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Metabolites
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Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases
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Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases

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

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 21267

Special Issue Editor

Dr. Judith Nzoughet Kouassi

E-Mail Website
Guest Editor
Faculté de Pharmacie, University Paris Cité, Paris, France
Interests: biomarkers discovery; human diseases; mass-spectrometry-based metabolomics; database; molecular networking; falsified medicines

Special Issue Information

Dear Colleagues,

Metabolomics and lipidomics have proven to be powerful strategies that enable the identification of metabolic signatures of pathological conditions, thus offering means for unraveling biomarkers and drug discovery.

This Special Issue on “Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases” will serve as a platform dedicated to this pursuit.

During the last decade, metabolomics and lipidomics in studying biological processes and metabolic responses of the population to pathophysiological stimuli, genetic modifications, and environmental challenges have revealed new therapeutic avenues.

This Special Issue will cover research targets of outstanding medical and biological interests, in human diseases and experimental models, using a range of cutting-edge technologies and data analysis tools. Several topics including sample preparation and detection techniques, bioinformatics and data analysis, and metabolic and molecular networks will be addressed, with the aim of improving diagnosis, prognosis, and therapeutic monitoring in inherited and common human diseases.

Dr. Judith Nzoughet Kouassi
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 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

  • biomarkers discovery
  • metabolomics
  • lipidomics
  • human diseases
  • bioinformatics and data analysis
  • metabolic and molecular networks

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Published Papers (10 papers)

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Research

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15 pages, 4456 KiB  
Article
Analysis of the Urine Volatilome of COVID-19 Patients and the Possible Metabolic Alterations Produced by the Disease
by Jennifer Narro-Serrano, Maruan Shalabi-Benavent, José María Álamo-Marzo, Álvaro Maximiliam Seijo-García and Frutos Carlos Marhuenda-Egea
Metabolites 2024, 14(11), 638; https://doi.org/10.3390/metabo14110638 - 19 Nov 2024
Viewed by 1254
Abstract
Alterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, [...] Read more.
Alterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, including results from different sample treatments in the analysis of volatile organic compounds (VOCs) present in urine samples from patients with COVID-19. Between September 2021 and May 2022, urine samples were collected from 35 hospitalized COVID-19 patients and 32 healthy controls. The samples were analyzed by headspace (HS) solid phase microextraction (SPME) coupled to gas chromatography–mass spectrometry (GC-MS). Analyses were conducted on untreated urine samples and on samples that underwent specific pretreatments: lyophilization and treatment with sulfuric acid. Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Subwindow Permutation Analysis (SPA) models were established to distinguish patterns between COVID-19 patients and healthy controls. The results identify compounds that are present in different proportions in urine samples from COVID-19 patients compared to those from healthy individuals. Analysis of urine samples using HS-SPME-GC-MS reveals differences between COVID-19 patients and healthy individuals. These differences are more pronounced when methods that enhance VOC formation are used. However, these pretreatments can cause reactions between sample components, creating additional products or removing compounds, so biomarker selection could be altered. Therefore, using a combination of methods may be more informative when evaluating metabolic alterations caused by viral infections and would allow for a better selection of biomarkers. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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18 pages, 3074 KiB  
Article
Causal Metabolomic and Lipidomic Analysis of Circulating Plasma Metabolites in Autism: A Comprehensive Mendelian Randomization Study with Independent Cohort Validation
by Zhifan Li, Yanrong Li, Xinrong Tang, Abao Xing, Jianlin Lin, Junrong Li, Junjun Ji, Tiantian Cai, Ke Zheng, Sai Sachin Lingampelly and Kefeng Li
Metabolites 2024, 14(10), 557; https://doi.org/10.3390/metabo14100557 - 17 Oct 2024
Cited by 1 | Viewed by 1407
Abstract
Background: The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Methods: Bidirectional two-sample [...] Read more.
Background: The increasing prevalence of autism spectrum disorder (ASD) highlights the need for objective diagnostic markers and a better understanding of its pathogenesis. Metabolic differences have been observed between individuals with and without ASD, but their causal relevance remains unclear. Methods: Bidirectional two-sample Mendelian randomization (MR) was used to assess causal associations between circulating plasma metabolites and ASD using large-scale genome-wide association study (GWAS) datasets—comprising 1091 metabolites, 309 ratios, and 179 lipids—and three European autism datasets (PGC 2015: n = 10,610 and 10,263; 2017: n = 46,351). Inverse-variance weighted (IVW) and weighted median methods were employed, along with robust sensitivity and power analyses followed by independent cohort validation. Results: Higher genetically predicted levels of sphingomyelin (SM) (d17:1/16:0) (OR, 1.129; 95% CI, 1.024–1.245; p = 0.015) were causally linked to increased ASD risk. Additionally, ASD children had higher plasma creatine/carnitine ratios. These MR findings were validated in an independent US autism cohort using machine learning analysis. Conclusion: Utilizing large datasets, two MR approaches, robust sensitivity analyses, and independent validation, our novel findings provide evidence for the potential roles of metabolomics and circulating metabolites in ASD diagnosis and etiology. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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25 pages, 2150 KiB  
Article
Multimodal Mass Spectrometry Imaging of an Osteosarcoma Multicellular Tumour Spheroid Model to Investigate Drug-Induced Response
by Sophie M. Pearce, Neil A. Cross, David P. Smith, Malcolm R. Clench, Lucy E. Flint, Gregory Hamm, Richard Goodwin, James I. Langridge, Emmanuelle Claude and Laura M. Cole
Metabolites 2024, 14(6), 315; https://doi.org/10.3390/metabo14060315 - 29 May 2024
Cited by 1 | Viewed by 3081
Abstract
A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating [...] Read more.
A multimodal mass spectrometry imaging (MSI) approach was used to investigate the chemotherapy drug-induced response of a Multicellular Tumour Spheroid (MCTS) 3D cell culture model of osteosarcoma (OS). The work addresses the critical demand for enhanced translatable early drug discovery approaches by demonstrating a robust spatially resolved molecular distribution analysis in tumour models following chemotherapeutic intervention. Advanced high-resolution techniques were employed, including desorption electrospray ionisation (DESI) mass spectrometry imaging (MSI), to assess the interplay between metabolic and cellular pathways in response to chemotherapeutic intervention. Endogenous metabolite distributions of the human OS tumour models were complemented with subcellularly resolved protein localisation by the detection of metal-tagged antibodies using Imaging Mass Cytometry (IMC). The first application of matrix-assisted laser desorption ionization–immunohistochemistry (MALDI-IHC) of 3D cell culture models is reported here. Protein localisation and expression following an acute dosage of the chemotherapy drug doxorubicin demonstrated novel indications for mechanisms of region-specific tumour survival and cell-cycle-specific drug-induced responses. Previously unknown doxorubicin-induced metabolite upregulation was revealed by DESI-MSI of MCTSs, which may be used to inform mechanisms of chemotherapeutic resistance. The demonstration of specific tumour survival mechanisms that are characteristic of those reported for in vivo tumours has underscored the increasing value of this approach as a tool to investigate drug resistance. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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19 pages, 20557 KiB  
Article
Metabolomic Profiling of Bipolar Disorder by 1H-NMR in Serbian Patients
by Katarina Simić, Zoran Miladinović, Nina Todorović, Snežana Trifunović, Nataša Avramović, Aleksandra Gavrilović, Silvana Jovanović, Dejan Gođevac, Ljubodrag Vujisić, Vele Tešević, Ljubica Tasic and Boris Mandić
Metabolites 2023, 13(5), 607; https://doi.org/10.3390/metabo13050607 - 28 Apr 2023
Cited by 6 | Viewed by 2640
Abstract
Bipolar disorder (BD) is a brain disorder that causes changes in a person’s mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity [...] Read more.
Bipolar disorder (BD) is a brain disorder that causes changes in a person’s mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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15 pages, 2760 KiB  
Article
Metabolomic Profiling in Mouse Model of Menopause-Associated Asthma
by William P. Pederson, Laurie M. Ellerman, Yan Jin, Haiwei Gu and Julie G. Ledford
Metabolites 2023, 13(4), 546; https://doi.org/10.3390/metabo13040546 - 11 Apr 2023
Cited by 3 | Viewed by 2613
Abstract
Menopause-associated asthma impacts a subset of women, tends to be more severe, and is less responsive to current treatments. We recently developed a model of menopause-associated asthma using 4-Vinylcyclohexene Diepoxide (VCD) and house dust mites (HDM). The goal of this study was to [...] Read more.
Menopause-associated asthma impacts a subset of women, tends to be more severe, and is less responsive to current treatments. We recently developed a model of menopause-associated asthma using 4-Vinylcyclohexene Diepoxide (VCD) and house dust mites (HDM). The goal of this study was to uncover potential biomarkers and drivers of menopause-onset asthma by assessing serum and bronchoalveolar lavage fluid (BALF) samples from mice with and without menopause and HDM challenge by large-scale targeted metabolomics. Female mice were treated with VCD/HDM to model menopause-associated asthma, and serum and BALF samples were processed for large-scale targeted metabolomic assessment. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) was used to examine metabolites of potential biological significance. We identified over 50 individual metabolites, impacting 46 metabolic pathways, in the serum and BALF that were significantly different across the four study groups. In particular, glutamate, GABA, phosphocreatine, and pyroglutamic acid, which are involved in glutamate/glutamine, glutathione, and arginine and proline metabolisms, were significantly impacted in the menopausal HDM-challenged mice. Additionally, several metabolites had significant correlations with total airway resistance including glutamic acid, histamine, uridine, cytosine, cytidine, and acetamide. Using metabolic profiling, we identified metabolites and metabolic pathways that may aid in discriminating potential biomarkers for and drivers of menopause-associated asthma. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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12 pages, 1185 KiB  
Article
Association of Metabolomic Biomarkers with Sleeve Gastrectomy Weight Loss Outcomes
by Wendy M. Miller, Kathryn M. Ziegler, Ali Yilmaz, Nazia Saiyed, Ilyas Ustun, Sumeyya Akyol, Jay Idler, Matthew D. Sims, Michael E. Maddens and Stewart F. Graham
Metabolites 2023, 13(4), 506; https://doi.org/10.3390/metabo13040506 - 31 Mar 2023
Cited by 6 | Viewed by 2066
Abstract
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 [...] Read more.
This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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11 pages, 718 KiB  
Article
Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics
by Marion Brandolini-Bunlon, Benoit Jaillais, Véronique Cariou, Blandine Comte, Estelle Pujos-Guillot and Evelyne Vigneau
Metabolites 2023, 13(3), 373; https://doi.org/10.3390/metabo13030373 - 2 Mar 2023
Cited by 1 | Viewed by 1557
Abstract
In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of [...] Read more.
In nutrition and health research, untargeted metabolomics is actually analyzed simultaneously with clinical data to improve prediction and better understand pathological status. This can be modeled using a multiblock supervised model with several input data blocks (metabolomics, clinical data) being potential predictors of the outcome to be explained. Alternatively, this configuration can be represented with a path diagram where the input blocks are each connected by links directed to the outcome—as in multiblock supervised modeling—and are also related to each other, thus allowing one to account for block effects. On the basis of a path model, we show herein how to estimate the effect of an input block, either on its own or conditionally to other(s), on the output response, respectively called “global” and “partial” effects, by percentages of explained variance in dedicated PLS regression models. These effects have been computed in two different path diagrams in a case study relative to metabolic syndrome, involving metabolomics and clinical data from an older men′s cohort (NuAge). From the two effects associated with each path, the results highlighted the complementary information provided by metabolomics to clinical data and, reciprocally, in the metabolic syndrome exploration. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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11 pages, 1602 KiB  
Article
A Metabolomic Profile of Seminal Fluid in Extremely Severe Oligozoopermia Suggesting an Epididymal Involvement
by Orianne Serri, Magalie Boguenet, Juan Manuel Chao de la Barca, Pierre-Emmanuel Bouet, Hady El Hachem, Odile Blanchet, Pascal Reynier and Pascale May-Panloup
Metabolites 2022, 12(12), 1266; https://doi.org/10.3390/metabo12121266 - 15 Dec 2022
Cited by 4 | Viewed by 1667
Abstract
Male infertility has increased in the last decade. Pathophysiologic mechanisms behind extreme oligospermia (EO) are not yet fully understood. In new “omics” approaches, metabolomic can offer new information and help elucidate these mechanisms. We performed a metabolomics study of the seminal fluid (SF) [...] Read more.
Male infertility has increased in the last decade. Pathophysiologic mechanisms behind extreme oligospermia (EO) are not yet fully understood. In new “omics” approaches, metabolomic can offer new information and help elucidate these mechanisms. We performed a metabolomics study of the seminal fluid (SF) in order to understand the mechanisms implicated in EO. We realized a targeted quantitative analysis using high performance liquid chromatography and mass spectrometry to compare the SF metabolomic profile of 19 men with EO with that of 22 men with a history of vasectomy (V) and 20 men with normal semen parameters (C). A total of 114 metabolites were identified. We obtained a multivariate OPLS-DA model discriminating the three groups. Signatures show significantly higher levels of amino acids and polyamines in C group. The sum of polyunsaturated fatty acids and free carnitine progressively decrease between the three groups (C > EO > V) and sphingomyelins are significantly lower in V group. Our signature characterizing EO includes metabolites already linked to infertility in previous studies. The similarities between the signatures of the EO and V groups are clear evidence of epididymal dysfunction in the case of testicular damage. This study shows the complexity of the metabolomic dysfunction occurring in the SF of EO men and underlines the importance of metabolomics in understanding male infertility. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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Review

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23 pages, 960 KiB  
Review
Diagnosis, Severity, and Prognosis from Potential Biomarkers of COVID-19 in Urine: A Review of Clinical and Omics Results
by Jennifer Narro-Serrano and Frutos Carlos Marhuenda-Egea
Metabolites 2024, 14(12), 724; https://doi.org/10.3390/metabo14120724 - 22 Dec 2024
Viewed by 1491
Abstract
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has spurred an extraordinary scientific effort to better understand the disease’s pathophysiology and develop diagnostic and prognostic tools to guide more precise and effective clinical management. Among the biological samples analyzed for biomarker identification, urine [...] Read more.
The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has spurred an extraordinary scientific effort to better understand the disease’s pathophysiology and develop diagnostic and prognostic tools to guide more precise and effective clinical management. Among the biological samples analyzed for biomarker identification, urine stands out due to its low risk of infection, non-invasive collection, and suitability for frequent, large-volume sampling. Integrating data from omics studies with standard biochemical analyses offers a deeper and more comprehensive understanding of COVID-19. This review aims to provide a detailed summary of studies published to date that have applied omics and clinical analyses on urine samples to identify potential biomarkers for COVID-19. In July 2024, an advanced search was conducted in Web of Science using the query: “covid* (Topic) AND urine (Topic) AND metabol* (Topic)”. The search included results published up to 14 October 2024. The studies retrieved from this digital search were evaluated through a two-step screening process: first by reviewing titles and abstracts for eligibility, and then by retrieving and assessing the full texts of articles that met the specific criteria. The initial search retrieved 913 studies, of which 45 articles were ultimately included in this review. The most robust biomarkers identified include kynurenine, neopterin, total proteins, red blood cells, ACE2, citric acid, ketone bodies, hypoxanthine, amino acids, and glucose. The biological causes underlying these alterations reflect the multisystemic impact of COVID-19, highlighting key processes such as systemic inflammation, renal dysfunction, critical hypoxia, and metabolic stress. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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17 pages, 2119 KiB  
Protocol
Sphingolipids in Childhood Asthma and Obesity (SOAP Study): A Protocol of a Cross-Sectional Study
by Belavendra Antonisamy, Harshita Shailesh, Yahya Hani, Lina Hayati M. Ahmed, Safa Noor, Salma Yahya Ahmed, Mohamed Alfaki, Abidan Muhayimana, Shana Sunny Jacob, Saroja Kotegar Balayya, Oleksandr Soloviov, Li Liu, Lisa Sara Mathew, Kun Wang, Sara Tomei, Alia Al Massih, Rebecca Mathew, Mohammed Yousuf Karim, Manjunath Ramanjaneya, Stefan Worgall and Ibrahim A. Janahiadd Show full author list remove Hide full author list
Metabolites 2023, 13(11), 1146; https://doi.org/10.3390/metabo13111146 - 11 Nov 2023
Cited by 2 | Viewed by 2443
Abstract
Asthma and obesity are two of the most common chronic conditions in children and adolescents. There is increasing evidence that sphingolipid metabolism is altered in childhood asthma and is linked to airway hyperreactivity. Dysregulated sphingolipid metabolism is also reported in obesity. However, the [...] Read more.
Asthma and obesity are two of the most common chronic conditions in children and adolescents. There is increasing evidence that sphingolipid metabolism is altered in childhood asthma and is linked to airway hyperreactivity. Dysregulated sphingolipid metabolism is also reported in obesity. However, the functional link between sphingolipid metabolism, asthma, and obesity is not completely understood. This paper describes the protocol of an ongoing study on sphingolipids that aims to examine the pathophysiology of sphingolipids in childhood asthma and obesity. In addition, this study aims to explore the novel biomarkers through a comprehensive multi-omics approach including genomics, genome-wide DNA methylation, RNA-Seq, microRNA (miRNA) profiling, lipidomics, metabolomics, and cytokine profiling. This is a cross-sectional study aiming to recruit 440 children from different groups: children with asthma and normal weight (n = 100), asthma with overweight or obesity (n = 100), overweight or obesity (n = 100), normal weight (n = 70), and siblings of asthmatic children with normal weight, overweight, or obesity (n = 70). These participants will be recruited from the pediatric pulmonology, pediatric endocrinology, and general pediatric outpatient clinics at Sidra Medicine, Doha, Qatar. Information will be obtained from self-reported questionnaires on asthma, quality of life, food frequency (FFQ), and a 3-day food diary that are completed by the children and their parents. Clinical measurements will include anthropometry, blood pressure, biochemistry, bioelectrical impedance, and pulmonary function tests. Blood samples will be obtained for sphingolipid analysis, serine palmitoyltransferase (SPT) assay, whole-genome sequencing (WGS), genome-wide DNA methylation study, RNA-Seq, miRNA profiling, metabolomics, lipidomics, and cytokine analysis. Group comparisons of continuous outcome variables will be carried out by a one-way analysis of variance or the Kruskal–Wallis test using an appropriate pairwise multiple comparison test. The chi-squared test or a Fisher’s exact test will be used to test the associations between categorical variables. Finally, multivariate analysis will be carried out to integrate the clinical data with multi-omics data. This study will help us to understand the role of dysregulated sphingolipid metabolism in obesity and asthma. In addition, the multi-omics data from the study will help to identify novel genetic and epigenetic signatures, inflammatory markers, and mechanistic pathways that link asthma and obesity in children. Furthermore, the integration of clinical and multi-omics data will help us to uncover the potential interactions between these diseases and to offer a new paradigm for the treatment of pediatric obesity-associated asthma. Full article
(This article belongs to the Special Issue Advanced Metabolomics and Lipidomics Approaches in Studying Human Diseases)
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