(2024) 22:205
Owaki et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03422-y

BMC Medicine

RESEARCH ARTICLE

Open Access

Effectiveness of genetic feedback on alcohol
metabolism to reduce alcohol consumption
in young adults: an open‑label randomized
controlled trial
Yukiko Owaki1,2* , Hisashi Yoshimoto2,3*, Go Saito2,4, Shohei Dobashi2, Satoshi Kushio5, Akihiro Nakamura5,
Takahiro Goto5, Yusuke Togo5, Kazumasa Mori5 and Hideki Hokazono5

Abstract
Background It is unclear whether brief interventions using the combined classification of alcohol-metabolizing
enzymes aldehyde dehydrogenase 2 (ALDH2) and alcohol dehydrogenase 1B (ADH1B) together with behavioral
changes in alcohol use can reduce excessive alcohol consumption. This study aimed to examine the effects of a brief
intervention based on the screening of ALDH2 and ADH1B gene polymorphisms on alcohol consumption in Japaneseyoung adults.
Methods In this open-label randomized controlled trial, we enrolled adults aged 20–30 years who had excessive
drinking behavior (average amount of alcohol consumed: men, ≥ 4 drinks/per day and women, ≥ 2 drinks/per day;1 drink = 10 g of pure alcohol equivalent). Participants were randomized into intervention or control group using
a simple random number table. The intervention group underwent saliva-based genotyping of alcohol-metabolizing
enzymes (ALDH2 and ADH1B), which were classified into five types. A 30-min in-person or online educational counseling was conducted approximately 1 month later based on genotyping test results and their own drinking records.
The control group received traditional alcohol education. Average daily alcohol consumption was calculated basedon the drinking diary, which was recorded at baseline and at 3 and 6 months of follow-up. The primary endpoint
was average daily alcohol consumption, and the secondary endpoints were the alcohol-use disorder identification
test for consumption (AUDIT-C) score and behavioral modification stages assessed using a transtheoretical model.
Results Participants were allocated to the intervention (n = 100) and control (n = 96) groups using simple randomization. Overall, 28 (29.2%) participants in the control group and 21 (21.0%) in the intervention group did not complete
the follow-up. Average alcohol consumption decreased significantly from baseline to 3 and 6 months in the intervention group but not in the control group. The reduction from baseline alcohol consumption values and AUDIT-C scoreat 3 months were greater in the intervention group than in the control group (p < 0.001). In addition, the behavioral
modification stages were significantly changed by the intervention (p < 0.001).

*Correspondence:
Yukiko Owaki
owaki-yu@gchs.ac.jp; Oowaki.yuki@md.tsukuba.ac.jp
Hisashi Yoshimoto
hisashiyoshi@md.tsukuba.ac.jp
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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Owaki et al. BMC Medicine(2024) 22:205

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Conclusions Genetic testing for alcohol-metabolizing enzymes and health guidance on type-specific excessive
drinking may be useful for reducing sustained average alcohol consumption associated with behavioral modification.
Trial registration R000050379, UMIN000044148, Registered on June 1, 2021.
Keywords Excessive drinking, ALDH2, ADH1B, Gene polymorphisms, Japanese, College students, Transtheoreticalmodel, Young adults, Randomized controlled trial

Background
Excessive alcohol consumption causes various healthproblems, and many studies on countermeasures havebeen performed [1–11]. Abnormal alcohol consumption
not only causes acute alcohol poisoning but also results
in dangerous behaviors in the intoxicated state, such as
those leading to injuries, rape, and suicide [1, 2]. Young
adulthood is especially recognized as a significant risk
period for increased levels of alcohol abuse behaviors
such as binge and high-intensity drinking [4]. Moreover,
the occurrence of high levels of alcohol consumption
with other transdiagnostic factors early in life can leadto alcohol-use disorder in later stages of life [5, 12], suggesting the importance of designing public health strategies to prevent high-risk levels of alcohol consumption inyoung adults.
Young people with alcohol-use disorders have beenreported to have low rates of health literacy [13]. Consequently, the effects of education on drinking behavior (i.e., brief interventions targeting young people) on
alcohol consumption have recently been examined. Brief
interventions for preventing high-risk levels of alcoholuse behavior include counseling sessions, which typicallyconsist of face-to-face counseling sessions delivered bya trained interventionist (e.g., physicians, psychologists,
nurses, or social workers), designed to increase people’s
awareness of their alcohol use and its consequences [14].
Previous meta-analytic evidence has shown that brief
alcohol interventions are efficacious in reducing alcohol
use among young adults [15, 16]; nonetheless, the effects
can be modest in magnitude [4]. Therefore, an effective
method other than brief interventions or methods that
promote the effectiveness of brief interventions should beestablished.
Focusing on the genetic polymorphisms of alcoholmetabolizing enzymes, East Asia, which accounts forapproximately 22% of the world’s population, has specific genetic polymorphisms in alcohol-metabolizing
enzymes aldehyde dehydrogenase 2 (ALDH2) and alcohol dehydrogenase 1 B (ADH1B) [6, 7]. Functional variants in ALDH2 and ADH1B make the population lesstolerant to alcohol consumption [8]. Recent studies
have indicated that genetic polymorphisms of alcoholmetabolizing enzymes, including ALDH2 and ADH1B,
are associated with various alcohol-related diseases

such as cancer, heart disease, and liver disease [7,9–11]. Understanding an individual’s alcohol-metabolizing enzyme genotype and implementing preventive
behaviors accordingly may contribute to solving alcohol-related health problems.
Hendershot et al. screened genetic polymorphisms of
ALDH2 in 200 Asian-American young adults and provided health risk information on alcohol-related cancer and addiction along with the test results [17]. The
results showed that participants with low ALDH2 activity(ALDH2*1/*2 genotypes) significantly reduced the frequency and amount of alcohol consumption 30 days after
the intervention [17]. Furthermore, Ishikawa showed thatthe average daily alcohol consumption of the intervention group that received health guidance in addition to
information on ALDH2 polymorphisms was significantly
lower than that of the control group that did not receive
genetic information 6 months after the intervention[18]. To the best of our knowledge, no intervention has
been designed based on genetic information feedbackof ADH1B polymorphism, which is also associated withalcohol-related diseases, similar to those associated with
ALDH2. ALDH2 and ADH1B genetic polymorphisms are
independent of each other, and both genotypes may be
dysfunctional or only one may degenerate. Hence, genetic
polymorphism testing for both genotypes can further
subdivide the classification of alcohol tolerance, and brief
interventions based on the subdivisions may be useful
as novel approaches to reduce alcohol consumption. In
addition, a previous study has suggested that reduction
in alcohol consumption due to a brief intervention using
genetic information feedback was attributed to behavioral changes toward alcohol consumption [19]; however,no study has investigated the effects of brief interventions on alcohol-use behavior in relation to alterations inbehavioral modification stages.
Based on this background, we developed a method
to classify five major types of drinking habits based on
ALDH2 and ADH1B gene test results and implement a
simple intervention based on these five types (Table 1)[20]. Therefore, in this study, we aimed to examine theeffects of a brief intervention based on the screening of
ALDH2 and ADH1B gene polymorphism on alcohol consumption (which is called screening and brief intervention [SBI]) in Japanese young adults aged 20–30 years

Owaki et al. BMC Medicine(2024) 22:205

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Table 1 Composition of the five genotypes of alcohol-metabolizing enzymes and associated health risks [20, 22]
Aldehyde dehydrogenase

Alcohol dehydrogenase

Classification
by type (%)aComments on constitution and health risks

ALDH2 *1/*1 (activity)

ADH1B *1/*1 (low activity)

A (3%)

Highest risk of alcohol addiction

ADH1B *1/*2 (activity)

B (50%)

Decomposition of alcohol and acetaldehyde is fast. Care should be taken
not to drink too much

ADH1B *1/*1

C (3%)The decomposition of alcohol and acetaldehyde is slow, and the cancer risk
is high, owing to acetaldehyde

ADH1B *1/*2

D (40%)

Face turns reddish immediately. Nausea and other discomfort can occur.
Symptoms and health problems are likely to occur

E (4%)

Drinking is intolerable because aldehyde cannot be decomposed
Very small amounts of alcohol cause unpleasant symptoms such as hot
flushes, drowsiness, palpitations, and nausea
Even a small amount of alcohol poses a risk of acute alcohol intoxication

ADH1B *2/*2 ( activity)
ALDH2 *1/*2 (low activity)

ADH1B *2/*2
ALDH2 *2/*2 (Inactive)

ADH1B *1/*1
ADH1B *1/*2
ADH1B *2/*2aPercentage of Japanesewith excessive drinking habits in relation to behavioral
change in a transtheoretical model (TTM) [21].

Methods
This study was an open-label, randomized controlled trial
and was conducted at University of Tsukuba according to
the study protocol published in 2022 [22].
Participants

The inclusion criteria were as follows: (i) undergraduate and postgraduate students, faculty, and staff of the
University of Tsukuba; (ii) those with excessive drinking habits of pure alcohol consumption (average amountof alcohol consumed: men, ≥ 4 drinks/per day andwomen, ≥ 2 drinks/per day; 1 drink = 10 g of pure alcohol
equivalent), which increases the risk of lifestyle-relateddiseases according to Japanese standards; (iii) those aged
20–30 years; and (iv) those with good health with nohealth interference in their daily life, studies, or employment and with no history of prior or current illness. The
exclusion criteria were as follows: (i) those who did notwish to disclose their alcohol constitution test results and(ii) those with difficulty communicating in Japanese.
Participation was terminated if the participants opted
to discontinue at any point in the study.
Informed consent and randomization

Potential study participants, recruited through poster
advertisement and snowball sampling at the university,
received a written explanation of the study and agreed toparticipate by signing a consent form. Participants wereassigned to the intervention (individual intervention on
drinking habits was provided; at enrollment, participants underwent genetic testing for alcohol-metabolizing
enzymes using saliva, and approximately 1 month later,
they received a brief intervention that included the test

results and intervention instructions) or control (noconstitutional testing was conducted during the study
period, and only conventional educational materials were
provided) group.
Measures

A self-administered questionnaire anonymized by ID
number was used to investigate the following: basic
attributes (age, sex, main field of study, living with family, employment status, participation in clubs, hobbies,
and activities) and outcome measures (primary outcome:average daily alcohol consumption; secondary outcomes:
stages of change, Alcohol Use Disorders Identification
Test for consumption [AUDIT-C], and AUDIT as screening tests for excessive alcohol consumption) [23]. The
researcher (primarily the first author) sent the questionnaire and the drinking calendar to the participants via
e-mail. The participants were requested to fill out thequestionnaires and return them within 1 week of receiving them. If the filled questionnaires were not returned,
an optional reminder e-mail was sent within 1 month.
Data were collected from the returned attachments.
The intervention group was surveyed 1 month after theintervention and 3 and 6 months after obtaining consent
for participation in this study. Data from a calendar thatrecorded the nature and amount of alcohol consumption were collected concurrently. In the control group,
data from a calendar that simultaneously recorded alcohol consumption and quantity were collected at 1, 3, and6 months after the participants provided consent forparticipation in this study. Initially, we planned to use
the values at 1 month after the intervention for comparisons between the groups, but the timing of the observation was different from that of the control group due toa slight delay in the return of the genotype test results.

Owaki et al. BMC Medicine(2024) 22:205

Therefore, only the data at baseline and at 3 and 6 months
were used for the analysis in this study.
Primary outcome

The primary outcome was the average daily alcohol consumption (g of pure alcohol equivalent) of participants,
which was calculated using a generally accepted formulaand the following procedure.
A drinking calendar showed drinking examples thatwere similar to those in the questionnaire, and participants were requested to record the content (type andconcentration of alcohol) and amount of alcohol consumed on each drinking day during a 6-month period.
From the calendar records, the net alcohol consumed(g) was calculated using the following generally accepted
formula: net alcohol content (g) = volume of liquor(mL) × degree or %/100 × 0.8 (specific gravity). This formula was used to calculate the net alcohol content foreach study period, which was divided by the number ofdrinking days, to calculate the average daily alcohol consumption (g of net alcohol).
The following examples are included in the alcohol
consumption questionnaire.
Conversion for alcoholic beverages was as follows:
sake: 15% alcohol content, 180 mL = 2 drinks; beer: 5%
alcohol content, 500 mL [medium bottle, medium mug,or canned beer] = 2 drinks; whiskey and brandy: 43%
alcohol by volume, 60 mL [double] = 2 drinks; shochu:
25% alcohol by volume, 100 mL [half cup] = 2 drinks;
canned cocktail: 7% alcohol by volume, 350 mL = 2
drinks; cocktails, fruity tastes, etc.: 5% alcohol by volume,
350 mL [can] = 1.5 drinks, 500 mL [can] = 2 drinks; wine:
12% alcohol by volume, 150 mL [glass] = 1.5 drinks; andumeshu: 12% alcohol by volume, 90 mL [small glass] = 1
drink. In the survey, respondents were asked to indicate
the average number of drinks they consumed per day (1drink = 10 g pure alcohol) on their drinking days during
the past month.
Secondary outcomes

The AUDIT and AUDIT-C were assessed. The AUDIT(10 items, 40-point scale) was developed by the World
Health Organization (WHO) as a screening method forexcessive drinking. The question items and response
options for AUDIT are shown in Additional file 1:
Table S1; they are rated by summing the number of relevant options for the question items. The validity andeffectiveness of the Japanese version of the AUDIT havebeen validated by Hiro et al. [24]. At present, in terms ofscreening and brief intervention for excessive drinking
in Japan, an AUDIT score of 7 or less is considered lowrisk drinking, whereas an AUDIT score of 8 or more suggests excessive drinking [25]. The AUDIT-C is a simple

Page 4 of 15

screening test (12-point scale) consisting of the AUDITQuestions 1–3. In Japan, a score of ≥ 6 for men and ≥ 4
for women is considered excessive drinking [25].
Moreover, the five stages of change in drinking behavior include precontemplation, contemplation, preparation, action, and maintenance [21]. The stages ofchange model is a theory used to understand current
motivational perceptions and stages of behavior change
to provide more appropriate health guidance. Specifically, the survey required responses from the following
five options: “precontemplation” with no intention tochange behavior within 6 months, “contemplation” withintention to change behavior within 6 months, “preparation” with intention to change behavior within 1 month,“action” with less than 6 months since behavioral change,and “maintenance” with more than 6 months sincebehavioral change.
Alcohol‑metabolizing enzymes related to genotype testing
and five types of classification

All participants in the intervention group underwent
genetic testing for alcohol-metabolizing enzymes andreceived a report of their results. In the control group,
those who requested testing were tested at the end of thestudy period, and they received their results.
Those with type A had low ADH1B activity and high
ALDH2 activity, resulting in long-lasting effects of alcohol and rapid degradation of acetaldehyde. Therefore,
those with type A had the highest preference for alcoholand were most likely to become dependent. Those with
type B had both ADH1B and ALDH2 activities and werethe most susceptible to drinking among the five types;
therefore, such individuals needed to be careful not todrink excessively. Those with type C had slow decomposition of alcohol and acetaldehyde; therefore, it was difficult for them to be aware of acetaldehyde generation anddiscomfort, but they had a risk of carcinogenesis. Thosewith type D showed rapid alcohol decomposition andslow acetaldehyde decomposition. Therefore, their bloodconcentration of acetaldehyde increases, causing immediate facial redness, nausea, and other unpleasant symptoms and acetaldehyde-related health problems. Thosewith type E had inactive ALDH2, could not decompose
acetaldehyde, and were at risk of acute alcohol intoxication, even with small amounts of alcohol.
Intervention using genotype test information

Individual interventions using genotype results were conducted at University of Tsukuba according to the study
protocol [22]. Both the intervention and control groups
lived a normal life with no special restrictions. The intervention was based on individual test results, baseline
questionnaire data, drinking calendar records, and healthOwaki et al. BMC Medicine(2024) 22:205

guidance materials and included conventional teaching materials [26]. The common and main content of theintervention for the five types of alcohol-metabolizing
enzyme genotyping concerned how to deal with alcohol according to the participants’ alcohol consumption,mechanism of alcohol absorption, and alcohol intake
in nutrition, eating, and drinking. The specifics are as
follows:• Alcohol constitution of the participants (from A to
E and characteristics of each constitution type)—
alcohol degradation mechanisms, metabolic enzyme
functions, and constitution based on the combination of alcohol-metabolizing enzymes• Disease risks associated with alcohol consumption
and points to note regarding alcohol constitution(comments were provided by the five constitution
types)• How to deal with alcohol according to participants’alcohol constitution, how to plan drinking/eating interms of nutrition, and how to slow down alcohol
metabolism• Drinking habit screening test (AUDIT-C)• How to drink appropriately and rest days
• Inappropriate drinking (including drinking during
pregnancy and lactation, drunk driving, alcohol harassment, and habitual heavy drinking)Interventions were primarily conducted by the firstauthor who is a licensed nurse. Before the intervention,
two main physicians in charge of the outpatient alcohol
consumption reduction clinic at University of Tsukuba
Hospital trained and advised the researcher on the brief
intervention.
Of note, face-to-face (n = 18) or online interventions(n = 82) were conducted according to the participants’needs as the study was conducted during the coronavirus
disease 2019 pandemic and infection control measures
were required.
Conventional teaching materials for the control group

In the control group, conventional educational materials (an alcohol handbook for college students) [26] were
provided to the participants to read during the consent
process.
The main components of the conventional material[26] are as follows:• Recollections of the loss of a child or parent due to afatal accident or illness caused by drinking• Current drinking styles of college students (results of
the Drinking Attitudes Survey)

Page 5 of 15• Conversion of the amount of alcohol consumed on agiven drinking day using the drinking amount conversion chart and confirmation of current drinking
amount using the drinking amount ranking chart(males and females)• Stages from tipsiness to death due to acute alcohol
intoxication• Monitoring and dealing with binge drinking, alcohol,
and harassment• Devices and specific examples of appropriate drinking at drinking parties• Knowledge about alcohol (alcohol-related problems:such as violence, depression, sleep-related problems,family and workplace problems, and mental andphysical illness)

Sample size

The total sample size of this study was 199, calculated with an effect size of 0.2, an alpha error of 0.05,
and a power of 0.8 using a power analysis software (G*Power3.1, University of Kiel, Germany) [27], as reported
previously [17, 28]. In total, 200 participants (100 in theintervention and control groups) were enrolled.
Statistical analysis

The available data for all participants were included
according to the original allocation in an intention-totreat analysis. The unsubmitted data of participants who
dropped out were treated as missing data. Statisticalanalysis for the continuous variable scale was performed
after confirmation of normality using the Shapiro–Wilk
test. None of the continuous variables was normally distributed. For the baseline characteristics of age, AUDITC, AUDIT, average number of drinks, and average daily
alcohol consumption, Wilcoxon’s rank-sum test was used
for comparisons between the control and intervention
groups. For the baseline characteristics of distributionof females, carrier status, status of employment, circle
and hobby activities, and status of living (with family ornot), chi-square test was used for comparisons between
the groups. Moreover, the differences in the distributionof classifications based on alcohol-metabolizing enzyme
genotype testing and behavioral alteration stage between
the groups were compared using Fisher’s exact probability test.
The time-course changes in outcomes (e.g., alcohol
consumption and AUDIT-C) were compared between
the two groups using mainly two-way (group × time)
analysis of variance (ANOVA). However, as many dropouts were unexpectedly observed in the current study, we
judged that two-way ANOVA was not suitable for comparison between the groups for which randomization

Owaki et al. BMC Medicine(2024) 22:205

might not be preserved. Therefore, the analytical method
was divided into two viewpoints as follows: time-series
comparisons within the same group and group comparisons at the same time point, in which all available data foreach analysis were used. Thus, the time-course changes inalcohol consumption and AUDIT-C score regarding thevalues at baseline versus at 3 and 6 months of follow-up
and the values at 3 versus 6 months were compared using
Wilcoxon’s signed-rank test with Bonferroni correction
within the same group, respectively. For group comparisons at the same time point, the changes from baseline
alcohol consumption values and AUDIT-C scores werecompared at 3 and 6 months.
The statistical significance level was set at less than 5%(p < 0.05), and Stata/SE 18.0 (Stata Corp., USA) was used
for the analysis. All graphs were generated using Prism
version 9.5.1. (GraphPad, USA).
Ethics and dissemination

This study was approved by the Ethics Committee of the
University of Tsukuba (Human Genome and Genetic
Analysis Research). The recruitment of participants
began in July 2021, and the survey was completed in June
2023.
The researchers strictly maintained a list of participants matched by ID numbers and managed the data sothat participant names and genotypic constitution information were not directly linked. Data entry and analysis
were performed by the first author using a personal computer with security measures, and the results were confirmed by the co-researcher.
This study complied with the Ethical Guidelines for
Human Genome and Genetic Analysis Research issued
by the Ministry of Health, Labor, and Welfare of Japan.
Enrollment in the UMIN clinical trial was completed onJune 1, 2021.

Results
Participants

Figure 1 presents the Consolidated Standards of Reporting Trials flowchart for this study. Undergraduate andpostgraduate students and university staff were invited
to participate in the study between July 2021 and October 2022, and enrollment was completed by November
2022. The final follow-up was completed in June 2023.
Consequently, 204 patients were enrolled in this study. Of
these, four patients withdrew from this trial during thestudy period after enrollment (three withdrew because
they were too busy to drink and one withdrew because
a physician ordered abstinence from alcohol due toalcohol-related illness), and four who could not be contacted were excluded from the analysis. In total, 196 individuals were selected for this study. Participants in the

Page 6 of 15

intervention group were randomly allocated using a simple randomization method (intervention group, n = 100;control group, n = 96) and underwent the genotype test
(ALDH2 and ADH1B: 5 types) with consent at enrollment. Individual intervention was conducted after thetest results were returned (approximately 1 month later).
Finally, 28 (29.2%) participants in the control group and21 (21.0%) in the intervention group did not complete
follow-up assessments.
Table 2 shows the baseline characteristics (i.e., age, sex,social position, AUDIT, AUDIT-C score, average daily
average number of drinks, and baseline average daily
alcohol consumption per month [calculated in g purealcohol equivalent as “total alcohol equivalent for eachstudy period divided by the number of drinking days”]).
No significant differences were observed between thecontrol and intervention groups in terms of age, carrier status, employment, participation status in circle
or hobby activities, current living status, proportion ofclassification based on alcohol-metabolizing enzyme
genotype testing, AUDIT and AUDIT-C scores, number of drinks consumed, and alcohol consumption value.
Unexpectedly, the proportions of men and women in thegroups were significantly different between the control
and intervention groups (p < 0.01).
Time‑course changes in alcohol consumption and AUDIT‑C
score

Figure 2 shows the time-course changes in alcohol consumption from baseline to 3 and 6 months and from
3 to 6 months. In the control group, no significant differences were observed between any of the time points(Fig. 2a–c). Alcohol consumption in the intervention
group decreased from baseline to 3 and 6 months (bothp < 0.01, Fig. 2d, e). However, it was significantly higher at
6 months than at 3 months (p < 0.05, Fig. 2f ).
Figure 3 demonstrates the time-course changes in the
AUDIT-C score from baseline to 3 and 6 months andfrom 3 to 6 months. In the control group, no significant
differences were observed in AUDIT-C scores between
baseline and 3 months and between 3 and 6 months(Fig. 3a, c), whereas those at 6 months were significantly
decreased from baseline (p < 0.01, Fig. 3b). In the intervention group, the AUDIT-C score was significantly
reduced from baseline to 3 and 6 months (both p < 0.001,
Fig. 3d, e) but was not significantly different between 3
and 6 months (Fig. 3f ).
Differences in changes in alcohol consumption
and AUDIT‑C scores from baseline between the control
and intervention groups

Unexpectedly, many dropouts were observed in the current study; thus, the changes in alcohol consumption andOwaki et al. BMC Medicine(2024) 22:205

Fig. 1 Consolidated Standards of Reporting Trials (CONSORT) flow chart

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Owaki et al. BMC Medicine(2024) 22:205

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Table 2 Baseline characteristics of the participants (n = 196)
Control (n = 96)

Intervention (n = 100)n

% or mean (median)n% or mean (median)Age (years)

96

22.4 (22.0)

100

22.7 (22.0)

0.319aFemale

64

66.7

47

47.0

0.005b

Variable

P-value

0.798bCarrier status
Undergraduate student

63

65.6

61

61.0

Graduate student

21

21.9

25

25.0

Faculty and staff

12

12.5

14

14.0
0.718b

Employment
Not working

13

13.5

15

15.0

Part-time job

71

74.0

69

69.0

University staffs, resident, teachers or part-time lecturers

12

12.5

16

16.0
0.720bCircle and hobby activities
Have

60

62.5

60

60.0

Not

36

37.5

40

40.0
0.604bCurrently status of living
Living with family

16

16.7

14

14.0

Living alone

80

83.3

86

86.0
0.979cClassification based on alcohol-metabolizing enzyme genotype testing
A

4

4.2

5

5.0

B

54

56.3

78

78.0

C

1

1.0

1

1.0

D

12

12.5

16

16.0

E

0

0.0

0

0.0

AUDIT

96

8.6 (8.0)100

9.1 (7.0)

0.543a

AUDIT-C96

5.6 (5.5)100

5.8 (6.0)

0.443aAverage number of drinks

96

5.4 (5.0)100

5.8 (5.2)

0.250aAverage daily alcohol consumption (g)9642.3 (39.6)

100

44.6 (38.8)

0.603aStatistical comparisons between control and intervention groups using aWilcoxon’s rank-sum test, bchi-square test, and cFisher’s exact probability test, respectively

AUDIT-C scores from baseline were compared between
the control and intervention groups at 3 and 6 months,
respectively (Fig. 4). We also compared the baseline characteristics after eliminating dropouts, and significant differences in the proportion of men and women between
the groups remained at 3 and 6 months (Additional file 1:
Table S2 and S3). Since sex is assumed to have a significant effect on the change in alcohol consumption during
alcohol reduction interventions [29], we compared thegroup differences in the changes in alcohol consumption
and AUDIT-C score at 3 and 6 months using one-way
analysis of covariance with sex as a covariate. Changes
in alcohol consumption from baseline were significantly
lower in the intervention group than in the control
group at 3 months (p < 0.05, Fig. 4a) but not at 6 months(Fig. 4b). The changes in AUDIT-C scores from baseline
were significantly lower in the intervention group thanin the control group at both 3 and 6 months (p < 0.01,
Fig. 4c; p < 0.01, Fig. 4d, respectively).

Alteration in behavioral modification stage
regarding alcohol‑use behavior due to the brief
intervention throughout the study period

The alteration in the behavioral modification stage
regarding alcohol-use behavior due to the brief intervention throughout the study period is shown in Table 3.
No significant difference was observed in the distribution of stages of change (precontemplation, contemplation, preparation, action, and maintenance) between thetwo groups at baseline. The differences in the proportionof the distribution of these behavioral modification stages
between the control and intervention groups were significant at 3 months (p < 0.001) but marginally significant at
6 months (p = 0.059).

Discussion
To the best of our knowledge, this is the first study to
examine the effectiveness of five types of genotypes
based on the alcohol-metabolizing enzyme (i.e., ALDH2

Owaki et al. BMC Medicine(2024) 22:205

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Fig. 2 Time-course changes in alcohol consumption in the control and intervention groups. a–c The results for the control group. d–f The results
for the intervention group. Baseline vs. 3 months (a and d), baseline vs. 6 months (b and e), and 3 vs. 6 months (c and f). The box represents
the interquartile range (IQR), with the lower edge of the box indicating the 25th percentile and the upper edge indicating the 75th percentile.
The whiskers extend from the box to the 2.5th and 97.5th percentiles. Wilcoxon’s signed-rank test was performed with the Bonferroni correction.
Briefly, since time-series comparisons were conducted three times within the same group, the p-values calculated by Wilcoxon’s test were tripled,
and those less than 0.05 were considered statistically significant. n.s., not significant; ***p < 0.001, **p < 0.01, and *p < 0.05

and ADH1B) and personalized brief interventions using
genotype information (SBI) on alcohol consumption anddrinking behavior in college students and adults aged
20–30 years. Although alcohol consumption and AUDITC scores did not change notably in the control group,
they significantly decreased in the intervention group.
Moreover, the reduction in these indicators from baseline
values was significantly greater in the intervention groupthan in the control group. We also observed alterationsin the behavioral modification stages of alcohol use by

the intervention. These results suggest that a personalized brief intervention using alcohol metabolism-related
genotype information is an effective strategy for reducing
alcohol consumption and preventing the development ofhigh-risk excessive drinking in young adults.
In this study, participants were assigned to two groups
using a simple randomization table to eliminate any
bias in the grouping. Theoretically, this randomization
method is known to eliminate differences between groups
for each indicator [30], and no differences, except for theOwaki et al. BMC Medicine(2024) 22:205

Page 10 of 15

Fig. 3 Time-course changes in AUDIT-C scores in the control and intervention groups. a–c The results for the control group. d–f The results
for the intervention group. Baseline vs. 3 months (a and d), baseline vs. 6 months (b and e), and 3 vs. 6 months (c and f). The box represents
the interquartile range (IQR), with the lower edge of the box indicating the 25th percentile and the upper edge indicating the 75th percentile.
The whiskers extend from the box to the 2.5th and 97.5th percentiles. Wilcoxon’s signed-rank test was performed with the Bonferroni correction.
Briefly, since time-series comparisons were conducted three times within the same group, the p-values calculated by Wilcoxon’s test were tripled,
and those less than 0.05 were considered statistically significant. n.s., not significant; ****p < 0.0001, **p < 0.01

sex composition ratio, were found between the groups.
Although the amount of alcohol consumption was largely
different between sexes [29], owing to the fact that no differences were found in terms of alcohol consumption atbaseline between the groups, and that we used sex as an
adjustment variable in the subsequent analysis of comparisons between groups, we believe that randomization itself was not a problem in this study. However, thisstudy had a relatively large number of dropouts during
the study period (29.2% in the control group and 21.0%

in the intervention group). As described in Fig. 1, thecontrol group had a large dropout rate at 1 month after
randomization, while the intervention group had almost
no dropout until the genetic test results were disclosed,
after which a large dropout was observed during thefollow-up period. Collectively, we speculate that the difficulty in maintaining and recording drinking calendar
and answering the questionnaire caused the dropout,rather than the genetic testing or brief intervention itself.
In this study, many participants used a calendar and did

Owaki et al. BMC Medicine(2024) 22:205

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Fig. 4 Changes in alcohol consumption and AUDIT-C scores from baseline between the control and intervention groups. a, b The changes
in alcohol consumption from baseline values at 3- and 6-month follow-up, respectively. c, d The changes in the AUDIT-C score from baseline
values at 3- and 6-month follow-up, respectively. The box represents the interquartile range (IQR), with the lower edge of the box indicating
the 25th percentile and the upper edge indicating the 75th percentile. The whiskers extend from the box to the 2.5th and 97.5th percentiles.
Statistical comparisons between the two groups in each panel were conducted using analysis of covariance (ANCOVA), with sex as a covariate. n.s.,
not significant; **p < 0.01, *p < 0.05

not forget to record their drinking behavior on drinking
occasions. Nevertheless, an electronic tool that can easily convert and record the amount of alcohol consumed
could be used in future studies.
However, as shown in Additional file 1:Tables S2 andS3, no differences in baseline characteristics, except forsex, were observed in the populations excluding dropouts, and the sample size for the alcohol consumption
and AUDIT-C score was large enough for analysis byseparating the analysis perspectives (i.e., comparing
time-series within the same group and comparing thechange from baseline at the same time point between thegroups). Hence, the dropout of participants in this study
did not appear to have a significant effect on the results.
In the intervention group, alcohol consumption
and AUDIT-C scores decreased significantly at 3 and6 months compared with those at baseline. Moreover,
the reduction in alcohol consumption (at 3 months) andOwaki et al. BMC Medicine(2024) 22:205

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Table 3 Alteration in behavioral modification stage distribution at baseline and 3- and 6-month follow-up
Time-course

Behavioral modification
stage

ControlIntervention

P-value

n

%

n

%

Precontemplation

85

89.5

86

86.0

Contemplation

5

5.3

4

4.0

Preparation

3

3.2

3

3.0

Action

1

1.0

4

4.0

Maintenance

1

1.0

3

3.0

Precontemplation

56

78.9

46

56.1

Contemplation

4

5.6

13

15.9

Preparation

7

9.9

2

2.4

Action

3

4.2

19

23.2

Maintenance

1

1.4

2

2.4

Precontemplation

48

70.6

41

51.9

Contemplation

7

10.3

13

16.5

Preparation

6

8.8

4

5.2

Action

5

7.4

16

20.2

Maintenance

2

2.9

5

6.3

Baseline(control: n = 95; intervention: n = 100)

0.653

3 months(control: n = 71; intervention: n = 82)

0.002

6 months(control: n = 68; intervention: n = 79)

0.059

Statistical comparisons between the control and intervention groups were performed using the Fisher’s exact probability test

AUDIT-C score (at 3 and 6 months) was significantly
greater in the intervention group than in the control
group. These results suggest that SBI in the current
study may be a useful approach for reducing alcohol
consumption among young excessive drinkers. Previous
studies have reported that brief intervention targeted at
individual ALDH2 genetic polymorphism significantly
decreased alcohol consumption [17, 18], which supports our present findings. However, these previous
studies did not compare the changes in alcohol consumption between the control and intervention groups,
indicating that the effectiveness of brief interventions
with genetic information was not clearly proven. Ourpresent results strictly prove that SBI induced a greater
reduction in alcohol consumption than the control condition. Previous studies have conducted SBI using only
ALDH2 genetic polymorphisms but did not use ADH1B
genotype information [17, 18]. In contrast, participantsin the intervention group received a brief intervention with genetic polymorphism information regarding
both the ALDH2 and ADH1B genetic polymorphisms.
Moreover, the biological and genetic information on
the pathogenesis of alcohol-related diseases and therationale for prevention as an intervention using easyto-understand pamphlets were also provided with theabove feedback from the genotype screening. Thus,

personalized interventions may have led to a greater
reduction in alcohol consumption in this study.
Regarding the reasons for the reduction in alcohol
consumption and AUDIT-C score by SBI, we focusedon behavioral changes in alcohol use using a TTM [21].
Interestingly, the results showed a significant difference in the composition of the behavioral change stages
between the intervention and control groups at 3 months,
suggesting that the proportion of “Precontemplation”
stage decreased and that of “Action” stage increasedin the intervention group. Therefore, it is possible thatthese behavioral changes contributed to the decrease inalcohol consumption values and AUDIT-C scores in theintervention group. Although studies have reported thatgenetic information feedback reduces alcohol consumption [17, 18], the factors that contribute to this reduction
have not been examined. Therefore, this study provides
the first evidence that SBI reduces alcohol consumption,
possibly through behavioral changes. Nevertheless, theextent to which the elements of SBI contribute to changes
in drinking behavior is currently unclear. Future studies
should address this issue in more detail.
In contrast, a significant reduction in alcohol consumption in the intervention group was attenuated from 3 to
6 months and a significantly greater reduction in alcohol
consumption was observed in the intervention group

Owaki et al. BMC Medicine(2024) 22:205

compared with the control group at 3 months but not
at 6 months. This result suggests that the alcohol consumption-reducing effect of SBI does not persist in thelong term. A recent umbrella review concluded that cognitive behavioral therapy (CBT) and motivational interviewing (MI) show effectiveness in the relative long-term(more than 6 months) reduction of alcohol consumption [31]. Both CBT and MI are usually conducted overmultiple sessions [32, 33]; reports of some cases indicatethat more sessions induce a greater reduction in alcohol
consumption than a single session [34]. Because the SBI
was implemented only once in this study, future studies
should investigate the effectiveness of multiple educational campaigns conducted on a periodic schedule (e.g.,
once every 3–6 months) to sustain the effectiveness of
the SBI.
Additionally, despite the lack of a special intervention,
the AUDIT-C score decreased from baseline to 6 months
in the control group. The participants in the control
group were asked to read and study the same traditional
educational materials for university students as the intervention group. Generally, visualizing personal health data
motivates users to increase or maintain their activities[34]. In addition, the Hawthorne effect on alcohol-use
behavior may have occurred in this study [35]. Accordingly, participating in this study may have contributed
to the reduction in alcohol consumption among young
adults, regardless of whether genetic testing information
was fed back to the participants.
In this study, we provide a new approach to reducing
alcohol consumption among excessive drinkers, which
is not only an academic contribution but also beneficial
to public health. Considering the current situation inwhich individual differences in health literacy exist, withthose with low health literacy in childhood maintaining
the health literacy at low levels in later life [36], SBI may
be effective in the workplace and among other adult age
groups. It is desirable to expand the target population in
the future.
This study had some limitations. First, 21 participants(21.0%) in the intervention group and 28 (29.2%) in thecontrol group were lost to follow-up during the study
period. However, a previously reported intervention
reducing alcohol consumption for college students confirmed that approximately 40% participants drop out
at 6 months [37], indicating that the compliance rate ofour study was relatively high. In addition, the number ofparticipants who completed the current study up to the
6-month point was similar to that reported in a previous
study [28]. Hence, our sample size, excluding the dropouts, may be valid for the investigation of our researchquestions and might not have a significant impact on
the data interpretation and conclusions of this study.

Page 13 of 15

Nevertheless, the possibility that some participants
dropped out owing to unchanged or increased alcohol consumption cannot be denied. Thus, we may haveunder- or over-estimated the effects of SBI on alcoholuse behavior.
Second, data on alcohol consumption and AUDITC were self-reported by the participants; therefore,
interpreting the results requires caution. Third, we did
not compare the effect of SBI on alcohol-use behavior between the five types of alcohol-metabolizing
enzyme genotypes or sexes because of the small sample
size, whereas previous studies demonstrated that the
responsiveness of alcohol-reducing interventions differed between the genotypes and sexes [17, 29]. In contrast, a significant reduction in alcohol consumption
was observed in the intervention group, which had arelatively high proportion of women; this finding may beclinically important because alcohol-related health problems have been increased recently not only among men
but also among women [38]. Finally, we cannot ignore
the unmeasurable confounding factors that may haveaffected the results of this study. Future studies should
include larger populations with better outcomes.

Conclusions
The effectiveness of our individualized intervention forlifestyle modification, considering genetic constitution
information, was confirmed by the significant decrease inaverage daily alcohol consumption and AUDIT-C scoresfor up to 6 months associated with a significant increasein the proportion of participants who transitioned fromthe precontemplation stage to the contemplation andaction stages. This type of intervention might be an effective strategy for preventing excessive drinking behavior
in young adults.
Abbreviations
ALDH2	Aldehyde dehydrogenase 2
ADH1B	Alcohol dehydrogenase 1B
AUDIT	Alcohol Use Disorders Identification Test
AUDIT-C	AUDIT-Consumption
SBI	Screening and brief intervention
TTM	Transtheoretical model

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03422-y.
Additional file 1: Table S1 Alcohol-use disorders identification test (AUDIT,
AUDIT-C). Table S2 Baseline characteristics of the participants, excluding
dropouts up to 3 months from baseline. Table S3 Baseline characteristicsof the participants, excluding dropouts up to 6 months from baseline.
Acknowledgements
The authors would like to thank Editage (www.​edita​ge.​jp) for the English
language review. In addition, we would like to express our gratitude to Mr. Kei
Hayashi, director of the Sanwa Laboratory of Sanwa Shurui Company, a joint

Owaki et al. BMC Medicine(2024) 22:205

research facility, and the co-authors who helped with the genetic testing of
alcohol metabolism enzymes, preparation of results reports, and developmentof research plans. We would also like to express our deepest gratitude to theparticipants for their cooperation with this study.
Authors’ contributions
YO, HY, and GS contributed to the development of the study design, ethicalprocedures, participant recruitment, intervention, and data collection. SK, AN,
TG, YT, KM, and HH advised on the study design, prepared the genetic testing
results report, coordinated the study implementation, and presented the test
results. YO and SD performed data analysis. YO, HY, SD, SK, and AN conducteddata interpretation. YO and SD drafted the manuscript. HY, GS, SK, AN, TG, YT,
KM, and HH critically revised the manuscript. All authors read and approved
the final manuscript.
Funding
This study was supported by JSPS KAKENHI (grant number JP 20K10779). This
was a Grant-in-Aid for Scientific Research provided by the Ministry of Education,
Japan Society for the Promotion of Science (JSPS). The funder was not involved incollecting, analyzing, and interpreting the study data or writing the manuscript.
Availability of data and materials
All data required to support the protocol will be supplied on request, with theapproval of the participants. Datasets generated and/or analyzed during thecurrent study are not published and are not available, but after data collection
is completed, they will be available from the corresponding author and firstauthor upon reasonable request (in Japanese). The datasets and statisticalcodes analyzed during the current study as well as the full protocol are available from the corresponding authors upon reasonable request.

Page 14 of 15

4.
5.
6.

7.
8.
9.
10.

11.

12.

Declarations

13.

Ethics approval and consent to participate
The study was approved by the Institutional Review Board of Medical Doctors, University of Tsukuba (Human Genome/Gene Analysis Research) ([2021]
G294, April 19, 2021). All participants included in the study provided written
informed consent.

14.

Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Nursing, School of Nursing, Gunma Prefectural Collegeof Health Sciences, Maebashi, Japan. 2 Research and Development Center
for Lifestyle Innovation, University of Tsukuba, Tsukuba, Japan. 3 Department of Family Medicine, General Practice and Community Health, Instituteof Medicine, University of Tsukuba, Tsukuba, Japan. 4 Department of Primary
Care and Medical Education, Graduate School of Comprehensive Human
Sciences, University of Tsukuba, Tsukuba, Japan. 5 Sanwa Laboratory, Sanwa
Shurui Company, Usa, Japan.
Received: 18 January 2024 Accepted: 9 May 2024

15.
16.
17.
18.
19.
20.
21.
22.

23.
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