Mixed-effect Bayesian network reveals personal effects of nutrition

Abstract Nutrition experts know by their experience that people can react very differently to the same nutrition. If we could systematically quantify these differences, it would enable more personal dietary understanding and guidance. This work proposes a mixed-effect Bayesian network as a method fo...

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Autores principales:	Jari Turkia, Lauri Mehtätalo, Ursula Schwab, Ville Hautamäki
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c30b3c3843694311966697eb7f5310e8
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spelling	oai:doaj.org-article:c30b3c3843694311966697eb7f5310e82021-12-02T17:52:25ZMixed-effect Bayesian network reveals personal effects of nutrition10.1038/s41598-021-91437-32045-2322https://doaj.org/article/c30b3c3843694311966697eb7f5310e82021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91437-3https://doaj.org/toc/2045-2322Abstract Nutrition experts know by their experience that people can react very differently to the same nutrition. If we could systematically quantify these differences, it would enable more personal dietary understanding and guidance. This work proposes a mixed-effect Bayesian network as a method for modeling the multivariate system of nutrition effects. Estimation of this network reveals a system of both population-wide and personal correlations between nutrients and their biological responses. Fully Bayesian estimation in the method allows managing the uncertainty in parameters and incorporating the existing nutritional knowledge into the model. The method is evaluated by modeling data from a dietary intervention study, called Sysdimet, which contains personal observations from food records and the corresponding fasting concentrations of blood cholesterol, glucose, and insulin. The model’s usefulness in nutritional guidance is evaluated by predicting personally if a given diet increases or decreases future levels of concentrations. The proposed method is shown to be comparable with the well-performing Extreme Gradient Boosting (XGBoost) decision tree method in classifying the directions of concentration increases and decreases. In addition to classification, we can also predict the precise concentration level and use the biologically interpretable model parameters to understand what personal effects contribute to the concentration. We found considerable personal differences in the contributing nutrients, and while these nutritional effects are previously known at a population level, recognizing their personal differences would result in more accurate estimates and more effective nutritional guidance.Jari TurkiaLauri MehtätaloUrsula SchwabVille HautamäkiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jari Turkia Lauri Mehtätalo Ursula Schwab Ville Hautamäki Mixed-effect Bayesian network reveals personal effects of nutrition
description	Abstract Nutrition experts know by their experience that people can react very differently to the same nutrition. If we could systematically quantify these differences, it would enable more personal dietary understanding and guidance. This work proposes a mixed-effect Bayesian network as a method for modeling the multivariate system of nutrition effects. Estimation of this network reveals a system of both population-wide and personal correlations between nutrients and their biological responses. Fully Bayesian estimation in the method allows managing the uncertainty in parameters and incorporating the existing nutritional knowledge into the model. The method is evaluated by modeling data from a dietary intervention study, called Sysdimet, which contains personal observations from food records and the corresponding fasting concentrations of blood cholesterol, glucose, and insulin. The model’s usefulness in nutritional guidance is evaluated by predicting personally if a given diet increases or decreases future levels of concentrations. The proposed method is shown to be comparable with the well-performing Extreme Gradient Boosting (XGBoost) decision tree method in classifying the directions of concentration increases and decreases. In addition to classification, we can also predict the precise concentration level and use the biologically interpretable model parameters to understand what personal effects contribute to the concentration. We found considerable personal differences in the contributing nutrients, and while these nutritional effects are previously known at a population level, recognizing their personal differences would result in more accurate estimates and more effective nutritional guidance.
format	article
author	Jari Turkia Lauri Mehtätalo Ursula Schwab Ville Hautamäki
author_facet	Jari Turkia Lauri Mehtätalo Ursula Schwab Ville Hautamäki
author_sort	Jari Turkia
title	Mixed-effect Bayesian network reveals personal effects of nutrition
title_short	Mixed-effect Bayesian network reveals personal effects of nutrition
title_full	Mixed-effect Bayesian network reveals personal effects of nutrition
title_fullStr	Mixed-effect Bayesian network reveals personal effects of nutrition
title_full_unstemmed	Mixed-effect Bayesian network reveals personal effects of nutrition
title_sort	mixed-effect bayesian network reveals personal effects of nutrition
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c30b3c3843694311966697eb7f5310e8
work_keys_str_mv	AT jariturkia mixedeffectbayesiannetworkrevealspersonaleffectsofnutrition AT laurimehtatalo mixedeffectbayesiannetworkrevealspersonaleffectsofnutrition AT ursulaschwab mixedeffectbayesiannetworkrevealspersonaleffectsofnutrition AT villehautamaki mixedeffectbayesiannetworkrevealspersonaleffectsofnutrition
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Mixed-effect Bayesian network reveals personal effects of nutrition

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