Metabolic Syndrome and Non-alcoholic Liver Disease

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Lipid Metabolism".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 2331

Special Issue Editors


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Guest Editor
Internal Medicine Department, University of Medicine and Pharmacy of Craiova, Filantropia Hospital of Craiova, 200143 Craiova, Romania
Interests: metabolic syndrome; non-alcoholic liver disease; insulin resistance; fatty liver disease; obesity; type 2 diabetes; liver cirrhosis; dyslipidemia; NAFLD treatment; risk factors

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Guest Editor
School of Nursing, Healthcare Genetics Program, Clemson University, Clemson, SC 29646, USA
Interests: autism spectrum disorder; Phelan-McDermid syndrome; cancer; overgrowth; liver disease; gut microbiota; COVID-19
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Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a platform for researchers to present their latest findings and insights in the field of metabolic syndrome and non-alcoholic liver disease (NAFLD). The focus of this Special Issue is to explore the complex interplay between metabolic syndrome and NAFLD, shedding light on its underlying mechanisms, clinical implications, and potential therapeutic strategies.

This Special Issue welcomes contributions that address various aspects of metabolic syndrome and NAFLD, including (but not limited to) the pathophysiology, epidemiology, diagnostic tools, treatment modalities, and preventive measures. Research articles, reviews, and original studies that elucidate the link between metabolic syndrome and NAFLD, as well as those focusing on novel approaches for managing these interconnected conditions, are encouraged.

The purpose of this Special Issue is to foster a deeper understanding of the relationship between metabolic syndrome and NAFLD, considering the escalating global burden of both conditions. By gathering diverse perspectives and cutting-edge research, we aim to enhance the knowledge base and provide valuable insights for clinicians, researchers, and public health practitioners. Ultimately, we strive to facilitate the development of more effective strategies for the prevention, diagnosis, and management of metabolic syndrome and NAFLD, addressing the unmet clinical needs in this field.

Potential authors are invited to contribute original research articles, reviews, and commentaries that delve into the intricate links between metabolic syndrome and NAFLD, as well as propose innovative approaches for addressing these significant health challenges. We welcome submissions that offer new perspectives, present compelling data, and contribute to advancing the field's understanding of metabolic syndrome and NAFLD. Through this Special Issue, we aim to encourage dialogue, share best practices, and pave the way for improved clinical outcomes in these interconnected domains.

Prof. Dr. Mircea-Catalin Fortofoiu
Dr. Luigi Boccuto
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

  • metabolic syndrome
  • non-alcoholic fatty liver disease (NAFLD)
  • insulin resistance
  • obesity
  • type 2 diabetes
  • liver cirrhosis
  • dyslipidemia
  • NAFLD treatment
  • hepatic steatosis
  • cardiovascular risk

Published Papers (3 papers)

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Research

14 pages, 1658 KiB  
Article
Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques
by Sergio Gil-Rojas, Miguel Suárez, Pablo Martínez-Blanco, Ana M. Torres, Natalia Martínez-García, Pilar Blasco, Miguel Torralba and Jorge Mateo
Metabolites 2024, 14(6), 305; https://doi.org/10.3390/metabo14060305 - 27 May 2024
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Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine [...] Read more.
Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients. Full article
(This article belongs to the Special Issue Metabolic Syndrome and Non-alcoholic Liver Disease)
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13 pages, 1058 KiB  
Article
Accumulation of Non-Pathological Liver Fat Is Associated with the Loss of Glyoxalase I Activity in Humans
by Andreas Peter, Erwin Schleicher, Elisabeth Kliemank, Julia Szendroedi, Alfred Königsrainer, Hans-Ulrich Häring, Peter P. Nawroth and Thomas Fleming
Metabolites 2024, 14(4), 209; https://doi.org/10.3390/metabo14040209 - 7 Apr 2024
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Abstract
The underlying molecular mechanisms for the development of non-alcoholic fatty liver (NAFL) and its progression to advanced liver diseases remain elusive. Glyoxalase 1 (Glo1) loss, leading to elevated methylglyoxal (MG) and dicarbonyl stress, has been implicated in various diseases, including obesity-related conditions. This [...] Read more.
The underlying molecular mechanisms for the development of non-alcoholic fatty liver (NAFL) and its progression to advanced liver diseases remain elusive. Glyoxalase 1 (Glo1) loss, leading to elevated methylglyoxal (MG) and dicarbonyl stress, has been implicated in various diseases, including obesity-related conditions. This study aimed to investigate changes in the glyoxalase system in individuals with non-pathological liver fat. Liver biopsies were obtained from 30 individuals with a narrow range of BMI (24.6–29.8 kg/m2). Whole-body insulin sensitivity was assessed using HOMA-IR. Liver biopsies were analyzed for total triglyceride content, Glo1 and Glo2 mRNA, protein expression, and activity. Liquid chromatography–tandem mass spectrometry determined liver dicarbonyl content and oxidation and glycation biomarkers. Liver Glo1 activity showed an inverse correlation with HOMA-IR and liver triglyceride content, but not BMI. Despite reduced Glo1 activity, no associations were found with elevated liver dicarbonyls or glycation markers. A sex dimorphism was observed in Glo1, with females exhibiting significantly lower liver Glo1 protein expression and activity, and higher liver MG-H1 content compared to males. This study demonstrates that increasing liver fat, even within a non-pathological range, is associated with reduced Glo1 activity. Full article
(This article belongs to the Special Issue Metabolic Syndrome and Non-alcoholic Liver Disease)
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25 pages, 314 KiB  
Article
Evolutive Models, Algorithms and Predictive Parameters for the Progression of Hepatic Steatosis
by Marinela Sînziana Tudor, Veronica Gheorman, Georgiana-Mihaela Simeanu, Adrian Dobrinescu, Vlad Pădureanu, Venera Cristina Dinescu and Mircea-Cătălin Forțofoiu
Metabolites 2024, 14(4), 198; https://doi.org/10.3390/metabo14040198 - 3 Apr 2024
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Abstract
The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition [...] Read more.
The utilization of evolutive models and algorithms for predicting the evolution of hepatic steatosis holds immense potential benefits. These computational approaches enable the analysis of complex datasets, capturing temporal dynamics and providing personalized prognostic insights. By optimizing intervention planning and identifying critical transition points, they promise to revolutionize our approach to understanding and managing hepatic steatosis progression, ultimately leading to enhanced patient care and outcomes in clinical settings. This paradigm shift towards a more dynamic, personalized, and comprehensive approach to hepatic steatosis progression signifies a significant advancement in healthcare. The application of evolutive models and algorithms allows for a nuanced characterization of disease trajectories, facilitating tailored interventions and optimizing clinical decision-making. Furthermore, these computational tools offer a framework for integrating diverse data sources, creating a more holistic understanding of hepatic steatosis progression. In summary, the potential benefits encompass the ability to analyze complex datasets, capture temporal dynamics, provide personalized prognostic insights, optimize intervention planning, identify critical transition points, and integrate diverse data sources. The application of evolutive models and algorithms has the potential to revolutionize our understanding and management of hepatic steatosis, ultimately leading to improved patient outcomes in clinical settings. Full article
(This article belongs to the Special Issue Metabolic Syndrome and Non-alcoholic Liver Disease)
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