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Risk_of_cardiovascular_disease.pdf
RESEARCH ARTICLE
Risk of cardiovascular disease in patients with
alcohol use disorder: A population-based
retrospective cohort study
Chieh SungID 1,2, Chi-Hsiang ChungID
3,4, Fu-Huang Lin3*, Wu-Chien ChienID 3,5,6*, Chien-
An Sun7,8, Chang-Huei Tsao9,10, Chih-Erh Weng2
1 Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, 2 Department of
Nursing, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan, 3 School of Public Health, National
Defense Medical Center, Taipei, Taiwan, 4 Taiwanese Injury Prevention and Safety Promotion Association,
Taipei, Taiwan, 5 Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan,
6 Department of Medical Research, Tri-Service General Hospital, National Defense Medical Center, Taipei,
Taiwan, 7 Department of Public Health, College of Medicine, Fu-Jen Catholic University, New Taipei City,
Taiwan, 8 Big Data Research Center, College of Medicine, Fu-Jen Catholic University, New Taipei City,
Taiwan, 9 Department of Medical Research, Tri-Service General Hospital, Taipei City, Taiwan,
10 Department of Microbiology & Immunology, National Defense Medical Center, Taipei City, Taiwan
* [email protected] (F-HL); [email protected] (W-CC)
Abstract
The complex effects of alcohol consumption on the cardiovascular system vary with mean
daily consumption and duration of intake. This population-based retrospective cohort study
aimed to explore the risk of cardiovascular disease (CVD) in patients with alcohol use disor-
der (AUD). Data was collected from the Taiwan National Health Insurance Research Data-
base from 2000 to 2013. A total of 7,420 patients with AUD were included in our study
group, and 29,680 age- and sex-matched controls without AUD in the control group. Cox
proportional hazard regression analysis was used to investigate the effects of AUD on the
risk of CVD. Most patients were men aged 25–44 years. At the end of the follow-up period,
the AUD group had a significantly higher incidence of CVD (27.39% vs. 19.97%, P<0.001)
and more comorbidities than the control group. The AUD group also exhibited a significantly
higher incidence of CVD than the control group based on the Cox regression analysis and
Fine and Gray’s competing risk model (adjusted hazard ratio [AHR] = 1.447, 95% confi-
dence interval [CI] = 1.372–1.52 5, P<0.001). Furthermore, male sex, diabetes mellitus,
hypertension, hyperlipidemia, chronic kidney disease, chronic obstructive pulmonary dis-
ease, anxiety, depression, and a high Charlson Comorbidity Index were also associated
with an increased risk of CVD. Patients with AUD in different CVD subgroups, such as those
with CVD, ischemic heart disease (IHD), and stroke, were at a significantly higher risk of dis-
ease than those without AUD; CVD (AHR = 1.447, 95% CI = 1.372–1.525, P<0.001), IHD
(AHR = 1.304, 95% CI = 1.214–1.401, P<0.001), and stroke (AHR = 1.640, 95% CI =
1.519–1.770, P<0.001). The risk also significantly differed among patients in the different
CVD subgroups. We observed an association between AUD and development of CVD even
after adjusting for several comorbidities and medications in our nationwide population
cohort.
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OPEN ACCESS
Citation: Sung C, Chung C-H, Lin F-H, Chien W-C,
Sun C-A, Tsao C-H, et al. (2022) Risk of
cardiovascular disease in patients with alcohol use
disorder: A population-based retrospective cohort
study. PLoS ONE 17(10): e0276690. https://doi.
org/10.1371/journal.pone.0276690
Editor: Kuang-Hsi Chang, Tungs’ Taichung
MetroHarbor Hospital, TAIWAN
Received: March 30, 2022
Accepted: October 11, 2022
Published: October 25, 2022
Copyright: © 2022 Sung et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: This study uses third
party data. Taiwan launched a single-payer National
Health Insurance program on March 1, 1995. The
database of this program contains registration files
and original claim data for reimbursement. Large
computerized databases derived from this system
by the National Health Insurance Administration.
Investigators interested may submit a formal
proposal to NHIRD(http://nhird.nhri.org.tw). We
have provided S1 Table in terms of outcome
measures and comorbidities, abbreviations and
ICD-9-CM, which describes the relevant dataset
Introduction
Alcohol use disorder (AUD) is characterized by compulsive alcohol seeking, loss of control
with regard to limiting intake, and persistent alcohol use despite awareness of the harmful con-
sequences such as alcoholic liver disease, cancer, cardiovascular disease (CVD), cirrhosis, and
neuropsychiatric disorders [1–3].
Heterogeneous associations exist between the level of alcohol consumption and the initial
presentation of CVD. Previous studies indicate that low-to-moderate levels of alcohol con-
sumption could reduce the risk of most CVDs. Thus, the relationship between alcohol con-
sumption and CVDs is complex and controversial [4]. Hence, to enhance the understanding
of the risk of CVDs associated with AUD, we conducted a large, nationwide, population-based
nested cohort study using Taiwan’s National Health Insurance Research Database (NHIRD).
Methods
Data source
The National Health Insurance Program (NHI) was launched in Taiwan in 1995 and covers
more than 99% of the Taiwanese population (more than 23 million beneficiaries). The NHIRD
contains the following encrypted data: patient identification number; date of birth; sex; dates
of admission and discharge; worldwide class of sicknesses, 9th Revision, medical modification
(ICD-nine-CM) diagnostic and system codes (as many as 5 each); and outcomes. The longitu-
dinal medical health insurance Database 2005 (LHID 2005), which we used, is a subset of the
NHIRD. The LHID 2005 carries approximately 1 million randomly selected records of benefi-
ciaries, representing approximately 5% of the population in Taiwan in 2005, for scientific utili-
zation. Statistics from 2000–2013 were extracted from the NHIRD.
Analysis of data from 2000 to 2013 using the Universal Health Coverage database, Tandem
「Inpatient expenditures by admissions (DD)」,「Registry for contracted medical facilities
(HOSB)」,「Registry for beneficiaries(ID)」 ,「Registry for catastrophic illness patients
(HV)」, variables include diagnosis, surgery, disposition, hospitalization and discharge dates,
length of stay and medical costs;「Registry for contracted medical facilities (HOSB)」the
variables include hospital location and hospital level.
According to the law, medical institutions are required to report outpatient (including
emergency) and inpatient expenses to the Health Insurance Bureau every month. Therefore,
Therefore, health insurance data is a representative empirical data in the field of medical and
health-related research, and analytical results thereof can be used as a reference for medical
and health policies, providing an important research resource. The NHI Administration peri-
odically reviews medical records in a random manner to verify the accuracy of diagnoses. This
review was conducted in accordance with the World Medical Association Code of Ethics (Hel-
sinki Declaration). This study was approved by the Institutional Review Board of Tri-Service
General Hospital at the National Defense Medical Center in Taipei, Taiwan, and the require-
ment of individual consent was waived because all identifying data were encrypted (TSGH
IRB No. B-111-10). The NHIRD is a publicly available database that contains depersonalized
patient information to ensure patient anonymity.
Study sample
The study comprised a cohort of patients aged above 18 years from the LHID 2005 database
who were newly diagnosed with alcohol use disorder, namely alcoholic psychosis (ICD-9-CM
291), alcohol abuse (ICD-9-CM 303.0, 305.0), and alcohol dependency syndrome (ICD-9-CM
303.9). We utilized the LHID to estimate the incidence of alcohol-related illnesses as previously
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names, variables, descriptions to be requested. We
have also added a minimal data set from the
National Health Insurance Research Database
(NHIRD) in our methods section. The authors
confirm they did not have any special access
privileges.
Funding: This study was funded by the TSGH-B-
111018 Special plan.
Competing interests: The authors have declared
that no competing interests exist.
specified in the Centers for Disease Control and Prevention’s "Chronic Causes" of "Alcohol-
Related ICD Codes"(https://nccd.cdc.gov/DPH/ARDI/Info/ICDCodes.aspx) and as previously
documented in the literature [5,6]. CVD was identified using the codes for ischemic heart dis-
ease (IHD) (ICD-9-CM 410–414) and stroke (ICD-9-CM 430–438). We excluded patients
with a history of AUD, aged<18 years of age, whose sex was unknown, who had CVD before
tracking, with incomplete tracking data, and who were diagnosed with AUD before the index
date, the inclusion and exclusion criteria are shown in Fig 1. Those who comprised the control
group were also selected from the LHID 2005. The study and control cohorts were selected
with 1:4 matching according to sex, age, and index date. The date of the diagnosis of an alco-
hol-related disease was used as the index date.
Outcome measurement and comorbidities
Patients with baseline comorbidities, IHD (ICD-9-CM 410–414), stroke (ICD-9-CM 430–
438), diabetes mellitus (DM) (ICD-9-CM 250), hyperlipidemia (ICD-9-CM 272.0–272.4),
hypertension (HTN) (ICD-9-CM 401–405), obesity (ICD-9-CM 278), depression (ICD-
9-CM296.2e296.3, 300.4), anxiety (ICD-9-CM 300.02), chronic kidney disease (CKD) (ICD-
9-CM 585), chronic obstructive pulmonary disease (COPD) (ICD-9-CM 490e496), liver cir-
rhosis (ICD-9-CM571), tobacco use disorder (ICD-9-CM350.1), and drug use disorder (ICD-
9-CM304, 305.2–305.9), are listed in S1 Table.
All patients were followed up from the index date until the first diagnosis of CVD, death,
withdrawal from the NHI program, or 31 December 2013. The covariates included sex, age
Fig 1. The flowchart of study sample selection from the National Health Insurance Research Database in Taiwan. Abbreviations:
AUD, alochol use disorder; CVD, cardiovascular disease.
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group (18–24, 25–44, 45–64,�65), geographical area of residence (), urbanization level of resi-
dence (levels 1–4), low-income, catastrophic illness, Charlson comorbidity index—revised
(CCI_R), season at diagnosis of CVD (spring, summer, autumn, winter), level of care (hospital
center, regional hospital, local hospital). The urbanization level of residence was defined
according to the population and various indicators of development: level 1 was defined as a
population >1,250,000, with a specific designation of political, economic, cultural, and metro-
politan development; level 2 was defined as a population of 500,000–1,249,999, with an impor-
tant role in politics, economy, and culture; levels 3 and 4 were defined as populations of
149,999–499,999 and<149,999, respectively.
Statistical analysis
The clinical characteristics of patients enrolled in the study are expressed in numerical form.
We compared the distribution of categorical characteristics and baseline comorbidities
between the case and control groups using Fisher’s exact test and the chi-squared test. Contin-
uous variables are presented as means and standard deviations and were compared using t-
tests. As the primary goal of the study was to determine whether the clinical characteristics of
the patients were associated with the development of CVD, Fine and Gray’s survival analysis
and regression analysis were used to determine the risk of CVD (competing with mortality),
and the results are presented as hazard ratios (HRs) with the associated 95% confidence inter-
vals (CIs). Associations between time-to-event outcomes and clinical characteristics were
examined using the Kaplan–Meier method and multivariate Cox regression analysis with step-
wise selection. The results are presented as adjusted HRs with the corresponding 95% CIs. All
statistical analyses were performed using IBM SPSS Statistics for Windows version 22.0.
(released 2013, IBM Corp., Armonk, NY, USA). A two-tailed P-value of<0.05 was considered
statistically significant.
Results
Among the 987,403 patients in the LHID 2000–2013, 12,601 were diagnosed with AUD; 7,420
patients were assigned to the study cohort and 29,680 age-, sex-, and comorbidity-matched
patients were assigned to the comparison (control) cohort (Fig 1).
The baseline data of the patient and control groups are shown in Table 1. The patients were
predominantly men (92.84%), with an average age of 43.12 ± 11.85 years. Our findings
revealed that low-income, DM, liver cirrhosis, CKD, drug use disorder, anxiety, depression,
location, Urbanization level, and level of care significantly differed between the study and con-
trol groups. In most patients with alcohol-related diseases, the diseases were diagnosed and
treated in northern Taiwan, and middle Taiwan, with a combination of urbanization level 1
and 2 cities, and these patients were predominantly treated in regional hospital, or a local hos-
pital. There were no significant differences in sex, age, CCI, and season between the groups.
Fig 2 shows the Kaplan–Meier survival curve of patients with CVD stratified by AUD using
the log-rank test; patients with AUD had a significantly higher cumulative risk of developing
CVD 14 years after the index date (log-rank test, P<0.001).
As indicated in Table 2, at the end of the 14-year follow-up period, patients with AUD had
significantly higher incidences of CVD (27.39% vs 19.97%, P<0.001) and several comorbidities
than did controls without AUD.
Patients with AUD also exhibited a significantly higher incidence of CVD than did controls
without AUD, according to the Cox regression analysis (adjusted HR [AHR] = 1.447, 95%
CI = 1.372–1.525, P<0.001). In addition, male sex (AHR = 1.206, 95% CI = 1.096–1.327,
P<0.001), DM (AHR = 1.363, 95% CI = 1.293–1.437, P<0.001), HTN (AHR = 1.699, 95%
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Table 1. Characteristics of the patient and control groups at baseline.
AUD Total With Without P
Variables n % n % n %
Total 37,100 7,420 20.00 29,680 80.00
Sex 0.999
Male 34,445 92.84 6,889 92.84 27,556 92.84
Female 2,655 7.16 531 7.16 2,124 7.16
Age (mean ± SD, y) 43.29 ± 13.19 43.12 ± 11.85 43.32 ± 13.50 0.223
Age groups (y) 0.999
18–24 1,115 3.01 223 3.01 892 3.01
25–44 22,075 59.50 4,415 59.50 17,660 59.50
45–64 11,695 31.52 2,339 31.52 9,356 31.52
�65 2,215 5.97 443 5.97 1,772 5.97
Low-income <0.001
Without 36,675 98.85 7,300 98.38 29,375 98.97
With 425 1.15 120 1.62 305 1.03
Catastrophic illness 0.638
Without 34,040 91.75 6,798 91.62 27,242 91.79
With 3,060 8.25 622 8.38 2,438 8.21
DM <0.001
Without 34,586 93.22 6,781 91.39 27,805 93.68
With 2,514 6.78 639 8.61 1,875 6.32
HTN 0.219
Without 35,078 94.55 6,994 94.26 28,084 94.62
With 2,022 5.45 426 5.74 1,596 5.38
Hyperlipidemia <0.001
Without 36,107 97.32 7,066 95.23 29,041 97.85
With 993 2.68 354 4.77 639 2.15
Obesity 0.901
Without 37,084 99.96 7,417 99.96 29,667 99.96
With 16 0.04 3 0.04 13 0.04
Liver cirrhosis <0.001
Without 32,111 86.55 4,467 60.20 27,644 93.14
With 4,989 13.45 2,953 39.80 2,036 6.86
CKD <0.001
Without 36,224 97.64 7,294 98.30 28,930 97.47
With 876 2.36 126 1.70 750 2.53
COPD 0.756
Without 36,055 97.18 7,207 97.13 28,848 97.20
With 1,045 2.82 213 2.87 832 2.80
Tobacco use disorder 0.617
Without 37,099 100.00 7,420 100.00 29,679 100.00
With 1 0.00 0 0.00 1 0.00
Drug use disorder <0.001
Without 37,021 99.79 7,358 99.16 29,663 99.94
With 79 0.21 62 0.84 17 0.06
Anxiety <0.001
Without 36,961 99.63 7,359 99.18 29,602 99.74
With 139 0.37 61 0.82 78 0.26
(Continued)
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CI = 1.615–1.787, P<0.001), hyperlipidemia (AHR = 1.869, 95% CI = 1.735–2.012, P<0.001),
CKD (AHR = 1.395, 95% CI = 1.273–1.529, P<0.001), COPD (AHR = 0.883, 95% CI = 0.810–
0.963, P<0.001), anxiety (AHR = 2.044, 95% CI = 1.597–2.616, P<0.001), depression
(AHR = 1.642, 95% CI = 1.510–1.807, P<0.001), and CCI (AHR = 1.262, 95% CI = 1.215–
1.312, P<0.001) were associated with an increased risk of CVD development (Table 3).
Table 4 presents the results of analyses, stratified by demographic factors and comorbidities
and Fine and Gray’s competing risk model. The incidence of CVD was higher in the case
cohort than in the control cohort (3,801.64 vs. 2,884.75 per 105 person-years), and the overall
incidence of CVD was 1.447-fold higher in the case cohort than in the control cohort. The risk
of CVD is higher for low-income AUD patients than for those without low-income, compared
with those without low-income households (AHR = 3.383; 95% CI = 3.209–3.566; with com-
peting risk model AHR = 2.293, 95% CI = 2.034–2.564, P<0.001). In addition, the risk of CVD
was 2.806 times higher in obese patients with AUD, and 2.089 times higher by the competing
risk model (AHR = 2.806; 95% CI = 2.662–2.958; With competing risk model AHR = 2.089,
95% CI = 1.853–2.336, P<0.001).
We categorized the CVD cohort into CVD subgroups according to the ICD-9-CM codes.
Table 5 shows that patients with AUD in different CVD subgroups, such as CVD, IHD, and
stroke, were at a significantly higher risk than those without AUD: CVD (AHR = 1.447, 95%
Table 1. (Continued)
AUD Total With Without P
Variables n % n % n %
Depression <0.001
Without 36,590 98.63 6,980 94.07 29,610 99.76
With 510 1.37 440 5.93 70 0.24
CCI_R 0.40 ± 1.44 0.44 ± 1.09 0.39 ± 1.52 0.935
Season 0.999
Spring (Mar–May) 9,155 24.68 1,831 24.68 7,324 24.68
Summer (Jun–Aug) 8,745 23.57 1,749 23.57 6,996 23.57
Autumn (Sep–Nov) 9,320 25.12 1,864 25.12 7,456 25.12
Winter (Dec–Feb) 9,880 26.63 1,976 26.63 7,904 26.63
Location <0.001
Northern Taiwan 14,715 39.66 2,823 38.05 11,892 40.07
Middle Taiwan 10,284 27.72 2,149 28.96 8,135 27.41
Southern Taiwan 9,422 25.40 1,683 22.68 7,739 26.07
Eastern Taiwan 2,501 6.74 715 9.64 1,786 6.02
Outlets islands 178 0.48 50 0.67 128 0.43
Urbanization level <0.001
1 (highest) 12,782 34.45 2,416 32.56 10,366 34.93
2 14,876 40.10 2,933 39.53 11,943 40.24
3 3,290 8.87 668 9.00 2,622 8.83
4 (lowest) 6,152 16.58 1,403 18.91 4,749 16.00
Level of care <0.001
Hospital center 10,701 28.84 1,742 23.48 8,959 30.19
Regional hospital 12,466 33.60 3,228 43.50 9,238 31.13
Local hospital 13,933 37.56 2,450 33.02 11,483 38.69
P: Chi-squared/Fisher’s exact test for categorical variables and t-test for continuous variables. AUD = Alcohol use disorder, DM = diabetes mellitus,
HTN = hypertension, COPD = chronic obstructive pulmonary disease, CKD = chronic kidney disease, CCI = Charlson comorbidity index, SD = standard deviation.
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CI = 1.372–1.525, P<0.001), IHD (AHR = 1.304, 95% CI = 1.214–1.401, P<0.001), and stroke
(AHR = 1.640, 95% CI = 1.519–1.770, P<0.001). Moreover, our findings revealed significant
differences in the risks of CVD, IHD, and stroke among subgroups with and without AUD. Of
note, in the AUD-stratified analysis, the effects of alcohol abuse on the risk of CVD, IHD, and
stroke were not significantly different, similar to the results of the competing risk model: CVD
(AHR = 1.500, 95% CI = 1.330–1.677, P<0.001), IHD (AHR = 1.424, 95% CI = 1.251–1.607,
P<0.001), and stroke (AHR = 1.596, 95% CI = 1.400–1.806, P<0.001). These results show the
importance of abstinence from alcohol.
Discussion
Alcohol has a strong effect on the human body and mind, even at low doses; its neurotoxic,
hepatotoxic, and carcinogenic properties make it a potent risk factor for disease burden [7].
To the best of our knowledge, this is the first national cohort study to establish a substantial
correlation between AUD and CVD. The results indicate that patients with AUD have an
increased risk of CVD. In addition, the risk of developing IHD and stroke was significantly
higher in patients with AUD than in those without AUD.
Several epidemiological studies published in the previous three decades have reported a car-
dio protective effect of low-to-moderate alcohol intake; however, the number of published
studies alone is not an indicator of the strength of the evidence on this effect, let alone a causal
effect. Many drinkers cite health benefits, mainly for cardio-protection, as a reason for
Fig 2. Kaplan–Meier curve of the CVD due to alcohol-related diseases. Abbreviations: CVD, cardiovascular disease;
AUD, alchol use disorder.
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Table 2. Characteristics of the patient and control groups at the study endpoint.
AUD Total With Without P
Variables n % n % n %
Total 37,100 7,420 20.00 29,680 80.00
CVD <0.001
Without 29,140 78.54 5,388 72.61 23,752 80.03
With 7,960 21.46 2,032 27.39 5,928 19.97
Sex 0.999
Male 34,445 92.84 6,889 92.84 27,556 92.84
Female 2,655 7.16 531 7.16 2,124 7.16
Age (y) 49.18 ± 14.48 49.33 ± 12.51 49.14 ± 14.94 0.329
Age groups (y) <0.001
18–24 524 1.41 52 0.70 472 1.59
25–44 15,783 42.54 2,963 39.93 12,820 43.19
45–64 14,981 40.38 3,496 47.12 11,485 38.70
�65 5,812 15.67 909 12.25 4,903 16.52
Low-income <0.001
Without 36,092 97.28 7,048 94.99 29,044 97.86
With 1,008 2.72 372 5.01 636 2.14
Catastrophic illness <0.001
Without 29,827 80.40 5,417 73.01 24,410 82.24
With 7,273 19.60 2,003 26.99 5,270 17.76
DM <0.001
Without 32,560 87.76 6,298 84.88 26,262 88.48
With 4,540 12.24 1,122 15.12 3,418 11.52
HTN <0.001
Without 32,226 86.86 6,607 89.04 25,619 86.32
With 4,874 13.14 813 10.96 4,061 13.68
Hyperlipidemia <0.001
Without 35,790 96.47 7,209 97.16 28,581 96.30
With 1,310 3.53 211 2.84 1,099 3.70
Obesity 0.030
Without 37,072 99.92 7,419 99.99 29,653 99.91
With 28 0.08 1 0.01 27 0.09
Liver cirrhosis <0.001
Without 32,626 87.94 4,999 67.37 27,627 93.08
With 4,474 12.06 2,421 32.63 2,053 6.92
CKD <0.001
Without 35,413 95.45 7,008 94.45 28,405 95.70
With 1,687 4.55 412 5.55 1,275 4.30
COPD <0.001
Without 34,693 93.51 6,736 90.78 27,957 94.19
With 2,407 6.49 684 9.22 1,723 5.81
Tobacco use disorder 0.002
Without 37,089 99.97 7,413 99.91 29,676 99.99
With 11 0.03 7 0.09 4 0.01
Drug use disorder <0.001
Without 37,066 99.91 7,404 99.78 29,662 99.94
With 34 0.09 16 0.22 18 0.06
(Continued)
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drinking alcohol, despite often-raised concerns in the scientific literature regarding the causal-
ity of a cardio protective effect.
The effect of alcohol on the risk of IHD also makes this an intriguing and sometimes con-
troversial topic in terms of disease epidemiology and public policy. The quality of epidemio-
logical studies has substantially improved in the previous three decades. However, several
studies have used recent abstainers as the reference group, and this can lead to systematic bias
and erroneous conclusions; hence, high-quality epidemiological evidence is needed to provide
a clear picture of the topic. When examining average alcohol consumption in comparison to
lifetime abstinence, the relationship between alcohol consumption and IHD risk follows a J-
curve. The curve shows a more detrimental association with lower average alcohol levels for
women than for men [8]. This is consistent with our literature, which indicates that patients
Table 2. (Continued)
AUD Total With Without P
Variables n % n % n %
Anxiety 0.075
Without 36,936 99.56 7,378 99.43 29,558 99.59
With 164 0.44 42 0.57 122 0.41
Depression <0.001
Without 36,677 98.86 7,173 96.67 29,504 99.41
With 423 1.14 247 3.33 176 0.59
CCI_R 0.20 ± 0.52 0.37 ± 0.60 0.16 ± 0.48 <0.001
Season 0.124
Spring (Mar–May) 9,046 24.38 1,808 24.37 7,238 24.39
Summer (Jun–Aug) 9,458 25.49 1,816 24.47 7,642 25.75
Autumn (Sep–Nov) 9,600 25.88 1,959 26.40 7,641 25.74
Winter (Dec–Feb) 8,996 24.25 1,837 24.76 7,159 24.12
Location <0.001
Northern Taiwan 14,573 39.28 2,759 37.18 11,814 39.80
Middle Taiwan 10,450 28.17 2,180 29.38 8,270 27.86
Southern Taiwan 9,354 25.21 1,706 22.99 7,648 25.77
Eastern Taiwan 2,564 6.91 730 9.84 1,834 6.18
Outlets islands 159 0.43 45 0.61 114 0.38
Urbanization level <0.001
1 (highest) 12,217 32.93 2,152 29.00 10,065 33.91
2 15,618 42.10 3,152 42.48 12,466 42.00
3 3,140 8.46 661 8.91 2,479 8.35
4 (lowest) 6,125 16.51 1,455 19.61 4,670 15.73
Level of care <0.001
Hospital center 12,053 32.49 2,046 27.57 10,007 33.72
Regional hospital 14,788 39.86 3,244 43.72 11,544 38.89
Local hospital 10,259 27.65 2,130 28.71 8,129 27.39
Mortality <0.001
Without 34,361 92.62 6,376 85.93 27,985 94.29
With 2,739 7.38 1,044 14.07 1,695 5.71
P: Chi-squared/Fisher’s exact test for categorical variables and t-test for continuous variables.
AUD = Alcohol use disorder, CVD = cardiovascular disease, DM = diabetes mellitus, HTN = hypertension, COPD = chronic obstructive pulmonary disease,
CKD = chronic kidney disease, CCI = Charlson comorbidity index.
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Table 3. Risk factors for cardiovascular disease according to Cox regression analysis.
Variables Crude HR 95% CI 95% CI P Adjusted HR 95% CI 95% CI P
AUD
Without Reference Reference
With 1.334 1.268 1.403 <0.001 1.447 1.372 1.525 <0.001
Sex
Male 1.266 1.151 1.392 <0.001 1.206 1.096 1.327 <0.001
Female Reference Reference
Age groups (y)
18–24 Reference Reference
25–44 1.106 0.626 1.952 0.728 1.032 0.528 1.647 0.809
45–64 1.478 0.838 2.605 0.177 1.095 0.621 1.933 0.753
�65 2.160 1.225 3.811 0.008 1.511 0.856 2.668 0.155
Low-income
Without Reference Reference
With 0.942 0.840 1.056 0.304 1.001 0.891 1.124 0.986
Catastrophic illness
Without Reference Reference
With 1.018 0.967 1.073 0.487 1.001 0.949 1.056 0.963
DM
Without Reference Reference
With 1.730 1.645 1.819 <0.001 1.363 1.293 1.437 <0.001
HTN
Without Reference Reference
With 1.997 1.906 2.092 <0.001 1.699 1.615 1.787 <0.001
Hyperlipidemia
Without Reference Reference
With 2.282 2.124 2.451 <0.001 1.869 1.735 2.012 <0.001
Obesity
Without Reference Reference
With 1.103 0.500 1.631 0.735 1.136 0.407 1.333 0.312
Liver cirrhosis
Without Reference Reference
With 1.111 1.035 1.192 0.004 1.006 0.854 1.204 0.061
CKD
Without Reference Reference
With 1.423 1.300 1.558 <0.001 1.395 1.273 1.529 <0.001
COPD
Without Reference Reference
With 0.943 0.866 1.027 0.178 0.883 0.810 0.963 0.005
Tobacco use disorder
Without Reference Reference
With 1.005 0.021 1.045 0.055 1.025 0.032 1.597 0.136
Drug use disorder
Without Reference Reference
With 1.007 0.109 1.047 0.060 1.064 0.149 1.445 0.185
Anxiety
Without Reference Reference
With 1.771 1.385 2.265 <0.001 2.044 1.597 2.616 <0.001
(Continued)
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with AUD have a higher mortality rate than those without AUD, and the gender component is
also consistent with the literature.
Average alcohol consumption alone is not sufficient to describe the alcohol-IHD relation-
ship. Drinking patterns play an important role, and both episodic and chronic heavy drinking
may negate any beneficial effect of alcohol consumption on IHD risk and even elevate the risk
substantially. Nevertheless, in several epidemiological and short-term experimental studies,
relative to lifetime abstinence, having one to two drinks per drinking day without episodic
heavy drinking showed a beneficial association with the risk of IHD [8]. The alcohol-IHD rela-
tionship fulfills the criteria for a causal association as proposed by Hill [9]. Whether one
detects an inverse, U-shaped, or J-shaped relationship depends on the distribution of drinking
patterns in a given population. The prevalence of heavy drinking patterns has been on the rise
in several countries, such as Canada, the US, the UK, and several Eastern European and Asian
countries [10–13]. In the US, episodic heavy drinking is more common than chronic heavy
drinking [13]. In our study, we showed that alcoholic psychoses, alcohol dependence and
CVD (IHD, stroke) were significantly related, but alcohol abuse was not significantly related to
CVD (IHD, stroke), which we believe is because alcohol abuse is likely to cause death before
CVD is diagnosed.
Table 3. (Continued)
Variables Crude HR 95% CI 95% CI P Adjusted HR 95% CI 95% CI P
Depression
Without Reference Reference
With 1.672 1.332 2.099 <0.001 1.642 1.510 1.807 <0.001
CCI_R 1.221 1.181 1.262 <0.001 1.262 1.215 1.312 <0.001
Season
Spring (Mar–May) Reference Reference
Summer (Jun–Aug) 0.907 0.852 0.966 0.002 0.929 0.873 0.989 0.021
Autumn (Sep–Nov) 0.814 0.765 0.866 <0.001 0.799 0.751 0.850 <0.001
Winter (Dec–Feb) 0.976 0.917 1.038 0.438 0.944 0.887 1.004 0.069
Location Multicollinearity with urbanization level
Northern Taiwan Reference Multicollinearity with urbanization level
Middle Taiwan 1.140 1.079 1.204 <0.001 Multicollinearity with urbanization level
Southern Taiwan 1.121 1.059 1.186 <0.001 Multicollinearity with urbanization level
Eastern Taiwan 1.157 1.063 1.260 0.001 Multicollinearity with urbanization level
Outlets islands 1.003 0.704 1.429 0.987 Multicollinearity with urbanization level
Urbanization level
1 (highest) 0.894 0.838 0.954 0.001 1.042 0.969 1.121 0.266
2 0.995 0.937 1.056 0.861 1.113 1.045 1.186 0.001
3 0.771 0.700 0.848 <0.001 0.800 0.726 0.881 <0.001
4 (lowest) Reference Reference
Level of care
Hospital center 1.938 1.890 1.988 <0.001 1.647 1.605 1.692 <0.001
Regional hospital 1.331 1.256 1.410 <0.001 1.628 1.594 1.664 <0.001
Local hospital Reference Reference
Adjusted HR: Adjusted variables as listed in the table.
HR = hazard ratio, CI = confidence interval, AUD = alcohol use disorder, CVD = cardiovascular disease, DM = diabetes mellitus, HTN = hypertension,
COPD = chronic obstructive pulmonary disease, CKD = chronic kidney disease, CCI = Charlson comorbidity index.
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Table 4. Risk factors for cardiovascular disease stratified by variables according to Cox regression analysis with/without Fine and Gray’s competing risk model.
AUD With Without (Reference) Without competing risk model With competing risk model
Stratified Events PYs Rate (per 105
PYs)
Events PYs Rate (per
105 PYs)
Adjusted
HR
95%
CI
95%
CI
P Adjusted
HR
95%
CI
95%
CI
P
Total 2,032 53,450.61 3,801.64 5,928 205,494.31 2,884.75 1.447 1.372 1.525 <
0.001
1.500 1.330 1.677 <
0.001
Sex
Male 1,900 49,093.23 3,870.19 5,599 191,461.56 2,924.35 1.453 1.378 1.532 <
0.001
1.503 1.333 1.681 <
0.001
Female 132 4,357.38 3,029.34 329 14,032.75 2,344.52 1.419 1.346 1.496 <
0.001
1.485 1.317 1.661 <
0.001
Age groups (y)
18–24 3 55.70 5,385.93 28 524.15 5,342.01 1.107 1.050 1.167 0.002 1.312 1.164 1.467 <
0.001
25–44 497 15,474.61 3,211.71 1,245 51,964.65 2,395.86 1.472 1.396 1.552 <
0.001
1.513 1.342 1.691 <
0.001
45–64 1,004 29,798.39 3,369.31 2,513 102,384.35 2,454.48 1.507 1.430 1.589 <
0.001
1.531 1.358 1.712 <
0.001
�65 528 8,121.90 6,500.94 2,142 50,621.16 4,231.43 1.687 1.600 1.778 <
0.001
1.619 1.437 1.811 <
0.001
Low-income
Without 1,841 49,929.71 3,687.18 5,813 198,962.74 2,921.65 1.386 1.314 1.461 <
0.001
1.468 1.302 1.641 <
0.001
With 191 3,520.90 5,424.75 115 6,531.57 1,760.68 3.383 3.209 3.566 <
0.001
2.293 2.034 2.564 <
0.001
Catastrophic
illness
Without 1,265 37,050.17 3,414.29 4,510 161,935.27 2,785.06 1.346 1.277 1.419 <
0.001
1.446 1.283 1.618 <
0.001
With 767 16,400.43 4,676.71 1,418 43,559.05 3,255.35 1.577 1.496 1.663 <
0.001
1.566 1.389 1.751 <
0.001
DM
Without 1,397 43,942.70 3,179.14 4,309 171,996.76 2,505.28 1.393 1.322 1.469 <
0.001
1.472 1.306 1.646 <
0.001
With 635 9,507.91 6,678.65 1,619 33,497.56 4,833.19 1.517 1.439 1.599 <
0.001
1.536 1.362 1.717 <
0.001
HTN
Without 1,448 45,320.96 3,194.99 3,697 161,055.46 2,295.48 1.528 1.450 1.611 <
0.001
1.541 1.367 1.724 <
0.001
With 584 8,129.65 7,183.58 2,231 44,438.85 5,020.38 1.571 1.490 1.656 <
0.001
1.563 1.386 1.748 <
0.001
Hyperlipidemia
Without 1,818 51,537.74 3,527.51 5,199 194,566.19 2,672.10 1.449 1.375 1.528 <
0.001
1.501 1.332 1.679 <
0.001
With 214 1,912.87 11,187.39 729 10,928.13 6,670.86 1.841 1.747 1.941 <
0.001
1.692 1.501 1.892 <
0.001
Obesity
Without 2,031 53,436.82 3,800.75 5,918 205,141.82 2,884.83 1.447 1.372 1.525 <
0.001
1.500 1.330 1.677 <
0.001
With 1 13.79 7,251.07 10 352.50 2,836.92 2.806 2.662 2.958 <
0.001
2.089 1.853 2.336 <
0.001
Liver cirrhosis
Without 1,506 40,431.30 3,724.84 5,594 192,819.39 2,901.16 1.410 1.337 1.486 <
0.001
1.480 1.313 1.655 <
0.001
(Continued)
PLOS ONE Cardiovascular risk and alcohol use disorder
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Table 4. (Continued)
AUD With Without (Reference) Without competing risk model With competing risk model
Stratified Events PYs Rate (per 105
PYs)
Events PYs Rate (per
105 PYs)
Adjusted
HR
95%
CI
95%
CI
P Adjusted
HR
95%
CI
95%
CI
P
With 526 13,019.31 4,040.15 334 12,674.92 2,635.12 1.683 1.597 1.775 <
0.001
1.618 1.435 1.809 <
0.001
CKD
Without 1,865 50,571.41 3,687.85 5,530 196,456.66 2,814.87 1.438 1.364 1.516 <
0.001
1.495 1.327 1.672 <
0.001
With 167 2,879.20 5,800.22 398 9,037.65 4,403.80 1.446 1.372 1.525 <
0.001
1.499 1.330 1.677 <
0.001
COPD
Without 1,882 48,125.71 3,910.59 5,510 191,156.62 2,882.45 1.490 1.413 1.570 <
0.001
1.522 1.350 1.702 <
0.001
With 150 5,324.89 2,816.96 418 14,337.69 2,915.39 1.061 1.006 1.118 0.043 1.284 1.139 1.436 <
0.001
Tobacco use
disorder
Without 2,031 53,322.17 3,808.92 5,928 205,430.44 2,885.65 1.449 1.375 1.528 <
0.001
1.501 1.332 1.678 <
0.001
With 1 128.44 778.57 0 63.88 0.00 1 - - 0.898 1 - - 0.886
Drug use
disorder
Without 2,030 53,332.33 3,806.32 5,926 205,337.62 2,885.98 1.448 1.374 1.527 <
0.001
1.500 1.331 1.678 <
0.001
With 2 118.28 1,690.90 2 156.69 1,276.38 1.454 1.380 1.533 <
0.001
1.504 1.334 1.681 <
0.001
Anxiety
Without 2,009 53,153.43 3,779.62 5,881 204,592.76 2,874.49 1.444 1.369 1.522 <
0.001
1.498 1.329 1.675 <
0.001
With 23 297.18 7,739.52 47 901.56 5,213.21 1.630 1.546 1.718 <
0.001
1.592 1.412 1.780 <
0.001
Depression
Without 1,963 51,368.56 3,821.40 5,888 203,648.32 2,891.26 1.451 1.376 1.530 <
0.001
1.502 1.332 1.680 <
0.001
With 69 2,082.05 3,314.04 40 1,845.99 2,166.86 1.679 1.593 1.770 <
0.001
1.616 1.433 1.807 <
0.001
Season
Spring (Mar–
May)
563 12,838.73 4,385.17 1,467 48,180.82 3,044.78 1.581 1.500 1.667 <
0.001
1.568 1.391 1.753 <
0.001
Summer (Jun–
Aug)
441 12,373.98 3,563.93 1,471 52,006.30 2,828.50 1.383 1.312 1.458 <
0.001
1.466 1.301 1.640 <
0.001
Autumn (Sep–
Nov)
503 15,196.56 3,309.96 1,510 56,803.93 2,658.27 1.367 1.297 1.441 <
0.001
1.458 1.293 1.630 <
0.001
Winter (Dec–Feb) 525 13,041.33 4,025.66 1,480 48,503.26 3,051.34 1.448 1.374 1.527 <
0.001
1.501 1.331 1.678 <
0.001
Urbanization
level
1 (highest) 529 14,666.80 3,606.79 1,741 63,300.22 2,750.39 1.440 1.366 1.518 <
0.001
1.496 1.327 1.673 <
0.001
2 976 22,981.01 4,246.98 2,704 88,191.25 3,066.06 1.521 1.443 1.603 <
0.001
1.538 1.364 1.719 <
0.001
3 162 5,037.67 3,215.77 485 17,574.24 2,759.72 1.279 1.214 1.349 <
0.001
1.410 1.251 1.577 <
0.001
(Continued)
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Furthermore, the overall risk-benefit relationship of any form of alcohol consumption on
an individual level must be judged cautiously in light of the well-known detrimental effects of
alcohol use on other disease outcomes, such as injuries and cancer [3,14,15]. Hence, making
recommendations for clinical practice is challenging because of the simultaneous beneficial
and detrimental effects of, on average, low alcohol consumption, and because evidence from
randomized controlled trials on the long-term effects of alcohol consumption is lacking. This
has been confirmed in our study that chronic diseases are positively correlated in patients with
AUD.
There is no control mechanism for alcohol purchase as there is for prescription drugs
because alcohol is freely available for self- and over-medication. Therefore, alcohol consump-
tion should not be considered an option for the prevention of IHD. In terms of public alcohol
policy, the picture is clear: alcohol consumption should be as low as possible, no amount of
consumption is safe, and episodic and chronic heavy drinking should be strongly discouraged
[16,17].
There are two major stroke subtypes with differing etiologies: ischemic stroke (IS) (based
on ischemic disease processes) and hemorrhagic stroke (HS) (based on hemorrhagic processes,
i.e., bleeding processes). Given the higher prevalence of IS than HS, IS typically drives investi-
gations on stroke. With similarities in etiologies, one would expect IS to show a similar rela-
tionship with alcohol consumption as IHD. Indeed, several studies have demonstrated that the
association between average alcohol consumption and IS follows a J-curve [18–21]. The risk
for intracerebral and subarachnoid HS increased with every drink, and the consumption of
>48 g per day resulted in a relative risk of 1.67 (95% CI: 1.25–2.23) for intracerebral stroke
and 1.82 (95% CI: 1.18–2.82) for subarachnoid HS [20,22]. One study suggested that heavy
alcohol intake is associated with an increased risk of stroke and that low-to-moderate alcohol
intake may be protective against total and IS risk [23]. Another study suggested that an alcohol
intake of<15 g/day is associated with a reduced risk of total stroke and stroke mortality [24].
This point echoes our study.
Epidemiological studies indicate a complex relationship between various dimensions of
alcohol consumption and CVD outcomes. Most epidemiological studies have relied on a single
measurement of alcohol intake at baseline. It is assumed that the self-reported drinking levels,
Table 4. (Continued)
AUD With Without (Reference) Without competing risk model With competing risk model
Stratified Events PYs Rate (per 105
PYs)
Events PYs Rate (per
105 PYs)
Adjusted
HR
95%
CI
95%
CI
P Adjusted
HR
95%
CI
95%
CI
P
4 (lowest) 365 10,765.12 3,390.58 998 36,428.61 2,739.60 1.359 1.289 1.433 <
0.001
1.453 1.289 1.625 <
0.001
Level of care
Hospital center 633 14,984.41 4,224.39 1,952 67,396.82 2,896.28 1.601 1.519 1.688 <
0.001
1.578 1.400 1.764 <
0.001
Regional hospital 854 25,476.91 3,352.05 2,488 95,137.59 2,615.16 1.407 1.335 1.484 <
0.001
1.479 1.312 1.654 <
0.001
Local hospital 545 12,989.29 4,195.77 1,488 42,959.90 3,463.70 1.330 1.262 1.402 <
0.001
1.438 1.276 1.608 <
0.001
Adjusted HR: The multivariable analysis included sex, age, covariates, and comorbidities (hypertension [HTN], diabetes mellitus [DM], hyperlipidemia, ischemic heart
diseases, congestive heart failure, chronic obstructive pulmonary disease [COPD], liver disease, rheumatic disease, connective tissue disease, multiple sclerosis,
osteoporosis).
PYs = person-years, adjusted HR = adjusted hazard ratio (adjusted for the variables listed in Table 3), CI = confidence interval, AUD = alcohol use disorder,
CVD = cardiovascular disease, CKD = chronic kidney disease.
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Table 5. Sensitivity for factors of CVD subgroups among different AUD types by Cox regression analysis with/without Fine and Gray’s competing risk model.
Without competing risk model With competing risk model
Sensitivity
test
CVD
subgroups
AUD types Population Events PYs Rate (per
105 PYs)
Adjusted
HR
95%
CI
95%
CI
P Adjusted
HR
95%
CI
95%
CI
P
Any of the
listed
CVD Without
AUD
29,680 5,928 205,494.31 2,884.75 Reference
With AUD 7,420 2,032 53,450.61 3,801.64 1.447 1.372 1.525 <
0.001
1.500 1.330 1.677 <
0.001
Alcoholic
psychoses
2,313 621 15,806.69 3,928.72 1.515 1.391 1.650 <
0.001
1.535 1.339 1.744 <
0.001
Alcohol
dependence
4,862 1,358 35,631.90 3,811.19 1.434 1.350 1.524 <
0.001
1.493 1.319 1.676 <
0.001
Alcohol abuse 245 53 2,012.02 2,634.17 1.139 0.869 1.494 0.347 1.331 0.958 1.660 0.163
IHD Without
AUD
29,680 3,531 205,494.31 1,718.30 Reference
With AUD 7,420 1,084 53,450.61 2,028.04 1.304 1.214 1.401 <
0.001
1.424 1.251 1.607 <
0.001
Alcoholic
psychoses
2,313 300 15,806.69 1,897.93 1.282 1.135 1.448 <
0.001
1.412 1.210 1.634 <
0.001
Alcohol
dependence
4,862 757 35,631.90 2,124.50 1.331 1.228 1.444 <
0.001
1.439 1.258 1.632 <
0.001
Alcohol abuse 245 27 2,012.02 1,341.94 0.938 0.642 1.371 0.742 1.208 0.810 1.590 0.484
Stroke Without
AUD
29,680 2,557 205,494.31 1,244.32 Reference
With AUD 7,420 993 53,450.61 1,857.79 1.640 1.519 1.770 <
0.001
1.596 1.400 1.806 <
0.001
Alcoholic
psychoses
2,313 339 15,806.69 2,144.66 1.829 1.627 2.056 <
0.001
1.686 1.449 1.947 <
0.001
Alcohol
dependence
4,862 626 35,631.90 1,756.85 1.565 1.431 1.712 <
0.001
1.560 1.359 1.777 <
0.001
Alcohol abuse 245 28 2,012.02 1,391.64 1.439 0.990 2.091 0.056 1.496 1.003 1.964 0.048
Respectively IHD Without
AUD
29,680 4,016 207,458.20 1,935.81 Reference
With AUD 7,420 1,253 54,091.79 2,316.43 1.330 1.245 1.422 <
0.001
1.438 1.267 1.619 <
0.001
Alcoholic
psychoses
2,313 354 15,993.44 2,213.41 1.330 1.189 1.487 <
0.001
1.438 1.238 1.656 <
0.001
Alcohol
dependence
4,862 865 36,070.83 2,398.06 1.345 1.247 1.451 <
0.001
1.446 1.268 1.636 <
0.001
Alcohol abuse 245 34 2,027.52 1,676.93 1.039 0.741 1.458 0.823 1.271 0.878 1.640 0.359
Stroke Without
AUD
29,680 3,076 207,938.71 1,479.28 Reference
With AUD 7,420 1,178 54,275.30 2,170.42 1.645 1.534 1.765 <
0.001
1.599 1.407 1.804 <
0.001
Alcoholic
psychoses
2,313 385 16,001.33 2,406.05 1.794 1.609 2.001 <
0.001
1.670 1.440 1.921 <
0.001
Alcohol
dependence
4,862 765 36,261.95 2,109.65 1.602 1.477 1.737 <
0.001
1.578 1.380 1.790 <
0.001
Alcohol abuse 245 28 2,012.02 1,391.64 1.219 0.839 1.770 0.299 1.376 0.940 1.806 0.191
PYs = person-years, adjusted HR = adjusted hazard ratio (adjusted for the variables listed in Table 3), CI = confidence interval, IHD = ischemic heart disease,
CVD = cardiovascular disease, AUD = alcohol use disorder.
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including drinking patterns, preferably remain the same before and after the baseline measure-
ment; however, this is not the case for many people, and even lifetime abstainers are difficult
to identify [25]. However, in our study, we found that alcoholic psychoses and alcohol depen-
dence were significantly associated with CVD, and alcohol abuse was associated with a high
mortality rate; hence, our results indicate the importance of initial abstinence from alcohol.
This study has some limitations. Although the study extensively adjusted the multivariate
logistic regression models, there may still be residual confounders. First, the NHIRD does not
provide detailed information on variables such as socioeconomic factors, occupation,
unhealthy behaviors, amount of alcohol consumption, and the genetic background of the sub-
jects. In addition, the NHIRD does not collect data on sleep quantity. A previous study found
that sleep duration may be a risk factor for future alcohol-related diseases [26]. Additionally,
the study participants were selected on the basis of their medical records in the NHIRD. When
patients with CVDs or AUD choose not to undergo treatment in the hospital, their data are
not recorded in the NHIRD; hence, many cases may be missed. Finally, AUD may be divided
into different stages based on the patient’s temporal exposure to alcohol; this study did not
take the stage of alcohol use into account. Thus, our results may have underestimated the prev-
alence of CVDs and AUD.
Conclusion
This study found a significantly higher risk of diagnosis for CVD in patients with AUD, and
we also observed an association between alcohol-related diseases and the development of CVD
even after adjusting for several comorbidities and sensitivity test in a nationwide cohort. If the
association reflects a causal effect, these findings strongly suggest that clinicians should inform
the patients about the risk of CVD and the benefits of quitting alcohol and that the earlier you
stop drinking, the better the cardiovascular benefits.
Supporting information
S1 Table. Abbreviation and ICD-9-CM.
(DOCX)
Acknowledgments
The authors thank and appreciate the Health and Welfare Data Science Center, Ministry of
Health and Welfare (HWDC, MOHW), Taiwan, for the provision of access to the NHIRD.
Author Contributions
Conceptualization: Chieh Sung, Fu-Huang Lin, Wu-Chien Chien, Chien-An Sun, Chih-Erh
Weng.
Data curation: Chieh Sung, Chi-Hsiang Chung, Wu-Chien Chien.
Formal analysis: Chieh Sung, Chi-Hsiang Chung, Chang-Huei Tsao.
Methodology: Chi-Hsiang Chung, Fu-Huang Lin, Wu-Chien Chien, Chien-An Sun, Chang-
Huei Tsao.
Validation: Chi-Hsiang Chung, Chien-An Sun.
Writing – original draft: Chieh Sung, Fu-Huang Lin, Wu-Chien Chien, Chih-Erh Weng.
Writing – review & editing: Chieh Sung, Fu-Huang Lin, Wu-Chien Chien.
PLOS ONE Cardiovascular risk and alcohol use disorder
PLOS ONE | https://doi.org/10.1371/journal.pone.0276690 October 25, 2022 16 / 17
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