DNA Test Offers Early Obesity Risk Prediction for Children

rapid Summary

• The World Obesity Federation predicts over half the global population may have overweight or obesity by 2035.
• Researchers developed a polygenic risk score (PGS) using genetic data from over five million people to predict obesity risk, even in early childhood.
• The PGS is effective in identifying individuals at higher genetic risk of developing obesity, enabling early lifestyle interventions.
• The study is part of the Genetic Investigation of Anthropometric traits (GIANT) Consortium and involved contributions from 600 scientists across 500 institutions globally. Data was also provided by consumer genetics company 23andMe.
• The new PGS is twice as effective at predicting obesity compared to previous tests, analyzing subtle genetic variations that affect factors like appetite through brain function.
• Despite its advancements, the PGS performs considerably better for populations with European-like ancestry than those with African ancestry.
• Higher genetic predisposition correlates with greater responsiveness to weight-loss interventions but also faster weight regain post-intervention.

Indian Opinion analysis

The development of this advanced polygenic risk score marks a significant scientific advancement in addressing global health crises such as obesity. For India, which faces rising rates of childhood and adult obesity alongside ongoing challenges related to malnutrition, integrating such predictive tools could help enhance public health strategies. Early identification of at-risk individuals can enable targeted interventions tailored for diverse demographics and prevent long-term healthcare costs.

However,India’s genetic diversity poses a challenge since current tests are less effective for non-European ancestries. Tailoring these technologies to local population groups would require deeper genomic studies within Indian communities. Moreover, while promising on paper, accessibility and affordability might limit practical deployment in resource-limited settings unless policies promote widespread adoption.

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