TY - JOUR
T1 - Shotgun mass spectrometry-based lipid profiling identifies and distinguishes between chronic inflammatory diseases
T2 - Lipid profiling of chronic diseases
AU - Matthiesen, Rune
AU - Lauber, Chris
AU - Sampaio, Julio L.
AU - Domingues, Neuza
AU - Alves, Liliana
AU - Gerl, Mathias J.
AU - Almeida, Manuel S.
AU - Rodrigues, Gustavo
AU - Araújo Gonçalves, Pedro
AU - Ferreira, Jorge
AU - Borbinha, Cláudia
AU - Pedro Marto, João
AU - Neves, Marisa
AU - Batista, Frederico
AU - Viana-Baptista, Miguel
AU - Alves, Jose
AU - Simons, Kai
AU - Vaz, Winchil L.C.
AU - Vieira, Otilia V.
N1 - Funding Information:
This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Fundação para a Ciência e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016.
Funding Information:
This work was supported by PTDC/MED-PAT/29395/2017 financially supported by Funda??o para a Ci?ncia e a Tecnologia (FCT), through national funds and co-funded by FEDER under the PT2020 Partnership. ND was a holder of PhD fellowship from the FCT (Ref. No.: SFRH/BD/51877/2012), attributed by the Inter-University Doctoral Programme in Ageing and Chronic Disease (PhDOC). LA was a holder of a FCT PhD fellowship (PD/BD/114254/2016), attributed by the ProRegem Doctoral Programme in 2016. Anonymized data described in the manuscript are available in supplementary materials. Appendix. Supplementary materials, All data and R analysis is provided in the link: https://github.com/ruma1974/lipidProfillingRM/tree/master. It includes an R package with additional plotting functionality.
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/8
Y1 - 2021/8
N2 - Background: Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings: Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment. Interpretation: Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Funding: Full list of funding sources at the end of the manuscript.
AB - Background: Localized stress and cell death in chronic inflammatory diseases may release tissue-specific lipids into the circulation causing the blood plasma lipidome to reflect the type of inflammation. However, deep lipid profiles of major chronic inflammatory diseases have not been compared. Methods: Plasma lipidomes of patients suffering from two etiologically distinct chronic inflammatory diseases, atherosclerosis-related vascular disease, including cardiovascular (CVD) and ischemic stroke (IS), and systemic lupus erythematosus (SLE), were screened by a top-down shotgun mass spectrometry-based analysis without liquid chromatographic separation and compared to each other and to age-matched controls. Lipid profiling of 596 lipids was performed on a cohort of 427 individuals. Machine learning classifiers based on the plasma lipidomes were used to distinguish the two chronic inflammatory diseases from each other and from the controls. Findings: Analysis of the lipidomes enabled separation of the studied chronic inflammatory diseases from controls based on independent validation test set classification performance (CVD vs control - Sensitivity: 0.94, Specificity: 0.88; IS vs control - Sensitivity: 1.0, Specificity: 1.0; SLE vs control – Sensitivity: 1, Specificity: 0.93) and from each other (SLE vs CVD ‒ Sensitivity: 0.91, Specificity: 1; IS vs SLE - Sensitivity: 1, Specificity: 0.82). Preliminary linear discriminant analysis plots using all data clearly separated the clinical groups from each other and from the controls, and partially separated CVD severities, as classified into five clinical groups. Dysregulated lipids are partially but not fully counterbalanced by statin treatment. Interpretation: Dysregulation of the plasma lipidome is characteristic of chronic inflammatory diseases. Lipid profiling accurately identifies the diseases and in the case of CVD also identifies sub-classes. Funding: Full list of funding sources at the end of the manuscript.
KW - Dyslipidemia
KW - Lipid biomarker
KW - Lipid profiling
KW - Systemic lupus erythematosus
KW - Vascular diseases
UR - http://www.scopus.com/inward/record.url?scp=85111046215&partnerID=8YFLogxK
U2 - 10.1016/j.ebiom.2021.103504
DO - 10.1016/j.ebiom.2021.103504
M3 - Article
AN - SCOPUS:85111046215
SN - 2352-3964
VL - 70
JO - EBioMedicine
JF - EBioMedicine
M1 - 103504
ER -