Machine learning based prediction and the influence of complement – coagulation pathway proteins on clinical outcome: results from the NEURAPRO trial
Background: Functional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR.
Materials and methods: We carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome.
Results and conclusion: A prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.
Irish Health Research Board research grant (HRB ILP POR 2019–005)
Health Research Board (HRB) under grant number HRB/HRA/PHR/2015–1293
Science Foundation Ireland (SFI) under Grant Number 413 16/RC/3948
European Regional Development Fund
FutureNeuro industry partners
Wellcome Trust and the Health Research Board (Grant Number 203930/B/16/Z)
Health Service Executive National Doctors Training and Planning and the Health and Social Care, Research and Development Division, Northern Ireland
Wellcome Flagship Innovations Award (220438Z/20/Z)
NHMRC Senior Research Fellowship (1137687)
University of Melbourne Dame Kate Campbell Fellowship
MRFF RART Fellowship
The Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme, reference 18/RI/5702
CommentsThe original article is available at https://www.sciencedirect.com/
Published CitationSusai SR. et al. Machine learning based prediction and the influence of complement - coagulation pathway proteins on clinical outcome: results from the NEURAPRO trial. Brain Behav Immun. 2022;103:50-60
Publication Date24 March 2022
- Beaumont Hospital
- Neurological and Psychiatric Disorders
- Population Health and Health Services
- Immunity, Infection and Inflammation
- Published Version (Version of Record)