Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine

Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia

INTRODUCTION

Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient.

OBJECTIVES

In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers.

METHODS

Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated.

RESULTS

Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61).

CONCLUSION

The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker.

Solanidine is a sensitive and specific dietary biomarker for CYP2D6 activity

BACKGROUND

Individual assessment of CYP enzyme activities can be challenging. Recently, the potato alkaloid solanidine was suggested as a biomarker for CYP2D6 activity. Here, we aimed to characterize the sensitivity and specificity of solanidine as a CYP2D6 biomarker among Finnish volunteers with known CYP2D6 genotypes.

RESULTS

Using non-targeted metabolomics analysis, we identified 9152 metabolite features in the fasting plasma samples of 356 healthy volunteers. Machine learning models suggested strong association between CYP2D6 genotype-based phenotype classes with a metabolite feature identified as solanidine. Plasma solanidine concentration was 1887% higher in genetically poor CYP2D6 metabolizers (gPM) (n = 9; 95% confidence interval 755%, 4515%; P = 1.88 × 10-11), 74% higher in intermediate CYP2D6 metabolizers (gIM) (n = 89; 27%, 138%; P = 6.40 × 10-4), and 35% lower in ultrarapid CYP2D6 metabolizers (gUM) (n = 20; 64%, - 17%; P = 0.151) than in genetically normal CYP2D6 metabolizers (gNM; n = 196). The solanidine metabolites m/z 444 and 430 to solanidine concentration ratios showed even stronger associations with CYP2D6 phenotypes. Furthermore, the areas under the receiver operating characteristic and precision-recall curves for these metabolic ratios showed equal or better performances for identifying the gPM, gIM, and gUM phenotype groups than the other metabolites, their ratios to solanidine, or solanidine alone. In vitro studies with human recombinant CYP enzymes showed that solanidine was metabolized mainly by CYP2D6, with a minor contribution from CYP3A4/5. In human liver microsomes, the CYP2D6 inhibitor paroxetine nearly completely (95%) inhibited the metabolism of solanidine. In a genome-wide association study, several variants near the CYP2D6 gene associated with plasma solanidine metabolite ratios.

CONCLUSIONS

These results are in line with earlier studies and further indicate that solanidine and its metabolites are sensitive and specific biomarkers for measuring CYP2D6 activity. Since potato consumption is common worldwide, this biomarker could be useful for evaluating CYP2D6-mediated drug-drug interactions and to improve prediction of CYP2D6 activity in addition to genotyping.

Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review

Endometrial cancer, the most prevalent gynecological malignancy in developed countries, is experiencing a sustained rise in both its incidence and mortality rates, primarily attributed to extended life expectancy and lifestyle factors. Currently, the absence of precise diagnostic tools hampers the effective management of the expanding population of women at risk of developing this disease. Furthermore, patients diagnosed with endometrial cancer require precise risk stratification to align with optimal treatment planning. Metabolomics technology offers a unique insight into the molecular landscape of endometrial cancer, providing a promising approach to address these unmet needs. This comprehensive literature review initiates with an overview of metabolomic technologies and their intrinsic workflow components, aiming to establish a fundamental understanding for the readers. Subsequently, a detailed exploration of the existing body of research is undertaken with the objective of identifying metabolite biomarkers capable of enhancing current strategies for endometrial cancer diagnosis, prognosis, and recurrence monitoring. Metabolomics holds vast potential to revolutionize the management of endometrial cancer by providing accuracy and valuable insights into crucial aspects.

Metabolomics of Personalized Body Elements in Thai Traditional Medicine Response to Herbal Medicine for Body Elements Balancing in Healthy Volunteers

Background

In Thai traditional medicine (TTM), the dominant body element called "Dhat Chao Ruean" (DCR) is an integral part in the diagnostic process of Thai traditional medicine. TTM practitioners usually use Thai herbal Benjakul formula (BKF) for adjusting and balancing the body elements. However, the effects of BKF on metabolism and individual response to it have not been studied yet.

Methods

This study proposed to investigate the metabolic profiling in 24 volunteers categorized by their types of birth month DCR (bDCR) after the administration of BKF (450 mg, three tablets three times a day before meals) for seven days. Differences in metabolic profiling between bDCR groups were investigated by using liquid chromatography coupled with mass spectrometry for untargeted analysis, and in addition, the safety was assessed by testing the plasma biochemical level.

Results

This study identified 57 biomarkers in positive ESI and 12 in negative ESI. Piperine was found in varying amount among the participants but it was the highest in the earth group. In addition, this study found that elemicin, phenylpropionic acid, ricinoleic acid, and β-sitosterol are important substances in a single herb of BKF. Regarding biochemical tests, the results indicated that BKF can decrease the lipid profile and it has no toxic effects on liver and kidney functions.

Conclusion

The findings indicated that it is safe to use BKF which can help to improve health in chronic diseases by adjusting abnormality of the elements of the body. In addition, the information gathered from this study is valuable for further study in the field of Thai traditional medicine.

Kynurenine pathway dynamics in patients with schizophrenia spectrum disorders across the disease trajectory

The aim of this study was to evaluate how schizophrenia spectrum disorders (SSD) and applied long-term (5.1 years) antipsychotic (AP) treatment affect the serum levels of tryptophan (Trp) metabolites. A total of 112 adults (54 first-episode psychosis [FEP] patients and 58 control subjects [CSs]) participated in the study. The investigated changes in the metabolite levels appeared against a background of persistent increase in BMI and waist circumference among the patients. Regarding the kynurenine (KYN) pathway, the strongest changes were seen in AP-naïve FEP patients. Trp, KYN, kynurenic acid (KYNA), and anthranilic acid (ANT) levels were significantly reduced in blood samples from patients in the early stage of the disease. Furthermore, 3-OH-kynurenine (3-HK) and quinolinic acid (QUIN) levels were somewhat lower in these patients. Most of these changes in the KYN pathway became weaker with AP treatment. The levels of serotonin and its metabolite 5-HIAA tended to be higher at 5.1 years in patients showing the relation of elevated serotonin turnover to increased BMI and waist circumference. The similar trend was evident for the ratio between xanthurenic acid (XA) and KYNA with strong link to the elevated BMI. Altogether, the present study supports the role of Trp-metabolites in the development of obesity and metabolic syndrome in SSD patients.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Sperm physiology and in vitro fertilising ability rely on basal metabolic activity: insights from the pig model

Whether basal metabolic activity in sperm has any influence on their fertilising capacity has not been explored. Using the pig as a model, the present study investigated the relationship of energetic metabolism with sperm quality and function (assessed through computer-assisted sperm analysis and flow cytometry), and fertility (in vitro fertilisation (IVF) outcomes). In semen samples from 16 boars, levels of metabolites related to glycolysis, ketogenesis and Krebs cycle were determined through a targeted metabolomics approach using liquid chromatography-tandem mass spectrometry. High-quality sperm are associated to greater levels of glycolysis-derived metabolites, and oocyte fertilisation and embryo development are conditioned by the sperm metabolic status. Interestingly, glycolysis appears to be the preferred catabolic pathway of the sperm giving rise to greater percentages of embryos at day 6. In conclusion, this study shows that the basal metabolic activity of sperm influences their function, even beyond fertilisation.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND

The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles.

MATERIALS AND METHODS

Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases.

RESULTS

According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion.

CONCLUSIONS

Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Serum lipidomic profiling by UHPLC-MS/MS may be able to detect early-stage endometrial cancer

Nowadays, screening for endometrial cancer (EC) primarily relies on clinical symptoms and imaging, which makes it difficult to detect early-stage disease. Here, we conducted a widely targeted lipidomic analysis of 38 human serum samples in a discovery set and 40 human serum samples in a validation set to profile the dysregulated lipid species and establish lipid biomarkers for early-stage EC. This comprehensive lipidomic determination of 616 serum lipids indicated significant differences between early-stage EC patients and healthy controls. Three phases of lipid biomarker investigation (discovery, validation, and determination of the lipid biomarker panel) were performed, which revealed the upregulation of some sphingolipid, glycerophospholipid, and glycerolipids and downregulation of some carnitine. Consistently, the perturbation of sphingolipid and glycerophospholipid metabolism was also observed from pathway enrichment analysis. Moreover, a lipid biomarker panel, including ursodeoxycholic acid, PC(O-14:0_20:4), and Cer(d18:1/18:0), was established. This panel was assessed as an effective diagnostic model to distinguish early-stage EC patients from healthy controls and atypical endometrial hyperplasia patients within the area under the receiver operating characteristic curve (AUC) reaching 0.903 and 0.928, respectively. In particular, the comparison results of the diagnostic efficacy indicated that the lipid biomarker panel was superior to clinically established indicators for EC diagnosis, including HE4, CA125, CA153, and CA199, suggesting that it could be used as an excellent supplementary method for the diagnosis of early-stage EC. In conclusion, we established a novel and non-invasive lipid biomarker for early-stage EC detection and these findings may provide new insight into the pathological mechanisms of EC.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Prospective Metabolomic Studies in Precision Medicine: The AKRIBEA Project

For a long time, conventional medicine has analysed biomolecules to diagnose diseases. Yet, this approach has proven valid only for a limited number of metabolites and often through a bijective relationship with the disease (i.e. glucose relationship with diabetes), ultimately offering incomplete diagnostic value. Nowadays, precision medicine emerges as an option to improve the prevention and/or treatment of numerous pathologies, focusing on the molecular mechanisms, acting in a patient-specific dimension, and leveraging multiple contributing factors such as genetic, environmental, or lifestyle. Metabolomics grasps the required subcellular complexity while being sensitive to all these factors, which results in a most suitable technique for precision medicine. The aim of this chapter is to describe how NMR-based metabolomics can be integrated in the design of a precision medicine strategy, using the Precision Medicine Initiative of the Basque Country (the AKRIBEA project) as a case study. To that end, we will illustrate the procedures to be followed when conducting an NMR-based metabolomics study with a large cohort of individuals, emphasizing the critical points. The chapter will conclude with the discussion of some relevant biomedical applications.

Upregulation of leucine-rich alpha-2 glycoprotein: A key regulator of inflammation and joint fibrosis in patients with severe knee osteoarthritis

Introduction

Osteoarthritis (OA) is a degenerative disease of the joints mainly affecting older individuals. Since the etiology behind the progression of OA is not well understood, several associated consequences, such as synovial joint stiffness and its progression due to joint fibrosis, are still poorly understood. Although a lot of developments have been achieved in the diagnosis and management of OA, synovial fibrosis remains one of the major challenging consequences. The present study was therefore focused on understanding the mechanism of synovial fibrosis, which may further contribute to improving symptomatic treatments, leading to overall improvements in the treatment outcomes of patients with OA.

Methods

We used advanced proteomic techniques including isobaric tag for relative and absolute quantitation and sequential window acquisition of all theoretical mass spectra for the identification of differentially expressed proteins in the plasma samples of patients with OA. An in silico study was carried out to evaluate the association of the identified proteins with their biological processes related to fibrosis and remodeling of the extracellular matrix (ECM). The most significantly upregulated protein was then validated by Western blot and enzyme-linked immunosorbent assay. The target protein was then further investigated for its role in inflammation and joint fibrosis using an in vitro study model.

Results

Leucine-rich alpha-2 glycoprotein (LRG1) was found to be the most highly differentially expressed upregulated (9.4-fold) protein in the plasma samples of patients with OA compared to healthy controls. The knockdown of LRG1 followed by in vitro studies revealed that this protein promotes the secretion of the ECM in synovial cells and actively plays a role in wound healing and cell migration. The knockdown of LRG1 further confirmed the reduction of the inflammatory- and fibrosis-related markers in primary cells.

Conclusion

LRG1 was identified as a highly significant upregulated protein in the plasma samples of patients with OA. It was found to be associated with increased fibrosis and cell migration, leading to enhanced inflammation and joint stiffness in OA pathogenesis.

CRISPR screens guide the way for PARP and ATR inhibitor biomarker discovery

DNA repair pathways are heavily studied for their role in cancer initiation and progression. Due to the large amount of inherent DNA damage in cancer cells, tumor cells profoundly rely on proper DNA repair for efficient cell cycle progression. Several current chemotherapeutics promote excessive DNA damage in cancer cells, thus leading to cell death during cell cycle progression. However, if the tumor has efficient DNA repair mechanisms, DNA-damaging therapeutics may not be as effective. Therefore, directly inhibiting DNA repair pathways alone and in combination with chemotherapeutics that cause DNA damage may result in improved clinical outcomes. Nevertheless, tumors can acquire resistance to DNA repair inhibitors. It is essential to understand the genetic mechanisms underlying this resistance. Genome-wide CRISPR screening has emerged as a powerful tool to identify biomarkers of resistance or sensitivity to DNA repair inhibitors. CRISPR knockout and CRISPR activation screens can be designed to investigate how the loss or overexpression of any human gene impacts resistance or sensitivity to specific inhibitors. This review will address the role of CRISPR screening in identifying biomarkers of resistance and sensitivity to DNA repair pathway inhibitors. We will focus on inhibitors targeting the PARP1 and ATR enzymes, and how the biomarkers identified from CRISPR screens can help inform the treatment plan for cancer patients.

A Marked Low-Grade Inflammation and a Significant Deterioration in Metabolic Status in First-Episode Schizophrenia: A Five-Year Follow-Up Study

The objective of this study was to evaluate how schizophrenia spectrum disorders and applied long-term (5.1 years) antipsychotic (AP) treatment affect the serum level of acylcarnitines (ACs), cytokines and metabolic biomarkers and to characterize the dynamics of inflammatory and metabolic changes in the early course of the disorder. A total of 112 adults participated in the study (54 patients with first-episode psychosis (FEP) and 58 control subjects). Biomolecule profiles were measured at the onset of first-episode psychosis and 0.6 years and 5.1 years after the initiation of APs. The results of the present study confirmed that specific metabolic-inflammatory imbalance characterizes AP-naïve patients. Short-term (0.6-years) AP treatment has a favourable effect on psychotic symptoms, as well as the recovery of metabolic flexibility and resolution of low-level inflammation. However, 5.1 years of AP treatment resulted in weight gain and increased serum levels of interleukin (IL)-2, IL-4, IL-6, IL-10, interferon-γ, hexoses, acetylcarnitine, short-chain ACs (C3, C4) and long-chain ACs (C16:2, C18:1, C18:2). In conclusion, despite the improvement in psychotic symptoms, 5.1 years of AP treatment was accompanied by a pronounced metabolic-inflammatory imbalance, which was confirmed by the presence of enhanced pro-inflammatory activity and increased obesity with changes in the metabolism of carbohydrates, lipids, and their metabolites.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

The Immunometabolic Atlas: A tool for design and interpretation of metabolomics studies in immunology

Immunometabolism, which concerns the interplay between metabolism and the immune system, is increasingly recognized as a potential source of novel drug targets and biomarkers. In this context, the use of metabolomics to identify metabolic characteristics associated with specific functional immune response processes is of value. Currently, there is a lack of tools to determine known associations between metabolites and immune processes. Consequently, interpretation of metabolites in metabolomics studies in terms of their role in the immune system, or selection of the most relevant metabolite classes to include in metabolomics studies, is challenging. Here, we describe the Immunometabolic Atlas (IMA), a public web application and library of R functions to infer immune processes associated with specific metabolites and vice versa. The IMA derives metabolite-immune process associations utilizing a protein-metabolite network analysis algorithm that associates immune system-associated annotated proteins in Gene Ontology to metabolites. We evaluated IMA inferred metabolite-immune system associations using a text mining strategy, identifying substantial overlap, but also demonstrating a significant chemical space of immune system-associated metabolites that should be confirmed experimentally. Overall, the IMA facilitates the interpretation and design of immunometabolomics studies by the association of metabolites to specific immune processes.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data

Gas chromatography-coupled mass spectrometry (GC-MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC-MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra

Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.

Metabolomics Signature and Potential Application of Serum Polyunsaturated Fatty Acids Metabolism in Patients With Vitiligo

Vitiligo is a depigmented skin disorder caused by a variety of factors, including autoimmune, metabolic disturbance or their combined effect, etc. Non-targeted metabolomic analyses have denoted that dysregulated fatty acids metabolic pathways are involved in the pathogenesis of vitiligo. However, the exact category of fatty acids that participate in vitiligo development and how they functionally affect CD8⁺ T cells remain undefined. We aimed to determine the difference in specific fatty acids among vitiligo patients and healthy individuals and to investigate their association with clinical features in patients with vitiligo. Serum levels of fatty acids in 48 vitiligo patients and 28 healthy individuals were quantified by performing ultra-performance liquid chromatography-tandem mass spectrometry. Univariate and multivariate analyses were carried out to evaluate the significance of differences. Moreover, flow cytometry was used to explore the effect of indicated fatty acids on the function of CD8⁺ T cells derived from patients with vitiligo. We demonstrated that serological level of alpha-linolenic acid (ALA) was markedly upregulated, while that of arachidonic acid (ARA), arachidic acid (AA) and behenic acid were significantly downregulated in patients with vitiligo. Moreover, ALA levels were positively associated with vitiligo area scoring index (VASI) and ARA was a probable biomarker for vitiligo. We also revealed that supplementation with ARA or nordihydroguaiaretic acid (NDGA) could suppress the function of CD8⁺ T cells. Our results showed that vitiligo serum has disorder-specific phenotype profiles of fatty acids described by dysregulated metabolism of polyunsaturated fatty acids. Supplementation with ARA or NDGA might promote vitiligo treatment. These findings provide novel insights into vitiligo pathogenesis that might add to therapeutic options.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

The Expanded Endocannabinoid System Contributes to Metabolic and Body Mass Shifts in First-Episode Schizophrenia: A 5-Year Follow-Up Study

Alterations in the expanded endocannabinoid system (eECS) and cell membrane composition have been implicated in the pathophysiology of schizophrenia spectrum disorders. We enrolled 54 antipsychotic (AP)-naïve first-episode psychosis (FEP) patients and 58 controls and applied a targeted metabolomics approach followed by multivariate data analysis to investigate the profile changes in the serum levels of endocannabinoids: 2-arachidonoylglycerol (2-AG) and anandamide, endocannabinoids-like N-acylethanolamines (NAEs: linoleoylethanolamide, oleoylethanolamide, and palmitoylethanolamide), and their dominating lipid precursor’s phosphatidylcholines. Biomolecule profiles were measured at the onset of first-episode psychosis (FEP) and 0.6 years and 5.1 years after the initiation of AP treatment. The results indicated that FEP might be characterized by elevated concentrations of NAEs and by decreased 2-AG levels. At this stage of the disease, the NAE-mediated upregulation of peroxisome proliferator-activated receptors (PPARs) manifested themselves in energy expenditure. A 5-year disease progression and AP treatment adverse effects led to a robust increase in 2-AG levels, which contributed to strengthened cannabinoid (CB1) receptor-mediated effects, which manifested in obesity. Dynamic 2-AG, NAEs, and their precursors in terms of phosphatidylcholines are relevant to the description of the metabolic shifts resulting from the altered eECS function during and after FEP.

Metabolome Genome-Wide Association Study Identifies 74 Novel Genomic Regions Influencing Plasma Metabolites Levels

Metabolites are small products of metabolism that provide a snapshot of the wellbeing of an organism and the mechanisms that control key physiological processes involved in health and disease. Here we report the results of a genome-wide association study of 722 circulating metabolite levels in 8809 subjects of European origin, providing both breadth and depth. These analyses identified 202 unique genomic regions whose variations are associated with the circulating levels of 478 different metabolites. Replication with a subset of 208 metabolites that were available in an independent dataset for a cohort of 1768 European subjects confirmed the robust associations, including 74 novel genomic regions not associated with any metabolites in previous works. This study enhances our knowledge of genetic mechanisms controlling human metabolism. Our findings have major potential for identifying novel targets and developing new therapeutic strategies.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Enantioselectivity Effects in Clinical Metabolomics and Lipidomics

Metabolomics and lipidomics have demonstrated increasing importance in underlying biochemical mechanisms involved in the pathogenesis of diseases to identify novel drug targets and/or biomarkers for establishing therapeutic approaches for human health. Particularly, bioactive metabolites and lipids have biological activity and have been implicated in various biological processes in physiological conditions. Thus, comprehensive metabolites, and lipids profiling are required to obtain further advances in understanding pathophysiological changes that occur in cells and tissues. Chirality is one of the most important phenomena in living organisms and has attracted long-term interest in medical and natural science. Enantioselective separation plays a pivotal role in understanding the distribution and physiological function of a diversity of chiral bioactive molecules. In this context, it has been the goal of method development for targeted and untargeted metabolomics and lipidomic assays. Herein we will highlight the benefits and challenges involved in these stereoselective analyses for clinical samples.

METABOLOMICS REVEALS BIOMARKERS IN HUMAN URINE AND PLASMA TO PREDICT CYP2D6 ACTIVITY

Background and Purpose

Individualized assessment of cytochrome P450 2D6 (CYP2D6) activity is usually performed through phenotyping following administration of a probe drug to measure the enzyme's activity. To avoid any iatrogenic harm (allergic drug reaction, dosing error) related to the probe drug, the development of non‐burdensome tools for real‐time phenotyping of CYP2D6 could significantly contribute to precision medicine. This study focuses on the identification of markers of the CYP2D6 enzyme in human biofluids using an LC‐high‐resolution mass spectrometry‐based metabolomic approach.

Experimental Approach

Plasma and urine samples from healthy volunteers were analysed before and after intake of a daily dose of paroxetine 20 mg over 7 days. CYP2D6 genotyping and phenotyping, using single oral dose of dextromethorphan 5 mg, were also performed in all participants.

Key Results

We report four metabolites of solanidine and two unknown compounds as possible novel CYP2D6 markers. Mean relative intensities of these features were significantly reduced during the inhibition session compared with the control session (n = 37). Semi‐quantitative analysis showed that the largest decrease (−85%) was observed for the ion m/z 432.3108 normalized to solanidine (m/z 398.3417). Mean relative intensities of these ions were significantly higher in the CYP2D6 normal–ultrarapid metabolizer group (n = 37) compared with the poor metabolizer group (n = 6). Solanidine intensity was more than 15 times higher in CYP2D6‐deficient individuals compared with other volunteers.

Conclusion and Implications

The applied untargeted metabolomic strategy identified potential novel markers capable of semi‐quantitatively predicting CYP2D6 activity, a promising discovery for personalized medicine.

A Clinical Trial for the Identification of Metabolic Biomarkers in Hashimoto’s Thyroiditis and in Psoriasis: Study Protocol

Hashimoto’s thyroiditis and psoriasis are inflammatory disorders that significantly impact patients’ quality of life, stressing the need for novel biomarkers of early diagnosis. This randomized clinical trial (NCT04693936) aims to identify Hashimoto’s thyroiditis’ and psoriasis’ metabolic biomarkers and to investigate the effect of environmental factors on the disease-related metabolic imprint and quality of life. Patients with Hashimoto’s thyroiditis, patients with psoriasis, and healthy individuals aged 18–60 will be recruited, enrolled according to eligibility criteria (medical history, clinical thyroid markers and the PASI score) and randomized to two groups. The intervention group will receive a combination of nutraceuticals for 6 months as part of a Mediterranean diet, and the control group will follow their usual diet. Data will be collected at baseline and the end of the study, including metabolite levels, lifestyle and anthropometric measurements, adherence to the Mediterranean diet (through the Mediterranean Diet Score) and disease-specific quality of life (through the Thyroid Patient Report Outcome for Hashimoto’s group, and the Dermatology Life Quality Index for the psoriasis group). This study will investigate metabolic biomarkers and related changes in Hashimoto’s thyroiditis and psoriasis and evaluate the association of metabolic changes with dietary factors and quality of life.

Dried Blood Spot (DBS) Methodology Study for Biomarker Discovery in Lysosomal Storage Disease (LSD)

Lysosomal storage diseases (LSDs) are a heterogeneous group of inherited metabolic diseases caused by mutations in genes encoding for proteins involved in the lysosomal degradation of macromolecules. They occur in approximately 1 in 5000 live births and pose a lifelong risk. Therefore, to achieve the maximum benefit from LSDs therapies, a fast and early diagnosis of the disease is required. In this framework, biomarker discovery is a significant factor in disease diagnosis and in predicting its outcomes. On the other hand, the dried blood spot (DBS) based metabolomics platform can open up new pathways for studying non-directional hypothesis approaches to biomarker discovery. This study aims to increase the efficiency of the developed methods for biomarker development in the context of rare diseases, with an improved impact on the reliability of the detected compounds. Thereby, we conducted two independent experiments and integrated them into the screening of the human blood metabolome: (1) comparison of EDTA blood and filter cards in terms of their suitability for metabolomics studies; (2) optimization of the extraction method: a side-by-side comparison of a series of buffers to the best utility to the disease of interest. The findings were compared to previous studies across parameters such as metabolite coverage, sample type suitability, and stability. The results indicate that measurements of metabolites are susceptible to differences in pre-analytical conditions and extraction solvents. This proposed approach can increase the positive rate of the future development of biomarkers. Altogether, the procedure can be easily adapted and applied to other studies, where the limited number of samples is a common barrier.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Application of artificial intelligence in gastroenterology: Potential role in clinical practice

Artificial intelligence (AI) based on deep learning boosted medical research in the past years and is expected to enormously change the style of health care in many aspects in the foreseeable future. Nowadays, there are exploding volumes of healthcare-related data being generated daily. Because of its time-sensitive characteristics, being able to process large amounts of data in real-time fashion is crucial in healthcare settings. In gastroenterology practice, AI can manage and interpret the sheer amount of data with different formats coming from a myriad of sources, including currently used endoscopic or imaging devices, digital record systems, and electronic health records, or from other sources such as governmental databases, social media, or wearable devices over a long period. Traditional gastroenterology is nowadays beginning to transform to a new personalized, predictive, and preventive paradigm. Evidence-based practices and recent studies are coming out every day, and big data-based approaches and the progress in basic sciences and its emerging applications are now becoming the indispensable part of precision medicine. In gastroenterology, AI can be applied in disease diagnosis, treatment guidance, outcome prediction, and reducing workload of the healthcare staff. As the healthcare community begins to embrace AI technology, how to seamlessly construct an interoperable platform to accommodate data with high variety and veracity with high velocity and implement AI in the clinical workflow would be the future challenges.

Metabolic dysfunction in pregnancy: Fingerprinting the maternal metabolome using proton nuclear magnetic resonance spectroscopy

Aims

Maternal metabolic disorders place the mother at risk for negative pregnancy outcomes with potentially long-term health impacts for the child. Metabolic syndrome, a cluster of features associated with increased risk of metabolic disorders, such as cardiovascular disease, diabetes and stroke, affects roughly one in five Canadians. Metabolomics is a relatively new technique that may be a useful tool to identify women at risk of metabolic disorders. This study set out to characterize urinary metabolic biomarkers of pregnant women with obesity and of pregnant women who later developed gestational diabetes mellitus (pre-GDM), compared to controls.

Methods and Materials

Second trimester urine samples were collected through the Alberta Pregnancy Outcomes and Nutrition (APrON) cohort and examined with 1H nuclear magnetic resonance (NMR) spectroscopy. Multivariate analysis was used to examine group differences, and machine learning feature selection tools identified the metabolites contributing to separation.

Results

Obesity and pre-GDM metabolomes were distinct from controls and from each other. In each comparison, the glycine, serine and threonine pathways were the most impacted. Pantothenate, formic acid and glycine were downregulated by obesity, while formic acid, dimethylamine and galactose were downregulated in pre-GDM. The three most impacted metabolites for the comparison of obesity versus pre-GDM groups were upregulated creatine/caffeine, downregulated sarcosine/dimethylamine and upregulated maltose/sucrose in individuals who later developed GDM.

Conclusion

These findings suggest a role for urinary metabolomics in the prediction of GDM and metabolic marker identification for potential diagnostics and prognostics in women at risk.

No population left behind: Improving paediatric drug safety using informatics and systems biology

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age-dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data-driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

In Vivo Targeted Metabolomic Profiling of Prostanit, a Novel Anti-PAD NO-Donating Alprostadil-Based Drug

Prostanit is a novel drug developed for the treatment of peripheral arterial diseases. It consists of a prostaglandin E1 (PGE1) moiety with two nitric oxide (NO) donor fragments, which provide a combined vasodilation effect on smooth muscles and vascular spastic reaction. Prostanit pharmacokinetics, however, remains poorly investigated. Thus, the object of this study was to investigate the pharmacokinetics of Prostanit-related and -affected metabolites in rabbit plasma using the liquid chromatography-mass spectrometry (LC-MS) approach. Besides, NO generation from Prostanit in isolated rat aorta and human smooth muscle cells was studied using the Griess method. In plasma, Prostanit was rapidly metabolized to 1,3-dinitroglycerol (1,3-DNG), PGE1, and 13,14-dihydro-15-keto-PGE1. Simultaneously, the constant growth of amino acid (proline, 4-hydroxyproline, alanine, phenylalanine, etc.), steroid (androsterone and corticosterone), and purine (adenosine, adenosine-5 monophosphate, and guanosine) levels was observed. Glycine, aspartate, cortisol, and testosterone levels were decreased. Ex vivo Prostanit induced both NO synthase-dependent and -independent NO generation. The observed pharmacokinetic properties suggested some novel beneficial activities (i.e., effect prolongation and anti-inflammation). These properties may provide a basis for future research of the effectiveness and safety of Prostanit, as well as for its characterization from a clinical perspective.

Supercritical fluid chromatography - Mass spectrometry in metabolomics: Past, present, and future perspectives

Metabolomics, which consists of the comprehensive analysis of metabolites within a biological system, has been playing a growing role in the implementation of personalized medicine in modern healthcare. A wide range of analytical approaches are used in metabolomics, notably mass spectrometry (MS) combined to liquid chromatography (LC), gas chromatography (GC), or capillary electrophoresis (CE). However, none of these methods enable a comprehensive analysis of the metabolome, due to its extreme complexity and the large differences in physico-chemical properties between metabolite classes. In this context, supercritical fluid chromatography (SFC) represents a promising alternative approach to improve the metabolome coverage, while further increasing the analysis throughput. SFC, which uses supercritical CO₂ as mobile phase, leads to numerous advantages such as improved kinetic performance and lower environmental impact. This chromatographic technique has gained a significant interest since the introduction of advanced instrumentation, together with the introduction of dedicated interfaces for hyphenating SFC to MS. Moreover, new developments in SFC column chemistry (including sub-2 µm particles), as well as the use of large amounts of organic modifiers and additives in the CO₂-based mobile phase, significantly extended the application range of SFC, enabling the simultaneous analysis of a large diversity of metabolites. Over the last years, several applications have been reported in metabolomics using SFC-MS - from lipophilic compounds, such as steroids and other lipids, to highly polar compounds, such as carbohydrates, amino acids, or nucleosides. With all these advantages, SFC-MS is promised to a bright future in the field of metabolomics.

Metabolomics of the diabetic nephropathy: behind the fingerprint of development and progression indicators

Current diagnostic methods are not very sensitive to detect the initial stages diabetic nephropathy of type 2. In this work, a review of metabolomic approximation studies for the identification of biomarkers of this disease with potential to differentiate between early stages, evaluate and direct treatment and help slow kidney damage. Using public (Pubmed and Google Scholar) and private (Scopus and Web of Knowledge) databases, a systematic search of the information published related to metabolomics of diabetic nephropathy in different biospecimens (urine, serum, plasma and blood) was made. Later, the MetaboAnalyst 4.0 software was used to identify the metabolic pathways associated with these metabolites. Groups of potential metabolites were identified for monitoring diabetic nephropathy with the available literature data. In the urine, oxide-3-hydroxyisovalerate, TMAO, aconite and citrate and hydroxypropionate derivatives are highlighted; meanwhile, in the serum: citrate, creatinine, arginine and its derivatives; and in the plasma: amino acids such as histidine, methionine and arginine has a potential contribution. Using MetaboAnalyst 4.0 the metabolic pathways related to these metabolites were related. The search for biomarkers to measure the progression of diabetic nephropathy, together with analytical strategies for their detection and quantification, are the starting point for designing new methods of clinical chemistry analysis. The association between the metabolic pathway dysfunction could be useful for the overall assessment of the treatment and clinical follow-up of this disease.

Metabolomics approach revealed robust changes in amino acid and biogenic amine signatures in patients with schizophrenia in the early course of the disease

The primary objective of this study was to evaluate how schizophrenia (SCH) spectrum disorders and applied antipsychotic (AP) treatment affect serum level of amino acids (AAs) and biogenic amines (BAs) in the early course of the disorder. We measured 21 different AAs and 10 BAs in a sample of antipsychotic (AP)-naïve first-episode psychosis (FEP) patients (n = 52) at baseline, after 0.6-year as well as after 5.1-year treatment compared to control subjects (CSs, n = 37). Serum levels of metabolites were determined with AbsoluteIDQ p180 kit using flow injection analysis tandem mass spectrometry and liquid chromatography technique. Elevated level of taurine and reduced level of proline and alpha-aminoadipic acid (alpha-AAA) were established as metabolites with significant change in AP-naïve FEP patients compared to CSs. The following 0.6-year treatment restored these alterations. However, further continuous 5.1-year AP treatment changed the metabolic profile substantially. Significantly elevated levels of asparagine, glutamine, methionine, ornithine and taurine, alongside with decreased levels of aspartate, glutamate and alpha-AAA were observed in the patient group compared to CSs. These biomolecule profile alterations provide further insights into the pathophysiology of SCH spectrum disorders and broaden our understanding of the impact of AP treatment in the early stages of the disease.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

Widespread application of omic technologies is evolving our understanding of population health and holds promise in providing precise guidance for selection of therapeutic interventions based on patient biology. The opportunity to use hundreds of analytes for diagnostic assessment of human health compared to the current use of 10–20 analytes will provide greater accuracy in deconstructing the complexity of human biology in disease states. Conventional biochemical measurements like cholesterol, creatinine, and urea nitrogen are currently used to assess health status; however, metabolomics captures a comprehensive set of analytes characterizing the human phenotype and its complex metabolic processes in real-time. Unlike conventional clinical analytes, metabolomic profiles are dramatically influenced by demographic and environmental factors that affect the range of normal values and increase the risk of false biomarker discovery. This review addresses the challenges and opportunities created by the evolving field of clinical metabolomics and highlights features of study design and bioinformatics necessary to maximize the utility of metabolomics data across demographic groups.

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

BACKGROUND

Precision medicine (PM) is playing a more and more important role in clinical practice. In recent years, the scale of PM research has been growing rapidly. Many reviews have been published to facilitate a better understanding of the status of PM research. However, there is still a lack of research on the intellectual structure in terms of topics.

OBJECTIVE

This study aimed to identify the intellectual structure and evolutionary trends of PM research through the application of various social network analysis and visualization methods.

METHODS

The bibliographies of papers published between 2009 and 2018 were extracted from the Web of Science database. Based on the statistics of keywords in the papers, a coword network was generated and used to calculate network indicators of both the entire network and local networks. Communities were then detected to identify subdirections of PM research. Topological maps of networks, including networks between communities and within each community, were drawn to reveal the correlation structure. An evolutionary graph and a strategic graph were finally produced to reveal research venation and trends in discipline communities.

RESULTS

The results showed that PM research involves extensive themes and, overall, is not balanced. A minority of themes with a high frequency and network indicators, such as Biomarkers, Genomics, Cancer, Therapy, Genetics, Drug, Target Therapy, Pharmacogenomics, Pharmacogenetics, and Molecular, can be considered the core areas of PM research. However, there were five balanced theme directions with distinguished status and tendencies: Cancer, Biomarkers, Genomics, Drug, and Therapy. These were shown to be the main branches that were both focused and well developed. Therapy, though, was shown to be isolated and undeveloped.

CONCLUSIONS

The hotspots, structures, evolutions, and development trends of PM research in the past ten years were revealed using social network analysis and visualization. In general, PM research is unbalanced, but its subdirections are balanced. The clear evolutionary and developmental trend indicates that PM research has matured in recent years. The implications of this study involving PM research will provide reasonable and effective support for researchers, funders, policymakers, and clinicians.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Metabolic Fingerprint of Chronic Obstructive Lung Diseases: A New Diagnostic Perspective

Chronic obstructive lung disease (COLD) is a group of airway diseases, previously known as emphysema and chronic bronchitis. The heterogeneity of COLD does not allow early diagnosis and leads to increased morbidity and mortality. The increasing number of COLD incidences stresses the need for precision medicine approaches that are specific to the patient. Metabolomics is an emerging technology that allows for the discrimination of metabolic changes in the cell as a result of environmental factors and specific genetic background. Thus, quantification of metabolites in human biofluids can provide insights into the metabolic state of the individual in real time and unravel the presence of, or predisposition to, a disease. In this article, the advantages of and potential barriers to putting metabolomics into clinical practice for COLD are discussed. Today, metabolomics is mostly lab-based, and research studies with novel COLD-specific biomarkers are continuously being published. Several obstacles in the research and the market field hamper the translation of these data into clinical practice. However, technological and computational advances will facilitate the clinical interpretation of data and provide healthcare professionals with the tools to prevent, diagnose, and treat COLD with precision in the coming decades.