(2023) 21:509
Park et al. BMC Medicine
https://doi.org/10.1186/s12916-023-03229-3

BMC Medicine

RESEARCH ARTICLE

Open Access

Risk of newly developed atrial fibrillation
by alcohol consumption differs according
to genetic predisposition to alcohol
metabolism: a large‑scale cohort study with UK
Biobank
Chan Soon Park1, Jaewon Choi2, JungMin Choi1, Kyung‑Yeon Lee1, Hyo‑Jeong Ahn1, Soonil Kwon1,
So‑Ryoung Lee1, Eue‑Keun Choi1,3, Soo Heon Kwak2,3 and Seil Oh1,3*   

Abstract
Background The predictive relationship between mild-to-moderate alcohol consumption and the risk of incident
atrial fibrillation (AF) remains controversial.
Objective We investigated whether the relationship between alcohol consumption and the risk of incident AF could
be associated with the genetic predisposition to alcohol metabolism.
Methods A total of 399,329 subjects with genetic data from the UK Biobank database, enrolled between 2006
and 2010, were identified and followed for incident AF until 2021. Genetic predisposition to alcohol metabolism
was stratified according to the polygenic risk score (PRS) tertiles. Alcohol consumption was categorized as non-drink‑
ers, mild-to-moderate drinkers (< 30 g/day), and heavy drinkers (≥ 30 g/day).
Results During the follow-up (median 12.2 years), 19,237 cases of AF occurred. When stratified by PRS tertiles, there
was a significant relationship between genetic predisposition to alcohol metabolism and actual alcohol consumption
habits (P < 0.001). Mild-to-moderate drinkers showed a decreased risk of AF (HR 0.96, 95% CI 0.92–0.99), and heavy
drinkers showed an increased risk of AF (HR 1.06, 95% CI 1.02–1.10) compared to non-drinkers. When stratified accord‑
ing to PRS tertiles for genetic predisposition to alcohol metabolism, mild-to-moderate drinkers had equivalent AF
risks, and heavy drinkers showed increased AF risk in the low PRS tertile group. However, mild-to-moderate drinkers
had decreased AF risks and heavy drinkers showed similar risks of AF in the middle/high PRS tertile groups.
Conclusions Differential associations between alcohol consumption habits and incident AF across genetic predispo‑
sition to alcohol metabolism were observed; individuals with genetic predisposition to low alcohol metabolism were
more susceptible to AF.
Keywords Atrial fibrillation, Alcohol consumption, Genetic predisposition to disease, Prognosis

*Correspondence:
Seil Oh
seil@snu.ac.kr
Full list of author information is available at the end of the article
© The Author(s) 2023. 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 the
original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
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Park et al. BMC Medicine

(2023) 21:509

Background
Atrial fibrillation (AF) is one of the most common clinical arrhythmias and a major cause of stroke, dementia,
heart failure, and mortality worldwide [1–3]. For this reason, many attempts have been made to improve the clinical prognosis of AF patients, including refining clinical
risk stratification for predicting stroke and thromboembolism, such as ­CHA2DS2-VASc scores [4]. In addition,
interventional treatment of AF with catheter ablation has
become the cornerstone of AF management [5]. Despite
these advances, recent studies have shown unacceptably
high prevalence and incidence of AF [6, 7], indicating the
need for tailored prevention strategies to improve clinical
outcomes further.
Various studies have demonstrated that unhealthy
lifestyle behaviors such as smoking, obesity, and a lack
of regular physical activity are associated with a significantly increased risk of AF [8–12]. Moreover, the risk of
cardiovascular diseases such as coronary artery disease
and heart failure had a J-shaped association with alcohol
consumption [13, 14]. However, reports are contradictory
as to whether mild or moderate alcohol consumption are
associated with a decreased risk of incident AF [15–18].
This may be due to unmeasured contributing factors that
may influence the association between alcohol consumption and the risk of AF. It has been reported that heterogeneity of genetic traits for alcohol metabolism could
play a pivotal role in the genetic liability to alcohol consumption habits [19]. Polymorphisms of genes related
to alcohol metabolism, such as alcohol dehydrogenase
and aldehyde dehydrogenase, could affect differential
response after alcohol consumption [20, 21] as well as differential alcohol consumption habits [19, 22]. Therefore,
it could be speculated that the genetic predisposition to
alcohol metabolism may alter the association between
alcohol consumption habits and the risk of incident AF;
if an individual has low levels of alcohol metabolic capacity, they might experience more side effects than beneficial effects from alcohol consumption and become less
likely to drink alcohol. Unfortunately, there is little data
exploring the association between genetic predisposition
to alcohol metabolism, alcohol consumption habits, and
the risk of AF.
This study aimed to verify whether the association
between alcohol consumption habits and incident AF
risk differs according to genetic predisposition to alcohol
metabolism, using a large prospective cohort from the
UK Biobank.
Methods
Ethical statement and data availability

This study was conducted in accordance with the principles of the Declaration of Helsinki and approved by

Page 2 of 10

the Institutional Review Board (IRB No. E-2203-0051302). The need for informed consent was waived as
anonymized data were used. For reasonable requests,
data are available through approval and oversight by the
UK Biobank. The UK Biobank has approval from the
North West Multi-centre Research Ethics Committee as
a Research Tissue Bank approval.
Data source and study population

The UK Biobank is a large-scale prospective cohort study
of approximately 500,000 volunteers enrolled from 2006
to 2010 in the UK. The detailed design and preliminary
results have been published elsewhere [23]. In brief, the
UK Biobank database includes individual demographic
information, diagnostic history, and genomic data. All
participants signed a written informed consent form,
and the UK Biobank received ethical approval from the
National Health Service Research Ethics Service (reference 11/NW/0382).
The study design is illustrated in Fig. 1. In total, 502,392
individuals enrolled between 2006 and 2010 were identified. Subjects with a history of AF before enrollment (n =
3650) were excluded. Moreover, individuals without valid
genetic data (n = 22,187) and those without valid answers
to alcohol consumption questionnaires (n = 1061) were
also excluded. Among these, we conducted this study
among individuals in the white British population who
self-reported as white British and were verified through
principal component analysis of genetic ancestry. A final
total of 399,329 individuals were included in this study
and followed up until 2021. The index date was the baseline visit to the UK Biobank.
Definitions of alcohol consumption habit and genetic
predisposition to alcohol metabolism

Using the UK Biobank database, the average alcohol
intake per day (g/day) was calculated to evaluate alcohol
consumption, and the participants were subsequently
categorized into non-drinkers, mild-to-moderate drinkers (<30 g/day), and heavy drinkers (≥30 g/day).
Participants in the UK Biobank were genotyped using
the Affymetrix UK Biobank Lung Exome Evaluation (UK
BiLEVE) Axiom array or the Affymetrix UK Biobank
Axiom array. As previously reported, the UK Biobank
team performed central quality control and imputation [24]. To define genetic predisposition to alcohol
metabolism, we calculated polygenic risk score (PRS) as
the sum of the single nucleotide polymorphism (SNP)’s
allele dosages of risk-increasing alleles weighted by their
reported log odds ratios. SNPs associated with alcohol metabolism were identified from an existing large
genome-wide association study from European ancestry
(Supplemental Table 1) [19], which has been explored in

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Fig. 1 Study population. Study design using data from UK Biobank

other studies [25]. To compare effect sizes corresponding
to the continuous PRS, we transformed each PRS of the
corresponding analytical data set to the standard normal
distribution (Supplemental Fig. 1). According to the PRS,
we stratified subjects into three groups based on tertiles:
the low PRS tertile group implies a genetic predisposition
of slower alcohol metabolism, while the high PRS tertile
group implies a genetic predisposition of faster alcohol
metabolism.
Definitions of covariates and the study endpoint

Demographic data, anthropometric data, and medical
history of hypertension, diabetes mellitus, myocardial
infarction, dyslipidemia, chronic kidney disease, heart
failure, and stroke were obtained. In addition, data on
smoking history were collected. Smoking history was
assessed using standardized questionnaires. Detailed
definitions of comorbidities are provided in Supplemental Table 2.
Newly diagnosed AF was defined as the study endpoint. The endpoint was defined based on ICD-10 codes
(I48). The follow-up duration was defined as the interval
between the index date and the occurrence of incident
AF.
Statistical analysis

Data are presented as numbers and frequencies for categorical variables and mean ± standard deviation or
median with interquartile range for continuous variables.
For categorical variables, the chi-square test or Fisher’s
exact test was used as appropriate. One-way analysis

of variance was used to analyze continuous variables
between two or more groups. The annual event incidence rate (aIR) was calculated as the number of events
per 1000 person-years (PY). Multivariate Cox proportional hazard regression models were used to estimate
hazard ratios (HRs) and corresponding 95% confidence
intervals (CIs) for the associations between genetic predisposition to alcohol metabolism, real alcohol consumption habits, and the risk of incident AF. Variables that
initially achieved P < 0.2 in the univariate Cox regression
analysis or those with clinical relevance in terms of AF
risks were included in a multivariable model (Supplemental Table 3). The multivariable models were adjusted
for covariates, including age, sex, previous history of
hypertension, diabetes mellitus, myocardial infarction, dyslipidemia, chronic kidney disease, heart failure,
stroke, and PRS tertile for alcohol metabolism or alcohol consumption habit, respectively. Subgroup analyses
were conducted according to the PRS tertiles for alcohol
metabolism and alcohol consumption habits using Cox
models. The joint association was demonstrated, with
individuals who had heavy drinking habits and low PRS
tertile serving as the reference group. We further investigated whether the effects of genetic predisposition to
alcohol metabolism could be associated with the risks
of incident AF across varying levels of alcohol consumption, using structural equation model (SEM) analysis.
With SEM, we aimed to explore the complex association between genetic predisposition, alcohol consumption, and risks of incident AF. A two-sided P value of
<0.05 was considered statistically significant. Statistical

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analyses were performed using R programming version 4.2.1 (The R Foundation for Statistical Computing,
Vienna, Austria). We manipulated the imputed genotype
data and calculated the individual-level polygenic risk
score using PLINK version 1.90 (https://​www.​cog-​genom​
ics.​org/​plink/) [26].

Results
Baseline characteristics of the study population

In total, 399,329 individuals without previous history of
AF (mean age 56.8 ± 8.0 years; men, 181,981 [45.6%])
were analyzed in this study. Hypertension was diagnosed
in 104,879 individuals (26.3%), diabetes mellitus in 16,148
individuals (4.0%), myocardial infarction in 21,369 individuals (5.4%), dyslipidemia in 47,650 individuals (11.9%),
chronic kidney disease in 16,695 individuals (4.2%), heart
failure in 12,550 individuals (3.1%), and stroke in 8455
individuals (2.1%). More than half of the included participants were never smokers (54.7%), and approximately
one-third were ex-smokers (35.0%).
Based on alcohol consumption habits, individuals
were classified into three groups: non-drinkers, mildto-moderate drinkers, and heavy drinkers. The baseline
characteristics of each group are presented in Table 1.
Heavy drinkers showed male preponderance, had a more
frequent history of hypertension and dyslipidemia and

substantially higher rates of smoking than non-drinkers and mild-to-moderate drinkers. They also showed a
genetic predisposition to higher alcohol metabolism, as
calculated by the PRS. In contrast, non-drinkers seemed
to have a greater history of diabetes mellitus than other
groups. The baseline characteristics classified by a genetic
predisposition to alcohol metabolism according to PRS
are shown in Table 2. Individuals with a higher PRS for
alcohol metabolism tended to consume more alcohol.
Predictive implication of a genetic predisposition
to alcohol metabolism and real alcohol consumption habit
for incident AF

During a median follow-up of 12.2 years (interquartile
range, 11.5–12.8), there were 19,237 new cases of AF
(4.0%). As shown in Table 3, the aIRs of AF were 5.50,
4.73, and 6.90 per 1000 PY for non-drinkers, mild-tomoderate drinkers, and heavy drinkers, respectively. In
the univariate analysis, mild-to-moderate drinkers were
associated with a lower risk of AF (HR 0.89, 95% CI
0.86–0.92), but heavy drinkers were associated with an
increased risk of AF (HR 1.12, 95% CI 1.08–1.16). After
adjusting for covariates, mild-to-moderate drinkers
showed a decreased risk of AF (HR 0.96, 95% CI 0.92–
0.99), while heavy drinkers consistently showed a higher
risk of AF (HR 1.06, 95% CI 1.02–1.10). When analyzed

Table 1 Baseline characteristics according to alcohol consumption habit
Non-drinkers (n = 114,528) Mild-to-moderate drinkers (n =
207,254)

Heavy drinkers (n =
77,547)

P-value

Age, years

57.0±8.2

56.9±8.0

56.4±79

<0.001

Sex, %

36,978 (32.3)

83,621 (40.3)

61,382 (79.2)

<0.001

BMI, kg/m2

28.2±5.5

26.7±4.4

27.8±4.2

<0.001

Hypertension

32,398 (28.3)

48,433 (23.4)

24,048 (31.0)

<0.001

Diabetes mellitus

7014 (6.1)

5998 (2.9)

3336 (4.0)

<0.001

Myocardial infarction

6993 (6.1)

9237 (4.5)

5139 (6.6)

<0.001

Dyslipidemia

14,528 (12.7)

21,954 (10.6)

1168 (14.4)

<0.001

Chronic kidney disease

6791 (5.9)

7182 (3.5)

2722 (3.5)

<0.001

Heart failure

4528 (4.0)

5134 (2.5)

2888 (3.7)

<0.001

Stroke

2689 (2.3)

3823 (1.8)

1943 (2.5)

<0.001

SBP, mmHg

137.0±18.8

137.4±18.5

142.8±18.0

<0.001

DBP, mmHg

81.3±10.1

81.8±9.9

85.3±10.0

<0.001

Never smoker

69,351 (60.8)

120,481 (58.3)

28,467 (37.0)

Ex-smoker

32,184 (28.2)

71,657 (34.7)

35,468 (46.1)

Current smoker

12,514 (11.0)

14,508 (7.0)

13,056 (17.0)

PRS for alcohol use

0.011±0.021

0.012±0.020

0.013±0.019

Demographic data

Past medical history, %

Physical examination

Smoking, %

<0.001

BMI Body mass index, PRS Polygenic risk score, S(D)BP Systolic (diastolic) blood pressure

<0.001

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Table 2 Baseline characteristics classified by genetic predisposition to alcohol metabolism according to PRS
Low tertile (n = 133,054)

Middle tertile (n = 132,363)

High tertile (n = 133,912)

P-value

Age, years

56.9±8.0

56.8±8.0

56.8±8.0

0.015

Sex, %

60,811(45.7)

60,469 (45.7)

60,701 (45.3)

<0.091

BMI, kg/m2

27.3±4.7

27.4±4.7

27.4±4.8

<0.001

Hypertension

34,487 (25.9)

34,950 (26.4)

35,442 (26.5)

0.002

Diabetes mellitus

5273 (4.0)

5340 (4.0)

5535 (4.1)

0.081

Myocardial infarction

7010 (5.3)

6993 (5.3)

7366 (5.5)

0.012

Dyslipidemia

15,740 (11.8)

15,971 (12.1)

15,939 (11.9)

0.157

Chronic kidney disease

5644 (4.2)

5505 (4.2)

5546 (4.1)

0.384

Heart failure

4132 (3.1)

4183 (3.2)

4235 (#.2)

0.634

Stroke

2744 (2.1)

2836 (2.1)

2875 (2.1)

0.233

SBP, mmHg

138.2±18.5

138.4±18.7

138.4±18.7

<0.001

DBP, mmHg

82.2±10.1

82.3±10.1

82.4±10.1

<0.001

Never smoker

72,788 (54.9)

72,298 (54.8)

73,213 (54.8)

Ex-smoker

46,598 (35.2)

46,241 (35.1)

46,770 (35.0)

Current smoker

13,182 (9.9)

13,388 (10.1)

13,508 (10.1)

2.1±2.7

2.2±2.8

2.3±2.9

Demographic data

Past medical history, %

Physical examination

Smoking, %

0.169

Alcohol consumption, unit/day

<0.001

BMI Body mass index, PRS Polygenic risk score, S(D)BP Systolic (diastolic) blood pressure

Table 3 Associations of alcohol consumption habit and PRS for alcohol metabolism with atrial fibrillation
Variables

Total number Atrial fibrillation Incidence rates Unadjusted HR (95%
CI)

P-value Adjusted HRa (95% CI) P-value

Alcohol consumption habit
Non-drinkers

114,528

5640

5.50

1 (reference)

Mild-to-moderate
drinkers

207,254

8781

4.73

0.89 (0.86–0.92)

Heavy drinkers

77,547

4816

6.90

1.12 (1.08–1.16)

Per 1 alcohol unit/day
increment

<0.001

1 (reference)

<0.001

0.96 (0.92–0.99)
1.06 (1.02–1.10)

1.02 (1.02–1.03)

<0.001

1.01 (1.01–1.02)

<0.001

0.837

1 (reference)

0.388

Genetic predisposition to alcohol metabolism
Low tertile

133,054

6259

5.26

1 (reference)

Middle tertile

132,363

6465

5.45

1.01 (0.98–1.05)

High tertile

133,912

6513

5.42

1.00 (0.97–1.04)

Per 1 SD increment
in PRS

1.01 (1.00–1.02)

1.01 (0.97–1.04)
0.99 (0.96–1.03)
0.221

1.00 (0.99–1.02)

0.551

Follow-up duration was presented as person-years. Incidence rates were presented as per 1000 person-years
CI Confidence interval, HR Hazard ratio, PRS Polygenic risk score, SD Standard deviation
a

Adjusted for age, sex, previous history of hypertension, diabetes mellitus, myocardial infarction, dyslipidemia, chronic kidney disease, heart failure, stroke, and PRS
tertile for alcohol metabolism or alcohol consumption habit, respectively

as a continuous variable, one alcohol unit increment per
day was significantly related to an increased risk of AF
(HR 1.01, 95% CI 1.01–1.02).
When individuals were stratified according to PRS tertiles, the aIRs of AF were 5.26, 5.45, and 5.42 per 1000

PY for the low, middle, and high tertiles, respectively
(Table 3). In the univariate analysis, the middle tertile
group (HR 1.01, 95% CI 0.98–1.05) and high tertile group
(HR 1.00, 95% CI 0.97–1.04) had similar risks of incident
AF compared to the low tertile group. Similarly, the risk

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of incident AF was consistently equivalent in the middle
tertile group (HR 1.01, 95% CI 0.97–1.04) and high tertile
group (HR 0.99, 95% CI 0.96–1.03) compared to the low
tertile group in the multivariate analysis.
Subgroup analyses for AF

To determine whether the predicted risks of incident
AF, based on real alcohol consumption habits, could be
modified by a genetic predisposition to alcohol metabolism, we performed a subgroup analysis according to
PRS for alcohol metabolism (Fig. 2). In the low PRS
tertile group, mild-to-moderate drinkers showed similar risks of AF (HR 0.97, 95% CI 0.92–1.03), whereas
heavy drinkers showed a higher AF (HR 1.10, 95% CI
1.02–1.18) than non-drinkers. In contrast, in the middle
PRS tertile group, mild-to-moderate drinkers and heavy
drinkers showed equivalent risks of AF (HR 0.95, 95% CI
0.90–1.01 and HR 1.06, 95% CI 0.99–1.14), compared to
non-drinkers. Similar findings were observed in the high
PRS tertile group; mild-to-moderate drinkers and heavy
drinkers showed a similar risk of AF (HR 0.94, 95% CI
0.89–1.00 and HR 1.02, 95% CI 0.95–1.10) compared to
non-drinkers. The subgroup analysis according to alcohol consumption habits is presented in Supplemental

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Table 4. When we analyzed joint association across
genetic predisposition to alcohol metabolism and alcohol consumption habits, individuals in the low PRS tertile group appeared to be more susceptible to incident
AF among drinkers (Fig. 3). In SEM analysis, we found
associations between genetic predisposition to alcohol
metabolism, alcohol consumption habits, and the risk of
AF (Supplemental Table 5) after assessment of the goodness-of-fit parameters indicated an adequate fir for this
model (Supplemental Table 6).

Discussion
This study investigated the differential association
between actual alcohol consumption habits and incident AF based on genetic predisposition to alcohol
metabolism. The major findings are summarized as follows (Fig. 4): First, when the PRS for alcohol metabolism was calculated, there was a significant correlation
between genetic predisposition to alcohol metabolism
and real alcohol consumption habits. Second, there
was significant association between risks of alcohol
consumption habits and risks of incident AF. Third,
there was no difference in risks of incident AF across
PRS tertiles (P = 0.221). Fourth, there was a significant

Fig. 2 Association between alcohol consumption habit and risks of incident AF according to genetic predisposition to alcohol metabolism.
Subgroup analysis according to PRS tertiles for alcohol metabolism was performed to determine whether the prognostic effect of alcohol
consumption habits could be modified by genetic predisposition to alcohol metabolism. AF, atrial fibrillation; CI, confidence interval; HR, hazard
ratio; PRS, polygenic risk score. *Adjusted for age, sex, previous history of hypertension, diabetes mellitus, myocardial infarction, dyslipidemia,
chronic kidney disease, heart failure, and stroke

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Fig. 3 Joint association of alcohol consumption habits and tertiles of genetic predisposition to alcohol metabolism with risks of incident AF.
Individuals with heavy drinking habits and low PRS tertile served as the reference group for analysis. Hazard ratios with 95% confidence intervals
are displayed as dot and whisker plots, adjusted for covariates. Covariates include age, sex, previous history of hypertension, diabetes mellitus,
myocardial infarction, dyslipidemia, chronic kidney disease, heart failure, and stroke

Fig. 4 Summarizing illustration. Schematic diagram depicting the association between genetic and acquired traits for alcohol consumption
and risks of AF. AF, atrial fibrillation

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interaction between alcohol consumption habits and
genetic predisposition to alcohol metabolism for incident AF (P for interaction <0.001).
As the number of elderly people increases, the prevalence of AF, the most common clinical arrhythmia, is
increasing [1–3]. Since AF is not only a debilitating disease but also could cause comorbidities such as stroke,
dementia, and heart failure, there is a demand for effective prevention strategies to mitigate the disease burden. There have been numerous efforts to unveil the
association between unhealthy lifestyle behaviors and
risks of incident AF, enabling the identification of correctable AF risk factors [8–11]. Alcohol consumption is
prevalent worldwide and is an important issue in lifestyle behavior modification [27–29]. A previous study
reported that approximately 5% of deaths worldwide
could be attributable to alcohol consumption [29]. Due
to the detrimental relationship between alcohol consumption and health problems, the association between
alcohol consumption and cardiovascular diseases has
been studied over the past several decades.
Interestingly, the association between alcohol consumption and cardiovascular diseases is complex [30].
There are reports that mild alcohol consumption might
be beneficial to coronary artery disease and heart failure while excessive alcohol consumption could cause
grave prognosis [13, 14]. An anti-inflammatory effect
and improved insulin sensitivity from small quantities of alcohol consumption were provided as plausible
explanations for the aforementioned J-shaped associations between alcohol consumption and cardiovascular diseases [31, 32]. Our study, based on the UK
biobank database, also showed nonlinear associations
between alcohol consumption and risks of incident
AF, as presented in Table 3. Indeed, the association
between alcohol consumption and the risk of incident
AF remains controversial. Notably, previous studies
have reported either a J-shaped association or linear
association between alcohol consumption and the risk
of AF [12, 15–17]. Several studies utilizing Mendelian
Randomization have demonstrated that even mild-tomoderate alcohol consumption could increase the risk
of cardiovascular diseases, including AF [33, 34]. However, a nonlinear Mendelian Randomization study has
also shown that the increase in risk within the range
of mild-to-moderate drinking is minimal [35]. Taken
together, the complex association between alcohol
consumption and cardiovascular diseases still remains
inconclusive and demands further investigation. Meanwhile, we attempted to explore whether the genetic
predisposition to alcohol metabolism might influence
the association between alcohol consumption and AF
risks; we found that there could be individuals who are

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more susceptible to AF from the same level of alcohol
consumption.
Alcohol, especially ethanol, is a substance in alcoholic beverages and is mainly metabolized in the liver
[36]. Alcohol metabolism is known to be significantly
influenced by genetic factors, including genetic polymorphisms of alcohol dehydrogenase and aldehyde
dehydrogenase [36, 37]. Previous studies have reported
that genetic polymorphisms in alcohol metabolism are
associated with altered levels of acetaldehyde and differential responses to unpleasant experiences after alcohol
consumption [20, 21], resulting in increased or decreased
susceptibility to habitual alcohol consumption [19, 22].
This suggests genetically decreased alcohol metabolism
may result in higher acetaldehyde levels, even after drinking small amounts of alcohol, and could cause flushing
syndrome increasing the risk of incident AF [22, 38]. In
our data, individuals in the low PRS tertile group showed
the lowest alcohol consumption. In contrast, the high
PRS tertile group showed the highest alcohol consumption (Table 2). Considering the importance of genetic
predisposition to alcohol metabolism and actual alcohol
consumption habits, we designed this study to explore
the differential predictive relationship of alcohol metabolism according to genetic background. The authors
hypothesized that the genetic predisposition to metabolize alcohol could differentiate an individual’s susceptibility to complications and benefits associated with alcohol
consumption. If someone has low levels of alcohol metabolic capacity, they might experience relatively more side
effects and fewer beneficial effects from alcohol consumption compared to those with high levels of alcohol
metabolic capacity. Using the UK Biobank database, we
found that the predicted risk of incident AF according to
of alcohol consumption differed based on genetic background stratified by PRS tertiles (Fig. 2), which was in
line with our hypothesis. Although the genetic ability to
metabolize alcohol and/or alcohol metabolites itself was
not associated with risks of increased AF as in our report
as well as a previous study,22 genetic predisposition to
alcohol metabolism could alter the relationship between
alcohol consumption habits and risks of AF.
To the best of our knowledge, this study is the first
to show comprehensive relationships between genetic
predisposition to alcohol metabolism, alcohol consumption habits, and risk of incident AF in a sizable
cohort with long-term follow-up. This study had two
strengths. The UK Biobank is a large population-based
prospective cohort study with genotype data. The
authors acknowledge that a randomized controlled
trial is the best to verify the hypothesis, and an ethical issue could not be avoided in a trial demanding
alcohol metabolism. Therefore, a well-designed and

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well-controlled observational study could be an optimal
alternative and provide valuable information. Second,
as results of quality control regarding genotype data
in the UK Biobank have been reported elsewhere [21],
we could explore comprehensive association between
genetic predisposition to alcohol metabolism, alcohol consumption habits, and risks of AF with reliable
genetic databases.
This study has several limitations. First, alcohol consumption habits might have been altered from baseline
during follow-up, for which information was not available. Second, since the study included only individuals
in the UK, it is uncertain whether this study could be
extrapolated to other ethnicities and countries. However, this large population-based study enabled use of
a notably large number of subjects with long-term follow-up, effectively reflecting the phenomenon observed
in real-world practice. Additionally, we employed
stepwise selections to identify significant confounders, a method that could not exclude the possibility of
collider bias during the analysis. Finally, in this study,
newly diagnosed AF was defined as the study endpoint
based on ICD-10 codes. While continuous ECG monitoring for each individual could potentially detect more
cases of AF, it was not feasible in this retrospective
cohort study due to its observational nature.

Conclusions
In this large nationwide study using a prospective registry, we found associations between alcohol consumption habits and incident AF differed according to the
genetic predisposition to alcohol metabolism; authors
found that PRS might help identify individuals who are
more susceptible to developing incident AF than others. Alcohol consumption of more than 30 g/day was
associated with increased risks of incident AF in the
low PRS tertile group, but the significance was attenuated in the middle/high PRS tertile group. Therefore,
alcohol consumption might be more harmful among
those with a genetic predisposition to low alcohol
metabolism. These findings highlight the importance of
assessing genetic predisposition to alcohol metabolism
for risk prediction and emphasize the significance of
tailored preventive strategies for AF.
Abbreviations
AF	Atrial fibrillation
aIR	Annual incidence rate
CI	Confidence interval
HR	Hazard ratio
PRS	Polygenic risk score
SNP	Single nucleotide polymorphism

Page 9 of 10

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​023-​03229-3.
Additional file 1: Supplemental Figure 1. Distribution of Polygenic risk
score for alcohol metabolism PRS, polygenic risk score. Supplemental
Table 1. A list of 10 SNPs known to be associated with alcohol metabo‑
lism. Supplemental Table 2. Definitions of comorbidities and outcomes.
Supplemental Table 3. Univariable Cox-proportional hazard regression
analysis for overall subjects and for white British subjects. Supplemental
Table 4. Associations of PRS for alcohol metabolism and atrial fibrillation
across alcohol consumption habits. Supplemental Table 5. Structural
equation models for alcohol consumption, genetic predisposition to
alcohol metabolism, and atrial fibrillation. Supplemental Table 6. The
statistical fit of structural equation models.
Acknowledgements
None.
Authors’ contributions
CSP contributed to the conception and design of the work, data interpreta‑
tion, data analysis, and drafting of the manuscript. SO contributed to the
conception, design, data acquisition, data interpretation, and critical revision
of the manuscript. JC and SHK contributed to the data acquisition, data
interpretation, data analysis, and critical revision of the manuscript. JMC, K-YL,
H-JA, SK, S-RL, and E-KC contributed to the conception and design of the work
and critically revised the manuscript. All authors read and approved the final
manuscript.
Funding
This work was supported by grant no. 04-2022-2260 from the SNUH Research
Fund.
Availability of data and materials
UKB data are available in a public, open access repository. This research has
been conducted using the UK Biobank Resource under Application Number
91312. The UK Biobank data are available on application to the UK Biobank
(www.​ukbio​bank.​ac.​uk/). For reasonable requests, data are available through
approval and oversight by the UK Biobank.

Declarations
Ethics approval and consent to participate
The UK Biobank has approval from the North West Multi-centre Research
Ethics Committee as a Research Tissue Bank approval. All participants signed
informed consent. This study was conducted in accordance with the princi‑
ples of the Declaration of Helsinki and approved by the Institutional Review
Board of the Seoul National University Hospital (IRB No. E-2203-005-1302). The
need for informed consent was waived as anonymized data were used.
Consent for publication
Not applicable.
Competing interests
EKC received research grants from Bayer, BMS/Pfizer, Biosense Webster, Chong
Kun Dang, Daiichi-Sankyo, Samjinpharm, Sanofi-Aventis, Seers Technology,
Skylabs, and Yuhan.
Author details
1
Cardiovascular Center, Seoul National University Hospital, Seoul, Republic
of Korea. 2 Division of Data Science Research, Innovative Biomedical Technol‑
ogy Research Institute, Seoul National University Hospital, Seoul, Republic
of Korea. 3 Department of Internal Medicine, Seoul National University College
of Medicine, Seoul, Republic of Korea.
Received: 4 August 2023 Accepted: 13 December 2023

Park et al. BMC Medicine

(2023) 21:509

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