If you’re trying to lose weight, what are
your chances of success? Your brain may hold the key.
Scientists at Wake Forest Baptist Medical Center believe
they may have found a way to predict who will be successful in their
weight-loss efforts with a quick, non-invasive brain scan.
In findings from a small study published in the current
online issue of the journal Obesity, the researchers were able to predict weight
loss success with 78 percent accuracy based on the brain volume of the study
“A simple test that can predict intentional weight loss
success using structural brain characteristics could ultimately be used to tailor
treatment for patients,” said Jonathan Burdette, M.D., professor of radiology
at Wake Forest School of Medicine, part of Wake Forest Baptist, and co-author
of the study.
“For example, people identified at high risk for failure
might benefit from intensive treatment and close guidance. People identified as
having a high probability for success might best respond to less intensive
In the study, 52 participants, age 60 to 79, were recruited
from the Cooperative Lifestyle Interventions Programs II (CLIP-II) project. The
participants were overweight or obese (BMI greater than 28 and less than 42)
and had a history of either cardiovascular disease or metabolic syndrome.
All participants had a baseline MRI scan and then were
randomized to one of three groups – diet only, diet plus aerobic exercise
training or diet plus resistance exercise training. The goal of the 18-month diet
and exercise program was a weight loss of 7 to 10 percent of body mass.
Basic brain structure information garnered from the MRIs was
classified using a support vector machine, a type of computerized predictive
algorithm. Predictions were based on baseline brain gray and white matter
volume from the participants’ MRIs and compared to the study participants’ actual
weight loss after the 18 months. Brain gray matter volume provided higher
prediction accuracy compared with white matter and the combination of the two
outperformed either one alone, Burdette said.
The study’s small sample size was a limitation, Burdette
said, but the researchers hope to include more people in follow-up studies and
broaden the types of interventions to help improve the predictive nature of the
“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,” he said.
Support for the study was provided by National Heart,
Lung, and Blood Institute grant R18HL07644, the Wake Forest Claude D. Pepper
Older Americans Independence Center, National Institute of Aging grant P3AG021332,
and the Translational Science Center at Wake Forest University.
Co-authors are: Fatemeh Mokhtari, M. Sc., Brielle M.
Paolini, Ph.D., and Paul Laurienti, M.D., Ph.D., of Wake Forest Baptist; W.
Jack Rejeski, Ph.D., and Anthony P. Marsh, Ph.D., of Wake Forest University.