A prospective observational cohort study to identify inflammatory biomarkers for the diagnosis and prognosis of patients with sepsis

Valentino D'Onofrio, Dries Heylen, Murih Pusparum, Inge Grondman, Johan Vanwalleghem, Agnes Meersman, Reinoud Cartuyvels, Peter Messiaen, Leo A B Joosten, Mihai G Netea, Dirk Valkenborg, Gökhan Ertaylan, Inge C Gyssens, Valentino D'Onofrio, Dries Heylen, Murih Pusparum, Inge Grondman, Johan Vanwalleghem, Agnes Meersman, Reinoud Cartuyvels, Peter Messiaen, Leo A B Joosten, Mihai G Netea, Dirk Valkenborg, Gökhan Ertaylan, Inge C Gyssens

Abstract

Background: Sepsis is a life-threatening organ dysfunction. A fast diagnosis is crucial for patient management. Proteins that are synthesized during the inflammatory response can be used as biomarkers, helping in a rapid clinical assessment or an early diagnosis of infection. The aim of this study was to identify biomarkers of inflammation for the diagnosis and prognosis of infection in patients with suspected sepsis.

Methods: In total 406 episodes were included in a prospective cohort study. Plasma was collected from all patients with suspected sepsis, for whom blood cultures were drawn, in the emergency department (ED), the department of infectious diseases, or the haemodialysis unit on the first day of a new episode. Samples were analysed using a 92-plex proteomic panel based on a proximity extension assay with oligonucleotide-labelled antibody probe pairs (OLink, Uppsala, Sweden). Supervised and unsupervised differential expression analyses and pathway enrichment analyses were performed to search for inflammatory proteins that were different between patients with viral or bacterial sepsis and between patients with worse or less severe outcome.

Results: Supervised differential expression analysis revealed 21 proteins that were significantly lower in circulation of patients with viral infections compared to patients with bacterial infections. More strongly, higher expression levels were observed for 38 proteins in patients with high SOFA scores (> 4), and for 21 proteins in patients with worse outcome. These proteins are mostly involved in pathways known to be activated early in the inflammatory response. Unsupervised, hierarchical clustering confirmed that inflammatory response was more strongly related to disease severity than to aetiology.

Conclusion: Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. These proteins are mostly related to disease severity. Within the setting of an emergency department, they could be used for outcome prediction, patient monitoring, and directing diagnostics.

Trail registration number: clinicaltrial.gov identifier NCT03841162.

Keywords: Biomarkers; Disease severity; Inflammation; Sepsis.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Venn diagram showing all differentially expressed proteins between three different groupings, together with overlapping findings. Three different groupings are: aetiology (influenza versus bacterial), severity (SOFA score  4), and outcome (less severe outcome versus worse outcome)
Fig. 2
Fig. 2
A principal component analysis to detect clustering between patients with influenza (pink), bacterial pneumonia (blue) and other bacterial infections (green). B principal component analysis to detect clustering based on SOFA score
Fig. 3
Fig. 3
Hierarchical clustering plot
Fig. 4
Fig. 4
HSP60 and HSP70/TLR signalling pathway involved in the activation of the inflammatory response
Fig. 5
Fig. 5
Model optimization for aetiology, disease severity and outcome, starting from the maximum number of parameters resulting from the elastic net regression models. The most optimal model was chosen based on the minimum decline of 5% in area under the ROC curve (AUROC) and sensitivity with the lowest number of proteins in the model. Aetiology: viral vs. bacterial sepsis, the most optimal model had an AUROC of 94% and sensitivity of 86% with six proteins in the model. Disease severity: SOFA score > 4 vs. SOFA score 

Fig. 6

Proteins in the most optimal…

Fig. 6

Proteins in the most optimal models, accurately predicting differences in aetiology, disease severity…

Fig. 6
Proteins in the most optimal models, accurately predicting differences in aetiology, disease severity and outcome
Fig. 6
Fig. 6
Proteins in the most optimal models, accurately predicting differences in aetiology, disease severity and outcome

References

    1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3) JAMA. 2016;315(8):801–810. doi: 10.1001/jama.2016.0287.
    1. Esposito S, De Simone G, Boccia G, De Caro F, Pagliano P. Sepsis and septic shock: New definitions, new diagnostic and therapeutic approaches. J Glob Antimicrob Resist. 2017;10:204–212. doi: 10.1016/j.jgar.2017.06.013.
    1. Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 2006;34(6):1589–1596. doi: 10.1097/01.CCM.0000217961.75225.E9.
    1. László I, Trásy D, Molnár Z, Fazakas J. Sepsis: from pathophysiology to individualized patient care. J Immunol Res. 2015;2015:510436. doi: 10.1155/2015/510436.
    1. van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 2017;17(7):407–420. doi: 10.1038/nri.2017.36.
    1. Faix JD. Biomarkers of sepsis. Crit Rev Clin Lab Sci. 2013;50(1):23–36. doi: 10.3109/10408363.2013.764490.
    1. Smolár M, Dedinská I, Hošala M, Mazúch J, Laca L. Importance of markers of sepsis in surgical patients. Am Surg. 2018;84(6):1058–1063. doi: 10.1177/000313481808400666.
    1. Fan SL, Miller NS, Lee J, Remick DG. Diagnosing sepsis - The role of laboratory medicine. Clin Chim Acta. 2016;460:203–210. doi: 10.1016/j.cca.2016.07.002.
    1. Vincent JL, Beumier M. Diagnostic and prognostic markers in sepsis. Expert Rev Anti Infect Ther. 2013;11(3):265–275. doi: 10.1586/eri.13.9.
    1. Wong HR, Lindsell CJ, Pettilä V, Meyer NJ, Thair SA, Karlsson S, et al. A multibiomarker-based outcome risk stratification model for adult septic shock*. Crit Care Med. 2014;42(4):781–789. doi: 10.1097/CCM.0000000000000106.
    1. Sardesai AU, Tanak AS, Krishnan S, Striegel DA, Schully KL, Clark DV, et al. An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm. Sci Rep. 2021;11(1):16905. doi: 10.1038/s41598-021-96081-5.
    1. Wong HR, Walley KR, Pettilä V, Meyer NJ, Russell JA, Karlsson S, et al. Comparing the prognostic performance of ASSIST to interleukin-6 and procalcitonin in patients with severe sepsis or septic shock. Biomarkers. 2015;20(2):132–135. doi: 10.3109/1354750X.2014.1000971.
    1. Parlato M, Philippart F, Rouquette A, Moucadel V, Puchois V, Blein S, et al. Circulating biomarkers may be unable to detect infection at the early phase of sepsis in ICU patients: the CAPTAIN prospective multicenter cohort study. Intensive Care Med. 2018;44(7):1061–1070. doi: 10.1007/s00134-018-5228-3.
    1. Prucha M, Bellingan G, Zazula R. Sepsis biomarkers. Clin Chim Acta. 2015;440:97–103. doi: 10.1016/j.cca.2014.11.012.
    1. D'Onofrio V, Meersman A, Vijgen S, Cartuyvels R, Messiaen P, Gyssens IC. Risk factors for mortality, intensive care unit admission, and bacteremia in patients suspected of sepsis at the emergency department: a prospective cohort study. Open Forum. Infect Dis. 2020;8(1):ofaa594. doi: 10.1093/ofid/ofaa594.
    1. Linsen L, Vanhees K, Vanoppen E, Ulenaers K, Driessens S, Penders J, et al. Raising to the challenge: building a federated biobank to accelerate translational research-The University Biobank Limburg. Front Med (Lausanne) 2019;6:224. doi: 10.3389/fmed.2019.00224.
    1. Kyriazopoulou E, Leventogiannis K, Norrby-Teglund A, Dimopoulos G, Pantazi A, Orfanos SE, et al. Macrophage activation-like syndrome: an immunological entity associated with rapid progression to death in sepsis. BMC Med. 2017;15(1):172. doi: 10.1186/s12916-017-0930-5.
    1. CDC. Collecting Cultures: a Clinician Guide 2019 . Accessed 28 May 2021.
    1. Wiersinga WJ, Bonten MJ, Boersma WG, Jonkers RE, Aleva RM, Kullberg BJ, et al. Management of community-acquired pneumonia in adults: 2016 guideline update from the Dutch Working Party on Antibiotic Policy (SWAB) and Dutch Association of Chest Physicians (NVALT) Neth J Med. 2018;76(1):4–13.
    1. United States . Department of Health and Human Services, United States. Food and Drug Administration, Center for Drug Evaluation and Research (U.S.). Complicated urinary tract infections: developing drugs for treatment. Silver Spring: Center for Drug Evaluation and Research; 2018.
    1. European Centre for Disease Prevention and Control . Annual epidemiological report on communicable diseases in Europe 2008. European Centre for Disease Prevention and Control: Stockholm; 2008.
    1. Assarsson E, Lundberg M, Holmquist G, Björkesten J, Thorsen SB, Ekman D, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE. 2014;9(4):e95192. doi: 10.1371/journal.pone.0095192.
    1. Olink. Olink target 96 inflammation panel 2019. . Accessed 28 May 2021.
    1. Chawla N, Bowyer K, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. doi: 10.1613/jair.953.
    1. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14(1):R15. doi: 10.1186/cc8872.
    1. Giannakopoulos K, Hoffmann U, Ansari U, Bertsch T, Borggrefe M, Akin I, et al. The use of biomarkers in sepsis: a systematic review. Curr Pharm Biotechnol. 2017;18(6):499–507. doi: 10.2174/1389201018666170601080111.
    1. van Engelen TSR, Wiersinga WJ, Scicluna BP, van der Poll T. Biomarkers in Sepsis. Crit Care Clin. 2018;34(1):139–152. doi: 10.1016/j.ccc.2017.08.010.
    1. Simon L, Gauvin F, Amre DK, Saint-Louis P, Lacroix J. Serum procalcitonin and C-reactive protein levels as markers of bacterial infection: a systematic review and meta-analysis. Clin Infect Dis. 2004;39(2):206–217. doi: 10.1086/421997.
    1. Shu T, Ning W, Wu D, Xu J, Han Q, Huang M, et al. Plasma proteomics identify biomarkers and pathogenesis of COVID-19. Immunity. 2020;53(5):1108–22.e5. doi: 10.1016/j.immuni.2020.10.008.
    1. Seymour CW, Kennedy JN, Wang S, Chang CH, Elliott CF, Xu Z, et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA. 2019;321(20):2003–2017. doi: 10.1001/jama.2019.5791.
    1. DeCoux A, Tian Y, DeLeon-Pennell KY, Nguyen NT, de Castro Brás LE, Flynn ER, et al. Plasma glycoproteomics reveals sepsis outcomes linked to distinct proteins in common pathways. Crit Care Med. 2015;43(10):2049–2058. doi: 10.1097/CCM.0000000000001134.
    1. Mickiewicz B, Tam P, Jenne CN, Leger C, Wong J, Winston BW, et al. Integration of metabolic and inflammatory mediator profiles as a potential prognostic approach for septic shock in the intensive care unit. Crit Care. 2015;19(1):11. doi: 10.1186/s13054-014-0729-0.
    1. Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR, Vincent JL. Sepsis and septic shock. Nat Rev Dis Primers. 2016;2:16045. doi: 10.1038/nrdp.2016.45.
    1. Su Y, Chen D, Yuan D, Lausted C, Choi J, Dai CL, et al. Multi-omics resolves a sharp disease-state shift between mild and moderate COVID-19. Cell. 2020;183(6):1479–95.e20. doi: 10.1016/j.cell.2020.10.037.
    1. Raymond SL, Hawkins RB, Stortz JA, Murphy TJ, Ungaro R, Dirain ML, et al. Sepsis is associated with reduced spontaneous neutrophil migration velocity in human adults. PLoS ONE. 2018;13(10):e0205327. doi: 10.1371/journal.pone.0205327.
    1. Arraes SM, Freitas MS, da Silva SV, de Paula Neto HA, Alves-Filho JC, Auxiliadora Martins M, et al. Impaired neutrophil chemotaxis in sepsis associates with GRK expression and inhibition of actin assembly and tyrosine phosphorylation. Blood. 2006;108(9):2906–2913. doi: 10.1182/blood-2006-05-024638.
    1. Chishti AD, Shenton BK, Kirby JA, Baudouin SV. Neutrophil chemotaxis and receptor expression in clinical septic shock. Intensive Care Med. 2004;30(4):605–611. doi: 10.1007/s00134-004-2175-y.
    1. van de Veerdonk FL, Janssen NAF, Grondman I, de Nooijer AH, Koeken VACM, Matzaraki V, et al. A systems approach to inflammation identifies therapeutic targets in SARS-CoV-2 infection. medRxiv. 2020;8:420.

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