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Updated: May 14, 2026

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
Published on: May 6, 2022
Genome-wide DNA methylation signatures in blood associated with pediatric obesity
Barbara Slapnik1,2, Robert Šket1,2, Blaž Vrhovšek1,2
1University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Insights
Epigenetic changes in children with obesity were identified using DNA methylation profiling. These blood-based markers may aid in early risk prediction and personalized interventions for pediatric obesity.
Area of Science:
- Genetics
- Epigenetics
- Pediatrics
Background:
- Pediatric obesity is a major nutritional disorder linked to numerous comorbidities.
- Epigenetic mechanisms, especially DNA methylation, are crucial for understanding disease risk and developing interventions.
- Genome-wide DNA methylation profiling offers potential for early detection and personalized treatment of pediatric obesity.
Purpose of the Study:
- To compare genome-wide DNA methylation profiles in blood from children with and without obesity.
- To identify differentially methylated regions associated with pediatric obesity.
- To explore the potential of blood-based epigenetic markers for risk prediction and stratification.
Main Methods:
- Utilized Oxford Nanopore Technologies for long-read, genome-wide DNA methylation profiling.
- Analyzed blood samples from children with obesity and matched controls.
- Applied two independent analytical tools to identify differentially methylated regions (DMRs), excluding cases of monogenic obesity.
Main Results:
- Identified seven consensus differentially methylated regions (DMRs) in children with obesity, associated with metabolism and comorbidities.
- Observed hypermethylation in genes such as PM20D1 and GM2A, impacting metabolic and adipogenic pathways.
- Detected hypomethylation in genes like S100A14, S100A16, and SNTG2, linked to obesity and its complications.
Conclusions:
- Long-read genome-wide methylation profiling effectively detects obesity-associated epigenetic changes in children.
- Blood-based epigenetic markers in metabolic and obesity-related genes show promise for early risk prediction and patient stratification.
- Further validation in larger, diverse cohorts is needed to confirm translational relevance.
Background:
Pediatric obesity is the most prevalent nutritional disorder in children and adolescents and is associated with multiple comorbidities. Understanding epigenetic mechanisms, particularly DNA methylation, offers potential for early risk prediction, prevention, and personalized interventions. In this study, genome-wide DNA methylation profiles in blood from children with obesity and matched controls were compared using Oxford Nanopore Technologies. Two independent analytical tools were applied to identify differentially methylated regions. Clinical data included family history, weight, BMI, and age at first examination. Participants with monogenic obesity, identified via short-read whole-exome sequencing, were excluded.
Results:
The cohort included five girls and five boys with obesity (median age 14.33, IQR 1.65; median BMI SDS 3.42, IQR 0.55) and matched controls (median age 13.94, IQR 0.52; median BMI SDS 0.26, IQR 1.71). Seven consensus differentially methylated regions were consistently identified, overlapping genes involved in metabolism and obesity-related comorbidities. Hypermethylation was observed in PM20D1, PM20D1-AS1, AC119673.2 (26.05% ± 0.78%), and GM2A (33.60% ± 1.10%) in children with obesity, primarily affecting metabolic and adipogenic pathways. Hypomethylation was detected in genes linked to obesity and its comorbidities, including S100A14, S100A16 (- 25.4% ± 0.48%), SNTG2 (- 25.55% ± 0.18%), ADARB2, LINC00200 (- 21.35% ± 0.98%), LRRC32, AP001189.1 (- 27.25%), CBLN3 and KHNYN (- 23.20% ± 0.80%).
Conclusions:
Long-read genome-wide methylation profiling can detect obesity-associated epigenetic loci in children. Blood-based markers in genes regulating metabolism and obesity comorbidities could support early risk prediction, patient stratification, and targeted prevention. Extending this work to larger and more diverse cohorts will be necessary to evaluate the robustness of these results and their potential translational relevance.
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