Multi-Ancestry Genetic Risk Score Shows Value of Global Collaboration in Metabolic Disease, With Akl Fahed, MD, MPH

A multi-ancestry metabolic polygenic risk score (PRS) of 20 metabolic traits from over 8.5 million individuals points to the utility of global collaboration in genetic research.1

The metabolic PRS outperformed existing ones in predicting obesity and T2D across 6 ancestries, effectively identifying individuals at high risk for metabolic multimorbidity and predicting clinical outcomes, including GLP-1 receptor agonist initiation.

“We haven’t yet seen this level of integration for diabetes and obesity. However, these scores are becoming more widely available,” Akl Fahed, MD, MPH, Interventional Cardiologist at Massachusetts General Hospital and Instructor in Medicine at Harvard Medical School, said in an interview. “Many health systems are beginning to implement them, and individuals can also access them through direct-to-consumer approaches.”

The metabolic diseases, obesity, and T2D have a shared pathophysiology. Traditionally, previous polygenic risk scores focused on them separately, with study investigators noting that the single-disease approach has fallen short in capturing the full dimension of metabolic dysfunction.1,2

To address this gap in research, investigators derived a biologically enriched metabolic PRS, composite score that uses multi-ancestry genome-wide association studies of 20 metabolic traits from over 8.5 million individuals, optimized to predict obesity and T2D.1

The model was trained and tested in the UK Biobank before being externally validated in 3 multi-ethnic cohorts comprising up to 300,000 participants. According to investigators, the risk scores identified individuals at high risk for clinical outcomes such as cardiovascular disease and stroke. In a median 5.5 years follow-up, even individuals with a high PRS who were healthy at baseline were approximately 2 times as likely, specifically, those in the top decile compared to the middle quintile, to eventually receive GLP-1 agonist medications or bariatric surgery.1

Investigators see the biologically enriched metabolic PRS as an opportunity to work together with traditional metrics, such as body mass index, to add more information to disease prediction and management approaches for metabolic diseases.1

As uptake of these tools grows, additional clinical trials will be needed to guide how clinicians should respond to elevated genetic risk. Fahed noted that while identifying individuals at high risk for obesity may support earlier lifestyle intervention, it remains unclear whether this should also prompt earlier use of pharmacologic options, including GLP-1–based therapies or other incretin agents, an area that will require further study. He also emphasized that the findings reflect a large-scale international effort, with contributions from researchers across the United States, the Middle East, China, and other regions.

"We relied on harmonizing data from multiple large biobanks and working closely with methodological experts. The field of genomics depends on this kind of global collaboration and access to shared data. Without publicly available datasets, work like this simply wouldn’t be possible.”

For more from Fahed, watch Part 1 of the interview with HCPLive, where he discusses efforts to address longstanding Eurocentric bias in genomics. He explains how expanding genomic datasets to include more non-European populations, alongside advances in analytic methods, is helping improve the accuracy and generalizability of polygenic risk scores across diverse ancestries.

Editor’s Note: Fahed reports relevant disclosures with Goodpath, MyOme, HeartFlow, and Foresite Labs.

References

1. New genetic risk score better predicts diabetes, obesity and downstream complications. EurekAlert! Published March 16, 2026. https://www.eurekalert.org/news-releases/1119751

2. Ghatan S, Jeroen van Rooij, Mandy van Hoek, et al. Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations. Genome Medicine. 2024;16(1). doi:https://doi.org/10.1186/s13073-023-01255-7

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