Targeted Clinical Metabolite Profiling Platform for the Stratification of Diabetic Patients

Wide-scope targeted analysis of bioactive lipids in human plasma by LC/MS/MS

Quantitative information on blood metabolites can be used in developing advanced medical strategies such as early detection and prevention of disease. Monitoring bioactive lipids such as steroids, bile acids, and PUFA metabolites could be a valuable indicator of health status. However, a method for simultaneously measuring these bioactive lipids has not yet been developed. Here, we report a LC/MS/MS method that can simultaneously measure 144 bioactive lipids, including steroids, bile acids, and PUFA metabolites, from human plasma, and a sample preparation method for these targets. Protein removal by methanol precipitation and purification of bioactive lipids by solid-phase extraction improved the recovery of the targeted compounds in human plasma samples, demonstrating the importance of sample preparation methods for a wide range of bioactive lipid analyses. Using the developed method, we studied the plasma from healthy human volunteers and confirmed the presence of bioactive lipid molecules associated with sex differences and circadian rhythms. The developed method of bioactive lipid analysis can be applied to health monitoring and disease biomarker discovery in precision medicine.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Cardiometabolic Care: Assessing Patients with Diabetes Mellitus with No Overt Cardiovascular Disease in the Light of Heart Failure Development Risk

The mechanisms leading to the development of heart failure (HF) in diabetes mellitus (DM) patients are multifactorial. Assessing the risk of HF development in patients with DM is valuable not only for the identification of a high-risk subgroup, but also equally important for defining low-risk subpopulations. Nowadays, DM and HF have been recognized as sharing similar metabolic pathways. Moreover, the clinical manifestation of HF can be independent of LVEF classification. Consequently, approaching HF should be through structural, hemodynamic and functional evaluation. Thus, both imaging parameters and biomarkers are important tools for the recognition of diabetic patients at risk of HF manifestation and HF phenotypes, and arrhythmogenic risk, and eventually for prognosis, aiming to improve patients' outcomes utilizing drugs and non-pharmaceutical cardioprotective tools such as diet modification.

The association of specific types of vegetables consumption with 10-year type II diabetes risk: Findings from the ATTICA cohort study

BACKGROUND

The present study aimed to investigate the association between vegetable consumption, in total as well as per type/category, and 10-year type-2 diabetes mellitus (T2DM) incidence.

METHODS

The ATTICA study was conducted during 2001-2012 in 3042 apparently healthy adults living in Athens area, Greece. A detailed biochemical, clinical, and lifestyle evaluation was performed; vegetable consumption (total, per type) was evaluated through a validated semi-quantitative food frequency questionnaire. After excluding those with no complete information of diabetes status or those lost at the 10-year follow-up, data from 1485 participants were used for the current analysis.

RESULTS

After adjusting for several participants' characteristics, including overall dietary habits, it was observed that participants consuming at least 4 servings/day of vegetables had a 0.42-times lower risk of developing T2DM (hazard ratio [HR] = 0.42; 95% confidence interval [CI] = 0.29-0.61); the benefits of consumption were greater in women (HR = 0.29; 95% CI = 0.16-0.53) compared to men (HR = 0.56; 95% CI = 0.34-0.92). Only 33% of the sample consumed vegetables 4 servings/day. The most significant associations were observed for allium vegetables in women and for red/orange/yellow vegetables, as well as for legumes in men.

CONCLUSIONS

The intake of at least 4 servings/day of vegetables was associated with a considerably reduced risk of T2DM, independently of other dietary habits; underlying the need for further elaboration of current dietary recommendations at the population level.

Mitofusins Mfn1 and Mfn2 Are Required to Preserve Glucose- but Not Incretin-Stimulated β-Cell Connectivity and Insulin Secretion

Mitochondrial glucose metabolism is essential for stimulated insulin release from pancreatic β-cells. Whether mitofusin gene expression, and hence, mitochondrial network integrity, is important for glucose or incretin signaling has not previously been explored. Here, we generated mice with β-cell-selective, adult-restricted deletion knock-out (dKO) of the mitofusin genes Mfn1 and Mfn2 (βMfn1/2 dKO). βMfn1/2-dKO mice displayed elevated fed and fasted glycemia and a more than fivefold decrease in plasma insulin. Mitochondrial length, glucose-induced polarization, ATP synthesis, and cytosolic and mitochondrial Ca2+ increases were all reduced in dKO islets. In contrast, oral glucose tolerance was more modestly affected in βMfn1/2-dKO mice, and glucagon-like peptide 1 or glucose-dependent insulinotropic peptide receptor agonists largely corrected defective glucose-stimulated insulin secretion through enhanced EPAC-dependent signaling. Correspondingly, cAMP increases in the cytosol, as measured with an Epac-camps-based sensor, were exaggerated in dKO mice. Mitochondrial fusion and fission cycles are thus essential in the β-cell to maintain normal glucose, but not incretin, sensing. These findings broaden our understanding of the roles of mitofusins in β-cells, the potential contributions of altered mitochondrial dynamics to diabetes development, and the impact of incretins on this process.

Effects of Butyrate Supplementation on Inflammation and Kidney Parameters in Type 1 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial

Type 1 diabetes is associated with increased intestinal inflammation and decreased abundance of butyrate-producing bacteria. We investigated the effect of butyrate on inflammation, kidney parameters, HbA1c, serum metabolites and gastrointestinal symptoms in persons with type 1 diabetes, albuminuria and intestinal inflammation. We conducted a randomized placebo-controlled, double-blind, parallel clinical study involving 53 participants randomized to 3.6 g sodium butyrate daily or placebo for 12 weeks. The primary endpoint was the change in fecal calprotectin. Additional endpoints were the change in fecal short chain fatty acids, intestinal alkaline phosphatase activity and immunoglobulins, serum lipopolysaccharide, CRP, albuminuria, kidney function, HbA1c, metabolites and gastrointestinal symptoms. The mean age was 54 ± 13 years, and the median [Q1:Q3] urinary albumin excretion was 46 [14:121] mg/g. The median fecal calprotectin in the butyrate group was 48 [26:100] μg/g at baseline, and the change was −1.0 [−20:10] μg/g; the median in the placebo group was 61 [25:139] μg/g at baseline, and the change was −12 [−95:1] μg/g. The difference between the groups was not significant (p = 0.24); neither did we find an effect of butyrate compared to placebo on the other inflammatory markers, kidney parameters, HbA1c, metabolites nor gastrointestinal symptoms. Twelve weeks of butyrate supplementation did not reduce intestinal inflammation in persons with type 1 diabetes, albuminuria and intestinal inflammation.

Liraglutide Lowers Palmitoleate Levels in Type 2 Diabetes. A Post Hoc Analysis of the LIRAFLAME Randomized Placebo-Controlled Trial

Background

Liraglutide is a glucose-lowering medication used to treat type 2 diabetes and obesity. It is a GLP-1 receptor agonist with downstream metabolic changes beyond the incretin system, such as reducing the risk of cardiovascular complications. The understanding of these changes is critical for improving treatment outcomes. Herein, we present a post hoc experimental analysis using metabolomic phenotyping to discover molecular mecphanisms in response to liraglutide.

Method

Plasma samples were obtained from The LiraFlame Study (ClinicalTrials.gov identifier: NCT03449654), a randomized double-blinded placebo-controlled clinical trial, including 102 participants with type 2 diabetes randomized to either liraglutide or placebo treatment for 26 weeks. Mass spectrometry-based metabolomics analyses were carried out on samples from baseline and the end of the trial. Metabolites (n=114) were categorized into pathways and linear mixed models were constructed to evaluate the association between changes in metabolites and liraglutide treatment.

Results

We found the free fatty acid palmitoleate was significantly reduced in the liraglutide group compared to placebo (adjusted for multiple testing p-value = 0.04). The activity of stearoyl-CoA desaturase-1 (SCD1), the rate limiting enzyme for converting palmitate into palmitoleate, was found significantly downregulated by liraglutide treatment compared to placebo (p-value = 0.01). These metabolic changes have demonstrated to be linked to insulin sensitivity and cardiovascular health.

High-Throughput UHPLC-MS to Screen Metabolites in Feces for Gut Metabolic Health

Feces are the product of our diets and have been linked to diseases of the gut, including Chron’s disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n = 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. MS analyses of the prepared fecal samples have shown reproducibility and coverage of n = 28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD (limit of detection), LOQ (limit of quantification), range of linearity and method recovery. The average concentrations for 135 healthy participants are reported here for clinical applications. Our high-throughput method provides a novel tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

Distinct Molecular Signatures of Clinical Clusters in People With Type 2 Diabetes: An IMI-RHAPSODY Study

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity, investigators of a previous study clustered people with diabetes according to five diabetes subtypes. The aim of the current study is to investigate the etiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic (N = 12,828), metabolomic (N = 2,945), lipidomic (N = 2,593), and proteomic (N = 1,170) data were obtained in plasma. For each data type, each cluster was compared with the other four clusters as the reference. The insulin-resistant cluster showed the most distinct molecular signature, with higher branched-chain amino acid, diacylglycerol, and triacylglycerol levels and aberrant protein levels in plasma were enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher levels of cytokines. The mild diabetes cluster with high HDL showed the most beneficial molecular profile with effects opposite of those seen in the insulin-resistant cluster. This study shows that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.

Prediction of Type 1 Diabetes at Birth: Cord Blood Metabolites vs Genetic Risk Score in the Norwegian Mother, Father, and Child Cohort

BACKGROUND AND AIM

Genetic markers are established as predictive of type 1 diabetes, but unknown early life environment is believed to be involved. Umbilical cord blood may reflect perinatal metabolism and exposures. We studied whether selected polar metabolites in cord blood contribute to prediction of type 1 diabetes.

METHODS

Using a targeted UHPLC-QQQ-MS platform, we quantified 27 low-molecular-weight metabolites (including amino acids, small organic acids, and bile acids) in 166 children, who later developed type 1 diabetes, and 177 random control children in the Norwegian Mother, Father, and Child cohort. We analyzed the data using logistic regression (estimating odds ratios per SD [adjusted odds ratio (aOR)]), area under the receiver operating characteristic curve (AUC), and k-means clustering. Metabolites were compared to a genetic risk score based on 51 established non-HLA single-nucleotide polymorphisms, and a 4-category HLA risk group.

RESULTS

The strongest associations for metabolites were aminoadipic acid (aOR = 1.23; 95% CI, 0.97-1.55), indoxyl sulfate (aOR = 1.15; 95% CI, 0.87-1.51), and tryptophan (aOR = 0.84; 95% CI, 0.65-1.10), with other aORs close to 1.0, and none significantly associated with type 1 diabetes. K-means clustering identified 6 clusters, none of which were associated with type 1 diabetes. Cross-validated AUC showed no predictive value of metabolites (AUC 0.49), whereas the non-HLA genetic risk score AUC was 0.56 and the HLA risk group AUC was 0.78.

CONCLUSIONS

In this large study, we found no support of a predictive role of cord blood concentrations of selected bile acids and other small polar metabolites in the development of type 1 diabetes.

Intravital imaging of islet Ca2+ dynamics reveals enhanced β cell connectivity after bariatric surgery in mice

Bariatric surgery improves both insulin sensitivity and secretion and can induce diabetes remission. However, the mechanisms and time courses of these changes, particularly the impact on β cell function, are difficult to monitor directly. In this study, we investigated the effect of Vertical Sleeve Gastrectomy (VSG) on β cell function in vivo by imaging Ca²⁺ dynamics in islets engrafted into the anterior eye chamber. Mirroring its clinical utility, VSG in mice results in significantly improved glucose tolerance, and enhanced insulin secretion. We reveal that these benefits are underpinned by augmented β cell function and coordinated activity across the islet. These effects involve changes in circulating GLP-1 levels which may act both directly and indirectly on the β cell, in the latter case through changes in body weight. Thus, bariatric surgery leads to time-dependent increases in β cell function and intra-islet connectivity which are likely to contribute to diabetes remission.

Deciphering Prunus Responses to PPV Infection: A Way toward the Use of Metabolomics Approach for the Diagnostic of Sharka Disease

Sharka disease, caused by Plum pox virus (PPV), induces several changes in Prunus. In leaf tissues, the infection may cause oxidative stress and disrupt the photosynthetic process. Moreover, several defense responses can be activated after PPV infection and have been detected at the phytohormonal, transcriptomic, proteomic, and even translatome levels. As proposed in this review, some responses may be systemic and earlier to the onset of symptoms. Nevertheless, these changes are highly dependent among species, variety, sensitivity, and tissue type. In the case of fruit tissues, PPV infection can modify the ripening process, induced by an alteration of the primary metabolism, including sugars and organic acids, and secondary metabolism, including phenolic compounds. Interestingly, metabolomics is an emerging tool to better understand Prunus–PPV interactions mainly in primary and secondary metabolisms. Moreover, through untargeted metabolomics analyses, specific and early candidate biomarkers of PPV infection can be detected. Nevertheless, these candidate biomarkers need to be validated before being selected for a diagnostic or prognosis by targeted analyses. The development of a new method for early detection of PPV-infected trees would be crucial for better management of the outbreak, especially since there is no curative treatment.

Metabolite Signature of Albuminuria Involves Amino Acid Pathways in 8661 Finnish Men Without Diabetes

OBJECTIVE

To investigate the metabolite signature of albuminuria in individuals without diabetes or chronic kidney disease to identify possible mechanisms that result in increased albuminuria and elevated risk of type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

The study cohort was a population-based Metabolic Syndrome In Men (METSIM) study including 8861 middle-aged and elderly Finnish men without diabetes or chronic kidney disease at baseline. A total of 5504 men participated in a 7.5-year follow-up study, and 5181 of them had metabolomics data measured by Metabolon's ultrahigh performance liquid chromatography-tandem mass spectroscopy.

RESULTS

We found 32 metabolites significantly (P < 5.8 × 10-5) and positively associated with the urinary albumin excretion (UAE) rate. These metabolites were especially downstream metabolites in the amino acid metabolism pathways (threonine, phenylalanine, leucine, arginine). In our 7.5-year follow-up study, UAE was significantly associated with a 19% increase (hazard ratio 1.19; 95% confidence interval, 1.13-1.25) in the risk of T2D after the adjustment for confounding factors. Conversion to diabetes was more strongly associated with a decrease in insulin secretion than a decrease in insulin sensitivity.

CONCLUSIONS

Metabolic signature of UAE included multiple metabolites, especially from the amino acid metabolism pathways known to be associated with low-grade inflammation, and accumulation of reactive oxygen species that play an important role in the pathogenesis of UAE. These metabolites were primarily associated with an increase in UAE and were secondarily associated with a decrease in insulin secretion and insulin sensitivity, resulting in an increased risk of incident T2D.

Gut microbiota profile and selected plasma metabolites in type 1 diabetes without and with stratification by albuminuria

AIMS/HYPOTHESIS

Abnormal gut microbiota and blood metabolome profiles have been reported both in children and adults with uncomplicated type 1 diabetes as well as in adults with type 1 diabetes and advanced stages of diabetic nephropathy. In this study we aimed to investigate the gut microbiota and a panel of targeted plasma metabolites in individuals with type 1 diabetes of long duration without and with different levels of albuminuria.

METHODS

In a cross-sectional study we included 161 individuals with type 1 diabetes and 50 healthy control individuals. Individuals with type 1 diabetes were categorised into three groups according to historically measured albuminuria: (1) normoalbuminuria (<3.39 mg/mmol); (2) microalbuminuria (3.39-33.79 mg/mmol); and (3) macroalbuminuria (≥33.90 mg/mmol). From faecal samples, the gut microbiota composition at genus level was characterised by 16S rRNA gene amplicon sequencing and in plasma a targeted profile of 31 metabolites was analysed with ultra HPLC coupled to MS/MS.

RESULTS

Study participants were aged 60 ± 11 years (mean ± SD) and 42% were women. The individuals with type 1 diabetes had had diabetes for a mean of 42 ± 15 years and had an eGFR of 75 ± 25 ml min^-1 (1.73 m)^-2. Measures of the gut microbial beta diversity differed significantly between healthy controls and individuals with type 1 diabetes, either with micro- or macroalbuminuria. Taxonomic analyses showed that 79 of 324 genera differed in relative abundance between individuals with type 1 diabetes and healthy controls and ten genera differed significantly among the three albuminuria groups with type 1 diabetes. For the measured plasma metabolites, 11 of 31 metabolites differed significantly between individuals with type 1 diabetes and healthy controls. When individuals with type 1 diabetes were stratified by the level of albuminuria, individuals with macroalbuminuria had higher plasma concentrations of indoxyl sulphate and L-citrulline than those with normo- or microalbuminuria and higher plasma levels of homocitrulline and L-kynurenine compared with individuals with normoalbuminuria. Whereas plasma concentrations of tryptophan were lower in individuals with macroalbuminuria compared with those with normoalbuminuria.

CONCLUSIONS/INTERPRETATION

We demonstrate that individuals with type 1 diabetes of long duration are characterised by aberrant profiles of gut microbiota and plasma metabolites. Moreover, individuals with type 1 diabetes with initial stages of diabetic nephropathy show different gut microbiota and plasma metabolite profiles depending on the level of albuminuria. Graphical abstract.

Novel Biomarkers to Distinguish between Type 3c and Type 2 Diabetes Mellitus by Untargeted Metabolomics

Pancreatogenic diabetes mellitus (T3cDM) is a highly frequent complication of pancreatic disease, especially chronic pancreatitis, and it is often misdiagnosed as type 2 diabetes mellitus (T2DM). A correct diagnosis allows the appropriate treatment of these patients, improving their quality of life, and various technologies have been employed over recent years to search for specific biomarkers of each disease. The main aim of this metabolomic project was to find differential metabolites between T3cDM and T2DM. Reverse-phase liquid chromatography coupled to high-resolution mass spectrometry was performed in serum samples from patients with T3cDM and T2DM. Multivariate Principal Component and Partial Least Squares-Discriminant analyses were employed to evaluate between-group variations. Univariate and multivariate analyses were used to identify potential candidates and the area under the receiver-operating characteristic (ROC) curve was calculated to evaluate their diagnostic value. A panel of five differential metabolites obtained an area under the ROC curve of 0.946. In this study, we demonstrate the usefulness of untargeted metabolomics for the differential diagnosis between T3cDM and T2DM and propose a panel of five metabolites that appear altered in the comparison between patients with these diseases.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Dyslipidemia: A Trigger for Coronary Heart Disease in Romanian Patients with Diabetes

Previous studies have reported age and gender disparities in the occurrence and therapeutic approach of dyslipidemia and (or) coronary heart disease (CHD) in patients with type 2 diabetes mellitus (T2DM). We aimed to investigate these differences in Romanian patients with T2DM. A cross-sectional, observational, retrospective study was conducted using the medical records of T2DM patients who attended the outpatient facility of the Internal Medicine Clinic of the Clinical Emergency Hospital of Bucharest, Romania for routine check-ups in a six-month period. We analyzed the records of 217 diabetic patients (mean age 69 ± 11 years; 51.15% women). We found no significant gender differences in the occurrence of dyslipidemia, CHD or CHD + dyslipidemia or in terms of statin prescription. However; patients aged 65 years or older were significantly more affected by dyslipidemia, CHD or CHD + dyslipidemia, versus subjects aged <65 years. Further, they were more likely to be prescribed statin therapy (p < 0.0001 for all). Statins were prescribed to 67.24% of the patients with dyslipidemia; 61.01% of the subjects with CHD; and to 91.48% of the patients who had both conditions. e recorded no gender differences in the occurrence of CHD and (or) dyslipidemia in Romanian T2DM patients. Patients aged 65 years or older had a higher prevalence of CHD and/or dyslipidemia, and were more likely to be prescribed statins, versus younger counterparts. However, many T2DM patients with CHD and (or) dyslipidemia were undertreated: Nearly 33% of the subjects with dyslipidemia, and nearly 40% of the ones with CHD were not prescribed statins.

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

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.