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Multi-Omics Analysis of Morbid Obesity Using a Patented Unsupervised Machine Learning Platform: Genomic, Biochemical,
Irena Šnajdar1,2, Luka Bulić1,3, Andrea Skelin1,4
1St. Catherine Specialty Hospital, 10000 Zagreb, Croatia.
International Journal of Molecular Sciences
|February 13, 2026
Summary
Personalized nutrition plans based on multi-omic data significantly reduced BMI and biological age in morbidly obese patients. This precision approach, using AI and whole-genome sequencing, offers a promising strategy for obesity management.
Area of Science:
- Genetics and Genomics
- Metabolic Disorders
- Nutritional Science
Background:
- Morbid obesity is a complex condition with metabolic and inflammatory issues.
- Patients often exhibit insulin resistance, inflammation, and accelerated biological aging.
Purpose of the Study:
- To evaluate the impact of personalized nutrition plans on morbidly obese patients.
- To observe changes in clinical, biochemical, hormonal, and glycomic parameters.
Main Methods:
- 14 morbidly obese patients underwent whole-genome sequencing (WGS) and multi-omic data analysis.
- AI-driven clustering identified nutrigenetic profiles for personalized dietary recommendations.
- Intervention lasted six months, with assessments at baseline and end.
Main Results:
- Patients showed reduced BMI (52.09 to 34.6 kg/m²), p < 0.01.
- Biological age (GlycanAge) decreased significantly (56 to 48 years), p < 0.01.
- Genetic analysis revealed risk alleles in obesity-associated genes like FTO and IL-6.
Conclusions:
- Personalized nutrigenetic diets effectively reduce BMI and biological age markers.
- Multi-omics and AI-driven approaches are valuable for precision obesity management.
- Morbid obesity has a pro-inflammatory molecular signature linked to accelerated glycomic aging.
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