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Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR).
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 1, article id e0147670Article in journal (Refereed) Published
Abstract [en]

A metabolomics approach for prediction of bacteremic sepsis in patients in the emergency room (ER) was investigated. In a prospective study, whole blood samples from 65 patients with bacteremic sepsis and 49 ER controls were compared. The blood samples were analyzed using gas chromatography coupled to time-of-flight mass spectrometry. Multivariate and logistic regression modeling using metabolites identified by chromatography or using conventional laboratory parameters and clinical scores of infection were employed. A predictive model of bacteremic sepsis with 107 metabolites was developed and validated. The number of metabolites was reduced stepwise until identifying a set of 6 predictive metabolites. A 6-metabolite predictive logistic regression model showed a sensitivity of 0.91(95% CI 0.69-0.99) and a specificity 0.84 (95% CI 0.58-0.94) with an AUC of 0.93 (95% CI 0.89-1.01). Myristic acid was the single most predictive metabolite, with a sensitivity of 1.00 (95% CI 0.85-1.00) and specificity of 0.95 (95% CI 0.74-0.99), and performed better than various combinations of conventional laboratory and clinical parameters. We found that a metabolomics approach for analysis of acute blood samples was useful for identification of patients with bacteremic sepsis. Metabolomics should be further evaluated as a new tool for infection diagnostics.

Place, publisher, year, edition, pages
2016. Vol. 11, no 1, article id e0147670
National Category
Pharmaceutical Sciences Anesthesiology and Intensive Care
Identifiers
URN: urn:nbn:se:umu:diva-130006DOI: 10.1371/journal.pone.0147670ISI: 000368655300138PubMedID: 26800189OAI: oai:DiVA.org:umu-130006DiVA, id: diva2:1063920
Available from: 2017-01-11 Created: 2017-01-11 Last updated: 2018-05-02Bibliographically approved
In thesis
1. Improved diagnosis and prediction of community-acquired pneumonia
Open this publication in new window or tab >>Improved diagnosis and prediction of community-acquired pneumonia
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Förbättrad diagnostik och prediktion vid samhällsförvärvad pneumoni
Abstract [en]

Community-acquired pneumonia (CAP) is a major cause of morbidity and mortality worldwide. Although there is wide variation in the microbial etiology, CAP may manifest with similar symptoms, making institution of proper treatment challenging. Therefore, etiological diagnosis is important to ensure that correct treatment and necessary infection control measures are instituted. This provides a challenge for conventional microbial diagnostic methods, typically based on culture and direct antigen tests. Moreover, existing molecular biomarkers have poor prognostic value. Few studies have investigated the global metabolic response during infection and virtually nothing is known about early responses after the start of antimicrobial treatment. The aim of this work was to improve diagnostic and predictive methods for CAP.

In paper I, a qPCR panel targeting 15 pathogens known to cause CAP was developed and evaluated. It combined identification of bacterial pathogens and viruses in the same diagnostic platform. The method proved to be robust and the results consistent with those obtained by standard methods. The panel approach, compared to conventional, selective diagnostics, detected a larger number of pathogens. In Paper II, whole blood samples from 65 patients with bacteremic sepsis were analyzed for metabolite profiles. Forty-nine patients with symptoms of sepsis, but later attributed to other diagnoses, were matched according to age and sex and served as a control group. Six metabolites were identified, all of which predicted growth of bacteria in blood culture. One of the metabolites, myristic acid, alone predicted bacteremic sepsis with a sensitivity of 100% and a specificity of 95%. Paper III and IV were based on a clinical study enrolling 35 patients with suspected CAP in need of hospital care. The aim was to study the metabolic response during the early phase of acute infection. The qPCR panel developed in Paper I was used to obtain the microbial etiological diagnosis. Paper IV focused on the global metabolic response and highlighted the dynamics of changes in major metabolic pathways during early recovery. A specific metabolite pattern for M. pneumoniae etiology was found. Four metabolites accurately predicted all but one patient as either M. pneumoniae etiology or not. Paper III looked at phospholipid levels during the first 48 hours after hospital admission. It was found that all major phospholipid species, especially the lysophosphatidyl-cholines, were pronouncedly decreased during acute infection. Levels started to increase the day after admission, reaching statistical significance at 48 hours. Paper II-IV showed that metabolomics might be used to study a number of different aspects of infection, such as etiology, disease progress and recovery. Knowledge of the metabolic profiles of patients may not only be utilized for biomarker discovery, as proposed in this work, but also for the future development of targeted therapies and supportive treatment.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2018. p. 80
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1960
Keyword
Community-acquired pneumonia, infection, diagnosis, qPCR, metabolites, metabolomics
National Category
Infectious Medicine
Identifiers
urn:nbn:se:umu:diva-147064 (URN)978-91-7601-873-6 (ISBN)
Public defence
2018-05-25, Bergasalen (Q0), Norrlands universitetssjukhus, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2018-05-04 Created: 2018-04-25 Last updated: 2018-05-04Bibliographically approved

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Kauppi, Anna M.Edin, AliciaSjöstedt, AndersGylfe, ÅsaJohansson, Anders
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