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Background <p>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.</p> Results <p>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 <i>PM20D1</i>, <i>PM20D1-AS1</i>, <i>AC119673.2</i> (26.05% ± 0.78%), and <i>GM2A</i> (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 <i>S100A14</i>, <i>S100A16</i> (− 25.4% ± 0.48%), <i>SNTG2</i> (− 25.55% ± 0.18%), <i>ADARB2</i>, <i>LINC00200</i> (− 21.35% ± 0.98%), <i>LRRC32</i>, <i>AP001189.1</i> (− 27.25%), <i>CBLN3</i> and <i>KHNYN</i> (− 23.20% ± 0.80%).</p> Conclusions <p>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.</p>

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Genome-wide DNA methylation signatures in blood associated with pediatric obesity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Barbara Slapnik,
  • Robert Šket,
  • Blaž Vrhovšek,
  • Primož Kotnik,
  • Tadej Battelino,
  • Jernej Kovač

摘要

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.