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Clinical predictors of conversion to bipolar disorder in a prospective longitudinal familial high-risk sample: focus on depressive features

Published online by Cambridge University Press:  07 November 2017

Andrew Frankland
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia
Gloria Roberts
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia
Ellen Holmes-Preston
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia
Tania Perich
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia Western Sydney University, Sydney, Australia
Florence Levy
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Prince of Wales Hospital, Sydney, Australia
Rhoshel Lenroot
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Neuroscience Research Australia, Sydney, Australia
Dusan Hadzi-Pavlovic
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia
Michael Breakspear
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia QIMR Berghofer, Brisbane, Australia
Philip B. Mitchell*
Affiliation:
School of Psychiatry, University of New South Wales, Sydney, Australia Black Dog Institute, Sydney, Australia
*
Author for correspondence: Philip B. Mitchell, E-mail: phil.mitchell@unsw.edu.au

Abstract

Background

Identifying clinical features that predict conversion to bipolar disorder (BD) in those at high familial risk (HR) would assist in identifying a more focused population for early intervention.

Method

In total 287 participants aged 12–30 (163 HR with a first-degree relative with BD and 124 controls (CONs)) were followed annually for a median of 5 years. We used the baseline presence of DSM-IV depressive, anxiety, behavioural and substance use disorders, as well as a constellation of specific depressive symptoms (as identified by the Probabilistic Approach to Bipolar Depression) to predict the subsequent development of hypo/manic episodes.

Results

At baseline, HR participants were significantly more likely to report ⩾4 Probabilistic features (40.4%) when depressed than CONs (6.7%; p < .05). Nineteen HR subjects later developed either threshold (n = 8; 4.9%) or subthreshold (n = 11; 6.7%) hypo/mania. The presence of ⩾4 Probabilistic features was associated with a seven-fold increase in the risk of ‘conversion’ to threshold BD (hazard ratio = 6.9, p < .05) above and beyond the fourteen-fold increase in risk related to major depressive episodes (MDEs) per se (hazard ratio = 13.9, p < .05). Individual depressive features predicting conversion were psychomotor retardation and ⩾5 MDEs. Behavioural disorders only predicted conversion to subthreshold BD (hazard ratio = 5.23, p < .01), while anxiety and substance disorders did not predict either threshold or subthreshold hypo/mania.

Conclusions

This study suggests that specific depressive characteristics substantially increase the risk of young people at familial risk of BD going on to develop future hypo/manic episodes and may identify a more targeted HR population for the development of early intervention programs.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2017 

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