Special Issue "Metabolomics of Complex Traits"

A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 March 2019).

Special Issue Editor

Prof. Guangju Zhai
E-Mail Website
Guest Editor
Faculty of Medicine, Memorial University of Newfoundland, Canada
Interests: population-based studies; genomics; metabolomics; biomarker discovery; musculoskeletal diseases

Special Issue Information

Dear Colleagues,

Metabolomics is a relative young member of the "OMICs" family, which uses state-of-art analytical chemistry techniques and advanced computational methods to comprehensively characterize small molecules (metabolites) in biological fluids and tissues. Metabolites represent both the downstream output of the genome and the upstream input from the environment, and are directly linked to the cellular function and phenotypes. The study of metabolites not only enables the identification of disease biomarkers but also provides unique insights into the fundamental causes of disease. Recent advances in metabolomics technologies results in a growing number of applications in biomedical research of complex traits, and such applications have already identified a number of unexpected chemical causes or metabolic pathways for several important complex diseases including atherosclerosis, diabetes, cancer, and osteoarthritis. In this Special issue, we seek both review articles and original research with a focus on studies of metabolomics in complex diseases and traits, which will provide all readers with an overview of the application of metabolomics in complex disease and summarize the most recent new knowledge and advances in the field.

Prof. Guangju Zhai
Guest Editor

Manuscript Submission Information

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Keywords

  • Metabolomics (MS-based and NMR-based)
  • Targeted and Untargeted Metabolomics
  • Biomarker Discovery
  • Complex Diseases and Traits
  • Pharmacometabolomics
  • Precision Medicine

Published Papers (8 papers)

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Research

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Open AccessArticle
Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel
Metabolites 2019, 9(5), 90; https://doi.org/10.3390/metabo9050090 - 07 May 2019
Cited by 1
Abstract
The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples [...] Read more.
The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Open AccessArticle
Ageing Investigation Using Two-Time-Point Metabolomics Data from KORA and CARLA Studies
Metabolites 2019, 9(3), 44; https://doi.org/10.3390/metabo9030044 - 05 Mar 2019
Cited by 1
Abstract
Ageing, one of the largest risk factors for many complex diseases, is highly interconnected to metabolic processes. Investigating the changes in metabolite concentration during ageing among healthy individuals offers us unique insights to healthy ageing. We aim to identify ageing-associated metabolites that are [...] Read more.
Ageing, one of the largest risk factors for many complex diseases, is highly interconnected to metabolic processes. Investigating the changes in metabolite concentration during ageing among healthy individuals offers us unique insights to healthy ageing. We aim to identify ageing-associated metabolites that are independent from chronological age to deepen our understanding of the long-term changes in metabolites upon ageing. Sex-stratified longitudinal analyses were performed using fasting serum samples of 590 healthy KORA individuals (317 women and 273 men) who participated in both baseline (KORA S4) and seven-year follow-up (KORA F4) studies. Replication was conducted using serum samples of 386 healthy CARLA participants (195 women and 191 men) in both baseline (CARLA-0) and four-year follow-up (CARLA-1) studies. Generalized estimation equation models were performed on each metabolite to identify ageing-associated metabolites after adjusting for baseline chronological age, body mass index, physical activity, smoking status, alcohol intake and systolic blood pressure. Literature researches were conducted to understand their biochemical relevance. Out of 122 metabolites analysed, we identified and replicated five (C18, arginine, ornithine, serine and tyrosine) and four (arginine, ornithine, PC aa C36:3 and PC ae C40:5) significant metabolites in women and men respectively. Arginine decreased, while ornithine increased in both sexes. These metabolites are involved in several ageing processes: apoptosis, mitochondrial dysfunction, inflammation, lipid metabolism, autophagy and oxidative stress resistance. The study reveals several significant ageing-associated metabolite changes with two-time-point measurements on healthy individuals. Larger studies are required to confirm our findings. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Open AccessArticle
MALDI-TOF-MS Analysis in the Identification of Urine Proteomic Patterns of Gestational Trophoblastic Disease
Metabolites 2019, 9(2), 30; https://doi.org/10.3390/metabo9020030 - 09 Feb 2019
Abstract
Gestational trophoblastic disease (GTD) is a group of highly aggressive, rare tumors. Human chorionic gonadotropin is a common biomarker used in the diagnosis and monitoring of GTD. To improve our knowledge of the pathology of GTD, we performed protein-peptide profiling on the urine [...] Read more.
Gestational trophoblastic disease (GTD) is a group of highly aggressive, rare tumors. Human chorionic gonadotropin is a common biomarker used in the diagnosis and monitoring of GTD. To improve our knowledge of the pathology of GTD, we performed protein-peptide profiling on the urine of patients affected with gestational trophoblastic neoplasm (GTN). We analyzed urine samples from patients diagnosed with GTN (n = 26) and from healthy pregnant and non-pregnant controls (n = 17) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Ions were examined in a linear mode over a m/z range of 1000–10,000. All GTN urine samples were analyzed before and after treatment and compared with those of the controls. The statistical analyses included multivariate classification algorithms as well as ROC curves. Urine sample analyses revealed there were significant differences in the composition of the ions between the evaluated groups. Comparing the pre-treatment and group with the pregnant controls, we identified two discriminatory proteins: hemoglobin subunit α (m/z = 1951.81) and complement C4A (m/z = 1895.43). Then, comparing urine samples from the post-treatment cases with those from the non-pregnant controls, we identified the peptides uromodulin fragments (m/z = 1682.34 and 1913.54) and complement C4A (m/z = 1895.43). Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Review

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Open AccessReview
The Metabolomic Signatures of Weight Change
Metabolites 2019, 9(4), 67; https://doi.org/10.3390/metabo9040067 - 04 Apr 2019
Abstract
Obesity represents a major health concern, not just in the West but increasingly in low and middle income countries. In order to develop successful strategies for losing weight, it is essential to understand the molecular pathogenesis of weight change. A number of pathways, [...] Read more.
Obesity represents a major health concern, not just in the West but increasingly in low and middle income countries. In order to develop successful strategies for losing weight, it is essential to understand the molecular pathogenesis of weight change. A number of pathways, implicating oxidative stress but also the fundamental regulatory of insulin, have been implicated in weight gain and in the regulation of energy expenditure. In addition, a considerable body of work has highlighted the role of metabolites generated by the gut microbiome, in particular short chain fatty acids, in both processes. The current review provides a brief understanding of the mechanisms underlying the associations of weight change with changes in lipid and amino acid metabolism, energy metabolism, dietary composition and insulin dynamics, as well as the influence of the gut microbiome. The changes in metabolomic profiles and the models outlined can be used as an accurate predictor for obesity and obesity related disorders. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Open AccessReview
Computational Methods for the Discovery of Metabolic Markers of Complex Traits
Metabolites 2019, 9(4), 66; https://doi.org/10.3390/metabo9040066 - 04 Apr 2019
Abstract
Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the [...] Read more.
Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Open AccessReview
Alteration of Metabolic Pathways in Osteoarthritis
Metabolites 2019, 9(1), 11; https://doi.org/10.3390/metabo9010011 - 09 Jan 2019
Cited by 3
Abstract
Sir Archibald Edward Garrod, who pioneered the field of inborn errors of metabolism and first elucidated the biochemical basis of alkaptonuria over 100 years ago, suggested that inborn errors of metabolism were “merely extreme examples of variations of chemical behavior which are probably [...] Read more.
Sir Archibald Edward Garrod, who pioneered the field of inborn errors of metabolism and first elucidated the biochemical basis of alkaptonuria over 100 years ago, suggested that inborn errors of metabolism were “merely extreme examples of variations of chemical behavior which are probably everywhere present in minor degrees, just as no two individuals of a species are absolutely identical in bodily structure neither are their chemical processes carried out on exactly the same lines”, and that this “chemical individuality [confers] predisposition to and immunities from various mishaps which are spoken of as diseases”. Indeed, with advances in analytical biochemistry, especially the development of metabolomics in the post-genomic era, emerging data have been demonstrating that the levels of many metabolites do show substantial interindividual variation, and some of which are likely to be associated with common diseases, such as osteoarthritis (OA). Much work has been reported in the literature on the metabolomics of OA in recent years. In this narrative review, we provided an overview of the identified alteration of metabolic pathways in OA and discussed the role of those identified metabolites and related pathways in OA diagnosis, prognosis, and treatment. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Open AccessReview
The Metabolome and Osteoarthritis: Possible Contributions to Symptoms and Pathology
Metabolites 2018, 8(4), 92; https://doi.org/10.3390/metabo8040092 - 13 Dec 2018
Cited by 3
Abstract
Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of [...] Read more.
Osteoarthritis (OA) is a progressive, deteriorative disease of articular joints. Although traditionally viewed as a local pathology, biomarker exploration has shown that systemic changes can be observed. These include changes to cytokines, microRNAs, and more recently, metabolites. The metabolome is the set of metabolites within a biological sample and includes circulating amino acids, lipids, and sugar moieties. Recent studies suggest that metabolites in the synovial fluid and blood could be used as biomarkers for OA incidence, prognosis, and response to therapy. However, based on clinical, demographic, and anthropometric factors, the local synovial joint and circulating metabolomes may be patient specific, with select subsets of metabolites contributing to OA disease. This review explores the contribution of the local and systemic metabolite changes to OA, and their potential impact on OA symptoms and disease pathogenesis. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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Other

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Open AccessPerspective
The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective
Metabolites 2019, 9(7), 126; https://doi.org/10.3390/metabo9070126 - 02 Jul 2019
Abstract
Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. However, as yet, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as [...] Read more.
Unmet clinical diagnostic needs exist for many complex diseases, which it is hoped will be solved by the discovery of metabolomics biomarkers. However, as yet, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely. Full article
(This article belongs to the Special Issue Metabolomics of Complex Traits)
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