Obesity is a chronic and complex disease that is increasing alarmingly worldwide. World Obesity Federation warns that if we don't change course, by 2035 more than half of the world's population will be overweight or obese. This problem not only impacts physical health, but also psychological well-being and healthcare systems. In this context, genomics and, specifically, polygenic scores (PGS) promise to identify people at higher risk of developing obesity even before excess weight appears.

PGS are statistical calculations that sum the effect of thousands or millions of genetic variants associated with a disease. For obesity, they combine variants that influence appetite, metabolism, response to diet, and physical activity. Although genetics does not determine our destiny, understanding our predisposition can help us design individualized prevention and treatment strategies.

The study that revolutionized the field

In August 2025, a paper published in Nature Medicine developed a PGS for obesity with data from over five million people from diverse backgrounds and tested it on over 500,000 participants. The result is a score almost twice as accurate than previous versions. According to the team from the NNF Centre for Basic Metabolic Research at the University of Copenhagen, the new PGS can predict the risk of obesity in childhood, even before the age of five【368994030973972†L1068-L1094】【180417826758558†L46-L116】.

The researchers observed that adding PGS to basic data such as birth weight or body mass index (BMI) in the early years barely improves prediction at three to five years of age. However, from eight years of age onwards, Incorporating PGS almost doubles the amount of BMI variation explained: for example, it goes from 11 % to 21 % at age eight and from 13 % to 26 % at age fifteen【368994030973972†L1083-L1096】. When trying to predict an adult's BMI from childhood data, measuring BMI at age eight explains 44 % of the variation and adding PGS increases it to 49 %; At younger ages, the impact of PGS is greater: from 22 % to 35 % if measured at five years of age and from 8 % to 26 % at twelve months of age【368994030973972†L1090-L1095】.

The study also shows that this multiethnic PGS is more accurate than previous scores. In European populations, it explains up to 17.6 % of BMI variation, while in populations with a larger African component, the figure drops to 5 %–6 % and reaches 2.2 % in a rural Ugandan cohort【368994030973972†L942-L948】. These differences reflect the lower number of people of African descent in genetic studies and warn of the need to increase diversity in research to avoid widening health inequalities.

What does predicting childhood obesity offer us?

Knowing that a child is at high risk doesn't mean they will develop obesity; genetics is only one piece of the puzzle. The Nature article shows that, by combining PGS with lifestyle interventions such as balanced diets and physical activity, participants with higher genetic risk responded better, losing more weight in the first few months. However, they also tended to regain weight faster after the interventions were completed【368994030973972†L1140-L1151】. This finding suggests that individuals with genetic susceptibility may require long-term maintenance strategies and ongoing support.

From a clinical perspective, identifying high-risk children can allow:

• Early intervention: Adjusting nutrition, encouraging active play, and improving sleep during critical stages of development.
• Personalized attention: Design nutrition and physical activity plans tailored to family needs and preferences, taking advantage of digital tools such as the app Caloo.
• Continuous monitoring: Conduct regular check-ups with health professionals, such as nutritionists or pediatricians, to adjust interventions and prevent excessive weight gain.
• Psychological support: Support families in stress management and self-care, avoiding stigmatizing children due to their genetic predisposition.

In practice, the PGS are not diagnosticA child with a low PGS can develop obesity due to unhealthy habits, while a child with a high PGS can maintain normal weight with an active lifestyle and a balanced diet. The application of these scores should be accompanied by a comprehensive assessment of the child's environment, diet, and lifestyle.

Implications for personalized nutrition

Precision nutrition seeks to adapt dietary recommendations to each individual's genetic, epigenetic, microbiota, and lifestyle profile. PGS are another tool in this comprehensive approach. Mefood Omics We combine omics data (genomics, metagenomics, metabolomics) with behavioral questionnaires to offer personalized eating plans tailored to your health and goals.

Integrating the obesity PGS into a personalized nutrition program allows:

• Identify people who could benefit from intensive interventions weight control from an early age.
• Adjust the energy density of the diet (for example, prioritizing foods rich in fiber and micronutrients) and avoiding ultra-processed ingredients that increase the risk of obesity.
• Optimize the frequency of meals and the duration of overnight fasts as a function of insulin sensitivity and genes involved in energy metabolism.
• Take advantage of the intestinal microbiota, since some bacteria influence energy absorption and modulate the immune response. In Alimentomics We explore how microbiome composition interacts with genetics and diet to influence weight.

However, the clinical application of PGS is still under study and should be performed under the supervision of qualified professionals. Furthermore, more data are needed in non-European populations to reduce bias and improve its usefulness worldwide.

Limitations and ethical considerations

Although the results of the new PGS are promising, there are important challenges:

• Genetic diversity: Most data come from Europeans. Low accuracy in African and other minority populations may exacerbate health inequalities. 【368994030973972†L942-L949】
• Interaction with the environment: The PGS does not replace factors such as diet, physical activity, sleep, stress, and socioeconomic environment. As we will see in the post 3, using genetics alone can give a false sense of security or alarm.
• Privacy and use of dataSharing genetic information with companies requires legal guarantees and transparency. post 2 analyzes the risks of using direct-to-consumer genetic tests and proposes measures to protect users.
• Professional supportInterpreting a PGS requires statistical and clinical knowledge. Inadequate communication could lead to anxiety or poor decisions.

As consumers, we must demand that these tools be developed with ethics, diversity, and professional support. Oorenji We offer nutritional coaching and education to help you make informed, science-based decisions.

Conclusion: genetics at the service of prevention

Polygenic obesity scores represent a step forward in preventive medicine. By predicting who is likely to gain excess weight from a very early age, they allow for the design of more effective and personalized interventions. However, they must be interpreted in the context of lifestyle and environment. Science is no substitute for common sense: a balanced diet, regular exercise, adequate rest, and stress management remain fundamental pillars. To understand your risk and receive a tailored nutritional plan, we invite you to explore our tools at Mefood Omics and Caloo.

References

1. Polygenic prediction of body mass index and obesity across the life course and across ancestries – Nature Medicine
2. Scientists built a test that predicts obesity. The results are wild – SciTechDaily
3. Polygenic prediction of BMI: Early-life prediction and intervention – Nature Medicine (figure analysis)
4. The 2023 Atlas of Obesity – World Obesity Federation