Medium-chain Fatty Acids as Biomarkers of Mitochondrial Dysfunction in Traumatic Brain Injury

Traumatic brain injury (TBI) is a complex disorder with variable eti-ology and severity that nowadays stands as a major cause of death and disability worldwide, principally among children and young people. Despite the implementation of intensive care strategies at early stages following the injury, long-term morbidity of severe TBI still remains high, and many patients may show significant neurologic sequelae even after recovery, which usually persist for years. For this reason, there is a critical need to get a deeper insight into pathological mechanisms occurring in brain after trauma and discover potential biomarkers that could help in diagnosis, staging disease severity, monitoring disease progression, the identification of complications, as well as the development of better strategies for treatment and rehabilitation after injury. TBI pathology begins with a mechanical brain damage, which is followed by complex and dynamic perturbations in multiple molecular pathways in glia and neurons. Thus, holistic approaches such as metabolomics stand out as suitable tools for characterizing these metabolic alterations. Metabolo-mics can be defined as the comprehensive study of the entire set of me-tabolites from a cell, tissue, organ, body fluid or organism at a specific time, as well as of the metabolic changes observed in response to a genetic or environmental perturbation. However, only a few authors have previously reported the application of metabolomic techniques for investigating TBI pathogenesis, usually by employing nuclear magnetic resonance (1 H-NMR) to study the brain and blood metabolome from different animal models (Viant et al., 2005; Bahado-Singh et al., 2016a; Bahado-Singh et al., 2016b). Thereby, it was demonstrated that numerous significant disturbances in TBI might be associated with oxi-dative stress, membrane disruption, failures in energy metabolism and neuronal injury, among other pathological processes. In the current issue of EBioMedicine, Orešič et al. (2016) describe the application of a metabolomic platform based on bi-dimensional gas chromatography coupled to high-resolution mass spectrometry (GC × GC-TOF-MS) to identify biomarkers in serum samples that could associate with disease severity and predict the outcomes of TBI patients. For this purpose, two independent cohorts were enrolled with the aim to validate results obtained in the discovery phase, comprising mild, moderate and severe TBI patients as well as orthopedic controls. Various metabolomic alterations were detected in serum samples, following the same pattern in all patients but with a proportional degree of change depending on the disease severity, thus suggesting that TBI is characterized by a specific metabotype. Moreover, …