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Author's personal copy
Journal of Affective Disorders 135 (2011) 10–19
Contents lists available at ScienceDirect
Journal of Affective Disorders
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j a d
Review
Insomnia as a predictor of depression: A meta-analytic evaluation of
longitudinal epidemiological studies
Chiara Baglioni a,⁎, Gemma Battagliese b, Bernd Feige a, Kai Spiegelhalder a, Christoph Nissen a,
Ulrich Voderholzer a,c, Caterina Lombardo b, Dieter Riemann a
a
b
c
Department of Psychiatry & Psychotherapy, University of Freiburg Medical Center, Hauptstraße 5, 79104 Freiburg, Germany
Department of Psychology, “Sapienza” University of Rome, Via dei Marsi 78, 00185 Roma, Italy
Medizinisch-Psychosomatische Klinik, Am Roseneck 6, Prien, Germany
a r t i c l e
i n f o
Article history:
Received 17 November 2010
Received in revised form 10 January 2011
Accepted 11 January 2011
Available online 5 February 2011
Keywords:
Insomnia
Depression
Longitudinal
Epidemiologic
Predictor
Meta-analysis
a b s t r a c t
Background: In many patients with depression, symptoms of insomnia herald the onset of the
disorder and may persist into remission or recovery, even after adequate treatment. Several
studies have raised the question whether insomniac symptoms may constitute an independent
clinical predictor of depression. This meta-analysis is aimed at evaluating quantitatively if
insomnia constitutes a predictor of depression.
Methods: PubMed, Medline, PsycInfo, and PsycArticles databases were searched from 1980
until 2010 to identify longitudinal epidemiological studies simultaneously investigating
insomniac complaints and depressed psychopathology. Effects were summarized using the
logarithms of the odds ratios for insomnia at baseline to predict depression at follow-up.
Studies were pooled with both fixed- and random-effects meta-analytic models in order to
evaluate the concordance. Heterogeneity test and sensitivity analysis were computed.
Results: Twenty-one studies met inclusion criteria. Considering all studies together,
heterogeneity was found. The random-effects model showed an overall odds ratio for
insomnia to predict depression of 2.60 (confidence interval [CI]: 1.98–3.42). When the analysis
was adjusted for outliers, the studies were not longer heterogeneous. The fixed-effects model
showed an overall odds ratio of 2.10 (CI: 1.86–2.38).
Limitations: The main limit is that included studies did not always consider the role of other
intervening variables.
Conclusions: Non-depressed people with insomnia have a twofold risk to develop depression,
compared to people with no sleep difficulties. Thus, early treatment programs for insomnia
might reduce the risk for developing depression in the general population and be considered a
helpful general preventive strategy in the area of mental health care.
© 2011 Elsevier B.V. All rights reserved.
Contents
1.
2.
Introduction . . . . . . . . . . . . .
Methods . . . . . . . . . . . . . . .
2.1.
Search method for identification
2.2.
Studies selection . . . . . . . .
2.3.
Data extraction . . . . . . . .
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⁎ Corresponding author. Tel.: + 49 761 270 6589; fax: + 49 761 270 6619.
E-mail address: chiara.baglioni@uniklinik-freiburg.de (C. Baglioni).
0165-0327/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jad.2011.01.011
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2.4.
Meta-analytic calculations . . . . . .
2.5.
Publication biases . . . . . . . . . .
2.6.
Incidence of depression . . . . . . .
3.
Results . . . . . . . . . . . . . . . . . .
3.1.
Total sample . . . . . . . . . . . .
3.2.
Sensitivity analysis: excluding outliers
3.3.
Subgroup analysis: age groups . . . .
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Publication bias . . . . . . . . . . .
3.5.
Incidence of depression . . . . . . .
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Discussion. . . . . . . . . . . . . . . . .
Role of funding source . . . . . . . . . . . . .
Conflict of interest . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . .
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1. Introduction
Depression is the leading cause of disability in both
women and men in the United States and worldwide and one
of the 10 leading disorders for global disease burden (Lopez et
al., 2006). In the United States, it has been reported that each
year more than 19 million adult Americans suffer from a
depressive illness and the direct and indirect costs of the
disorder are more than $30 billion (Lopez et al., 2006).
However, consensus reports show that individuals with
depression are being underdiagnosed and undertreated
(Hirschfeld et al., 1997). Although a substantial increase in
the prescription of antidepressants has been observed over
the past twenty years, data suggest that this increase is
mainly due to increased long-term prescribing, rather than to
changes in the recognition or case definition of depression
(Moore et al., 2009). In order to narrowing the gap between
prevalence and primary care assistance, the recognition of
clinical early predictors of depression seems of utmost
relevance.
Major depression commonly co-occurs with symptoms of
insomnia (Riemann and Voderholzer, 2003; Tsuno et al., 2005),
defined in the DSM-IV-TR as difficulties in initiating/maintaining sleep or non-restorative sleep, accompanied by decreased
daytime functioning, persisting for at least four weeks. Already
40 years ago, Winokur et al. (1969) described in a sample of
1257 individuals with depression that all of them had
insomniac symptoms. Although this relationship between
depression and insomnia is well known and its description
dates back to the founder of modern psychiatry (Kraepelin,
1909), its conceptualization has radically changed during the
last decade. Insomnia has been traditionally conceptualized as a
symptom of psychopathology, especially depression (overview
see Riemann et al., 2001; Staner, 2010). More recently,
insomnia has been considered as a primary disorder if it is
present without the co-existence of other clinically relevant
psychiatric or medical diseases, and as a secondary disorder if
otherwise. Nevertheless, with respect to the link to depression,
chronic insomnia can also exist years before the first onset of a
depressive episode. Consequently, it has been suggested that
“comorbid” insomnia may be a more appropriate term than
“secondary” insomnia (McCrae and Lichstein, 2001; NIH, 2005;
Lichstein, 2006). This new approach identifies insomnia and
depression as two independent diagnostic entities with
different clinical courses and characteristics (Staner, 2010).
The next edition of DSM, DSM-V, will probably abandon the
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primary/secondary concept and instead introduce “insomnia
disorder” as the main diagnostic category for insomnia,
allowing specification whether or not it is co-morbid with
another mental or medical disorder (Reynolds and Redline,
2010).
The close link between insomnia and depression suggests
that the conditions are not just randomly associated.
Insomnia is now considered not only a symptom of but also
a possible predictor of depression. Ford and Kamerow (1989)
were the first to note that such a relationship exists based on
data from a longitudinal epidemiological study. Since then
more than 40 studies have been published evaluating the
predictor question (overview see Riemann, 2009; Baglioni et
al., 2010a). However, despite the large amount of data
collected up to now, no systematic meta-analytic evaluation
of longitudinal studies has been performed about this type of
relationship. Such an analysis might have strong clinical
implications. If insomnia is indeed a predictor for depression,
early and adequate treatment of insomnia might contribute to
the prevention of the future development of depression. This
view seems to be supported by some studies showing that
adding cognitive-behavior treatment for insomnia (CBT-I) is
efficacious also in patients with both symptoms of insomnia
and depression and guarantees a better treatment outcome in
this population than standard antidepressive treatment alone
(Taylor et al., 2007; Manber et al., 2008).
The aim of the current review was to quantitatively
summarize the results of studies which have longitudinally
investigated the role of insomnia as a predictor for depression
with a meta-analytic strategy. We hypothesized that symptoms of insomnia predict depression in different age-samples.
2. Methods
The meta-analysis was performed according to the
MOOSE (Stroup et al., 2000) and the PRISMA (Liberati et al.,
2009) guidelines. The first and second authors independently
conducted the literature search, screened the titles and the
abstracts of potentially eligible studies identified, examined
the full text and extracted the data for the analyses from the
selected studies.
2.1. Search method for identification of studies
Longitudinal studies were identified via literature search
using PUBMED, MEDLINE, PsycINFO, and PsycArticles and
Author's personal copy
12
C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
performed for all languages. The search was conducted from
1980 until February 2010. Indeed, as the DSM III was
published in 1980, studies published before did not have
the possibility to refer to the modern definition of psychiatric
conditions. Key search terms included: “insomnia” linked to
“depression” and “longitudinal” or “epidemiology” or “prospective” or “risk factors”. Terms were searched as keywords,
capturing the title and abstract. Further studies were added
by examining the reference lists of the papers found by the
literature search. Unpublished studies were not included.
2.2. Studies selection
After a first screening of titles and abstracts, the full texts
were examined to determine whether they met the inclusion
criteria. Studies were only considered when “sleep problems”
were defined as difficulties in initiating/maintaining sleep or
non-restorative sleep (i.e. insomniac symptoms). Moreover,
studies were selected only if depression was measured
through standard measures or consistently with the DSM IV
definition. Additionally, studies were included only if participants with significant depression at baseline were excluded
for the analysis or if the effect of symptoms of insomnia in
predicting depression was controlled for other depressive
symptoms.
Only those studies in which odds ratios were reported or
computable from the given data were selected in order to
compute meta-analytic calculations. Additionally, studies
were considered only if the follow-up measurement was
conducted after at least 12 months in order to evaluate the
long-term relationship between insomnia and depression.
First authors were contacted for supplemental data to the
published information. All authors contacted replied confirming or adding information about the suitability/unsuitability of their study for the meta-analysis calculations.
2.3. Data extraction
For each study, data on a number of procedural variables
were collected in order to assess the quality of the studies.
Specifically, the variables considered were: sample size,
length of the follow-up period (in months), and definition
of insomnia on the basis of DSM-IV-TR criteria. With respect
to the definition of insomnia, three groups were identified:
meeting only the sleep difficulties criterion (difficulties in
initiating/maintaining sleep or non-restorative sleep); meeting the sleep difficulties and the duration criteria (at least
1 month); meeting the sleep difficulties, the duration, and the
daytime consequences criteria. Additional descriptive information collected were gender, age, publication year, and
number of follow-up assessments. Furthermore, in order to
compute meta-analytic parameters for dichotomous outcomes, for each study the odds ratio was collected with 95%
confidence intervals (CI), as index of the predictive value of
insomnia for future onset of depression.
2.4. Meta-analytic calculations
The logarithms (logs) of the odds ratios and their CIs were
used for meta-analytic calculations. The analysis was applied
to the whole sample of studies and the studies were pooled
with both fixed-effects and random-effects meta-analytic
models.That is, the concordance between the two models
supplies first information concerning heterogeneity among
the studies: if the fixed and the random models give identical
results it is unlikely that there is important statistical
heterogeneity. In contrast, if the two models vary, heterogeneity needs to be assessed. In order to test for heterogeneity,
chi-square tests and the I2 statistic derived from the chi-square
values were used. I2 describes the proportion of total variation
in the estimates of effect size that is due to heterogeneity
among studies (Higgins and Thompson, 2002). An alpha error
b0.20 and an I2 of at least 50% were taken as indicators of
heterogeneity. Based on the results, we considered the
fixed-effects model when studies were not heterogeneous,
as a fixed model assumes that all studies share a common
true effect size (Borenstein et al., 2009). If studies were
heterogeneous, a random-effects model was used. In
sensitivity analyses, we considered standardized residuals
to identify outcomes that were outliers. Studies with
standardized residuals greater or equal to + 3 and/or
lower or equal to − 3 were deleted from the analysis
(Hedges and Olkin, 1985), and the meta-analytic calculations were performed again. Moreover, a subgroup analysis
was conducted considering 4 age groups and performing
the meta-analytic calculations for each group: 1) children
and adolescents, 2) working-age, 3) elderly, 4) mixed-age.
All meta-analytic calculations were made with the
software program Comprehensive Meta-Analysis, version 2
(Borenstein et al., 2006).
2.5. Publication biases
Potential publication or retrieval bias was assessed using
visual assessment of the funnel plot. This was computed
considering the standard error of the log odds ratio against
the log odds ratio. Publication bias may lead to asymmetrical
funnel plots. Furthermore, we tested the sensitivity of our
results to potential unpublished studies using the revisited
file-drawer test for meta-analysis (Rosenberg, 2005). This
test determines the number of non-significant, unpublished (or missing) studies that would need to be added to
a meta-analysis to reduce an overall statistically significant
observed result to non-significance. This number is called
the “fail safe number”. If the number is large relative to the
number of observed studies, one can feel fairly confident in
the summary conclusions.
2.6. Incidence of depression
Incidence of depression was considered for those studies
which reported: a) the percentage relative to the incidence of
new episodes of depression; b) the number of individuals
with sleep difficulties and no depression at baseline who
developed new depression at follow-up and the number of
individuals with no sleep difficulties and no depression at
baseline who developed new depression at follow-up. For
those studies a mean percentage indicating the incidence of
depression in the two groups with and without sleep
difficulties was calculated.
Author's personal copy
C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
3. Results
Fig. 1 illustrates the search flow. The literature search
yielded 46 longitudinal studies considering both sleep
difficulties and major depression. Five studies were excluded
as they focussed on the effect of depression predicting sleep
difficulties (Morgan et al., 1989; Hohagen et al., 1993; Foley et
al., 1999a; Patten et al., 2000; Jansson and Linton, 2006). Two
studies were excluded because the follow-up assessment was
conducted after less than 12 months (Hohagen et al., 1994;
Schramm et al., 1995). Three studies were excluded as sleep
difficulties were defined only as excessive sleepiness (Barkow
et al., 2001), intake of sleeping pills (Harlow et al., 1991), or
“sleep rhythm” problems (Ong et al., 2006). One study was
excluded as the odds ratio was not reported and not computable
(Rodin et al., 1988). Finally, one study was excluded because it
focussed on the risk for non remission of depression, instead of
new onset of the disorder (Pigeon et al., 2008); and one study
was excluded because it focussed on the risk for suicidality,
instead of the onset of depression (Cukrowicz et al., 2006).
A further selection was made in order to ensure homogeneity of insomnia definitions. Specifically, 4 studies were
excluded as insomnia was considered together with hypersomnia and parasomnias in a single variable (Gregory and O'Connor,
2002; Gregory et al., 2005; Gregory et al., 2008; Gregory et al.,
2009). Eight more studies were excluded as an unspecified
variable “sleep disturbances” was taken into account as
predictor (Kennedy et al., 1990; Dryman and Eaton, 1991;
Green et al., 1992; Livingston et al., 1993; Paffenbarger et al.,
1994; Berger et al., 1998; Prince et al., 1998; Livingston et al.,
2000). Our final selection included 21 studies.
Table 1 shows the list of the studies and the description of
the study characteristics. The mean sample size at the followup (t1) was n = 3200 (sd = 5556), ranging from n = 147
(Perlis et al., 2006) to n = 25,130 (Neckelmann et al., 2007).
13
The mean age of the participants was 46 years (sd = 22.4)
with means ranging from 6 (Johnson et al., 2000) to 80 years
(Foley et al., 1999b). In 3 studies, the age of the participants
was b18 years — a) children and adolescent group (Johnson
et al., 2000; Roberts et al., 2002; Roane and Taylor, 2008).
Seven studies considered working-age adults — b) workingage group. In 6 studies, the age of the participants ranged
between 18 and 60 years (Vollrath et al., 1989; Breslau et al.,
1996; Chang et al., 1997; Mallon et al., 2000; Buysse et al.,
2008; Jansson-Fröjmark and Lindblom, 2008). In one of these
7 studies, the age of the participants ranged between 33 and
71 years, but all participants were employees (Szklo-Coxe et al.,
2010). In 6 studies, the age of the participants was N60 years —
c) elderly group (Brabbins et al., 1993; Foley et al., 1999b; Hein
et al., 2003; Perlis et al., 2006; Cho et al., 2008; Kim et al., 2009).
In 4 studies, working-age adults and elderly were considered
together — d) mixed-age group (Ford and Kamerow, 1989;
Weissman et al., 1997; Morphy et al., 2007; Neckelmann et al.,
2007). Additionally, Roberts et al. (Roberts et al., 2000) were
also considered in the mixed-age group as participants were
aged 50 or older, thus, the sample included both working-age
individuals and elderly.
Only in one study, all participants were male (Chang et al.,
1997), while in all other studies, the mean of the percentage
of females was 55% (sd = 4.5), percentages ranging from 46%
(38) to 62% (33).
Four studies focussed on clinical samples. Hein et al.
(2003) recruited participants with a positive family history of
depression, Brabbins et al.(1993) and Perlis et al. (2006)
recruited patients of general practitioners, and Roberts et al.
(2002) recruited adolescents in managed care and use of
services for both psychiatric and somatic complaints. All
other studies focussed on the general population.
All studies assessed insomnia and depression through
subjective self-report, but one additionally measured
Fig. 1. The search flow: results of search for articles.
2006
2007
2007
2008
2008
2008
Perlis et al.
Morphy et al.
Neckelmann et al.
Buysse et al.
Cho et al.
Jansson-Fröjmark
and Lindblom
Roane and Taylor
Kim et al.
Szklo-Coxe et al.
dur
dur
dur
dur
and
and
and
and
day
day
day
day
sd and dur
sd, dur and day
sd
sd
sd
sd,
sd,
sd,
sd,
sd, dur and day
sd and dur
sd and dur
sd and dur
sd
sd and dur
sd
sd and dur
sd
sd
sd, dur and day
sd
In-home interview
Geriatric Mental State
Zung Self-Rating Depression Scale
SCID and HAMD
HADS
HADS
SPIKE
SCID and BDI
HADS
78
24
44
12
12
132
240
24
12
Diagnostic Interview Schedule
12
Psychiatric interview
84
Geriatric Mental State
36
NIHM Diagnostic Interview Schedule
42
Checklists. medical reports and self-reports
408
Diagnostic Interview Schedule
12
CES-D
36
CBCL and TRF
60
HADS
144
12 Items from the DSM-12D
12
Diagnostic Interview Schedule for Children
12
Composite International Diagnostic Interview
60
1
1
1
1
1
1
6
3
1
1
2
1
1
7
1
3
1
1
1
1
1
7954
457
701
979
941
7113
6899
717
1244
2370
3136
664
N (t1)
45.79
21.00
69.76
26.14
26.00
48.23
80.09
6.00
55.00
64.90
15.00
60.00
4494
1204
1533
OR (95% CI)
3582 16.00
c&a
909 72.20
elderly
555 53.60 working
[1.30–36.09]
[1.37–5.37]
[0.80–1.60]
[1.10–2.10]
[1.07–8.75]
[2.11–5.83]
2.20 [1.34–3.58]
2.10 [1.50–3.00]
2.49 [0.83–7.50]
6.86
2.71
1.10
1.60
3.05
3.51
mixed 39.80 [19.8–80.00]
working
2.16 [1.17–3.99]
elderly
1.39 [1.07–1.79]
working
2.10 [1.10–4.00]
working
1.90 [1.20–3.20]
mixed
5.40 [2.60–11.30]
elderly
1.70 [1.29–2.23]
c&a
1.53 [0.35–6.54]
working
2.78 [1.59–4.90]
mixed
4.85 [3.09–7.61]
c&a
1.92 [1.30–1.92]
elderly
2.40 [1.28–4.52]
Mean Age
age
group
247
147 72.00
elderly
2363
1589 50.00
mixed
74,977 25,130 54.07
mixed
591
278 19.50 working
351
329 69.00
elderly
1812
1489 42.00 working
10,534
591
1070
1007
1053
18,571
9282
823
1870
2730
4175
775
FU in
N of N (t0)
months FU
Abbreviations: NIHM: National Institute of Mental Health; CES-D: Centre for Epidemiologic Studies Depression; CBCL: Child Behavior Checklist; TRF: Teacher Report Form; HADS: Hospital Anxiety and Depression Scale;
DSM-12D: 12-item scale for DSM depression; HAMD: Hamilton Rating Scale for Depression; SCID: Structured Clinical Interview for DSM Disorders; SPIKE: Structured Psychopathological Interview and Rating of Social
Consequences of Psychic Disturbances for Epidemiology; PSQI: Pittsburgh Sleep Quality Index; BDI: Beck Depression Inventory; PSG: polisomnographic assessment; sd: sleep difficulties criterion; dur: duration criterion;
day: daytime consequences criterion; working: working-age group; elderly: elderly group; c&a: children and adolescents group; mixed: mixed-age group.
2008
2009
2010
1989
1989
1993
1996
1997
1997
1999
2000
2000
2000
2002
2003
Ford and Kamerow
Vollrath et al.
Brabbins et al.
Breslau et al.
Chang et al.
Weissman et al.
Foley et al.
Johnson et al.
Mallon et al.
Roberts et al.
Roberts et al.
Hein et al.
DSM-IV-TR insomnia Depression measures
criteria satisfied
14
Diagnostic Interview Schedule
Psychiatric interview
Geriatric Mental State
NIHM Diagnostic Interview Schedule
Habit Survey Questionnaire
Questionnaires
Interview
1 item from the CBCL
Uppsala Sleep Inventory
2 Items from the DSM-12D
Questionnaires
Composite International Diagnostic
Interview
HAMD (sleep items)
Jenkins Sleep Scale
Questionnaires
SPIKE and visual analogue scales
PSQI
Basic Nordic Sleep Questionnaires and
Uppsala Sleep Inventory
In-home interview
Interview
PSG + interview and self-reported
symptoms
PublYear Insomnia measures
Authors
Table 1
Study characteristics.
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C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
insomnia through polysomnographic assessment (SzkloCoxe et al., 2010).
On average, the follow-up assessment was conducted
after 71 months (sd = 96.0), ranging from 12 months to
408 months. Classifying the studies on the basis of the
median value, 11 studies conducted the follow-up assessment after 1 to 3 years (Ford and Kamerow, 1989; Brabbins et
al., 1993; Weissman et al., 1997; Foley et al., 1999b; Roberts et
al., 2000; Roberts et al., 2002; Perlis et al., 2006; Morphy et al.,
2007; Morphy et al., 2007; Cho et al., 2008; Jansson-Fröjmark
and Lindblom, 2008; Kim et al., 2009), while 10 studies
conducted the follow-up assessment after more than 3 years
(Vollrath et al., 1989; Breslau et al., 1996; Chang et al., 1997;
Johnson et al., 2000; Mallon et al., 2000; Hein et al., 2003;
Neckelmann et al., 2007; Buysse et al., 2008; Roane and
Taylor, 2008; Szklo-Coxe et al., 2010). Five studies reported
more than 1 follow-up assessment (Vollrath et al., 1989;
Chang et al., 1997; Foley et al., 1999b; Buysse et al., 2008; Cho
et al., 2008). All other studies reported only one follow-up
assessment.
Seven studies diagnosed insomnia on the basis of all
DSM-IV criteria: sleep difficulties, duration and daytime
consequences (Ford and Kamerow, 1989; Roberts et al.,
2002; Neckelmann et al., 2007; Buysse et al., 2008; Cho et
al., 2008; Jansson-Fröjmark and Lindblom, 2008; Kim et al.,
2009). Six studies took into consideration only sleep
difficulties and duration criteria (Vollrath et al., 1989;
Brabbins et al., 1993; Breslau et al., 1996; Weissman et al.,
1997; Johnson et al., 2000; Roane and Taylor, 2008), and
eight studies based the diagnosis only on the sleep
difficulties criterion (Chang et al., 1997; Foley et al.,
1999b; Mallon et al., 2000; Roberts et al., 2000; Hein et al.,
2003; Perlis et al., 2006; Morphy et al., 2007; Szklo-Coxe et
al., 2010).
With respect to the publication year (median value), ten
studies were published before 2001 (Ford and Kamerow,
1989; Vollrath et al., 1989; Brabbins et al., 1993; Breslau et al.,
1996; Chang et al., 1997; Weissman et al., 1997; Foley et al.,
1999b; Mallon et al., 2000; Johnson et al., 2000; Roberts et al.,
2000), while the other 11 studies were published after 2001.
3.1. Total sample
The fixed-effects meta-analytic model (2.07; CI: 1.87–
2.29) and the random-effects meta-analytic model (2.60; CI:
1.98–3.42) were not concordant. The test for heterogeneity
showed a significant index (Q-value = 124.7; df(Q) = 20;
p b 0.001; and I2 = 83.96). The between-studies variance was
τ 2 = 0.30 (standard error = 0.14; variance = 0.02; and
τ = 0.55). The weights and the standardized residuals for
each study are reported in Table 2. Due to the presence of
heterogeneity, the random-effects meta-analytic model was
selected (z = 6.85; N = 21, and p b 0.001).
15
Table 2
Weights and standardized residuals for each study.
Study
Weights
(fixed)
Standardized
residuals (fixed)
Ford and Kamerow 1989
Vollrath et al. 1989
Brabbins et al. 1993
Breslau et al. 1996
Chang et al. 1997
Weissman et al. 1997
Foley et al. 1999
Johnson et al. 2000
Mallon et al. 2000
Roberts et al. 2000
Roberts et al. 2002
Hein et al. 2003
Perlis et al. 2006
Morphy et al. 2007
Neckelmann et al. 2007
Buysse et al. 2008
Cho et al. 2008
Jansson-Fröjmark and Lindblom 2008
Roane and Taylor, 2008
Kim et al. 2009
Szklo-Coxe et al. 2010
2.10
2.72
15.45
2.45
4.25
1.89
13.65
0.48
3.25
5.03
6.82
2.57
0.37
2.19
8.51
9.78
0.93
3.96
4.24
8.51
0.84
8.39
0.14
− 3.30
0.04
− 0.35
2.58
− 1.52
− 0.41
1.05
3.80
− 0.40
0.46
1.42
0.78
− 3.74
− 1.65
0.73
2.08
0.25
0.08
0.33
p b 0.22; and I2 = 20.30). The fixed-effects meta-analytic model
showed an overall odds ratio of 2.10 (CI: 1.86–2.38; z = 11.96;
N = 17, p b 0.001). This show that non-depressed people with
insomnia have a twofold risk to develop depression, compared
to people with no sleep difficulties. The forest plot of these 17
studies is shown in Fig. 2.
3.3. Subgroup analysis: age groups
Results showed that the working age group was not
heterogeneous (Q-value = 7.8; df(Q) = 5; p = 0.2; and
I2 = 35.7). The group of elderly was marginally heterogeneous
(Q-value= 8.8; df(Q) = 5; p = 0.1; and I2 = 43.1). However,
when excluding the studies with clinical samples (Perlis et al.,
2006; Hein et al., 2003; Brabbins et al., 1993), which were
conducted on samples of elderly participants, the elderly group
was not anymore heterogeneous (Q-value = 1.7; df(Q) = 2;
p = 0.4; and I2 = 0.0). The group of children and adolescents
was also not heterogeneous (Q-value= 0.3; df(Q) = 2; p = 0.9;
and I2 = 0.0). On the other hand, the mixed age group was
heterogeneous.
Considering the working age group, the fixed-effects
model showed an odds ratio value of 2.1 (CI: 1.7–2.6;
z = 7.4, N = 6, and p b 0.001). Considering the elderly group,
without the three studies conducted on clinical samples, the
fixed-effects model showed an odds ratio value of 1.9 (CI:
1.6–2.3; z = 5.9, N = 4, and p b 0.001). With respect to the
group of children and adolescents, the fixed-effects model
showed an odds ratio value of 2.0 (CI: 1.5–2.7; z = 4.6; N = 3;
and p b 0.001).
3.2. Sensitivity analysis: excluding outliers
3.4. Publication bias
Two studies had standardized residuals greater than
+3LW43,46, and two studies had standardized residuals lower
than −3LW39,27. The exclusion of these 4 studies explained most
of the heterogeneity among the studies. The remaining studies,
thus, were not heterogeneous (Q-value = 20.08; df(Q) = 16;
The funnel plot was asymmetric with results in the same
direction for most outcomes. However, the computation of
the revisited file-drawer test for meta-analysis showed that
the fail safe number (significance level: 0.05) of studies
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C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
Fig. 2. Meta-analysis of the effects of insomnia for future depression after exclusion of the outliers (fixed-effects meta-analytic model).
necessary to “nullify” the average effect was n = 1782. Thus,
the fail-safe number was immensely larger than the number
of original studies (n = 21).
3.5. Incidence of depression
Of the 21 studies considered, 6 reported the information
about the number of individuals with insomnia (and no
depression) at baseline who developed depression at followup and the number of those with no sleep difficulties (and no
depression) at baseline who developed depression at followup (Szklo-Coxe et al., 2010; Morphy et al., 2007; Perlis et al.,
2006; Chang et al., 1997; Weissman et al., 1997; Vollrath et
al., 1989). Considering these studies together, within a total of
797 individuals with insomnia at baseline, 90 developed new
depression at follow-up (incidence in percentage: 11.3).
Within a total of 6919 individuals with no sleep difficulties at
baseline, 168 developed new depression at follow-up
(incidence in percentage: 2.4). Three more studies reported
the incidence of depression in percentage respectively for
both the group with and without insomnia at baseline (Roane
and Taylor, 2008: 22.9 and 10.2; Breslau et al., 1996: 15.9 and
4.6; Ford and Kamerow, 1989: 22.9 and 10.2).
Calculating a mean incidence in percent considering these
9 studies together, we found that in the group of those with
insomnia at baseline, the incidence value is 13.1, while in the
group of those without sleep difficulties at baseline, the
incidence value is 4.0.
4. Discussion
The results of the present analysis indicate that nondepressed subjects with insomnia have a twofold risk to
develop depression, compared to people with no sleep
difficulties. The pooled estimates were high despite the
wide variability in study populations, design and measures,
and persisted to be high after exclusion of the outliers
(OR = 2.10; 95%; and CI: 1.86–2.38). More specifically, the
incidence of depression in the group with insomnia (and no
depression) at baseline was significantly higher (incidence in
percentage: 13.1) than the incidence of depression in the
group without sleep difficulties (and no depression) at
baseline (incidence in percentage: 4.0). The incidence in
percentage of depression in the general population has been
reported to be 9.9 (Murphy et al., 2002). That is, in a specific
group with sleep difficulties, the incidence of depression is
higher in comparison to the general population. It is also
interesting to notice that in a specific group of people with no
sleep difficulties, the incidence of depression is much lower as
compared to the general population.
Subgroup analysis considering the different age-groups
showed that the effect of insomnia in predicting subsequent
depression is similar in children and adolescents, workingage individuals, and elderly individuals.
Our analysis is the first attempt to quantitatively summarize the results of longitudinal studies on the role of insomnia
as a predictor of depression. Insomnia is one of the most
common health problems in industrialized countries, affecting approximately 10% of the general population in its chronic
form and about 30% occasionally (Ohayon, 2009). The
understanding of the role of sleep in depression has been
suggested to yield important insights into the pathophysiology of the disorder (Murray and Lopez, 1996). Based on this,
the results of the present analysis indicate important research
and clinical implications. Psychophysiological mechanisms
through which insomnia predicts depression are still not
clear. Recent interest has been dedicated to the role of
insomnia in emotion regulation, which might explain why
insomnia leads to depression (Koffel and Watson, 2009;
Baglioni et al., 2010a). From a neurobiological perspective, a
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C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
dysfunction in sleep-wake regulating neural circuitries may
lead to alterations in emotional reactivity (Riemann et al.,
2010). Moreover, emotional stimuli interact with the basic
homeostatic and circadian drives for sleep through the
interaction of affect-related and sleep-related brain regions
(Saper et al., 2005). A few studies have found altered
emotional responses in people with insomnia as compared
to good sleepers, both using subjective (Scott and Judge,
2006) and objective measures (Baglioni et al., 2010b).
However, further investigation is needed, especially with
regard to the role of age and gender.
With respect to clinical implications, treating insomnia in
its early stage might be effective in ameliorating sleep quality
and in preventing mood dysfunctions (Riemann, 2009;
Baglioni et al., 2010a). However, although cognitive-behavior
therapy for insomnia (CBT-I) is known to be effective and
exerts stable long-term results (Riemann and Perlis, 2009)
only a minority of afflicted individuals are treated in this way
because its implementation is presently mostly restricted to
academic and research contexts. Dissemination of CBT-I
protocols, for example through general practitioners, might
lead to better results. Easy accessible intervention programs
for insomnia for the general population could be a very
important goal for the next years as a preventive strategy for
depression.
The quality of the original studies was appraised through
the consideration of mean sample size and length of followup periods, as well as the evaluation of the definition of
insomnia based on DSM-IV-TR criteria. As a whole, the studies
included in this meta-analysis reported data from a large
number of individuals taken mostly from the general
population in different countries (USA, UK, Switzerland,
Germany, Sweden, Norway, and South Korea). Additionally,
a great number of these studies followed the participants for
more than 3 years. That is, symptoms of insomnia may have
existed already many years before the onset of a depressive
episode. Three of the 17 selected studies, after exclusion of
outliers, applied the follow-up assessment after more than
10 years (Chang et al., 1997; Mallon et al., 2000; Buysse et al.,
2008). As the prodromal period of a first episode of major
depression may be up to 8–10 years (Dryman and Eaton,
1991), these 3 studies suggest that insomnia could be not
only a prodrome of, but also an independent risk factor of
depression, at least in the 18–60 years old group. However, it
could also be argued that insomnia represents the first
clinical sign of depression. In order to deepen this issue,
further investigations should focus on possible neurobiological links between insomnia and depression. Moreover,
prospective intervention studies focussing on insomnia in
different age-samples should be conducted in order to
assess the expected decrease of the incidence of depression. Finally, it is noteworthy that of the 10 studies
published before 2001, only one defined insomnia on the
basis of sleep difficulties, duration and daytime consequences, whereas, of the 11 studies published after 2001, 7
studies did so. This reflects the changes in the conceptualization of insomnia in the last 20 years.
The four outlier studies (Ford and Kamerow, 1989;
Brabbins et al., 1993; Roberts et al., 2000; Neckelmann et
al., 2007) did not included smaller sample sizes, or shorter
length of follow-up periods, or satisfied less the DSM-IV-TR
17
criteria for insomnia, compared to the other studies. As 3 of
them recruited both working-age participants and elderly
(Ford and Kamerow, 1989; Roberts et al., 2000; Neckelmann
et al., 2007), it might be argued that studies with mixed-age
samples could have required the use of several different sites
for recruitment and this might have influenced the carefulness of the selection. However, other two studies did so and
did not belong to the outlier group (Weissman et al., 1997;
Morphy et al., 2007). Anyway, the effects reported in the 4
outlier studies were in the same direction of the pooled
estimate of the effect resulting from the remaining 17 studies.
Quantitative reviews, as meta-analysis, have the potential
to be affected by publication bias. However, our analysis
indicates that a publication bias was not the source of the
overall results of our meta-analysis, as the “fail safe number”
was much larger than the number of the original studies.
Some limitations of the current analysis have to be
addressed. First, results cannot be generalized with respect to
gender. The majority of the included studies reported a
summarized odds ratio for both women and men. Additionally,
as studies differed with respect to the definition of insomnia, it
is impossible to evaluate whether clinical symptoms of
insomnia might have a greater impact in predicting depression
or not as compared to the experience of poor sleep. Further,
insomnia is a heterogeneous disorder including different
problems related to poor sleep and its consequences. As the
definition of insomnia considered in the selected studies,
independently on how accurate it was, referred to a representative heterogeneous sample of people with sleep difficulties, it
would be interesting to conduct longitudinal studies on the
causal relationship between insomnia and depression taking
into account insomnia subtypes. Moreover, there is a need for
future research to evaluate the predictor role of insomnia for
depression, rigorously independently from all other related
variables, as alcohol or drug abuse, other somatic or psychiatric
diseases, and/or medication status. Additionally, studies on
children and adolescents are still too few and the issue needs
further investigation. These studies appear of great interest, as
interventions in early periods of life could lead to significant
benefits for the individual and improve public health outcomes.
The link between insomnia and mood regulation is
consistent with the clinical evidence that insomnia commonly co-occurs with all most relevant and frequent psychiatric
conditions. The transdiagnostic nature of insomnia with
respect to psychiatric disorders (Harvey, 2009) suggests
that future research strategies in insomnia address neurobiological underpinnings in relation to other mental disorders
and clarify a possible preventive role of insomnia treatment in
general for mental health.
Role of funding source
Dr. Baglioni and Prof. Dr. Riemann have received funding from the
European Community's Seventh Framework Programme (People, Marie
Curie Actions, Intra-European Fellowship, FP7-PEOPLE-IEF-2008) under
grant agreement n. 235321 and from OPTIMI (FP7-JCT-2009-4; 248544)
for this work. The sources of funding had no role in study design; in the
collection, analysis and interpretation of data; in the writing of the report;
and in the decision to submit the paper for publication.
Conflict of interest
The corresponding author, Dr. Baglioni, confirms that she had full access
to all the data in the study and had final responsibility for the decision to
submit for publication.
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18
C. Baglioni et al. / Journal of Affective Disorders 135 (2011) 10–19
Dr. Nissen has received speaker honoraria from Sanofi-Aventis and Lundbeck.
Dr. Voderholzer has received speaker honoraria from Sanofi-Aventis, Lundbeck,
Pfizer, Cephalon, and Lilly. He has been principal investigator of an investigator
initiated trial sponsored by Lundbeck. Dr. Riemann received research support from
Takeda, Sanofi-Aventis, Organon, Actelion and Omron. He was on the speaker's
bureau of Sanofi-Aventis, Takeda, Servier, Lundbeck, Boehringer-Ingelheim, GSK,
Cephalon and Merz Pharmaceuticals. He was also a member of advisory boards for
Sanofi-Aventis, Lundbeck, GSK, Takeda and Actelion. Dr. Nissen, Dr. Voderholzer,
and Dr. Riemann declare that the above mentioned activities have no influence on
the content of this article. All the other authors reported no conflicts of interest.
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