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Journal
of
Anxiety
Disorders
27 (2013) 475–
484
Contents
lists
available
at
SciVerse
ScienceDirect
Journal
of
Anxiety
Disorders
Genetic
and
environmental
influences
on
relationship
between
anxiety
sensitivity
and
anxiety
subscales
in
children
M.A.
Waszczuk
∗
,
H.M.S.
Zavos,
T.C.
Eley
King’s
College
London,
MRC
Social,
Genetic
and
Developmental
Psychiatry
Centre,
Institute
of
Psychiatry,
United
Kingdom
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
18
September
2012
Received
in
revised
form
28
May
2013
Accepted
28
May
2013
Keywords:
Anxiety
Anxiety
sensitivity
Genetics
Twins
Panic
disorder
Separation
anxiety
General
anxiety
a
b
s
t
r
a
c
t
Anxiety
sensitivity,
a
belief
that
symptoms
of
anxiety
are
harmful,
has
been
proposed
to
influence
devel-
opment
of
panic
disorder.
Recent
research
suggests
it
may
be
a
vulnerability
factor
for
many
anxiety
subtypes.
Moderate
genetic
influences
have
been
implicated
for
both
anxiety
sensitivity
and
anxiety,
however,
little
is
known
about
the
aetiology
of
the
relationship
between
these
traits
in
children.
Self-
reports
of
anxiety
sensitivity
and
anxiety
symptoms
were
collected
from
approximately
300
twin
pairs
at
two
time
points.
Partial
correlations
indicated
that
anxiety
sensitivity
at
age
8
was
broadly
associated
with
most
anxiety
subtypes
at
age
10
(r
=
0.11–0.17,
p
<
0.05).
The
associations
were
largely
unidirectional,
underpinned
by
stable
genetic
influences.
Non-shared
environment
had
unique
influences
on
variables.
Phenotypic
results
showed
that
anxiety
sensitivity
is
a
broad
predictor
of
anxiety
symptoms
in
childhood.
Genetic
results
suggest
that
childhood
is
a
developmental
period
characterised
by
genetic
stability
and
time-specific
environmental
influences
on
anxiety-related
traits.
© 2013 Elsevier Ltd.
1.
Introduction
1.1.
Anxiety
disorders
Anxiety
is
one
of
the
most
prevalent
psychiatric
conditions
amongst
young
people
(Beesdo
et
al.,
2010).
About
10%
experi-
ence
anxiety
by
the
age
of
16
(Costello,
Mustillo,
Erkanli,
Keeler,
&
Angold,
2003),
with
lifetime
prevalence
estimated
at
around
29%
and
mean
onset
age
of
11
years
(Kessler
et
al.,
2005).
Anxiety
disorders
have
negative
impact
on
child
development,
disturbing
well-being
and
impairing
academic
performance
and
interpersonal
interactions
(Langley,
Bergman,
McCracken,
&
Piacentini,
2004;
Van
Ameringen,
Mancini,
&
Farvolden,
2003).
They
are
also
reliable
pre-
dictors
of
long-term
mental
health
difficulties
(Gregory
et
al.,
2007;
Otto
et
al.,
2001).
Anxiety
is
a
broad
term
bringing
together
specific
disorders,
such
as
generalised
anxiety
disorder,
panic
disorder
or
phobias,
that
are
characterised
by
excessive,
persistent
and
impair-
ing
worry
or
fear
(American
Psychiatric
Association,
2000).
It
is
important
to
investigate
developmental
trajectories
of
each
anx-
iety
disorder
in
order
to
learn
about
the
specific
as
well
as
shared
aetiology.
Although
anxiety
disorders
are
characterised
by
homotypic
continuity
(prediction
of
disorder
by
the
same
disorder)
and
∗
Corresponding
author
at:
King’s
College
London,
MRC
Social,
Genetic
and
Devel-
opmental
Psychiatry
Centre,
Institute
of
Psychiatry,
De
Crespigny
Park,
London,
SE5
8AF,
United
Kingdom.
Tel.:
+44
020
7848
0039.
E-mail
address:
monika.waszczuk@kcl.ac.uk
(M.A.
Waszczuk).
heterotypic
continuity
(prediction
of
disorder
by
another
disor-
der),
certain
anxiety
disorders
seem
to
co-vary
more
than
others
(
Gregory
et
al.,
2007).
Panic
disorder
and
separation
anxiety
are
thought
to
show
such
close
developmental
relationship,
called
the
separation
anxiety
hypothesis
(Klein,
1964;
Silove,
Manicavasagar,
Curtis,
&
Blaszczynski,
1996).
The
two
conditions
share
common
physiological
perturbations,
such
as
somatic
symptoms
(Pine
et
al.,
2005;
Slattery
et
al.,
2002).
Separation
anxiety
in
childhood
has
been
associated
with
increased
risk
of
panic
disorder
in
adult-
hood
(Klein,
1995;
Silove,
Manicavasagar,
Vasey,
&
Dadds,
2001;
Kossowsky
et
al.,
2013),
a
longitudinal
relationship
which
has
been
shown
to
be
influenced
by
a
shared
genetic
diathesis
(Roberson-
Nay,
Eaves,
Hettema,
Kendler,
&
Silberg,
2012).
However,
the
specificity
of
this
developmental
relationship
is
not
clear,
as
some
studies
identified
separation
anxiety
as
a
general
risk
factor
for
multiple
adult
anxiety
and
nonanxious
disorders
(Aschenbrand,
Kendall,
Webb,
Safford,
&
Flannery-Schroeder,
2003;
Kossowsky
et
al.,
2013).
Despite
some
evidence
of
clinical,
developmental
and
biological
similarity
between
separation
anxiety
and
panic
disor-
der,
little
is
known
about
shared
aetiology
of
these
anxiety
subtypes
in
childhood.
1.2.
Anxiety
sensitivity
Evidence
from
twin
studies
suggests
moderate
genetic
and
environmental
influences
on
anxiety
in
childhood
and
across
the
lifespan,
implicating
a
complex
aetiology
(Gregory
&
Eley,
2009).
Another
risk
factor
for
anxiety
might
be
biased
cognitions.
These
0887-6185
©
2013 Elsevier Ltd.
http://dx.doi.org/10.1016/j.janxdis.2013.05.008
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
476 M.A.
Waszczuk
et
al.
/
Journal
of
Anxiety
Disorders
27 (2013) 475–
484
are
thought
to
play
a
role
in
both
the
emergence
and
maintenance
of
anxiety
disorders
(Clark,
1986;
Ehlers,
1991).
The
biases
can
influence
information
processing
at
automatic
information
encod-
ing
stage
(attentional
biases),
as
well
as
at
interpretational
stage
(interpretation
and
memory
biases)
(Muris
&
Field,
2008).
Anxiety
sensitivity
represents
one
such
bias:
a
tendency
to
perceive
bodily
cues
related
to
experiencing
anxiety
as
having
threatening
or
dan-
gerous
consequences
(Reiss,
1986).
It
is
distinct
from
trait
anxiety,
which
refers
to
the
extent
to
which
individual
is
fearful
and
prone
to
anxiety,
while
anxiety
sensitivity
is
a
fear
of
experiencing
anx-
iety
symptoms
themselves
(Taylor,
1996;
Zinbarg,
Brown,
Barlow,
&
Rapee,
2001).
Anxiety
sensitivity
is
thought
to
be
underscored
by
information
processing
abnormalities
in
the
brain
circuitry
(Paulus
&
Stein,
2006)
and
variation
in
the
trait
is
due
to
both
genetic
and
environmental
influences
(Zavos,
Gregory,
&
Eley,
2012).
Anx-
iety
sensitivity
emerges
in
middle
childhood
(Reiss,
Silverman,
&
Weems,
2001),
a
period
characterised
by
a
cognitive
developmen-
tal
stage
of
concrete
operations
and
an
overall
cognitive
maturation
(
Bibace
&
Walsh,
1981;
Piaget,
1952),
corresponding
to
acquisi-
tion
of
ability
to
consider
physical
symptoms
in
relation
to
anxiety
from
the
age
of
7
(Muris
et
al.,
2008).
Childhood
anxiety
sensitivity
shows
significant
homotypic
continuity,
as
well
as
predicts
future
anxiety
symptoms
when
accounting
for
the
current
anxious
state
(
Rabian,
Embry,
&
MacIntyre,
1999;
Weems,
Hammond-Laurence,
Silverman,
&
Ginsburg,
1998).
Importantly,
anxiety
sensitivity
and
anxiety
symptoms
both
emerge
at
similar
age,
making
it
an
ideal
time
to
investigate
potential
aetiological
relationship
of
the
two
constructs.
1.3.
Anxiety
sensitivity
–
specific
or
broad
risk
factor?
Anxiety
sensitivity
was
originally
proposed
as
a
specific
risk
factor
for
panic
disorder.
The
presence
of
this
cognitive
bias
in
childhood
has
been
found
to
predict
panic
attacks
concurrently
(
Calamari
et
al.,
2001;
Mattis
&
Ollendick,
1997),
as
well
as
longitu-
dinally
in
adulthood
(Maller
&
Reiss,
1992;
Schmidt
et
al.,
2006).
Several
studies
in
adults
have
found
that
cognitive-behavioural
therapy
and
pharmaceutical
treatment
targeted
at
panic
reduce
anxiety
sensitivity,
and
this
decline
in
cognitive
bias
was
found
to
mediate
the
treatment
(Simon
et
al.,
2004;
Smits,
Powers,
Cho,
&
Telch,
2004).
Furthermore,
one
study
found
that
children
with
good
heart
beat
perception,
which
indicates
good
awareness
of
and
attention
to
own
body
state,
show
the
highest
level
of
panic
and
somatic
symptoms,
but
also
heightened
separation
anxiety
symp-
toms
(Eley,
Stirling,
Ehlers,
Gregory,
&
Clark,
2004).
This
could
be
due
to
a
close
developmental
relationship
between
panic
disor-
der
and
separation
anxiety.
Anxiety
sensitivity,
therefore,
could
be
investigated
as
a
specific
risk
factor
not
only
for
panic,
but
also
for
separation
anxiety.
Other
studies
have
shown
a
much
broader
relationship
between
anxiety
sensitivity
and
anxiety
subtypes
(Schmidt
et
al.,
2010;
Taylor,
2003),
suggesting
that
anxiety
sensitivity
might
be
a
risk
factor
for
a
range
of
internalising
symptoms
(Plehn
&
Peterson,
2002
).
Two
recent
meta-analyses
of
adult
studies
support
this
view
(
Naragon-Gainey,
2010;
Olatunji
&
Wolitzky-Taylor,
2009),
find-
ing
that
anxiety
sensitivity
was
significantly
related
to
all
anxiety
subtypes
and
depression.
The
associations
were
strongest
between
anxiety
sensitivity
and
panic,
general
anxiety
and
post-traumatic
stress
disorder,
suggesting
some
degree
of
specificity.
A
meta-
analysis
of
studies
of
anxiety
sensitivity
in
childhood
(Noël
&
Francis,
2011)
confirmed
that
anxiety
sensitivity
was
associated
with
higher
anxiety
levels.
Few
studies
have
looked
at
the
associa-
tions
between
anxiety
sensitivity
and
specific
anxiety
subtypes
in
young
people,
but
preliminary
results
based
on
2
studies
suggested
a
degree
of
specificity
to
panic
symptoms.
The
majority
of
studies
that
found
association
between
anxi-
ety
sensitivity
and
anxiety
are
cross-sectional
and
are
therefore
not
able
to
establish
whether
anxiety
sensitivity
predates
anxi-
ety
symptoms,
or
is
a
consequence
of
anxiety.
Interestingly,
some
longitudinal
studies
have
directly
addressed
this
question
and
sug-
gest
that
the
relationship
might
be
bidirectional.
For
example,
one
study
found
a
reciprocal
longitudinal
associations
between
anxiety
sensitivity
and
both
anxiety
and
depression
in
adolescence
(Zavos,
Rijsdijk,
&
Eley,
2012),
while
another
found
that
the
experience
of
panic
and
anxiety
symptoms
in
adulthood
lead
to
an
increase
in
anxiety
sensitivity
(Schmidt,
Lerew,
&
Joiner,
2000).
This
suggests
that
anxiety
sensitivity
increases
subsequent
anxiety,
but
also
that
symptoms
of
anxiety
themselves
increase
levels
of
anxiety
sensitiv-
ity.
However,
none
of
the
studies
have
investigated
these
reciprocal
processes
in
younger
age
groups,
when
both
anxiety
sensitivity
and
anxiety
disorders
emerge
and
when
it
might
be
possible
to
establish
whether
anxiety
sensitivity
predates
anxiety
symptoms.
1.4.
Genetics
of
anxiety
sensitivity
and
anxiety
Very
little
is
known
about
the
mechanisms
underpinning
the
association
between
anxiety
sensitivity
and
anxiety
symptoms.
To
date,
there
are
no
multivariate
twin
studies
investigating
genetic
and
environmental
relationship
of
these
constructs
in
adults.
In
adolescence,
anxiety
sensitivity
and
anxiety
were
found
to
have
high
and
significant
genetic
correlations
(Zavos,
Rijsdijk,
Gregory,
&
Eley,
2010).
This
suggests
that
genetic
factors
are
important
in
the
concurrent
association
between
anxiety
sensitivity
and
anxi-
ety
in
young
people.
In
childhood,
a
very
high
genetic
correlation
has
been
reported
between
anxiety
sensitivity
and
panic
symp-
toms
(r
=
0.98;
Eley,
Gregory,
Clark,
&
Ehlers,
2007),
suggesting
a
substantial
overlap
of
genetic
influences
on
the
two
constructs.
This
is
consistent
with
the
pattern
found
in
the
adolescent
sam-
ple,
but
longitudinal
associations
and
specificity
to
other
anxiety
subtypes
have
not
been
addressed.
In
sum,
the
genetic
and
environ-
mental
influences
underpinning
the
relationship
between
anxiety
sensitivity
and
specific
anxiety
subtypes
remain
largely
unknown.
Recently,
twin
studies
have
begun
investigating
developmen-
tal
patterns
of
genetic
and
environmental
effects
in
longitudinal
study
designs,
in
order
to
see
how
these
influences
operate
over
time
(Ronald,
2011).
Genetic
influences
on
anxiety
sensitivity
have
been
found
to
be
largely
stable,
with
new
genetic
influences
emerg-
ing
late
in
adolescence
(Zavos,
Gregory,
et
al.,
2012).
Similarly,
genetic
stability
in
anxiety
has
been
observed
during
childhood,
with
new
genetic
influences
emerging
in
early
and
late
adoles-
cence,
and
in
early
adulthood
(Kendler,
Gardner,
&
Lichtenstein,
2008;
Trzaskowski,
Zavos,
Haworth,
Plomin,
&
Eley,
2011).
Unlike
genetic
effects,
environmental
influences
are
more
time-specific,
possibly
because
non-shared
environmental
experiences
such
as
stressful
life
events
are
transient
(Kendler,
Gardner,
Annas,
et
al.,
2008;
Kendler,
Gardner,
&
Lichtenstein,
2008;
Lau
&
Eley,
2006;
Trzaskowski
et
al.,
2011).
However,
there
is
also
evidence
that
idiosyncratic
experiences
may
contribute
to
the
continuity
of
anxiety
(Kendler
et
al.,
2011),
suggesting
some
non-shared
environ-
ment
stability
over
time.
Overall,
very
few
studies
have
addressed
these
developmental
questions
and
even
fewer
have
explored
genetic
stability
and
change
on
the
co-morbidity
between
two
traits
or
disorders.
To
our
knowledge,
the
stability
of
genetic
and
envi-
ronmental
influences
on
relationship
between
anxiety
sensitivity
and
anxiety
subtypes
during
childhood
has
not
been
investigated.
1.5.
Aims
The
current
study
aimed
to
investigate
the
developmental
asso-
ciation
between
anxiety
sensitivity
and
anxiety
disorders
when
these
problems
first
emerge
in
middle
childhood
(8–10
years
old).
M.A.
Waszczuk
et
al.
/
Journal
of
Anxiety
Disorders
27 (2013) 475–
484 477
Five
anxiety
subtypes
were
chosen
–
panic/somatic
symptoms,
general
anxiety,
separation
anxiety,
school
anxiety
and
social
pho-
bia.
Using
a
prospective
study
design,
the
phenotypic
associations
were
first
investigated
to
examine
if
anxiety
sensitivity
predicted
future
anxiety
symptoms
over
and
above
any
concurrent
associa-
tions
with
other
anxiety
subscales.
It
was
also
hypothesised,
based
on
previous
literature,
that
the
longitudinal
relationship
between
anxiety
sensitivity
and
anxiety
subtypes
would
be
bidirectional.
Second,
the
genetic
and
environmental
influences
underpinning
these
longitudinal
relationships
were
explored,
controlling
for
the
concurrent
relationship
between
the
variables.
It
was
pre-
dicted
that
the
multivariate
genetic
findings
would
mirror
the
phenotypic
results.
Finally,
the
degree
of
genetic
and
environ-
mental
continuity
over
time
was
investigated.
It
was
expected
that
in
the
child
sample
environmental
influences
would
be
time-
specific
and
genetic
influences
would
be
relatively
stable
over
time.
2.
Methods
2.1.
Participants
The
present
analyses
use
data
from
the
ECHO
study
(see
Lau,
Gregory,
Goldwin,
Pine,
&
Eley,
2007
for
more
details),
a
spin-
off
from
a
larger
longitudinal
sample
of
twins
born
in
England
and
Wales
during
1994–1996
(TEDS;
Trouton,
Spinath,
&
Plomin,
2002
).
In
order
to
maximise
power
and
include
children
with
high
emotional
symptoms,
the
majority
of
twins
(N
=
247
pairs)
were
recruited
due
to
one
or
both
of
them
scoring
within
top
15%
on
child
anxiety
at
age
7,
as
reported
by
parents.
A
smaller
group
of
‘control’
pairs
were
chosen,
out
of
which
none
of
the
twins
scored
high
on
anxiety
symptoms
(N
=
53
pairs).
This
selection
ensured
that
the
data
represented
a
full
range
of
scores
on
test
measures.
The
sample
characteristics
at
both
waves
are
presented
in
Table
1.
A
total
of
11
twin
pairs
(4%)
were
excluded
because
at
least
one
of
the
twins
had
co-morbid
diagnosis
of
neurological
impairments,
autistic
spectrum
disorders,
severe
receptive
lan-
guage
impairments
or
persistent
attentional
difficulties.
Zygosity
was
established
using
parent-report
questionnaires.
This
method
is
estimated
to
be
90%
accurate
(Goldsmith,
1991).
Where
zygos-
ity
was
ambiguous,
DNA
was
collected
from
cheek
swabs
in
order
to
assign
zygosity
using
highly
polymorphic
markers
of
99.9%
accuracy
(Price
et
al.,
2000).
The
social-economic
status
(SES)
of
ECHO
participants
was
somewhat
higher
than
a
population
based
sample,
where
for
example
32%
of
parents
were
in
edu-
cation
until
18
years
or
more
(Meltzer,
Gatward,
Goodman,
&
Ford,
2000).
For
both
waves,
parents
provided
written
informed
consent
through
the
post
prior
to
data
collection.
Data
collection
was
con-
ducted
at
the
Institute
of
Psychiatry
(King’s
College
London,
United
Kingdom),
apart
from
a
small
number
of
children
who
were
vis-
ited
in
their
homes.
The
study
was
granted
ethical
approval
by
the
Maudsley
Hospital
Ethics
Committee
(London,
United
Kingdom).
In
order
to
be
able
to
generalise
the
results
from
this
selected
sample
to
the
whole
population,
a
weight
was
incorporated
into
all
analyses.
The
weight
controls
for
biases
due
to
ascertainment
–
oversampling
symptomatic
children.
It
also
controls
for
two
response
biases:
higher
SES
of
families
from
TEDS
sample
who
agreed
to
participate
in
ECHO
study
as
compared
to
the
whole
sample,
and
higher
attrition
rate
in
the
families
with
mothers
reporting
higher
levels
of
emotional
problems
and
experiencing
more
negative
life
events.
The
weight
used
the
ratio
of
the
selection
probability
of
high
symptom
families
to
that
of
nonsymptomatic
families
to
control
for
bias
associated
with
ascertainment
across
waves,
and
the
inverse
of
the
predicted
probability
of
families
Table
1
Sample
characteristics.
Total
tested
Top
15%
score
on
anxiety/
controls
Male/female
White/non-
white
ethnic
background
Mother
employed/
unemployed
Father
employed/
unemployed
Mothers
in
education
above
age
18/below
age
18
Fathers
in
education
above
age
18/below
age
18
MZ/DZ/
unknown
Mean
age
(range)
Total
after
exclusions
Twin
pairs
Wave
1
300
247/53
(82%)
130.5/169.5
(43%)
256/44
(85%)
215/85
(72%)
269/31
(90%)
157/143
(52%)
175/125
(58%)
100/199/1
(33%)
8
years
and
6
months
(8
years
2
months–8
years
11
months)
289
Twin
pairs
Wave
2
250
203/47
(81%)
109/141
(44%)
216/34
(86%)
186/64
(74%)
225/25
(90%)
140/110
(56%)
144/106
(58%)
83/167
(33%)
10
years
1
month
(9
years
7
months-10
years
10
months)
248
anxiety
sensitivity;
249
anxiety
478 M.A.
Waszczuk
et
al.
/
Journal
of
Anxiety
Disorders
27 (2013) 475–
484
remaining
at
Wave
2
to
control
for
bias
associated
with
attrition.
In
short,
lower
weights
were
assigned
to
individuals
from
categories
over-represented
in
the
sample,
and
higher
weights
to
individuals
from
categories
under-represented
in
the
sample
relative
to
the
population
distribution.
2.2.
Measures
The
questionnaires
were
administered
on
a
laptop
computer
by
a
member
of
the
research
team
at
both
waves.
Items
were
read
aloud
if
child
had
difficulty
reading
them.
2.2.1.
Anxiety
sensitivity
The
Child
Anxiety
Sensitivity
Index
(CASI;
Silverman,
Fleisig,
Rabian,
&
Peterson,
1991)
was
used
to
measure
children’s
sensitiv-
ity
to
different
symptoms
of
anxiety.
Children
were
asked
to
rate
on
a
3
point
Likert
scale
(1
=
none,
2
=
some,
3
=
a
lot)
the
18
question-
naire
items
which
included
statements
such
as
‘Unusual
feelings
in
my
body
scare
me’.
The
construct
validity
of
CASI
is
good,
as
sug-
gested
by
the
high
correlations
with
fear
scores
in
normative
and
clinical
samples
(Silverman
et
al.,
1991).
The
internal
consistency
of
the
CASI
measure
is
also
very
good
(˛
=
0.87),
with
test–retest
reliability
ranges
between
0.70
and
0.80
(Reiss,
1986;
Silverman
et
al.,
1991).
In
the
current
sample
the
internal
consistency
was
comparable
to
published
statistics:
˛
=
0.80
at
both
waves.
2.2.2.
Anxiety
The
Screen
for
Child
Anxiety
Related
Emotional
Disorders
(SCARED;
Birmaher
et
al.,
1999)
was
used
to
assess
anxiety
disor-
der
symptoms.
Children
ranked,
on
a
3
point
Likert
scale
(0
=
almost
never,
2
=
often),
how
often
in
the
last
3
months
they
experi-
enced
symptoms
described
by
the
41
items
of
the
questionnaire.
The
items
can
be
summed
up
to
create
total
anxiety
score,
but
can
also
be
used
to
create
5
DSM-IV-related
anxiety
subscales:
panic/somatic,
general
anxiety,
separation
anxiety,
social
anxiety
and
school
phobia.
An
example
of
a
panic/somatic
subscale
item
is
‘When
I
get
frightened,
I
feel
like
passing
out‘,
while
separation
anxiety
is
measured
by
items
such
as
‘I
worry
that
bad
things
will
happen
to
my
parents’.
The
psychometric
properties
of
the
SCARED
have
been
extensively
reviewed
(Birmaher
et
al.,
1999;
Monga
et
al.,
2000
)
and
are
very
high,
showing
very
good
internal
consistency
(˛
=
0.90
for
the
total
score,
˛
=
0.78–0.87
for
the
subscales)
and
test–retest
reliability
ranging
between
0.60
and
0.90.
The
SCARED
also
discriminates
well
between
anxiety
and
other
psychiatric
dis-
orders,
including
co-morbid
conditions
such
as
depression.
In
this
sample
the
internal
consistency
ranged
between
˛
=
0.88–0.90
at
both
time
points.
For
individual
subscales,
internal
consistency
was
between
˛
=
0.50
(school
phobia)
to
˛
=
0.75
(panic/somatic
scale)
at
wave
1,
and
˛
=
0.58
(social
and
school
phobia)
and
˛
=
0.76
(panic/somatic
scale)
at
wave
2.
2.3.
Statistical
analyses
2.3.1.
Phenotypic
analyses
Descriptive
statistics
were
calculated
using
Stata
(StataCorp.,
2007
).
Variance,
distribution
and
means
were
estimated
for
all
variables.
Associations
between
anxiety
sensitivity
and
anxiety
subtypes,
both
concurrent
and
longitudinal,
were
explored
using
full
and
partial
correlations.
For
example,
in
order
to
investigate
the
longitudinal
relationship
between
anxiety
sensitivity
at
wave
1
and
panic/somatic
symptoms
at
wave
2,
the
scores
on
all
anx-
iety
scales
at
wave
1
were
controlled.
This
allowed
investigating
the
longitudinal
links
over
and
above
the
relationships
with
other
variables
at
time
1
that
might
confound
the
longitudinal
associa-
tion
due
to
high
co-morbidity
between
anxiety
symptoms.
These
descriptive
analyses
were
performed
on
untransformed
variables
to
allow
for
comparisons
with
other
samples.
2.3.2.
Genetics
analyses
The
twin
design
compares
the
degree
of
similarity
between
MZ
(sharing
100%
of
the
genes)
and
DZ
(sharing
on
average
50%
of
their
genes)
twin
pairs.
These
relative
differences
in
within-pair
correlations
allow
to
disentangle
the
influences
caused
by
additive
genetics
(A),
common
environment
(C)
and
non-shared
environ-
ment
(E).
For
more
details
of
quantitative
genetic
methods
see
Rijsdijk
and
Sham
(2002).
Models
were
fitted
using
the
OpenMx
program
(Boker
et
al.,
2011
)
in
R
(www.R-project.org;
TeamRDC,
2010),
a
structural
equa-
tion
modelling
package
for
the
analysis
of
genetically
informative
data
that
controls
for
non-independence
of
family
members
data.
Sampling
weights
were
incorporated
into
analyses,
but
did
not
influence
the
results
in
a
manner
that
would
alter
the
interpre-
tation.
As
is
standard
in
model
fitting,
the
variables
were
regressed
for
age
and
sex,
and
the
variables
with
skew
greater
than
1
were
transformed
to
ensure
normal
distribution
(anxiety
sensitivity,
panic/somatic
and
general
anxiety
time
2
were
square
transformed,
school
phobia
time
2
was
log
transformed).
Sex
differences
in
genetic
and
environmental
variance
components
were
not
exam-
ined
owing
to
the
relatively
small
sample
size.
In
order
to
maximise
the
sample,
raw
data
was
modelled.
Satu-
rated
models
were
run
for
each
set
of
variables.
To
assess
model-fit
to
saturated
model,
a
fit
index,
twice
the
negative
loglikelihood
(−2LL)
of
the
data
was
calculated.
The
difference
in
this
statistic
between
two
models
is
distributed
as
chi-square,
with
the
degrees
of
freedom
being
the
difference
in
degrees
of
freedom
between
the
two
models.
A
p-value
associated
with
chi-square
was
calculated
to
test
for
the
significance
of
the
discrepancy
between
the
raw
data
outcomes
and
the
expected
parameters.
The
goodness-of-fit
was
also
indexed
by
the
Akaike
information
criterion
(AIC).
Informa-
tion
about
the
precision
of
parameter
estimate
was
obtained
by
95%
likelihood-based
confidence
intervals
(CIs).
The
univariate
analyses
assessing
the
A,
C
and
E
influences
on
each
variable
were
conducted
at
both
waves.
Next,
three
longitu-
dinal
trivariate
Cholesky
decomposition
models
were
fitted
(see
Fig.
1).
The
model
assumes
three
distinct
sets
of
genetic
and
envi-
ronmental
influences
(A1
to
A3,
C1
to
C3
and
E1
to
E3)
on
each
variable
(though
the
paths
a1
to
a3,
c1
to
c3
and
e1
to
e3).
A1,
C1
and
E1
are
common
factors
influences
on
all
variables;
A2,
C2
and
E2
influence
only
the
second
and
third
variable;
and
A3,
C3
and
E3
are
unique
influences
specific
to
the
third
variable
only.
The
paths
from
each
factor
to
the
measured
variable
are
denoted
by
lower
case
letters
with
subscripts
denoting
the
number
of
the
latent
factor
and
the
measured
variable
(for
example
A1
influence
on
second
variable
is
denoted
by
a1
2
).
Total
A,
C
and
E
effects
on
each
individual
measure
can
be
obtained
by
summing
all
genetic
and
environmental
paths
to
that
measure.
Although
any
ordering
of
the
variables
explains
the
variance–covariance
matrix
between
variables
equally
well,
the
current
variables
are
ordered
in
a
way
to
investigate
whether
any
longitudinal
genetic
or
environmental
relationships
remained
between
anxiety
sensitivity
at
time
1
and
each
of
the
anxiety
subscales
at
wave
2
after
accounting
for
scores
on
the
same
anxiety
subscale
at
time
1.
Unlike
phenotypic
analyses,
which
controlled
for
all
concurrent
variables
at
time
1,
multivariate
analyses
controlled
only
for
one
concurrent
variable
at
time
1
due
to
problems
associated
with
interpreting
Cholesky
decompositions
of
more
than
4
variables.
There
was
an
item
on
the
panic/somatic
scale
that
reflected
anx-
iety
sensitivity
as
much
as
panic/somatic
subscale
(‘I
am
afraid
of
having
anxiety
(or
panic)
attacks’),
so
all
the
analyses
were
repeated
with
a
reduced
version
of
the
panic/somatic
subscale
that
did
not
include
this
item.
This
exclusion
did
not
make
significant
difference
M.A.
Waszczuk
et
al.
/
Journal
of
Anxiety
Disorders
27 (2013) 475–
484 479
Fig.
1.
Trivariate
Cholesky
decomposition.
Note:
A
denotes
additive
genetic
effects;
C
shared
environmental
effects;
E
non-shared
environmental
effects.
Variance
paths,
which
must
be
squared
to
estimate
the
proportion
of
variance
accounted
for,
are
represented
by
lowercase
letters
and
followed
by
two
numerals,
e.g.
a1
1
,
c2
2
,
e3
3
.
to
the
results
and
for
this
reason
the
results
for
the
entire
scale
are
presented.
3.
Results
3.1.
Phenotypic
results
The
descriptive
statistics
for
both
waves
are
presented
in
Table
2.
There
was
a
significant
drop
in
scores
on
anxiety
sen-
sitivity
(t(495)
=
2.85,
p
=
0.001,
d
=
0.26)
and
all
anxiety
scales
(t(497)
=
6.65,
p
<
0.001,
d
=
0.60)
from
8
to
10
years.
As
expected,
there
were
significant
sex
differences
in
the
scores.
Females
scored
significantly
higher
than
males
on
anxiety
sensitivity
time
1
(t(576)
=
2.07,
p
=
0.02,
d
=
0.17)
and
on
total
anxiety
scores
at
both
times
(time
1:
t(576)
=
3.20,
p
<
0.001,
d
=
0.27;
time
2:
t(496)
=
2.33,
p
=
0.01,
d
=
0.21).
The
within-time
and
longitudinal
correlations
between
vari-
ables
were
all
significant
at
the
p
<
0.001
level,
as
presented
in
Table
S.1.
The
more
stringent
longitudinal
partial
correlations,
which
con-
trolled
for
all
variables
at
time
1,
are
presented
in
Table
3.
Anxiety
sensitivity
time
1
was
found
to
be
most
strongly
correlated
with
panic/somatic
and
separation
anxiety
subtypes
at
time
2
over
and
above
associations
with
other
time
1
variables
(r
=
0.17
and
r
=
0.16
respectively,
both
p
<
0.001).
Anxiety
sensitivity
was
also
signifi-
cantly
correlated
with
general
and
social
anxiety
subtypes
at
time
2
after
controlling
for
the
confounding
associations
(r
=
0.11
both,
p
<
0.05).
Anxiety
sensitivity
had
no
significant
longitudinal
rela-
tionship
with
school
phobia
subscale
over
and
above
associations
with
other
variables
at
time
1.
It
is
important
to
note
that
weaker
relationship
could
be
due
to
poorer
internal
consistency
of
school
phobia
measure
at
both
waves.
Post
hoc
comparisons
revealed
that
the
longitudinal
partial
correlation
coefficients
were
not
signifi-
cantly
different
from
each
other.
Table
3
shows
that
longitudinal
associations
were
not
bidirec-
tional,
as
none
of
the
anxiety
subtypes
at
time
1
were
significantly
correlated
to
anxiety
sensitivity
at
time
2,
over
and
above
concur-
rent
associations,
with
the
exception
of
general
anxiety
(r
=
0.10,
Table
2
Descriptive
Statistics.
Anxiety
sensitivity Total
anxiety Somatic/panic
General
anxiety Separation
anxiety Social
phobia School
phobia
Mean
(SD,
range)
Time
1
31.31
(6.24,
18–52)
29.39
(12.63,
0–68)
7.15
(4.53,
0–22)
5.52
(3.51,
0–16)
7.46
(3.53,
0–16)
6.8
(2.96,
0–14)
2.45
(1.72,
0–8)
Time
2 30.32
(5.51,
18–51) 25.17
(11.59,
1–71)
5.71
(3.93,
0–24)
5.08
(3.46,
0–17)
6.06
(3.24,
0–16)
6.27
(3.03,
0–14)
2.04
(1.59,
0–8)
Note:
Results
presented
on
untransformed
variables
for
comparison
with
other
published
samples.
N
for
all
variables
at
time
1
=
578
(289
twin
pairs).
At
time
2,
N
=
496
(248
twin
pairs)
for
anxiety
sensitivity
and
N
=
498
(249
twin
pairs)
for
all
anxiety
subtypes.
480 M.A.
Waszczuk
et
al.
/
Journal
of
Anxiety
Disorders
27 (2013) 475–
484
Table
3
Longitudinal
partial
correlations
between
anxiety
sensitivity
and
anxiety
subtypes.
Time
1
Anxiety
sensitivity
Panic/somatic
General
anxiety
Separation
anxiety
Social
phobia
School
phobia
Time
2
Anxiety
Sensitivity
0.18
***
0.05
0.10
*
0.03
0.04
−0.06
Somatic/panic
0.17
***
0.15
***
0.10
*
−0.02
−0.01
0.05
General
anxiety
0.11
*
0.01
0.21
***
−0.03
0.04
0.01
Separation
anxiety 0.16
***
−0.03 0.07
0.22
***
−0.01
0.00
Social
phobia
0.11
*
−0.05
0.05
0.05
0.20
***
−0.01
School
phobia
0.03
0.03
0.01
0.05
−0.04
0.26
***
Note:
Analyses
controlled
for
all
other
variables
at
time
1.
The
significant
correlations
are
presented
in
bold.
*
p
<
0.05.
***
p
<
0.001.
p
<
0.05).
There
were
significant
longitudinal
homotypic
correla-
tions
of
each
anxiety
subtype
(r
=
0.15–0.26,
p
<
0.001).
Almost
no
longitudinal
heterotypic
correlations
between
the
variables
were
evident,
confirming
that
the
SCARED
scales
are
well
designed
to
differentiate
between
anxiety
subtypes.
Supplementary
data
associated
with
this
article
can
be
found,
in
the
online
version,
at
http://dx.doi.org/10.1016/j.janxdis.
2013.05.008
.
3.2.
Genetic
results
The
within-pair
correlations
for
MZ
and
DZ
pairs
and
the
univariate
model-fitting
results
are
presented
in
Table
S.2.
They
indicate
small
to
moderate
genetic
effects
on
all
variables
(ran-
ging
from
5%
on
social
phobia
time
1
to
39%
on
social
phobia
time
2),
non-significant
shared
environmental
influences
and
large
non-shared
environmental
influences
(ranging
from
59%
on
anxiety
sensitivity
time
2
to
95%
on
social
phobia
time
1).
The
trivariate
twin
analyses
focused
on
the
longitudinal
rela-
tionship
between
anxiety
sensitivity
and
three
anxiety
subtypes:
panic/somatic,
general
and
separation
anxiety.
Panic/somatic
and
separation
anxiety
subtypes
were
selected
because
they
had
the
strongest
longitudinal
phenotypic
association
with
anxiety
sen-
sitivity.
General
anxiety
was
selected