Dysregulated Lipid Metabolism Precedes Onset of Psychosis

Alex M. Dickens; Partho Sen; Matthew J. Kempton; Neus Barrantes-Vidal; Conrad Iyegbe; Merete Nordentoft; Thomas Pollak; Anita Riecher-Rossler; Stephan Ruhrmann; Gabriele Sachs; Rodrigo Bressan; Marie-Odile Krebs; G. Paul Amminger; Lieuwe de Haan; Mark van der Gaag; Lucia Valmaggia; Tuulia Hyotylainen; Matej Oresic; Philip McGuire; EU-GEI High Risk Study Group; Philippe Delespaul; Bart Rutten; Jim van Os

doi:10.1016/j.biopsych.2020.07.012

Dysregulated Lipid Metabolism Precedes Onset of Psychosis

Alex M. Dickens, Partho Sen, Matthew J. Kempton, Neus Barrantes-Vidal, Conrad Iyegbe, Merete Nordentoft, Thomas Pollak, Anita Riecher-Rossler, Stephan Ruhrmann, Gabriele Sachs, Rodrigo Bressan, Marie-Odile Krebs, G. Paul Amminger, Lieuwe de Haan, Mark van der Gaag, Lucia Valmaggia, Tuulia Hyotylainen, Matej Oresic^*, Philip McGuire^*, EU-GEI High Risk Study GroupPhilippe Delespaul, Bart Rutten, Jim van Os

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

BACKGROUND: A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group.

METHODS: Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes.

RESULTS: At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01).

CONCLUSIONS: Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.

Original language	English
Pages (from-to)	288-297
Number of pages	10
Journal	Biological Psychiatry
Volume	89
Issue number	3
DOIs	https://doi.org/10.1016/j.biopsych.2020.07.012
Publication status	Published - 1 Feb 2021

Keywords

DISEASE
FATTY-ACIDS
INDIVIDUALS
MECHANISMS
PLASMALOGENS
RISK
SCHIZOPHRENIA
SPHINGOMYELIN

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Dickens, A. M., Sen, P., Kempton, M. J., Barrantes-Vidal, N., Iyegbe, C., Nordentoft, M., Pollak, T., Riecher-Rossler, A., Ruhrmann, S., Sachs, G., Bressan, R., Krebs, M.-O., Amminger, G. P., de Haan, L., van der Gaag, M., Valmaggia, L., Hyotylainen, T., Oresic, M., McGuire, P., ... van Os, J. (2021). Dysregulated Lipid Metabolism Precedes Onset of Psychosis. Biological Psychiatry, 89(3), 288-297. https://doi.org/10.1016/j.biopsych.2020.07.012

@article{fca74274651b49c29891817768d4ef5a,

title = "Dysregulated Lipid Metabolism Precedes Onset of Psychosis",

abstract = "BACKGROUND: A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group.METHODS: Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes.RESULTS: At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01).CONCLUSIONS: Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.",

keywords = "DISEASE, FATTY-ACIDS, INDIVIDUALS, MECHANISMS, PLASMALOGENS, RISK, SCHIZOPHRENIA, SPHINGOMYELIN",

author = "Dickens, {Alex M.} and Partho Sen and Kempton, {Matthew J.} and Neus Barrantes-Vidal and Conrad Iyegbe and Merete Nordentoft and Thomas Pollak and Anita Riecher-Rossler and Stephan Ruhrmann and Gabriele Sachs and Rodrigo Bressan and Marie-Odile Krebs and Amminger, {G. Paul} and {de Haan}, Lieuwe and {van der Gaag}, Mark and Lucia Valmaggia and Tuulia Hyotylainen and Matej Oresic and Philip McGuire and {EU-GEI High Risk Study Group} and Philippe Delespaul and Bart Rutten and {van Os}, Jim",

note = "Funding Information: This study has received funding from the European Union{\textquoteright}s Seventh Framework Programme for projects EU-GEI—European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (Grant No. HEALTH-F2-2010-241909 [to PMG, NB-V, MN, AR-R, SR, GS, RB, M-OK, GPA, LdH, and MvdG]) and METSY—Neuroimaging Platform for Characterization of Metabolic Comorbidities in Psychotic Disorders (Grant No. 602478 [to MO]). Additional support was provided by a Medical Research Council Fellowship (Grant No. MR/J008915/1 [to MJK]), Ministerio de Ciencia, Innovaci{\'o}n e Universidades (Grant No. PSI2017-87512-C2-1-R ), and Generalitat de Catalunya (Grant No. 2017SGR1612 and ICREA Academia Award [to NB-V]). Funding Information: This study has received funding from the European Union's Seventh Framework Programme for projects EU-GEI—European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (Grant No. HEALTH-F2-2010-241909 [to PMG, NB-V, MN, AR-R, SR, GS, RB, M-OK, GPA, LdH, and MvdG]) and METSY—Neuroimaging Platform for Characterization of Metabolic Comorbidities in Psychotic Disorders (Grant No. 602478 [to MO]). Additional support was provided by a Medical Research Council Fellowship (Grant No. MR/J008915/1 [to MJK]), Ministerio de Ciencia, Innovaci{\'o}n e Universidades (Grant No. PSI2017-87512-C2-1-R), and Generalitat de Catalunya (Grant No. 2017SGR1612 and ICREA Academia Award [to NB-V]). We thank Cecilia Carlsson for technical assistance in lipidomic analysis. We would also like to acknowledge the Turku Metabolomics Centre, which is a part of Biocentre Finland, for their role in this study. The lipidomics dataset and the relevant clinical metadata generated in this study are available from the EU-GEI management group on reasonable request. We also thank the following EU-GEI High Risk Study Group collaborators: Maria Calem, Stefania Tognin, Gemma Modino, Tamar C. Kraan, Daniella S. van Dam, Nadine Burger, Barnaby Nelson, Patrick McGorry, Christos Pantelis, Athena Politis, Joanne Goodall, Stefan Borgwardt, Charlotte Rapp, Sarah Ittig, Erich Studerus, Renata Smieskova, Ary Gadelha, Elisa Brietzke, Graccielle Asevedo, Elson Asevedo, Andre Zugman, Tecelli Dom{\'i}nguez-Mart{\'i}nez, Anna Racciopi, Thomas R. Kwapil, Manel Monsonet, Araceli Rosa, Ariel Frajerman, Boris Chaumette, Julie Bourgin, Oussama Kebir, C{\'e}lia Jantac, Dorte Nordholm, Lasse Randers, Kristine Krakauer, Louise Glenth{\o}j, Birte Glenth{\o}j, Dominika Gebhard, Julia Arnhold, Joachim Klosterk{\"o}tter, Iris Lasser, Bernadette Winklbaur, Philippe A. Delespaul, Bart P. Rutten, and Jim van Os. The authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: {\textcopyright} 2020 Society of Biological Psychiatry",

year = "2021",

month = feb,

day = "1",

doi = "10.1016/j.biopsych.2020.07.012",

language = "English",

volume = "89",

pages = "288--297",

journal = "Biological Psychiatry",

issn = "0006-3223",

publisher = "Elsevier Science",

number = "3",

}

Dickens, AM, Sen, P, Kempton, MJ, Barrantes-Vidal, N, Iyegbe, C, Nordentoft, M, Pollak, T, Riecher-Rossler, A, Ruhrmann, S, Sachs, G, Bressan, R, Krebs, M-O, Amminger, GP, de Haan, L, van der Gaag, M, Valmaggia, L, Hyotylainen, T, Oresic, M, McGuire, P, EU-GEI High Risk Study Group, Delespaul, P , Rutten, B & van Os, J 2021, 'Dysregulated Lipid Metabolism Precedes Onset of Psychosis', Biological Psychiatry, vol. 89, no. 3, pp. 288-297. https://doi.org/10.1016/j.biopsych.2020.07.012

TY - JOUR

T1 - Dysregulated Lipid Metabolism Precedes Onset of Psychosis

AU - Dickens, Alex M.

AU - Sen, Partho

AU - Kempton, Matthew J.

AU - Barrantes-Vidal, Neus

AU - Iyegbe, Conrad

AU - Nordentoft, Merete

AU - Pollak, Thomas

AU - Riecher-Rossler, Anita

AU - Ruhrmann, Stephan

AU - Sachs, Gabriele

AU - Bressan, Rodrigo

AU - Krebs, Marie-Odile

AU - Amminger, G. Paul

AU - de Haan, Lieuwe

AU - van der Gaag, Mark

AU - Valmaggia, Lucia

AU - Hyotylainen, Tuulia

AU - Oresic, Matej

AU - McGuire, Philip

AU - EU-GEI High Risk Study Group

AU - Delespaul, Philippe

AU - Rutten, Bart

AU - van Os, Jim

N1 - Funding Information: This study has received funding from the European Union’s Seventh Framework Programme for projects EU-GEI—European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (Grant No. HEALTH-F2-2010-241909 [to PMG, NB-V, MN, AR-R, SR, GS, RB, M-OK, GPA, LdH, and MvdG]) and METSY—Neuroimaging Platform for Characterization of Metabolic Comorbidities in Psychotic Disorders (Grant No. 602478 [to MO]). Additional support was provided by a Medical Research Council Fellowship (Grant No. MR/J008915/1 [to MJK]), Ministerio de Ciencia, Innovación e Universidades (Grant No. PSI2017-87512-C2-1-R ), and Generalitat de Catalunya (Grant No. 2017SGR1612 and ICREA Academia Award [to NB-V]). Funding Information: This study has received funding from the European Union's Seventh Framework Programme for projects EU-GEI—European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (Grant No. HEALTH-F2-2010-241909 [to PMG, NB-V, MN, AR-R, SR, GS, RB, M-OK, GPA, LdH, and MvdG]) and METSY—Neuroimaging Platform for Characterization of Metabolic Comorbidities in Psychotic Disorders (Grant No. 602478 [to MO]). Additional support was provided by a Medical Research Council Fellowship (Grant No. MR/J008915/1 [to MJK]), Ministerio de Ciencia, Innovación e Universidades (Grant No. PSI2017-87512-C2-1-R), and Generalitat de Catalunya (Grant No. 2017SGR1612 and ICREA Academia Award [to NB-V]). We thank Cecilia Carlsson for technical assistance in lipidomic analysis. We would also like to acknowledge the Turku Metabolomics Centre, which is a part of Biocentre Finland, for their role in this study. The lipidomics dataset and the relevant clinical metadata generated in this study are available from the EU-GEI management group on reasonable request. We also thank the following EU-GEI High Risk Study Group collaborators: Maria Calem, Stefania Tognin, Gemma Modino, Tamar C. Kraan, Daniella S. van Dam, Nadine Burger, Barnaby Nelson, Patrick McGorry, Christos Pantelis, Athena Politis, Joanne Goodall, Stefan Borgwardt, Charlotte Rapp, Sarah Ittig, Erich Studerus, Renata Smieskova, Ary Gadelha, Elisa Brietzke, Graccielle Asevedo, Elson Asevedo, Andre Zugman, Tecelli Domínguez-Martínez, Anna Racciopi, Thomas R. Kwapil, Manel Monsonet, Araceli Rosa, Ariel Frajerman, Boris Chaumette, Julie Bourgin, Oussama Kebir, Célia Jantac, Dorte Nordholm, Lasse Randers, Kristine Krakauer, Louise Glenthøj, Birte Glenthøj, Dominika Gebhard, Julia Arnhold, Joachim Klosterkötter, Iris Lasser, Bernadette Winklbaur, Philippe A. Delespaul, Bart P. Rutten, and Jim van Os. The authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2020 Society of Biological Psychiatry

PY - 2021/2/1

Y1 - 2021/2/1

N2 - BACKGROUND: A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group.METHODS: Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes.RESULTS: At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01).CONCLUSIONS: Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.

AB - BACKGROUND: A key clinical challenge in the management of individuals at clinical high risk for psychosis (CHR) is that it is difficult to predict their future clinical outcomes. Here, we investigated if the levels of circulating molecular lipids are related to adverse clinical outcomes in this group.METHODS: Serum lipidomic analysis was performed in 263 CHR individuals and 51 healthy control subjects, who were then clinically monitored for up to 5 years. Machine learning was used to identify lipid profiles that discriminated between CHR and control subjects, and between subgroups of CHR subjects with distinct clinical outcomes.RESULTS: At baseline, compared with control subjects, CHR subjects (independent of outcome) had higher levels of triacylglycerols with a low acyl carbon number and a double bond count, as well as higher levels of lipids in general. CHR subjects who subsequently developed psychosis (n = 50) were distinguished from those that did not (n = 213) on the basis of lipid profile at baseline using a model with an area under the receiver operating curve of 0.81 (95% confidence interval = 0.69-0.93). CHR subjects who became psychotic had lower levels of ether phospholipids than CHR individuals who did not (p < .01).CONCLUSIONS: Collectively, these data suggest that lipidomic abnormalities predate the onset of psychosis and that blood lipidomic measures may be useful in predicting which CHR individuals are most likely to develop psychosis.

KW - DISEASE

KW - FATTY-ACIDS

KW - INDIVIDUALS

KW - MECHANISMS

KW - PLASMALOGENS

KW - RISK

KW - SCHIZOPHRENIA

KW - SPHINGOMYELIN

U2 - 10.1016/j.biopsych.2020.07.012

DO - 10.1016/j.biopsych.2020.07.012

M3 - Article

C2 - 32928501

SN - 0006-3223

VL - 89

SP - 288

EP - 297

JO - Biological Psychiatry

JF - Biological Psychiatry

IS - 3

ER -