Early-Stage Biomarkers: Metabolomics in Preclinical and Prodromal Disease Detection

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

Deadline for manuscript submissions: 15 April 2026 | Viewed by 106

Special Issue Editor


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Guest Editor
Department of Endocrinology, Metabolism and Lipid Research, Washington University School of Medicine, St. Louis, MO 63110, USA
Interests: chromatography; mass spectrometry; medicinal plants; secondary me-tabolites; purification; biomarker discovery

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue entitled “Early-Stage Biomarkers: Metabolomics in Preclinical and Prodromal Disease Detection.” This Special Issue aims to spotlight the emerging role of metabolomics in identifying biomarkers at the earliest stages of disease progression prior to clinical diagnosis.

For diseases such as cancer, neurodegeneration, cardiovascular disorders, and many others, pathology often advances silently, narrowing the window for timely intervention. This Special Issue seeks to address this critical gap by focusing on metabolomics as a tool to capture early biochemical changes that occur in preclinical and prodromal phases and ultimately enabling earlier detection, improved prognosis, and more effective prevention strategies.

Focus

We invite contributions that leverage untargeted and targeted metabolomics approaches to detect and, importantly, validate subtle metabolic alterations that precede overt disease symptoms. Beyond discovery, this Special Issue strongly encourages studies emphasizing robust validation in large cohorts or independent datasets as an essential step for translating biomarkers into clinically actionable tools.

Submissions may involve human clinical studies, animal models, or in vitro systems, provided they aim to discover, replicate, or validate biomarkers relevant to early diagnosis, risk stratification, or patient monitoring.

Scope

This Special Issue welcomes original research articles, comprehensive reviews, and case studies covering (but not limited to):

  • Methodological and analytical advancements in metabolomics for early-stage biomarker discovery and validation.
  • Large-scale cohort studies and multi-site collaborations focused on replicating and confirming biomarker performance.
  • Integration with other omics (genomics, proteomics, transcriptomics, exposomics) for a systems-level understanding.
  • AI and machine learning approaches for biomarker selection, pattern recognition, and predictive modeling.
  • Applications across a wide spectrum of diseases, including Cancer, Alzheimer’s, Parkinson’s, diabetes, and cardiovascular disorders, cerebrovascular disease, autoimmune disorders, infectious diseases, and psychiatric conditions—areas where early detection could transform outcomes.
  • Translational research bridging discovery science to clinical implementation, including biomarker qualification and regulatory considerations.

Purpose

Our goal is to provide a platform that fosters interdisciplinary collaboration and highlights research with direct translational impact. By promoting rigorous validation of metabolite-based biomarkers, this Special Issue aims to accelerate the development of reliable early detection tools, support preventive healthcare strategies, and reduce the global healthcare burden.

Audience

We encourage potential authors from academia, clinical research, and the biotechnology sector to contribute their latest findings and insights in order to progress the field from discovery to clinical readiness.

Dr. Sonali Mishra
Guest Editor

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

  • early-stage biomarkers
  • metabolomics
  • biomarker validation
  • untargeted and targeted approaches
  • cohort studies
  • multi-omics integration
  • machine learning / AI
  • translational research
  • early disease detection
  • cancer, neurodegenerative, cerebrovascular, autoimmune, infectious, and psychiatric disorders

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Published Papers (1 paper)

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Research

14 pages, 790 KB  
Article
A Novel Single-Test Approach for GDM Diagnosis: Identification and Prediction of High-Risk Postprandial Hyperglycemia
by Hao Wu, Danqing Chen, Xue Li, Menglin Zhou and Qi Wu
Metabolites 2026, 16(1), 27; https://doi.org/10.3390/metabo16010027 (registering DOI) - 25 Dec 2025
Abstract
Background: Early prediction of gestational diabetes mellitus (GDM) remains a major clinical challenge, and the current oral glucose tolerance test (OGTT) is time-consuming and inconvenient for clinical routine. This study aimed to develop a novel predictive model for postprandial hyperglycemia GDM (pp-GDM) and [...] Read more.
Background: Early prediction of gestational diabetes mellitus (GDM) remains a major clinical challenge, and the current oral glucose tolerance test (OGTT) is time-consuming and inconvenient for clinical routine. This study aimed to develop a novel predictive model for postprandial hyperglycemia GDM (pp-GDM) and postprandial glucose elevation using fasting serological and metabolic profiles. Method: We used High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS) to analyze fasting plasma amino acid profiles at 24–28 weeks of gestation for 60 pp-GDM patients and 120 controls. Binary logistic regression model was constructed to identify potential biomarkers for pp-GDM prediction. Results: By incorporating amino acid indicators such as isoleucine, phenylalanine, threonine, and aspartate into the predictive model alongside traditional predictors (including BMI at sampling, fasting insulin, glycated hemoglobin, and uric acid), the overall predictive performance was significantly improved from 78.2% to 91.1%. A clinically practical nomogram for risk assessment was subsequently developed. Conclusions: This fasting metabolite-based model provides a reliable tool for early prediction of pp-GDM and postprandial hyperglycemia, which may reduce the need for OGTT and facilitate timely clinical decision making. Full article
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