Baseline gray- and white-matter volume predict successful weight loss in the elderly.
OBJECTIVE
The purpose of this study was to investigate whether structural brain phenotypes could be used to predict weight loss success following behavioral interventions in older adults with overweight or obesity and cardiometabolic dysfunction.


METHODS
A support vector machine with a repeated random subsampling validation approach was used to classify participants into the upper and lower halves of the weight loss distribution following 18 months of a weight loss intervention. Predictions were based on baseline brain gray matter and white matter volume from 52 individuals who completed the intervention and a magnetic resonance imaging session.


RESULTS
The support vector machine resulted in an average classification accuracy of 72.62% based on gray matter and white matter volume. A receiver operating characteristic analysis indicated that classification performance was robust based on an area under the curve of 0.82.


CONCLUSIONS
Findings suggest that baseline brain structure was able to predict weight loss success following 18 months of treatment. The identification of brain structure as a predictor of successful weight loss was an innovative approach to identifying phenotypes for responsiveness to intensive lifestyle interventions. This phenotype could prove useful in future research focusing on the tailoring of treatment for weight loss.
Department of Radiology, Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.|Department of Biomedical Engineering, Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Winston Salem, North Carolina, USA.
Fatemeh
F
Mokhtari
Department of Radiology, Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.
Brielle M
BM
Paolini
Department of Radiology, Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.
Jonathan H
JH
Burdette
Department of Biomedical Engineering, Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Winston Salem, North Carolina, USA.|Translational Science Center, Wake Forest University, Winston Salem, North Carolina, USA.|Department of Health and Exercise Science, Wake Forest University, Winston Salem, North Carolina, USA.|Department of Geriatric Medicine, Wake Forest University, Winston Salem, North Carolina, USA.
Anthony P
AP
Marsh
Translational Science Center, Wake Forest University, Winston Salem, North Carolina, USA.|Department of Health and Exercise Science, Wake Forest University, Winston Salem, North Carolina, USA.|Department of Geriatric Medicine, Wake Forest University, Winston Salem, North Carolina, USA.
W Jack
WJ
Rejeski
Department of Radiology, Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston Salem, North Carolina, USA.|Translational Science Center, Wake Forest University, Winston Salem, North Carolina, USA.
Paul J
PJ
Laurienti
D000368:Aged; D001521:Behavior Therapy; D005260:Female; D066128:Gray Matter / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000628:therapy; D050177:Overweight / Q000628:therapy*; D012372:ROC Curve; D016896:Treatment Outcome; D015431:Weight Loss; D066127:White Matter / Q000033:anatomy & histology* / Q000000981:diagnostic imaging