For decades now, obesity has been defined by a number on a scale or where you land on the body mass index (BMI) measure. But a groundbreaking study has found that it’s far more complex, uncovering 11 distinct biological types of obesity, each with unique causes and health risks that could change how we treat and understand the disease.
An international team of researchers led by scientists at the Broad Institute of MIT and Harvard, as well as scientists from the UK, Finland, Japan, Mexico, and South Korea, looked at the DNA of two million people to investigate a potential link between genes and obesity. They sourced health data from a range of prominent studies – the GIANT Consortium, UK Biobank, All of Us Research Program (USA) and the Mass General Brigham Biobank. Additional datasets from Finland, Mexico, Japan and South Korea were also analyzed. Ultimately, the data contained people of all sizes and from European, East Asian, African, South Asian, Latino and Middle Eastern backgrounds.
Using this massive and diverse collection of data, the researchers looked for genetic patterns linked to measures such as waist and hip size, and fat distribution. What they found was 743 genetic loci involved in body size and shape – and 86 of those were previously unknown. This confirmed that obesity is highly polygenic, meaning more than a single gene plays some sort of role in the process.
The researchers also developed partitioned polygenic scores – genetic risk scores, based on the small effects of variants throughout an individual’s DNA, that don’t just estimate the likelihood of obesity, but reveal how their biology influences weight gain. The score adds up the small effects of many different gene variants across a person’s DNA. In this study, the team created separate polygenic scores for each of the 11 obesity types, allowing them to predict not just who is at risk, but how and why.
The team isolated 11 specific endotypes – the biological mechanism behind a condition’s outward appearance (phenotype). In this case, two people may have the same BMI and appear equally obese, but the reason for their weight gain could be entirely different.
What’s more, each of these 11 biological types of obesity were driven by different factors, including mechanisms in the brain, immune cells, insulin pathways and fat-storing tissues. So, it begs the question: If obesity looks similar on the outside but at its root has a different cause, a one-size-fits-all weight loss approach based only on phenotype – like BMI – doesn’t tackle the root cause.
“These 11 components represent distinct biological mechanisms driving obesity-related traits, rather than general obesity per se,” said one of the researchers. “Each endotype has a unique pattern of association with metabolic biomarkers and disease outcomes, suggesting differing clinical consequences.”
Each of the 11 endotypes of obesity identified had different underlying processes – including appetite and energy regulation in the brain, insulin processing, visceral and subcutaneous fat storage regulation, fatty liver disease and lipid (fat) imbalances, neuroendocrine signaling issues and immune dysregulation. There was some crossover, too, with many impacting different aspects of insulin regulation and fat metabolism.
These endotypes were not just “fat genes”, however; they also revealed one’s risk of other health issues such as type 2 diabetes, fatty liver disease and heart conditions. The findings pave the way for endotype-specific treatments for obesity, which could be more effective than approaching weight management based on BMI and also tackle the many health risks driven by these genes.
“Our findings suggest that obesity is not a single disease entity but rather a collection of biologically distinct subtypes, or endotypes,” the researchers noted. “These results can inform personalized prevention strategies and guide therapeutic development by targeting specific pathways relevant to each endotype.”
In the meantime, the researchers have made their data and polygenetic scores available, in the hope that other scientists will build on their work and ultimately develop better clinical screening and targeted therapeutics based on every patient’s unique genetic blueprint.
The research was published in preprint form on medRxiv, and appears on the National Library of Medicine site.
Source: medRxiv
