Understanding coronary atherosclerosis in relation to obesity: is getting the distribution of body fatness using dual-energy X-ray absorptiometry worth the effort? A novel perspective using Bayesian Networks

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 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.

Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver

The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.