A new polygenic risk score enhances obesity risk prediction from childhood to adulthood, offering new insights for early intervention and prevention strategies.
New genetic test predicts obesity risk in early childhood | Image Credit: © adrian_ilie825 - stock.adobe.com.
An international team of researchers has developed a polygenic risk score (PGS) that significantly improves the prediction of obesity risk from early childhood through adulthood. Drawing from the genetic data of more than 5 million individuals, the new PGS was found to be twice as effective as the previous best-performing genetic test in predicting future obesity risk, particularly among individuals of European ancestry. The findings were published in Nature Medicine.1,2
The new PGS was created using genome-wide association study (GWAS) summary statistics from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and 23andMe, Inc. This dataset included individuals from diverse ancestral backgrounds: 71.1% of European ancestry, 14.4% American, 8.4% East Asian, 4.6% African, and 1.5% South Asian ancestry. Researchers developed both ancestry-specific and multi-ancestry PGSs using these data.
The study team tested the PGS in multiple cohorts including the UK Biobank, Million Veteran Program (MVP), BioMe Biobank, and others. In European-ancestry individuals from the UK Biobank, the multi-ancestry PGS explained 17.6% of the variation in body mass index (BMI), an improvement over the 8.5% explained by the earlier PGS developed by Khera et al. Performance was lower in populations with African-like ancestry, where the explained variance ranged from 2.2% in the Uganda General Population Cohort to 6.3% in African American populations.
“This new polygenic score is a dramatic improvement in predictive power and a leap forward in the genetic prediction of obesity risk, which brings us much closer to clinically useful genetic testing,” said Ruth Loos, PhD, senior author of the study and professor at the University of Copenhagen’s NNF Center for Basic Metabolic Research.
Importantly, the score demonstrated predictive value early in life. In the Avon Longitudinal Study of Parents and Children (ALSPAC), children with higher PGSs showed accelerated BMI gain beginning at 2.5 years of age and had an earlier adiposity rebound compared to peers. Adding the PGS to predictors available at birth nearly doubled the explained variance in BMI at age 8—from 11% to 21%. Including the PGS alongside BMI measurements at age 5 increased the explained variance in BMI at age 18 from 22% to 35%.
“What makes the score so powerful is its ability to predict, before the age of 5, whether a child is likely to develop obesity in adulthood, well before other risk factors start to shape their weight later in childhood,” said lead author Roelof Smit, PhD. “Intervening at this point can have a huge impact.”
Researchers also evaluated the PGS in 2 randomized controlled trials of lifestyle interventions—the Diabetes Prevention Program and the Look AHEAD study. Individuals with higher PGSs lost more weight during the first year of intervention (–0.55 kg per standard deviation increase in PGS), but also regained weight more quickly in subsequent years.
The findings emphasize that while genetics confer risk, they do not determine outcomes. “Genetics is not destiny,” the press release stated. Individuals with a high genetic risk of obesity still responded to behavioral interventions, albeit with a tendency toward faster weight regain when the intervention ceased.
The authors acknowledged that predictive performance varied by ancestry, with better results in individuals of European ancestry. Despite the diverse composition of the training dataset, prediction in individuals with African ancestry remained limited. This discrepancy is likely due to underrepresentation of African ancestry populations in GWAS data and differences in genetic architecture, such as allele frequencies and linkage disequilibrium patterns.
The researchers concluded that polygenic prediction may support early identification of children at elevated risk of obesity and inform targeted prevention efforts. However, they cautioned that further research is needed to improve predictive accuracy across diverse ancestral backgrounds and to evaluate the implementation of PGS in clinical settings.
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