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Disentangling the causal interrelationship between negative life events
and depressive symptoms in women: a longitudinal twin study
M. Wichers, H. H. Maes, N. Jacobs, C. Derom, E. Thiery and K. S. Kendler
Psychological Medicine / Volume 42 / Issue 09 / September 2012, pp 1801 1814
DOI: 10.1017/S003329171100300X, Published online: 25 January 2012
Link to this article: http://journals.cambridge.org/abstract_S003329171100300X
How to cite this article:
M. Wichers, H. H. Maes, N. Jacobs, C. Derom, E. Thiery and K. S. Kendler (2012). Disentangling the causal inter
relationship between negative life events and depressive symptoms in women: a longitudinal twin study. Psychological
Medicine,42, pp 18011814 doi:10.1017/S003329171100300X
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Disentangling the causal inter-relationship between
negative life events and depressive symptoms in
women: a longitudinal twin study
M. Wichers1*, H. H. Maes2,3, N. Jacobs1,4, C. Derom5, E. Thiery6and K. S. Kendler2
1Department of Psychiatry and Neuropsychology, South Limburg Mental Health Research and Teaching Network, EURON, Maastricht
University, Maastricht, The Netherlands
2Virginia Commonwealth University, Department of Psychiatry and Human and Molecular Genetics, Virginia Institute for Psychiatric and
Behavioral Genetics, Richmond, VA, USA
3Virginia Commonwealth University, Massey Cancer Center, Richmond, VA, USA
4Faculty of Psychology, Open University of the Netherlands, Heerlen, The Netherlands
5Department of Human Genetics, University Hospital Gasthuisberg, Katholieke Universiteit Leuven, Belgium
6Association for Scientific Research in Multiple Births, Ghent, Belgium
Background. Negative life events are strongly associated with the development of depression. However, the etiologic
relationship between life events and depression is complex. Evidence suggests that life events can cause depression,
and depression increases the risk for life events. Additionally, third factors influencing both phenotypes may be
involved. In this work we sought to disentangle these relationships using a genetically informative longitudinal
Method. Adult female twins (n=536, including 281 twin pairs) were followed up for measurements of negative life
event exposure and depressive symptoms. Four follow-ups were completed, each approximately 3 months apart.
Model fitting was carried out using the Mx program.
Results. The best-fitting model included causal paths from life events to depressive symptoms for genetic and shared
environmental risk factors, whereas paths from depressive symptoms to life events were apparent for shared
environmental factors. Shared latent influence on both phenotypes was found for individual-specific effects.
Conclusions. Life events and depressive symptoms have complex inter-relationships that differ across sources of
variance. The results of the model, if replicated, indicate that reducing life event exposure would reduce depressive
symptoms and that lowering depressive symptoms would decrease the occurrence of negative life events.
Received 17 August 2010; Revised 8 December 2011; Accepted 15 December 2011; First published online 25 January 2012
Key words: Depressive symptoms, longitudinal studies, statistical modelling, stressful events, twins.
There is strong accumulated evidence that negative
life events play a role in the development of major
depression (Kendler et al. 1999, 2001a; Rijsdijk et al.
2001; Paykel, 2003; Hammen, 2005). Interpersonal
‘loss’ factors, such as bereavement or separation, and
other loss experiences such as loss of self-esteem, loss
of employment or respected status in the community,
or loss of cherished ideas and humiliation (Kendler
et al. 2003b), are reported to be potent elicitors of de-
pressive reactions (Brown et al. 1995; Farmer &
McGuffin, 2003). However, the relationship between
negative life events and depression is more complex
and dynamic than can be accounted for solely by a
causal effect of negative life events on depression.
Several studies have shown that exposure to negative
life events itself is partly under genetic control
(Kendler et al. 1993a; Kendler & Baker, 2007;
Vinkhuyzen et al. 2010).
Hammen (1991) introduced the concept of stress
generation to describe the finding that people with a
history of depression were more likely to expose
themselves to negative life events than people without
a history of depression (Kendler & Karkowski-
Shuman, 1997). The fact that depressed individuals
contribute to their experience of stress suggests a
causal path from depression to life event exposure. A
recent prospective study (n=826) examined the tem-
poral associations between initial chronic stress,
neuroticism and follow-up depression severity in a
* Address for correspondence: Dr M. Wichers, Department of
Psychiatry and Psychology, South Limburg Mental Health Research
and Teaching Network, EURON, Maastricht University,
Vijverdalseweg 1, Concorde building, Maastricht, The Netherlands.
Psychological Medicine (2012), 42, 1801–1814.
f Cambridge University Press 2012
sample of out-patients (Brown & Rosellini, 2011).
Evidence was found for both causation (association
between initial chronic stress and follow-up de-
pression severity) and the stress generation hypothesis
(association between initial depressive severity and
follow-up levels of chronic stress). Two other studies,
using latent modeling with cross-lagged paths, also
found reciprocal associations between major life
events and depressive symptoms, supporting the hy-
potheses of both the causation model and the stress
generation model. However, in the latter study re-
ciprocal associations were found only in girls (Ge et al.
1994; Cole et al. 2006). Furthermore, another recent
study reported only modest support for 1-year lagged
paths from depressive symptoms to major events, and
little evidence for paths of the opposite direction
(Pettit et al. 2011). Other studies have tried to examine
directionality by separating the effects of ‘dependent’
from ‘independent’ life events. Dependent life events
refer to events that the individual him/herself could
have contributed to, such as interpersonal conflicts;
independent events refer to those that were not under
the control of the individual, such as death of a spouse
or a child. Kercher et al. (2009) showed, using path
analysis, that not only did depressive symptoms pre-
dict later dependent life events but also dependent life
events mediated the effects of neuroticism on later
depressive symptoms. Other studies (Kendler et al.
1999; Silberg et al. 2001) showed that independent life
events predicted future onset of major depression.
Thus, previous studies have tried to disentangle the
direction of effects and found evidence for bidirec-
However, instead of reciprocal causation, shared
causal influence on both life events and depression
may also explain the reported phenotypic correlation
between negative life events and depression. Potential
third factors that are related to both phenotypes are,
for example, neuroticism (Kendler et al. 1993b, 2003a;
Van Os & Jones, 1999) or socio-economic status (Brady
& Matthews, 2002; Wang et al. 2010). Neuroticism has
been shown phenotypically and genetically to be re-
lated to risk for major depression and depressive
symptoms (Kendler et al. 1993b; Van Os & Jones,
1999). In addition, neuroticism is associated with
negative life event exposure (Kendler et al. 2003a). As
life events are subject to a degree of genetic control,
shared genes that influence both life events and de-
pression could be involved. The presence of shared
causal influence can be examined using genetically
sensitive designs, such as twins studies (see online
supplementary material for an explanation of basic
behavioral genetic principles and the co-twin control
method). The fact that within monozygotic (MZ)
twins pairs, matched for both genotype and family
environment, life event exposure increased the risk for
onset of major depression suggests causal influence of
life events on major depression. As this effect was
smaller within MZ pairs than dizygotic (DZ) pairs or
within the entire population, it was concluded that
part of the association (about 1/3) was non-causal
and explained by genetic factors that influence
both depression liability and exposure to life events.
However, in another study that also used the co-twin
control method (Middeldorp et al. 2008), it was found
that the genes that influenced anxious depression did
not overlap with genes influencing life event exposure.
One study (Thapar et al. 1998) used structural equation
modeling to carry out bivariate genetic analyses of
twin data on life events and depressive symptoms.
The model supported the presence of shared genetic
influences on both phenotypes. However, in this
study, the bivariate model was not compared to other
models of causal paths from life events to depressive
symptoms or the other way around, which may or
may not have fitted the data better.
Thus, studies have examined both directionality of
effect and potential shared genetic influence, some
(Kendler et al. 1999; Silberg et al. 2001; Middeldorp
et al. 2008) within the same sample; however, there is
disagreement on the role of shared genetic influence.
The extent to which shared genes, instead of causal
influence of depressive symptoms on new life events,
explain the phenotypic association has clinical rel-
evance because stress generation can be hypothesized
as a contributing mechanism to the recurrence of de-
pressive episodes (Hammen, 2005). Furthermore,
multiple and contrasting pathways of causation may
be operating simultaneously depending on the nature
of the risk factor. However, no study has yet examined
how several sources of variance (additive genetic ef-
fects, shared and individual-specific environmental
effects) differentially impact on the association be-
tween life events and depression.
Therefore, the current longitudinal twin study
examined the dynamic within and cross-time associ-
ations between negative life events and depressive
symptoms using structural equation modeling. To test
for directionality of effects and shared sources of in-
fluence several models were compared: (i) models
with causal paths from life events to depressive
symptoms, (ii) models with causal paths from de-
pressive symptoms to life events, and (iii) models
without causal paths but with factors for depressive
symptoms and life events that were allowed to corre-
late (which would suggest shared influences). To our
knowledge, this is the first longitudinal study using
structural equation modeling to disentangle the nature
of the dynamic associations between negative life
events and depressive symptoms over time.
1802 M. Wichers et al.
Subjects (n=621) were taking part in an ongoing,
longitudinal, general population twin study on gene–
environment interaction in affective disorders, which
has been described in detail elsewhere, and showed a
very high degree of compliance with research pro-
cedures (Jacobs et al. 2005). The sample consisted of
Following exclusion of those individuals who were
non-twin sisters and those with missing data on zy-
gosity, 536 subjects (who were part of 281 twin pairs)
had valid life events measurements at the first follow-
up of the study. Given evidence for qualitative differ-
ences in the type of environmental stressors that are
associated with depression in men and women
(Kendler et al. 2001b, 2006) and potential gender dif-
ferences inthe temporal
stressors and depressive symptoms (Ge et al. 1994), a
female-only sample was chosen to improve hom-
ogeneity. The study was approved by the standing
ethics committee and subjects provided written in-
formed consent. Zygosity was determined through
sequential analysis based on sex, fetal membranes,
blood groups and DNA fingerprints (Derom et al.
2006). In 81 pairs, determination of zygosity was based
on self and mother’s report of standard questions
about physical similarity and the degree to which the
twins are confused (Spitz et al. 1996; Peeters et al. 1998;
Christiansen et al. 2003) and, if necessary, on examin-
ation of DNA fingerprints.
Subjects were assessed five times at approximately 3-
to 4-monthly intervals. The average number of days
between T0 and T1 was 132, between T1 and T2 n=91,
between T2 and T3 n=116 and between T3 and T4
n=91. At T0 assessments were performed at the home
of the individuals. For the collection of follow-up data,
questionnaires were sent to the participants.
An inventory of recent life events was made based on
the event list of the Interview for Recent Life Events
(Paykel, 1997). Participants reported on the occurrence
of 61 events in the past 6 months (at baseline) and
since the last measurement occasion (at follow-up)
and rated their impact on a five-point scale (from
1=very pleasant to 5=very unpleasant). These recent
life events were in the domain of 10 categories: work;
education; finance; health; bereavement; migration;
courtship, marriage and cohabitation; legal, family
and social relationships, all representing dateable
occurrences involving changes in the external social
environment. Events rated as unpleasant (i.e. a score
of 4=unpleasant or 5=very unpleasant) were in-
cluded in the analysis, and a variable was constructed
representing the number of such unpleasant events
that had occurred since the last measurement oc-
casion. In the analyses, a negative life event (LE) score
was used and coded as follows: 0 LE=0, 1 LE=1, 2
LE=2, 3 LE=3, 4 LE=4, o5 LE=5, resulting in six
categories of life event exposure. As the first (baseline)
measurement of negative life events represented oc-
currences in the past 6 months whereas the follow-up
measurements all represented occurrences since the
past measurement occasion (approximately 3-month
intervals), only the four follow-up measurements were
used for the analyses to ensure that all four measure-
ments used the same phenotype.
For the measurements of depressive symptoms, a
validated self-report measure was used. At baseline
and at each of the four follow-ups, subjects filled in the
90-item Symptom Checklist (SCL-90; Derogatis et al.
1973). The dimension of depressive symptomatology
consists of 16 items such as ‘feeling low in energy or
slowed down’, ‘feeling no interest in things’ or ‘ex-
periencing feelings of worthlessness’. Subjects were
instructed to rate the degree of discomfort associated
with each depressive symptom during the past week
on a five-point scale ranging from ‘not at all’ to ‘ex-
tremely’. A continuous weighted depression score
(sum of scores of the depression items divided by
number of items filled in) was calculated at each
measurement occasion. Table 1 shows the number of
subjects at each time point, the time interval between
time points and the average life event and SCL-90
scores at each time point. The mean age of the sample
at T1 was 28 years (range=18–46 years). For infor-
mation on attrition, see supplementary online ma-
terial. As the measurements of life events and
depressive symptoms were analyzed within one
model, only the four follow-up measurements of de-
pressive symptoms were used and data were trans-
formed into six categories of symptoms, each with
equal numbers of observations (see online material for
a further explanation of this choice).
Model fitting was performed using the Mx program
(Neale et al. 2003). Several different plausible models
that may explain the observed phenotypic association
between depressive symptoms and life events were
modeled and compared. The models were chosen to
reflect the different possibilities of how negative life
events and depressive symptoms may be associated
A longitudinal twin study on life events and depressive symptoms1803
with one another (whether they might be causally af-
fecting each other (bi- or unidirectionally) or whether
a common latent construct is affecting both pheno-
types simultaneously. Similar types of models have
been tested in a previous study on the relationship
between peer deviance and conduct disorder (Kendler
et al. 2008).
The models are illustrated in Fig. 1 for the genetic
paths, but apply equally to shared and individual-
specific environmental paths. The first one assumes
one latent factor for each phenotype that influences
either exposure to life events or experience of de-
pressive symptoms at all time points. This model is
called the ‘causal factor model’ (Fig. 1a). It also in-
cludes causal paths between life events and depressive
symptoms. Three different causal factor models were
tested: (1) a model with bidirectional cross-time causal
paths from life events to depressive symptoms (from
LE1to DS2; from LE2to DS3; from LE3to DS4) and vice
versa (from DS1to LE2; from DS2to LE3; from DS3to
LE4); (2) a model including only unidirectional paths
from life events to depressive symptoms; and (3) a
model including only unidirectional paths from de-
pressive symptoms to life events. The latter two mod-
els are nested within the first bidirectional model.
The second model (Fig. 1b), which is called the
‘simple causal model’, assumes that separate inde-
pendent latent factors influence life events and de-
pressive symptoms at each of the four measurements
occasions instead of having one common factor for
each phenotype. Three versions of this model were
also tested, one with bidirectional paths between the
phenotypes and two with either one of the unidirec-
The third model (Fig. 1c) is called the ‘correlated
factor model’. This model postulates that phenotypic
correlations between life events and depression arise
from a correlation between the latent factors that in-
fluence life events and depression.
Initially, all models include cross-time within-
phenotype paths (hereafter ‘simplex paths’: for
example, paths from T1 to T2, T2 to T3 and T3 to T4
within both phenotypes). See online material for fur-
ther details on model characteristics.
Nested models were compared by evaluating de-
cline in fit using x2(df) tests. For the evaluation of non-
nested models the Bayesian Information Criterion
(BIC; Schwartz, 1978) was used. The model with the
lowest BIC is considered as the model with the best
balance between explanatory power and parsimony.
Information Criterion (AIC). The optimum fit as con-
sidered by the AIC is typically shifted more towards
high explanatory power and less to parsimony as
compared to the BIC. The BIC is the criterion used in
Table 1. Descriptive information of measurements of negative life events and depressive symptoms
Life events (n)a
mean (S.D.; range)
mean (S.D.; range)b
Time interval (days)
536 (MZ: 329)
1.34 (1.46; 0–17)
476 (MZ: 2 99)
1.50 (0.57; 1–4.77)
535 (MZ: 327)
1.24 (1.43; 0–9)
473 (MZ: 293)
1.48 (0.55; 1–4.31)
532 (MZ: 324)
1.00 (1.40; 0–21)c
448 (MZ: 281)
1.47 (0.56; 1–4.78)
535 (MZ: 328)
0.88 (1.37; 0–13)c
437 (MZ: 272)
1.46 (0.60; 1–4.46)
MZ, Monozygotic; S.D., standard deviation.
aNumber of individuals following exclusion of subjects with missing zygosity status and non-twin sisters.
bNo significant differences between SCL-90 scores at T1 compared to other time points.
cSignificant differences (p<0.05) between life event score at T1 and life event score at T3 and T4.
1804M. Wichers et al.
this study because it performs well with such complex
models (Markon & Krueger, 2004). For completeness,
however, both the AIC and the BIC values are shown
in the description of the results.
First, we fitted a fully saturated model with separate
means, variances and covariances, as a baseline for
model comparisons. Second, we then tested assump-
tions of the twin modeling such as equality of means
and variances by twin order and zygosity. Third, a
triple Cholesky decomposition model for genetic fac-
tors (A), shared environmental (C) and individual-
specific factors (E) was fitted. This is a saturated model
of the observed genetic and environmental variances
and covariances (see online material for a further ex-
planation of the Cholesky decomposition and an
overview of fit indices for all models tested). We sim-
plified genetic factors first, then shared environmental
and then individual-specific factors (see Table 2). The
same model testing procedure was followed for each
variance component. First, for both the causal factor
and simple causal models, the bidirectional models
were compared to the unidirectional models to see
whether fit would significantly deteriorate after re-
moving one of the directional paths. The best of all
causal factor models and the best of all simple causal
models were retained. Second, the non-nested best-
fitting causal factor, simple causal model and corre-
lated factor model were compared using the BIC.
Finally, we tested whether simplex paths could be
dropped from the best-fitting model resulting from the
LE t1 LE t2LE t3 LE t4
LE t1LE t2 LE t3LE t4
DS t2DS t3
DS t1DS t2 DS t3DS t4
LE t2 LE t3LE t4
DS t1DS t2
DS t3DS t4
Fig. 1. (a) The causal factor model; (b) the simple causal model; and (c) the correlated factor model. * This model was also
tested with the lower arrows pointing in the opposite direction (from depressive symptoms towards life events: DS t1 to LE t2,
DS t2 to LE t3 and DS t3 to LE t4) and with the lower arrows in both directions simultaneously.
A longitudinal twin study on life events and depressive symptoms 1805
Table 2. Model testing procedure for variance components (a) A, (b) C and (c) E
(a) Variance component AModel
x2LL change (df)Resulta
Variance component A: bi- and unidirectional causal factor models
Causal factor: nested model evaluationI
q can be dropped
p cannot be dropped
Variance component A: bi- and unidirectional simple causal models
Simple causal: nested model evaluation IV
q can be dropped
p can be dropped
Variance component A: comparison non-nested best models
Comparison of non-nested best models using BIC Description
Variance component A: comparison simplex paths of best-fitting model
Simplex paths: nested model evaluation of best-fitting modelDescription
x2LL change (df)Result
Best model CF: LEpDS
Drop of simplex paths
Simplex paths can be
x2LL df AIC BIC
Best model for AIIc CF: LEpDS
Drop of simplex paths
M. Wichers et al.
(b) Variance component C using best model for A (IIc)Model
x2LL change (df)Resulta
Variance component C: bi- and unidirectional causal factor models
Causal factor: nested model evaluationVIII
q cannot be dropped
p can be dropped
Variance component C: bi- and unidirectional simple causal models
Simple causal: nested model evaluationXI
q cannot be dropped
p cannot be dropped
Variance component C: comparison non-nested best models
Comparison of non-nested best models using BIC criterion Description
x2LL df AIC BIC
Variance component C: comparison simplex paths of best-fitting model
Simplex paths: nested model evaluation of best-fitting modelDescription
x2LL change (df) Resulta
Best model CF: LEqDS
Drop of simplex paths
Simplex paths can be dropped
x2LL df AIC BIC
Best model for CXcCF: LEqDS
Drop of simplex paths
(c) Variance component E using best model for A and C (Xc) Model
x2LL change (df )Resulta
Variance component E: bi- and unidirectional causal factor model
Causal factor: nested model evaluationXV
q cannot be dropped
p can be dropped
A longitudinal twin study on life events and depressive symptoms
Table 2 (cont.)
(c) Variance component E using best model for A and C (Xc)Model
Variance component E: bi- and unidirectional simple causal models
Simple causal: nested model evaluationXVIII
p cannot be dropped
p cannot be dropped
Variance component E: comparison non-nested best models
x2LL df AICBIC
Comparison of non-nested best models using BICXVII
Variance component E: comparison simplex paths of best-fitting model
x2LL and df changeResult*
Simplex paths: nested model evaluation
of best-fitting model
XXIBest model: corr. factor10998.747 3921––
XXIcE Simplex paths11007.896 3923 9.149 (2)Simplex paths cannot
x2LLdf AIC BIC
Best model for EXXICorr. factor10998.747 39213156.747
A, Additive genetic effects; C, shared environmental effects; E, individual-specific effects; x2LL, x2 log likelihood; df, degrees of freedom; AIC, Akaike Information Criterion; BIC,
Bayesian Information Criterion; LE, life events; DS, depressive symptoms; Corr. factor, correlated factor model; CF, causal factor model; SC, simple causal model.
First, nested models were evaluated by the decline in fit (x2LL) in relation to df using x2tests. In a second step, the best models resulting from these evaluations were compared to other
non-nested models and evaluated using the BIC. In a third step, the best of these models was selected and we tested whether simplex paths could be dropped, again by evaluating the
decline in fit using x2tests. The best-fitting A model was used for further testing of the C model and, similarly, the best resulting C model was used for further testing of the E model (see
online material for additional information). Note that we may repeat the statistics of the models for different comparisons; however, the model number reflects this.
aCritical x2values are 3.84 (df=1) and 5.99 (df=2). Above the critical values there is a significant deterioration of fit.
Best-fitting models are marked in bold.
M. Wichers et al.
latter evaluation. The final model was then used in
further testing of other (C and E) variance com-
ponents. Because directional effects were not hypoth-
esized to be different across time points, the simplex
paths in addition to the causal paths were a priori set to
the same value.
As temporal changes in variance in longitudinal
studies with repeated measurements are informative
about the underlying developmental process (Eaves
et al. 1986), path coefficients of the model on the first
occasion only were standardized so that the pheno-
typic variance is unity. Variances at subsequent oc-
casions were expressed relative to their initial values.
Therefore, the path coefficients can exceed unity, par-
ticularly when variances are increasing over time.
Table 3 shows the within- and across-time phenotypic
correlations between negative life events and de-
pressive symptoms. Correlations below the diagonal
depict the prediction of depressive symptoms by pre-
viously experienced life events. Those above the di-
agonal are informative for the prediction of life event
exposure by prior depressive symptoms. These two
sets of correlations are broadly similar in magnitude,
suggesting that causal effects are probably operating
in both directions. However, the hypothesis that a
third factor influences both phenotypes is also con-
sistent with this pattern of correlations.
First, fully saturated models were fitted on the ob-
served variables. The model in which means and vari-
ances were equated across groups (twin 1, twin 2, MZ
and DZ twins) did not fit significantly worse than the
fully saturated model as evaluated by the BIC. Second,
a Cholesky model with equal means and variances
across groups but with different thresholds per time
point was tested and compared to the same model but
with equal thresholds across time. The latter fitted best
and was used for further model testing (for infor-
mation on fit statistics of these models see online ma-
terial). Table 2 summarizes the results from the
correlational and causal models tested (see online
material for further information on the model testing
For the genetic factor (A), the causal factor model
with paths going from negative life events to de-
pressive symptoms (model II) provided the best fit. In
addition, simplex paths could be dropped from this
model without deterioration in fit. It should be noted
that the nested model evaluation of the simple causal
models could not distinguish the two unidirectional
models. Both unidirectional models were preferred
over the bidirectional model. The differences in BIC
between model II on the one hand and models V and
VI on the other were small (Table 2a). For the shared
environmental factor (C), model X was the best fit.
This model also had a causal factor structure, with one
latent factor per phenotype influencing observations at
all time points. In contrast to the previous model, this
one included unidirectional causal paths from de-
pressive symptoms to life events. Also here, simplex
paths could be dropped without fit deterioration
(Table 2b). Finally, the individual-specific factor (E)
was simplified. The best-fitting model for the E factor
was the correlated factor model (model XXI). Simplex
paths could not be dropped from this model without
significant deterioration in fit (Table 2c).
The overall best-fit model (Fig. 2) had the following
four key features. First, the genetic risk factors for
negative life events and depressive symptoms could
be best understood as two single common factors, so
Table 3. Within- and cross-time cross-phenotype correlations
Depressive symptoms t1
Depressive symptoms t2
Depressive symptoms t3
Depressive symptoms t4
Numbers in bold represent correlations between depressive symptoms and
life events later in time. Numbers in italics represents correlations between life
events and depressive symptoms later in time. Diagonals represent within-time
A longitudinal twin study on life events and depressive symptoms1809
that the same genetic factors influenced the pheno-
types at all four time points (Fig. 2a). Second, the
causal paths in the genetic portion of the model went
from negative life events to depression. Third, the
shared environmental factors showed a similar struc-
ture to that seen for genetic factors, but with causal
LE t1LE t2 LE t3 LE t4
LE t1 LE t2 LE t3 LE t4
DS t1DS t2 DS t3 DS t4
DS t3DS t4
0.77 0.69 0.820.83
0.42 0.72 0.53 0.49
Fig. 2. Resulting best-fit model of the additive genetic (A), shared environmental (B) and individual-specific (E) influences on
negative life event exposure and depressive symptoms. (a) The causal factor model with paths from negative life events to
depressive symptoms (see Table 2a). (b) The causal factor model with paths from depressive symptoms to negative life events
(see Table 2b). (c) The correlated factor model with simplex paths and additional observation-specific effects (see Table 2c).
1810M. Wichers et al.
paths going in the opposite direction, that is from de-
pressive symptoms to life events (Fig. 2b). Fourth, by
contrast, the individual-specific environmental influ-
ences on life events and depressive symptoms could
best be modeled as two correlated latent factors
(Fig. 2c). In addition, forward transmission was pres-
ent in the E model for both negative life events and
depressive symptoms from T1 to T2, T2 to T3 and T3 to
T4. That is, levels of life event exposure at one time
point had a direct impact on levels of life event ex-
posure at the next time point. Furthermore, depressive
symptoms at one time period directly impacted on
depressive symptoms at the next time point.
Table 4 shows the estimates for a2, c2and e2for
negative life events and depressive symptoms at all
time points, obtained from the best-fit model (model
This study sought to clarify the causal relationship
between exposure to negative life events and de-
pressive symptoms using a longitudinal genetically
informative design. The most striking features of the
best-fitting model are the following. Life events
and depressive symptoms had a complex inter-
relationship that differed depending on the source of
variance considered. The best-fitting model was a
combination of model specifications with directional
paths across time between the two phenotypes and
with a correlated factor structure, implying shared
influences on both phenotypes. Thus, both causal
paths and shared influences explained the phenotypic
correlations between negative life events and de-
pressive symptoms. Furthermore, causal paths be-
tween the two phenotypes went in both directions,
depending on the source of variance. Genetic factors
impacted on exposure to life events, which in turn
influenced the risk for depressive symptoms. Thus,
although the exposure to life events itself was influ-
enced by genetic factors, the life events were causal to
the development of depressive symptoms. This model
thus shows two different paths by which genes may
influence depressive symptoms. First, there is the
direct path of additive genetic influences on depress-
ive symptoms. These are probably genes that affect
people’s vulnerability to depression by acting on bio-
logical, cognitive or psychological processes, for ex-
ample by influencing people’s affective processing or
genes associated with increased stress responses to
negative situations (Wichers et al. 2007, 2009). These
genes thus act ‘inside the skin’. Second, there is an
indirect path from genes to depressive symptoms via
exposure to negative life events. These genes act on
depression by creating an environment (outside the
skin) that exposes the individual to negative life
events. Genes that influence the ability to decide on
the important choices in life (e.g. choosing one’s
marital partner, study, job) may impact on exposure to
life events. Moreover, genetic influences on having
low emotional intelligence or a difficult personality
(e.g. high levels of neuroticism) may set people up for
the loss of relationships, friendships or jobs. The
indirect path is a typical example of active gene–
environment correlation (Plomin et al. 1977). Acc-
ording to the current model, however, it can be cal-
culated from the path coefficients and the total vari-
ance that only around 1–2% of the variance at each
time point was explained by indirect (outside the skin)
paths and 98–99% by the direct (inside the skin)
pathway (exact numbers of standardized total effects
of the direct and indirect genetic paths available upon
request). However, this does not mean that the in-
direct pathway is non-existent. This model examined
effects across time. It is possible that the effects of this
path may be larger when examining the effects of life
events on depressive symptoms within time, examin-
ing the effects of life events of the past 3 months, in-
stead of the life events as reported one time point
For shared environmental influences the coefficients
of the latent factor on depressive symptoms were
modest; however, the strength of the causal path from
depressive symptoms to life events was fairly large
(see Fig. 2). An example of such shared environmental
influences is parental divorce or shared adverse up-
bringing leading to adult depressive symptoms. The
expression of these depressive symptoms may then
further increase the risk for life events, such as having
a divorce or having relational problems themselves.
Thus, also here there is a direct path to the experience
of negative life events and an indirect path via the
experience of depressive symptoms. The indirect
Table 4. Variance components for life events and depressive
symptoms at all time points
Percentage explained variance for A, C and E
Life eventsDepressive symptoms
T1 T2T3 T4 T1T2 T3 T4
A, Additive genetic effects; C, shared environmental
effects; E, individual-specific effects.
A longitudinal twin study on life events and depressive symptoms 1811
paths (from DS T2 to LE T3 and from DS T3 to LE T4)
contribute meaningful percentages of the total effect of
the shared environment, 31% and 57% respectively.
This finding is consistent with Hammen’s hypothesis
of stress generation and the idea that the relationship
between life events and depressive symptoms is bi-
directional (Hammen, 1991, 2005). The fact that ex-
perience of depressive symptoms themselves selects
an environment of increased stress exposure would
imply that lowering depressive symptoms also posi-
tively impacts on the environment people create
around themselves (Hammen, 1991). This finding also
emphasizes the need to resolve residual symptoms
following a depressive episode to prevent recurrence
(Kennedy & Paykel, 2004).
For individual-specific effects the correlated factor
structure fitted best. Thus, phenotypic correlations
between life events and depression arise partly from a
correlation between the E latent factors that influence
life events and depression. Shared individual-specific
effects are both affecting risk for life events and de-
pressive symptoms. These could involve experiences
such as a physical illness, simultaneously leading to
the loss of a job and to feeling down. Another example
is being bullied in childhood, leading both to an
altered way of coping with daily life situations, result-
ing in an increased level of life events, and to a more
active stress system, resulting in mood symptoms.
Causal paths versus shared causal influence
The finding that genes impact on depressive symp-
toms through their effect on exposure to negative life
events is in agreement with previous studies. Kendler
& Karkowski-Shuman (1997) concluded that genetic
risk factors for major depressive disorder increase the
probability of experiencing stressful life events. That
study, however, mentioned the possibility that not
only causal effects from life events to depression but
also shared causal influence on both phenotypes
might have explained the findings. Neuroticism seems
a likely candidate because effects of neuroticism on
both phenotypes have been shown (Kendler et al.
1993b, 2003a) and neuroticism is partially heritable
(Viken et al. 1994; Jang et al. 1996). However, a study
using path analyses (Kercher et al. 2009) showed that
the best-fitting model did not include any direct paths
from neuroticism to depression. Instead it included
paths from neuroticism to dependent negative life
events and negative thoughts, which in turn, had
causal paths to depression. These findings are con-
sistent with those of the current study, in which the
best-fitting model showed genetic effects on negative
life events, which in turn had causal paths to de-
The current model thus suggests that shared genetic
and environmental influences on negative life events
and depressive symptoms are expressed at the
phenotypic level (at the level of observed negative life
events that cause depressive symptoms) and not at the
latent level (the level of the latent genetic factor that
has direct paths to both negative life events and de-
pressive symptoms). The fact that the current design is
able to distinguish models favoring shared influences
expressed at the phenotypic level from those favoring
shared genetic influence at the latent level is not a
trivial or purely theoretical advantage. These two in-
terpretations have substantially different implications
for the prevention of depressive symptoms or negative
life events. If shared genes indeed impact on depress-
ive symptoms at the phenotypic level, through the
generation of negative life events, it follows that de-
creasing life event exposure should decrease the risk
for depressive symptoms, whereas this would not be
the case when shared genes exert their effects at the
latent level. Likewise, it follows that decreasing the
level of depressive symptoms should decrease the risk
for negative life events. The current results revealed a
fairly large effect of the indirect path to life events, but
small effects of the indirect path to depressive symp-
toms. Because of the clinical relevance of the outcome,
there is an urgent need for replication and further
examination of these effects.
A drawback of the current model, however, is that it
leaves little room for the effect of independent nega-
tive life events on depressive symptoms. Previous
work has clearly shown that fateful negative events
that are uncontrollable by people themselves impact
on the risk for depression (Kendler et al. 1999, 2000).
The model, therefore, must be interpreted with cau-
tion. Moreover, explanations for the lack of effects of
independent life events were investigated further.
When life events were split into dependent and inde-
pendent life events, regression analyses showed that
more items in the list of life events were dependent
(marital discord, fights and arguments with friends or
family, loss of job, etc.) than independent (death of
partner or close relatives, serious illness, partner or
close relatives with serious illness, etc.). In addition,
the dependent life events were far more frequent than
the independent ones, and the dependent life events
hada much stronger
Therefore, it may be that dependent life events domi-
nated the impact of independent life events in the
process of model fit comparisons. Separate analyses
for independent and dependent life events might have
given a different picture for the associations between
independent life events and depressive symptoms,
with stronger causal paths of purely environmentally
influenced life events on depressive symptoms.
1812M. Wichers et al.
However, because independent life events were rare,
such an analysis would be likely to suffer from a lack
of power. Dependent life events are usually more
dominant in younger life. The mean age of the twins
was 28 years, which is relatively young, and which
might explain the high frequency of dependent com-
pared to independent life events in this sample.
Models II, V and VI, in the specification of the A vari-
ance component, showed only very small differences
in BIC values (nBIC <2). Therefore, caution is war-
ranted regarding the resulting model. The study may
have lacked the power to differentiate well between
these models. Although not small, the current sample
(n=536) is smaller than the sample size of a previous
study (n=1492) that used similar model-fitting ana-
lyses (Kendler et al. 2008). The models, however, in the
specification of the C and E variance component could
be differentiated with sufficient confidence (Raftery,
1995). In addition, the measurements rely on retro-
spective self-report of negative life events and de-
pressive symptoms. Finally, this was a female sample
only. Therefore, the results may not be generalizable to
To summarize, the current study suggests that both
reciprocal causation, using cross-time intervals of ap-
proximately 3 months, and shared latent influences
explained the inter-relationship between negative life
events and depressive symptoms. The results of our
study should be interpreted with caution as these
questions need to be addressed by further studies and
replicated before they deserve wide acceptance.
Supplementary material accompanies this paper on
the Journal’s website (http://journals.cambridge.org/
Organization for Scientific Research; the Fund for
Scientific Research, Flanders and Twins, a non-profit
association for scientific research in multiple births
(Belgium) (to the East Flanders Prospective Survey);
and the Dutch Medical Council (VENI grant no.
916.76.147) (to Dr M. Wichers). We thank all twins for
researchwas supported bythe Dutch
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