Diets, nutrients, genes and the microbiome: recent advances in personalised nutrition

Abstract

As individuals seek increasingly individualised nutrition and lifestyle guidance, numerous apps and nutrition programs 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 practise 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.

Understanding Choline Bioavailability and Utilization: First Step Toward Personalizing Choline Nutrition

“…189 Amidst a barricade of challenges impeding the application of tailored nutritional guidance, as discussed in great detail in a review by Verma et al, 205 some of the recent advancements include new biomarkers as such epigenetic markers and microbial enterotypes as well as the usage of deep learning technology. 206 Concerning personalized nutrition and TMA, an oral carnitine challenge test was recently developed by Wu et al to differentiate individual TMAO productivity using the presence of key TMA producers such as E. timonensis or I. massiliensis to establish a TMAO-producer prediction model that attained a predictive value of 92%. 151 As an extension, such a method can be used to studying choline bioavailability in different demographics and even used as a predictive model for risk association to various choline deficiency diseases.…”

GeNuIne (gene–nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B12and D deficiencies and metabolic diseases

“…Given that the genetic profile varies across various ethnic groups (33,34) , it is crucial to explore gene-diet interactions in multiple ethnicities, which will enable us to personalise diet according to each ethnic group (23,35) . To address this issue, the GeNuIne Collaboration was initiated in 2013 to implement nutrigenetic studies on metabolic disease-related traits using population-based studies from multiple ethnic groups in lower-middle income countries (6,7) .…”

“…Although some studies have shown that increased physical activity levels and healthy diet can attenuate the effect of genetic variants on metabolic traits, other studies have shown conflicting results (17) , which could be attributed to genetic heterogeneity and differences in the dietary patterns across multiple ethnic groups (7,18,19) . Majority of the gene-lifestyle interactions have focused on metabolic disease-related outcomes (20)(21)(22)(23) and only a few have focused on vitamins B 12 and D, two critical vitamins which have been shown to be associated with age-related cardiometabolic diseases (24) .…”

A nutrigenetic approach to examine the relationship between vitamin B12 status and cardio‐metabolic traits in multiple ethnic groups – findings from the GeNuIne Collaboration

“…To address all these issues, the GeNuIne Collaboration (Vimaleswaran, 2017) was initiated to investigate the effect of gene-lifestyle interactions on cardio-metabolic disease risk factors using population-based studies from various ethnic groups in lower-middle income countries (LMICs): Brazil (Surendran et al, 2019), Sri Lanka (Surendran et al, 2019b), India (Surendran et al, 2019c) and Indonesia (Surendran et al, 2019d). Another objective of the GeNuIne Collaboration was to look at gene-lifestyle interactions on vitamin B12 concentrations (Matusheski et al, 2021). A summary of the general characteristics of the populations and the SNPs investigated are provided in Tables 1 and 2.…”