Development and External Validation of The Psychosis Metabolic Risk Calculator (PsyMetRiC): A Cardiometabolic Risk Prediction Algorithm for Young People with Psychosis

Benjamin I Perry, Emanuele F Osimo, Rachel Upthegrove, Pavan Mallikarjun, Jessica Yorke, Jan Stochl, Jesus Perez, Stan Zammit, Oliver Howes, Peter B Jones, Golam M Khandaker

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

Young people with psychosis are at high risk of developing cardiometabolic disorders. However, a suitable cardiometabolic risk prediction algorithm for this group is lacking. Therefore, we aimed to develop and externally validate a cardiometabolic risk prediction algorithm tailored for young people (aged 16-35 years) with psychosis.
Original languageEnglish
Pages (from-to)589-598
Number of pages10
JournalLancet Psychiatry
Volume8
Issue number7
Early online date1 Jun 2021
DOIs
Publication statusPublished - Jul 2021

Bibliographical note

Funding Information:
BIP received funding from the NIHR (doctoral research fellowship DRF-2018-11-ST2-018). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. EFO is a Chain-Florey clinical PhD fellow, funded jointly by the Medical Research Council (MRC) and the NIHR Imperial BRC. GMK received funding from the Wellcome Trust (intermediate clinical fellowship grant 201486/Z/16/Z); MQ: Transforming Mental Health (data science award grant MQDS17/40); the MRC (MICA: Mental Health Data Pathfinder grant MC_PC_17213; and Therapeutic Target Validation in Mental Health grant MR/S037675/1); and the BMA Foundation (J Moulton grant 2019). PBJ received funding from the MRC and MQ (as described earlier), and programmatic funding from the NIHR (RP-PG- 0616-20003). JS, JP, and PBJ received salary support from the NIHR Applied Research Collaboration East of England. RU received funding from the NIHR (Health Technology Assessment grant 127700) and MRC (Therapeutic Target Validation in Mental Health grant MR/S037675/1). The MRC, Wellcome Trust (grant 102215/2/13/2), and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. This research was specifically funded by the Wellcome Trust (grant 08426812/Z/07/Z), Wellcome Trust and MRC (grant 217065/Z/19/Z), and MRC (grant MR/M006727/1). SZ is supported by the NIHR BRC at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. Access to London EIS data were supported in part by the NIHR Cambridge BRC, and by the NIHR BRC at South London and Maudsley NHS Foundation Trust and King's College London. We thank all the families who took part in ALSPAC, the midwives for their help in recruiting them, and the whole ALSPAC team, including interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. We also thank Rachel Temple, Anja Hollowell, and Thomas Kabir from the McPin Foundation for their kind support in facilitating the authors' attendance at YPAG meetings, and all participants of the YPAG for their enthusiasm for the project and insightful comments. Finally, we thank Megan Pritchard (SLaM BRC Nucleus) and Jonathan Lewis (Cambridgeshire and Peterborough NHS Foundation Trust Database Manager) for their invaluable help and support in setting up the local database search queries.

Funding Information:
BIP received funding from the NIHR (doctoral research fellowship DRF-2018-11-ST2-018). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. EFO is a Chain-Florey clinical PhD fellow, funded jointly by the Medical Research Council (MRC) and the NIHR Imperial BRC. GMK received funding from the Wellcome Trust (intermediate clinical fellowship grant 201486/Z/16/Z); MQ: Transforming Mental Health (data science award grant MQDS17/40); the MRC (MICA: Mental Health Data Pathfinder grant MC_PC_17213; and Therapeutic Target Validation in Mental Health grant MR/S037675/1); and the BMA Foundation (J Moulton grant 2019). PBJ received funding from the MRC and MQ (as described earlier), and programmatic funding from the NIHR (RP-PG- 0616-20003). JS, JP, and PBJ received salary support from the NIHR Applied Research Collaboration East of England. RU received funding from the NIHR (Health Technology Assessment grant 127700) and MRC (Therapeutic Target Validation in Mental Health grant MR/S037675/1). The MRC, Wellcome Trust (grant 102215/2/13/2), and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website. This research was specifically funded by the Wellcome Trust (grant 08426812/Z/07/Z), Wellcome Trust and MRC (grant 217065/Z/19/Z), and MRC (grant MR/M006727/1). SZ is supported by the NIHR BRC at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. Access to London EIS data were supported in part by the NIHR Cambridge BRC, and by the NIHR BRC at South London and Maudsley NHS Foundation Trust and King's College London. We thank all the families who took part in ALSPAC, the midwives for their help in recruiting them, and the whole ALSPAC team, including interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. We also thank Rachel?Temple, Anja Hollowell, and Thomas Kabir from the McPin Foundation for their kind support in facilitating the authors' attendance at YPAG meetings, and all participants of the YPAG for their enthusiasm for the project and insightful comments. Finally, we thank Megan Pritchard (SLaM BRC Nucleus) and Jonathan Lewis (Cambridgeshire and Peterborough NHS Foundation Trust Database Manager) for their invaluable help and support in setting up the local database search queries.

Publisher Copyright:
© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

Keywords

  • psychosis
  • young people
  • risk prediction algorithm
  • metabolic syndrome
  • cardiometabolic disorders
  • ALSPAC

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