Metabolomics最新文献

Introduction: Gliomas represent the tumors of the central nervous system that originate from glial cells. Overall survival predictions and treatment regimen selection are based on accurate tumor diagnosis and grading. However, the diagnosis of glioma remains critically dependent on either invasive biopsies or advanced imaging.

Objective: This exploratory study aims to assess the diagnostic potential of urine specimens for discriminating gliomas from controls and identify the dysregulated pathways in a North Indian cohort. Urine is an ideal non-invasive candidate, requires no prior preparation, and considerably increases patient compliance.

Method: Urine samples from 50 glioma patients were analysed with ¹H NMR (Nuclear Magnetic Resonance) spectroscopy and compared with those of healthy controls. Statistical analysis was performed in MetaboAnalyst 6.0 to identify significantly perturbed metabolites. Diagnostic performance was assessed using the Receiver Operating Characteristic (ROC) curve, and the Random Forest model was used to evaluate classification accuracy. Pathway enrichment and topology analysis based on the KEGG (Kyoto Encyclopedia of Genes and Genomes) database were performed to identify dysregulated pathways.

Results: ¹H NMR metabolic analysis of urine samples revealed seven statistically significant (p < 0.05) metabolites namely acetate, pyruvate, creatinine, dimethylamine, glutamine, alanine and carnitine. This panel of metabolites displayed excellent diagnostic capability with an Area Under the Curve of 0.90 as measured by a multivariate ROC curve. The random forest model efficiently differentiated glioma from control samples using significant metabolites. Disruption in the primary energy pathways of the body and in the metabolism of major amino acids was observed in the pathway analysis.

Conclusion: Integration of these urinary signatures into current clinical practice can serve as an additional diagnostic tool and a non-invasive screening method for populations at risk. They can also be monitored in real time, thus aiding in adaptive treatment strategies and therapy assessment.

Background: Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic targets by analyzing metabolic profiles. In analyzing these profiles, traditional bioinformatic and statistical approaches, while valuable, often struggle to process high-dimensional and nonlinear metabolic data, lacking the sensitivity and adaptability that artificial intelligence (AI) and machine learning (ML) techniques provide. The integration of AI/ML has greatly enhanced the metabolomics field, enabling biomarker identification, disease prediction, and classification of metabolic patterns at an unprecedented level.

Aim of review: This study analyses and compares the scientific goals, methodologies, datasets, and sources of AI/ML approaches applied to metabolomic data, as well as assessing their implications in precision medicine. We systematically reviewed recent advancements in AI/ML applications to metabolomic data, focusing on peer-reviewed research indexed in PubMed. Significant number of studies were analyzed, covering diseases such as cancer, cardiovascular diseases, and diabetes. Our results showed that the most used AI/ML techniques were SVM, RF, Gradient Boosting, and Logistic Regression, highlighting their effectiveness in processing complex metabolic data. Despite these advancements, key challenges persist in AI/ML applications to metabolomics data, including small cohort sizes, data heterogeneity, and the need for improved model interpretability, and these challenges must be considered for future use.

Key scientific concepts of review: Ultimately, our findings underscore the transformative potential of AI/ML in metabolomics and its critical role in advancing precision medicine by uncovering novel metabolic pathways, improving treatment strategies, and enabling the earlier diagnosis of diseases through predictive metabolic profiling.

Background: Disabling hearing loss affects millions of adults world-wide. Metabolomics investigations are comprehensive assessments of an individual's metabolic processes that could provide insight into biological pathways underlying auditory dysfunction, yet data are limited.

Methods: We conducted a cross-sectional investigation of the association of plasma metabolite profiles and self-reported adult-onset moderate or severe hearing loss among 3925 women, including 1167 hearing loss cases and 2758 controls in the Nurses' Health Study. Information on hearing status at the time of the blood collection and on relevant risk factors was collected on biennial questionnaires. Metabolic profiling was conducted by liquid chromatography-mass spectrometry. The independent associations of 278 metabolites with hearing loss was assessed in logistic regression models adjusted for age, fasting status, race/ethnicity, co-morbidities, medication use and biobehavioral factors. The false discovery rate was controlled at 5% through the q-value approach. Metabolite Set Enrichment Analysis was conducted to identify metabolite classes that are enriched for concordant associations with hearing loss.

Results: We identified 10 metabolites that were significantly associated (q value < 0.05) with moderate or severe hearing loss in multivariable-adjusted models. Steroid esters were enriched for negative associations, while triglycerides were enriched for positive associations. Triglycerides with fewer double bonds were enriched for significant, positive associations with hearing loss (p = 0.04).

Conclusion: In this population-based investigation, we identified that triglycerides were enriched for positive associations, while steroid esters were inversely associated with adult-onset moderate or severe hearing loss. This study indicates that metabolic perturbations may contribute to the pathoetiology underlying adult-onset hearing loss.

Introduction: The COVID-19 pandemic and liver diseases both cause significant metabolic disturbances, yet the specific mechanisms driving these changes remain poorly understood.

Objectives: This study aimed to elucidate and compare the urinary metabolomic profiles of COVID-19 patients, individuals with liver diseases, and healthy controls to determine common and unique metabolic signatures between diseases.

Methods: Untargeted metabolomic profiling was performed using liquid chromatography-mass spectrometry (LC-MS) on urine samples from COVID-19 patients (n = 102), liver disease patients (n = 100), and healthy controls (n = 101). Differential metabolite abundance, pathway enrichment, network topology, and Random Forest-based machine learning analyses were performed.

Results: Both COVID-19 and liver disease exhibited extensive metabolic reprogramming. COVID-19 patients showed suppression of Vitamin B6 and purine metabolism, indicating impaired energy production and antioxidant defense. Liver disease patients exhibited reduced primary bile acid biosynthesis and pantothenate metabolism, reflecting hepatic dysfunction. Random Forest models robustly discriminated disease from healthy states, with binary models for COVID-19 and liver disease achieving AUCs of 0.998, and a multiclass model distinguishing all three groups with 91.9% accuracy. Both conditions shared perturbations in amino acid and steroid-related pathways, reflecting common systemic stress. Importantly, unique metabolites, such as N-Acetylvaline, Succinyladenosine, and S-adenosylhomocysteine in COVID-19, and 3-Hydroxysebacic acid, Asn-Trp, and bile acid derivatives in liver disease, emerged as highly specific biomarkers, highlighting systemic viral stress versus chronic hepatic metabolic adaptation and warranting future validation.

Conclusion: These findings enhance our understanding of disease-specific metabolic remodelling and point to potential biomarkers for diagnosis and therapeutic targeting.

Introduction: Early diagnosis of cancer is essential for improving patient outcomes. Metabolomics analysis has shown promise in detecting cancer and distinguishing its metastatic burdens in previous studies.

Objectives: We hypothesized that metabolomics data can differentiate between people with and without cancer at a population level, uncovering new biomarkers and deepening our understanding of cancer metabolism.

Methods: A total of 1,386 metabolites were measured by two commonly used metabolomics platforms: Nightingale and Metabolon, in baseline plasma samples from participants in the population-based Rotterdam Study, with sample sizes of 2,538 and 5,057, respectively. Logistic regression and competing risk Cox proportional hazards models were employed to examine associations between these metabolites and both baseline prevalent and incident during follow-up of all and cause-specific cancers. Statistical significance was defined by a false discovery rate (FDR) < 0.05.

Results: There were 654 cancer cases at baseline, and 618 new cases also occurred during follow-up of nearly 10 years. In the cross-sectional study, 68, 7, and 10 metabolites were significantly associated with prevalent blood, colorectal, and all cancer, after multivariate adjustment. In the longitudinal study, 19, 11, 2, 3, and 1 metabolites were significantly associated with incident blood, colorectal, lung, prostate, and all cancer, respectively. Among these, 17 and 2 metabolites were associated with both prevalent and incident blood and colorectal cancer.

Conclusions: This study indicates several circulating metabolites that are associated with different cancers. These metabolites may contribute to better understanding of the metabolic pathways of cancer and serve as biomarkers for early cancer diagnosis.

Background and objective: Multidrug-resistant (MDR) bacterial infections are a leading cause of sepsis-related death. A rapid method to identify patients with MDR infections upon hospital admission is urgently needed. This study aimed to characterize the distinct plasma metabolomic signatures associated with MDR gram-positive (G+) and gram-negative (G-) sepsis and to develop predictive models for rapid, risk stratification during the initial clinical encounter.

Methods: Two independent cohorts of septic patients were recruited, with 198 subjects (117 MDR and 81 susceptible) in the discovery cohort, and 198 patients (95 MDR and 103 susceptible) in the validation cohort. Plasma metabolomic profiling was performed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Multiple machine learning algorithms were employed to identify differential metabolomic signatures and to construct and validate multi-metabolite models for the early identification of MDR bacteria.

Results: Distinct metabolomic signatures were identified for both MDR G- and G+ infections. MDR G- sepsis showed significant elevations in metabolites related to host inflammatory responses, such as histamine, alongside decreased levels of gut microbiota-derived metabolites, including cholic acid and benzoic acid, indicating profound host-microbe dysregulation. Conversely, MDR G+ sepsis was characterized by alterations in energy and amino acid metabolism, notably elevated 2-hydroxyglutarate, a marker of mitochondrial stress. An 8-metabolite model for MDR G- infection achieved excellent discrimination in both the discovery (AUROC = 0.885, 95% CI: 0.787-0.982) and validation (AUROC = 0.878, 95% CI: 0.782-0.951) cohorts. The model for MDR G+ infection demonstrated good predictive performance (AUROC = 0.763 and 0.715 in discovery and validation, respectively).

Conclusion: This study identifies robust and distinct plasma metabolomic signatures that differentiate MDR from antibiotic-susceptible sepsis. These findings support the development of rapid, metabolomics-based testing using admission plasma to risk-stratify patients. This approach could guide early, stewardship-aligned antimicrobial decisions while conventional culture results are pending, potentially improving clinical outcomes.

Introduction: Blood microsampling (BμS) has emerged as an alternative to invasive sampling methods, including blood and plasma sampling. Several studies have shown that BμS are suitable alternatives for analyzing endogenous metabolites and for metabolomics applications. Dried blood spots (DBS) have long been used for clinical applications, particularly for newborn screening. New quantitative BμS have emerged, including volumetric absorptive microsampling (VAMS).

Objectives: We aimed to develop an extraction protocol from BµS for non-targeted metabolomics analysis using a reversed-phase liquid chromatography/mass spectrometry (RPLC-MS) method for the mid- to non-polar metabolome and a hydrophilic interaction chromatography/mass spectrometry (HILIC-MS) method for the polar metabolome, based on existing protocols from the literature. To improve coverage, two new HILIC-MS methods have been developed.

Methods: We used an in-house RPLC-MS method for the analysis of mid- to non-polar metabolites. Two new HILIC-MS/MS methods were developed using 73 chemical reference standards of polar metabolites from various classes. To optimize extraction, five procedures were investigated and compared to identify the most appropriate protocol for extracting metabolites from BµS for non-targeted metabolomics analysis. The final workflow was optimized on both DBS and VAMS.

Results and conclusion: We developed and optimized a 15-minute HILIC-MS method that included column re-equilibration. Our experiments showed that using a 20% H₂O/80% MeOH (v/v) mixture for extraction, with sample rehydration, is a good compromise for detecting many metabolite features. Our extraction and LC-MS methodology covered metabolites from many pathways, including amino acids, acylcarnitines, and bile acids.

Background: The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof.

Aim of review: In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers "identified" and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication.

Key scientific concepts of review: Poor metabolite identification potentially represents an existential threat to the credibility of untargeted "discovery" metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.