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Nutrients
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Nutrigenomics and Exposomics: Precision Nutrition in Metabolic Diseases
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Nutrigenomics and Exposomics: Precision Nutrition in Metabolic Diseases

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrigenetics and Nutrigenomics".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 3867

Special Issue Editor

Dr. Ghada Soliman

E-Mail Website
Guest Editor
City University of New York (CUNY), Graduate School of Public Health and Health Policy, New York, NY, USA
Interests: nutrient metabolism; metabolomics; precision nutrition; mTOR signaling network; exposome; cardiometabolic diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The exposome and systems biology have the capacity to address unmet precision nutrition and public health needs. The exposome is defined as the totality of internal and external exposures across the lifespan and their health effects. The external exposome includes diet, nutrients, water, chemicals, pollutants, toxins, and other environmental and geospatial exposures. The internal exposome can be measured by untargeted metabolomics, encompassing the small metabolites resulting from all the metabolic processes. As such, the cumulative exposome can interact with the genetic predisposition to alter the metabolome and trigger the cardiometabolic disease onset or progression. In parallel, the biology systems approach facilitates the integration of large databases, including genomics, epigenomics, proteomics, metabolomics, and exposomics. Taken together, the exposome and biology system approaches can put into focus the signaling pathways, metabolic processes, and gene–environment interactions that can lead to the development of early diagnostic biomarkers, unravel new mechanism-based treatments, and assess the impact of the current treatments of metabolic diseases.

This Special Issue will address the food and dietary exposome, nutrigenomics, nutrients, beverages, phytochemicals, processed food, metabolomics, and the impact on the system biology and cardiometabolic disease risk, which may contribute to the applications of precision nutrition for therapeutic interventions in metabolic diseases.

Dr. Ghada Soliman
Guest Editor

Manuscript Submission Information

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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. Nutrients is an international peer-reviewed open access semimonthly 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 2900 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

  • nutrigenomics
  • internal exposome
  • external exposome
  • diet and nutrients
  • cardiometabolic diseases
  • Type 1 Diabetes (T1D)
  • Type 2 Diabetes (T2D)
  • insulin resistance
  • metabolic syndrome
  • precision nutrition
  • personalized medicine
  • pathomechanisms
  • omics platform
  • genomics
  • metabolomics
  • transcriptomics
  • proteomics
  • lipidomics
  • metagenomics
  • epigenomics
  • biosamples

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Published Papers (2 papers)

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Research

14 pages, 1279 KiB  
Article
Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery
by Fatma Hilal Yagin, Fahaid Al-Hashem, Irshad Ahmad, Fuzail Ahmad and Abedalrhman Alkhateeb
Nutrients 2024, 16(10), 1537; https://doi.org/10.3390/nu16101537 - 20 May 2024
Cited by 3 | Viewed by 1370
Abstract
Background: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Methods: Univariate and multivariate analyses such as fold change, a receiver operating [...] Read more.
Background: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques. Methods: Univariate and multivariate analyses such as fold change, a receiver operating characteristic curve (ROC), and Partial Least-Squares Discriminant Analysis (PLS-DA) were used to identify biomarker metabolites that showed significant concentration in T2D patients. Three tree-based algorithms [eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Adaptive Boosting (AdaBoost)] that demonstrated robustness in high-dimensional data analysis were used to create a diagnostic model for T2D. Results: As a result of the biomarker discovery process validated with three different approaches, Pyruvate, D-Rhamnose, AMP, pipecolate, Tetradecenoic acid, Tetradecanoic acid, Dodecanediothioic acid, Prostaglandin E3/D3 (isobars), ADP and Hexadecenoic acid were determined as potential biomarkers for T2D. Our results showed that the XGBoost model [accuracy = 0.831, F1-score = 0.845, sensitivity = 0.882, specificity = 0.774, positive predictive value (PPV) = 0.811, negative-PV (NPV) = 0.857 and Area under the ROC curve (AUC) = 0.887] had the slight highest performance measures. Conclusions: ML integrated with bioinformatics techniques offers accurate and positive T2D candidate biomarker discovery. The XGBoost model can successfully distinguish T2D based on metabolites. Full article
(This article belongs to the Special Issue Nutrigenomics and Exposomics: Precision Nutrition in Metabolic Diseases)
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12 pages, 1055 KiB  
Article
Characterization of Postprandial Bile Acid Profiles and Glucose Metabolism in Cerebrotendinous Xanthomatosis
by Soumia Majait, Emma C. E. Meessen, Frederic Maxime Vaz, E. Marleen Kemper, Samuel van Nierop, Steven W. Olde Damink, Frank G. Schaap, Johannes A. Romijn, Max Nieuwdorp, Aad Verrips, Filip Krag Knop and Maarten R. Soeters
Nutrients 2023, 15(21), 4625; https://doi.org/10.3390/nu15214625 - 31 Oct 2023
Viewed by 1634
Abstract
Cerebrotendinous xanthomatosis (CTX) is a rare inherited disease characterized by sterol 27-hydroxylase (CYP27A1) deficiency and, thus, a lack of bile acid synthesis with a marked accumulation of 7α-hydroxylated bile acid precursors. In addition to their renowned lipid-emulgating role, bile acids have been shown [...] Read more.
Cerebrotendinous xanthomatosis (CTX) is a rare inherited disease characterized by sterol 27-hydroxylase (CYP27A1) deficiency and, thus, a lack of bile acid synthesis with a marked accumulation of 7α-hydroxylated bile acid precursors. In addition to their renowned lipid-emulgating role, bile acids have been shown to stimulate secretion of the glucose-lowering and satiety-promoting gut hormone glucagon-like peptide 1 (GLP-1). In this paper, we examined postprandial bile acid, glucose, insulin, GLP-1 and fibroblast growth factor 19 (FGF19) plasma profiles in patients with CTX and matched healthy controls. Seven patients and seven age, gender and body mass index matched controls were included and subjected to a 4 h mixed meal test with regular blood sampling. CTX patients withdrew from chenodeoxycholic acid (CDCA) and statin therapy three weeks prior to the test. Postprandial levels of total bile acids were significantly lower in CTX patients and consisted of residual CDCA with low amounts of ursodeoxycholic acid (UDCA). The postprandial plasma glucose peak concentration occurred later in CTX patients compared to controls, and patients’ insulin levels remained elevated for a longer time. Postprandial GLP-1 levels were slightly higher in CTX subjects whereas postprandial FGF19 levels were lower in CTX subjects. This novel characterization of CTX patients reveals very low circulating bile acid levels and FGF19 levels, aberrant postprandial glucose and insulin profiles, and elevated postprandial GLP-1 responses. Full article
(This article belongs to the Special Issue Nutrigenomics and Exposomics: Precision Nutrition in Metabolic Diseases)
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