Genetic Risk Scores Offer New Hope in Obesity Prediction
Obesity affects 23.4% of Indonesian adults. New research using ancestry-adjusted Polygenic Risk Scores (PRS) helps identify individuals at high genetic risk. Based on local data and the 1000 Genomes Project, this method improves prediction accuracy and supports more inclusive personalized medicine in Indonesia.
7/16/20251 min read
INAVIMED, July 2025 — As obesity rates in Indonesia continue to climb, new research highlights genetics as a key factor in understanding and managing this growing health issue.
Genetic Clues to Obesity: What They Are & Why They Matter
Recent studies using Genome-Wide Association Studies (GWAS) have identified multiple genetic markers—called Single Nucleotide Polymorphisms (SNPs)—linked to obesity and BMI. These SNPs are used to calculate a Polygenic Risk Score (PRS), which estimates an individual's genetic risk for obesity.
In Indonesia, a pioneering study involving 5,800 participants and data from the 1000 Genomes Project applied Principal Components Analysis (PCA) to adjust the PRS for local genetic backgrounds. This matters because most global PRS models are based on European populations, which may not reflect Southeast Asian diversity.
Beyond the Scale: How Ancestry-Adjusted PRS Can Improve Healthcare
Tailoring PRS to Indonesian genetics improves early identification of high-risk individuals and supports more personalized prevention strategies. In fact, the adjusted model of the study identified 21.4% of Indonesians as high-risk—closely matching national obesity data (23.4%) from Riskesdas. Without ancestry adjustments, models may misclassify risk in underrepresented groups, worsening health disparities.
Final Takeaway: Ancestry-Adjusted Genomics Is Important
The takeaway? Accounting for ancestry in genetic prediction works — and it matters. As precision medicine becomes a central pillar of healthcare innovation, inclusivity must remain a priority. This pioneering study demonstrates the importance of adapting genetic tools to local populations, not just importing models built elsewhere. It is very important to reduce health gaps around the world. For countries like Indonesia, this is a chance to become a leader in making personalized medicine fair and useful for everyone.
Keywords: PRS Indonesia, genetic obesity risk, polygenic risk score, obesity prediction, genomics in Indonesia
References:
Siswanto JV, Mutiara B, Austin F, Susanto J, Tan CT, Kresnadi RU, et al. Ancestry-adjusted polygenic risk scores for predicting obesity risk in the Indonesian population. 2025 May 16 [cited 2025 Jul 16]; Available from: https://doi.org/10.48550/arXiv.2505.13503
