ARDS Metabolic Fingerprints: Characterization, Benchmarking and Potential Mechanistic Interpretation

Sayed Metwaly; Andréanne Côté; Sarah J Donnelly; Mohammad M Banoei; Chel Hee Lee; Graciela Andonegui; Bryan G. Yipp; Hans J. Vogel; Oliver Fiehn; Brent W. Winston; Canadian Critical Care Translational Biology Group (CCCTBG)

doi:10.1152/ajplung.00077.2021

ARDS Metabolic Fingerprints: Characterization, Benchmarking and Potential Mechanistic Interpretation

Sayed Metwaly, Andréanne Côté, Sarah J Donnelly, Mohammad M Banoei, Chel Hee Lee, Graciela Andonegui, Bryan G. Yipp, Hans J. Vogel, Oliver Fiehn, Brent W. Winston^* (Corresponding Author), Canadian Critical Care Translational Biology Group (CCCTBG)

^*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

Aims: In this study we aimed to identify ARDS metabolic fingerprints in selected patient cohorts, compare the metabolic profiles of direct vs indirect ARDS and hypoinflammatory vs hyperinflammatory ARDS. Hypothesis: We hypothesize that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints which set ARDS apart from other ICU conditions, help examine ARDS subphenotypes and clinical subgroups.Subjects: 108 ARDS patients and 27 ICU ventilated controls were analyzed. Samples were randomly divided into 2/3 training and 1/3 test sets. Methods: Samples were analyzed using proton nuclear magnetic resonance spectroscopy (1H-NMR) and gas chromatography mass spectrometry (GC-MS). 12 proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct vs indirect ARDS subgroups and hypoinflammatory vs hyperinflammatory ARDS subgroups were compared.Results: Serum metabolomics and proteins/cytokines have similar AUROC when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibit correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups are metabolically distinct.Limitations: Our identified metabolic fingerprints are not ARDS diagnostic biomarkers. Further research is required to ascertain generalizability.Conclusions: ARDS patients are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

Original language	English
Pages (from-to)	L79-L90
Number of pages	12
Journal	American Journal of Physiology-Lung Cellular and Molecular Physiology
Volume	321
Issue number	1
Early online date	5 May 2021
DOIs	https://doi.org/10.1152/ajplung.00077.2021
Publication status	Published - 1 Jul 2021

Bibliographical note

ACKNOWLEDGMENTS
We thank Dr. Patricia Liaw (McMaster University) for doing the protein C measurement levels. We thank Josee Wong and the Critical Care Epidemiologic and Biologic Tissue Resource, a tissue bank at the University of Calgary, for managing samples and patient data. We thank all the patients, ICU nurses, and ICU physicians at the University of Calgary for participating and assisting in this study.
GRANTS
This work was partly funded by a team grant from Alberta Innovates-Health Solutions to the Alberta Sepsis Network, a grant from Alberta’s Health Research Innovation Strategy, an Emerging Team Grant from the Faculty of Medicine (Cumming School of Medicine), University of Calgary, Alberta Health Services (to B. W. Winston and H. J. Vogel), a grant from the Lung Association of Alberta and the NWT (to B. W. Winston), and a grant from the Canadian Intensive Care Foundation (to B. W. Winston).

Keywords

Acute Respiratory Distress Syndrome (ARDS)
ARDS metabolic fingerprint
ARDS heterogeneity
ARDS metabolomics
dysfunctional folate metabolism

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Cite this

Metwaly, S., Côté, A., Donnelly, S. J., Banoei, M. M., Lee, C. H., Andonegui, G., Yipp, B. G., Vogel, H. J., Fiehn, O., Winston, B. W., & Canadian Critical Care Translational Biology Group (CCCTBG) (2021). ARDS Metabolic Fingerprints: Characterization, Benchmarking and Potential Mechanistic Interpretation. American Journal of Physiology-Lung Cellular and Molecular Physiology, 321(1), L79-L90. https://doi.org/10.1152/ajplung.00077.2021

Metwaly, S, Côté, A, Donnelly, SJ, Banoei, MM, Lee, CH, Andonegui, G, Yipp, BG, Vogel, HJ, Fiehn, O, Winston, BW & Canadian Critical Care Translational Biology Group (CCCTBG) 2021, 'ARDS Metabolic Fingerprints: Characterization, Benchmarking and Potential Mechanistic Interpretation', American Journal of Physiology-Lung Cellular and Molecular Physiology, vol. 321, no. 1, pp. L79-L90. https://doi.org/10.1152/ajplung.00077.2021

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title = "ARDS Metabolic Fingerprints: Characterization, Benchmarking and Potential Mechanistic Interpretation",

abstract = "Aims: In this study we aimed to identify ARDS metabolic fingerprints in selected patient cohorts, compare the metabolic profiles of direct vs indirect ARDS and hypoinflammatory vs hyperinflammatory ARDS. Hypothesis: We hypothesize that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints which set ARDS apart from other ICU conditions, help examine ARDS subphenotypes and clinical subgroups.Subjects: 108 ARDS patients and 27 ICU ventilated controls were analyzed. Samples were randomly divided into 2/3 training and 1/3 test sets. Methods: Samples were analyzed using proton nuclear magnetic resonance spectroscopy (1H-NMR) and gas chromatography mass spectrometry (GC-MS). 12 proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct vs indirect ARDS subgroups and hypoinflammatory vs hyperinflammatory ARDS subgroups were compared.Results: Serum metabolomics and proteins/cytokines have similar AUROC when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibit correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups are metabolically distinct.Limitations: Our identified metabolic fingerprints are not ARDS diagnostic biomarkers. Further research is required to ascertain generalizability.Conclusions: ARDS patients are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.",

keywords = "Acute Respiratory Distress Syndrome (ARDS), ARDS metabolic fingerprint, ARDS heterogeneity, ARDS metabolomics, dysfunctional folate metabolism",

author = "Sayed Metwaly and Andr{\'e}anne C{\^o}t{\'e} and Donnelly, {Sarah J} and Banoei, {Mohammad M} and Lee, {Chel Hee} and Graciela Andonegui and Yipp, {Bryan G.} and Vogel, {Hans J.} and Oliver Fiehn and Winston, {Brent W.} and {Canadian Critical Care Translational Biology Group (CCCTBG)}",

note = "ACKNOWLEDGMENTS We thank Dr. Patricia Liaw (McMaster University) for doing the protein C measurement levels. We thank Josee Wong and the Critical Care Epidemiologic and Biologic Tissue Resource, a tissue bank at the University of Calgary, for managing samples and patient data. We thank all the patients, ICU nurses, and ICU physicians at the University of Calgary for participating and assisting in this study. GRANTS This work was partly funded by a team grant from Alberta Innovates-Health Solutions to the Alberta Sepsis Network, a grant from Alberta{\textquoteright}s Health Research Innovation Strategy, an Emerging Team Grant from the Faculty of Medicine (Cumming School of Medicine), University of Calgary, Alberta Health Services (to B. W. Winston and H. J. Vogel), a grant from the Lung Association of Alberta and the NWT (to B. W. Winston), and a grant from the Canadian Intensive Care Foundation (to B. W. Winston).",

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doi = "10.1152/ajplung.00077.2021",

language = "English",

volume = "321",

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journal = "American Journal of Physiology-Lung Cellular and Molecular Physiology",

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TY - JOUR

T1 - ARDS Metabolic Fingerprints

T2 - Characterization, Benchmarking and Potential Mechanistic Interpretation

AU - Metwaly, Sayed

AU - Côté, Andréanne

AU - Donnelly, Sarah J

AU - Banoei, Mohammad M

AU - Lee, Chel Hee

AU - Andonegui, Graciela

AU - Yipp, Bryan G.

AU - Vogel, Hans J.

AU - Fiehn, Oliver

AU - Winston, Brent W.

AU - Canadian Critical Care Translational Biology Group (CCCTBG)

N1 - ACKNOWLEDGMENTS We thank Dr. Patricia Liaw (McMaster University) for doing the protein C measurement levels. We thank Josee Wong and the Critical Care Epidemiologic and Biologic Tissue Resource, a tissue bank at the University of Calgary, for managing samples and patient data. We thank all the patients, ICU nurses, and ICU physicians at the University of Calgary for participating and assisting in this study. GRANTS This work was partly funded by a team grant from Alberta Innovates-Health Solutions to the Alberta Sepsis Network, a grant from Alberta’s Health Research Innovation Strategy, an Emerging Team Grant from the Faculty of Medicine (Cumming School of Medicine), University of Calgary, Alberta Health Services (to B. W. Winston and H. J. Vogel), a grant from the Lung Association of Alberta and the NWT (to B. W. Winston), and a grant from the Canadian Intensive Care Foundation (to B. W. Winston).

PY - 2021/7/1

Y1 - 2021/7/1

N2 - Aims: In this study we aimed to identify ARDS metabolic fingerprints in selected patient cohorts, compare the metabolic profiles of direct vs indirect ARDS and hypoinflammatory vs hyperinflammatory ARDS. Hypothesis: We hypothesize that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints which set ARDS apart from other ICU conditions, help examine ARDS subphenotypes and clinical subgroups.Subjects: 108 ARDS patients and 27 ICU ventilated controls were analyzed. Samples were randomly divided into 2/3 training and 1/3 test sets. Methods: Samples were analyzed using proton nuclear magnetic resonance spectroscopy (1H-NMR) and gas chromatography mass spectrometry (GC-MS). 12 proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct vs indirect ARDS subgroups and hypoinflammatory vs hyperinflammatory ARDS subgroups were compared.Results: Serum metabolomics and proteins/cytokines have similar AUROC when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibit correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups are metabolically distinct.Limitations: Our identified metabolic fingerprints are not ARDS diagnostic biomarkers. Further research is required to ascertain generalizability.Conclusions: ARDS patients are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

AB - Aims: In this study we aimed to identify ARDS metabolic fingerprints in selected patient cohorts, compare the metabolic profiles of direct vs indirect ARDS and hypoinflammatory vs hyperinflammatory ARDS. Hypothesis: We hypothesize that the biological and inflammatory processes in ARDS would manifest as unique metabolomic fingerprints which set ARDS apart from other ICU conditions, help examine ARDS subphenotypes and clinical subgroups.Subjects: 108 ARDS patients and 27 ICU ventilated controls were analyzed. Samples were randomly divided into 2/3 training and 1/3 test sets. Methods: Samples were analyzed using proton nuclear magnetic resonance spectroscopy (1H-NMR) and gas chromatography mass spectrometry (GC-MS). 12 proteins/cytokines were also measured. Orthogonal partial least squares discriminant analysis (OPLS-DA) was utilized to select the most differentiating ARDS metabolites and protein/cytokines. Predictive performance of OPLS-DA models was measured in the test set. Temporal changes of metabolites were examined as patients progressed through ARDS until clinical recovery. Metabolic profiles of direct vs indirect ARDS subgroups and hypoinflammatory vs hyperinflammatory ARDS subgroups were compared.Results: Serum metabolomics and proteins/cytokines have similar AUROC when distinguishing ARDS from ICU controls. Pathway analysis of ARDS differentiating metabolites identified a dominant involvement of serine-glycine metabolism. In longitudinal tracking, the identified pathway metabolites generally exhibit correction by 7-14 days, coinciding with clinical improvement. ARDS subphenotypes and clinical subgroups are metabolically distinct.Limitations: Our identified metabolic fingerprints are not ARDS diagnostic biomarkers. Further research is required to ascertain generalizability.Conclusions: ARDS patients are metabolically different from ICU controls. ARDS subphenotypes and clinical subgroups are metabolically distinct.

KW - Acute Respiratory Distress Syndrome (ARDS)

KW - ARDS metabolic fingerprint

KW - ARDS heterogeneity

KW - ARDS metabolomics

KW - dysfunctional folate metabolism

U2 - 10.1152/ajplung.00077.2021

DO - 10.1152/ajplung.00077.2021

M3 - Article

SN - 1040-0605

VL - 321

SP - L79-L90

JO - American Journal of Physiology-Lung Cellular and Molecular Physiology

JF - American Journal of Physiology-Lung Cellular and Molecular Physiology

IS - 1

ER -