Integrative Analysis of Circulating Metabolite Profiles and Magnetic Resonance Imaging Metrics in Patients with Traumatic Brain Injury

Abstract

Recent evidence suggests that patients with traumatic brain injuries (TBIs) have a distinct circulating metabolic profile. However, it is unclear if this metabolomic profile corresponds to changes in brain morphology as observed by magnetic resonance imaging (MRI). The aim of this study was to explore how circulating serum metabolites, following TBI, relate to structural MRI (sMRI) findings. Serum samples were collected upon admission to the emergency department from patients suffering from acute TBI and metabolites were measured using mass spectrometry-based metabolomics. Most of these patients sustained a mild TBI. In the same patients, sMRIs were taken and volumetric data were extracted (138 metrics). From a pool of 203 eligible screened patients, 96 met the inclusion criteria for this study. Metabolites were summarized as eight clusters and sMRI data were reduced to 15 independent components (ICs). Partial correlation analysis showed that four metabolite clusters had significant associations with specific ICs, reflecting both the grey and white matter brain injury. Multiple machine learning approaches were then applied in order to investigate if circulating metabolites could distinguish between positive and negative sMRI findings. A logistic regression model was developed, comprised of two metabolic predictors (erythronic acid and myo-inositol), which, together with neurofilament light polypeptide (NF-L), discriminated positive and negative sMRI findings with an area under the curve of the receiver-operating characteristic of 0.85 (specificity = 0.89, sensitivity = 0.65). The results of this study show that metabolomic analysis of blood samples upon admission, either alone or in combination with protein biomarkers, can provide valuable information about the impact of TBI on brain structural changes.