Molecular 'signature' more precise than BMI for obesity-related disease risk

Oct. 11 (UPI) — Researchers have developed a more precise method than body mass index for predicting risk for diabetes and cardiovascular disease.

The method developed by scientists at Scripps Research and collaborating corporate and academic partners uses distinct molecular “signatures” from people with obesity. The findings were published Thursday in the journal Cell Metabolism.

“While BMI correlates well and to a large extent with individual health outcomes, it does not have the sensitivity to identify outliers, some of which carry unique health consequences,” the authors wrote in the study, which is the measure of body fat based on height and weight that applies to adult men and women.

For a person who is 5 feet 9 inches tall, a BMI of 18.5 to 24.9, which corresponds to 125 pounds to 168 pounds, is considered a healthy weight, according to the Centers for Disease Control and Prevention.

Instead, researchers found predictors in the body’s metabolites, which are molecules produced for people to live, breathe and eat.

“By looking at metabolome changes, we could identify individuals with a several-fold increase in their risk of developing of diabetes and cardiovascular disease over the ensuing years,” Dr. Amalio Telenti, professor of genomics at Scripps Research and previously a scientific leader at Human Longevity Inc., said in a press release.

The scientists assessed the relationship between disease risk and the “metabolome” — a person’s collection of hundreds of metabolites. Obesity profoundly alters the metabolome, including certain metabolites associated with an increase in intra-abdominal fat sitting behind the abdominal wall. The researchers identified specific signatures that predicted higher risk.

Telenti and colleagues from HLI, the J. Craig Venter Institute and other partner organizations analyzed 2,396 people’s body composition.

Nearly one-third of the approximately 1,000 metabolites measured in the study were associated with BMI, and 49 had a strong signature of the relationship between BMI, obesity, metabolic disease and the genetics of BMI.

Scientists could predict a person’s obesity status with an 80 percent to 90 percent accuracy rate based on their metabolite levels.

Metabolome changes weren’t always associated with whether a person was actually obese. Some people were lean but still at risk of disease. The researchers believe this information can help doctors predict future disease risk or enroll patients in clinical trials.

By combining many measurements to create a “signature” of a disease, they can find individuals with genetic variants associated with morbid obesity.

“We generated a signature of obesity, but with different experimental and machine learning approaches, we could have also generated more targeted biomarkers for diseases like diabetes and liver steatosis,” Telenti said.

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