Prospective progression from high-prevalence disorders to bipolar disorder: exploring characteristics of pre-illness stages Citation: Ratheesh, Aswin, Cotton, Susan M., Betts, Jennifer K., Chanen, Andrew, Nelson, Barnaby, Davey, Christopher G., McGorry, Patrick D., Berk, Michael and Bechdolf, Andreas 2015, Prospective progression from high-prevalence disorders to bipolar disorder: exploring characteristics of pre-illness stages, Journal of affective disorders, vol. 183, pp. 45-48 DOI: 10.1016/j.jad.2015.04.025 This is the accepted manuscript. ©2015, Elsevier

This manuscript version is made available under a Creative Commons Attribution Non-Commercial No-Derivatives 4.0 Licence. Available from Deakin Research Online: http://hdl.handle.net/10536/DRO/DU:30073836

Ratheesh 2014 High prevalence disorders to BD

Prospective progression from high- prevalence disorders to bipolar disorder: exploring

characteristics of pre-illness stages

Aswin Ratheesh1,2, Susan M Cotton1,2, Jennifer K Betts1,2, Andrew Chanen1,2, Barnaby Nelson1,2, Christopher G. Davey1,2, Patrick D McGorry1,2, Michael Berk1,3,4, Andreas Bechdolf2,5,6 1Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia 2Centre for Youth Mental Health, The University of Melbourne, Parkville, Australia 3IMPACT Strategic Research Centre, Deakin University, Geelong, Australia 4Florey Institute of Neurosciences and Mental Health, Parkville, Australia 5Department of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Klinikum am Urban, Charite Medical University, Berlin, Germany 6Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany

Corresponding Author:

Dr Aswin Ratheesh, Psychiatrist, Hon Lecturer, Orygen, 35 Poplar Road, Parkville, VIC 3052, Australia. Email: aswinr@unimelb.edu.au Ph: +61 3 9342 2800

Ratheesh 2014 High prevalence disorders to BD

Abstract

Background: Identification of risk factors within precursor syndromes, such as depression, anxiety or substance use disorders (SUD), might help to pinpoint high-risk stages where preventive interventions for Bipolar Disorder (BD) could be evaluated. Methods: We examined baseline demographic, clinical, quality of life, and temperament measures along with risk clusters among 52 young people seeking help for depression, anxiety or SUDs without psychosis or BD. The risk clusters included Bipolar At-Risk (BAR) and the Bipolarity Index as measures of bipolarity and the Ultra-High Risk assessment for psychosis. The participants were followed up for 12 months to identify conversion to BD. Those who converted and did not convert to BD were compared using Chi-Square and Mann Whitney U tests. Results: The sample was predominantly female (85%) and a majority had prior treatment (64%). Four participants converted to BD over the 1-year follow up period. Having an alcohol use disorder at baseline (75% vs 8%, �2=14.1, p<0.001) or a family history of SUD (67% vs 12.5%, �2=6.0, p=0.01) were associated with development of BD. The sub-threshold mania subgroup of BAR criteria was also associated with 12-month BD outcomes. The severity of depressive symptoms and cannabis use had high effects sizes of association with BD outcomes, without statistical significance. Conclusions and Limitations: The small number of conversions limited the power of the study to identify associations with risk factors that have previously been reported to predict BD. However, subthreshold affective symptoms and SUDs might predict the onset of BD among help-seeking young people with high-prevalence disorders. Keywords: bipolar, depression, anxiety, substance, staging, prospective, at-risk

Ratheesh 2014 High prevalence disorders to BD

Background Bipolar disorder (BD) is a leading cause of disability and morbidity (Whiteford et al., 2013). Early intervention and prevention efforts might help decrease this morbidity, but such efforts require identification of precursor syndromes for BD. Familial high risk (Duffy et al., 2014; Egeland et al., 2012) and naturalistic cohort studies (Angst et al., 2005; Fiedorowicz et al., 2011) indicate that depression and anxiety symptoms might be precursor syndromes for BD. A longitudinal relationship is also evident between substance use disorders (SUD) and manic and hypomanic symptoms (Henquet et al., 2006; Manwani et al., 2006). The concept of high prevalence disorders such as depression, anxiety and SUD being precursor syndromes in some cases for lower prevalence syndromes such as BD, has received support in familial (Duffy et al., 2014), epidemiological (Beesdo et al., 2009) and clinical populations (Fiedorowicz et al., 2011). We prospectively examined factors associated with risk of developing BD over 12 months among young people seeking help for current depression, anxiety or SUD. Methods

Participants: A subsample was selected from a cohort of help-seeking young people aged 15-25 years attending a tertiary youth mental health service in Melbourne, Australia. For the primary study, 70 participants were selected from 559 help-seeking young people attending Orygen Youth Health (OYH) on the basis that they did not have threshold (hypo)mania or psychosis and consented to research follow up. The details of the selection criteria have previously been described (Bechdolf et al., 2014). Of these participants, a sub-group was selected on the basis of meeting at-risk criteria for BD (Bipolar At-Risk or BAR) (Bechdolf et al., 2010). The BAR criteria were: (i) age between 15 and 25 years; (ii) sub-threshold manic symptoms; and (iii) sub-threshold depression in combination with either a) cyclothymic features or b) family history of BD. Subthreshold mania was defined as Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) criteria for hypomania but for a shorter duration (between 2 and 4 days) and at a lower threshold for Criterion B symptoms (two associated with elation and three with irritability), and without consideration of criteria C through E. Subthreshold depression was defined in line with the criteria for Major Depressive Episodes in DSM-IV, but for a shorter duration of 1 week and with a requirement for a lower number of primary symptoms (three including the major criteria) and excluding criteria B through E. Among the participants followed up for 12 months, the current report examines a subgroup of 52 with one or more baseline DSM-IV diagnoses — of major depressive disorder (MDD), an anxiety disorder or a substance use disorder using the Structured Clinical Interview for DSM-IV Axis I Disorders – Patient Edition (SCID IV-I/P) (First et al., 2002). Consistent with the DSM 5 (American Psychiatric Association, 2013), substance abuse and dependence was combined as a single diagnosis of SUD. The Melbourne Health Human Research Ethics Committee approved the research project [No: 2008.613]. Measures: In addition to the SCID-I/P and the BAR criteria, participants were assessed at baseline on dimensional measures of psychopathology including the Young Mania Rating Scale (YMRS, Young et al., 1978) as a cross sectional measure of manic symptoms and the Montgomery Åsberg Depression Rating Scale (MADRS, Montgomery and Asberg, 1979) and the Bipolar Depression Rating Scale (BDRS, Berk et al., 2007) for depressive symptoms. Brown scales for Attention Deficit Disorder (ADD)(Brown, 1996) were used as a self-report measure of dimensional ADD related pathology. The 69-item version of Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Auto-questionnaire (TEMPS-A, Akiskal et al., 2005) was used as a measure of temperament. Family history was assessed using the Family Interview for Genetic Studies (Maxwell, 1992). As with the SCID-I/P, the substance use diagnoses of dependence and abuse were combined to be a single diagnosis of substance use disorder among family members. The Comprehensive Assessment of At-Risk Mental States (CAARMS, Yung et al., 2005) was used to identify those at Ultra-High Risk (UHR) of transition to psychosis. The Bipolarity Index (BI, Sachs, 2004)) was used as a composite measure of bipolar risk in addition to BAR criteria. In addition to the SCID IV-I/P,

Ratheesh 2014 High prevalence disorders to BD

Additions Severity Index (McLellan et al., 1980) was used to quantify the number of days alcohol or cannabis was used in the month prior to baseline assessment. The subjective quality of life (QoL) was measured using the Modular System for Quality of Life (MSQoL, Pukrop et al., 1999). The socio-demographic details were collected using a proforma devised for this purpose. At the completion of the 12 month follow-up, the primary measure of conversion to BD was made using the Longitudinal Interval Follow-up Evaluation (LIFE) for DSM IV diagnoses (Keller et al., 1997). Statistical analyses: The differences between the converters and non-convertors to BD were examined using chi-square tests for categorical variables and Mann-Whitney U tests for continuous variables. Effect sizes were also determined due to the risk of type II error, using Odds Ratios (OR) or Area Under the receiver-operating characteristic Curve (AUC). Corrections for multiple comparisons were not performed, as this was an exploratory study. Results At baseline, the sample was of a mean age of 19.7 years, predominantly female (85%) (Table1) and were moderately unwell given 75% (n=39) had a previous suicide attempt, 64% (n=33) had previous psychiatric treatment and 32% (n=16) had a previous psychiatric hospitalization. At 12-month follow up, four participants (7.7%) had developed BD, all of whom were female. With respect to their DSM-IV diagnoses, three developed BD II and one developed BD not otherwise specified (NOS). Two of the participants who later developed BD II and two who did not develop BD were prescribed SSRIs at baseline. Two of the four participants who converted to BD were prescribed an anti-depressant at baseline and two were not. There were no significant differences between groups with respect to frequency of antidepressant or medication use. None of the participants were prescribed stimulants. Baseline predictors of developing BD were having an alcohol use disorder, or having a family history of SUD (Table 2). In addition, converters had significantly lower physical health QoL (converters M=34.1, SD=7.7, non-converters M=44.5, SD=6.7; U=15.5, p=0.03). Examination of the BI and BAR criteria indicated that subthreshold (hypo)manic symptoms or episodes were significantly associated with later development of BD. There were differences between the groups using the ‘Episode Characteristics’ subscale of BI, which measures subthreshold (hypo)manic symptoms, as a continuous measure [converters M=5.0,SD=0; nonconverters M=2.4 (SD=2.3); U= 30, p=0.02] and using the subthreshold mania subgroup of BAR as a discontinuous measure [converters n=2, 29%; nonconverters n=2, 4%; 2=5.0, p=0.03]. Though not statistically significant, the AUC values indicative of overall classification ability, was high for severity of depression based on MADRS (AUC=0.74, SD=0.16) and BDRS (AUC=0.72,SD=0.13) scores as well as the number of days of cannabis consumed in the 30 days prior (AUC= 0.70, SD=0.16) using ASI at baseline assessment. Discussion

Of the 52 help-seeking young people with depressive, anxiety or substance use disorders, development of BD over the following 12 months was predicted by baseline sub-threshold manic symptoms, alcohol use disorders, family history of substance use disorders and lower physical health QoL. SUDs among participants and their families in the ‘prodromal’ period of BD could be related to BD for a number of reasons. One is that there could be shared genetic and environmental vulnerabilities among people with SUDs and BD. Presence of BD among probands was associated with a higher prevalence of SUDs among family members in an association study (Biederman et al., 2000) while a genome-wide association study has identified shared genes of risk between BD and SUD (Johnson et al., 2009). Such shared risk factors may include impulsivity and a propensity for risk-taking, which have been associated specifically with

Ratheesh 2014 High prevalence disorders to BD

persons with bipolar disorder, especially when not taking antipsychotic medication (Reddy et al., 2014). A second possibility is that the prevalence of SUDs among converters might represent self-medication for sub-threshold symptoms or associated cognitive difficulties. In a cross-sectional study, a quarter of individuals who experienced mood symptoms reported using drugs or alcohol to self-medicate themselves during their mood episodes, the highest being among BDI participants (Bolton et al., 2009). Substance abuse, particularly cannabis could also possibly act as a factor associated with onset of mania as indicated by a previous prospective study (Baethge et al., 2008) examining the co-occurrence of SUD and BD. Poorer physical QoL has not been previously noted in at-risk samples to the authors’ knowledge. While this could be a chance finding due to the small sample size, it is possible that some items in the physical QoL measure represent anergic symptoms of depression. In addition to this, the severity of depression measured on MADRS and BDRS in the current study had high classificatory ability for BD based on AUC, even though this was not statistically significant. Severity of depression has been previously identified to be a risk factor for prospective conversion to BD among in and outpatients with MDD (Holma et al., 2008). The role of sub-threshold manic symptoms across risk clusters such as BAR and BI in predicting BD may partly be explained by the relatively selective sampling of participants that fulfill the BAR criteria. However, previous studies have indicated a role for subthreshold manic symptoms (Fiedorowicz et al., 2011; Tijssen et al., 2010) in predicting BD. The diverse risk factors also indicate that clusters of risk factors such as BAR could be more useful than individual risk factors in predicting onset of BD. Using characteristics such as the severity of depression, and the prevalence of alcohol and substance use disorders could enhance the predictive abilities of these risk clusters. Limitations The primarily limitation of the study is the small number of conversions, particularly the absence of conversions to BD-I. This might be related to the short duration of follow-up as well as the over-representation of women in the baseline sample and among convertors. However, the conversion rate per year in the current study is substantially higher than that in previous samples (Akiskal et al., 1995; Angst et al., 2005). The possibility of type II error due to the small sample size of converters may also explain the lack of association with other measures that have previously been associated with onset of BD such as temperament, attentional difficulties and family history of BD. In addition, women are over-represented both in the intake sample as well as among convertors. Conclusions Larger samples of at-risk participants, screened to identify multiple risk factors with adequate power to identify the development of BD, preferably over a longer time period might help clarify the specificity and magnitude of the predictive ability of these factors for BD. Among a group of help seeking young people with high levels of baseline anxiety, depression, attentional difficulties and markers of severity such as previous hospitalization and suicidality, other risk factors such as sub-threshold symptoms and SUDs could improve the prediction of BD onset. References

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Ratheesh 2014 High prevalence disorders to BD

Akiskal, H.S., Maser, J.D., Zeller, P.J., Endicott, J., Coryell, W., Keller, M., Warshaw, M., Clayton, P., Goodwin, F., 1995. Switching from 'unipolar' to bipolar II. An 11-year prospective study of clinical and temperamental predictors in 559 patients. Arch Gen Psychiatry 52, 114-123. American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders: DSM-5. American Psychiatric Association, Arlington, VA. Angst, J., Sellaro, R., Stassen, H.H., Gamma, A., 2005. Diagnostic conversion from depression to bipolar disorders: results of a long-term prospective study of hospital admissions. Journal of affective disorders 84, 149-157. Baethge, C., Hennen, J., Khalsa, H.-M.K., Salvatore, P., Tohen, M., Baldessarini, R.J., 2008. Sequencing of substance use and affective morbidity in 166 first-episode bipolar I disorder patients. Bipolar Disorders 10, 738-741. Bechdolf, A., Nelson, B., Cotton, S.M., Chanen, A., Thompson, A., Kettle, J., Conus, P., Amminger, G.P., Yung, A.R., Berk, M., McGorry, P.D., 2010. A preliminary evaluation of the validity of at-risk criteria for bipolar disorders in help-seeking adolescents and young adults. Journal of affective disorders 127, 316-320. Bechdolf, A., Ratheesh, A., Cotton, S.M., Nelson, B., Chanen, A., Betts, J., Bingmann, T., Yung, A., Berk, M., McGorry, P., 2014. The predictive validity of Bipolar At-risk (prodromal) criteria in help seeking adolescents and young adults: a prospective study Bipolar Disorders. Beesdo, K., Höfler, M., Leibenluft, E., Lieb, R., Bauer, M., Pfennig, A., 2009. Mood episodes and mood disorders: patterns of incidence and conversion in the first three decades of life. Bipolar Disorders 11, 637-649. Berk, M., Malhi, G.S., Cahill, C., Carman, A.C., Hadzi-Pavlovic, D., Hawkins, M.T., Tohen, M., Mitchell, P.B., 2007. The Bipolar Depression Rating Scale (BDRS): its development, validation and utility. Bipolar Disord 9, 571-579. Biederman, J., Faraone, S.V., Wozniak, J., Monuteaux, M.C., 2000. Parsing the association between bipolar, conduct, and substance use disorders: a familial risk analysis. Biological psychiatry 48, 1037-1044. Bolton, J.M., Robinson, J., Sareen, J., 2009. Self-medication of mood disorders with alcohol and drugs in the National Epidemiologic Survey on Alcohol and Related Conditions. J Affect Disord 115, 367-375. Brown, T.E., 1996. Brown Attention-Deficit Disorder Scales: Manuel. The Psychological Corporation, San Antonio. Duffy, A., Horrocks, J., Doucette, S., Keown-Stoneman, C., McCloskey, S., Grof, P., 2014. The developmental trajectory of bipolar disorder. Br J Psychiatry 204, 122-128. Egeland, J.A., Endicott, J., Hostetter, A.M., Allen, C.R., Pauls, D.L., Shaw, J.A., 2012. A 16-Year Prospective Study of Prodromal Features Prior to BPI Onset in Well Amish Children. J Affect Disord 142, 186-192. Fiedorowicz, J.G., Endicott, J., Leon, A.C., Solomon, D.A., Keller, M.B., Coryell, W.H., 2011. Subthreshold hypomanic symptoms in progression from unipolar major depression to bipolar disorder. The American Journal Of Psychiatry 168, 40-48. First, M.B., Spitzer, R.L., Gibbon Miriam, Williams, J.B.W., 2002. Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition (SCID-I/P). Biometrics Research, New York State Psychiatric Institute, New York. Henquet, C., Krabbendam, L., de Graaf, R., ten Have, M., van Os, J., 2006. Cannabis use and expression of mania in the general population. J Affect Disord 95, 103-110.

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Ratheesh 2014 High prevalence disorders to BD

Table 1: Baseline clinical and demographic characteristics (N=52) Characteristic at Baseline % (N) Mean (SD)

Female gender 85% (44) Age, in years 19.7 (2.8) Educational status1

Year 10 or less 38% (20) Year 11 or 12 56% (29)

More than year 12 6% (3) No history of migration 86% (45) Previous medical condition 29% (15) Previous suicide attempt 75% (39) Previous psychiatric hospitalization 32% (16) Current diagnosis

Major Depressive Disorder 65% (34) Anxiety disorder 79% (41)

Substance Use Disorder 37% (19) ADD diagnoses2 75% (39) UHR for psychosis 21% (11) BAR criteria at baseline 50% (26) ADD- Attention Deficit Disorder; UHR- Ultra High Risk Criteria, based on the Comprehensive Assessment of At-Risk Mental States, BAR- Bipolar At-Risk 1- Enrolled or completed; 2-Based on cut offs of 45 for adults and 60 for adolescents using the Brown ADD scale

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C

0.65

0.

46-0

.84

Rat

hees

h 20

14 H

igh

prev

alen

ce d

isor

ders

to B

D

Lif

e M

(SD

) 42

.4 (

6.4)�

45.3

(11

.5)�

U

43.0

0.

39

AU

C

0.35

0.

13-0

.57

CI-

Con

fide

nce

Inte

rval

, OR

- O

dds

Rat

io, A

UC

- A

rea

Und

er th

e R

ecei

ver

Ope

ratin

g C

hara

cter

istic

Cur

ve, Y

MR

S- Y

oung

Man

ia R

atin

g Sc

ale,

MA

DR

S- M

ontg

omer

y A

sber

g D

epre

ssio

n R

atin

g Sc

ale,

BD

RS-

Bip

olar

Dep

ress

ion

Rat

ing

Scal

e, A

DD

- A

ttent

ion

Def

icit

Dis

orde

r, T

EM

PS- T

empe

ram

ent E

valu

atio

n of

Mem

phis

-Pis

a-Sa

n D

iego

Aut

oque

stio

nnai

re, U

HR

- U

ltra

Hig

h R

isk,

MSQ

oL-

Mod

ular

Sys

tem

for

Qua

lity

of L

ife

# Dat

a av

aila

ble

for

25 p

artic

ipan

ts; * D

ata

avai

labl

e fo

r 43

par

ticip

ants

; ^ Dat

a av

aila

ble

for

42 p

artic

ipan

ts

Rat

hees

h 20

14 H

igh

prev

alen

ce d

isor

ders

to B

D

Rol

e of

Fun

ding

Sou

rce

The

pri

mar

y st

udy

was

fund

ed b

y a

NA

RSA

D in

depe

nden

t inv

estig

ator

to A

B a

nd b

y th

e G

erm

an R

esea

rch

Foun

datio

n (B

e 36

97/1

-1).

The

fun

ding

sou

rces

ha

d no

invo

lvem

ent i

n th

e st

udy

desi

gn, a

naly

sis,

pre

para

tion

of th

e m

anus

crip

t and

the

deci

sion

to s

ubm

it th

e m

anus

crip

t.

Rat

hees

h 20

14 H

igh

prev

alen

ce d

isor

ders

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D

The

aut

hors

wis

h to

ack

now

ledg

e th

e G

erm

an R

esea

rch

Foun

datio

n (B

e 36

97/1

-1)

and

Bra

in a

nd B

ehav

ior

Res

earc

h Fo

unda

tion

thro

ugh

the

NA

RSA

D

gran

ts fo

r fu

ndin

g th

e st

udy.

Rat

hees

h 20

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igh

prev

alen

ce d

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The

aut

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hav

e no

con

flic

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f in

tere

st to

dec

lare

.

Rat

hees

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igh

prev

alen

ce d

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ders

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D

Con

trib

utor

s

A R

athe

esh,

S C

otto

n, M

Ber

k, C

Dav

ey a

nd A

Bec

hdol

f co

ncep

tual

ized

the

curr

ent r

epor

t. D

rs R

athe

esh

and

Cot

ton

wer

e in

volv

ed in

the

data

an

alys

is. D

r R

athe

esh

prep

ared

the

firs

t dra

ft a

nd a

ll au

thor

s co

ntri

bute

d to

the

editi

ng a

nd d

evel

opm

ent o

f th

e m

anus

crip

t. J

Bet

ts, A

Cha

nen,

B

Nel

son

& P

D M

cGor

ry w

ere

invo

lved

in th

e de

sign

, con

cept

ualiz

atio

n an

d ex

ecut

ion

of th

e pr

imar

y st

udy

and

cont

ribu

ted

to th

e fi

nal d

raft

of

the

man

uscr

ipt.

Rat

hees

h 20

14 H

igh

prev

alen

ce d

isor

ders

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Hig

hlig

hts

•

Alc

ohol

use

dis

orde

rs a

nd a

fam

ily h

isto

ry o

f su

bsta

nce

use

diso

rder

s m

ay p

redi

ct o

nset

of

BD

am

ong

yout

h w

ith d

epre

ssio

n, a

nxie

ty a

nd

subs

tanc

e us

e di

sord

ers

•

Subt

hres

hold

man

ic s

ubgr

oup

of B

ipol

ar-A

t-R

isk

(BA

R)

crite

ria

also

pre

dict

s co

nver

sion

to B

D

•

Seve

rity

of

depr

essi

on a

nd c

anna

bis

use

as r

isk

fact

ors

of c

onve

rsio

n m

ay m

erit

furt

her

stud

y

Rat

hees

h 20

14 H

igh

prev

alen

ce d

isor

ders

to B

D