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Meta-analysis
Risk factors and peripheral biomarkers for
schizophrenia spectrum disorders: an
umbrella review of meta-analyses
Belbasis L, K€
ohler CA, Stefanis N, Stubbs B, van Os J, Vieta E,
Seeman MV, Arango C, Carvalho AF, Evangelou E. Risk factors and
peripheral biomarkers for schizophrenia spectrum disorders: an
umbrella review of meta-analyses
Objective: This study aimed to systematically appraise the meta-
analyses of observational studies on risk factors and peripheral
biomarkers for schizophrenia spectrum disorders.
Methods: We conducted an umbrella review to capture all meta-
analyses and Mendelian randomization studies that examined
associations between non-genetic risk factors and schizophrenia
spectrum disorders. For each eligible meta-analysis, we estimated the
summary effect size estimate, its 95% confidence and prediction
intervals and the I
2
metric. Additionally, evidence for small-study
effects and excess significance bias was assessed.
Results: Overall, we found 41 eligible papers including 98 associations.
Sixty-two associations had a nominally significant (P-value <0.05)
effect. Seventy-two of the associations exhibited large or very large
between-study heterogeneity, while 13 associations had evidence for
small-study effects. Excess significance bias was found in 18
associations. Only five factors (childhood adversities, cannabis use,
history of obstetric complications, stressful events during adulthood,
and serum folate level) showed robust evidence.
Conclusion: Despite identifying 98 associations, there is only robust
evidence to suggest that cannabis use, exposure to stressful events
during childhood and adulthood, history of obstetric complications,
and low serum folate level confer a higher risk for developing
schizophrenia spectrum disorders. The evidence on peripheral
biomarkers for schizophrenia spectrum disorders remains limited.
L. Belbasis
1
,C.A.K
€
ohler
2
,
N. Stefanis
3
, B. Stubbs
4,5
,
J. van Os
6,7
, E. Vieta
8
,
M. V. Seeman
9
, C. Arango
10
,
A. F. Carvalho
11,12
,
E. Evangelou
1,13
1
Department of Hygiene and Epidemiology, University of
Ioannina Medical School, Ioannina, Greece,
2
Translational Psychiatry Research Group, Department of
Clinical Medicine, Federal University of Cear
a Medical
School, Fortaleza, Brazil,
3
Department of Psychiatry,
Eginition Hospital, National and Kapodistrian University
of Athens Medical School, Athens, Greece,
4
Department
of Physiotherapy, South London and Maudsley NHS
Foundation Trust, London, UK,
5
Department of Health
Service and Population Research, Institute of Psychiatry,
King’s College London, London, UK,
6
Department of
Psychiatry, Brain Center Rudolf Magnus, University
Medical Centre Utrecht, Utrecht, The Netherlands,
7
Department of Psychosis Studies, Institute of
Psychiatry, King’s College London, London, UK,
8
Bipolar
Disorder Unit, Institute of Neuroscience, University of
Barcelona, IDIBAPS and CIBERSAM, Barcelona, Spain,
9
Institute of Medical Science, University of Toronto,
Toronto, ON, Canada,
10
Department of Child and
Adolescent Psychiatry, University Hospital Gregorio
Mara~
n
on, Complutense University of Madrid Medical
School, CIBERSAM, Madrid, Spain,
11
Department of
Psychiatry, University of Toronto, Toronto, ON, Canada,
12
Centre for Addiction & Mental Health (CAMH),
Toronto, ON, Canada and
13
Department of Epidemiology
and Biostatistics, School of Public Health, Imperial
College London, London, UK
Key words: epidemiology; meta-analysis; psychosis; risk
factors; schizophrenia
Evangelos Evangelou, Department of Hygiene and
Epidemiology, University of Ioannina Medical School,
Ioannina, Greece.
E-mail: vangelis@cc.uoi.gr
Accepted for publication December 4, 2017
1
Acta Psychiatr Scand 201 : 1–10 ©2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
All rights reserved
DOI: 10.1111/acps.12847 ACTA PSYCHIATRICA SCANDINAVICA
7
Summations
•This large-scale umbrella review assessed 98 associations of non-genetic risk factors with
schizophrenia that were examined in meta-analyses and mendelian randomization studies.
•Even though 62 associations were nominally statistical significant (P-value<0.05) most of the meta-
analyses showed large heterogeneity, evidence of small-study effects and excess of significant findings.
•Cannabis use, childhood adversities, history of obstetric complications, stressful events during adult-
hood and serum folate level showed robust evidence of association.
Considerations
•We considered only associations that have been examined in a meta-analysis, therefore potentially
interesting risk factors that have been assessed in single studies, such as socioeconomic status, have
not been considered.
•The associations of adult stressful events, obstetric complications and serum folate level were not
examined in prospective studies.
Introduction
Schizophrenia is a mental disorder with a med-
ian lifetime prevalence of 4.0 per 1000 individu-
als (1). Its onset characteristically occurs in
adolescence or early adulthood (2). A diagnosis
of schizophrenia is associated with significant
disability and with premature all-cause mortality
(3, 4). The death rate is predominantly due to
suicide early in the course of the disorder and
cardio-metabolic disturbances later in life (1, 4,
5). Accumulating evidence indicates that a com-
plex interplay of genetic and environmental risk
factors probably underpins the emergence of
schizophrenia (6).
Over the past twenty-five years, the field has
witnessed an explosion in observational studies
investigating putative environmental risk factors.
Two previously published field-wide systematic
reviews found that few non-genetic risk factors
were supported by good quality evidence (7, 8).
These reviews, however, did not provide a quanti-
tative appraisal of epidemiological credibility nor
did they explore potential biases (7, 8).
Aims of the study
In this study, we conducted an umbrella review
of meta-analyses on risk factors and peripheral
biomarkers for schizophrenia and other psychotic
disorders. We assessed the potential for bias in
this literature, and we identified associations sup-
ported by the most robust epidemiological evi-
dence. The potential causal association between
risk factors and schizophrenia was examined by
systematically searching for Mendelian random-
ization (MR) studies, a novel epidemiological
study design allowing for better control of
reverse causality and confounding (9).
Methods
This study is an umbrella review, which is a sys-
tematic collection and critical evaluation of multi-
ple systematic reviews and meta-analyses on a
specific research topic (10). Similar efforts have
been published for other chronic medical condi-
tions. (11–16)
Search strategy and eligibility criteria
We systematically searched PubMed from inception
to January 5, 2017 to identify meta-analyses of
observational studies examining associations of
schizophrenia spectrum disorders in adults and
either environmental (i.e., non-genetic) risk factors
or peripheral biomarkers. The following search
algorithm was used: (schizophrenia OR psychosis)
AND (‘systematic review*’ OR meta-analys*).
Peripheral biomarkers were defined as biomarkers
measurable in serum/plasma, saliva, or urine. Based
on a definition by the World Health Organization
(17), a risk factor was defined as any attribute, char-
acteristic or exposure of an individual that increases
the likelihood of developing a disease or injury.
We excluded meta-analyses that investigated
genetic variants, neuroimaging markers, or cere-
brospinal fluid markers. Also, we excluded meta-
analyses that examined environmental risk factors
for the transition to psychosis in individuals at
ultra-high risk, because these factors have been
recently examined in a published meta-analysis
and study-specific data are not publicly available
(18). We considered as eligible meta-analyses that
2
Belbasis et al.
included at least three independent samples. Lan-
guage restrictions were not applied. When more
than one meta-analysis was available on the same
association, we included the one with the largest
number of non-overlapping prospective observa-
tional studies.
Moreover, we performed an additional search
on PubMed to systematically capture MR studies
for schizophrenia spectrum disorders using the fol-
lowing search algorithm: ‘mendelian randomiza-
tion’ OR ‘mendelian randomisation’. An MR
study is an epidemiological study design using tri-
angulation methods to address the causal relation-
ship between an exposure and an outcome in
observational studies (19). It is an application of
the technique of instrumental variables with geno-
type acting as an instrument for the exposure of
interest. The term ‘Mendelian randomization’ is a
method of using measured variation in genes of
known function to examine the effect of an expo-
sure on a disease (20). We did not include MR
studies that used only summary-level data from
candidate gene association studies.
Initial title screening was performed by one
researcher (LB), and two independent researchers
extracted the data (LB and EE). Discrepancies
were discussed, and consensus was reached.
Data extraction
From each meta-analysis, we abstracted informa-
tion on first author, year and journal of publica-
tion, examined risk factors, number and study
design of component studies, and study-specific
risk estimates (i.e., risk ratio, odds ratio, hazard
ratio, Cohen’s d, or Hedges’ g). We additionally
recorded whether the eligible meta-analyses per-
formed a qualitative assessment of component
studies based on predefined quality scores or
scales, such as the Newcastle–Ottawa scale. For
each eligible meta-analysis, we examined whether
component studies included overlapping samples;
in such circumstances, we considered only the
component study with the largest sample size.
From each MR study, we extracted the follow-
ing information: first author, year and journal of
publication, sample size, effect size metric, causal
effect size estimate along its 95% confidence
interval (CI), P-value, study design, and genetic
instrument.
Statistical analysis
Measures of standardized mean difference (i.e.,
Cohen’s d and Hedges’ g) were transformed to
odds ratio. For each meta-analysis, we estimated
the summary effect size estimate and its 95% CI,
using both fixed-effect and random-effects models
(21, 22). We also estimated the 95% prediction
interval, which accounts for between-study hetero-
geneity and evaluates the uncertainty for the effect
that would be expected in a new study addressing
that same association (23, 24). For the largest
study of each meta-analysis, we estimated the SE
of the effect size to examine whether it was less
than 0.10.
Between-study heterogeneity was quantified
using the I
2
metric. I
2
ranges between 0% and
100% and quantifies the variability in effect esti-
mates that is due to heterogeneity rather than to
sampling error (25). Values exceeding 50% or 75%
are considered to represent large or very large
heterogeneity respectively.
We assessed small-study effects using the Egger’s
regression asymmetry test (26, 27). A P-value
<0.10 combined with a more conservative effect in
the largest study than in random-effects meta-ana-
lysis was judged to provide adequate evidence for
small-study effects. We also applied the excess sta-
tistical significance test, which evaluates whether
there is a relative excess of statistically significant
findings in the published literature (28). It is a sta-
tistical test that assesses whether the observed
number of studies with statistically significant
results is larger than expected. Excess statistical
significance was claimed at two-sided P-value
<0.10 (29).
Assessment of epidemiological credibility
To identify associations with robust evidence, we
applied a set of methodological criteria, which
have been previously applied in other research
fields (11–16). For evidence to be convincing,
>1000 cases were required as well as a highly signif-
icant association (P-value <10
6
by random-effects
model), no evidence of small-study effects or excess
significance bias, a 95% prediction interval exclud-
ing the null value and no large between-study
heterogeneity (I
2
<50%). Highly suggestive evi-
dence required >1000 cases, a highly significant
association (P-value <10
6
by random-effects
model) and a statistically significant effect in the
largest study. Suggestive evidence required >1000
cases and P-value <0.001 by the random-effects
model, whereas the remaining nominally signifi-
cant risk factors (P-value <0.05) were supported by
weak evidence.
For associations with convincing or highly sug-
gestive evidence, to examine the temporal relation-
ship between the exposure and the outcome, we
performed a sensitivity analysis synthesizing
3
Risk factors and biomarkers for schizophrenia
evidence from prospective cohort studies only. An
additional sensitivity analysis was performed for
component studies that applied a structured diag-
nostic interview for case definition.
Results
Overall, we searched 3499 articles and 41 articles
fulfilled the eligibility criteria (Fig. 1). During full-
text screening, 31 articles were excluded, because a
larger meta-analysis examining the same associa-
tion was available, whereas 28 systematic reviews
were excluded because there was no quantitative
synthesis of the evidence. The 41 eligible papers
examined a total of 98 associations (41 environ-
mental factors and 57 peripheral biomarkers).
Ten of the 41 papers (24%) reported a qualita-
tive assessment of component studies through a
standardized tool. Seven of these used the
Newcastle–Ottawa scale, and three papers used a
scoring system based on the STROBE statement
(30). Six additional papers (15%) used a tailor-
made assessment tool.
Environmental risk factors
Overall, 41 environmental risk factors were exam-
ined for an association with schizophrenia. Eleven
associations (stressful events during adulthood,
Borna disease virus infection, general academic
achievement, handedness, cannabis use, tobacco
smoking, traumatic brain injury, obstetric compli-
cations, advanced paternal age, childhood adversi-
ties, and urbanicity) were studied in at least 1000
cases.
Thirty associations (73%) presented a nomi-
nally significant summary effect, 21 associations
remained significant at P-value <0.001, but only
3499 articles reviewed by
title screening
477 articles reviewed by
abstract screening
41 eligible articles (98 associations) published
until January 5, 2017
282 articles reviewed by
full text screening
241 articles were excluded
167 were articles on other research topics
31 were not the largest meta-analysis
28 were systematic reviews without quantitative synthesis
10 were meta-analyses not presenting study-specific effect estimates
3 were meta-analyses with fundamental statistical errors
2 were meta-analyses including less than 3 component studies
3022 articles were excluded
1288 were treatment studies
539 were articles on other research topics
526 were articles about genetic epidemiology
332 were diagnostic, prognostic or screening studies
241 were editorials or narrative reviews
37 were articles about health economics and quality of life
34 were methodological papers
25 were incidence or prevalence studies
195 articles were excluded
83 were articles on other research topics
49 were diagnostic, prognostic or screening studies
33 were editorials or narrative reviews
16 were treatment studies
8 were incidence or prevalence studies
6 were articles about health economics and quality of life
Fig. 1. Flowchart of literature search.
4
Belbasis et al.
14 associations achieved a P-value <10
6
under
the random-effects model (Table S1). Only four
associations (stressful events during adulthood,
cannabis use, childhood adversities, and obstetric
complications) had a P-value <10
6
and were
studied in at least 1000 cases. Seven associations
(23%) were reported in meta-analyses that
included a largest study with a SE <0.10 (stress-
ful events during adulthood, general academic
achievement, handedness, tobacco smoking, trau-
matic brain injury, paternal age, and urbanicity).
The result of the largest study was more conser-
vative than the summary result in 19 associations
(46%).
In twenty associations (49%), small or moderate
between-study heterogeneity was found
(I
2
<50%). In 10 meta-analyses (24%), 95% pre-
diction intervals excluded the null value. Five asso-
ciations (handedness, childhood social withdrawal,
Toxoplasma gondii infection, traumatic brain
injury, and cooperativeness) were reported in
meta-analyses with evidence for small-study
effects. Six associations (handedness, childhood
social withdrawal, Toxoplasma gondii infection,
cooperativeness, openness, and agreeableness)
were reported in meta-analyses with evidence for
excess significance bias (Table S1). The excess sta-
tistical significance test was not performed in four
associations (parental communication deviance,
tobacco smoking, paternal age, and urbanicity),
because the study-specific sample sizes were not
available and power calculations could not be per-
formed.
Peripheral biomarkers
Fifty-seven associations of peripheral biomarkers
and risk for schizophrenia were identified. Twelve
associations (21%), pertaining to serum BDNF,
serum vitamin B
12
, serum CRP, serum interleukin-
6, serum antigliandin IgA and IgG, serum anti-
TTG2 IgA, serum leptin, serum folate, serum
TNF-a, serum morning cortisol, and plasma adi-
ponectin, were studied in a total sample of more
than 1000 cases.
Thirty-two of 57 associations (56%) presented a
nominally significant effect, 16 associations
remained significant at P-value <0.001, and four of
these (serum S100B, serum DPA, serum folate,
and plasma TAS) had a P-value <10
6
(Table S2).
Only the association of schizophrenia with serum
folate level had a P-value <10
6
and included a
total number of cases greater than 1000. Also, only
the association with serum TNF-ahad largest
study with a SE <0.10. The effect of the largest
study was more conservative than the summary
effect in 35 associations (61%).
Six associations (11%) presented small or mod-
erate between-study heterogeneity (I
2
<50%), and
42 associations (77%) had an I
2
>75%. Only the
association with impaired glucose tolerance had a
95% prediction interval excluding the null value,
but this association was supported by a trivial
number of cases. Thirteen associations (23%)
rested on evidence suggestive of small-study
effects, and 21 associations (37%) showed evidence
for excess statistical significance (Table S2).
Assessment of epidemiological credibility
By applying a standardized procedure, we found
one association supported by convincing evidence
(i.e., history of obstetric complications). Also, we
identified four associations (4%) supported by
highly suggestive evidence. These associations were
as follows: exposure to stressful events during
adulthood, exposure to childhood adversities, can-
nabis use, and serum folate level. Seven associa-
tions (7%) had suggestive evidence (urbanicity,
Borna disease virus infection, advanced paternal
age, tobacco smoking, serum interleukin-6, serum
BDNF, and serum CRP). The associations
supported by convincing, highly suggestive, and
suggestive evidence are presented in Table 1. Fifty-
one associations (52%) presented weak evidence
for an association with schizophrenia. The remain-
ing associations had a non-significant effect
(P-value >0.05).
In the sensitivity analysis that included only
prospective cohort studies, the evidence for the
association of cannabis use and exposure to child-
hood adversities with schizophrenia risk remained
highly suggestive (Table 2). The association of
exposure to stressful events during adulthood, his-
tory of obstetric complications, and serum folate
level were not studied in any prospective cohort
study. In the sensitivity analysis that included only
component studies using a structured diagnostic
interview, the association of childhood adversities
remained highly suggestive, the association of
stressful events during adulthood became sugges-
tive only, and the association of obstetric compli-
cations became weak (Table 2). For cannabis use
and serum folate level, fewer than three component
studies using a structured diagnostic interview
were available.
Mendelian randomization studies in schizophrenia
We identified five MR studies with a total of six
MR analyses. These MR studies examined the
5
Risk factors and biomarkers for schizophrenia
Table 2. Sensitivity analyses among associations with convincing and highly suggestive evidence
Reference Risk factor
Level of
comparison
Number of
datasets
Number of
cases/controls
Random-effects
summary
OR (95% CI) P random
95%
prediction
interval I
2
Grading
Sensitivity analysis on prospective cohort studies
Marconi et al. (2016) (36) Cannabis use Ever vs. never use 6 1294/49 202 3.84 (2.34–6.29) 1.02 910
7
0.74–19.80 80.5 Highly
suggestive
Varese et al. (2012) (61) Childhood
adversities
Yes vs. no 8 4085/30 681 2.57 (1.94–3.40) 5.67 910
11
1.12–5.90 67 Highly
suggestive
Sensitivity analysis on studies using ICD or DSM codes and structured interview for case definition
Beards et al. (2013) (56) Adult stressful events Yes vs. no 7 1479/8141 2.93 (1.83–4.68) 7.53 910
6
0.66–12.91 80 Suggestive
Geddes et al. (1995) (57) Obstetric
complications
Yes vs. no 14 633/18 232 1.85 (1.40–2.44) 1.48 910
5
1.10–3.10 13.4 Weak
Varese et al. (2012) (61) Childhood adversities Yes vs. no 10 2136/19 128 2.95 (2.33–3.74) 2.75 910
19
1.51–5.76 57.3 Highly
suggestive
CI, confidence interval; OR, odds ratio.
Table 1. Characteristics of associations with convincing, highly suggestive, and suggestive evidence
Reference Risk factor
Level of
comparison
Number of
cases/controls
Number of
datasets
Random-effects
summary
OR (95% CI) P random
95%
prediction
interval I
2
Small-study
effects/Excess
significance bias Grading
Arias et al.
(2012) (45)
BDV infection Yes vs. no 1930/2150 17 2.23 (1.47–3.39) 1.77 910
4
0.76–6.59 36.4 No/No Suggestive
Beards et al.
(2013) (56)
Adult stressful
events
Yes vs. no 2218/17 628 13 3.11 (2.31–4.18) 5.21 910
14
1.19–8.13 74.8 No/No Highly
suggestive
Geddes and
Lawrie
(1995) (57)
Obstetric
complications
Yes vs. no 1000/19 101 18 1.97 (1.55–2.50) 2.87 910
8
1.16–3.36 18.9 No/No Convincing
Goldsmith et al.
(2016) (58)
Serum IL-6 High levels
vs. low levels
1398/1391 29 2.61 (1.55–4.40) 3.25 910
4
0.15–44.49 91.6 No/Yes Suggestive
Green et al.
(2011) (59)
Serum BDNF High levels
vs. low levels
1189/969 17 0.34 (0.18–0.62) 5.29 910
4
0.02–4.66 91.5 No/No Suggestive
Gurillo et al.
(2015) (60)
Tobacco
smoking
Ever vs. never NA/NA 17 2.34 (1.65–3.33) 2.15 910
6
0.63–8.75 88.3 No/NA Suggestive
Inoshita et al.
(2016) (53)
Serum CRP High levels
vs. low levels
1664/3070 15 2.96 (1.75–5.01) 5.28 910
5
0.34–25.43 93.7 No/No Suggestive
Marconi et al.
(2016) (36)
Cannabis use Ever vs. never 4036/62 780 10 3.90 (2.84–5.35) 3.41 910
17
1.33–11.45 81.7 No/No Highly
suggestive
Torrey et al.
(2009) (43)
Paternal age >35 years vs.
<35 years
NA/NA 10 1.28 (1.11–1.48) 9.11 910
4
0.82–1.99 74.9 No/NA Suggestive
Varese et al.
(2012) (61)
Childhood
adversities
Yes vs. no 7738/67 009 34 2.80 (2.34–3.34) 5.33 910
30
1.22–6.42 72.5 No/No Highly
suggestive
Vassos et al.
(2012) (42)
Urbanicity Highest vs.
lowest category
NA/NA 8 2.39 (1.63–3.51) 9.37 910
6
0.57–9.93 99.1 No/NA Suggestive
Wang et al.
(2016) (62)
Serum folate High levels vs.
low levels
1773/1930 26 0.37 (0.27–0.51) 1.23 910
9
0.08–1.73 83.4 No/No Highly
suggestive
BDV, Borna disease virus; BDNF, brain-derived neurotrophic factor; CI, confidence interval; CRP, C-reactive protein; IL-6, interleukin-6; NA, not available; OR, odds ratio.
Table 3. Characteristics of Mendelian randomization studies for schizophrenia
Author, Year Risk factor Level of comparison
Genetic
instrument
Number of SNPs
in genetic instrument
Number of
cases
Effect size
metric
Causal effect
size (95% CI) P-value
Gage et al. (2016) (32) Cannabis use Users vs. non-users GRS 12 36 989 OR 1.01 (0.93–1.10) NS
Prins et al. (2016) (33) Serum CRP Per 1 mg/l increase in ln(CRP) GRS 3 34 241 OR 0.90 (0.82–0.99) NS
Prins et al. (2016) (33) Serum CRP Per 1 mg/l increase in ln(CRP) GRS 15 25 629 OR 0.86 (0.79–0.94) 0.001
Taylor et al. (2016) (35) Serum 25(OH)D Per 10% increase GRS 4 34 241 OR 0.99 (0.97–1.02) NS
Vaucher et al. (2017) (31) Cannabis use Users vs. non-users GRS 10 34 241 OR 1.41 (1.09–1.83) NR
Wium-Andersen et al.
(2014) (34)
Serum CRP Highest vs. lowest quartile GRS 4 168 HR 1.40 (0.46–4.25) NS
CI, confidence interval; CRP, C-reactive protein; HR, hazard ratio; GRS, genetic risk score; NR, not reported; NS, not significant; OR, odds ratio; SNPs, single nucleotide polymor-
phisms.
6
Belbasis et al.
causal association of cannabis use (31, 32), serum
CRP (33, 34), and serum vitamin D (35) with risk
for schizophrenia (Table 3). All MR studies con-
structed a genetic risk score as an instrumental
variable. Three MR studies used summary-level
data, one MR study used individual-level data,
whereas one MR study used both summary-level
and individual-level data. One MR study was
based on a trivial number of schizophrenia cases
(34), whereas the remaining MR studies included
at least 25 000 cases. A significant protective effect
was observed for higher serum level of CRP, and a
non-significant effect was found for serum level of
vitamin D. The MR studies on cannabis use
showed conflicting results.
Discussion
We critically appraised almost 100 associations
between risk factors and peripheral biomarkers for
psychotic disorders in the schizophrenia spectrum.
More than two-thirds of the examined associations
presented a nominally significant effect, but most
of these associations were based on weak evidence
due to either a small number of cases or a P-value
close to the significance threshold. This is a com-
mon phenomenon which has also been observed in
previously published umbrella reviews on other
chronic conditions (11–15). Our analysis indicated
that the association of schizophrenia with exposure
to physical or psychological adversities during
childhood and adulthood and with cannabis use
was based on robust evidence and showed a large
effect size (i.e., an odds ratio >2). Furthermore, a
history of obstetric complications was associated
with increased risk for schizophrenia in offspring.
Also, serum folate level was lower in patients with
schizophrenia than in healthy controls, and this
association was also supported by robust evidence.
Cannabis use was associated with a very large
risk for schizophrenia spectrum disorders with no
evidence of bias. Large between-study heterogene-
ity was observed, but the 95% prediction interval
excluded the null value. Several methodological
aspects deserve consideration. First, the degree of
cannabis exposure across studies varied, whereas
available evidence indicates that heavy use may
confer a higher risk for psychosis than light use
(36). In addition, the increasing availability of
high-potency cannabis and synthetic cannabinoids
could modify the magnitude of this association
(37). Cannabis use is associated with motivation
and cognitive impairments (38), where there is evi-
dence indicating more pronounced effects if canna-
bis use starts in adolescence (38). Such factors may
therefore influence the role of cannabis use as a
risk factor for psychosis across studies, but two
published MR studies on this association showed
conflicting results (31, 32)
Exposure to physical and psychological adversi-
ties during childhood showed highly suggestive evi-
dence for an increased risk for schizophrenia. This
meta-analysis presented a very large heterogeneity
estimate, but the 95% prediction interval excluded
the null value. Also, this association remained sig-
nificant in our sensitivity analyses. Exposure to
childhood adversities has been linked with later
drug use disorders, indicating a possible correla-
tion between the two highly suggestive risk factors
(39, 40).
Furthermore, a history of obstetric complica-
tions was associated with an almost twofold
increase in risk for schizophrenia in offspring. This
meta-analysis presented a highly significant effect,
a small between-study heterogeneity, and a 95%
prediction interval excluding the null value,
whereas evidence for small-study effects and excess
significance bias was absent. This finding is aligned
with the neurodevelopmental hypothesis for
schizophrenia, which supports that risk factors for
schizophrenia affect early neurodevelopment dur-
ing pregnancy (41).
Traditional risk factors, such as urban environ-
ment (42), advanced paternal age (43), a history of
traumatic brain injury (44), and perinatal infec-
tions (45), did not present robust evidence as risk
factors for schizophrenia spectrum disorders. This
observation does not mean that these factors or
other factors and exposures that are difficult to be
measured should be ignored or ruled out from fur-
ther research. For example, in the case of urban
environment, nearly all the criteria for highly sug-
gestive evidence were fulfilled, but the P-value was
slightly larger than 10
6
. Further well-designed
prospective studies may provide convincing evi-
dence for an association with schizophrenia spec-
trum disorders.
Migration status is also considered a traditional
risk factor for schizophrenia, and our literature
search captured three meta-analyses that could be
considered eligible and examined this association
(46–48). These studies showed significant associa-
tions for migration status; however, we were not
able to further evaluate the findings of these meta-
analyses as they suffered from fundamental errors
in the synthesis of the available data, such as inclu-
sion of studies with overlapping populations. Fur-
thermore, these studies showed very large
heterogeneity (I
2
>75% in all cases) denoting
potential problems from systematic biases and
accumulation of very heterogeneous populations
that did not allow for robust conclusions. Of
7
Risk factors and biomarkers for schizophrenia
course, observed heterogeneity may rather point at
the necessity to explore underlying sources of vari-
ation that may be genuine, especially for findings
that have been consistently replicated in the past.
Fifty-seven biomarkers for schizophrenia spec-
trum disorders have been studied in meta-analyses
of observational studies. The identification of
robust biomarkers associated with the schizophre-
nia spectrum could lead to a better understanding
of the pathophysiology and at the same time could
offer clinicians a valuable tool for diagnosis in the
emerging framework of precision psychiatry (49).
However, in most cases, the sample size of compo-
nent studies was small, or the P-value was close to
the nominal significance threshold. Similar findings
were observed in umbrella reviews on peripheral
biomarkers for depression and bipolar disorder
(50, 51).
Serum folate level was significantly lower in
patients with schizophrenia than in healthy con-
trols, and this association was supported by highly
suggestive evidence. A field synopsis for genetic
associations in schizophrenia has shown strong
epidemiological credibility between rs1801131, a
polymorphism in the methylene tetrahydrofolate
reductase gene, and risk for schizophrenia (52).
However, the evidence for the link between serum
folate level and schizophrenia is based on case–
control studies and there is a lack of prospective
cohort studies supporting this association; there-
fore, results should be interpreted with caution.
Increased serum CRP level presented suggestive
evidence for an increased risk for schizophrenia
spectrum disorders (53). In contrast, the available
MR study indicated a causal protective effect for
elevated levels of serum CRP level (33). Although
there is accumulating evidence that peripheral
immune activation could play a pathophysiological
role in schizophrenia, the results of the MR study
questions whether the observed CRP elevation in
schizophrenia is a consequence of illness activity
rather than a risk factor for schizophrenia (33).
Previous studies could have been affected by
potential biases regarding the causes of elevated
CRP level in patients with schizophrenia, such as
reverse causality (33).
Limitations
Our umbrella review has some limitations. We did
not conduct a qualitative assessment of component
studies as this should be performed in the original
systematic reviews and meta-analyses through
standardized tools, such as Newcastle–Ottawa
scale. However, only a small proportion of eligible
meta-analyses included a standardized qualitative
assessment of component studies. Also, we consid-
ered only associations that have been examined in
a meta-analysis, so we did not include potentially
important factors such as socioeconomic status.
Furthermore, psychotic disorders are a very hetero-
geneous group of psychiatric conditions, and the
combination of studies on various schizophrenia
spectrum disorders in the same meta-analysis could
be a potential source of between-study heterogene-
ity. However, it was not feasible to identify the
component studies that are focused exclusively on
schizophrenia, given that this process is beyond the
scope of an umbrella review.
To conclude, our umbrella review found a wide
range of risk factors and biomarkers for
schizophrenia spectrum disorders. Although the
majority of associations were statistically signifi-
cant at P-value <0.05, only exposure to childhood
adversities and cannabis use were supported by
robust evidence. The associations of adult stressful
events, obstetric complications, and serum folate
level with risk for schizophrenia presented convinc-
ing or highly suggestive evidence, but these associa-
tions were not examined in prospective studies. We
have shown that the contribution of environmental
factors and biomarkers to the development of psy-
chotic disorders remains incompletely elucidated.
Both child maltreatment and cannabis use are
potentially modifiable leading to reduced incidence
of schizophrenia (54, 55). Randomized evidence,
however, is still needed before establishing a causal
association between child maltreatment or canna-
bis use and schizophrenia spectrum disorders.
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Supporting Information
Additional Supporting Information may be found in the online
version of this article:
Table S1. Characteristics of 41 associations between environ-
mental factors and risk for schizophrenia spectrum disorders.
Table S2. Characteristics of 57 associations between biomark-
ers and risk for schizophrenia spectrum disorders.
10
Belbasis et al.
