New genetic risk score better predicts metabolic conditions

By integrating genetic findings from several of the world's largest biobanks, investigators from Mass General Brigham built metabolic a polygenic risk score (PRS) for predicting obesity and type 2 diabetes (T2D), which capture multiple genetic risk factors to provide an estimate for whether a person may develop a complex medical condition and how they might fare long-term, and outperformed existing disease-prediction models and predicted downstream morbidity and clinical interventions.

Dr Min Seo Kim

"Our intention was to not only capture the risk of being diagnosed with obesity or diabetes, but also to better predict health consequences across the life course by integrating many aspects of metabolic function," said co-first author, Dr Min Seo Kim. "In the future, this genomic approach could complement established clinical risk factors to inform patient care and preventative strategies."

The metabolic PRS designed by the researchers includes one version optimized for obesity and another for T2D. Both scores look beyond widely utilized variables, such as body mass index, and focus on genes associated with 20 different traits related to metabolic function, such as fat distribution and insulin and glucose control.

The team used genome-wide association studies (GWAS) performed on some of the largest datasets worldwide, which collectively encompass over 8.5 million participants globally.

The researchers found that the risk scores identified individuals at high risk for clinical outcomes like cardiovascular disease and stroke. Individuals with a high PRS who were initially healthy were about twice as likely to later receive GLP‑1 agonist medications or bariatric surgery compared to those with mid-range PRS scores, during a follow-up period of a median of 5.5 years.

The use of multi-ancestry GWAS data, with a particular focus on non-European populations, enabled the construction of obesity and T2D risk scores that surpassed prior PRS models in African, East Asian, and South Asian individuals.

Going forward, the researchers hope to continue refining understandings of the genetic subtypes of T2D and obesity to improve patient classification and stratification for clinical trials and ultimately foster more tailored interventions.

"We want clinicians to be able to think about metabolic conditions in terms beyond body mass index, with a focus more broadly on underlying genetic susceptibility," said co-senior author, Dr Akl Fahed of the Cardiovascular Research Center at Massachusetts General Hospital and an interventional cardiologist with the Mass General Brigham Heart and Vascular Institute. "Early identification of people who are likely to have a worse trajectory of poor metabolic health, before they even develop these conditions, can help us improve prevention and clinical interventions. That is how we can cure disease, and that is the bold mission that we are after."

The findings were reported in the paper, 'Metabolic Polygenic Risk Scores for Prediction of Obesity, Type 2 Diabetes, and Related Morbidities', published in Cell Metabolism. To access this paper, please click here