Type 2 diabetes (T2D) and obesity are metabolic disorders with many causes, including overlapping and distinct genetic features. A polygenic risk score (PRS) can capture multiple genetic risk factors to generate an estimate for whether a person may develop a complex medical condition and how they might fare long term. By integrating genetic data from several of the world’s largest biobanks, investigators at Mass General Brigham have now built metabolic a PRS (MetPRS) for predicting obesity and T2D, which outperformed existing disease-prediction models and predicted downstream morbidity and clinical 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 Akl Fahed, MD, MPH, at 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.”
Fahed is co-senior and co-corresponding author of the team’s published paper in Cell Metabolism, titled “Metabolic polygenic risk scores for prediction of obesity, type 2 diabetes, and related morbidities,” in which they concluded that their findings “… highlight the ability of MetPRS to identify individuals at high risk for multimorbid obesity or T2D, offering a meaningful improvement over existing PRSs in capturing management critical populations … The biologically enriched MetPRS has the potential to add an extra layer of information to disease prediction and management approaches for metabolic diseases.”
“The polygenic risk score (PRS) has emerged as a powerful tool for assessing composite genetic risks, gaining traction for its potential clinical applications,” the authors wrote. Obesity and T2D are metabolic diseases that have shared pathophysiology, and while traditional polygenic risk scores (PRSs) have focused on these conditions individually, “… the single-disease approach falls short in capturing the full dimension of metabolic dysfunction,” the researchers added. “The PRSs for obesity and T2D have been developed individually based on a single-disease approach thus far, without accounting for the multivariable nature of these conditions.”
The new metabolic PRS designed by Fahed and team includes one version optimized for obesity (O-MetPRS) and another for T2D (D-MetPRS). 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. To create their PRS the team used datasets from some of the largest multi-ancestry genome-wide association studies (GWAS), which collectively encompass over 8.5 million participants globally. They validated and tested their model in the UK Biobank, and carried out further testing in three multi-ethnic cohorts including nearly 300,000 participants.
“This study introduces MetPRS for obesity and T2D based on a novel approach that captures the genetic architecture of metabolic dysfunction by synthesizing genetic association data from related measures and indices,” the team reported. “By integrating genetic information for related diseases and measures, we developed a metabolic PRS (MetPRS) optimized for obesity (O-MetPRS) and T2D (D-MetPRS) …” The team’s evaluations demonstrated that MetPRS outperformed all previous PRSs in predicting obesity and T2D across multiple ancestries. Their findings, they reported “… highlight the ability of MetPRS to identify individuals at high risk for multimorbid obesity or T2D, offering a meaningful improvement over existing PRSs in capturing management critical populations.”
The team found that the risk scores identified individuals at high risk for clinical outcomes such as 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 median 5.5 years. “Our analysis demonstrates that MetPRS predicts the likelihood of GLP-1 receptor agonist prescription among individuals free of obesity and T2D at baseline over a follow-up period of up to 13 years,” they stated.
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. “We demonstrate that MetPRS outperformed all previous PRSs in predicting obesity and T2D across multiple ancestries …” they wrote. “Given its reliable performance among various ancestries, MetPRS can be readily implemented in ethnically diverse settings and contexts.”
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. “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 Min Seo Kim, MD, MSc. “In the future, this genomic approach could complement established clinical risk factors to inform patient care and preventative strategies.”
In their report the authors commented, “Obesity and T2D are not only major health challenges on their own but also serve as strong risk factors and precursors for a wide array of downstream diseases and complications … Our findings contribute to the growing body of evidence that multi-ancestry genetic studies can facilitate more inclusive and widely applicable health services and alleviate the risk of propagating potential healthcare disparities in genomic medicine.”
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