02. Advancing Biostatistical Approaches in Clinical Research: Lessons from Human Metabolism Assessment

Indirect calorimetry assesses metabolism using respiratory oxygen and carbon dioxide gas exchange. Whole room indirect calorimeters detect small, dynamic changes in metabolic parameters, such as carbohydrate oxidation. The area under the curve (AUC) is commonly used to evaluate these acute temporal changes by summarizing the time series into a single parameter, but limits power and specificity in describing the temporal changes. Here, we evaluate the potential for Bayesian hierarchical modeling to describe individual trends in time series metabolic data and compare the resulting estimates of aggregated data to that of the commonly used AUC. Carbohydrate utilization was assessed over time after participants consumed drinks containing one of three sugars: sucrose, dextrose, and fructose. Carbohydrate oxidation profiles over time were summarized and compared across groups, and individuals, using cubic splines and an appropriate Bayesian hierarchical model. The resulting models demonstrated that the carbohydrate oxidation profile for dextrose differed statistically from sucrose and fructose, with the posterior probability for all spline coefficients for sucrose and fructose being less than 5%. Additionally, the Bayesian model estimates of the AUCs for dextrose, fructose, and sucrose were 4.43, 22.4, and 30.5, respectively. Comparatively, the estimated AUC using the trapezoidal rule and raw data were 0.6, 19.9, and 24.6, respectively. Our developed model was able to characterize the trajectory of carbohydrate oxidation following the consumption of one of three sugars more precisely than AUC alone. This innovation has relevant applications in the areas of human metabolism and physiology to better describe a human's response to other stimuli. Future work includes exploring different parametric forms outside of the cubic spline in measuring metabolism.

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Women in Statistics and Data Science 2024