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Metabolites
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Biomarkers and Human Blood Metabolites
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Biomarkers and Human Blood Metabolites

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Advances in Metabolomics".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 20829

Special Issue Editors

Dr. Hiroshi Kondoh

E-Mail Website
Guest Editor
Geriatric Unit, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
Interests: ageing; metabolites; ageing-related diseases; glycolysis; senescence
Dr. Akiyoshi Hirayama

E-Mail Website
Guest Editor
Institute for Advanced Biosciences, Keio University, Yamagata 997-0052, Japan
Interests: metabolomics; capillary electrophoresis; mass spectrometry; liquid chromatography; ion chromatography; metabolism; biomarker; kidney disease; extracellular vesicles

Special Issue Information

Dear Colleagues,

Metabolomics, a tool to evaluate metabolite profiles in cells and organisms, employs techniques such as liquid chromatography (LC)-mass spectrometry (MS) to reveal complex, but highly integrated biological processes. As metabolomics detects, identifies, and quantifies small organic metabolites, it permits comprehensive evaluation of metabolic mechanisms of physiological responses and diseases and of biological effects of drugs, nutrients, and environmental stressors.

Because all tissues and organs are supplied by the circulatory system, human blood samples are expected to document not only individual genetic variability, but also differences in physiological responses and homeostatic mechanisms. This Special Issue highlights the use of metabolomics in human blood biomarker research. Specific areas include, but not limited to, are the disease biomarkers, biomarkers of exposure, and indicators of  physiological responses, which reflect environmental conditions, genetic and epigenetic factors, nutritional status, and lifestyle. Manuscripts dealing with other relevant issues would be also desirable.

Dr. Hiroshi Kondoh
Dr. Akiyoshi Hirayama
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Blood metabolites as human biomarkers
  • Identification of disease and non-disease biomarkers
  • Understanding the pathogenesis of the diseases through metabolites
  • Physiological response against environmental stress or stimuli
  • Exposure to toxin or chemical drugs
  • Lifestyle, nutrition, and genetic or epigenetic factors

Published Papers (7 papers)

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Research

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14 pages, 2225 KiB  
Article
Targeted Metabolomics Analysis of Bile Acids in Patients with Idiosyncratic Drug-Induced Liver Injury
by Zhongyang Xie, Lingjian Zhang, Ermei Chen, Juan Lu, Lanlan Xiao, Qiuhong Liu, Danhua Zhu, Fen Zhang, Xiaowei Xu and Lanjuan Li
Metabolites 2021, 11(12), 852; https://doi.org/10.3390/metabo11120852 - 8 Dec 2021
Cited by 19 | Viewed by 2945
Abstract
Drug-induced liver injury (DILI) is rare but clinically important due to a high rate of mortality. However, specific biomarkers for diagnosing and predicting the severity and prognosis of DILI are lacking. Here, we used targeted metabolomics to identify and quantify specific types of [...] Read more.
Drug-induced liver injury (DILI) is rare but clinically important due to a high rate of mortality. However, specific biomarkers for diagnosing and predicting the severity and prognosis of DILI are lacking. Here, we used targeted metabolomics to identify and quantify specific types of bile acids that can predict the severity of DILI. A total of 161 DILI patients were enrolled in this prospective cohort study, as well as 31 health controls. A targeted metabolomics method was used to identify 24 types of bile acids. DILI patients were divided into mild, moderate, and severe groups according to disease severity. A multivariate analysis was performed to identify characteristic bile acids. Then the patients were divided into severe and non-severe groups, and logistic regression was used to identify bile acids that could predict DILI severity. Among the enrolled DILI patients, 32 were in the mild group, 90 were in the moderate group, and 39 were in the severe group. Orthogonal partial least squares-discriminant analysis (OPLS-DA) modeling clearly discriminated among the different groups. Among the four groups, glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholic acid (DCA), Nor Cholic acid (NorCA), glycocholic acid (GCA), and taurocholic acid (TCA) showed significant differences in concentration between at least two groups. NorCA, GCDCA, and TCDCA were all independent risk factors that differentiated severe DILI patients from the other groups. The area under the receiver operating characteristic curve (AUROC) of GCDCA, TCDCA, and NorCA was 0.856, 0.792, and 0.753, respectively. Together, these three bile acids had an AUROC of 0.895 for predicting severe DILI patients. DILI patients with different disease severities have specific bile acid metabolomics. NorCA, GCDTA, and TCDCA were independent risk factors for differentiating severe DILI patients from less-severe patients and have the potential to predict DILI severity. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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12 pages, 1060 KiB  
Article
Vitamin-D Binding Protein Gene Polymorphisms and Serum 25-Hydroxyvitamin-D in a Turkish Population
by Lutfiye Karcıoğlu Batur, Ahmet Özaydın, Murat Emrah Maviş, Gökçe Göksu Gürsu, Laurence Harbige and Nezih Hekim
Metabolites 2021, 11(10), 696; https://doi.org/10.3390/metabo11100696 - 12 Oct 2021
Cited by 8 | Viewed by 2607
Abstract
The rs7041 and rs4588 polymorphisms found in the GC gene, encoding vitamin D-binding protein (DBP), have distinct biochemical phenotypes. The aim of this study was to investigate vitamin D parameters with these polymorphisms, in individuals with possible vitamin D deficiency. The most common [...] Read more.
The rs7041 and rs4588 polymorphisms found in the GC gene, encoding vitamin D-binding protein (DBP), have distinct biochemical phenotypes. The aim of this study was to investigate vitamin D parameters with these polymorphisms, in individuals with possible vitamin D deficiency. The most common (49% of the cohort) genotype in rs7041 was GT, especially among individuals with high levels of free 25(OH)D calculated but with low levels of bioavailable 25(OH)D, and in rs4588 it was AC in particular among the individuals with low levels of bioavailable 25(OH)D. The most common phenotypes were Gc1s/2 (35.3%) and Gc1s/1s (31.4%), and Gc1f/1f was rare (5.9%). The variations in free and bioavailable 25(OH)D levels among healthy Turkish individuals may be attributed to the variations in total 25(OH)D as well as GC gene polymorphisms. The Turkish population shares a similarity for allele frequencies of rs7041 with the European population and similarity for allele frequencies of rs4588 with Gujarati Indians, and this may also be important in relation to certain ethnic populations showing associations between vitamin D and COVID-19. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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15 pages, 1711 KiB  
Article
Metabolomics Signature of Plasma Renin Activity and Linkage with Blood Pressure Response to Beta Blockers and Thiazide Diuretics in Hypertensive European American Patients
by Mai Mehanna, Caitrin W. McDonough, Steven M. Smith, Yan Gong, John G. Gums, Arlene B. Chapman, Julie A. Johnson, Lauren McIntyre and Rhonda M. Cooper-DeHoff
Metabolites 2021, 11(9), 645; https://doi.org/10.3390/metabo11090645 - 21 Sep 2021
Cited by 7 | Viewed by 2815
Abstract
Plasma renin activity (PRA) is a predictive biomarker of blood pressure (BP) response to antihypertensives in European–American hypertensive patients. We aimed to identify the metabolic signatures of baseline PRA and the linkages with BP response to β-blockers and thiazides. Using data from the [...] Read more.
Plasma renin activity (PRA) is a predictive biomarker of blood pressure (BP) response to antihypertensives in European–American hypertensive patients. We aimed to identify the metabolic signatures of baseline PRA and the linkages with BP response to β-blockers and thiazides. Using data from the Pharmacogenomic Evaluation of Antihypertensive Responses-2 (PEAR-2) trial, multivariable linear regression adjusting for age, sex and baseline systolic-BP (SBP) was performed on European–American individuals treated with metoprolol (n = 198) and chlorthalidone (n = 181), to test associations between 856 metabolites and baseline PRA. Metabolites with a false discovery rate (FDR) < 0.05 or p < 0.01 were tested for replication in 463 European–American individuals treated with atenolol or hydrochlorothiazide. Replicated metabolites were then tested for validation based on the directionality of association with BP response. Sixty-three metabolites were associated with baseline PRA, of which nine, including six lipids, were replicated. Of those replicated, two metabolites associated with higher baseline PRA were validated: caprate was associated with greater metoprolol SBP response (β = −1.7 ± 0.6, p = 0.006) and sphingosine-1-phosphate was associated with reduced hydrochlorothiazide SBP response (β = 7.6 ± 2.8, p = 0.007). These metabolites are clustered with metabolites involved in sphingolipid, phospholipid, and purine metabolic pathways. The identified metabolic signatures provide insights into the mechanisms underlying BP response. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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20 pages, 2842 KiB  
Article
Dysregulated Alanine as a Potential Predictive Marker of Glioma—An Insight from Untargeted HRMAS-NMR and Machine Learning Data
by Safia Firdous, Rizwan Abid, Zubair Nawaz, Faisal Bukhari, Ammar Anwer, Leo L. Cheng and Saima Sadaf
Metabolites 2021, 11(8), 507; https://doi.org/10.3390/metabo11080507 - 1 Aug 2021
Cited by 8 | Viewed by 2831
Abstract
Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest [...] Read more.
Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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11 pages, 273 KiB  
Article
A Metabolomic Profile Predictive of New Osteoporosis or Sarcopenia Development
by Kana Miyamoto, Akiyoshi Hirayama, Yuiko Sato, Satsuki Ikeda, Midori Maruyama, Tomoyoshi Soga, Masaru Tomita, Masaya Nakamura, Morio Matsumoto, Noriko Yoshimura and Takeshi Miyamoto
Metabolites 2021, 11(5), 278; https://doi.org/10.3390/metabo11050278 - 28 Apr 2021
Cited by 13 | Viewed by 2684
Abstract
The increasing number of patients with osteoporosis and sarcopenia is a global concern among countries with progressively aging societies. The high medical costs of treating those patients suggest that prevention rather than treatment is preferable. We enrolled 729 subjects who attended both the [...] Read more.
The increasing number of patients with osteoporosis and sarcopenia is a global concern among countries with progressively aging societies. The high medical costs of treating those patients suggest that prevention rather than treatment is preferable. We enrolled 729 subjects who attended both the second and third surveys of the Research on Osteoarthritis/Osteoporosis Against Disability (ROAD) study. Blood samples were collected from subjects at the second survey, and then a comprehensive metabolomic analysis was performed. It was found that 35 had newly developed osteoporosis at the third survey performed four years later, and 39 were newly diagnosed with sarcopenia at the third survey. In the second survey, we found that serum Gly levels were significantly higher even after adjustment for age, sex, and BMI in subjects with newly developed osteoporosis relative to those who remained osteoporosis-negative during the four-year follow-up. We also show that serum taurine levels were significantly lower at the second survey, even after adjustment for age, sex, and BMI in subjects with newly developed sarcopenia during the four-year follow-up compared with those not diagnosed with sarcopenia at the second or third surveys. Though our sample size and odds ratios were small, increased Gly and decreased taurine levels were found to be predictive of new development of osteoporosis and sarcopenia, respectively, within four years. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)

Review

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19 pages, 912 KiB  
Review
Metabolomics Studies in Psoriatic Disease: A Review
by John Koussiouris, Nikita Looby, Melanie Anderson, Vathany Kulasingam and Vinod Chandran
Metabolites 2021, 11(6), 375; https://doi.org/10.3390/metabo11060375 - 10 Jun 2021
Cited by 13 | Viewed by 3431
Abstract
Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision [...] Read more.
Metabolomics investigates a broad range of small molecules, allowing researchers to understand disease-related changes downstream of the genome and proteome in response to external environmental stimuli. It is an emerging technology that holds promise in identifying biomarkers and informing the practice of precision medicine. In this review, we summarize the studies that have examined endogenous metabolites in patients with psoriasis and/or psoriatic arthritis using nuclear magnetic resonance (NMR) or mass spectrometry (MS) and were published through 26 January 2021. A standardized protocol was used for extracting data from full-text articles identified by searching OVID Medline ALL, OVID Embase, OVID Cochrane Central Register of Controlled Trials and BIOSIS Citation Index in Web of Science. Thirty-two studies were identified, investigating various sample matrices and employing a wide variety of methods for each step of the metabolomics workflow. The vast majority of studies identified metabolites, mostly amino acids and lipids that may be associated with psoriasis diagnosis and activity. Further exploration is needed to identify and validate metabolomic biomarkers that can accurately and reliably predict which psoriasis patients will develop psoriatic arthritis, differentiate psoriatic arthritis patients from patients with other inflammatory arthritides and measure psoriatic arthritis activity. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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14 pages, 324 KiB  
Review
Alterations of Extracellular Matrix Components in the Course of Juvenile Idiopathic Arthritis
by Magdalena Wojdas, Klaudia Dąbkowska and Katarzyna Winsz-Szczotka
Metabolites 2021, 11(3), 132; https://doi.org/10.3390/metabo11030132 - 25 Feb 2021
Cited by 11 | Viewed by 1870
Abstract
Juvenile idiopathic arthritis (JIA) is the most common group of chronic connective tissue diseases in children that is accompanied by joint structure and function disorders. Inflammation underlying the pathogenic changes in JIA, caused by hypersecretion of proinflammatory cytokines, leads to the destruction of [...] Read more.
Juvenile idiopathic arthritis (JIA) is the most common group of chronic connective tissue diseases in children that is accompanied by joint structure and function disorders. Inflammation underlying the pathogenic changes in JIA, caused by hypersecretion of proinflammatory cytokines, leads to the destruction of articular cartilage. The degradation which progresses with the duration of JIA is not compensated by the extent of repair processes. These disorders are attributed in particular to changes in homeostasis of extracellular matrix (ECM) components, including proteoglycans, that forms articular cartilage. Changes in metabolism of matrix components, associated with the disturbance of their degradation and biosynthesis processes, are the basis of the progressive wear of joint structures observed in the course of JIA. Clinical evaluation and radiographic imaging are current methods to identify the destruction. The aim of this paper is to review enzymatic and non-enzymatic factors involved in catabolism of matrix components and molecules stimulating their biosynthesis. Therefore, we discuss the changes in these factors in body fluids of children with JIA and their potential diagnostic use in the assessment of disease activity. Understanding the changes in ECM components in the course of the child-hood arthritis may provide the introduction of both new diagnostic tools and new therapeutic strategies in children with JIA. Full article
(This article belongs to the Special Issue Biomarkers and Human Blood Metabolites)
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