As individuals seek increasingly individualised nutrition and lifestyle guidance, numerous apps and nutrition programmes have emerged. However, complex individual variations in dietary behaviours, genotypes, gene expression and composition of the microbiome are increasingly recognised. Advances in digital tools and artificial intelligence can help individuals more easily track nutrient intakes and identify nutritional gaps. However, the influence of these nutrients on health outcomes can vary widely among individuals depending upon life stage, genetics and microbial composition. For example, folate may elicit favourable epigenetic effects on brain development during a critical developmental time window of pregnancy. Genes affecting vitamin B12 metabolism may lead to cardiometabolic traits that play an essential role in the context of obesity. Finally, an individual's gut microbial composition can determine their response to dietary fibre interventions during weight loss. These recent advances in understanding can lead to a more complete and integrated approach to promoting optimal health through personalised nutrition, in clinical practice settings and for individuals in their daily lives. The purpose of this review is to summarise presentations made during the DSM Science and Technology Award Symposium at the 13th European Nutrition Conference, which focused on personalised nutrition and novel technologies for health in the modern world.
Bibliographical noteFunding Information:
S. M. and B. K. S. would like to thank Tamara Bucher from the University of Newcastle, Australia, for providing the fake-food image data set. A. C., H. M. and K. P. would like to acknowledge the researchers on the ‘EpiFASSTT’ and ‘EpiBrain’ projects. S. S. and K. S. V. acknowledge support from the British Council, Newton Fund, British Nutrition Foundation and the authors of the GeNuIne Collaboration(54).
S. M. and B. K. S. were supported by the European Union’s Horizon 2020 research and innovation programmes (grant numbers 863059 – FNS-Cloud, 769661 – SAAM); and the Slovenian Research Agency (grant number P2-0098). The European Union and Slovenian Research Agency had no role in the design, analysis or writing of this article. A. C., H. M. and K. P. were supported in part by the HSC Research and Development Division of the Public Health Agency, Northern Ireland (Enabling Research Award STL/5043/14), the Biology and Biological Sciences Research Council and the Economic and Social Research Council (Grant Ref: ES/N000323/1 ‘EpiFASSTT’) and the European JPI ERA-HDHL “Nutrition & the Epigenome” scheme jointly funded by the Biology and Biological Sciences Research Council and the Medical Research Council (Grant Ref: BB/ S020330/1 ‘EpiBrain’). The Northern Ireland Department for Economy (DfE) funded the PhD studentship for Aoife Caffrey. The funders had no role in the design, analysis or writing of this article. S. S. and K. S. V. were supported in part by GeNuIne Collaboration: British Nutrition Foundation; Sri Lankan (Genetics of Obesity and Diabetes) study: Farnborough College of Technology, UK; Indonesian (MINANG) study: British Council Newton Fund Researcher Links Travel Grant: 2016-RLTG7-10215; and Indian (Chennai Urban Rural Epidemiology Study) study: Research Society for the Study of Diabetes in India (RSSDI) (Project No: RSSDI/HQ/Grants/2014/250).
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- Personalised nutrition
- Deep learning
- Vitamin B 12