Search Results (409)

Chiang Mai, Thailand, experiences seasonal fine particulate matter (PM_2.5) pollution associated with metabolic diseases, but the underlying mechanisms remain unclear. This prospective observational study compared plasma metabolomes of 25 healthy adults in Samoeng District, a highly affected area, between low and high PM_2.5 exposure seasons using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Twenty-six metabolites differentiating haze and non-haze seasons were identified using PLS-DA (VIP > 1.5). During the haze season, 11 were elevated, whereas 15 were decreased. Among the elevated metabolites, the top five—maleylacetoacetic acid, deoxyribose 5-phosphate, betaine, 3-hydroxyanthranilic acid, and 1-methyladenosine—were associated with inflammation, increased reactive oxygen species, nitric oxide inhibition, and altered amino acid metabolism. The top five decreased metabolites—deoxyguanosine, D-arabitol, glycerophosphocholine, ophthalmic acid, and oxaloacetic acid—were involved in several metabolic pathways, particularly those involved in energy metabolism. A total of 56 metabolic pathways were altered by high PM_2.5 exposure, including pathways related to amino acids, lipids, sugars, nucleotides, vitamins, and energy metabolism. High PM_2.5 exposure disrupts metabolites and pathways, inducing inflammation, oxidative stress, impaired lipid/energy metabolism, insulin resistance, and high blood pressure. These alterations may increase the risk of metabolic and cardiovascular diseases, with dysregulated metabolites serving as potential biomarkers. These findings highlight the molecular impact of air pollution in affected populations and may support preventive strategies and public health policy development in affected regions. Further studies are needed to clarify these findings. Full article

Ketosis is one of the most economically significant metabolic disorders affecting periparturient dairy cows, causing production losses and predisposing animals to secondary complications. Current blood-based diagnostics are invasive and provide limited insight into the underlying metabolic perturbations. This study employed an integrated three-platform metabolomics approach to characterize milk metabolite alterations in ketotic Holstein dairy cows and to evaluate milk-based biomarker panels for early ketosis detection. Milk samples from 20 healthy control (CON) cows and 6 ketotic cows were collected at 2 weeks postpartum and analyzed by direct injection/liquid chromatography–tandem mass spectrometry (DI/LC-MS/MS), proton nuclear magnetic resonance (¹H-NMR) spectroscopy, and inductively coupled plasma mass spectrometry (ICP-MS). Ketosis was confirmed by serum β-hydroxybutyrate concentrations ≥ 1400 μmol/L. Principal component analysis, partial least squares-discriminant analysis, and receiver operating characteristic (ROC) curve analyses were applied. All three platforms discriminated ketotic cows from healthy cows, with clear cluster separation validated by 2000 permutation tests (p < 0.05). DI/LC-MS/MS identified 16 significantly altered metabolites (p < 0.05), with butyrylcarnitine (C4), phosphatidylcholine 30:0 (PC 30:0), ether-linked phosphatidylcholine O-38:3 (PC O-38:3), and citrulline identified as the top discriminatory biomarkers (AUC = 0.920; 95% CI: 0.85–0.98; sensitivity = 91.7%; specificity = 93.3%). ICP-MS revealed significantly reduced selenium (Se, p = 0.017), manganese (Mn, p = 0.045), and chromium (Cr, p = 0.037), as well as elevated cobalt (Co, p = 0.014) in ketotic milk (AUC = 0.870). ¹H-NMR detected no individually significant metabolites; however, multivariate analysis distinguished groups (AUC = 0.890), with succinate (numerical fold change: +5.77×; p = 0.059), methanol (−1.94×; not significant), and acetate (+2.88×; not significant) as top VIP contributors. The combined multi-platform biomarker panel (joint classification using top VIP features from all three platforms, without formal data fusion) achieved superior diagnostic performance (AUC = 0.970; 95% CI: 0.93–1.00; sensitivity = 95.0%; specificity = 96.7%). These findings identify coordinated perturbations in glycerophospholipid metabolism, acylcarnitine profiles, amino acid homeostasis, antioxidant mineral status, and energy metabolism during early ketosis, and suggest that milk metabolomics is a promising non-invasive approach for precision dairy health monitoring, pending validation in independent cohorts. We acknowledge the small ketotic group size (n = 6) as a limitation; therefore, these findings should be considered discovery cohort observations requiring prospective validation before clinical translation. Full article

Background: Sepsis remains a leading cause of mortality in emergency and intensive care settings, with early diagnosis representing a critical determinant of patient outcomes. Despite advances in biomarker discovery, integrating platelet-derived metabolic signatures with explainable machine learning frameworks for sepsis prediction remains underexplored. The clinical adoption of predictive models has been hindered by the “black box” nature of conventional algorithms, limiting clinician trust and understanding. Objective: This study aimed to evaluate and validate an interpretable machine learning model utilizing platelet metabolomics data for accurate sepsis prediction while providing clinically meaningful explanations of the underlying metabolic disturbances that could inform therapeutic decision-making. Methods: We analyzed metabolomics data, comprising 25 sepsis patients diagnosed according to Sepsis-3 criteria and 14 age- and gender-matched non-sepsis from the emergency department. Platelet metabolite profiles were obtained via quantitative ¹H-NMR spectroscopy. Five machine learning algorithms were evaluated: Explainable Boosting Machine (EBM), Support Vector Machine (SVM), Logistic Regression (LR), Gradient Boosting Machine (GBM), and AdaBoost. Three biologically motivated metabolite ratios (adenosine triphosphate/adenosine diphosphate (ATP/ADP), ATP/adenosine monophosphate (AMP), Glutamine/Glutamate) were derived as additional features, yielding 22 candidate variables. Models were evaluated using a fully nested leave-one-out cross-validation (LOOCV) framework in which log transformation, KNN imputation, BorderlineSMOTE class balancing, and hyperparameter optimisation were performed exclusively within each training fold. Global and local interpretability analyses were performed to identify discriminative metabolites. Results: EBM achieved the highest ROC-AUC (0.864; 95% CI: 0.736–1.000), the highest PR-AUC (0.902; 95% CI: 0.783–0.997), and the best Brier score (0.189; 95% CI: 0.130–0.258) among all evaluated models, with sensitivity 0.880 (95% CI: 0.640–1.000; TP = 22/25) and specificity 0.714 (95% CI: 0.357–1.000; TN = 10/14). Global feature importance identified Carnitine, myo-Inositol, ADP, and O-Phosphoethanolamine as the leading single-feature predictors, alongside three pairwise interaction terms reflecting non-additive energy–amino acid metabolic relationships. Local explanations demonstrated that the ADP–Creatine interaction, Glutamine, and myo-Inositol drove correct sepsis classification in a representative true positive case. Conclusions: The EBM model demonstrated the highest discriminative performance and best calibration among all evaluated models, providing transparent mechanistic insights through global feature importance, and patient-level local explanations. These findings position the proposed framework as a proof-of-concept warranting external validation in larger, multi-centre cohorts before any clinical application is considered. Full article

Steatotic liver disease (SLD) is characterised by profound metabolic reprogramming, yet no single biomarker reliably distinguishes disease entities, stages or sex-specific risk profiles. By integrating serum metabolomic signatures as a liquid biopsy with tumour-associated CSC marker profiles in a sex-stratified analytical framework, we aimed to identify biologically meaningful differences and improve strategies for early, presymptomatic detection of SLD progression and HCC. The present study focuses on a targeted panel of 12 strongly dysregulated serum metabolites as candidate biomarkers of disease progression, quantified by NMR-based metabolomics and ELISA and complemented by CSC marker staining. We combined these NMR-based metabolomic ‘liquid biopsy’ data with circulating tumour-associated biomarkers, MELD-based risk assessment and tissue-level CSC marker expression across MetALD, MASLD, immune-mediated and cancerogenic liver disease, HCC and healthy controls. Female MetALD patients showed the second highest mortality after HCC, with lower survival than male cancer patients, despite MELD 3.0 assigning ~50% higher scores in women. MetALD mortality clustered with GP73, CD44, metabolomics and AA/3HB ratio, indicating a distinct, high-risk female phenotype. Integrating liquid-based metabolomic profiling, AA/3HB redox assessment, CSC markers and MELD 3.0 into sex-sensitive diagnostic pathways may improve early detection and risk stratification of alcohol-associated SLD, especially in women. Full article

Interpretation of pharmacometabolomics results, aiming particularly at biomarker (sets) discovery for drug exposure, remains a major challenge. The metabotyping of drug exposure depends on resolution of specific metabolomics techniques and comprises individual metabolic phenotypes (“metabotypes”), disease-, drug- and microbiome-specific patterns, as well as conditional metabolic states (e. g. fasting). In this clinical trial with 16 healthy participants, an exploratory objective was to evaluate the untargeted urinary metabolomics of voriconazole, administered in four single doses, using proton nuclear magnetic resonance (¹H-NMR) spectroscopy. Voriconazole is a second-generation triazole and a potent inhibitor of drug-metabolizing enzymes such as cytochrome P450 (CYP) isozymes CYP3A4 and CYP2C19. Therefore, identification of metabolites reflecting acute CYP3A4 inhibition was of particular interest. On two treatment days without and with voriconazole (with background microdosed midazolam and omeprazole administration for CYP3A4 and CYP2C19 phenotyping, respectively), spot urine was collected after overnight fasting (predose) and 4 h later (postdose fasting). In the postdose versus predose fingerprints, most changes at the annotated metabolite level were attributable to fasting metabolomics or potential confounders. ¹H-NMR spectroscopy identified neither a short-term voriconazole-specific signature nor patterns or metabolites potentially reflecting acute CYP3A4 inhibition. Our study emphasizes crucial significance of strict standardization of fasting time and minimization of confounder influences by clinical trial design as well as selection of adequate baselines and high-resolution analytical techniques in pharmacometabolomics research, especially for biomarker discovery. Full article

Background: Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS) shares numerous clinical features with Tourette syndrome (TS), notably the presence of tics and frequent comorbidities such as obsessive-compulsive disorder, irritability, and ADHD-like behaviors, often indistinguishable, particularly in the early stages of the two syndromes. Also, pathogenic similarities between PANS and TS constitute a diagnostic challenge, highlighting the need for biomarkers elucidating the underpinnings of the two disorders. In this context, metabolomics has emerged as a powerful tool for identifying distinct biochemical patterns in various diseases. We previously compared PANS, autism patients, and controls, identifying specific metabolic patterns. However, no studies have directly compared the metabolomic profiles of Tourette syndrome and PANS patients. The present study aims to compare the serum metabolomic profiles of TS patients with those of PANS and healthy controls to advance the molecular understanding and clinical differentiation of these two pediatric neuropsychiatric disorders. Methods: Thirty-four PANS patients and twenty-three Tourette patients were matched with twenty-five healthy subjects (C), and their blood samples were analyzed with ¹H NMR spectroscopy. Subsequently, data were analysed with multivariate and univariate statistical approaches. Results: Supervised models indicated that the metabolomic profile of TS patients was significantly different from that of controls (p = 0.02), with altered concentrations of glutamate, glycerol, glycine, lactate, and proline. No significant differences were found in the comparison between PANS and TS patients. Conclusions: These preliminary data suggest that Tourette and Pans also seem to share the metabolic profiles, while differences were found in TS patients compared to controls. On the other hand, the PANS phenotype comprises symptoms that largely overlap with those of all other NDDs, including TS, outlining a spectrum of disorders that share common pathogenetic pathways. Larger studies are needed to confirm these findings. Full article

The Ordos Basin hosts abundant lacustrine shale oil resources. Adequately retained hydrocarbons in source rocks, together with favorable mobility, are prerequisites for large-scale shale oil exploitation. Therefore, the quantitative characterization of retained hydrocarbon content and mobility is a core research focus in shale oil exploration and development. This study investigates Chang 7 shale with varying lithofacies and geochemical characteristics. Stepwise pyrolysis and pyrolysis gas chromatography–mass spectrometry (GC–MS) were applied to analyze retained hydrocarbons in different occurrence states, their compositions, and biomarkers. In addition, nuclear magnetic resonance (NMR) combined with CO₂ flooding experiments was conducted, and the collected products under different displacement pressures were analyzed using GC–MS. The aim was to quantitatively examine the variations in expelled oil volume, compositional differences during migration, and occurrence features of shale oil within reservoir micro-pores. The results show the following: (1) Organic-rich shale is characterized by higher proportions of light and medium hydrocarbons, lower heavy fractions, and elevated aromatic hydrocarbon content. In contrast, low-organic-carbon mudstone or siltstone contains more medium and heavy hydrocarbons, with lower light and aromatic fractions. The C13−/C14+ ratio increases with total organic carbon (TOC). (2) In black shale, oil displacement is mainly contributed by mesopores. At low pressures, oil expulsion is difficult and dominated by heavy hydrocarbons. When pressure reaches a threshold, the capillary-bound oil in micropores is released, increasing production and improving oil quality. Muddy siltstone shows higher displacement efficiency than black shale, with contributions from pores of all sizes. At low pressures, its expelled oil volume is larger and lighter than that of black shale. With increasing pressure, the oil yield rises significantly, and medium–large pores produce heavier fractions compared with micropores, likely because light hydrocarbons preferentially enter micropores and are less prone to dissipation. (3) The main controlling factors for shale oil enrichment include retained hydrocarbon content, mobile hydrocarbon fraction, fluidity, and engineering-related parameters. Thick shale layers with high organic matter abundance, high proportions of light–medium hydrocarbons, and favorable porosity–permeability conditions, as well as interbedded siltstone, are enriched in mobile hydrocarbons. Full article

The global seafood industry faces persistent challenges related to product quality, safety, and authenticity, driven by complex supply chains, increasing demand, and the perishable nature of aquatic products. Traditional analytical methods often fall short in providing rapid, comprehensive, and non-destructive insights into the intricate biochemical changes occurring in seafood. ¹H Nuclear Magnetic Resonance (¹H NMR) spectroscopy has emerged as a powerful and versatile tool for metabolomics, offering a holistic view of the low-molecular-mass compounds (metabolites) present in biological samples. The present study applied ¹H NMR for chemical fingerprint identification in mullets (Mugil liza) from Brazil. Dorsal muscle samples were taken from the fish during summer, autumn, and winter. The procedure involved freeze-drying the muscle tissue, thereafter extracting polar metabolites using designated solvents (methanol, water, and chloroform), and analyzing them using a 600 MHz spectrometer. As a result, 23 metabolites related to degradation biomarkers, essential metabolites, energy expenditure, and muscle structure were identified. The statistical analysis demonstrated a distinct separation between the geographical origins (RJ vs. SC), mostly influenced by variations in the concentrations of lactate, histidine, threonine, phenylalanine, and ornithine. Factors like fish size and seasonal variations did not markedly affect the overall metabolic profile, underscoring the reliability of these chemicals as stable origin indicators. The Principal Component Analysis identified two distinct groups of metabolites, establishing a profile for each geographical origin. The developed protocol can be applied to the processes of geographical identification. Thus, the ¹H NMR tool was efficient in determining metabolites that can be considered biomarkers in analyses for seafood traceability. Full article

Cerebrospinal fluid (CSF) oligoclonal band (OCB) analysis remains central to the diagnostic evaluation of neuroinflammatory diseases of the central nervous system (CNS), as it reflects intrathecal immunoglobulin synthesis. However, its reliance on lumbar puncture limits its applicability for screening and repeated longitudinal assessment. Serum metabolomics offers a minimally invasive strategy to explore peripheral biochemical correlates of central immune activity. Building on previous binary OCB comparisons, the present study extends serum metabolomic analysis to encompass all five classical OCB patterns, thereby capturing a broader immunological spectrum. A total of 92 adults undergoing diagnostic evaluation for suspected CNS inflammatory disorders were retrospectively stratified according to OCB type (Types 1–5). Serum samples were analysed using targeted ¹H-NMR spectroscopy on a Bruker Avance Neo 600 MHz platform and processed using Bruker’s IVDr pipeline. Group-wise differences were assessed using non-parametric statistical testing with false discovery rate (FDR) correction, complemented by effect size estimation, exploratory multivariate analyses, and Receiver Operating Characteristic (ROC) modelling. Distributional characteristics were further examined using boxplots and violin plots. Across analytical approaches, several metabolites—most prominently leucine, 2-oxoglutaric acid, histidine, threonine, and glycerol—exhibited nominal variation and moderate effect sizes across OCB patterns. Rather than discrete metabolic separation, these metabolites demonstrated graded shifts in central tendency accompanied by substantial overlap between groups. Unsupervised principal component analysis did not reveal robust clustering, while supervised multivariate models highlighted amino acid- and tricarboxylic acid cycle-related metabolites as contributors to partial differentiation. Post hoc power analysis indicated limited sensitivity to detect small-to-moderate effects under multiple-testing correction, supporting an exploratory interpretation of the findings. Taken together, this first targeted serum ¹H-NMR metabolomic evaluation spanning all classical OCB patterns suggests that peripheral metabolic profiles may reflect graded immunometabolic variation associated with intrathecal immune activity. While not intended for diagnostic classification, these findings provide a spectrum-based framework for integrating serum metabolomics with OCB phenotyping and identify candidate metabolites for future prospectively powered and clinically characterised studies. Full article

Tuberculosis (TB) remains a major global cause of morbidity and mortality. Current tools for monitoring treatment response rely on sputum-based microscopy and culture, which are often insensitive, time-consuming, and impractical in extrapulmonary or pediatric TB and in individuals unable to produce sputum. Metabolomics has emerged as a promising approach for identifying host-derived biomarkers that reflect treatment-associated immunometabolic changes; however, the available evidence remains heterogeneous and has not been comprehensively synthesized. We conducted a comprehensive literature review of human studies evaluating metabolomic biomarkers in relation to TB treatment response or outcomes. PubMed, Scopus, and EMBASE were searched for human studies evaluating targeted or untargeted metabolomics (NMR, LC-MS, GC-MS, CE-MS) in relation to treatment response or outcomes. Two reviewers independently screened studies, extracted data, and assessed risk of bias using QUIPS and PROBAST. Findings were synthesized using a structured framework organized across treatment stages and outcomes. Of 218 records identified, 139 titles and abstracts were screened and 42 full texts assessed; 15 studies met the inclusion criteria. Recurrent treatment-associated signals involved amino acid metabolism, particularly the tryptophan–kynurenine pathway, as well as vitamin and cofactor metabolites (pyridoxate, nicotinamide, trigonelline). Plasma studies frequently reported lipid remodeling and bile acid perturbations, whereas urine studies highlighted polyamine metabolism (e.g., N¹,N¹²-diacetylspermine) and fatty acid β-oxidation markers. Common limitations included inadequate adjustment for confounders and, in prediction models, small sample sizes and limited external validation. Metabolomics reveals reproducible but heterogeneous immunometabolic changes during TB therapy. Key pathways include tryptophan–kynurenine metabolism, vitamin and cofactor metabolism, lipid remodeling, and urine polyamine pathways. Standardization and prospective multicenter validation are needed for clinical translation. Full article

Faba bean-fed crispy tilapia represents a commercially valuable aquaculture product, renowned for its exceptional muscle firmness. However, the dynamic changes in muscle metabolite profiles during the tilapia crisping process remain largely unelucidated. In this study, proton nuclear magnetic resonance spectroscopy (¹H-NMR) combined with multivariate statistical analysis was employed to characterize and compare the muscle metabolomes of tilapia subjected to different crispness grades (CD0, CD2, CD4). A total of 11 differential metabolites were successfully identified, among which glycine, threonine, and trans-4-hydroxy-L-proline were demonstrated to be potential crispness-related biomarkers. Specifically, as the crispness grade increased from 0 to 4, the muscle contents of these key metabolites exhibited a consistent downward trend: glycine decreased significantly from 19.86 mM to 7.15 mM, threonine from 1.21 mM to 0.58 mM, and trans-4-hydroxy-L-proline from 2.25 mM to 0.89 mM. Subsequent metabolic pathway enrichment analysis further revealed that the glycine-serine-threonine metabolic pathway represented the most significantly perturbed pathway associated with the crisping process. Collectively, our findings demonstrate that faba bean-based feeding regimens enhance tilapia muscle crispness by orchestrating metabolite signatures involved in collagen biosynthesis and lipid metabolism. These results not only provide novel insights into the intrinsic molecular mechanisms underlying tilapia crisping but also establish a solid theoretical framework for the precise quality control and standardized production of high-quality crispy tilapia. Full article

Long-term adherence to plant-based diets can modify gut bacterial microbiota composition and metabolite profiles, which may be particularly relevant for postmenopausal women who frequently adopt such diets and experience age-related changes in nutrient absorption and metabolism. Fermented foods, commonly consumed in vegetarian diets, enhance dietary diversity and nutritional quality. This study compared gut bacterial microbiota and fecal metabolomes between vegetarians (VGs) and omnivores (OMs) and evaluated the contribution of fermented food intake. Thirty-two healthy postmenopausal Thai women (>55 years; 16 VGs, 16 OMs) were enrolled. Gut bacterial microbiota and fecal metabolites were analyzed using 16S rRNA metagenomic and untargeted ¹H-NMR metabolomics. The five most frequently consumed fermented foods were microbiologically characterized. Fermented food consumption was found to be significantly different between groups. OM participants reported infrequent consumption (<10% per week), whereas VG participants consumed fermented foods daily, often in multiple forms (>60% of weekly meals). VG participants exhibited enrichment of Prevotella, Faecalibacterium, and Blautia, while OM participants showed higher abundances of Bacteroides and Escherichia–Shigella. LEfSe identified Weissella as a bacterial taxon associated with the VG group. Functional prediction and metabolomic analyses indicated enhanced carbohydrate fermentation and increased short-chain fatty acid (SCFA) production in VGs, whereas OM profiles reflected greater protein catabolism. Fermented foods consumed by VGs shared microbial biomarkers with the VG gut bacterial microbiota and were rich in SCFAs and essential amino acids, supporting their potential role as microbial and metabolic contributors within the gut ecosystem and nutritional adequacy in postmenopausal vegetarians. Full article

Head and neck squamous cell carcinoma (HNSCC) continues to pose a major global health challenge, with over 600,000 new cases diagnosed annually and persistently poor survival outcomes despite advances in surgery, radiotherapy, and immunotherapy. Growing evidence implicates metabolic reprogramming, including enhanced glycolysis, glutaminolysis, lipid synthesis, and one-carbon/redox flux as a central driver of HNC initiation, progression, and therapy resistance. In contrast, metabolic crosstalk within the hypoxic, acidic tumor microenvironment (TME) further shapes immune evasion and stromal support. Recent innovations in mass spectrometry platforms (LC-MS, GC-MS, NMR) have attracted attention in clinical therapeutics, and spatial metabolomics imaging techniques now enable high-resolution in situ mapping of metabolites, revealing intratumoral heterogeneity and offering new insights into tumor-immune–stromal interactions and potential biomarkers for precision oncology. In this review, we integrate critical methodological considerations from sample collection and data-analysis workflows to analytical pitfalls with a balanced, pathway-focused analysis of HNSCC dysmetabolism, examine tumor immune stromal metabolic interactions, and highlight translational opportunities through emerging biomarkers, targeted inhibitors, and cutting-edge approaches such as single-cell and AI-driven metabolomics to chart a roadmap toward precision oncology for HNSCC. Full article

Background: Stable isotope-based analytical methods have brought about a significant transformation in the study of energy nutrient metabolism, enabling precise in vivo measurement of metabolic fluxes at systemic, tissue, and organ-specific levels in both healthy and diseased states. The regulation of these metabolic fluxes is governed by dynamic interactions between proteins, lipids, carbohydrates, and their precursors—such as glucose, fatty acids, and amino acids—as well as final metabolic products. Discussion: Advanced analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), which can offer enhanced precision, have been developed for investigating nutrient metabolism and fluxes in humans, providing precise information on metabolic pathways. These techniques have primarily utilized stable isotopes, such as ²H, ¹³C, ¹⁵N, and ¹⁸O, which have largely replaced radioactive isotopes and are now central to metabolic research. These isotopes have been used to label glucose, fatty acids, or amino acids—the main biomolecular precursors—enabling detailed investigation at systemic, tissue, and organ-specific levels of carbohydrate, lipid, and protein metabolism, and revealing pathway alterations associated with diseases conditions, such as diabetes, non-alcoholic fatty liver disease, cardiovascular disorders, and cancer. The use of deuterium oxide (D₂O) has allowed for long-term metabolic studies, providing a cost-effective and less invasive means to monitor metabolic changes over days to months. Total daily energy expenditure can be measured in free living conditions by the doubly stable isotopes ²H- and ¹⁸O-labeled water method. Stable isotope tracing, combined with advanced imaging and modeling, has also been instrumental in assessing body composition, energy expenditure, and nutrient bioavailability. Collectively, these methods have expanded our understanding of human physiology and disease, supporting the development of novel diagnostic tools, the identification of new biomarkers, and the tailoring of nutritional and therapeutic interventions. Conclusions: This review aimed to provide an overview of the applications of stable isotopes for the study of energy nutrient metabolic pathways. The ongoing integration of stable isotope approaches with artificial intelligence, omics technologies, and miniaturized detection techniques could promise to further refine our understanding of human metabolism and drive advances in personalized medicine. Full article

Background: Reliable identification remains a cornerstone of forensic investigations, particularly when encountering degraded remains or suboptimal biological evidence. This study evaluates the potential of dentine metabolomics, utilizing proton nuclear magnetic resonance (¹H-NMR) spectroscopy, to detect cancer-associated metabolic signatures in dental tissues for forensic applications. Methods: Forty-four non-carious second molars were analyzed, comprising 22 samples from deceased individuals with a documented history of cancer and 22 age- and sex-matched controls. Metabolomic profiling was conducted using ¹H-NMR spectroscopy to identify and quantify dentine metabolites. Statistical evaluation included unsupervised principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), receiver operating characteristic (ROC) curve analysis, and exploratory binary logistic regression. Results: Among the 209 identified metabolites, inosinic acid and 2-ketobutyric acid were identified as the most robust discriminative biomarkers across both multivariate and univariate frameworks. The exploration within-sample predictive model achieved a Nagelkerke R² of 0.822 and an overall classification accuracy of 90.9%, with a specificity of 95.5% and a sensitivity of 86.4%. These key metabolites are fundamentally associated with purine metabolism and oxidative stress pathways frequently dysregulated in oncogenesis. Conclusions: This pilot study suggests that dentine may retain metabolomic information associated with cancer comorbidity under heterogeneous postmortem conditions. However, the findings remain exploratory and require validation in larger cohorts with standardized postmortem variables before practical forensic implementation. Full article