Metabolomic Biomarkers Research Articles

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Salivary metabolomic biomarkers for non-invasive lung cancer detection.

Identifying novel biomarkers for early detection of lung cancer is crucial. Non-invasively available saliva is an ideal biofluid for biomarker exploration; however, the rationale underlying biomarker detection from organs distal to the oral cavity in saliva requires clarification. Therefore, we analyzed metabolomic profiles of cancer tissues compared with those of adjacent non-cancerous tissues, as well as plasma and saliva samples collected from patients with lung cancer (n = 109 pairs). Additionally, we analyzed plasma and saliva samples collected from control participants (n = 83 and 71, respectively). Capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry were performed to comprehensively quantify hydrophilic metabolites. Paired tissues were compared, revealing 53 significantly different metabolites. Plasma and saliva showed 44 and 40 significantly different metabolites, respectively, between patients and controls. Of these, 12 metabolites exhibited significant differences in all three comparisons and primarily belonged to the polyamine and amino acid pathways; N1-acetylspermidine exhibited the highest discrimination ability. A combination of 12 salivary metabolites was evaluated using a machine learning method to differentiate patients with lung cancer from controls. Salivary data were randomly split into training and validation datasets. Areas under the receiver operating characteristic curve were 0.744 for cross-validation using training data and 0.792 for validation data. This model exhibited a higher discrimination ability for N1-acetylspermidine than that for other metabolites. The probability of lung cancer calculated using this model was independent of most patient characteristics. These results suggest that consistently different salivary biomarkers in both plasma and lung tissues might facilitate non-invasive lung cancer screening.

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank

Aims/hypothesisThe aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers.MethodsFrom a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts.ResultsAt false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts.Conclusions/interpretationAltered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.Graphical

Influence of microbiota-associated metabolic reprogramming on clinical outcome in patients with melanoma from the randomized adjuvant dendritic cell-based MIND-DC trial

Tumor immunosurveillance plays a major role in melanoma, prompting the development of immunotherapy strategies. The gut microbiota composition, influencing peripheral and tumoral immune tonus, earned its credentials among predictors of survival in melanoma. The MIND-DC phase III trial (NCT02993315) randomized (2:1 ratio) 148 patients with stage IIIB/C melanoma to adjuvant treatment with autologous natural dendritic cell (nDC) or placebo (PL). Overall, 144 patients collected serum and stool samples before and after 2 bimonthly injections to perform metabolomics (MB) and metagenomics (MG) as prespecified exploratory analysis. Clinical outcomes are reported separately. Here we show that different microbes were associated with prognosis, with the health-related Faecalibacterium prausnitzii standing out as the main beneficial taxon for no recurrence at 2 years (p = 0.008 at baseline, nDC arm). Therapy coincided with major MB perturbations (acylcarnitines, carboxylic and fatty acids). Despite randomization, nDC arm exhibited MG and MB bias at baseline: relative under-representation of F. prausnitzii, and perturbations of primary biliary acids (BA). F. prausnitzii anticorrelated with BA, medium- and long-chain acylcarnitines. Combined, these MG and MB biomarkers markedly determined prognosis. Altogether, the host-microbial interaction may play a role in localized melanoma. We value systematic MG and MB profiling in randomized trials to avoid baseline differences attributed to host-microbe interactions.

Association of Blood Metabolomics Biomarkers with Brain Metabolites and Patient-Reported Outcomes as a New Approach in Individualized Diagnosis of Schizophrenia.

Given its polygenic nature, there is a need for a personalized approach to schizophrenia. The aim of the study was to select laboratory biomarkers from blood, brain imaging, and clinical assessment, with an emphasis on patients' self-report questionnaires. Metabolomics studies of serum samples from 51 patients and 45 healthy volunteers, based on the liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS/MS), led to the identification of 3 biochemical indicators (cortisol, glutamate, lactate) of schizophrenia. These metabolites were sequentially correlated with laboratory tests results, imaging results, and clinical assessment outcomes, including patient self-report outcomes. The hierarchical cluster analysis on the principal components (HCPC) was performed to identify the most homogeneous clinical groups. Significant correlations were noted between blood lactates and 11 clinical and 10 neuroimaging parameters. The increase in lactate and cortisol were significantly associated with a decrease in immunological parameters, especially with the level of reactive lymphocytes. The strongest correlations with the level of blood lactate and cortisol were demonstrated by brain glutamate, N-acetylaspartate and the concentrations of glutamate and glutamine, creatine and phosphocreatine in the prefrontal cortex. Metabolomics studies and the search for associations with brain parameters and self-reported outcomes may provide new diagnostic evidence to specific schizophrenia phenotypes.

Evaluation of a point-of-use device used for autoantibody analysis and its potential for following microcystin leucine-arginine exposure

Introduction: Globally, the need for measuring exposure to algal toxins has become urgent due to ever-increasing reports of contamination in sea and freshwater, in shellfish and fish stocks and in aerosols.Methods: To address this issue, we evaluated the potential of determining autoantibodies to a panel of biomarkers known to be elevated following exposure to the hepatotoxin microcystin leucine-arginine. The presence of autoantibodies, specific to four selected stress-response, metabolomic and chaperone biomarkers, namely, Heat shock protein 1, Triosephosphate isomerase, Peroxiredoxin 1 and Peroxiredoxin 2 was employed in screening 371 serum samples from microcystin-exposed individuals in Tanzania. In addition, the capacity of the LightDeck fluorescence-based detector, a point-of-use device, to monitor these autoantibody responses in comparison to enzyme-linked immunosorbent assay was evaluated.Results: By using the determination of autoantibodies to this novel panel of biomarkers an altered response was observed following microcystin exposure, with levels generally upregulated. The presence of elevated levels of microcystin leucine-arginine in water, as well as in food sources in Tanzania, may potentially have significant health effects on the population.Discussion: This novel biomarker panel may have potential for the detection of microcystin leucine-arginine exposure as well as various microcystin exposure-associated cancers (e.g., hepatocellular cancer and colorectal cancer). In addition, the utilisation of the LightDeck point-of-use device proved successful for the rapid analysis of this biomarker panel.

Metabolomic Biomarkers for Prognosis in Non-Muscle Invasive Bladder Cancer: A Comprehensive Systematic Review and Meta-Analysis

Metabolomic Biomarkers for Prognosis in Non-Muscle Invasive Bladder Cancer: A Comprehensive Systematic Review and Meta-Analysis

Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women.

Objective: the aim of this study was to identify plasma metabolomic markers of Dietary Approaches to Stop Hypertension (DASH) dietary patterns in pregnant women. Methods: This study included 186 women who had both dietary intake and metabolome measured from a nested case-control study within the NICHD Fetal Growth Studies-Singletons cohort (FGS). Dietary intakes were ascertained at 8-13 gestational weeks (GW) using the Food Frequency Questionnaire (FFQ) and DASH scores were calculated based on eight food and nutrient components. Fasting plasma samples were collected at 15-26 GW and untargeted metabolomic profiling was performed. Multivariable linear regression models were used to examine the association of individual metabolites with the DASH score. Least absolute shrinkage and selection operator (LASSO) regression was used to select a panel of metabolites jointly associated with the DASH score. Results: Of the total 460 known metabolites, 92 were individually associated with DASH score in linear regressions, 25 were selected as a panel by LASSO regressions, and 18 were identified by both methods. Among the top 18 metabolites, there were 11 lipids and lipid-like molecules (i.e., TG (49:1), TG (52:2), PC (31:0), PC (35:3), PC (36:4) C, PC (36:5) B, PC (38:4) B, PC (42:6), SM (d32:0), gamma-tocopherol, and dodecanoic acid), 5 organic acids and derivatives (i.e., asparagine, beta-alanine, glycine, taurine, and hydroxycarbamate), 1 organic oxygen compound (i.e., xylitol), and 1 organoheterocyclic compound (i.e., maleimide). Conclusions: our study identified plasma metabolomic markers for DASH dietary patterns in pregnant women, with most of being lipids and lipid-like molecules.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring.

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Metabolomic biomarkers for (R, S)-ketamine and (S)-ketamine in treatment-resistant depression and healthy controls: A systematic review.

Ketamine is increasingly used for treatment-resistant depression (TRD) while its mechanism of action is still being investigated. In this systematic review, we appraise the current evidence of metabolomic biomarkers for racemic ketamine and esketamine in patients with TRD and healthy controls (HCs). A comprehensive search of several databases (Ovid MEDLINE®, Embase, and Epub Ahead of Print) was performed from each database's inception to June 29, 2022, in any language, was conducted. We included studies wherein the metabolomic biomarkers for racemic ketamine or esketamine were investigated in TRD or HCs. Our main outcomes were to examine changes in metabolites among patients treated with ketamine/esketamine and explore the association with response to ketamine/esketamine. A total of 1859 abstracts were screened of which 11 were included for full-text review. Of these, a total of five articles were included (N = 147), including three RCTs (n = 129) and two open-label trials (n = 18). All studies used racemic ketamine; one study additionally used esketamine. The included studies evaluated patients with treatment-resistant bipolar depression (n = 22), unipolar depression (n = 91), and HCs (n = 34). The included studies reported alteration in several metabolites including acylcarnitines, lipids, kynurenine (KYN), and arginine with ketamine in TRD. Studies suggest the involvement of energy metabolism, KYN, and arginine pathways. In HCs, acetylcarnitine decreased post-infusion, whereas inconsistent findings were observed after the ketamine infusion in TRD patients. This systematic review provides preliminary evidence that ketamine may cause changes in several important pathways involved in energy metabolism and inflammation. Larger and more rigorous studies are needed.

Blood-Derived Metabolic Signatures as Biomarkers of Injury Severity in Traumatic Brain Injury: A Pilot Study.

Metabolomic biomarkers hold promise in aiding the diagnosis and prognostication of traumatic brain injury. In Canada, over 165,000 individuals annually suffer from a traumatic brain injury (TBI), making it one of the most prevalent neurological conditions. In this pilot investigation, we examined blood-derived biomarkers as proxy measures that can provide an objective approach to TBI diagnosis and monitoring. Using a 1H nuclear magnetic resonance (NMR)-based quantitative metabolic profiling approach, this study determined whether (1) blood-derived metabolites change during recovery in male participants with mild to severe TBI; (2) biological pathway analysis reflects mechanisms that mediate neural damage/repair throughout TBI recovery; and (3) changes in metabolites correlate to initial injury severity. Eight male participants with mild to severe TBI (with intracranial lesions) provided morning blood samples within 1-4 days and again 6 months post-TBI. Following NMR analysis, the samples were subjected to multivariate statistical and machine learning-based analyses. Statistical modelling displayed metabolic changes during recovery through group separation, and eight significant metabolic pathways were affected by TBI. Metabolic changes were correlated to injury severity. L-alanine (R= -0.63, p < 0.01) displayed a negative relationship with the Glasgow Coma Scale. This study provides pilot data to support the feasibility of using blood-derived metabolites to better understand changes in biochemistry following TBI.

P852 Metataxonomic and metabolomic profiling to predict response to anti-TNF therapy in Crohn’s Disease

Abstract Background The composition of the gut microbiota is associated with response to treatments in inflammatory bowel disease1. We sought to identify gut microbial and metabolomic biomarkers associated with response to anti-TNF therapy in Crohn’s disease. Methods Faecal and serum samples collected at baseline and week 14 of anti-TNF treatment in the "Predictors of anti-TNF treatment failure" (PANTS) study were used2. Response to treatment (defined clinically and biochemically) was determined at week 142. Faecal samples were processed by GA-map®, with targeted 16S rRNA PCR sequencing. Serum samples were analysed with targeted liquid chromatography tandem mass spectrometry for bile acids and tryptophan metabolites. Multivariate and univariate analyses, as well as Spearman’s rank test, were performed. Results Two-hundred and twenty-four patients with Crohn’s disease commencing anti-TNF therapy were included (54.1% anti-TNF responders, 48.6% female, median age 37.2 (26.5-50.0) years, 56.9% receiving immunomodulator-based treatment). A significantly higher proportion of non-responders received corticosteroid treatment compared to responders at baseline (p=0.0009). Sixty-nine bacterial taxa were identified. Significantly lower abundances of Clostridium difficile and Mycoplasma were identified in responders relative to non-responders at baseline (q-value=0.046 and q-value=0.034, respectively). No significant differences in the abundances of Faecalibacterium prausnitzii, Shigella or Escherichia Coli were found (all q-values &amp;gt; 0.05). Eighteen serum bile acids and thirteen tryptophan metabolites were identified. Significantly higher serum abundances of cholic acid, tryptophan and 3OH-kynurenine were identified in responders at baseline (q-values=0.049, 0.033, and 0.033, respectively). Exploration of microbial–metabolite associations revealed negative correlations between secondary bile acids and several bile salt hydrolase producing bacteria, such as Bifidobacterium (r ≤ -0.50). Positive correlations were also identified between xanthurenic acid and Bifidobacterium and Roseburia intestinalis (r ≥ 0.50). Increased abundances of short-chain fatty acid producing bacteria, such as Bifidobacterium and Bacteroides vulgatus, were noted in responders at week 14. Conclusion We have identified baseline differences in the faecal microbiota and serum metabolome of anti-TNF responders and non-responders. These findings require validation in larger cohorts to determine their robustness for potential use in treatment personalisation.

P140 Serum Metabolomic Biomarkers Can Identify and Characterize Associated Subtypes and Phenotypes in Inflammatory Bowel Disease

Abstract Background Metabolomics is increasingly recognized for uncovering pathophysiology and identifying diagnostic and prognostic biomarkers in inflammatory bowel disease (IBD). This study focuses on exploring comprehensive serum metabolomic profiles and corresponding specific metabolic pathways, in order to differentiate IBD patients from healthy individuals and to accurately identify IBD subtypes (Crohn's disease [CD], ulcerative colitis [UC]) through serum biomarkers with high patient acceptability. Methods Serum samples with matched clinical metadata were comprehensively collected from patients with IBD at Kyung Hee University Hospital, Seoul, Korea. Corresponding samples from normal controls (NCs) were obtained for comparison. Untargeted profiling was performed with Gas Chromatography-Time-Of-Flight-Mass Spectrometry, and targeted profiling of bile acids and tryptophan used Liquid Chromatography-Triple Quadrupole-Mass Spectrometry. The identification of distinct metabolites and potential biomarkers was achieved through extensive univariate and multivariate statistical analyses. Receiver operating characteristic (ROC) curves and metabolic pathway analyses were also conducted. Results A total of 346 subjects were analyzed, comprising 258 IBD patients (134 with CD and 124 with UC) and 88 NCs, Table 1. The patients with IBD were clearly clustered from the NCs, while IBD subgroups were not clearly separated from each other in the nontargeted profiling. Tryptophan and indole-3-acetic acid were elevated in both patients with CD and UC, while kynurenine and indole-3-propionic acid increased only in CD. Conversely, patients with UC had lower levels of indole-3-acetic acid, serotonin, and acetylcholine compared to CD. The ratio of primary and secondary bile acids was also significantly decreased in both patient groups compare with NCs. The ROC curves (Figure 1A) showed good discriminatory power for NCs vs CD (AUC:0.9738), NCs vs UC (AUC:0.9887), and UC vs CD (AUC:0.7140). Pathway analysis of the identified metabolites showed a sustained change in glyoxylate and dicarboxylate metabolism/alanine, aspartate and glutamate metabolism/glycine, serine and threonine metabolism associated with all comparisons (NCs vs UC, NCs vs CD, UC vs CD). Beta-alanine, arginine, and proline metabolism were linked to IBD compared to NCs. Glycerolipid metabolism distinctly differed between UC and CD (Figure 1B-1D). Network analysis revealed associations between metabolomic markers and specific clinical phenotypes of IBD subtypes. Conclusion This study demonstrates that serum metabolomic biomarkers are novel and effective tools for diagnosing IBD, as well as for identifying and characterizing its subtypes and associated phenotypes.

Microfluidics enabled multi-omics triple-shot mass spectrometry for cell-based therapies.

In recent years, cell-based therapies have transformed medical treatment. These therapies present a multitude of challenges associated with identifying the mechanism of action, developing accurate safety and potency assays, and achieving low-cost product manufacturing at scale. The complexity of the problem can be attributed to the intricate composition of the therapeutic products: living cells with complex biochemical compositions. Identifying and measuring critical quality attributes (CQAs) that impact therapy success is crucial for both the therapy development and its manufacturing. Unfortunately, current analytical methods and tools for identifying and measuring CQAs are limited in both scope and speed. This Perspective explores the potential for microfluidic-enabled mass spectrometry (MS) systems to comprehensively characterize CQAs for cell-based therapies, focusing on secretome, intracellular metabolome, and surfaceome biomarkers. Powerful microfluidic sampling and processing platforms have been recently presented for the secretome and intracellular metabolome, which could be implemented with MS for fast, locally sampled screening of the cell culture. However, surfaceome analysis remains limited by the lack of rapid isolation and enrichment methods. Developing innovative microfluidic approaches for surface marker analysis and integrating them with secretome and metabolome measurements using a common analytical platform hold the promise of enhancing our understanding of CQAs across all "omes," potentially revolutionizing cell-based therapy development and manufacturing for improved efficacy and patient accessibility.

Emerging Technologies and Advanced Biomarkers for Enhanced Toxicity Prediction and Safety Pharmacology

Background: Biomarkers are important tools in toxicology that can improve risk assessment, early detection of organ injury, and monitoring of pharmacological responses. However, rigorous validation is necessary to justify use for regulatory decisionmaking. Purpose: This article reviews the current landscape surrounding established and emerging biomarkers of toxicity across multiple techniques, with a focus on assessment of merits, limitations, and validation needs. Main Body: An overview of common clinical chemistry biomarkers used for safety monitoring of liver, kidney, heart, and inflammation is first provided, highlighting their biological relevance and applications in detecting organ dysfunction. Novel protein biomarkers emerging for enhanced sensitivity of injury detection are then discussed, such as microRNAs for liver and clusterin for kidney. Metabolomic biomarker approaches to assess biofluids and mitochondrial toxicity are outlined, along with toxicogenomic markers of susceptibility like HLA alleles. Non-invasive imaging techniques including ultrasound, MRI, and PET tracers for organ function are explored as techniques that provide additional modalities for biomarker measurement. Emerging tools like organ microphysiological systems and high-throughput omics for biomarker discovery are also described. Conclusion: While great progress has been made, translation and qualification of novel biomarkers remains challenging. This review synthesizes key developments across biomarker categories, evaluates readiness for regulatory use, and outlines strategic considerations for fit-for-purpose biomarker validation to advance integration into drug development programs.

Metabolomics-derived biomarkers for biosafety assessment of Gd-based nanoparticle magnetic resonance imaging contrast agents.

With the rapid development of nanotechnology and biomedicine, numerous gadolinium (Gd)-based nanoparticle MRI contrast agents have been widely investigated. Due to the unique physicochemical properties of nanoparticles and the complexity of biological systems, the biosafety of Gd-based nanoparticle MRI contrast agents has been paid more and more attention. Herein, for the first time, we employed an ultra-high performance liquid chromatography-electrospray ionization quadrupole time-of-flight/mass spectrometry (UPLC-ESI-QTOF/MS)-based metabolomics approach to investigate the potential toxicity of Gd-based nanoparticle MRI contrast agents. In this work, NaGdF4 and PEG-NaGdF4 nanoparticles were successfully constructed and selected as the representative Gd-based nanoparticle MRI contrast agents for the metabolomics analysis. Based on the results of metabolomics, more metabolic biomarkers and pathways were identified in the NaGdF4 group than those in the PEG-NaGdF4 group. Careful analysis of these metabolic biomarkers and pathways suggested that NaGdF4 nanoparticles induced disturbance of pyrimidine and purine metabolism, inflammatory response, and kidney injury to a certain extent compared with PEG-NaGdF4 nanoparticles. These results indicated that Gd-based nanoparticle contrast agents modified with PEG had better biosafety. Additionally, it was demonstrated that the discovery of characteristic metabolomics biomarkers induced by nanoparticles would provide a new approach for biosafety assessment and stimulate the development of nanomedicine.

Elucidation for the pharmacological effects and mechanism of Shen Bai formula in treating myocardial injury based on energy metabolism and serum metabolomic approaches

Ethnopharmacological relevanceShen Bai formula (SBF) is a proven effective traditional Chinese medicine for treating viral myocarditis (VMC) sequelae in clinic, and myocardial injury is the pathological basis of VMC sequelae. However, the pharmacological action and mechanism of SBF have not been systematically elucidated. Aim of the studyIn present research, the doxorubicin-induced myocardial injury rat model was used to evaluate the efficacy of SBF, and energy metabolism and metabolomics approaches were applied to elucidate the effects of SBF on myocardial injury. Materials and methodsThrough energy metabolism measurement system and UPLC-Q-TOF-MS/MS oriented blood metabolomics, directly reflected the therapeutic effect of SBF at a macro level, and identified biomarkers of myocardial injury in microcosmic, revealing its metabolomic mechanism. ResultsResults showed that SBF significantly improved the electrocardiogram (ECG), heart rate (HR), extent of myocardial tissue lesion, and ratio of heart and spleen. In addition, the serum levels of AST, CK, LDH, α-HBDH, cTnI, BNP, and MDA decreased, whereas SOD and ATP activity and content increased. Moreover, SBF increased locomotor activity and basic daily metabolism in rats with myocardial injury, restoring their usual level of energy metabolism. A total of 45 potential metabolomic biomarkers were identified. Among them, 44 biomarkers were significantly recalled by SBF, including representative biomarkers arachidonic acid (AA), 12-HETE, prostaglandin J2 (PGJ2), 15-deoxy-Δ-12,14-PGJ2, 15-keto-PGE2, 15(S)-HPETE, 15(S)-HETE, 8,11,14-eicosatrienoic acid and 9(S)-HODE, which involved AA metabolism, biosynthesis of unsaturated fatty acids and linoleic acid metabolism. ConclusionWe successfully replicated a myocardial injury rat model with the intraperitoneal injection of doxorubicin, and elucidated the mechanism of SBF in treating myocardial injury. This key mechanism may be achieved by targeting action on COX, Alox, CYP, and 15-PGDH to increase or decrease the level of myocardial injury biomarker, and then emphatically interven in AA metabolism, biosynthesis of unsaturated fatty acids and linoleic acid metabolism, and participate in regulating purine metabolism, sphingolipid metabolism, primary bile acid biosynthesis, and steroid hormone synthesis.

Explainable Artificial Intelligence Paves the Way in Precision Diagnostics and Biomarker Discovery for the Subclass of Diabetic Retinopathy in Type 2 Diabetics.

Diabetic retinopathy (DR), a common ocular microvascular complication of diabetes, contributes significantly to diabetes-related vision loss. This study addresses the imperative need for early diagnosis of DR and precise treatment strategies based on the explainable artificial intelligence (XAI) framework. The study integrated clinical, biochemical, and metabolomic biomarkers associated with the following classes: non-DR (NDR), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) in type 2 diabetes (T2D) patients. To create machine learning (ML) models, 10% of the data was divided into validation sets and 90% into discovery sets. The validation dataset was used for hyperparameter optimization and feature selection stages, while the discovery dataset was used to measure the performance of the models. A 10-fold cross-validation technique was used to evaluate the performance of ML models. Biomarker discovery was performed using minimum redundancy maximum relevance (mRMR), Boruta, and explainable boosting machine (EBM). The predictive proposed framework compares the results of eXtreme Gradient Boosting (XGBoost), natural gradient boosting for probabilistic prediction (NGBoost), and EBM models in determining the DR subclass. The hyperparameters of the models were optimized using Bayesian optimization. Combining EBM feature selection with XGBoost, the optimal model achieved (91.25 ± 1.88) % accuracy, (89.33 ± 1.80) % precision, (91.24 ± 1.67) % recall, (89.37 ± 1.52) % F1-Score, and (97.00 ± 0.25) % the area under the ROC curve (AUROC). According to the EBM explanation, the six most important biomarkers in determining the course of DR were tryptophan (Trp), phosphatidylcholine diacyl C42:2 (PC.aa.C42.2), butyrylcarnitine (C4), tyrosine (Tyr), hexadecanoyl carnitine (C16) and total dimethylarginine (DMA). The identified biomarkers may provide a better understanding of the progression of DR, paving the way for more precise and cost-effective diagnostic and treatment strategies.

Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis.

A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.

Effects of Zinc Supplementation on Metabolomic Profiles in Tanzanian Infants: A Randomized Trial

BackgroundProvision of zinc supplementation to young children has been associated with reduced infectious morbidity and better growth outcomes. However, the metabolic pathways underlying these outcomes are unclear, and metabolomic data from humans undergoing zinc supplementation, particularly infants, are generally lacking. ObjectivesThis study aimed to examine the effect of zinc supplementation on metabolic profiles in Tanzanian infants aged 6 wk and 6 mo. MethodsBlood samples were collected at age 6 wk and 6 mo from 50 Tanzanian infants who were enrolled in a randomized placebo-controlled trial of zinc supplementation (5 mg oral daily). Metabolomic analysis using an ultrahigh-performance liquid chromatography/tandem mass spectroscopy platform was performed to identify potential metabolomic profiles and biomarkers associated with zinc supplementation. Principal component analysis (PCA) was used to summarize metabolomic data from all samples. Two-way repeated measures analysis of variance with compound symmetry covariance structures were used to compare metabolome levels over time between infants in the 2 treatment arms. ResultsIn PCA, the samples tended to be more separated by child age (6 wk compared with 6 mo) than by zinc supplementation status. We found that zinc supplementation affected a variety of metabolites associated with amino acid, lipid, nucleotide, and xenobiotic metabolism, including indoleacetate in the tryptophan metabolism pathway; 3-methoxytrosine and 4-hydrxoyphenylphruvate in the tyrosine pathway; eicosanedioate, 2-aminooctanoate, and N-acetyl-2-aminooctanoate in the fatty acid pathway; and N6-succinyladenosine in the purine metabolism pathway. Compared to the relatively small number of metabolites associated with zinc supplements, many infant metabolites changed significantly from age 6 wk to 6 mo. ConclusionsZinc supplementation, despite having overall clinical benefits, appears to induce limited metabolomic changes in blood metabolites in young infants. Future larger studies may be warranted to further examine metabolic pathways associated with zinc supplementation.The parent trial was registered at clinicaltrials.gov as NCT00421668.