The role of metabolites and metabolomics in clinically applicable biomarkers of disease

GMAMDA: Predicting Metabolite-Disease Associations Based on Adaptive Hardness Negative Sampling and Adaptive Graph Multiple Convolution

Metabolites are small molecules produced during organism metabolism, with their abnormal concentrations closely linked to the onset and progression of various diseases. Accurate prediction of metabolite-disease associations is crucial for early diagnosis, mechanistic exploration, and treatment optimization. However, existing algorithms often overlook the integration of node features and neglect the impact of different hop domains on nodes in the processing of heterogeneous graphs. Furthermore, current methods solely rely on random sampling for selecting negative samples without considering their reliability, thereby compromising model stability. A novel metabolite-disease association prediction model, GMAMDA, is proposed to address these challenges. GMAMDA integrates adaptive hardness negative sampling, adaptive graph multiple convolution techniques, and a multiheterogeneous graph fusion strategy to forecast potential metabolite-disease associations. Initially, by computing multisource similarity information for metabolites and diseases, multiple heterogeneous graph networks are established for metabolite-disease association networks. Subsequently, the adaptive graph's multiconvolution mechanism is employed to generate feature-rich node representations across various heterogeneous graphs by dynamically leveraging information from different hop neighborhoods. The model then utilizes an adaptive hardness negative sampling approach based on principal component analysis to select negative samples with the highest information content for training, enabling the prediction of potential associations between new metabolites and diseases. Experimental findings demonstrate that GMAMDA outperforms state-of-the-art methods across various evaluation metrics, including AUC (0.9962 ± 0.0014), AUPR (0.9967 ± 0.0009), and accuracy (0.9733 ± 0.0042). Case studies focusing on Alzheimer's disease and kidney disease further validate GMAMDA's clinical potential in predicting metabolite markers.

Altered aminoacid and lipid metabolism in a rat orofacial inflammation model determined by omics approach: potential role in trigeminal sensitisation

BACKGROUND

Trigeminal activation and sensitisation involved in chronic inflammatory orofacial pain share several similarities with headaches, including migraine. Therefore, understanding the pathophysiological mechanisms is important to determine novel therapies, in which animal models are crucial. Here we aimed to identify key mediators, mechanisms and networks using unbiased multi-omic approaches in a rat orofacial inflammatory pain model.

METHODS

Complete Freund's Adjuvant (CFA, 50 µl, 1 mg/mL) was injected into the right whisker pad of male Wistar rats (n = 5-11/group), mechanonociceptive threshold was measured by von Frey filaments. Plasma concentrations of metabolites were measured both by targeted (MxP Quant 500 kit) and untargeted mass spectrometry methods on day 3 when maximal facial allodynia developed. Next-generation sequencing of the trigeminal ganglia (TG) was performed, furthermore, transcriptomic and plasma metabolomic data were analysed together.

RESULTS

Plasma carnosine, serotonin and fatty acid levels significantly increased, while tryptophan, kynurenine, tyrosine, phenylalanine, asparagine, glycerolipids, and sphingolipids decreased in response to orofacial inflammation. CFA upregulated the Cxcr3 chemokine receptor and downregulated GNRHR in the TG. Bioinformatic analysis revealed altered amino acid metabolism and fatty acid beta-oxidation involved in mitochondrial energy production, neuroinflammation and immune responses.

CONCLUSIONS

Integrated joint pathway analysis of metabolomic and transcriptomic data provides a useful approach to determine pathophysiological mechanisms of trigeminal sensitization and identify novel drug targets for orofacial pain and headaches.

Pharmacometabolomics: An emerging platform for understanding the pathophysiological processes and therapeutic interventions

Pharmacometabolomics has emerged as a new subclass of metabolomics, aiming to predict an individual's response to a drug or optimize therapy based on prior information on an individual's metabolic profile. Pharmacometabolomics is being explored in drug discovery, biomarker identification, disease diagnosis, monitoring of disease progression, and therapeutic intervention. The time points-based sample collection is essential to measure the response of individuals to pathophysiological processes and therapeutic interventions. Analytical techniques such as NMR, LC-MS, and GC-MS have been employed to assess a huge number of metabolites present in biological systems. NMR has an advantage over other analytical techniques as it provides a snapshot of tissue and biological fluids, however, it requires higher magnetic fields to achieve better resolution. GC-MS could cover a wide range of metabolites due to high resolution but requires derivatization for certain metabolites. LC-MS is equally competitive and separates a wide range of metabolites with diverse polarities but requires extensive method development. Several platforms have been developed to analyze the analytical data and provide meaningful results via data reduction methods. PCA and PLS-DA are the most common methods for reduction dimensionality through simplified multivariate data modeling. This manuscript brings insights into the overview of pharmacometabolomics experimental design and the application of various analytical techniques and multivariate statistical analysis in the various fields of medical research.

Potential biomarkers develop for predicting the prognosis of patients with esophageal squamous cell carcinoma after optimized chemoradiotherapy using serum metabolomics

BACKGROUND

Esophageal squamous cell carcinoma (ESCC), the most common type of esophageal cancer, characterized by low five-year survival rate, and concurrent chemoradiotherapy (CCRT) has been proposed to treat ESCC, while potential biomarkers for prognostic monitoring after optimized CCRT remains unknown.

METHODS

Serum samples from 45 patients with ESCC were collected and categorized into three groups: Control (pre-CCRT), CCRT (during CCRT), and CCRT-1 M (one-month post-CCRT). The therapeutic effect was evaluated using CT imaging and established evaluation criteria. Untargeted metabolomic analysis was performed on the serum samples to identify differential metabolites caused by CCRT treatment, assessing their potential for prognostic monitoring.

RESULTS

CCRT had significant therapeutic efficacy in patients with ESCC, as indicated by CT imaging and RECIST 1.1 solid tumor evaluation criteria. Notably, several metabolic markers were identified through non-targeted metabolomic analysis, highlighting changes following CCRT treatment. These differential metabolites are involved in the dysregulation of phenylalanine, tyrosine, and tryptophan biosynthesis, as well as histidine, arginine, and proline metabolism, and glycine, serine, and threonine metabolism, suggesting a reduction in glucose metabolism in patients with ESCC after CCRT. Additionally, ROC analysis indicated that the AUC of these metabolites exceeded 0.661, underscoring their diagnostic value for assessing CCRT efficacy and their potential use in prognostic monitoring. Comparative metabolomic analysis identified L-phenylalanine and lysine as promising serum biomarkers for predicting therapeutic outcomes.

CONCLUSIONS

CCRT shows considerable therapeutic benefit in patients with ESCC, with observed reductions in glucose metabolism post-treatment. L-phenylalanine and lysine may serve as potential serum biomarkers to predict CCRT efficacy.

Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis

Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for early diagnosis. This study aims to identify promising biomarkers for the early diagnosis of AL amyloidosis. Peripheral venous blood samples from 70 newly diagnosed systemic AL amyloidosis patients, 48 newly diagnosed multiple myeloma (MM) patients, and 29 healthy controls (HCs) were analyzed using high-performance liquid chromatography-mass spectrometry. Metabolomic profiling revealed distinct metabolic differences between the AL group and the controls (HCs and MM). Machine learning further identified that phytosphingosine and asymmetric dimethylarginine were significantly up-regulated in the AL group compared with HCs group, with area under curve (AUC) values of 0.990 and 0.904, sensitivity and specificity of (97%, 100%) and (88%, 93%), respectively. Compared with MM group, phytosphingosine was also significantly up-regulated in the AL group, with an AUC value of 0.779, sensitivity and specificity of (62%, 88%). Pathway analysis showed significant changes in starch and sucrose metabolism pathway, as well as pentose and glucuronate interconversions pathway between the AL and the controls. Metabolomics combined with machine learning identified phytosphingosine as a promising biomarker for early diagnosis of AL amyloidosis. Two metabolic pathways (starch and sucrose metabolism, pentose and glucuronate interconversions) may reflect the key pathological processes involved in the development and progression of AL amyloidosis. Further confirmation studies are warranted to validate its value in this field.

Innovations in acute and chronic pain biomarkers: enhancing diagnosis and personalized therapy

Pain affects millions worldwide, posing significant challenges in diagnosis and treatment. Despite advances in understanding pain mechanisms, there remains a critical need for validated biomarkers to enhance diagnosis, prognostication, and personalized therapy. This review synthesizes recent advancements in identifying and validating acute and chronic pain biomarkers, including imaging, molecular, sensory, and neurophysiological approaches. We emphasize the emergence of composite, multimodal strategies that integrate psychosocial factors to improve the precision and applicability of biomarkers in chronic pain management. Neuroimaging techniques like MRI and positron emission tomography provide insights into structural and functional abnormalities related to pain, while electrophysiological methods like electroencepholography and magnetoencepholography assess dysfunctional processing in the pain neuroaxis. Molecular biomarkers, including cytokines, proteomics, and metabolites, offer diagnostic and prognostic potential, though extensive validation is needed. Integrating these biomarkers with psychosocial factors into clinical practice can revolutionize pain management by enabling personalized treatment strategies, improving patient outcomes, and potentially reducing healthcare costs. Future directions include the development of composite biomarker signatures, advances in artificial intelligence, and biomarker signature integration into clinical decision support systems. Rigorous validation and standardization efforts are also necessary to ensure these biomarkers are clinically useful. Large-scale collaborative research will be vital to driving progress in this field and implementing these biomarkers in clinical practice. This comprehensive review highlights the potential of biomarkers to transform acute and chronic pain management, offering hope for improved diagnosis, treatment personalization, and patient outcomes.

A Protocol for Determination of Proteinogenic Amino Acids in Biological Fluids by the High-Speed UHPLC-MS Method: Application on Transgenic Spontaneously Hypertensive Rat-24 Plasma and Cerebrospinal Fluid Samples

Recently, proteinogenic amino acids have become very interesting molecules, accompanied by a large variety of metabolic processes in humans and are associated with various diseases. In the era of system biology, including a broad spectrum of associated disciplines (e.g., metabolomics, lipidomics, proteomics, etc.), the possibility of identifying trustworthy biomarkers of diseases becomes much more likely. Changes in amino acid levels in plasma, serum, or cerebrospinal fluid reflect physiological or pathological conditions and, therefore, their regular monitoring can lead to early detection of the occurrence of a disease. Therefore, the exact determination of amino acids in biological fluids is of great importance. However, it is necessary to dispose with an effective, accurate, precise, selective, and robust analytical method. This protocol describes the complex procedure of amino acid analysis based on a combination of UHPLC with single quadrupole MS. The protocol presents a highly reproducible and robust methodology that has already been established in the quality control of biopharmaceuticals and determination of proteinogenic amino acids in urine in our laboratory. Here, the application potential is extended to the most frequently investigated biological fluid, that is, plasma and to the cerebrospinal fluid, which is investigated in many neurological conditions.

Precision Dietary Intervention: Gut Microbiome and Meta-metabolome as Functional Readouts

Gut microbiome, the group of commensals residing within the intestinal tract, is closely associated with dietary patterns by interacting with food components. The gut microbiome is modifiable by the diet, and in turn, it utilizes the undigested food components as substrates and generates a group of small molecule-metabolites that addressed as "meta-metabolome" in this review. Profiling and mapping of meta-metabolome could yield insightful information at higher resolution and serve as functional readouts for precision nutrition and formation of personalized dietary strategies. For assessing the meta-metabolome, sample preparation is important, and it should aim for retrieval of gut microbial metabolites as intact as possible. The meta-metabolome can be investigated via untargeted and targeted meta-metabolomics with analytical platforms such as nuclear magnetic resonance spectroscopy and mass spectrometry. Employing flux analysis with meta-metabolomics using available database could further elucidate metabolic pathways that lead to biomarker discovery. In conclusion, integration of gut microbiome and meta-metabolomics is a promising supplementary approach to tailor precision dietary intervention. In this review, relationships among diet, gut microbiome, and meta-metabolome are elucidated, with an emphasis on recent advances in alternative analysis techniques proposed for nutritional research. We hope that this review will provide information for establishing pipelines complementary to traditional approaches for achieving precision dietary intervention.

Metabolome Alterations Associated with Three-Month Sitting-Time Reduction Among Sedentary Postmenopausal Latinas with Cardiometabolic Disease Risk

Background: Incidence of cardiometabolic disease among U.S. Hispanics/Latinos is higher than in non-Hispanic Whites. Prolonged sitting duration is prevalent in older adults, and compounded with menopause, greatly increases cardiometabolic risk in postmenopausal women. Metabolomic analyses of interventions to reduce sitting are lacking and mechanistic understanding of health-promoting behavior change in postmenopausal Latinas is needed. Methods: To address this knowledge gap, an exploratory analysis investigated the plasma metabolome impact of a 12-week increased standing intervention among sedentary postmenopausal Latinas with overweight or obesity. From a parent-randomized controlled trial, a subset of Best Responders (n = 43) was selected using parameters of highest mean change in sitting bout duration and total sitting time; baseline variable-Matched Controls (n = 43) were selected using random forest modeling. Targeted LC-MS/MS analysis of archived baseline and 12-week plasma samples was conducted. Metabolite change was determined using a covariate-controlled general linear model and multivariate testing was performed. A false discovery rate correction was applied to all analyses. Results: Best Responders significantly changed time sitting (-110.0 ± 11.0 min; -21%), standing (104.6 ± 10.1 min; 40%), and sitting in bouts >30 min (-102.3 ± 13.9 min; -35%) compared to Matched Controls (7.1 ± 9.8 min, -7.8 ± 9.0 min, and -4.6 ± 12.7 min, respectively; all p < 0.001). Twelve-week metabolite change was significantly different between the two groups for 24 metabolites (FDR < 0.05). These were primarily related to amino acid metabolism, improved blood flow, and ATP production. Enzyme enrichment analysis predicted significant changes regulating glutamate, histidine, phenylalanine, and mitochondrial short-chain fatty acid catabolism. Pathway analysis showed significant intervention effects on glutamate metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis, potentially indicating reduced cardiometabolic disease risk. Conclusions: Replacing nearly two hours of daily sitting time with standing and reduced prolonged sitting bouts significantly improved metabolomic profiles associated with cardiometabolic risk among postmenopausal Latinas.

Bioinert UHPLC system improves sensitivity and peak shapes for ionic metabolites

The analysis of ionic compounds by liquid chromatography is challenging due to the interaction of analytes with the metal surface of the instrument and the column, leading to poor peak shape and decreased sensitivity. The use of bioinert materials in the chromatographic system minimizes these unrequired interactions. In this work, the ultrahigh-performance liquid chromatography (UHPLC) with bioinert components was connected to a high-resolution mass spectrometer to develop a method for untargeted metabolomic analysis. 81 standards of metabolites were used for the development and optimization of the method. In comparison to the conventional chromatographic system, the application of bioinert technology resulted in significantly improved peak shapes and increased sensitivity, especially for metabolites containing phosphate groups. The calibration curves were constructed for the evaluation of the method performance, showing a wide dynamic range, low limit of detection, and linear regression coefficients higher than 0.99 for all standards. The optimized method was applied to the analysis of NIST SRM 1950 human plasma, which allowed the detection of 156 metabolites and polar lipids based on the combination of mass accuracy in the full-scan mass spectra in both polarity modes, characteristic fragment ions in MS/MS, and logical chromatographic behavior leading to the high confidence level of annotation/identification. We have demonstrated an improvement in the peak shapes and sensitivity of ionic metabolites using bioinert technology, which indicates the potential for the analysis of other ionic compounds, e.g., molecules containing phosphate groups.

Metabolomics Revealed Cadmium Exposure Associated with Alterations in Serum Metabolism in Children

Cadmium is a heavy metal contaminant known to cause various health issues. However, limited research exists on the serum metabolomic effects of cadmium exposure in children. In this study, we recruited 42 children to analyze their serum metabolomic profiles, along with measuring urinary cadmium and creatinine concentrations, to evaluate the impact of environmental cadmium exposure on serum metabolism. We also screened for potential biomarkers. The findings revealed that environmental cadmium exposure led to disruptions in amino acid metabolism, biosynthesis of secondary metabolites, endocrine function, lipid metabolism, nervous system function, sensory processes, and the metabolism of cofactors and vitamins in children. Lansioside C, Hydroxytanshinone, and 1-Methylinosine were identified as potential biomarkers. In conclusion, environmental cadmium exposure negatively impacts children's neurological development by inducing metabolic disturbances and increasing the risk of oxidative stress-related disorders. This study provides a valuable theoretical foundation for future efforts to prevent the harmful effects of cadmium exposure in children and mitigate associated health risks.

Combined urine proteomics and metabolomics analysis for the diagnosis of pulmonary tuberculosis

BACKGROUND

Tuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis.

METHODS

In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis.

RESULTS

A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570.

CONCLUSION

Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

In Situ Array Microextraction and Metabolic Profiling of Small Extracellular Vesicles to Guide and Monitor Maternal Delivery

The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large-scale extraction and high-throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on-plate aptamer array (16×24 spots) is developed for continuous scale-up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5 h) and sample volume by 95% (only 0.125 µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post-administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post-administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real-time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large-scale clinical samples for medicine innovation and advancement.

Exploring serum amino acid signatures as potential biomarkers in Hashimoto's thyroiditis patients

Hashimoto's thyroiditis (HT) is an autoimmune disease caused by the immune system attacking healthy tissues. However, the exact pathogenesis of HT remains unclear. Metabolomic analysis was performed to obtain information about the possible pathogenic mechanisms and diagnostic biomarkers of HT. The amino acid profile was analyzed using an LC-MS/MS method using serum samples obtained from 30 patients diagnosed with ultrasonographic imaging and laboratory markers (thyroid stimulating hormone) free thyroxine and thyroid peroxidase) and 30 healthy individuals. There were statistically significant changes in 27 amino acids out of 32 amino acids analyzed (p < 0.05). Based on the receiver operating characteristic curve analysis, the six amino acid (1-methylhistidine, cystine, norvaline, histidine, glutamic acid and leucine) biomarkers showed high sensitivity, specificity (area under the curve > 0.98), positive likelihood ratio and low negative likelihood ratio. Also, according to pathway analysis, degradation of phenylalanine, tyrosine and tryptophan biosynthesis was the highest metabolic pathway according to the impact value (p < 0.001 and impact value = 1.0). We provide serum amino acid profiles of patients with Hashimoto's thyroiditis and identify five potential biomarkers for early diagnosis by clinicians.

Cellular metabolomics study of the antitumor mechanism of Sijunzi decoction combined with mitomycin C

Mitomycin C (MMC) has an antitumor effect and is considered as a broad-spectrum antibiotic. Sijunzi Decoction (SJZD), a well-known ancient Chinese prescription, is widely used in the treatment of cancer when combined with chemotherapy drugs. Studies have shown that SJZD can be combined with other drugs to enhance the therapeutic effect against cancer and inhibit the toxicity of chemotherapy drugs, but the specific mechanism is not clear. Thus, we hope to further explore the antitumor mechanism of combined SJZD and MMC. 3-(4,5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide assay, flow cytometry, western blot, 1H NMR and HPLC-MS were used to study the mechanism at the cellular level. The results show that the combined administration can have a more significant effect on inhibiting the proliferation of cancer cells, promoting their apoptosis. Based on metabolomics, 38 biomarkers were found in the MMC group and 43 biomarkers were found in the combined administration group. Among them, 18 unique biomarkers were discovered in the combined administration group. Studies have shown that the antitumor mechanism of combined administration is related to amino acid metabolism, energy metabolism, lipid metabolism and nucleotide metabolism, among which amino acid metabolism is the most important. In addition, SJZD achieves the effect of toxin reduction and efficiency enhancement by improving the body's immunity and improving the oxidative stress environment.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Sitting Interruption Modalities during Prolonged Sitting Acutely Improve Postprandial Metabolome in a Crossover Pilot Trial among Postmenopausal Women

Older adults sit during most hours of the day; more than 30% are considered physically inactive. The accumulation of prolonged sitting time is an exercise-independent risk factor for aging-related conditions such as cardiometabolic disease and cancer. Archival plasma samples from a randomized controlled, four-condition crossover study conducted in 10 postmenopausal women with overweight or obesity were analyzed. During 5-hour conditions completed on separate days, the trial tested three interruption modalities: two-minute stands each 20 min (STS), hourly ten-minute standing breaks (Stand), hourly two-minute walks (Walk), and a controlled sit. Fasting baseline and 5-hour end point (2 h postprandial) samples were used for targeted metabolomic profiling. Condition-associated metabolome changes were compared using paired t-tests. STS eliminated the postprandial elevation of amino acid metabolites that was observed in the control. A norvaline derivative shown to have anti-hypertensive and -hyperglycemic effects was significantly increased during Stand and STS. Post-hoc testing identified 19 significantly different metabolites across the interventions. Tight metabolite clustering by condition was driven by amino acid, vasoactive, and sugar metabolites, as demonstrated by partial least squares-discriminant analyses. This exploratory study suggests that brief, low-intensity modalities of interrupting prolonged sitting can acutely elucidate beneficial cardiometabolic changes in postmenopausal women with cardiometabolic risk.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Metabolomic Profiles in Jamaican Children With and Without Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a wide range of behavioral and cognitive impairments. While genetic and environmental factors are known to contribute to its etiology, metabolic perturbations associated with ASD, which can potentially connect genetic and environmental factors, remain poorly understood. Therefore, we conducted a metabolomic case-control study and performed a comprehensive analysis to identify significant alterations in metabolite profiles between children with ASD and typically developing (TD) controls in order to identify specific metabolites that may serve as biomarkers for the disorder. We conducted metabolomic profiling on plasma samples from participants in the second phase of Epidemiological Research on Autism in Jamaica, an age and sex-matched cohort of 200 children with ASD and 200 TD controls (2-8 years old). Using high-throughput liquid chromatography-mass spectrometry techniques, we performed a targeted metabolite analysis, encompassing amino acids, lipids, carbohydrates, and other key metabolic compounds. After quality control and missing data imputation, we performed univariable and multivariable analysis using normalized metabolites while adjusting for covariates, age, sex, socioeconomic status, and child's parish of birth. Our findings revealed unique metabolic patterns in children with ASD for four metabolites compared to TD controls. Notably, three metabolites were fatty acids, including myristoleic acid, eicosatetraenoic acid, and octadecenoic acid. The amino acid sarcosine exhibited a significant association with ASD. These findings highlight the role of metabolites in the etiology of ASD and suggest opportunities for the development of targeted interventions.

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

NMR Precision Metabolomics: Dynamic Peak Sum Thresholding and Navigators for Highly Standardized and Reproducible Metabolite Profiling of Clinical Urine Samples

Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) 1H-13C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

Metabolites: a converging node of host and microbe to explain meta-organism

Meta-organisms encompassing the host and resident microbiota play a significant role in combatting diseases and responding to stress. Hence, there is growing traction to build a knowledge base about this ecosystem, particularly to characterize the bidirectional relationship between the host and microbiota. In this context, metabolomics has emerged as the major converging node of this entire ecosystem. Systematic comprehension of this resourceful omics component can elucidate the organism-specific response trajectory and the communication grid across the ecosystem embodying meta-organisms. Translating this knowledge into designing nutraceuticals and next-generation therapy are ongoing. Its major hindrance is a significant knowledge gap about the underlying mechanisms maintaining a delicate balance within this ecosystem. To bridge this knowledge gap, a holistic picture of the available information has been presented with a primary focus on the microbiota-metabolite relationship dynamics. The central theme of this article is the gut-brain axis and the participating microbial metabolites that impact cerebral functions.

Black raspberry-mediated metabolic changes in patients with familial adenomatous polyposis associated with rectal polyp regression

Familial adenomatous polyposis (FAP) patients face an almost certain 100% risk of developing colorectal cancer, necessitating prophylactic colectomy to prevent disease progression. A crucial goal is to hinder this progression. In a recent clinical trial involving 14 FAP patients, half received 60 g of black raspberry (BRB) powder orally and BRB suppositories at bedtime, while the other half received only BRB suppositories at bedtime over 9 months. This intervention led to a notable reduction in rectal polyps for 11 patients, although 3 showed no response. In this study, we delved into the metabolic changes induced by BRBs in the same patient cohort. Employing mass spectrometry-based non-targeted metabolomics, we analyzed pre- and post-BRB urinary and plasma samples from the 11 responders. The results showed significant alterations in 23 urinary and 6 plasma metabolites, influencing various pathways including polyamine, glutathione metabolism, the tricarboxylic acid cycle, inositol metabolism, and benzoate production. BRBs notably elevated levels of several metabolites associated with these pathways, suggesting a potential mechanism through which BRBs facilitate rectal polyp regression in FAP patients by modulating multiple metabolic pathways. Notably, metabolites derived from BRB polyphenols were significantly increased post-BRB intervention, emphasizing the potential therapeutic value of BRBs in FAP management.

Serological Exosome Metabolic Biopsy of Hepatocellular Carcinoma via Designed Core-Shell Nanoparticles

Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.

Integrative polygenic analysis of the protective effects of fatty acid metabolism on disease as modified by obesity

Dysregulation of fatty acid metabolites can play a crucial role in the progression of complex diseases, such as cardiovascular disease, digestive diseases, and metabolic diseases. Metabolites can have either protective or risk effects on a disease; however, the details of such associations remain contentious. In this study, we demonstrate an integrative PheWAS approach to establish high confidence, causally suggestive of metabolite-disease associations for three fatty acid metabolites, namely, omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, for 1,254 disease endpoints. Metabolite-disease associations were established if there was a concordant direction of effect and significance for metabolite level and genetic risk score for the metabolite. There was enrichment for metabolite associations with diseases of the respiratory system for omega-3 fatty acids, diseases of the circulatory system and endocrine system for omega-6 fatty acids, and diseases of the digestive system for docosahexaenoic acid. Upon performing Mendelian randomization on a subset of the outcomes, we identified 3, 6, and 15 significant diseases associated with omega-3 fatty acids, omega-6 fatty acids, and docosahexaenoic acid, respectively. We then demonstrate a class of prevalence-risk relationships indicative of (de)canalization of disease under high and low fatty acid metabolite levels. Finally, we show that the interaction between the metabolites and obesity demonstrates that the degree of protection afforded by fatty acid metabolites is strongly modulated by underlying metabolic health. This study evaluated the disease architectures of three polyunsaturated fatty acids (PUFAs), which were validated by several PheWAS modes of support. Our results not only highlight specific diseases associated with each metabolite but also disease group enrichments. In addition, we demonstrate an integrative PheWAS methodology that can be applied to other components of the human metabolome or other traits of interest. The results of this study can be used as an atlas to cross-compare genetic with non-genetic disease associations for the three PUFAs investigated. The findings can be explored through our R shiny app at https://pufa.biosci.gatech.edu.

Lipid metabolic vulnerabilities of multiple myeloma

Multiple myeloma (MM) is the second most common hematological malignancy worldwide, characterized by abnormal proliferation of malignant plasma cells within a tumor-permissive bone marrow microenvironment. Metabolic dysfunctions are emerging as key determinants in the pathobiology of MM. In this review, we highlight the metabolic features of MM, showing how alterations in various lipid pathways, mainly involving fatty acids, cholesterol and sphingolipids, affect the growth, survival and drug responsiveness of MM cells, as well as their cross-talk with other cellular components of the tumor microenvironment. These findings will provide a new path to understanding the mechanisms underlying how lipid vulnerabilities may arise and affect the phenotype of malignant plasma cells, highlighting novel druggable pathways with a significant impact on the management of MM.

Correlations for untargeted GC × GC-HRTOF-MS metabolomics of colorectal cancer

INTRODUCTION

Modern comprehensive instrumentations provide an unprecedented coverage of complex matrices in the form of high-dimensional, information rich data sets.

OBJECTIVES

In addition to the usual biomarker research that focuses on the detection of the studied condition, we aimed to define a proper strategy to conduct a correlation analysis on an untargeted colorectal cancer case study with a data set of 102 variables corresponding to metabolites obtained from serum samples analyzed with comprehensive two-dimensional gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC × GC-HRTOF-MS). Indeed, the strength of association existing between the metabolites contains potentially valuable information about the molecular mechanisms involved and the underlying metabolic network associated to a global perturbation, at no additional analytical effort.

METHODS

Following Anscombe's quartet, we took particular attention to four main aspects. First, the presence of non-linear relationships through the comparison of parametric and non-parametric correlation coefficients: Pearson's r, Spearman's rho, Kendall's tau and Goodman-Kruskal's gamma. Second, the visual control of the detected associations through scatterplots and their associated regressions and angles. Third, the effect and handling of atypical samples and values. Fourth, the role of the precision of the data on the attribution of the ranks through the presence of ties.

RESULTS

Kendall's tau was found the method of choice for the data set at hand. Its application highlighted 17 correlations significantly altered in the active state of colorectal cancer (CRC) in comparison to matched healthy controls (HC), from which 10 were specific to this state in comparison to the remission one (R-CRC) investigated on distinct patients. 15 metabolites involved in the correlations of interest, on the 25 unique ones obtained, were annotated (Metabolomics Standards Initiative level 2).

CONCLUSIONS

The metabolites highlighted could be used to better understand the pathology. The systematic investigation of the methodological aspects that we expose allows to implement correlation analysis to various fields and many specific cases.

Fructose-enabled killing of antibiotic-resistant Salmonella enteritidis by gentamicin: Insight from reprogramming metabolomics

Salmonella enterica is a food-borne pathogen that poses a severe threat to both poultry production and human health. Antibiotics are critical for the initial treatment of bacterial infections. However, the overuse and misuse of antibiotics results in the rapid evolution of antibiotic-resistant bacteria, and the discovery and development of new antibiotics are declining. Therefore, understanding antibiotic resistance mechanisms and developing novel control measures are essential. In the present study, GC-MS-based metabolomics analysis was performed to determine the metabolic profile of gentamicin sensitive (SE-S) and resistant (SE-R) S. enterica. Fructose was identified as a crucial biomarker. Further analysis demonstrated a global depressed central carbon metabolism and energy metabolism in SE-R. The decrease in the pyruvate cycle reduces the production of NADH and ATP, causing a decrease in membrane potential, which contributes to gentamicin resistance. Exogenous fructose potentiated the effectiveness of gentamicin in killing SE-R by promoting the pyruvate cycle, NADH, ATP and membrane potential, thereby increasing gentamicin intake. Further, fructose plus gentamicin improved the survival rate of chicken infected with gentamicin-resistant Salmonella in vivo. Given that metabolite structures are conserved across species, fructose identified from bacteria could be used as a biomarker for breeding disease-resistant phenotypes in chicken. Therefore, a novel strategy is proposed for fighting against antibiotic-resistant S. enterica, including exploring molecules suppressed by antibiotics and providing a new approach to find pathogen targets for disease resistance in chicken breeding.

PFAS Exposures and the Human Metabolome: A Systematic Review of Epidemiological Studies

Purpose of Review

There is a growing interest in understanding the health effects of exposure to per- and polyfluoroalkyl substances (PFAS) through the study of the human metabolome. In this systematic review, we aimed to identify consistent findings between PFAS and metabolomic signatures. We conducted a search matching specific keywords that was independently reviewed by two authors on two databases (EMBASE and PubMed) from their inception through July 19, 2022 following PRISMA guidelines.

Recent Findings

We identified a total of 28 eligible observational studies that evaluated the associations between 31 different PFAS exposures and metabolomics in humans. The most common exposure evaluated was legacy long-chain PFAS. Population sample sizes ranged from 40 to 1,105 participants at different stages across the lifespan. A total of 19 studies used a non-targeted metabolomics approach, 7 used targeted approaches, and 2 included both. The majority of studies were cross-sectional (n = 25), including four with prospective analyses of PFAS measured prior to metabolomics.

Summary

Most frequently reported associations across studies were observed between PFAS and amino acids, fatty acids, glycerophospholipids, glycerolipids, phosphosphingolipids, bile acids, ceramides, purines, and acylcarnitines. Corresponding metabolic pathways were also altered, including lipid, amino acid, carbohydrate, nucleotide, energy metabolism, glycan biosynthesis and metabolism, and metabolism of cofactors and vitamins. We found consistent evidence across studies indicating PFAS-induced alterations in lipid and amino acid metabolites, which may be involved in energy and cell membrane disruption.

Discovery and validation of metabolite markers in bloodstains for bloodstain age estimation

Bloodstain age estimation involves measuring time-dependent changes in the levels of biomolecules in bloodstains. Although several studies have identified bloodstain metabolites as markers for estimating bloodstain age, none have considered sex, age-related metabolomic differences, or long-time bloodstain age. Therefore, we aimed to identify metabolite markers for estimating the age of bloodstains at weekly intervals within 28 days and validate them through multiple reaction monitoring. Adenosine 5'-monophosphate, choline, and pyroglutamic acid were selected as markers. Seven metabolites were validated, including five previously reported metabolites, ergothioneine, hypoxanthine, L-isoleucine, L-tryptophan, and pyroglutamic acid. Choline and hypoxanthine can be used to differentiate bloodstains between days 0 and 14 after deposition at weekly intervals, whereas L-isoleucine and L-tryptophan can help distinguish bloodstains between 7 days before and 14 days after deposition. Evaluation of the changes in metabolite levels according to sex and age revealed that the average levels of all seven metabolites were higher in women on day 0. Moreover, the level of ergothioneine was significantly higher in elderly individuals than in young individuals at all time points. In this study, we confirmed the potential effectiveness of metabolites in bloodstains as forensic markers and provided a new perspective on metabolomic approaches linked to forensic science.

Behavioral metabolomics: how behavioral data can guide metabolomics research on neuropsychiatric disorders

INTRODUCTION

Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging.

OBJECTIVES

This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation.

METHODS

We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features.

RESULTS

MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure.

CONCLUSION

These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

Rapid skin biomarker discovery using hydrogel-phase sampling followed by semi-automated liquid-phase re-extraction high-resolution mass spectrometry

A facile and rapid skin metabolomics protocol is proposed. The liquid microjunction-surface sampling probe system has been partly automated, and used in conjunction with hydrogel probes for skin metabolite analysis. A control device was built to precisely control the segmented solvent flow and analyte re-extraction into the liquid microjunction. This mode provides rapid online re-extraction of the analytes from hydrogel probes. Humectant was added to the hydrogel, and moist heat treatment was used to make the hydrogel probes rugged for sampling in the clinical setting. The developed method was validated for the analysis of choline - a putative biomarker of psoriasis. A linear relationship over six calibration levels from 3.18 × 10^-5 to 3.18 × 10^-4 mol m^-2 has been obtained. The limit of detection was 6.6 × 10^-6 mol m^-2, while the recoveries range from 92 to 109%. The within-run and between-run precision were evaluated and the coefficients of variation range from 1.84 to 7.13%. Furthermore, the developed method has been used to screen patients (n = 10) and healthy participants (control group; n = 10) for psoriasis-related skin metabolites. Metabolomic profiling of the skin excretion-related signals identified potential biomarkers of psoriasis: choline, pipecolic acid, ornithine, urocanic acid, and methionine.

Clinical-Grade Patches as a Medium for Enrichment of Sweat-Extracellular Vesicles and Facilitating Their Metabolic Analysis

Cell-secreted extracellular vesicles (EVs), carrying components such as RNA, DNA, proteins, and metabolites, serve as candidates for developing non-invasive solutions for monitoring health and disease, owing to their capacity to cross various biological barriers and to become integrated into human sweat. However, the evidence for sweat-associated EVs providing clinically relevant information to use in disease diagnostics has not been reported. Developing cost-effective, easy, and reliable methodologies to investigate EVs' molecular load and composition in the sweat may help to validate their relevance in clinical diagnosis. We used clinical-grade dressing patches, with the aim being to accumulate, purify and characterize sweat EVs from healthy participants exposed to transient heat. The skin patch-based protocol described in this paper enables the enrichment of sweat EVs that express EV markers, such as CD63. A targeted metabolomics study of the sweat EVs identified 24 components. These are associated with amino acids, glutamate, glutathione, fatty acids, TCA, and glycolysis pathways. Furthermore, as a proof-of-concept, when comparing the metabolites' levels in sweat EVs isolated from healthy individuals with those of participants with Type 2 diabetes following heat exposure, our findings revealed that the metabolic patterns of sweat EVs may be linked with metabolic changes. Moreover, the concentration of these metabolites may reflect correlations with blood glucose and BMI. Together our data revealed that sweat EVs can be purified using routinely used clinical patches, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant disease biomarkers. This study thus provides a proof-of-concept towards a novel methodology that will focus on the use of the sweat EVs and their metabolites as a non-invasive approach, in order to monitor wellbeing and changes in diseases.

NMR-based metabolomics applied to ecotoxicology with zebrafish (Danio rerio) as a prominent model for metabolic profiling and biomarker discovery: Overviewing the most recent approaches

Metabolomics is an innovative approach used in the medical, toxicological, and biological sciences. As an interdisciplinary topic, metabolomics and its relation with the environment and toxicological research are extensive. The use of substances, such as drugs and pesticides, contributes to the continuous releasing of xenobiotics into the environment, harming organisms and their habitats. In this context, fish are important bioindicators of the environmental condition and have often been used as model species. Among them, zebrafish (Danio rerio) presents itself as a versatile and straightforward option due to its unique attributes for research. Zebrafish proves to be a valuable model for toxicity assays and also for metabolomics profiling by analytical tools. Thus, NMR-based metabolomics associated with statistical analysis can reasonably assist researchers in critical factors related to discovering and validating biomarkers through accurate diagnosis. Therefore, this review aimed to report the studies that applied zebrafish as a model for (eco)toxicological assays and essentially utilized NMR-based metabolomics analysis to assess the biochemical profile and thus suggest the potential biological marker.

Asymmetric dimethyl arginine in captive owl monkeys (Aotus sp)

Only four of 40 animals had measurable asymmetric dimethyl arginine (ADMA) levels. The young primate had the lowest value (53.4 ng/ml) when compared with the two adults (218.8 ± 9.3 ng/ml) and the elderly one (320.5 ng/ml). The ADMA levels in this study may relate to the echocardiographic abnormalities found, and possible hypertensive individuals.

Metabolomic profiling of overnight peritoneal dialysis effluents predicts the peritoneal equilibration test type

This study primarily aimed to evaluate whether peritoneal equilibration test (PET) results can be predicted through the metabolomic analysis of overnight peritoneal dialysis (PD) effluents. From a total of 125 patients, overnight PD effluents on the day of the first PET after PD initiation were analyzed. A modified 4.25% dextrose PET was performed, and the PET type was categorized according to the dialysate-to-plasma creatinine ratio at the 4-h dwell time during the PET as follows: high, high average, low average, or low transporter. Nuclear magnetic resonance (NMR)-based metabolomics was used to analyze the effluents and identify the metabolites. The predictive performances derived from the orthogonal projection to latent structure discriminant analysis (OPLS-DA) modeling of the NMR spectrum were estimated by calculating the area under the curve (AUC) using receiver operating characteristic curve analysis. The OPLS-DA score plot indicated significant metabolite differences between high and low PET types. The relative concentrations of alanine and creatinine were greater in the high transporter type than in the low transporter type. The relative concentrations of glucose and lactate were greater in the low transporter type than in the high transporter type. The AUC of a composite of four metabolites was 0.975 in distinguish between high and low PET types. Measured PET results correlated well with the total NMR metabolic profile of overnight PD effluents.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

[State of the art on the pathophysiology, diagnosis and treatment of non-alcoholic steatohepatitis (NASH)]

NAFLD or non-alcoholic fatty liver disease is one of the complications of obesity and diabetes, the prevalence of which is increasing. The causes of the pathology and its development towards its severe form, NASH or non-alcoholic steatohepatitis, are multiple and still poorly understood. Many different pharmacological classes are being tested in clinical trials to treat NASH, but no pharmaceutical treatment is currently on the market. Moreover, the diagnosis of certainty is only possible by liver biopsy and histological analysis, an invasive procedure with high risk for the patient. It is therefore necessary to better understand the natural history of the disease in order to identify therapeutic targets, but also to identify markers for the diagnosis and monitoring of the disease using a blood sample, which will allow an improvement in patient management.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.

Pre-analytical aspects in metabolomics of human biofluids - sample collection, handling, transport, and storage

Metabolomics is the field of omics research that offers valuable insights into the complex composition of biological samples. It has found wide application in clinical diagnostics, disease investigation, therapy prediction, monitoring of treatment efficiency, drug discovery, or in-depth analysis of sample composition. A suitable study design constitutes the fundamental requirements to ensure robust and reliable results from the study data. The study design process should include a careful selection of conditions for each experimental step, from sample collection to data analysis. The pre-analytical variability that can introduce bias to the subsequent analytical process, decrease the outcome reliability, and confuse the final results of the metabolomics research, should also be considered. Herein, we provide key information regarding the pre-analytical variables affecting the metabolomics studies of biological fluids that are the most desirable type of biological samples. Our work offers a practical review that can serve and guide metabolomics pre-analytical design. It indicates pre-analytical factors, which can introduce artificial data variation and should be identified and understood during experimental design (through literature overview or analytical experiments).

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (TCRC) and normal paired tissues (THC) along with 10 matched fecal samples (FCRC) and 10 healthy controls (FHC). The highest microbial phyla abundance from THC and TCRC were Firmicutes, while the dominant phyla from FHC and FCRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in THC than in TCRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in FHC than in FCRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between THC and TCRC groups and 700 metabolites different between FHC and FCRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Altered lipid metabolites accelerate early dysfunction of T cells in HIV-infected rapid progressors by impairing mitochondrial function

The complex mechanism of immune-system damage in HIV infection is incompletely understood. HIV-infected "rapid progressors" (RPs) have severe damage to the immune system early in HIV infection, which provides a "magnified" opportunity to study the interaction between HIV and the immune system. In this study, forty-four early HIV-infected patients (documented HIV acquisition within the previous 6 months) were enrolled. By study the plasma of 23 RPs (CD4⁺ T-cell count < 350 cells/µl within 1 year of infection) and 21 "normal progressors" (NPs; CD4⁺ T-cell count > 500 cells/μl after 1 year of infection), eleven lipid metabolites were identified that could distinguish most of the RPs from NPs using an unsupervised clustering method. Among them, the long chain fatty acid eicosenoate significantly inhibited the proliferation and secretion of cytokines and induced TIM-3 expression in CD4⁺ and CD8⁺ T cells. Eicosenoate also increased levels of reactive oxygen species (ROS) and decreased oxygen consumption rate (OCR) and mitochondrial mass in T cells, indicating impairment in mitochondrial function. In addition, we found that eicosenoate induced p53 expression in T cells, and inhibition of p53 effectively decreased mitochondrial ROS in T cells. More importantly, treatment of T cells with the mitochondrial-targeting antioxidant mito-TEMPO restored eicosenoate-induced T-cell functional impairment. These data suggest that the lipid metabolite eicosenoate inhibits immune T-cell function by increasing mitochondrial ROS by inducing p53 transcription. Our results provide a new mechanism of metabolite regulation of effector T-cell function and provides a potential therapeutic target for restoring T-cell function during HIV infection.

Cardiometabolic syndrome in HIV-positive and HIV-negative patients at Zewditu Memorial Hospital, Addis Ababa, Ethiopia: a comparative cohort study

Cardiometabolic syndrome (CMetS) has recently emerged as a serious public health concern, particularly for individuals living with chronic conditions. This study aimed to determine the incidence and prevalence of CMetS, as well as the risk factors linked with it, in HIV-positive and HIV-negative adult patients.

Methods

A comparative cohort study was designed. The National Cholesterol Education Program (NCEP) and the International Diabetes Federation (IDF) tools were used to determine the outcome variables. Association studies were done using logistic regression.

Result

CMetS was found to have a greater point and period prevalence, and incidence estimation in HIV-negative than HIV+ patients using both the NCEP and the IDF tools. Using the NCEP tool, the risk of obesity was 44.1% [odds ratio (OR) = 0.559, 95% confidence interval (CI), (0.380-0.824); P = 0.003] lower in HIV+ than in HIV-negative participants. By contrast, no apparent difference was noted using the IDF tool. Similarly, hyperglycemia [OR = 0.651, 95% CI (0.457-0.926); P = 0.017], and hypertension [OR = 0.391, 95% CI (0.271-0.563); P < 0.001] were shown to be lower in HIV+ patients than HIV-negative patients by 34.9% and 60.9%, respectively. The study revealed significant variation in all biomarkers across the follow-up period in both HIV+ and HIV-negative participants, except for SBP.

Conclusions

CMetS caused more overall disruption in HIV-negative people with chronic diseases than in HIV-positive people. All of the indicators used to assess the increased risk of CMetS were equally meaningful in HIV+ and HIV-negative subjects.

Altered Urinary Metabolomics in Hereditary Angioedema

Hereditary angioedema (HAE) is a rare and potentially life-threatening disease with heterogeneous clinical symptoms. The metabolomic profile of HAE remains unknown. Uncovering the metabolic signatures of HAE may provide inspiration for a comprehensive understanding of HAE pathogenesis and may help explore potential new metabolic biomarkers. We performed a comprehensive metabolic analysis using high-performance liquid chromatography−tandem mass spectrometry (HPLC-MS/MS). Urine samples from 34 HAE patients and 82 healthy controls (HCs) were collected to characterize the metabolic signatures associated with HAE. The metabolomes of HAE patients carrying different mutation types were also compared. A total of 795 metabolites were accurately detected and quantified. We considered 73 metabolites as differential metabolites in HAE patients (with an importance in projection (VIP) value > 1.0, q-value < 0.05, and fold change (FC) ≥ 1.2 or FC ≤ 0.8). Several metabolites associated with riboflavin metabolism, the citrate cycle, oxidative stress, and inflammation, including xanthine, oxypurinol, vitamin B2, and isocitrate, were significantly altered in HAE patients. No significantly different metabolites were found in HAE patients carrying different mutation types. The present study highlights that metabolic disturbances in the purine metabolism, riboflavin metabolism, and TCA cycle may be involved in the pathogenesis of HAE. Although biochemical significance requires further experimental verification, these findings may help to identify novel candidate metabolite biomarkers associated with HAE.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Metabolic profiling of induced acute pancreatitis and pancreatic cancer progression in a mutant Kras mouse model

Untargeted Nuclear Magnetic Resonance (NMR) metabolomics of polar extracts from the pancreata of a caerulin-induced mouse model of pancreatitis (Pt) and of a transgenic mouse model of pancreatic cancer (PCa) were used to find metabolic markers of Pt and to characterize the metabolic changes accompanying PCa progression. Using multivariate analysis a 10-metabolite metabolic signature specific to Pt tissue was found to distinguish the benign condition from both normal tissue and precancerous tissue (low grade pancreatic intraepithelial neoplasia, PanIN, lesions). The mice pancreata showed significant changes in the progression from normal tissue, through low-grade and high-grade PanIN lesions to pancreatic ductal adenocarcinoma (PDA). These included increased lactate production, amino acid changes consistent with enhanced anaplerosis, decreased concentrations of intermediates in membrane biosynthesis (phosphocholine and phosphoethanolamine) and decreased glycosylated uridine phosphates, reflecting activation of the hexosamine biosynthesis pathway and protein glycosylation.

Metabolomics profiles in acute-on-chronic liver failure: Unveiling pathogenesis and predicting progression

Acute-on-chronic liver failure (ACLF) usually develops based on acute decompensation (AD) of cirrhosis and is characterized by intense systemic inflammation, multiple organ failure, and high short-term mortality. Validated biomarkers for the diagnosis and prognosis of ACLF remain to be clarified. Metabolomics is an emerging method used to measure low-molecular-weight metabolites and is currently frequently implemented to understand pathophysiological processes involved in disease progression, as well as to search for new diagnostic or prognostic biomarkers of various disorders. The characterization of metabolites in ACLF has recently been described via metabolomics. The role of metabolites in the pathogenesis of ACLF deserves further investigation and improvement and could be the basis for the development of new diagnostic and therapeutic strategies. In this review, we focused on the contributions of metabolomics on uncovering metabolic profiles in patients with ACLF, the key metabolic pathways that are involved in the progression of ACLF, and the potential metabolite-associated therapeutic targets for ACLF.