring onlyin the early stage of middle adulthood, was associatedwith a lower AF risk in females. Associations of AF withweight change were similar across all levels of AF genetic
risk. Having a higher BMI in youth, particularly in males
or those with a low genetic risk of AF, is related to ahigher risk of AF in later life, independent of their subsequent BMI. These findings suggest a need to implement
weight management in youth and middle adulthood forthe primary prevention of AF. Results on interactions
with sex and genetic risk may assist future public healthprograms in identifying target intervention populations.
Abbreviations
AF	Atrial fibrillation
AFL 	Atrial flutter
ARIC	Atherosclerosis risk in communities
BMI 	Body mass index
CVD	Cardiovascular disease
FFQ 	Food frequency questionnaire
GRS 	Genetic risk score
GWAS	Genome-wide association studyHR	Hazard ratio
ICC	Intraclass correlation coefficient
ICD	International classification of diseases
MDC	Malmö Diet and Cancer
SD	Standard deviation
SNP	Single nucleotide polymorphism

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03565-y.
Additional file 1: Table S1. Baseline characteristics of study participants
according to weight change from baseline to follow-up examination.
Table S2. Baseline characteristics of study participants according to BMI atage 20 years. Table S3. Correlation coefficients for BMI at three time points
and weight changes during two intervals. Table S4. Assessments of themodified effect by key variables on the association between exposures
and AF. Table S5. Sensitivity analyses of the associations between weightchange from age 20 years to baseline and risk of AF. Table S6. Sensitivity analyses of the associations between weight change from baseline
to follow-up examination and risk of AF. Table S7. Sensitivity analyses of
the associations between BMI at age 20 years and risk of AF. Fig. S1. The
flowchart for selecting participants. Fig. S2. Dose–response association of
AF with weight change and BMI at age 20 years. Fig. S3. Dose–response
associations of AF with weight change from baseline to five-year followup examination stratified by smoking status. Fig. S4. Dose–response
associations of AF with percentage of weight change from age 20 years tobaseline (a) and percentage of weight change from baseline to five-year
follow-up examination (b) stratified by sex.
Acknowledgements
The authors would like to thank all the Malmö Diet and Cancer Study participants and staff for their contribution to the study.
Authors’ contributions
YD, YB, and ES designed the study; YD performed the statistical analysis andvisualization and wrote the paper; YB verified all the underlying data; YD, LQ,
YB, and ES contributed to the interpretation of results and revision of themanuscript. All authors read and approved the final version.

Du et al. BMC Medicine(2024) 22:345

Funding
Open access funding provided by Lund University. This study was funded by
the Swedish Research Council (2020–01412) and Heart and Lung Foundation(20220444), Crafoordska Stiftelsen, Albert Påhlssons Stiftelse.
Availability of data and materials
Supporting data are available from the corresponding author upon reasonable request but access to data must be granted by the MDC steeringcommittees.

Declarations

Page 10 of 11

9.

10.

11.
12.

Ethics approval and consent to participate
The study was approved by the Ethical Committee at the Medical Faculty at
Lund University (approval number: LU 51/90). All participants provided written
informed consent.

13.

Consent for publication
Not applicable.

14.

Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Epidemiology and Statistics, School of Public Health, Lanzhou
University, Lanzhou, Gansu, China. 2 Nutritional Epidemiology, Departmentof Clinical Sciences Malmö, Lund University, Malmö, Sweden. 3 Departmentof Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA. 4 Department of Nutrition, Harvard T.H. Chan Schoolof Public Health, Boston, MA, USA. 5 Department of Food and Meal Scienceand the Research Environment MEAL, Faculty of Natural Science, Kristianstad
University, Kristianstad, Sweden.

15.
16.

17.
18.

Received: 3 April 2024 Accepted: 15 August 2024
19.
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