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Metabolites
Special Issues
Proteomics and Metabolomics in Human Health and Disease
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Proteomics and Metabolomics in Human Health and Disease

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 4758

Special Issue Editors

Dr. Michele Costanzo

E-Mail Website
Guest Editor
Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, 80131 Naples, Italy
Interests: proteomics; metabolomics; lipidomics; interactomics; bioinformatics; mass spectrometry; metabolism; mitochondria; single-cell omics; PTMs
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Margherita Ruoppolo

E-Mail Website
Guest Editor
Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
Interests: inherited metabolic disorders; metabolomics; newborn screening; proteomics; protein-protein interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past two decades, the omics revolution has significantly advanced our understanding of human health and disease. Each omics discipline has developed into a specialized field, offering powerful insights into the complex molecular networks that underlie biological functions and dysfunctions.  

Proteomics and metabolomics show the ability to capture the dynamic biochemical landscape. Proteomics involves the large-scale study of protein expression and modifications to disclose pathological processes, while metabolomics focuses on small molecules and lipids, providing a real-time snapshot of metabolic activities and cellular phenotypes. Together, these approaches deliver a detailed molecular fingerprint that can reveal subtle, yet clinically significant, changes associated with disease onset, progression, or treatment response. Moreover, the integration of multiple omics data types has emerged as a transformative approach in biomedical research, enabling a more comprehensive exploration of disease mechanisms and the discovery of novel diagnostic and therapeutic targets.

This Special Issue invites contributions that apply proteomics and metabolomics to biomedical research, particularly those addressing disease-related questions through experimental innovation or computational integration. This collection will contribute to a more precise, system-level understanding of health and disease, with wide-ranging applications in both basic science and personalized medicine.

Dr. Michele Costanzo
Prof. Dr. Margherita Ruoppolo
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 250 words) can be sent to the Editorial Office for assessment.

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

  • proteomics
  • metabolomics
  • lipidomics
  • multi-omics
  • disease mechanisms
  • machine learning
  • omics integrations
  • artificial intelligence

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Published Papers (4 papers)

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Research

17 pages, 5410 KB  
Article
Bile and Serum Metabolomics in Living Donor Liver Transplantation: Exploratory Insights into Acute Rejection Biomarkers
by Yuta Hirata, Yasunaru Sakuma, Hideo Ogiso, Taiichi Wakiya, Takahiko Omameuda, Toshio Horiuchi, Noriki Okada, Yukihiro Sanada, Yasuharu Onishi, Hironori Yamaguchi, Ryozo Nagai and Kenichi Aizawa
Metabolites 2026, 16(4), 273; https://doi.org/10.3390/metabo16040273 - 17 Apr 2026
Viewed by 342
Abstract
Background: Acute rejection remains a major complication following liver transplantation, yet reliable noninvasive biomarkers for its early prediction and diagnosis remain unidentified. This exploratory study characterized bile and serum metabolites associated with acute rejection in living donor liver transplantation using comprehensive metabolomic profiling [...] Read more.
Background: Acute rejection remains a major complication following liver transplantation, yet reliable noninvasive biomarkers for its early prediction and diagnosis remain unidentified. This exploratory study characterized bile and serum metabolites associated with acute rejection in living donor liver transplantation using comprehensive metabolomic profiling combined with machine learning. Methods: Non-targeted metabolomics were performed on bile samples collected on post-operative day (POD) 1 (n = 38) and serum on POD 14 (n = 45) from liver transplant recipients. Partial least squares discriminant analysis-based variable selection was followed by logistic regression and least absolute shrinkage and selection operator models, which were evaluated via cross-validation in the discovery cohort to explore potential biomarkers for acute rejection. Results: A three-variable, bile-based model for predicting acute rejection achieved a mean cross-validated AUC of 0.872 (95% confidence interval: 0.814–0.930). Glycohyocholic acid and sulfolithocholylglycine were the main contributors. A nine-variable serum model for the Rejection Activity Index, including the change in γ-glutamyl transferase, showed a mean cross-validated R2 of 0.728 (95% confidence interval: 0.609–0.846), with methionine, creatine, and oxidized fatty acids contributing prominently. Conclusions: These findings suggest that metabolomic profiling combined with machine learning may provide candidate biomarkers for acute rejection after liver transplantation. However, given the exploratory nature of the study and the lack of external validation, the clinical utility of these metabolite signatures remains to be determined. Therefore, external validation in larger, independent cohorts will be required. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics in Human Health and Disease)
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30 pages, 1740 KB  
Article
Untargeted Metabolomics Profiling of a PFAS-Exposed Flemish Population
by María del Mar Delgado-Povedano, Haesong Sher, Leen Jacobs, Maria van de Lavoir, Rani Robeyns, Ann Colles, Eva Govarts, Elly Den Hond, Giulia Poma, Alexander L. N. van Nuijs and Adrian Covaci
Metabolites 2026, 16(2), 135; https://doi.org/10.3390/metabo16020135 - 15 Feb 2026
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Abstract
Background/Objectives: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants that accumulate in humans through everyday exposure pathways, raising concern about long-term metabolic health effects in exposed populations. This study aimed to characterize PFAS-associated serum metabolic alterations in a Flemish population residing [...] Read more.
Background/Objectives: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants that accumulate in humans through everyday exposure pathways, raising concern about long-term metabolic health effects in exposed populations. This study aimed to characterize PFAS-associated serum metabolic alterations in a Flemish population residing within a 3 km radius of a PFAS production facility using untargeted metabolomics and lipidomics. Methods: A cohort of 82 adults was stratified into high-exposure (n = 41, median total PFAS = 162.0 ng/mL) and low-exposure (n = 41, median total PFAS = 7.2 ng/mL) groups. Serum metabolic profiling was performed using four liquid chromatography–high-resolution mass spectrometry (LC-HRMS)-based platforms. Univariate and multivariate statistics were conducted to identify metabolites that were differentially expressed between both exposure groups. Results: The analysis revealed 38 altered metabolites. Overall, high PFAS exposure was characterized by upregulation of phosphatidylglycerols (PG), phosphatidylinositols, phosphatidylethanolamines (PE), and triacylglycerols (TG) and downregulation of sphingomyelins, with differential regulation of ceramides, hexosylceramides (HexCer), and phosphatidylcholines. Glycerophospholipid metabolism as well as sphingolipid metabolism pathways were identified as perturbed. Seven lipids and one amino acid showed weak-to-strong correlations (|r|= 0.23–0.61) with PFAS levels. A panel of five metabolites was selected to explore whether they collectively form a potential metabolic signature associated with PFAS exposure. This panel, including L-aspartic acid, PG 18:0_18:2, HexCer (d18:1/14:0), PE 16:0_18:3, and TG 16:0_20:5_22:6, showed moderate discrimination between residents with high and low PFAS levels (area under the curve, AUC = 0.753). Conclusions: This study identifies coordinated lipid metabolic changes associated with PFAS exposure and highlights a small, exploratory metabolite panel that may provide complementary insight into the biological effects of PFAS. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics in Human Health and Disease)
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15 pages, 963 KB  
Article
Development and Validation of a Targeted Metabolomic Tool for Metabotype Classification in Schoolchildren
by Sheyla Karina Hernández-Ramírez, Diego Arturo Velázquez-Trejo, Eduardo Sandoval-Colín, Cristóbal Fresno, Mariana Flores-Torres, Ernestina Polo-Oteyza, María José Garcés-Hernández, Nayely Garibay-Nieto, Isabel Ibarra-González, Marcela Vela-Amieva, Guadalupe Estrada-Gutierrez and Felipe Vadillo-Ortega
Metabolites 2026, 16(1), 44; https://doi.org/10.3390/metabo16010044 - 4 Jan 2026
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Abstract
Background: Metabolomic profiling can uncover metabolic differences among seemingly healthy children, providing opportunities for personalized medicine and early detection of risk biomarkers for future metabolic disorders. This study aimed to identify and internally validate metabotypes in apparently healthy schoolchildren using targeted serum metabolomics [...] Read more.
Background: Metabolomic profiling can uncover metabolic differences among seemingly healthy children, providing opportunities for personalized medicine and early detection of risk biomarkers for future metabolic disorders. This study aimed to identify and internally validate metabotypes in apparently healthy schoolchildren using targeted serum metabolomics and to assess the external validity of this metabotype classification tool in two separate groups of children. Methods: Data from schoolchildren aged 6–11 years were analyzed in two phases. In the first phase, we developed and validated a classification tool using targeted serum metabolomics in healthy children. Metabotypes were identified through unsupervised clustering with a self-organizing map, followed by assessment of cluster stability and classification accuracy. In the second phase, we tested the tool’s consistency by applying it to two additional groups: the same children from phase 1 after a 10-month physical activity intervention, and a separate group diagnosed with metabolic syndrome. Results: Three metabotypes were identified in healthy children: METBA (balanced profile), METLI (high lipid and glucose levels), and METAA (high amino acid levels). Internal validation showed strong cluster stability (ARI = 0.79) and high classification accuracy (0.95). After the intervention, 55% of children were reclassified, indicating diverse metabolic responses to physical activity. Among children with metabolic syndrome, 83% were classified as METLI and 13% as METAA. Conclusions. This tool revealed serum metabolomic diversity, enabling classification of healthy children into three distinct metabotypes. It also detects changes in metabotype classification associated with a physical activity intervention and identifies the majority of children diagnosed with metabolic syndrome within two groups. This supports the potential use of metabotypes as biomarkers and eventually for personalized interventions. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics in Human Health and Disease)
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16 pages, 568 KB  
Article
Effect of Creatinine on Various Clinical Outcomes in Patients with Severe Traumatic Brain Injury (TBI)
by Sarah Dawson-Moroz, Schneider Rancy, George Agriantonis, Kate Twelker, Navin D. Bhatia, Zahra Shafaee, Jennifer Whittington and Bharti Sharma
Metabolites 2025, 15(10), 657; https://doi.org/10.3390/metabo15100657 - 4 Oct 2025
Viewed by 1803
Abstract
Background: Traumatic brain injury (TBI) is a major public health concern. Creatinine (Cr) has been well studied as a marker of renal function, specifically the development of acute kidney injury (AKI) in TBI patients. We aimed to evaluate the effect of Cr on [...] Read more.
Background: Traumatic brain injury (TBI) is a major public health concern. Creatinine (Cr) has been well studied as a marker of renal function, specifically the development of acute kidney injury (AKI) in TBI patients. We aimed to evaluate the effect of Cr on various clinical outcomes in patients with severe TBI. Methods: We investigated the relationship between Cr levels at various time points and a range of clinical variables, using parametric and non-parametric statistical testing. Results: 1000 patients were included in our study. We found a significant association between sex and Cr level at intensive care unit (ICU) admission and ICU discharge. Cr was positively correlated with ISS at hospital admission, ICU admission, ICU discharge, and at death. Conversely, Cr was negatively correlated with GCS at hospital admission, ICU admission, ICU discharge, and at death. Larger decreases in Cr from Hospital to ICU admission were significantly correlated with increased vent days. Larger decreases in Cr from ICU admission to ICU discharge were significantly correlated with increased hospital length of stay (LOS), ICU LOS, and vent days, likely reflecting the degree of initial hypercreatinemia. For all patients, there were significant positive correlations between Cr at admission and ICU LOS, Cr at ICU admission and ICU LOS, and Cr at ICU admission and vent days. Conclusions: Our findings support existing literature that demonstrates a positive relationship between Cr levels, ICU LOS, and vent days amongst patients with severe TBI. These data suggest renal injury is predictive of TBI outcomes. Future research should investigate the role of renal therapeutic interventions in TBI recovery. Full article
(This article belongs to the Special Issue Proteomics and Metabolomics in Human Health and Disease)
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