Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.7
3.5
Journals
Metabolites
Special Issues
Metabolomics in Human Diseases and Health
share announcement

Metabolomics in Human Diseases and Health

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 7714

Special Issue Editors

Dr. Dimitris Kounatidis

E-Mail
Guest Editor
Diabetes Center, First Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
Interests: diabetes; lipids; biomarkers; lipoproteins; obesity; microbiota; sepsis; antibiotics
Special Issues, Collections and Topics in MDPI journals
Dr. Iordanis Mourouzis

E-Mail
Guest Editor
Diabetes Center, First Department of Propaedeutic Internal Medicine, Medical School, National and Kapodistrian University of Athens, Laiko General Hospital, 11527 Athens, Greece
Interests: pharmacology; cardiac remodeling; cardiac regeneration; heart failure; sepsis; hypertension; atherogenesis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metabolomics is a rapidly evolving field dedicated to the comprehensive identification and analysis of small molecules or metabolites present in various biological samples, including cells, tissues, and bodily fluids. Unlike other ‘omics’ technologies, metabolomics offers a direct reflection of the metabolic activity and status of cells and tissue, making it a promising tool for understanding molecular phenotypes. This field employs both untargeted and targeted approaches, with mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy being the primary analytical techniques. In the context of human health, metabolomics plays a pivotal role in identifying metabolic patterns associated with various diseases such as cancer, diabetes mellitus, cardiovascular diseases, and neurodegenerative disorders.

These unique signatures can serve as diagnostic markers, facilitating early disease detection, monitoring disease progression, and guiding personalized treatment strategies. Furthermore, metabolomics contributes to a deeper understanding of disease mechanisms, paving the way for the development of novel therapeutic interventions and precision-targeted treatments. It therefore holds significant potential for advancing our understanding of human diseases and improving patient care outcomes, offering valuable insights into diagnosis and prognosis.

The aim of this Special Issue, titled "Metabolomics in Human Diseases and Health", is to provide an in-depth overview of the field, focusing on the aspects mentioned above. We welcome original research and review articles that explore these themes and contribute to the advancement of metabolomics in human health.

Dr. Dimitris Kounatidis
Dr. Iordanis Mourouzis
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

  • metabolomics
  • NAFLD
  • biomarkers
  • obesity
  • mass spectrometry
  • NMR spectroscopy
  • diabetes mellitus
  • cancer
  • cardiovascular diseases
  • neurodegenerative diseases

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

23 pages, 3688 KiB  
Article
Targeted and Non-Targeted Metabolomic Evaluation of Cerebrospinal Fluid in Early Phase Schizophrenia: A Pilot Study from the Hopkins First Episode Psychosis Project
by George E. Jaskiw, Mark E. Obrenovich, Curtis J. Donskey, Farren B. S. Briggs, Sun Sunnie Chung, Anastasiya I. Kalinina, Austin Bolomey, Lindsay N. Hayes, Kun Yang, Robert H. Yolken and Akira Sawa
Metabolites 2025, 15(4), 275; https://doi.org/10.3390/metabo15040275 - 15 Apr 2025
Viewed by 295
Abstract
(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects [...] Read more.
(1) Background: The lack of reliable biomarkers remains a significant barrier to improving outcomes for patients with schizophrenia. While metabolomic analyses of blood, urine, and feces have been explored, results have been inconsistent. Compared to peripheral compartments, cerebrospinal fluid (CSF) more closely reflects the chemical composition of brain extracellular fluid. Given that brain dysregulation may be more pronounced during the first episode of psychosis (FEP), we hypothesized that metabolomic analysis of CSF from FEP patients could reveal disease-associated biomarkers. (2) Methods: We recruited 15 patients within 24 months of psychosis onset (DSM-4 criteria) and 14 control participants through the Johns Hopkins Schizophrenia Center. CSF samples were analyzed using both non-targeted and targeted liquid chromatography–mass spectrometry. (3) Results: The non-targeted analysis identified lower levels of N-acetylneuraminic acid and N-acetyl-L-aspartic acid in the FEP group, while levels of uric acid were elevated. The targeted analysis focused on indolic and phenolic molecules previously linked to neuropsychiatric disorders. Notably, L-phenylalanine and 4-hydroxycinnamic acid levels were lower in the FEP group, and this difference remained significant after adjusting for age and sex. However, none of the significant differences in analyte levels between the groups survived an adjustment for multiple comparisons. (4) Conclusions: Our intriguing but preliminary associations align with results from other investigational approaches and highlight potential CSF analytes that warrant further study in larger samples. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

21 pages, 2010 KiB  
Article
Longitudinal Metabolomics Data Analysis Informed by Mechanistic Models
by Lu Li, Huub Hoefsloot, Barbara M. Bakker, David Horner, Morten A. Rasmussen, Age K. Smilde and Evrim Acar
Metabolites 2025, 15(1), 2; https://doi.org/10.3390/metabo15010002 - 24 Dec 2024
Cited by 2 | Viewed by 891
Abstract
Background: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior [...] Read more.
Background: Metabolomics measurements are noisy, often characterized by a small sample size and missing entries. While data-driven methods have shown promise in terms of analyzing metabolomics data, e.g., revealing biomarkers of various phenotypes, metabolomics data analysis can significantly benefit from incorporating prior information about metabolic mechanisms. This paper introduces a novel data analysis approach to incorporate mechanistic models in metabolomics data analysis. Methods: We arranged time-resolved metabolomics measurements of plasma samples collected during a meal challenge test from the COPSAC2000 cohort as a third-order tensor: subjects by metabolites by time samples. Simulated challenge test data generated using a human whole-body metabolic model were also arranged as a third-order tensor: virtual subjects by metabolites by time samples. Real and simulated data sets were coupled in the metabolites mode and jointly analyzed using coupled tensor factorizations to reveal the underlying patterns. Results: Our experiments demonstrated that the joint analysis of simulated and real data had better performance in terms of pattern discovery, achieving higher correlations with a BMI (body mass index)-related phenotype compared to the analysis of only real data in males, while in females, the performance was comparable. We also demonstrated the advantages of such a joint analysis approach in the presence of incomplete measurements and its limitations in the presence of wrong prior information. Conclusions: The joint analysis of real measurements and simulated data (generated using a mechanistic model) through coupled tensor factorizations guides real data analysis with prior information encapsulated in mechanistic models and reveals interpretable patterns. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

17 pages, 2124 KiB  
Article
Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway
by Petr G. Lokhov, Elena E. Balashova, Dmitry L. Maslov, Oxana P. Trifonova and Alexander I. Archakov
Metabolites 2024, 14(11), 593; https://doi.org/10.3390/metabo14110593 - 4 Nov 2024
Viewed by 1121
Abstract
Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. [...] Read more.
Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. The use of accumulated metabolomic data to characterize this phenomenon at the system level may provide additional insight into the nature and strength of aging–disease relationships. Methods: For this purpose, metabolic changes associated with human aging and metabolic alterations under different pathological conditions were compared. To do this, the published results of metabolomic studies on human aging were compared with data on metabolite alterations collected in the human metabolome database through metabolite set enrichment analysis (MSEA) and combinatorial analysis. Results: It was found that human aging and pathological conditions involve the set of the same metabolic pathways with a probability of 99.96%. These data show the high identity of the aging process and the development of diseases at the metabolic level and allow to identify the set of metabolic pathways reflecting age-related changes closely associated with health. Based on these pathways, a metapathway was compiled, changes in which are simultaneously associated with health and age. Conclusions: The knowledge about the strength of the convergence of aging and pathological conditions has been supplemented by the rigor evidence at the metabolome level, which also made it possible to outline the age and health-relevant place in the human metabolism. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

14 pages, 2039 KiB  
Article
Metabolomic Effects of Liraglutide Therapy on the Plasma Metabolomic Profile of Patients with Obesity
by Assim A. Alfadda, Anas M. Abdel Rahman, Hicham Benabdelkamel, Reem AlMalki, Bashayr Alsuwayni, Abdulaziz Alhossan, Madhawi M. Aldhwayan, Ghalia N. Abdeen, Alexander Dimitri Miras and Afshan Masood
Metabolites 2024, 14(9), 500; https://doi.org/10.3390/metabo14090500 - 17 Sep 2024
Viewed by 1902
Abstract
Background: Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at [...] Read more.
Background: Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at changes in metabolite levels before and after liraglutide treatment in patients with obesity. To this end, in the present study we aimed to explore the changes in the plasma metabolomic profile, using liquid chromatography-high resolution mass spectrometry (LC-HRMS) in patients with obesity. Methods: A single-center prospective study was undertaken to evaluate the effectiveness of 3 mg liraglutide therapy in twenty-three patients (M/F: 8/15) with obesity, mean BMI 40.81 ± 5.04 kg/m2, and mean age of 36 ± 10.9 years, in two groups: at baseline (pre-treatment) and after 12 weeks of treatment (post-treatment). An untargeted metabolomic profiling was conducted in plasma from the pre-treatment and post-treatment groups using LC-HRMS, along with bioinformatics analysis using ingenuity pathway analysis (IPA). Results: The metabolomics analysis revealed a significant (FDR p-value ≤ 0.05, FC 1.5) dysregulation of 161 endogenous metabolites (97 upregulated and 64 downregulated) with distinct separation between the two groups. Among the significantly dysregulated metabolites, the majority of them were identified as belonging to the class of oxidized lipids (oxylipins) that includes arachidonic acid and its derivatives, phosphorglycerophosphates, N-acylated amino acids, steroid hormones, and bile acids. The biomarker analysis conducted using MetaboAnalyst showed PGP (a21:0/PG/F1alpha), an oxidized lipid, as the first metabolite among the list of the top 15 biomarkers, followed by cysteine and estrone. The IPA analysis showed that the dysregulated metabolites impacted the pathway related to cell signaling, free radical scavenging, and molecular transport, and were focused around the dysregulation of NF-κB, ERK, MAPK, PKc, VEGF, insulin, and pro-inflammatory cytokine signaling pathways. Conclusions: The findings suggest that liraglutide treatment reduces inflammation and modulates lipid metabolism and oxidative stress. Our study contributes to a better understanding of the drug’s multifaceted impact on overall metabolism in patients with obesity. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

19 pages, 1928 KiB  
Article
Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study
by Tobias Kipura, Madlen Hotze, Alexa Hofer, Anna-Sophia Egger, Lea E. Timpen, Christiane A. Opitz, Paul A. Townsend, Lee A. Gethings, Kathrin Thedieck and Marcel Kwiatkowski
Metabolites 2024, 14(7), 370; https://doi.org/10.3390/metabo14070370 - 30 Jun 2024
Cited by 3 | Viewed by 2164
Abstract
Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample [...] Read more.
Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

Review

Jump to: Research

20 pages, 1291 KiB  
Review
Metabolomics Insights into Gut Microbiota and Functional Constipation
by Fan Zheng, Yong Yang, Guanting Lu, Joo Shun Tan, Uma Mageswary, Yu Zhan, Mina Ehab Ayad, Yeong-Yeh Lee and Daoyuan Xie
Metabolites 2025, 15(4), 269; https://doi.org/10.3390/metabo15040269 - 12 Apr 2025
Viewed by 512
Abstract
Background: The composition and metabolic activity of the gut microbiota play a crucial role in various health conditions, including the occurrence and development of chronic constipation. Recent metabolomic advances reveal that gut microbiota-derived metabolites—such as SCFAs, bile acids, neurotransmitters, and microbial gases—play critical [...] Read more.
Background: The composition and metabolic activity of the gut microbiota play a crucial role in various health conditions, including the occurrence and development of chronic constipation. Recent metabolomic advances reveal that gut microbiota-derived metabolites—such as SCFAs, bile acids, neurotransmitters, and microbial gases—play critical roles in regulating intestinal function. Methods: We systematically analyzed the current literature on microbial metabolomics in chronic constipation. This review consolidates findings from high-throughput metabolomic techniques (GC-MS, LC-MS, NMR) comparing metabolic profiles of constipated patients with healthy individuals. It also examines diagnostic improvements and personalized treatments, including fecal microbiota transplantation and neuromodulation, guided by these metabolomic insights. Results: This review shows that reduced SCFA levels impair intestinal motility and promote inflammation. An altered bile acid metabolism—with decreased secondary bile acids like deoxycholic acid—disrupts receptor-mediated signaling, further affecting motility. Additionally, imbalances in amino acid metabolism and neurotransmitter production contribute to neuromuscular dysfunction, while variations in microbial gas production (e.g., methane vs. hydrogen) further modulate gut transit. Conclusions: Integrating metabolomics with gut microbiota research clarifies how specific microbial metabolites regulate gut function. These insights offer promising directions for precision diagnostics and targeted therapies to restore microbial balance and improve intestinal motility. Full article
(This article belongs to the Special Issue Metabolomics in Human Diseases and Health)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop