Alcohol Misuse in Bipolar Disorder.

8/10/2019 Alcohol Misuse in Bipolar Disorder.

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Review

Alcohol

misuse

in

bipolar

disorder.

A

systematic

review

andmeta-analysis

of

comorbidity

rates

A. Di Florio *, N. Craddock, M. van den Bree

Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Hadyn Ellis Building, Maindy Road, Cathays, Cardiff CF24

4HQ, United Kingdom

1. Introduction

Comorbidity has important implications for both research and

clinical practice [68]. Nevertheless, important issues about the

definition and evaluation of comorbidity remain to be elucidated in

psychiatric research [62,69].

From the classic phenomenological work of Emil Kraepelin [36]

to contemporary epidemiological studies [50] the comorbidity

between alcohol misuse and bipolar disorder (BD) has been

reported. Of all DSM-IV axis I disorders, BD has been reported to be

the most strongly linked with alcohol or drug abuse [25,41] and

compared with other primary psychiatric disorders, mania and

hypomania may have one of the highest associations with

alcoholism [25,41].

Although considerable research has been devoted to character-izing the link between alcohol related disorders and BD,

surprisingly, a preliminary search of MEDLINE, EMBASE and

PsychINFO did provide no evidence of any published systematic

reviews focusing on the comorbidity between the two disorders.

In this review, we explored the current state of knowledge on

the rates of alcohol use disorders (AUDs) in people affectedbyBD.

There are many definitions of alcohol related disorders. Here we

included in the definition of AUDs problem drinking (alcohol

abuse or harmful use) and addiction (alcoholism or alcohol

dependence).

We tested the following hypotheses:

AUDs are over-represented in people with BD compared to the

general population;

there is an effect of gender on the comorbidity between AUDs

and BD, with men having higher comorbidity rates than women.

Although it is a common observation in clinical practice that men

with BD are over-represented among patients with AUDs

comorbidity, evidence from both epidemiological and clinical

studies are contrasting;

methodological differences such as study design, geographical

location of the population, diagnostic criteria and instruments ofassessment have an effect on comorbidity rates reported in

studies. The rates of AUDs in the general population are

influenced by cultural [16] and genetic differences [65]. More-

over, diagnostic criteria and research protocol have been

implicated as additional sources of variability [53].

2. Methods

Search strategy and reporting are in accordance with the

recommendations of the Meta-analysis Of Observational Studies in

Epidemiology (MOOSE) group [57].

European Psychiatry 29 (2014) 117124

A R T I C L E I N F O

Article history:

Received 31 May 2013Received in revised form 18 July 2013

Accepted 27 July 2013

Available online 25 September 2013

Keywords:

Alcohol

use

disorders

Bipolar disorder

Comorbidity

Co-occurrence

A B S T R A C T

Aims:

To assess the comorbidity rates of alcohol use disorders (AUDs) in bipolar disorder (BD) and to

explore possible sources of heterogeneity.

Methods: Studieswere identified through database searches. Meta-analytic techniques were employed

to aggregate dataon lifetime comorbidity and to explore possible sourcesof heterogeneity. Funnel plots

were used to detect publication bias.

Results: In cl inical studies, AUDs affected more than one in three subjects with BD. Significant

heterogeneitywas found,whichwas largely explainedby thegeographical location of study populations

and gender ratio of participants. AUDs affected more than one in five women and two in five men.

Conclusion: AUDs are highly prevalent in BD. Our study revealed a substantial heterogeneity across

studies. Further research including control groups is needed. Patients with BD should be assessed for

current and previous AUDs.

2013 Elsevier Masson SAS. All rights reserved.

* Corresponding author. Tel.: +44 2920 744 826.

E-mail address: [email protected] (A. Di Florio).

Available

online

at

www.sciencedirect.com

0924-9338/$

see

front

matter

2013

Elsevier

Masson

SAS.

All

rights

reserved.

http://dx.doi.org/10.1016/j.eurpsy.2013.07.004
http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004mailto:[email protected]:[email protected]://www.sciencedirect.com/science/journal/09249338http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://www.sciencedirect.com/science/journal/09249338mailto:[email protected]://dx.doi.org/10.1016/j.eurpsy.2013.07.004http://crossmark.crossref.org/dialog/?doi=10.1016/j.eurpsy.2013.07.004&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.eurpsy.2013.07.004&domain=pdf


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2.1. Search strategy

An electronic search was conducted in databases MEDLINE,

EMBASE, PsychINFO by author A.D.F.

Keywords and Boolean operators employed (set by A.D.F.,

M.V.D.B. and N.C.) are listed in S1.

No language restrictions were applied to the search. Publica-

tions from 1978 to 2013 were retrieved.

The abstracts of the identified studies were independently

screened for inclusion in the review by two investigators (A.D.F.

and M.V.D.B.).

Bibliographies of eligible articles were checked for possible

other relevant studies (ancestor search).

2.2.

Inclusion

criteria

Studies were selected for inclusion if they met the following

criteria:

disorders defined according to DSM or ICD and RDC criteria;

for clinical studies, sample size greater than 50 individuals;

at least 95% of the sample older than 18;

study employed quantitative research methodology;

participants not selected on the basis of comorbidity with AUDs(probabilistic sample);

rates of AUDs in BD participants evaluated and reported;

study based on original research.

2.3. Data extraction

Data were extracted from full-text articles, with the exception

of a single conference abstract [8].

When a sample was reported in more than one paper, data were

extracted from the article reporting the largest sample, or from all

articles, in case they reported on different variables.

Information extracted from original publications is listed in S2.

Geographical locations were classified according to:

the continent where the study was conducted;

total per capita consumption of pure alcohol, in litres, for the

adult population, as reported by the World Health Organization

[70];

patterns of drinking score as calculated by the World Health

Organization [70].

In this review, comorbidity was defined as the association

between BD and AUDs within a given time frame and co-

occurrence as the cross-sectional association between disorders

i.e. both traits assessed and present at the same time [69].

2.4. Data analyses

Data analyses were conducted using R version 2.13.0 (Copyright

2011 The R Foundation for Statistical Computing, Vienna, Austria).

Meta-analytic techniques were employed to aggregate data on

lifetime comorbidity between AUDs and BD.

Firstly, the command metaprop was used to calculate the

pooled estimates of proportions with the corresponding 95%

confidence intervals on the base of the Freeman-Tukey double

arcsine transformation. A fixed as well as a random effects model

framework was used [21].

The association between gender and AUDs was also evaluated

using DerSimonian-Laird estimate (R metabin command) for

random effect models.

Heterogeneity of combined study results was assessed by

Q

test and inconsistency statistic (I

2

). A

P

value below

0.10 in

the

Q test was considered indicative of statistically significant

heterogeneity. Values of 25%, 50% and 75% were considered

to represent respectively low, medium and high heterogeneity

in the I2 statistic [29]. Since significant heterogeneity was

found across studies, only random effects estimates were

reported.

Possible sources of heterogeneity were explored using

univariate and multivariate random effects meta-regression

(R metafor package) [64].

Models were fitted using the rma function. For the purpose of

a reliable statistical analysis, Freeman-Tukey double arcsine

transformed proportions were calculated as outcome measure.

The random-effects models were fitted using the restricted

maximum-likelihood estimator and the empirical Bayes estimator.

The effects of the following independent covariates were

tested: geographical location of the study, diagnostic tools,

diagnostic criteria and sampling methods. Proportion of women

and average age at the interview were not included in the model, to

avoid aggregation bias (i.e., the relationship with patient

averages between trials may not be the same as the relationship

for patients within trial) [30]. Standard errors of the estimated

coefficients were adjusted with the Knapp and Hartung method

[35], to account for the uncertainty in the estimate of the amount of

(residual) heterogeneity.To evaluate publication bias, asymmetry in the funnel plots was

tested with mixed-effects meta-regression model (R regtest

command) using the standard error as the predictor [14].

3. Results

3.1. Search results

The flow chart of the search process is shown in Fig. 1. As a

result of the electronic and ancestor researches, 57 research

articles and one abstract [8], reporting on 46 original studies, were

included in the systematic review. Except for one paper in French

[59] and one paper in Flemish [63], all papers were in English.

Studies characteristics are summarised in Table 1.

3.2. Are alcohol use disorders over-represented in people suffering

with bipolar disorder compared to the general population?

The majority of epidemiological studies calculated the strength

of association with AUDs in BD compared to the general population

(Table 2).

3.2.1. Epidemiological studies

Information was extracted from eight epidemiological studies,

from ten published papers (table S3a).

Epidemiological studies conducted in Europe [41] and

North America [50,25,34,17,20,26] univocally reported that

the prevalence of

AUDs was significantly

increased amongparticipants with BD, regardless the diagnostic criteria (i.e.

DSM-III, DSM-III-R, DSM-IV) and the time window (i.e. current,

lifetime, other Table 2) employed. Results were heterogeneous

when alcohol abuse (AA) and alcohol dependence (AD) were

analysed separately. Only one study reported on the 12 month

incidence of AUDs [26]. This study found no differences between

BD and the general population and furthermore that partici-

pants with bipolar II disorder (BD-II) had a lower risk of AA

compared to the general population. Kessler et al. [34] reported

a similar finding for lifetime AA incidence in men with mania,

despite the fact that this study used different definitions

(lifetime prevalence) compared to Grant et al. [26] (12 months

incidence) as well as different diagnostic criteria (DSM-III-R

versus

DSM-IV).

A. Di Florio et al./European Psychiatry 29 (2014) 117124118


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The only epidemiological survey conducted in Asia reported no

effect of BD on the 12 months prevalence of any AUDs [10]

compared to the general population.

It was not possible to calculate a combined odds ratio for

epidemiological studies, because of the small number of studies

using

the

same

diagnostic

criteria

and

time

frames

(Table

2).

3.2.2.

Clinical

studies

Only one clinical study included a random control group. This

study found an excess of AUDs in BD [7].

Because of the lack of random control groups, it was not

possible to estimate the pooled effect of BD on AUDs in clinical

samples.

However,

lifetime

prevalence

of

AUDs

was

reported

by

31

Fig. 1. Flow chart of the selection process. Electronic search in grey, ancestor search in white. aStudies could be excluded because meeting more than one exclusion criterion.

A. Di Florio et al./ European Psychiatry 29 (2014) 117124 119


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independent

clinical

studies

[8,63,7,3,5,4,9,12,15,18,22,24,28,32,33,55,3840,4247,49,56,58,60,61,67], including 8309 subjects

with BD, allowing us to calculate pooled lifetime prevalence.

Overall, the pooled lifetime prevalence of AUDs in BD in these

31 studies was 35.1% [95%CI: 30.3%; 40.0%] using a random effect

model. Significant heterogeneity across studies emerged from both

Q test (Q = 615.27, df = 30, P


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4.

Discussion

In this systematic review, the currently available literature on

the burden of AUDs in BD was assessed.

We tested the hypotheses that AUDs are over-represented in

people suffering fromBDand that, amongparticipants with BD,men

have higher comorbidity rates than women. Moreover, we explored

the causes of heterogeneity in comorbidity rates between studies.

We found only one clinical study including a random control

group of participants without BD [7]. Epidemiological studies

conducted in North America and Europe unanimously found

significantly increased odds of AUDs in participants with BDcompared to the general population. However, sub-analyses

examining AA, AD or genders separately, produced greater

variability in findings. AUDs affected more than one in three

participants in clinical studies of BD, with men having 23 times

higher rates of AUDs than women. Prevalence estimates of AUD in

BD-I and of AD in BD were similar at about 3035%.

A large variability across studies was reported, with geogra-

phical location and gender ratio of participants explaining the

majority of the heterogeneity.

4.1. Rates of alcohol use disorders in bipolar disorder

To the best of our knowledge, this is the first systematic review

and meta-analysis on the comorbidity rates with AUDs in patients

with BD. We found a previously unreported large variability in

definitions, methodology and results between studies. Our reviewof the epidemiological studies indicated clear evidence of

increased odds of AUDs in BD only for studies conducted in

Western countries. Due to the differences in the outcome

measured and in the stratification of the samples, it was not

Table 2

Odds ratio for the associations with alcohol use disorders in bipolar disorders compared to the general population in epidemiological studies.

First author, year of publication BD Criteria Time window AUD OR

(95%CIs)

AA OR

(95%CIs)

AD OR

(95%CIs)

Regier, 1990 [50] Any DSM-III Lifetime 5.1a 4.6a 3.3a

BD-I 5.6a 5.5a 3.0a

BD-II 4.2a 3.1a 3.9a

Fogarty, 1994 [17] BD-I DSM-III Lifetime 2.5 (1.225.34)

Kessler,

1997

[34]

Mania

in

women

DSM-III-R

Lifetime

incidence

1.8

(0.74.7)

4.7

(0.828.2)Mania in men 0.1 (0.0030.5) 8.0 (4.514.5)

Merikangas 2008 [41];

Angst, 1998 [2]

BD-II DSM-III-R 18 years incidence 9.1 (2.731.2) 21.1 (6.667.5)

Grant, 2005 [50];

Goldstein, 2006 [23]

BD-I DSM-IV 12 months prevalence 3.5 (2.84.3) 1.3 (0.91.8) 5.9 (4.67.7)

Lifetime 3.3 (2.93.8) 1.0 (0.81.2) 5.2 (4.56.1)

Any 12 months prevalence 2.7 (2.223.31) 1.0 (0.81.4) 4.2 (3.25.3)

Glantz 2009 [26] BD DSM-IV Lifetime 2.7 (1.84.0) 5.6 (4.17.7)

Grant, 2009b [26] BD-I DSM-IV 12 months incidence 0.4 (0.151.24) 1.4 (0.752.71)

BD-II 0.2 (0.030.99) 1.3 (0.553.10)

Chou, 2012 [10] BD DSM-IV 12 months prevalence 0.38 (0.043.46) 2.04 (0.2318.25) c

Positive associations in bold, negative association in italics. BD: bipolar disorder; BD-I: bipolar I disorder; BD-II: bipolar II disorder; AA: alcohol abuse; AD: alcohol

dependence; AUD: alcohol use disorders.a Confidence

intervals

not

given.b 99% confidence intervals.c OR could not be calculated because the prevalence was too low.

Study

Randomeffectsmodel

Cardosoetal2008 (29)

Cassidy

et

al

2001 (57)

Dalton

et

al

2003

(32)

Escamillaetal2002(34)

Hendricketal2000(37)

Kawaetal2005(38)

Fryeetal2003 (58)

Lydall

et

al

2011

(42)Maier

et

al

1995

(43)

Morganetal2005(44)

Nivolietal2011(48)

Ostacheretal2010 (50)

Strakowskietal2005(51)

Suominenetal2009(52)

Tsaietal1997(53)

Tsai

et

al

2012

(54)

VanRoyetal2010(21)

Events

25

114

51

25

31

32

117

8018

23

143

429

40

56

8

31

9

Total

2610

54

191

129

92

64

90

162

19341

59

272

880

75

90

36

147

35

Males

Events

28

76

47

5

12

20

96

6311

4

99

518

21

37

2

10

6

Total

3462

132

201

207

113

59

121

188

31341

53

332

1274

69

101

65

159

34

Females

0.75

1.5

OddsRatio

OR

2.76

3.20

2.44

2.23

8.06

3.68

2.79

2.49

2.812.13

7.83

2.61

1.39

2.61

2.85

9.00

3.98

1.62

95%-CI

[2.17; 3.50]

[1.62; 6.31]

[1.62;

3.65]

[1.38;

3.60]

[2.94;22.07]

[1.65; 8.20]

[1.46; 5.31]

[1.59; 3.90]

[1.89;

4.18][0.85;

5.39]

[2.49;24.61]

[1.87; 3.65]

[1.17; 1.65]

[1.32; 5.18]

[1.58; 5.13]

[1.80;45.12]

[1.88;

8.45]

[0.51; 5.17]

W(random)

100%

5.7%

8.1%

7.4%

3.7%

4.8%

6.0%

7.8%

8.2%4.1%

3.1%

8.8%

10.1%

5.7%

6.5%

1.8%

5.2%

3.0%

Fig. 2. Forest plot of the association between lifetime comorbidity and gender in 17 clinical studies. OR indicates the increased risk of lifetime alcohol use disorders (AUDs) in

male

participants

with

bipolar

disorder

(BD)

compared

to

female

participants

with

BD.



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possible to investigate patterns of variability. Interestingly, the

epidemiological studies conducted over the past two decades

didnt find any association with AA, or even that BD might be

protective against incident AA [34,26].

4.2. Gender

In our meta-analysis of the clinical studies (Fig. 2) male gender

was associated with a higher lifetime prevalence of AUDs (44%),

compared to female gender (22%). Male BD patients had between 2

3 times greater risk of lifetime AUDs (pooled OR between 2 and 3).

We werent able to compare comorbidity estimates stratified by

gender with those in the general population, because of the lack of

clinical studies including a control sample. [28,19]. Gender

differences in alcohol consumption and AUDs, in fact, have been

reported in the general population. In an American survey, ethanol

consumptionandproportion ofheavydrinkers werealmost twice as

high inmen than inwomen [71].According to theAmericanNational

Comorbidity Survey, AUDs are more prevalent in men (19.6%) than

women (7.5%) (http://www.hcp.med.harvard.edu/ncs/ftpdir/NCS-

R_Lifetime_Prevalence_Estimates.pdf). Two previous clinical stu-

dies hypothesized that, although women with BD had lower risk of

AUDs than men with BD, the comorbidity rates may actually be

greater in women than in men, when compared to healthy controls.Contrary to this hypothesis, Kessler found that women with BD

didnt have an increased risk of incident AUDs compared to the

general population, while men with BD did have an increased risk of

AD, but not of AA.

Despite alcohol problems being related to male gender in the

general population and in the majority of clinical studies,

comorbidity with AUDs in women with BD is clinically relevant

with more than one in five reporting lifetime AUDs.

Gender may affect the identifications of patients with AUDs,

leading to overestimation in men. Recognition of AUDs by general

physicians is, in fact, higher for men and for women [13].

Further studies including control groups are needed to

elucidate the effect of gender on comorbidity with AUDs in BD.

4.3.

Geographical

location

We found that pooled comorbidity estimates for Asia were

significantly lower than those for North America and Europe. It is

well established thatAsianpopulations have a reduced riskofAUDs.

Criteria such as intoxication, dependence, impairment and abuse

can be challenged by cultural and genetic [65] differences between

populations. Genetic variations especially of ADH and ALDH2

(mitochondrial ALDH2 aldehyde dehydrogenase 2 family, http://

omim.org/entry/100650) [1] have been associated with the varia-

tion in risk of AD between populations. Environmental as well as

other genetic factors have been hypothesised to explain this

heterogeneity [65].

Interestingly,

we

found

a

positive

correlation

between

comor-bidity rates and pro capita consumption of alcohol, but no

correlation with risky patterns of drinking in the general

population. However, the majority of studies considered in this

review were conducted in the US, with a lack of data on Africa and

the vast majority of Asian countries (the ones included in this

review were conducted in South Korea and Taiwan only).

Further studies are needed to disentangle the cultural and

biological factors mediating the geographical differences in the

comorbidity risk of AUD.

4.4. Quality of studies

Although considerable literature has addressed the comorbid-

ity rates of AUDs in BD (we selected 131 pertinent papers), only 46

original studies (from 58 papers) were retrieved according to our

quality inclusion criteria (sample size larger than 50 subjects,

employment of main diagnostic system criteria, assessment

methods reported). The paucity of evidence may be related to

the fact that some studies with negative results may not have been

published (so called file drawer effect). Publication bias is a

greater factor in observational studies than in clinical trials [21].

However, we didnt find any evidence of publication bias, although

the results should be treated with some caution. Statistically

pooled information from observational data may produce precise

but spurious results [14]. In our analyses, we emphasized the

examination of possible sources of heterogeneity, as it has been

suggested that this, rather than the numerical combination of data,

should be the primary aim of reviews of observational studies [14].

4.5. Limitations and further directions

Our results need to be interpreted in the light of several

methodological limitations:

we did not use unpublished material;

we did not employ quality scoring to report and assess the

quality of the studies included, as this is still controversial [57].

Instead, we postulated a priori quality inclusion criteria.

The comorbidity between BD and AUDs is a broad research

topic, with many important aspects. For reasons of focus and

clarity, in this systematic review we used a limited set of

hypotheses, leaving several issues unaddressed:

we focused on BD as the primary diagnosis. The differences

between primary and secondary diagnosis were not

examined, neither was the causality between co-occurring AUDs

and BD. In a rare longitudinal study [66], 37% of participants with

BD reported co-occurring AUDs at intake, but only 3% had AUDs

at the 10-year follow-up. This suggests that AUDs in BD may be

secondary to the mood disorder, rather than independent or

primary. Recent analyses from the National Comorbidity SurveyReplication support this hypothesis, showing that active, but not

remitting, BD predicted incident AD [20];

due to the limited amount of data, it was not possible to conduct

separate analyses for problem drinking (AA or harmful use) and

addiction (alcoholism or AD). Notwithstanding, the differentia-

tion between the two sets of behaviours has important research

and clinical implications. There are two main views: one

supporting the hierarchical approach to the classification of

AUDs (where a diagnosis of AA is made only in absence of AD);

the other maintains that problem drinking and addiction are two

separate disorders. In the general population, there are clear

differences in family history and early childhood experiences

between subjects with AD and those with non-dependent

alcohol

disorders.

A

recent

genetic

linkage

study

reported

a

riskvariant for BD in families with AA, but not in families with AD,

supporting the second of the two views [51]. Methodologically,

alcohol abuse and harmful alcohol use are psychometrically less

robust than dependence, and it has been argued they represent

unsatisfactory constructs [27]. This may be particularly the case

of BD, where excessive alcohol use can be either a manifestation

of the impulse dyscontrol observed in acute mania or of self-

medication for both mania and depression;

although we had initially aimed to establish the odds ratio of

AUDs in BD compared to the general population, only one clinical

study included this information and the epidemiological studies

were too heterogeneous for meta-analysis;

geographical location and gender ratio explained about 70% of

heterogeneity.

However,

the

test

for

residual

heterogeneity

A. Di Florio et al./European Psychiatry 29 (2014) 117124122
http://www.hcp.med.harvard.edu/ncs/ftpdir/NCS-R_Lifetime_Prevalence_Estimates.pdfhttp://www.hcp.med.harvard.edu/ncs/ftpdir/NCS-R_Lifetime_Prevalence_Estimates.pdfhttp://omim.org/entry/100650http://omim.org/entry/100650http://omim.org/entry/100650http://omim.org/entry/100650http://www.hcp.med.harvard.edu/ncs/ftpdir/NCS-R_Lifetime_Prevalence_Estimates.pdfhttp://www.hcp.med.harvard.edu/ncs/ftpdir/NCS-R_Lifetime_Prevalence_Estimates.pdf


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suggests that there were other covariates not considered in the

model influencing the comorbidity rates, such as the level of

training of interviewers, other variables assessed and current

mood. Potential explanatory variables that we did not consider,

but that can contribute in explaining the association between BD

and AUDs, include:

comorbidity with anxiety disorder. It has for example been

reported that women, but not men, with BD and comorbid

social phobia have four-fold increased risk of AA compared to

those without social phobia [52],

duration of BD. It has been previously reported that medical

comorbidity in BD varies with age and duration of illness, with

systematic differences between patients at different stages of

the disease [54].

It has recently been advocated that substance use disorders and

psychiatric conditions are complicated and are probably best in

the realm of future research rather than nosology at this point [3].

The current nosological systems have been challenged by

comorbidity between disorders. Advances in neuroscience may

offer new insights into phenotypes [37]. In particular, one of the

specific objectives of the Psychiatric GWAS Consortium [11] is the

analysis of comorbidities between major psychiatric disorders.

This approach may lead to a better understanding of the biologicalmechanisms underpinning the relationship between AUDs and BD.

However, while we are waiting for more biological grounded

diagnostic and clinical approaches, clinical and epidemiological

studies may provide some important clues for the management of

patients with BD and AUDs.

Patients with BD in comorbidity with AUDs are usually

excluded from clinical trials. A recent research found that current

AUDs were the leading exclusion criterion in trials for acute mania

[31]. Our results indicate that the exclusion of patient with current

AUDs limits the generalizability and clinical utility of trials.

Moreover, due to the influence of gender on comorbidity rates, our

meta-analysis emphasises the need of systematically assessment

of gender differences among participants.

To conclude, clinicians should be aware of the high comorbidityof AUDs not only in men, but also in women with BD. Patients with

BD should be assessed for current and previous alcohol use and

misuse. Clinical trials should also take into account the high rates

and gender differences in comorbidity with AUDs in participants

with BD.

Disclosure

of

interest

The authors declare that they have no conflicts of interest

concerning this article.

Appendix

A.

Supplementary

data

Supplementary data associated with this article can be found,

in the online version, at http://dx.doi.org/10.1016/j.eurpsy.2013.

07.004.

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Alcohol Misuse in Bipolar Disorder.

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Transcript of Alcohol Misuse in Bipolar Disorder.