Metabolomics Profile in Acute Respiratory Distress Syndrome By Nuclear Magnetic Resonance Spectroscopy In Patients With Community-Acquired Pneumonia

Background: Acute respiratory distress syndrome (ARDS) is a challenging clinical problem. To date, no standardized diagnostic biomarker has been validated for community-acquired pneumonia (CAP)-induced ARDS. An integrated analysis of changes in the metabolic profile could help detect biomarkers for early prediction of ARDS development and evaluation of treatment efficacy.Methods: A total of 88 patients were enrolled for the final analysis and divided into two groups: the ARDS group (n = 43) and the no-ARDS group (n = 45). We examined differences in serum and urine metabolites and explored dynamic changes with nuclear magnetic resonance (NMR) spectroscopy.Results: A total of 20 serum and 42 urine metabolites were identified using NMR spectroscopy. Serum metabolites, including leucine, 3-hydroxybutyrate, lactate, acetone, citrate, and choline, and urine metabolites, including creatine and creatinine, could distinguish patients with CAP with and without ARDS, with areas under the receiver operating characteristic curve (AUC) values of 0.790 and 0.747, respectively. The treatment efficacy of patients with ARDS was achieved at an AUC of 0.862 with serum metabolites 3-hydroxybutyrate, lactate, acetone, acetoacetate, citrate, and choline, and of 0.691 with urine metabolites taurine and glucose. The treatment efficacy of patients without ARDS was achieved at an AUC of 0.845 with serum metabolites alanine, acetate, acetoacetate, glutamine, creatine, and glucose and 0.891 with urine metabolites choline, tryptamine, and 3-indoxyl sulfate. We also proposed a combined biomarker of associated serum and urine metabolites to predict ARDS in patients with CAP (AUC = 0.865) and evaluate treatment efficacy for patients with and without ARDS (AUC = 0.921 and 0.893, respectively).Conclusions: Serum and urine analyses showed that metabolomics provides potential circulatory markers for early prediction and evaluation of treatment efficacy in patients with CAP with and without ARDS.

Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage

Urine metabolomics profiling has potential for non-invasive RCC staging, in addition to providing metabolic insights into disease progression. In this study, we utilized liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (NMR), and machine learning (ML) for the discovery of urine metabolites associated with RCC progression. Two machine learning questions were posed in the study: Binary classification into early RCC (stage I and II) and advanced RCC stages (stage III and IV), and RCC tumor size estimation through regression analysis. A total of 82 RCC patients with known tumor size and metabolomic measurements were used for the regression task, and 70 RCC patients with complete tumor-nodes-metastasis (TNM) staging information were used for the classification tasks under ten-fold cross-validation conditions. A voting ensemble regression model consisting of elastic net, ridge, and support vector regressor predicted RCC tumor size with a R2 value of 0.58. A voting classifier model consisting of random forest, support vector machines, logistic regression, and adaptive boosting yielded an AUC of 0.96 and an accuracy of 87%. Some identified metabolites associated with renal cell carcinoma progression included 4-guanidinobutanoic acid, 7-aminomethyl-7-carbaguanine, 3-hydroxyanthranilic acid, lysyl-glycine, glycine, citrate, and pyruvate. Overall, we identified a urine metabolic phenotype associated with renal cell carcinoma stage, exploring the promise of a urine-based metabolomic assay for staging this disease.

Carbon Ion Radiotherapy Evokes a Metabolic Reprogramming and Individualized Response in Prostate Cancer

Introduction: Carbon ion radiotherapy (CIRT) is a novel treatment for prostate cancer (PCa). However, the underlying mechanism for the individualized response to CIRT is still not clear. Metabolic reprogramming is essential for tumor growth and proliferation. Although changes in metabolite profiles have been detected in patients with cancer treated with photon radiotherapy, there is limited data regarding CIRT-induced metabolic changes in PCa. Therefore, the study aimed to investigate the impact of metabolic reprogramming on individualized response to CIRT in patients with PCa.Materials and Methods: Urine samples were collected from pathologically confirmed patients with PCa before and after CIRT. A UPLC-MS/MS system was used for metabolite detection. XCMS online, MetDNA, and MS-DIAL were used for peak detection and identification of metabolites. Statistical analysis and metabolic pathway analysis were performed on MetaboAnalyst.Results: A total of 1,701 metabolites were monitored in this research. Principal component analysis (PCA) revealed a change in the patient's urine metabolite profiles following CIRT. Thirty-five metabolites were significantly altered, with the majority of them being amino acids. The arginine biosynthesis and histidine metabolism pathways were the most significantly altered pathways. Hierarchical cluster analysis (HCA) showed that after CIRT, the patients could be clustered into two groups according to their metabolite profiles. The arginine biosynthesis and phenylalanine, tyrosine, and tryptophan biosynthesis pathways are the most significantly discriminated pathways.Conclusion: Our preliminary findings indicate that metabolic reprogramming and inhibition are important mechanisms involved in response to CIRT in patients with PCa. Therefore, changes in urine metabolites could be used to timely assess the individualized response to CIRT.

Effects of COVID-19 pandemics on urinary metabolites in kidney stone patients: our kidney stone prevention clinic experience

Background The dietary habits and lifestyle changes during the COVID-19 pandemic could affect the urinary risk factors in kidney stone formers. In this study, we investigated the effects of the COVID-19 pandemic on 24-h urine metabolites, as a surrogate for dietary intake, in patients with kidney stones, in Tehran, Iran. Methods We evaluated the medical records of all patients with urolithiasis who visited in our stone prevention clinic from the beginning of COVID-19 in Iran to 1 year later (Feb 2020–Feb 2021) and compared it with the patients’ medical records in the same period a year before COVID-19 (Feb 2019–Feb 2020). Results The results of our stone prevention clinic showed a decrease in the number of visits during COVID-19. Twenty-four-hour urine urea, sodium, and potassium were significantly lower, and 24-h urine magnesium was significantly higher during COVID-19. Higher 24-h urine oxalate was only shown in patients with the first-time visit, whereas lower 24-h urine uric acid and citrate were only shown in patients with the follow-up visits. Conclusions COVID-19 pandemics may change some of the dietary habits of the patients, including lower salt, protein, and fruit and vegetable intake. Although economic issues, restricted access, or sanitation issues may be the reason for the undesirable dietary changes, the importance of a quality diet should be discussed with all patients, as possible. Since the number of patients visited in the stone clinic was lower during COVID-19, virtual visits could be an excellent alternative to motivate patients with kidney stones.

Alterations in Metabolome and Microbiome Associated with an Early Stress Stage in Male Wistar Rats: A Multi-Omics Approach

Stress disorders have dramatically increased in recent decades becoming the most prevalent psychiatric disorder in the United States and Europe. However, the diagnosis of stress disorders is currently based on symptom checklist and psychological questionnaires, thus making the identification of candidate biomarkers necessary to gain better insights into this pathology and its related metabolic alterations. Regarding the identification of potential biomarkers, omic profiling and metabolic footprint arise as promising approaches to recognize early biochemical changes in such disease and provide opportunities for the development of integrative candidate biomarkers. Here, we studied plasma and urine metabolites together with metagenomics in a 3 days Chronic Unpredictable Mild Stress (3d CUMS) animal approach that aims to focus on the early stress period of a well-established depression model. The multi-omics integration showed a profile composed by a signature of eight plasma metabolites, six urine metabolites and five microbes. Specifically, threonic acid, malic acid, alpha-ketoglutarate, succinic acid and cholesterol were proposed as key metabolites that could serve as key potential biomarkers in plasma metabolome of early stages of stress. Such findings targeted the threonic acid metabolism and the tricarboxylic acid (TCA) cycle as important pathways in early stress. Additionally, an increase in opportunistic microbes as virus of the Herpesvirales was observed in the microbiota as an effect of the primary stress stages. Our results provide an experimental biochemical characterization of the early stage of CUMS accompanied by a subsequent omic profiling and a metabolic footprinting that provide potential candidate biomarkers.

Network of Interactions Between Gut Microbiome, Host Biomarkers, and Urine Metabolome in Carotid Atherosclerosis

Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers concerning distinct clinical features. We integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite feature were covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. In a specific note about clinical parameters of liver function, we identified Eubacteriumeligens, Faecalibacteriumprausnitzii and Ruminococcuslactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerellanexills, Ruminococcusgnavus, Blautiahansenii, and Atopobiumparvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases. Deciphering the complexity of human physiology and disease requires a holistic and trans-omics approach integrating multi-layer data sets, including the gut microbiome and profiles of biological fluids. By studying the gut microbiome on carotid atherosclerosis, we identified microbial features associated with clinical parameters, and we observed that groups of urine metabolites correlated with groups of clinical parameters. Combining the three data sets, we revealed correlations of entities across the three systems, suggesting that physiological changes are reflected in each of the omics. Our findings provided insights into the interactive network between the gut microbiome, blood clinical parameters and the urine metabolome concerning physiological variations, and showed the promise of trans-omics study for biomarker discovery.

Characterization of LC-MS Based Urine Metabolomics in Healthy Children and Adults

Previous studies reported that gender, age, diets or lifestyles could influence urine metabolomics, which should be considered in biomarker discovery. As a consequence, for the baseline of urine metabolomics characteristics, it becomes critical to avoid confounding effects in clinical cohort studies. In this study, we provided a comprehensive characterization of urine metabolomics in a cohort of 348 healthy children (Aged 1~18) and 315 adults (Aged 20-78) for evaluation gender and age effects. Our results suggested that urine metabolites showed larger gender differences in children than in adults. For both male/boy and female/girl, each age group showed specific metabolic characterization. Especially, the pantothenate and CoA biosynthesis and alanine metabolism pathways were enriched in early life. Androgen and estrogen metabolism showed high activity during adolescence and youth stages. Pyrimidine metabolism was enriched in the old stage. This work could help us understand the baseline of urine metabolism characteristics and contribute to further studies of clinical disease biomarker discovery.