Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks.
Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6122792?pdf=render |
| _version_ | 1818849612570558464 |
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| author | Selene Xu Wesley Thompson Jacqueline Kerr Suneeta Godbole Dorothy D Sears Ruth Patterson Loki Natarajan |
| author_facet | Selene Xu Wesley Thompson Jacqueline Kerr Suneeta Godbole Dorothy D Sears Ruth Patterson Loki Natarajan |
| author_sort | Selene Xu |
| collection | DOAJ |
| description | Obesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising 333 postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors (e.g., sleep, physical activity), body mass index (BMI), and health outcomes (biomarkers and self-reported quality of life metrics). We used bootstrap resampling to assess network stability and accuracy, and Bayesian information criteria (BIC) to compare networks. Our results identified important behavioral subnetworks. BMI was the primary pathway linking behavioral factors to glucose regulation and inflammatory markers; the BMI-biomarker link was reproduced in 100% of resampled networks. Sleep quality was a hub impacting mental quality of life and physical health with > 95% resampling reproducibility. Omission of the BMI or sleep links significantly degraded the fit of the networks. Our findings suggest potential mechanistic pathways and useful intervention targets for future trials. Using our models, we can make quantitative predictions about health impacts that would result from targeted, weight loss and/or sleep improvement interventions. Importantly, this work highlights the utility of Bayesian networks in health behaviors research. |
| first_indexed | 2024-12-19T06:36:00Z |
| format | Article |
| id | doaj.art-76f34e19ea684de09a9feb787184dd27 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T06:36:00Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-76f34e19ea684de09a9feb787184dd272022-12-21T20:32:14ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01139e020292310.1371/journal.pone.0202923Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks.Selene XuWesley ThompsonJacqueline KerrSuneeta GodboleDorothy D SearsRuth PattersonLoki NatarajanObesity and its impact on health is a multifaceted phenomenon encompassing many factors, including demographics, environment, lifestyle, and psychosocial functioning. A systems science approach, investigating these many influences, is needed to capture the complexity and multidimensionality of obesity prevention to improve health. Leveraging baseline data from a unique clinical cohort comprising 333 postmenopausal overweight or obese breast cancer survivors participating in a weight-loss trial, we applied Bayesian networks, a machine learning approach, to infer interrelationships between lifestyle factors (e.g., sleep, physical activity), body mass index (BMI), and health outcomes (biomarkers and self-reported quality of life metrics). We used bootstrap resampling to assess network stability and accuracy, and Bayesian information criteria (BIC) to compare networks. Our results identified important behavioral subnetworks. BMI was the primary pathway linking behavioral factors to glucose regulation and inflammatory markers; the BMI-biomarker link was reproduced in 100% of resampled networks. Sleep quality was a hub impacting mental quality of life and physical health with > 95% resampling reproducibility. Omission of the BMI or sleep links significantly degraded the fit of the networks. Our findings suggest potential mechanistic pathways and useful intervention targets for future trials. Using our models, we can make quantitative predictions about health impacts that would result from targeted, weight loss and/or sleep improvement interventions. Importantly, this work highlights the utility of Bayesian networks in health behaviors research.http://europepmc.org/articles/PMC6122792?pdf=render |
| spellingShingle | Selene Xu Wesley Thompson Jacqueline Kerr Suneeta Godbole Dorothy D Sears Ruth Patterson Loki Natarajan Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. PLoS ONE |
| title | Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. |
| title_full | Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. |
| title_fullStr | Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. |
| title_full_unstemmed | Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. |
| title_short | Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks. |
| title_sort | modeling interrelationships between health behaviors in overweight breast cancer survivors applying bayesian networks |
| url | http://europepmc.org/articles/PMC6122792?pdf=render |
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