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Molecular Studies on Obesity and Related Diseases

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Metabolic Diseases, Exercise and Nutrition Research Group (DOMEN), Laboratory of Metabolic Diseases Glauto Tuquarre, Department of Biophysics and Physiology, Center for Health Sciences, Federal University of Piaui, Teresina 64049-550, PI, Brazil
Interests: clinical nutrition; oncology; colorectal cancer; gut microbiota; intestinal barrier integrity; metabolic and inflammatory pathways; ultra-processed foods; obesity; inflammatory bowel disease; metabolomics and metagenomics

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Guest Editor
Department of Biophysics and Physiology, Center for Health Sciences, Federal University of Piauí, Teresina 64049-550, Brazil
Interests: nutrition and cancer; supplementation; gut microbiota; exercise; translational studies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Laboratory of Physiology, Center for Drug Discovery and Innovative Medicines (MedInUP)/RISE-Health: Health Research Network, Department of Immuno-Physiology and Pharmacology, School of Medicine and Biomedical Science—University of Porto (ICBAS-UP), 4050-313 Porto, Portugal
Interests: exercise; gastrointestinal motility; intestinal permeability; enteric nervous system; gastrointestinal pharmacology; gastrointestinal physiology; GERD; Crohn's disease; supplementation; microbiota; exercise and cancer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Obesity is a complex, multifactorial, and progressive disease driven by the interaction between genetic, metabolic, environmental, and lifestyle factors. Recent advances in molecular biology have facilitated a deeper understanding of adipose tissue biology, immunometabolism, endocrine regulation, intracellular signaling, and host–microbiota interactions. These discoveries have revealed new mechanisms contributing not only to excessive adiposity but also to obesity-related diseases, including type 2 diabetes, cardiovascular disease, nonalcoholic fatty liver disease (NAFLD/NASH), gastrointestinal cancers, breast cancer, and chronic inflammation.

This Special Issue aims to gather cutting-edge molecular research addressing the pathogenesis, biomarkers, and therapeutic targets related to obesity and its comorbidities, integrating multi-omics technologies (genomics, transcriptomics, proteomics, metabolomics, epigenetics), advanced imaging, and cellular and preclinical models. Translational studies focusing on the metabolic effects of dietary interventions, physical activity, pharmacotherapy (e.g., GLP-1 analogues), and bariatric procedures are also highly encouraged.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Adipose tissue biology, browning, adipogenesis and thermogenesis;
  • Molecular mechanisms linking obesity to metabolic dysfunction;
  • Gut microbiota, intestinal barrier and immunometabolic signaling;
  • Epigenetic regulation and transcriptional networks in obesity;
  • Biomarkers for early detection of obesity-related comorbidities;
  • Molecular targets for drug development and precision medicine;
  • Multi-omics approaches to metabolic reprogramming;
  • Inflammation, oxidative stress and immune dysfunction in obesity;
  • Interaction between obesity and cancer development or progression;
  • Effects of diet, fasting, exercise and metabolic surgery on molecular pathways.

We believe this Special Issue will contribute to the translational understanding of obesity, enabling improved prevention, diagnosis and personalized therapies.

We look forward to receiving your valuable contributions.

Dr. Juliana Soares Severo
Dr. Francisco L. Torres-Leal
Dr. Moisés Tolentino B. Da Silva
Guest Editors

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Keywords

  • obesity
  • adipose tissue
  • immunometabolism
  • adipokines
  • metabolomics
  • gut microbiota
  • thermogenesis
  • NAFLD
  • inflammation
  • precision nutrition

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Published Papers (1 paper)

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14 pages, 1787 KB  
Article
Multi-Omics Analysis of Morbid Obesity Using a Patented Unsupervised Machine Learning Platform: Genomic, Biochemical, and Glycan Insights
by Irena Šnajdar, Luka Bulić, Andrea Skelin, Leo Mršić, Mateo Sokač, Maja Brkljačić, Martina Matovinović, Martina Linarić, Jelena Kovačić, Petar Brlek, Gordan Lauc, Martina Smolić and Dragan Primorac
Int. J. Mol. Sci. 2026, 27(3), 1551; https://doi.org/10.3390/ijms27031551 - 4 Feb 2026
Viewed by 611
Abstract
Morbid obesity is a complex, multifactorial disorder characterized by metabolic and inflammatory dysregulation. The aim of this study was to observe changes in obese patients adhering to a personalized nutrition plan based on multi-omic data. This study included 14 adult patients with a [...] Read more.
Morbid obesity is a complex, multifactorial disorder characterized by metabolic and inflammatory dysregulation. The aim of this study was to observe changes in obese patients adhering to a personalized nutrition plan based on multi-omic data. This study included 14 adult patients with a body mass index (BMI) > 40 kg/m2 who were consecutively recruited from those presenting to our outpatient clinic and who met the inclusion criteria. Clinical, biochemical, hormonal, and glycomic parameters were assessed, along with whole-genome sequencing (WGS) that included a focused analysis of obesity-associated genes and an extended analysis encompassing genes related to cardiometabolic disorders, hereditary cancer risk, and nutrigenetic profiles. Patients were stratified into nutrigenetic clusters using a patented unsupervised machine learning platform (German Patent Office, No. DE 20 2025 101 197 U1), which was employed to generate personalized nutrigenetic dietary recommendations for patients with morbid obesity to follow over a six-month period. At baseline, participants exhibited elevated glucose, insulin, homeostatic model assessment for insulin resistance (HOMA-IR), triglycerides, and C-reactive protein (CRP) levels, consistent with insulin resistance and chronic low-grade inflammation. The majority of participants harbored risk alleles within the fat mass and obesity-associated gene (FTO) and the interleukin-6 gene (IL-6), together with multiple additional significant variants identified across more than 40 genes implicated in metabolic regulation and nutritional status. Using an AI-driven clustering model, these genetic polymorphisms delineated a uniform cluster of patients with morbid obesity. The mean GlycanAge index (56 ± 12.45 years) substantially exceeded chronological age (32 ± 9.62 years), indicating accelerated biological aging. Following a six-month personalized nutrigenetic dietary intervention, significant reductions were observed in both BMI (from 52.09 ± 7.41 to 34.6 ± 9.06 kg/m2, p < 0.01) and GlycanAge index (from 56 ± 12.45 to 48 ± 14.83 years, p < 0.01). Morbid obesity is characterized by a pro-inflammatory and metabolically adverse molecular signature reflected in accelerated glycomic aging. Personalized nutrigenetic dietary interventions, derived from AI-driven analysis of whole-genome sequencing (WGS) data, effectively reduced both BMI and biological age markers, supporting integrative multi-omics and machine learning approaches as promising tools in precision-based obesity management. Full article
(This article belongs to the Special Issue Molecular Studies on Obesity and Related Diseases)
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