Personalised nutrition

The importance of diet on health is well established.^1,2,3 Different patterns of food intake are known to be linked to differing disease risks. Diets that are low in calcium and vitamin D, for example, are linked to increased risk of bone disease, and diets that are high in certain types of saturated fat are linked to increased risk of cardiovascular diseases.^4,5

As knowledge of the relationship between diet and disease develops, so does the knowledge that variation in response to food consumption also exists.^6,7,8 While research investigating these variations is ongoing, the focus is increasingly on identifying the cause of these variations and using knowledge of such variations to offer personalised advice. This has developed into an area referred to as personalised nutrition.

To date, there is no agreed definition of personalised nutrition. Within existing definitions is the common goal – to use information derived from the individual to deliver more specific dietary and lifestyle advice than that offered by existing standardised healthy eating guidelines.^9,10,11 Other definitions are more specific and driven by the approach or technology used to personalise the information or advice being given. Nutrigenomics, for instance, uses molecular tools to clarify and understand different responses to nutrient intakes in order to personalise advice for an individual or stratify advice for a group.¹⁰ The Food4Me project^9,12,13,14 defined personalised nutrition in terms of the manner and depth of personalisation and type of data on which advice was based: Level 1 advice based on individual dietary and anthropometric data, Level 2+ advice based on individual phenotypic data and Level 3+ advice based on individual genomic data. These levels offer flexibility in delivering personalised nutrition advice in that advice given may be based on any combination of personalised data across these levels.¹²

Regardless of how it is defined, to be able to offer personalised or tailored advice, knowledge on the drivers or causes of the variation in response to diet is required and should be used to tailor nutrition-related advice and, therefore, deliver personalised nutrition. This concept is not new and has been at the foundation of nutritional advice since the advent of dietary advice, with differing recommended intakes given for age, sex and lifestyle.¹⁴ Now however, novel technologies such as metabolomics and whole genome sequencing allow more complete characterisation of an individual and their response to food consumption and offer more potential to identify and characterise factors causing variation, which can in turn be harnessed in the provision of personalised nutrition.¹⁵

Factors influencing variation

Some studies have specifically captured and reported on variance in response to a given diet/intervention. To date, these include characteristics such as age, body mass index (BMI), circulating triacylglycerol, c-reactive protein or insulin levels.¹⁵ Alongside phenotypic characteristics influencing response to intervention studies, much work has focused on differing responses of genotypes to nutrition interventions. 

While there are many genetic variations known to influence the response to diet, the depth and level of evidence is varied.¹⁵ As an example, variations in genes involved in folate metabolism have been well researched with respect to response to consumption of B-vitamins and other nutrients.^16,17,18

One of the most interesting papers in this area, by Wilson et al, examined the response of methylenetetrahydrofolate reductase (MTHFR) TT genotype patients with risk of CVD. This study was a four-year follow on from a study where participants representing all three MTHFR 677CT genotypes were initially recruited to participate in a placebo-controlled riboflavin intervention for 16 weeks. In the initial study, the team found the TT group to be responsive with respect to reduction in blood pressure. To confirm these findings, the follow-up study, which only examined those with the TT genotype, confirmed the effect of consumption of riboflavin (1.6mg/d for 16 weeks) or placebo on blood pressure, conducted in a crossover style whereby the 2004 treatment groups were placed in opposite intervention groups. This study confirmed that riboflavin supplementation produced an overall decrease in systolic and diastolic blood pressure in this genotype group.¹⁹

Does it work?

While considered the ‘gold standard’ in study design, randomised controlled trials (RCTs) examining the efficacy of personalised nutrition remain few. Moreover, evidence for behaviour change following genetics-based advice is mixed.^20-26

One of the more recent and largest RCTs examining the impact of personalised nutrition was conducted as part of the Food4Me project. Volunteers (n = 1607) were recruited across seven European countries to take part in the internet-delivered RCT, which examined whether providing personalised nutrition advice based on individual diet, anthropometry and lifestyle, with the addition of information on phenotype and/or genotype, would promote sustained healthy changes in dietary behaviour. 

Participants were randomised to one of four groups: (control) dietary advice based on public health eating guidelines or advice based on baseline individual; (level 1) diet; (level 2) diet plus phenotype (anthropometry and blood biomarkers); or (level 3) diet plus phenotype plus genotype taking five diet-responsive genetic variants (FTO; TCF7L2; ApoE4; FADS1; and MTHFR). Changes in dietary intake, anthropometry and blood biomarkers measured at baseline at three and six months post intervention were examined.¹²

Results demonstrated that the provision of personalised advice resulted in greater improvements in dietary intake in the personalised groups compared to the control group, but the change in behaviour was similar irrespective of the type of information on which the personalised advice was based.^13,20,27,28

Results on the efficacy of personalised approaches to dietary health promotion are mixed, even when focused on specific areas such as weight loss. For instance, a study looking at the efficacy of a personalised web-based weight loss maintenance programme compared to standard treatment²⁹ not only found no significant weight rebound from the start of weight loss maintenance up to twelve months for either group, but also no significant differences in BMI between groups.²⁹

The ANODE study, however, was a 16-week RCT in a group of patients with type 2 diabetes and abdominal obesity who were randomised to a fully automated personalised programme that provided either personalised menus and a shopping list for the day, or the week, or general nutritional advice (control). 

The authors found a significant improvement in dietary habits and favourable clinical and laboratory changes in the personalised nutrition group.³⁰ The reasons for these mixed results remain unclear. Therefore, future research may need to embed psychological measures associated with behaviour change within trial designs in order to determine the reasons why interventions are more or less successful and to understand how best to encourage healthy dietary behaviour change.

Conclusions

Personalised nutrition has the potential to enable practitioners to provide a range of services tailored to the needs of the individual. However, before personalised nutrition can be offered more widely, a strictly evidence-based approach will be required along with a better understanding of how individuals respond to different types of advice and delivery.

References

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30. Hansel B et al. A fully automated web-based program improves lifestyle habits and Hba1c in patients with type 2 diabetes and abdominal obesity: randomized trial of patient e-coaching nutritional support (the ANODE study). J Med Internet Res 2017; 19 (11), e360