A groundbreaking study involving genetic data from over five million people has uncovered how our DNA can predict obesity risk as early as childhood. The new polygenic risk score outperforms previous methods, helping to identify high-risk children before weight issues develop paving the way for early lifestyle interventions.
A new genetic analysis using data from over five million people has provided a clearer understanding of the risk of going on to live with obesity. New research led by the Universities of Copenhagen and Bristol shows that analyzing genes at a young age may support early strategies to prevent obesity from developing later in life.
The World Obesity Federation expects more than half the global population to become overweight or obese by 2035. However, treatment strategies such as lifestyle change, surgery, and medications are not universally available or effective.
By drawing on genetic data from over five million people, an international team of researchers has created a measure called a polygenic risk score (PGS) that is reliably associated with adulthood obesity and shows consistent and indicative patterns in early childhood. The findings could help to identify children and adolescents at higher genetic risk of developing obesity in later life, who could benefit from targeted preventative strategies, such as lifestyle interventions, at a younger age.
“What makes the score so powerful is the consistency of associations between the genetic score and body mass index before the age of five and through to adulthood – timing that starts well before other risk factors start to shape their weight later in childhood. Intervening at this point could theoretically make a huge impact,” said Assistant Professor Roelof Smit at the University of Copenhagen and lead author of the research published in the journal Nature Medicine.
Twice as effective at predicting obesity as the next best method
The subtle variations in people’s genomes can have a real impact on health when acting together. Thousands of genetic variants have been identified that increase the risk of obesity, for example, variants that act in the brain and influence appetite. A PGS is like a calculator that combines the effects of the different risk variants that a person carries and provides an overall score. The PGS was able to explain almost a fifth (17%) of a person’s variation in body mass index, much higher than in previous studies.
To create these PGS, the scientists drew on the genetic data of more than five million people – the largest and most diverse genetic dataset ever – including genetic data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and consumer DNA testing firm, 23andMe. The researchers then tested whether their new PGS was associated with obesity using datasets of the physical and genetic characteristics of more than 500,000 people, including BMI data tracked over time from the Children of the 90s study. They found that their new PGS was twice as effective as the previous best method at predicting a person’s risk of developing obesity.
Dr Kaitlin Wade, Associate Professor in Epidemiology at the University of Bristol and second author on this paper, said: “Obesity is a major public health issue, with many factors contributing to its development, including genetics, environment, lifestyle and behaviour. These factors likely vary across a person’s life, and we believe that some of these originate in childhood.
“We were delighted to contribute data from the Children of the 90s study to this exceptional and insightful research into the genetic architecture of obesity. We hope this work will contribute to detecting individuals at high risk of developing obesity at an earlier age, which could have a vast clinical and public health impact in the future.”
Genetics is not destiny
The research team also investigated the relationship between a person’s genetic risk of obesity and the impact of lifestyle weight loss interventions, such as diet and exercise. They discovered that people with a higher genetic risk of obesity were more responsive to interventions but also regained weight more quickly when the interventions ended.
Despite drawing on the genomes of a wider population, the new PGS has its limitations. For example, it was far better at predicting obesity in people with European-like ancestry than in people with African ancestry. This flags the need for work like this in more representative groups.
