Search Results (1,060)

Background and Objectives: The uric acid to HDL-cholesterol ratio (UHR) has recently emerged as a promising biomarker reflecting systemic inflammation and metabolic disturbances. Elevated liver transaminases are clinical indicators of hepatic injury and underlying metabolic dysfunction. Many Middle Eastern countries face constrained clinical and laboratory resources, where access to comprehensive diagnostic tools may be limited. In such settings, identifying simple and easily accessible markers could offer significant practical value in detecting and monitoring health disorders. This study investigates the potential association between UHR and elevated liver transaminases levels in the Saudi general population. Materials and Methods: This retrospective cross-sectional study included 9618 subjects, and the association between the UHR and elevated liver transaminases, alanine transaminase (ALT), and aspartate transaminase (AST), was comprehensively analysed. In addition, the study assessed risk indicators including the prevalence ratio (PR) and odds ratio (OR) as well as the diagnostic accuracy of UHR and C-reactive protein (CRP) in detecting liver transaminases abnormalities, with analyses stratified by age and gender. Results: UHR was significantly elevated in subjects with increased ALT and AST activities, and this pattern was consistent across all age and gender categories. High UHR was significantly associated with elevated ALT (OR = 2.32, 95% CI: 2.12–2.53, p < 0.001) and AST (OR = 1.38, 95% CI: 1.25–1.52, p < 0.001), with stronger associations observed in males and for ALT activity. In addition, elevated UHR was more prevalent among individuals with increased liver transaminase activities. Receiver operating characteristic (ROC) analysis showed that UHR outperformed CRP in identifying elevated liver transaminases, with better discriminative ability for ALT than AST activity. Conclusions: These findings highlight a significant association between UHR and liver transaminase abnormalities in the general population, underscoring the potential utility of UHR as a simple and accessible indicator for liver function assessment in clinical settings. Full article

Bamboo shoot boiled liquid (BSBL), a processing byproduct containing soluble proteins, peptides, amino acids, carbohydrates, and phenolics, is typically discarded, causing resource waste and environmental issues. This study analyzed metabolic changes in BSBL during Pediococcus pentosaceus B49 fermentation. The result of partial least squares discriminant analysis (PLS-DA) revealed significant metabolite profile differences across fermentation times (0 h, 24 h, 48 h, 72 h, 96 h). The most substantial alterations occurred within the first 24 h, followed by stabilization. Compared to unfermented BSBL, fermented samples exhibited significantly elevated signal intensities for 5,7-dimethoxyflavone, cinnamic acid, 3,4-dihydro-2H-1-benzopyran-2-one, 6,8-dimethyl-4-hydroxycoumarin, and 2-hydroxycinnamic acid (p < 0.05), showing upward trends over time. Conversely, (+)-gallocatechin intensity decreased gradually. Bitter peptides, such as alanylisoleucine, isoleucylisoleucine, leucylvaline, and phenylalanylisoleucine, in BSBL exhibited a significant reduction following fermentation with P. pentosaceus B49 (p < 0.05). KEGG enrichment indicated tyrosine metabolism (ko00350) and arginine/proline metabolism (ko00330) as the most impacted pathways. These findings elucidate metabolic regulation in BSBL fermentation, supporting development of functional fermented bamboo products. Full article

Background: Type 2 diabetes (T2D) is a global health epidemic with rising prevalence within Asian populations, particularly amongst individuals with high visceral adiposity and ectopic organ fat, the so-called Thin-Outside, Fat-Inside phenotype. Metabolomic and microbiome shifts may herald T2D onset, presenting potential biomarkers and mechanistic insight into metabolic dysregulation. However, multi-omics datasets across ethnicities remain limited. Methods: We performed cross-sectional multi-omics analyses on 171 adults (99 Asian Chinese, 72 European Caucasian) from the New Zealand-based TOFI_Asia cohort at 4-years follow-up. Paired plasma and faecal samples were analysed using untargeted metabolomic profiling (polar/lipid fractions) and shotgun metagenomic sequencing, respectively. Sparse multi-block partial least squares regression and discriminant analysis (DIABLO) unveiled signatures associated with ethnicity, glycaemic status, and sex. Results: Ethnicity-based DIABLO modelling achieved a balanced error rate of 0.22, correctly classifying 76.54% of test samples. Polar metabolites had the highest discriminatory power (AUC = 0.96), with trigonelline enriched in European Caucasians and carnitine in Asian Chinese. Lipid profiles highlighted ethnicity-specific signatures: Asian Chinese showed enrichment of polyunsaturated triglycerides (TG.16:0_18:2_22:6, TG.18:1_18:2_22:6) and ether-linked phospholipids, while European Caucasians exhibited higher levels of saturated species (TG.16:0_16:0_14:1, TG.15:0_15:0_17:1). The bacteria Bifidobacterium pseudocatenulatum, Erysipelatoclostridium ramosum, and Enterocloster bolteae characterised Asian Chinese participants, while Oscillibacter sp. and Clostridium innocuum characterised European Caucasians. Cross-omic correlations highlighted negative correlations of Phocaeicola vulgatus with amino acids (r = −0.84 to −0.76), while E. ramosum and C. innocuum positively correlated with long-chain triglycerides (r = 0.55–0.62). Conclusions: Ethnicity drove robust multi-omic differentiation, revealing distinctive metabolic and microbial profiles potentially underlying the differential T2D risk between Asian Chinese and European Caucasians. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Inflammatory biomarkers, including high-sensitivity C-reactive protein (hsCRP), serum amyloid A (SAA), and interleukin-6 (IL-6), have been associated with an increased risk of future cardiovascular events. While they provide valuable prognostic information, these associations do not necessarily imply a direct causal role. The combined prognostic utility of these markers, however, remains insufficiently studied. We analysed 3300 well-characterised participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, all of whom underwent coronary angiography. Participants were stratified based on their serum concentrations of hsCRP, SAA, and IL-6. Associations between biomarker combinations and mortality were assessed using multivariate Cox regression and ROC analysis. Individuals with elevated hsCRP and SAA or IL-6 showed higher prevalence rates of coronary artery disease, heart failure, and adverse metabolic traits. These “both high” groups had lower estimated glomerular filtration rate, higher NT-proBNP, and increased HbA1c. Combined elevations of hsCRP and SAA were significantly associated with higher all-cause and cardiovascular mortality in partially adjusted models. However, these associations weakened after adjusting for IL-6. IL-6 alone demonstrated the highest predictive power (AUC: 0.638) and improved risk discrimination when included in multi-marker models. The co-elevation of hsCRP, SAA, and IL-6 identifies a high-risk phenotype characterised by greater cardiometabolic burden and increased mortality. IL-6 may reflect upstream inflammatory activity and could serve as a therapeutic target. Multi-marker inflammatory profiling holds promise for refining cardiovascular risk prediction and advancing personalised prevention strategies. Full article

Metabolome studies have already been carried out in patients with systemic sclerosis (SSc). However, polyneuropathy (PNP) as a complication of SSc has been overlooked in these studies. To the best of our knowledge, this is the first study to examine metabolic changes in SSc patients with PNP. Patients with SSc (n = 62) and a healthy control group (HC) (n = 72) were recruited from two Latvian hospitals. Blood plasma samples were collected and analyzed using an LC-MS-based targeted metabolomics workflow. Our plasma sample cohort consisted of 62 patients with SSc, 42% of whom had PNP. Differences between SSc patients and the HC group with fold changes > 2 were observed for aspartic acid, glutamic acid, valine, and citrulline, all of which were reduced. In contrast to the SSc to HC discrimination, no metabolites had a high fold change; only minor changes were observed using FC > 1.3. We identified elevated concentrations of kynurenine, asparagine, and alanine. Changes in metabolite regulation in patients with SSc, compared to controls, are not identical to those observed in SSc patients with PNP, with elevated concentrations of kynurenine and alanine specific to the SSc subgroup. SSc patients with PNP should probably be considered a distinct population with important metabolomic features. Full article

Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making. Full article

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares–discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD. Full article

Rhabdomyosarcoma (RMS), the most common pediatric soft tissue sarcoma, comprises embryonal (ERMS) and alveolar (ARMS) subtypes with distinct histopathological features, clinical outcomes, and therapeutic responses. To better characterize their molecular distinctions, we performed untargeted plasma proteomics and metabolomics profiling in children with ERMS (n = 18), ARMS (n = 17), and matched healthy controls (n = 18). Differential expression, functional enrichment (GO, KEGG, RaMP-DB), co-expression network analysis (WGCNA/WMCNA), and multi-omics integration (DIABLO, MOFA) revealed distinct molecular signatures for each subtype. ARMS displayed elevated oncogenic and stemness-associated proteins (e.g., cyclin E1, FAP, myotrophin) and metabolites involved in lipid transport, fatty acid metabolism, and polyamine biosynthesis. In contrast, ERMS was enriched in immune-related and myogenic proteins (e.g., myosin-9, SAA2, S100A11) and metabolites linked to glutamate/glycine metabolism and redox homeostasis. Pathway analyses highlighted subtype-specific activation of PI3K-Akt and Hippo signaling in ARMS and immune and coagulation pathways in ERMS. Additionally, the proteomics and metabolomics datasets showed association with clinical parameters, including disease stage, lymph node involvement, and age, demonstrating clear molecular discrimination consistent with clinical observation. Co-expression networks and integrative analyses further reinforced these distinctions, uncovering coordinated protein–metabolite modules. Our findings reveal novel, subtype-specific molecular programs in RMS and propose candidate biomarkers and pathways that may guide precision diagnostics and therapeutic targeting in pediatric sarcomas. Full article

The integration of HS-SPME-GC/MS and UPLC-MS/MS techniques enabled the profiling of volatile organic compounds (VOCs) and amino acids (AAs) in 18 crabapple flower cultivars, facilitating the development of a novel VOC–AA model. Among the 51 identified VOCs, benzyl alcohol, benzaldehyde, and ethyl benzoate were predominant, categorizing cultivars into fruit-almond, fruit-sweet, and mixed types. The amino acids, namely glutamic acid (Glu), asparagine (Asn), aspartic acid (Asp), serine (Ser), and alanine (Ala) constituted 83.6% of the total AAs identified. Notably, specific amino acids showed positive correlations with key VOCs, suggesting a metabolic regulatory mechanism. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, when combined with volatile organic compounds (VOCs) and amino acid profiles, enabled more effective aroma type classification, providing a robust foundation for further studies on aroma mechanisms and targeted breeding. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

Burnout syndrome is characterized mainly by three criteria (emotional exhaustion, depersonalization, and low personal accomplishment), and further exacerbated by night shift work, with profound implications for individual and societal well-being. The Maslach Burnout Inventory survey applied to 97 medical care professionals (with day and night work) revealed different scores for these criteria. Blood metabolic profiles were obtained by UHPLC-QTOF-ESI⁺-MS untargeted metabolomics and multivariate statistics using the Metaboanalyst 6.0 platform. The Partial Least Squares Discrimination scores and VIP values, Random Forest graphs, and Heatmaps, based on 99 identified metabolites, were complemented with Biomarker Analysis (AUC ranking) and Pathway Analysis of metabolic networks. The data obtained reflected the biochemical implications of night shift work and correlated with each criterion’s burnout scores. Four main metabolic pathways with important consequences in burnout were affected, namely lipid metabolism, especially steroid hormone synthesis and cortisol, the energetic mitochondrial metabolism involving acylated carnitines, fatty acids, and phospholipids as well polar metabolites’ metabolism, e.g., catecholamines (noradrenaline, acetyl serotonin), and some amino acids (tryptophan, tyrosine, aspartate, arginine, valine, lysine). These metabolic profiles suggest potential strategies for managing burnout levels in healthcare professionals, based on validated criteria, including night shift work management. Full article

Background: Adoptive cell therapies are emerging as a promising therapeutic option against hematological and solid malignancies. Memory-like natural killer (mlNK) cells are a specific subtype of NK cells generated after cytokine preactivation that have shown enhanced in vivo persistence after infusion into patients, an issue that has hindered traditional NK cell immunotherapy. However, the quality and variability of mlNK cell products remains poorly defined. Methods: In this study, we evaluated heterogeneity across critical functional and molecular aspects of mlNK cells generated from independent donors, including mlNK cytotoxicity, cluster formation, motility, mitochondria morphology, and gene expression. Results: We observed a correlation between changes in gene expression associated with glycolysis and key NK cell functions such as cytotoxicity and motility. For further characterization, we blocked glycolysis and oxidative phosphorylation (OXPHOS) and observed an impaired mlNK functional response, suggesting the importance of metabolism. Conclusions: Our findings provide insights into discriminating between mlNK cell products and how the predictive markers can identify optimal mlNK cell products for adoptive cell therapy of cancer. Full article

The gut microbiota critically influences lipid metabolism and fat deposition in pigs, processes that underpin pork quality preferences and differentiate the meat traits of Chinese indigenous breeds (fat-type) from those of Western commercial breeds (lean-type). To explore the mechanisms underlying breed-specific fatty acid absorption, we compared the rectal and colonic microbiota and metabolite profiles of Huainan and Large White pigs using 16S rRNA sequencing and untargeted metabolomics. HN pigs exhibited enriched Lactobacillus johnsonii and Lactobacillus amylovorus, along with a significantly higher Firmicutes/Bacteroidetes ratio. Functional predictions further revealed elevated microbial pathways related to glycolysis, pyruvate metabolism, and ABC transporters in HN pigs. Conversely, LW pigs showed increased abundance of potentially pro-inflammatory bacteria and enriched pathways for lipopolysaccharide (LPS) biosynthesis. Metabolites such as 4-ethyl-2-heptylthiazole and picolinic acid were significantly upregulated in HN pigs and served as robust biomarkers (Area Under the Curve, AUC = 1.0),with perfect discrimination observed in both rectal and colonic samples. Integrative analysis identified 52 co-enriched microbial and metabolic pathways in HN pigs, including short-chain fatty acid (SCFA) production, lipid biosynthesis and transport, amino acid metabolism, ABC transporter activity, and the PPAR signaling pathway, supporting a microbiota–metabolite axis that enhances fatty acid absorption and gut immune balance. These findings provide mechanistic insight into breed-specific fat deposition and offer candidate biomarkers for improving pork quality via precision nutrition and breeding. Full article

Cognitive decline is a common complication of diabetes mellitus, driven in part by oxidative stress and impaired glucose–insulin homeostasis. This study examined the neuroprotective effects of agmatine (200 mg/kg intraperitoneally) in female BALB/c diabetic mice. Several receptor pathways were examined using commercially available antagonists. Behavioral performance was evaluated using the novel object recognition test. Metabolic parameters, such as glucose and insulin levels, as well as antioxidants, including catalase (CAT), superoxide dismutase (SOD), and glutathione (GSH), were measured in blood and brain tissue. The diabetic mice exhibited impaired recognition memory (discrimination index = 0.08), hyperglycemia (24.3 mmol/L), decreased insulin levels (38.4 µU/mL), and diminished antioxidant defenses (CAT: 75.4 U/g tissue, SOD: 32.6 U/g tissue, and GSH: 8.3 mmol/g tissue). Agmatine treatment improved cognitive function and reversed the biochemical alterations. However, these effects were reduced when agmatine was co-administered with imidazoline I₂/I₃ receptor antagonists. Correlation analysis revealed that cognitive performance positively correlated with antioxidant enzyme levels and insulin levels and negatively correlated with glucose concentrations. Strong intercorrelations among CAT, SOD, and GSH levels suggest a coordinated antioxidant response. Overall, these results imply that agmatine’s neuroprotective effects are partially mediated by modulation of the oxidative balance and glucose–insulin regulation via imidazoline receptors. Full article