Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.9
3.7
Journals
Metabolites
Special Issues
Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by...
share announcement

Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by Advanced Methodologies in Lipidomics and Metabolomics

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Endocrinology and Clinical Metabolic Research".

Deadline for manuscript submissions: closed (15 May 2025) | Viewed by 5137

Special Issue Editors

Dr. Ying Zhang

E-Mail Website
Guest Editor
BioMarin Pharmaceuticals, Inc., San Rafael, CA 94901, USA
Interests: multi-omics; metabolomics; biomarker; genetic diseases
Dr. Kang Chen

E-Mail Website
Guest Editor
College of Food Science and Engineering, Ningbo University, Ningbo 315211, China
Interests: omics; chronic disease; immunology; food
Dr. Jun Ding

E-Mail Website
Guest Editor
Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
Interests: metabolomics; mass spectrometry; metabolome mapping; plant hormone analysis

Special Issue Information

Dear colleagues,

Chronic diseases are complex conditions that usually last 3 months or longer and may get worse over time. The most common types of chronic diseases are cancer, heart disease, chronic respiratory diseases, diabetes, arthritis and neurodegenerative disorders. Some clinical biomarkers lack the efficiency of detecting the pre-disease stage such as T2D. The risk of developing chronic diseases might be a combination of genetic and environmental factors such as T1D. The mechanisms of some chronic diseases are not well understood such as neurodegenerative disorders.

In this context, discovering novel biomarkers for early-stage disease diagnosis and risk prediction years before the disease onset can greatly benefit disease-affected populations. Metabolomics, aimed at the systematic measurement of all small molecules from a single biological sample, has been matured as an essential tool for such a purpose from different aspects. Untargeted metabolomics (and/or lipidomics) can provide a complete metabolic signature for novel biomarker discovery or understanding the fundamental mechanisms resulting in metabolic-state perturbations. Targeted metabolomics can provide a quantitative view of molecular targets within a pathway with more accurate information of metabolite alterations.

Therefore, this Special Issue seeks to bring together the state-of-the-art research on understanding and predicting the risk of disease progress by metabolomics/lipidomics. Works on innovative metabolomics methodologies for chronic diseases are also highly welcomed.

Dr. Ying Zhang
Dr. Kang Chen
Dr. Jun Ding
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

  • chronic diseases
  • lipidomics
  • metabolomics
  • risk prediction
  • biomarker discovery

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 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

13 pages, 1982 KiB  
Article
Lipid Subclasses Differentiate Insulin Resistance by Triglyceride–Glucose Index
by Khaled Naja, Najeha Anwardeen, Omar Albagha and Mohamed A. Elrayess
Metabolites 2025, 15(5), 342; https://doi.org/10.3390/metabo15050342 - 20 May 2025
Viewed by 429
Abstract
Background: Insulin resistance is a key driver of metabolic syndrome and related disorders, yet its underlying metabolic alterations remain incompletely understood. The Triglyceride–Glucose (TyG) index is an emerging, accessible marker for insulin resistance, with growing evidence supporting its clinical utility. This study aimed [...] Read more.
Background: Insulin resistance is a key driver of metabolic syndrome and related disorders, yet its underlying metabolic alterations remain incompletely understood. The Triglyceride–Glucose (TyG) index is an emerging, accessible marker for insulin resistance, with growing evidence supporting its clinical utility. This study aimed to characterize the metabolic profiles associated with insulin resistance using the TyG index in a large, population-based cohort, and to identify metabolic pathways potentially implicated in insulin resistance. Methods: Here, we conducted a cross-sectional study using data from the Qatar Biobank, including 1255 participants without diabetes classified as insulin-sensitive or insulin-resistant based on TyG index tertiles. Untargeted serum metabolomics profiling was performed using high-resolution mass spectrometry. Our statistical analyses included orthogonal partial least squares discriminate analysis and linear models. Results: Distinct metabolic signatures differentiated insulin-resistant from insulin-sensitive participants. Phosphatidylethanolamines, phosphatidylinositols, and phosphatidylcholines, were strongly associated with insulin resistance, while plasmalogens and sphingomyelins were consistently linked to insulin sensitivity. Conclusions: Lipid-centric pathways emerge as potential biomarkers and therapeutic targets for the early detection and personalized management of insulin resistance and related metabolic disorders. Longitudinal studies are warranted to validate causal relationships. Full article
(This article belongs to the Special Issue Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by Advanced Methodologies in Lipidomics and Metabolomics)
►▼ Show Figures

Figure 1

18 pages, 1328 KiB  
Article
Metabolomics Approach to Identify Biomarkers of Acute and Subacute Mastitis in Milk Samples: A Pilot Case–Control Study
by Paola Quifer-Rada, Laia Aguilar-Camprubí, Sara Samino, Nuria Amigó, Oria Soler and Alba Padró-Arocas
Metabolites 2024, 14(10), 566; https://doi.org/10.3390/metabo14100566 - 21 Oct 2024
Cited by 1 | Viewed by 2356
Abstract
Background and aims: Mastitis is one of the main complications during breastfeeding and contributes to the cessation of breastfeeding. However, the etiopathogenesis and diagnosis of mastitis are complex and not yet well defined. We aimed to identify metabolic and lipidic changes in [...] Read more.
Background and aims: Mastitis is one of the main complications during breastfeeding and contributes to the cessation of breastfeeding. However, the etiopathogenesis and diagnosis of mastitis are complex and not yet well defined. We aimed to identify metabolic and lipidic changes in human milk during acute and subacute mastitis in order to detect potential biomarkers of mastitis. Methods: We conducted a pilot case–control study including 14 breastfeeding women with acute mastitis, 32 with subacute mastitis symptoms, and 19 without any mastitis symptoms (control). Milk samples were collected and analyzed by proton nuclear magnetic resonance (H-NMR) for metabolomics analysis. To assess the association between the significant metabolites and lipids and the development of acute and subacute mastitis, multi-adjusted logistic regression models were developed. Results: The NMR-based metabolomics approach was able to identify and quantify a total of 40 metabolites in breast milk samples. After adjusting for confounding variables, acute mastitis was significantly associated with acetate (OR 3.9 IC 1.4–10.8), total cholesterol (OR 14 CI 3.2–62), esterified cholesterol (OR 3.3 CI 1.9–5.8), and sphingomyelin (OR 2.6 CI 1.2–5.8). The other metabolites presented weak association (OR < 2.5). Subacute mastitis was significantly associated with glutamine, lysophosphatidylcholine, phosphatidylcholine, plasmalogen, and total polyunsaturated fatty acids, but only cholesterol showed a strong association (OR > 2.5) with an OR of 2.6 (IC 1.1–6.6). Conclusions: Metabolic alteration in breast milk occurs during a process of both acute and subacute mastitis. Acetate, esterified cholesterol, lysophostidylcholine, and polyunsaturated fatty acids increased in both acute and subacute mastitis. However, according to the multi-adjusted regression logistic models, the candidate biomarkers for acute and subacute mastitis are cholesterol, lysophosphatidylcoholine, phosphatidylcholine, plasmalogen, and polyunsaturated fatty acids. Full article
(This article belongs to the Special Issue Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by Advanced Methodologies in Lipidomics and Metabolomics)
►▼ Show Figures

Figure 1

Review

Jump to: Research

29 pages, 876 KiB  
Review
SARS-CoV-2 in Asthmatic Children: Same Consequences in Different Endotypes?
by Alice Bosco, Vassilios Fanos, Serena Bosone, Valeria Incandela, Federica La Ciacera and Angelica Dessì
Metabolites 2025, 15(6), 406; https://doi.org/10.3390/metabo15060406 - 16 Jun 2025
Viewed by 228
Abstract
During the early stages of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, concerns arose regarding the susceptibility of asthmatic children, one of the most common chronic conditions in childhood and a major cause of hospitalization in pediatric settings. Unexpectedly, evidences showed [...] Read more.
During the early stages of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, concerns arose regarding the susceptibility of asthmatic children, one of the most common chronic conditions in childhood and a major cause of hospitalization in pediatric settings. Unexpectedly, evidences showed milder clinical courses and fewer asthma exacerbations in these patients, even if cases of critical and fatal infection, often related to specific clinical features of the patient, are not negligible. In this regard, obesity is considered not only an important comorbidity in patients with difficult-to-treat asthma but also a risk factor for more severe forms of COVID-19. These observations are of even greater concern in the context of an increase in childhood obesity that began even before the SARS-CoV-2 pandemic and has continued also as a consequence of it. Given asthma’s heterogeneity, especially in children, an endotype-based approach is crucial. This is possible through a detailed analysis of the complex metabolic pathways that correlate asthma, COVID-19 infection and obesity thanks to new high-through-put technologies, especially metabolomics, which with minimally invasive sampling, including on exhaled breath condensate (EBC), can provide precise and unbiased evidence in support of existing endotypes, making it possible to identify not only the most vulnerable individuals and thus risk stratification through specific biomarkers, but also new molecular and therapeutic targets. This review explores asthma endotypes by highlighting their shared immunometabolic pathways with COVID-19. Findings suggest that metabolomics could enable more accurate risk stratification and guide personalized interventions during viral pandemics, especially in the presence of relevant comorbidities such as obesity. Full article
(This article belongs to the Special Issue Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by Advanced Methodologies in Lipidomics and Metabolomics)
►▼ Show Figures

Graphical abstract

17 pages, 600 KiB  
Review
Reproductive Health in Women with Major β-Thalassemia: Evaluating Ovarian Reserve and Endocrine Complications
by Vasileios Tsilionis, Efthalia Moustakli, Stefanos Dafopoulos, Athanasios Zikopoulos, Sotirios Sotiriou, Athanasios Zachariou and Konstantinos Dafopoulos
Metabolites 2024, 14(12), 717; https://doi.org/10.3390/metabo14120717 - 20 Dec 2024
Cited by 1 | Viewed by 1461
Abstract
Thalassemia is an autosomal recessive hereditary chronic hemolytic anemia characterized by a partial or complete deficiency in the synthesis of alpha- or beta-globin chains, which are essential components of adult hemoglobin. Mutations in the globin genes lead to the production of unstable globin [...] Read more.
Thalassemia is an autosomal recessive hereditary chronic hemolytic anemia characterized by a partial or complete deficiency in the synthesis of alpha- or beta-globin chains, which are essential components of adult hemoglobin. Mutations in the globin genes lead to the production of unstable globin chains that precipitate within cells, causing hemolysis. This shortens the lifespan of mature red blood cells (RBCs) and results in the premature destruction of RBC precursors in the bone marrow. Regular red blood cell transfusions are the standard treatment for thalassemia. However, these transfusions can lead to increased iron overload, which can impair vital systems such as the liver, heart, ovaries, and endocrine system. Focusing on female reproductive endocrinology, recurrent blood transfusions can cause iron accumulation in the pituitary and hypothalamus, leading to hypogonadotropic hypogonadism (HH), the most common endocrinopathy in these patients, affecting 40–91% of women. Recurrent transfusions and the resulting iron overload can also lead to oxidative stress and ovarian damage in patients with beta-thalassemia major (BTM). Despite advancements in iron chelation therapy, hypothalamic–pituitary damage associated with HH contributes to subfertility and sexual dysfunction, often with little to no recovery. In women exposed to gonadotoxic drugs, particularly those with BTM, anti-Mullerian hormone (AMH)—a marker of ovarian reserve—is frequently used to assess ovarian damage. This review aims to explore the pathophysiology of β-thalassemia and its major clinical manifestations, with a focus on endocrine complications and their impact on ovarian reserve. It also investigates how metabolomics can provide insights into the disease’s metabolic alterations and inform current and emerging therapeutic strategies to mitigate complications and optimize patient outcomes, potentially leading to more effective and personalized treatments. Full article
(This article belongs to the Special Issue Risk Prediction and Novel Biomarker Discovery of Chronic Diseases by Advanced Methodologies in Lipidomics and Metabolomics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop