TY - JOUR
T1 - Machine learning based prediction and the influence of complement – Coagulation pathway proteins on clinical outcome
T2 - Results from the NEURAPRO trial
AU - Susai, Subash Raj
AU - Mongan, David
AU - Healy, Colm
AU - Cannon, Mary
AU - Cagney, Gerard
AU - Wynne, Kieran
AU - Byrne, Jonah F.
AU - Markulev, Connie
AU - Schäfer, Miriam R.
AU - Berger, Maximus
AU - Mossaheb, Nilufar
AU - Schlögelhofer, Monika
AU - Smesny, Stefan
AU - Hickie, Ian B.
AU - Berger, Gregor E.
AU - Chen, Eric Y. H.
AU - de Haan, Lieuwe
AU - Nieman, Dorien H.
AU - Nordentoft, Merete
AU - Riecher-Rössler, Anita
AU - Verma, Swapna
AU - Street, Rebekah
AU - Thompson, Andrew
AU - Ruth Yung, Alison
AU - Nelson, Barnaby
AU - McGorry, Patrick D.
AU - Föcking, Melanie
AU - Paul Amminger, G.
AU - Cotter, David
N1 - Funding Information: This work was supported by an Irish Health Research Board research grant to DC (HRB ILP POR 2019–005). This publication has emanated from research supported by Health Research Board (HRB) [to 411 DC, MF] under grant number HRB/HRA/PHR/2015–1293. The research was funded in 412 part by a research grant from Science Foundation Ireland (SFI) under Grant Number 413 16/RC/3948 and co-funded under the European Regional Development Fund and by 414 FutureNeuro industry partners. The immune marker study was supported by Orygen, Australia. 415 DM is a Fellow on the Irish Clinical Academic Training (ICAT) Programme which is supported 416 by the Wellcome Trust and the Health Research Board (Grant Number 203930/B/16/Z), the 417 Health Service Executive National Doctors Training and Planning and the Health and Social 418 Care, Research and Development Division, Northern Ireland. JFB was supported by a Wellcome Flagship Innovations Award (220438Z/20/Z). BN was supported by an NHMRC Senior Research Fellowship (1137687) and a University of Melbourne Dame Kate Campbell Fellowship. MB was supported by a MRFF RART Fellowship. Research reported in this publication was supported by The Comprehensive Molecular Analytical Platform (CMAP) under The SFI Research Infrastructure Programme, reference 18/RI/5702. Funding Information: We would like to thank the participants and their families. We express our thanks to Mr. John Butler from Mesoscale diagnostics for his guidance and support to run the multiplex assays. We thank Eugène Dillon from University College Dublin for helping us in obtaining proteomic data. We express our sincere thanks to Hok Pan Yuen from ORYGEN for his contribution in clinical data acquisition for the NEURAPRO study. Publisher Copyright: © 2022 Elsevier Inc.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Clinical high risk
KW - Functional outcome
KW - Prediction models
KW - Psychosis
KW - Schizophrenia
UR - http://www.scopus.com/inward/record.url?scp=85128186404&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.bbi.2022.03.013
DO - https://doi.org/10.1016/j.bbi.2022.03.013
M3 - Article
C2 - 35341915
SN - 0889-1591
VL - 103
SP - 50
EP - 60
JO - Brain, behavior, and immunity
JF - Brain, behavior, and immunity
ER -