For those seeking to lose weight, a quick brain scan may predict how successful you’ll be, says a new study from Wake Forest Baptist Medical Center.
Researchers had 52 study participants between the ages of 60 to 79 undergo an MRI, and they fed the brain-image results into a “computerized predictive algorithm,” according to the study.
Based on a person’s brain volume, the researchers were able to successfully predict weight loss after 18 months with 78 percent accuracy. In measuring brain volume, the researchers analyzed the amounts of gray and white matter in the subjects’ brains. Ultimately, gray matter volume showed to be more predictive than white matter, and, taken together, the two areas proved the most accurate in foretelling weight loss.
“A simple test that can predict intentional weight loss success using structural brain characteristics could ultimately be used to tailor treatment for patients,” said the study’s co-author, Jonathan Burdette, M.D., professor of radiology at Wake Forest School of Medicine, in a statement.
For the current study, the researchers divided the patients into three groups after obtaining their MRI results – one group dieted; a second dieted and exercised aerobically; a third dieted and exercised with resistance training.
Despite the varying factors weighing on the progress of each group, the MRI-driven prediction accuracy held steady.
“To our knowledge, this is the first study to demonstrate that the combination of a machine learning technology and structural brain phenotype can be used to predict weight loss success,” wrote the study authors in the journal Obesity.
Researchers call it “an innovative approach” that uses individual markers to gauge a person’s “responsiveness to intensive lifestyle interventions,” which have proven to be powerful weight loss methods.
A Step Forward for Personalized Medicine
“The findings presented are a first step in personalized medicine for the treatment of obesity. Specifically, if we are able to accurately identify those who will, or will not, succeed in weight loss programs, we can direct clinical effort and funding appropriately,” wrote Burdette and his team.
However, the researchers cautioned that similar personalized approaches are potentially cost-prohibitive. “Such efforts are clearly costly,” they noted.
But the benefits may be worth a high price tag, they added, because the personalized nature of such medicine can overcome individual limitations.
“For example, people identified at high risk for failure might benefit from intensive treatment and close guidance,” explained Burdette. “People identified as having a high probability for success might best respond to less intensive treatment.”
The research did note a range of study limitations – from a small sample size to not “independently predicting weight loss in males and females.”
Yet the findings remain exciting, for today and for future research. “Future studies will investigate whether functional brain networks in association with patterns of brain anatomy may improve prediction, as our recent research has demonstrated that brain circuits are associated with food craving and the self-regulation of eating behavior,” said Burdette.
The study received funding from the National Heart, Blood and Lung Institute (NHLBI).