Precision nutrition: A systematic literature review

Daniel Kirk,Cagatay Catal,Bedir Tekinerdogan

doi:10.1016/j.compbiomed.2021.104365

Daniel Kirk, Cagatay Catal + Show 1 more

Open Access

https://doi.org/10.1016/j.compbiomed.2021.104365

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Abstract

Precision Nutrition research aims to use personal information about individuals or groups of individuals to deliver nutritional advice that, theoretically, would be more suitable than generic advice. Machine learning, a subbranch of Artificial Intelligence, has promise to aid in the development of predictive models that are suitable for Precision Nutrition. As such, recent research has applied machine learning algorithms, tools, and techniques in precision nutrition for different purposes. However, a systematic overview of the state-of-the-art on the use of machine learning in Precision Nutrition is lacking. Therefore, we carried out a Systematic Literature Review (SLR) to provide an overview of where and how machine learning has been used in Precision Nutrition from various aspects, what such machine learning models use as input features, what the availability status of the data used in the literature is, and how the models are evaluated. Nine research questions were defined in this study. We retrieved 4930 papers from electronic databases and 60 primary studies were selected to respond to the research questions. All of the selected primary studies were also briefly discussed in this article. Our results show that fifteen problems spread across seven domains of nutrition and health are present. Four machine learning tasks are seen in the form of regression, classification, recommendation and clustering, with most of these utilizing a supervised approach. In total, 30 algorithms were used, with 19 appearing more than once. Models were through the use of four groups of approaches and 23 evaluation metrics. Personalized approaches are promising to reduce the burden of these current problems in nutrition research, and the current review shows Machine Learning can be incorporated into Precision Nutrition research with high performance. Precision Nutrition researchers should consider incorporating Machine Learning into their methods to facilitate the integration of many complex features, allowing for the development of high-performance Precision Nutrition approaches.

Highlights

Remarkable progress has been made over the last few decades in understanding how nutrition interacts with health
Pre diction of glycemia for each individual was attempted based on meal content, meal timing features, activity, blood features, continuous glucose monitoring (CGM) data, and data about the microbiome [5]
It was found that random forest (RF) consistently performed the best and high F-measures were found across the various experiments

Summary

Introduction

Remarkable progress has been made over the last few decades in understanding how nutrition interacts with health. Evidence for recommendations for healthy eating guidelines is often obtained from epidemiological or large clinical studies, wherein averages or generic cut-off points are made in an attempt to supply nutritional advice on a population level Such generalisation, practical, fails to capture the individualized nature of the biological effects of nutrition [2]. The level of detail that PN reaches to will depend on how much the differences within the same stratified group make to the final prediction outcome; how well these differences can be detected by the technology in use; and the cost-effectiveness trade-off between these two Taking these points into account, stratification seems likely to be the dominant choice

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