Abstract | Telomere length (TL) is a reliable biomarker for genome health which inversely correlates with morbidity and mortality. Telomere attrition, or the rate of telomere shortening, is an indicator of genome ageing and can predict life expectancy more accurately than chronological age. Increased telomere attrition has been linked to adverse environmental exposure and pre-pathological conditions. Negative associations between obesity and TL have been consistently reported in adults. In contrast, conclusive evidence linking paediatric obesity with telomere attrition is missing due to the heterogeneity of results from fewer studies on children and adolescents. Inconsistencies have been attributed to variability in study design, including populations' size and age and resolution of methodologies used. In this study, we conducted the first investigation to measure TL in paediatric obesity using single-telomere length analysis (STELA), the most sensitive method for TL measurements developed to date. We measured TL in adolescents recruited from schools and obesity clinics in London. DNA was extracted from unstimulated saliva samples, and participants were selected for analysis based on the quality of the extracted DNA. Based on their BMI, we divided the participants into three groups: healthy-weight controls, participants with obesity and participants with severe obesity. Through one-way ANOVA, we identified a significantly lower average of TL in participants with severe obesity than in the other two groups, but no significant difference when comparing healthy-weight participants with those with obesity. Our study furthers evidence of accelerated genome ageing in adolescents with severe obesity. Our findings also highlight how inconsistencies with obesity categorisation across studies could lead to heterogeneous results. Finally, we report for the first time the application of STELA in obesity and on DNA samples obtained non-invasively. This investigation is part of a study on genomic instability in childhood obesity aimed at identifying biomarkers to inform the prioritisation of clinical interventions. |
---|