Baseline gray- and white-matter volume predict successful weight loss in the elderly
| Autor: | Brielle Paolini, Paul J. Laurienti, Jonathan H. Burdette, W. Jack Rejeski, Anthony P. Marsh, Fatemeh Mokhtari |
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| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
Endocrinology Diabetes and Metabolism Medicine (miscellaneous) Overweight White matter 03 medical and health sciences 0302 clinical medicine Endocrinology Physical medicine and rehabilitation Weight loss medicine 030212 general & internal medicine Nutrition and Dietetics medicine.diagnostic_test business.industry Area under the curve Magnetic resonance imaging medicine.disease Obesity medicine.anatomical_structure Physical therapy Brain Gray Matter medicine.symptom business Gray (horse) 030217 neurology & neurosurgery |
| Zdroj: | Obesity. 24:2475-2480 |
| ISSN: | 1930-7381 |
| DOI: | 10.1002/oby.21652 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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