Obesity is a chronic, complex, and relapsing disease characterised by excessive fat accumulation that poses significant health risks. The World Health Organization (WHO) reports that obesity contributes to the deaths of at least 2.8 million people annually. Since 1990, adult obesity rates have more than doubled, and in 2022, an estimated 890 million adults worldwide were living with obesity — roughly 1 in 8 people. 

 

This is a substantial challenge, particularly in identifying which individuals with obesity are at the highest risk of related complications and determining how to prioritise their treatment. Precision medicine, which has driven major advancements in the prediction, prevention, diagnosis, and treatment of many diseases, holds promise for overcoming these obstacles. 

 

A recent study from the IMI SOPHIA consortium, published in Nature Medicine, introduces a new precision-prediction algorithm that identifies previously unknown subtypes of obesity associated with an increased risk of developing type 2 diabetes and cardiovascular disease. The study was led by researchers from the Lund University Diabetes Centre in Sweden, Maastricht Centre for Systems Biology, and Erasmus MC University Medical Centre in the Netherlands, in collaboration with the IMI SOPHIA consortium.

 

Obesity is both prevalent and diverse, meaning that the health risks for one person with obesity can differ greatly from those of another. Identifying those at the highest risk is essential for delivering precise and timely interventions. 

 

This research examined data from 170,000 adults across the U.K., the Netherlands, and Germany, developing a machine-learning algorithm that classifies obesity into five unique diagnostic profiles, each with varying risks of associated complications.

 

Key findings from this research show that about 80% of participants had health markers typical for their body weight. Approximately 8% of women showed higher-than-expected blood pressure, higher HDL, and a lower waist-to-hip ratio (WHR), with more fat in the hips and less around the waist. This pattern was not observed in men. Around 5% of women and 7% of men exhibited high LDL, elevated triglycerides, high WHR, and higher blood pressure than expected for their weight. Roughly 5% had elevated liver enzymes (ALT) and a high WHR relative to their weight. About 4% showed higher inflammation levels than expected and around 2.5% had high blood sugar and lower LDL than expected for their weight.

 

On a broad scale, higher body weight generally correlates with worse health outcomes. However, at the individual level, there are more complex patterns that can improve disease prediction. Certain biomarkers, such as fat or sugar levels in the blood, may be significantly different from what one might expect based on body weight alone, influencing individual risks for obesity-related complications. Standard clinical tools overlook about 1 in 5 people who could benefit from early intervention. This new algorithm can potentially assist clinicians and patients in making more informed health decisions.

 

Source: Lund University
Image Credit: iStock 

 


References:

Coral DE, Smit F, Farzaneh A et al. (2024) Subclassification of obesity for precision prediction of cardiometabolic diseases. Nat Med.



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diabetes, obesity, precision medicine, heart disease Subclassification of Obesity for Precision Prediction of Diabetes, Heart Disease