Poster No:
541
Submission Type:
Abstract Submission
Authors:
Rochelle Ye1, Cassandra Wannan1, Stephen Wood1, Joseph Kambeitz2, Lana Kambeitz-Ilankovic2, Stefan Borgwardt3, Paolo Brambilla4, Rebekka Lencer5, Eva Meisenzahl6, Raimo Salokangas7, Christos Pantelis8, Alessandro Bertolino9, Rachel Upthegrove10, Nikolaos Koutsouleris11, Barnaby Nelson8, Dominic Dwyer12
Institutions:
1University of Melbourne, Parkville, Victoria, 2University of Cologne, Cologne, Germany, 3University of Luebeck, Luebeck, Germany, 4University of Milan, Milan, Italy, 5University of Muenster, Muenster, Germany, 6Univesity of Duesseldorf, Duesseldorf, Germany, 7University of Turku, Turku, Finland, 8Orygen, Melbourne, Victoria, 9University of Bari, Bari, Italy, 10University of Oxford, Oxford, United Kingdom, 11University of Munich, Munich, Germany, 12Centre for Youth Mental Health, Melbourne, VIC
First Author:
Rochelle Ye
University of Melbourne
Parkville, Victoria
Co-Author(s):
Introduction:
Clinical staging is a dimensional approach to operationalising the progression of mental illness that can identify risk for developing serious mental illness (Stage 1b), the onset of diagnosable illness (Stage 2), and unremitting, persistent illness (Stage 2+; McGorry et al., 2007). Recently, clinical staging has moved towards developing transdiagnostic criteria that are not disorder-specific and can capture a range of mental illness, however, operationalisation and empirical evidence for these stages are limited. Clear psychometric criteria for determining stages still need to be validated, as most research relies on clinician or researcher-consensus (Iorfino et al., 2019; Addington et al., 2019). Another limitation is that clinical staging does not integrate negative symptoms, despite emerging evidence of it being transdiagnostic across diagnoses (Li et al., 2024). Further, underlying biological changes across clinical stages have not been assessed. Neuroimaging research suggests shared neural substrates across psychopathology (Goodkind et al., 2015) and schizophrenia polygenic risk scores (PRS) have been shown to explain around 3% of variance in mental health (Marsman et al., 2020). Demonstrating changes in brain volume and genetic risk across clinical stages may provide convergent validity for the psychometrically defined stages. We aim to (1) expand staging criteria to capture negative symptoms, (2) investigate longitudinal trajectories of stages, and (3) demonstrate differences in brain volume and (3) PRS across transdiagnostic clinical stages.
Methods:
749 help-seeking individuals and healthy controls from a longitudinal, multisite cohort study investigating prediction tools for psychosis-related outcomes (PRONIA) were stratified into transdiagnostic clinical stages. Participants were assessed on positive, negative, and depression symptoms, socio-occupational functioning, premorbid functioning, and quality of life. Schizophrenia PRS were calculated using the Psychiatric Genomic Consortium reference sample, generated over a range of 10 P-values. Group differences were determined using ANOVAs and significant differences were explored using post-hoc t-tests. 2-sided P values were assessed at a significance level of false discovery rate corrected P < .05. Linear mixed effects models were used to investigate longitudinal trajectories. Findings were validated in an unmatched validation set with three new sites (n = 610). Voxel-based morphometry (VBM) of T1-weighted MRI scans was performed using SPM12 in MATLAB (R2024a) to compare grey matter volume across clinical stages. VBM statistical t-maps were parcellated using Schaefer cortical and Tian subcortical atlases. To assess validation, region-wise correlation of the discovery and validation statistical maps were computed, and statistical significance was assessed using spin permutation tests.
Results:
Higher clinical stage was associated with higher symptomatology, poorer functioning, and poorer premorbid functioning (P < .001). Differences between stages were largely maintained over 18 months (P < .001). Clinical findings were replicated. Higher schizophrenia PRS was associated with higher clinical stage (P < .001). PRS findings were partially replicated. Greater grey matter volume abnormality was associated with higher clinical stage (P < .001). There was a moderate to strong correlation between the discovery and validation statistical maps (r = .44, P < .05).
Conclusions:
Our findings support transdiagnostic clinical staging as a promising method of characterising the progression of mental illness severity and identifying individuals with different histories, clinical presentations, prognoses, and biology. Underlying biological changes have been demonstrated for the first time with findings of a positive relationship between clinical stage, brain volume abnormalities and PRS. This could have implications for determining treatment type, duration, and target in clinical settings.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetics Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
Multivariate Approaches 2
Keywords:
Affective Disorders
Experimental Design
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
Treatment
Other - clinical staging; transdiagnostic
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Provide references using APA citation style.
1. Addington, J., Liu, L., Goldstein, B. I., Wang, J., Kennedy, S. H., Bray, S., ... & MacQueen, G. (2019). Clinical staging for youth at‐risk for serious mental illness. Early Intervention in Psychiatry, 13(6), 1416-1423.
2. Goodkind, M., Eickhoff, S. B., Oathes, D. J., Jiang, Y., Chang, A., Jones-Hagata, L. B., ... & Etkin, A. (2015). Identification of a common neurobiological substrate for mental illness. JAMA psychiatry, 72(4), 305-315.
3. Iorfino, F., Scott, E. M., Carpenter, J. S., Cross, S. P., Hermens, D. F., Killedar, M., ... & Hickie, I. B. (2019). Clinical stage transitions in persons aged 12 to 25 years presenting to early intervention mental health services with anxiety, mood, and psychotic disorders. JAMA psychiatry, 76(11), 1167-1175.
4. Li, S. B., Zhang, J. B., Liu, C., Wang, L. L., Hu, H. X., Chu, M. Y., ... & Chan, R. C. (2024). A transdiagnostic approach of negative symptoms in psychiatric disorders: replication of a two-factor structure in major depressive disorder and bipolar disorder. European Archives of Psychiatry and Clinical Neuroscience, 1-12.
5. Marsman, A., Pries, L. K., Ten Have, M., De Graaf, R., Van Dorsselaer, S., Bak, M., ... & Van Os, J. (2020). Do current measures of polygenic risk for mental disorders contribute to population variance in mental health?. Schizophrenia Bulletin, 46(6), 1353-1362
6. McGorry, P. D., Purcell, R., Hickie, I. B., Yung, A. R., Pantelis, C., & Jackson, H. J. (2007). Clinical staging: a heuristic model for psychiatry and youth mental health. Medical Journal of Australia, 187(S7), S40-S42.
No