ArticleObesity (Silver Spring, Md.)2016
Baseline gray- and white-matter volume predict successful weight loss in the elderly.
Article in Obesity (Silver Spring, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.NeuroImage · 2018Trial
- Medial orbitofrontal cortex structure, function, and cognition associates with weight loss for laparoscopic sleeve gastrectomy.Obesity (Silver Spring, Md.) · 2025Article
- Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions.Molecular psychiatry · 2023Review
- Computational approaches to predicting treatment response to obesity using neuroimaging.Reviews in endocrine & metabolic disorders · 2022Review
- Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Efficacy of weight loss intervention can be predicted based on early alterations of fMRI food cue reactivity in the striatum.NeuroImage. Clinical · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveThe 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.
methodsA 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.
resultsThe 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.
conclusionsFindings 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.
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Registered trials
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