Human Serum Metabolites Associate With Severity and Patient Outcomes in Traumatic Brain Injury

Matej Orešič, Jussi P Posti, Maja H Kamstrup-Nielsen, Riikka S K Takala, Hester F Lingsma, Ismo Mattila, Sirkku Jäntti, Ari J Katila, Keri L H Carpenter, Henna Ala-Seppälä, Anna Kyllönen, Henna-Riikka Maanpää, Jussi Tallus, Jonathan P Coles, Iiro Heino, Janek Frantzén, Peter J Hutchinson, David K Menon, Olli Tenovuo, Tuulia Hyötyläinen, Matej Orešič, Jussi P Posti, Maja H Kamstrup-Nielsen, Riikka S K Takala, Hester F Lingsma, Ismo Mattila, Sirkku Jäntti, Ari J Katila, Keri L H Carpenter, Henna Ala-Seppälä, Anna Kyllönen, Henna-Riikka Maanpää, Jussi Tallus, Jonathan P Coles, Iiro Heino, Janek Frantzén, Peter J Hutchinson, David K Menon, Olli Tenovuo, Tuulia Hyötyläinen

Abstract

Traumatic brain injury (TBI) is a major cause of death and disability worldwide, especially in children and young adults. TBI is an example of a medical condition where there are still major lacks in diagnostics and outcome prediction. Here we apply comprehensive metabolic profiling of serum samples from TBI patients and controls in two independent cohorts. The discovery study included 144 TBI patients, with the samples taken at the time of hospitalization. The patients were diagnosed as severe (sTBI; n=22), moderate (moTBI; n=14) or mild TBI (mTBI; n=108) according to Glasgow Coma Scale. The control group (n=28) comprised of acute orthopedic non-brain injuries. The validation study included sTBI (n=23), moTBI (n=7), mTBI (n=37) patients and controls (n=27). We show that two medium-chain fatty acids (decanoic and octanoic acids) and sugar derivatives including 2,3-bisphosphoglyceric acid are strongly associated with severity of TBI, and most of them are also detected at high concentrations in brain microdialysates of TBI patients. Based on metabolite concentrations from TBI patients at the time of hospitalization, an algorithm was developed that accurately predicted the patient outcomes (AUC=0.84 in validation cohort). Addition of the metabolites to the established clinical model (CRASH), comprising clinical and computed tomography data, significantly improved prediction of patient outcomes. The identified 'TBI metabotype' in serum, that may be indicative of disrupted blood-brain barrier, of protective physiological response and altered metabolism due to head trauma, offers a new avenue for the development of diagnostic and prognostic markers of broad spectrum of TBIs.

Keywords: Biomarkers; Mass spectrometry; Metabolomics; Traumatic brain injury.

Copyright © 2016. Published by Elsevier B.V.

Figures

Graphical abstract
Graphical abstract
Fig. 1
Fig. 1
Overview of the workflow to study metabolome in traumatic brain injury (TBI). (a) Serum metabolomics was performed in a series of samples from TBI patients and controls from the city of Turku, Finland. Significant metabolites associated with severity of TBI were identified. (b) The results from the Turku series were compared with metabolomics data from TBI patients and controls from the city of Cambridge, UK. (c) Brain microdialysate (BMD) samples were also analyzed from the severe TBI patients in Cambridge, for possible associations of BMD metabolite concentrations with the changes observed in serum. (d) In order to assess if metabolites can serve as predictor of outcomes in TBI patients, metabolites from the Turku dataset were screened for associations with patient outcomes. Predictive models were developed, which were independently tested in the Cambridge dataset.
Fig. 2
Fig. 2
Serum metabolome is associated with severity of TBI. (a) Principal Component Analysis (PCA) scores for the first two principal components (out of seven), using the dataset comprising 98 metabolites with FDR q t-test two sided unequal variance). (d) Scatter plot of log2 scaled mean metabolite levels in sTBI vs. mTBI patients, with significant metabolites in both groups marked with full circles (t-test two sided unequal variance). Regression lines in panels c and d are drawn based on significant metabolites in both groups. In panels b-d, the selected unidentified metabolites are listed in italic and annotated according to their structural class (Castillo et al., 2011). Abbreviations: 2,3-BPG, 2,3-bisphosphoglycerate; α-KG, alpha-ketoglutarate; DA, decanoic acid; IPA, indole-3-propionic acid; moTBI, moderate TBI; mTBI, mild TBI; OA, octanoic acid; PC, principal component; sTBI, severe TBI.
Fig. 3
Fig. 3
Serum metabolites found associated with TBI display similar associations in an independent cohort and are found enriched in brain microdialysates. (a) Scatter plot of log2 scaled mean metabolite levels in sTBI patients from the cities of Cambridge vs. Turku, with significant metabolites in both groups marked with full circles. Regression line is drawn based on significant metabolites in both groups. (b) Comparison of mean serum metabolite level changes (sTBI vs. Controls; Turku) and mean brain microdialysate (BMD) metabolite levels (normalized to mean serum metabolite levels in Controls from Turku), shown as a scatter plot of all metabolites detected both in serum and BMD. The metabolites that are found significantly associated with TBI in serum (dark blue circles) display a close association with BMD levels, while the non-associated metabolites (light blue circles) do not.
Fig. 4
Fig. 4
Serum metabolite levels at the time of hospitalization predict the patient outcomes in TBI. Performance of the model to predict patient outcomes in TBI, shown as ROC curves for the training (Turku) and validation (Cambridge) data. Abbreviation: AUC, area under the ROC curve.

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