Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Population scale proteomics enables adaptive digital twin modelling in sepsis

Scott, Aaron M. LU ; Mellhammar, Lisa LU ; Malmström, Erik LU ; Gustafsson, Axel Goch LU ; Bakochi, Anahita LU orcid ; Isaksson, Marc LU ; Mohanty, Tirthankar LU ; Thelaus, Louise LU ; Kahn, Fredrik LU and Malmström, Lars LU , et al. (2024)
Abstract
Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk... (More)
Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.Competing Interest StatementThe authors have declared no competing interest.Funding StatementL.M. is funded by the Swedish Research Council (grant number VR-2020-02419), the Wallenberg foundation (grant number 2016.0023) and Alfred Österlunds Foundation. J.M. is a Wallenberg academy fellow (KAW 2017.0271) and is also funded by the Swedish Research Council (Vetenskapsrådet, VR) (2019-01646 and 2018-05795), the Wallenberg foundation (KAW2016.0023, KAW2019.0353 and KAW2020.0299), and Alfred Österlunds Foundation. E.M. is funded by Wenner-Gren Foundation (FT2020-0003), the Crafoord Foundation, and the Swedish Society of Medicine (SLS-985287). F.K. is funded by Region Skåne ALF project and the Crafoord Foundation. A.L. is funded by the Swedish Research Council VR 2023-02707 and Region Skåne ALF project 2022-0146.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethical approval for the study was obtained from the Swedish National Ethics Committee (file numbers 2022-01454-01, 2014/741 and 2016/271).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesData produced in the present study are available upon reasonable request to the authors (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
publisher
medRxiv
DOI
10.1101/2024.03.20.24304575
language
English
LU publication?
yes
id
aad8e373-e597-4850-92dd-c9ee4b0cb82b
alternative location
http://medrxiv.org/content/early/2024/03/22/2024.03.20.24304575.abstract
date added to LUP
2024-04-09 16:29:09
date last changed
2024-04-09 16:29:09
@misc{aad8e373-e597-4850-92dd-c9ee4b0cb82b,
  abstract     = {{Sepsis is one of the leading causes of mortality in the world. Currently, the heterogeneity of sepsis makes it challenging to determine the molecular mechanisms that define the syndrome. Here, we leverage population scale proteomics to analyze a well-defined cohort of 1364 blood samples taken at time-of-admission to the emergency department from patients suspected of sepsis. We identified panels of proteins using explainable artificial intelligence that predict clinical outcomes and applied these panels to reduce high-dimensional proteomics data to a low-dimensional interpretable latent space (ILS). Using the ILS, we constructed an adaptive digital twin model that accurately predicted organ dysfunction, mortality, and early-mortality-risk patients using only data available at time-of-admission. In addition to being highly effective for investigating sepsis, this approach supports the flexible incorporation of new data and can generalize to other diseases to aid in translational research and the development of precision medicine.Competing Interest StatementThe authors have declared no competing interest.Funding StatementL.M. is funded by the Swedish Research Council (grant number VR-2020-02419), the Wallenberg foundation (grant number 2016.0023) and Alfred Österlunds Foundation. J.M. is a Wallenberg academy fellow (KAW 2017.0271) and is also funded by the Swedish Research Council (Vetenskapsrådet, VR) (2019-01646 and 2018-05795), the Wallenberg foundation (KAW2016.0023, KAW2019.0353 and KAW2020.0299), and Alfred Österlunds Foundation. E.M. is funded by Wenner-Gren Foundation (FT2020-0003), the Crafoord Foundation, and the Swedish Society of Medicine (SLS-985287). F.K. is funded by Region Skåne ALF project and the Crafoord Foundation. A.L. is funded by the Swedish Research Council VR 2023-02707 and Region Skåne ALF project 2022-0146.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethical approval for the study was obtained from the Swedish National Ethics Committee (file numbers 2022-01454-01, 2014/741 and 2016/271).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesData produced in the present study are available upon reasonable request to the authors}},
  author       = {{Scott, Aaron M. and Mellhammar, Lisa and Malmström, Erik and Gustafsson, Axel Goch and Bakochi, Anahita and Isaksson, Marc and Mohanty, Tirthankar and Thelaus, Louise and Kahn, Fredrik and Malmström, Lars and Malmström, Johan and Linder, Adam}},
  language     = {{eng}},
  month        = {{01}},
  note         = {{Preprint}},
  publisher    = {{medRxiv}},
  title        = {{Population scale proteomics enables adaptive digital twin modelling in sepsis}},
  url          = {{http://dx.doi.org/10.1101/2024.03.20.24304575}},
  doi          = {{10.1101/2024.03.20.24304575}},
  year         = {{2024}},
}