The evolution of personalised nutrition

The concept of personalised nutrition (Fig. 1), whereby dietary advice is tailored to an individual, is not new. Potentially, its origins date back to 1950 when a review paper discussed the concept of genetotrophic diseases and their relationship to nutrient intake⁽ Reference Williams ^{1 )}. This decade also saw the introduction of a controlled diet for successful treatment of phenylketonuria and hence, the beginnings of personalisation based on the identification of inborn errors of metabolism⁽ Reference Bickel, Gerrard and Hickmans ^{2 )}. Following on from this, the common APOE polymorphism was first reported in the 1977⁽ Reference Utermann, Hees and Steinmetz ^{3 )} and since then researchers have been aware of its varying effect on lipid metabolism disorders⁽ Reference Minihane, Khan and Leigh-Firbank ^{4 )}. Furthermore, recommended daily allowances are personalised based on age, gender and life cycle^{( 5 )}. However it was not until the completion of the human genome sequence in 2001 that the concept of personalised health including personalised nutrition was catapulted to the forefront and many believed that this would provide the solution to all chronic diseases⁽ Reference Venter, Adams and Myers ^{6 ,} Reference Lander, Linton and Birren ^{7 )}. Despite these advances, over a decade later, personalised nutrition is still not readily available for delivery at a population level.

Fig. 1.

The concept of personalised nutrition at an individual and group level. Non-personalised advice is generic healthy eating advice that is aimed at the population. This figure depicts the concept that personalised advice can be delivered at an individual level or at a group level to metabolically similar individuals.

The idea of personalised nutrition originally focused on functional genetic variations, known to affect gene–nutrient metabolism. In recent times, a more encompassing definition has emerged; personalised nutrition can be delivered at varying levels where one level builds on the foundations of another⁽ Reference Ronteltap, van Trijp and Berezowska ^{8 )}. Within this example, a three-tiered approach to personalised nutrition is taken, where level 1 personalised advice is based on assessment of the individual's diet, level 2 is based on diet and phenotypic markers such as BMI and blood markers e.g. cholesterol, glucose and level 3 personalised advice is based on diet, phenotype and genotypic information⁽ Reference Gibney and Walsh ^{9 )}. It is hypothesised that dietary advice that is personalised to the individual may be more successful in motivating changes in diet and lifestyle behaviour⁽ Reference Hamburg and Collins ^{10 )}. However, it is unknown what level of personalisation i.e. diet, phenotype and/or genotype is optimal for initiating such positive changes in diet and lifestyle. Therefore, the first objective of this review is to examine the effectiveness of personalised dietary advice in motivating behaviour change within the levels of personalised nutrition. The second objective is to explore new concepts to potentially deliver personalised nutrition on a wider scale. More specifically, the concept of metabotyping or the grouping of individuals with similar metabolic patterns (Fig. 1) will be discussed and its potential as a feasible public health tool for the delivery of personalised dietary advice will be examined.

Personalised nutrition – how effective is it?

The effectiveness of personalised nutrition can be examined within this concept of levels of personalised advice. While there are many studies in this area, in recent years, there has been a move towards internet-based interventions which can have many benefits over more traditional methods of intervention delivery such as convenience and scalability⁽ Reference Celis-Morales, Lara and Mathers ^{11 )}. As a result the present review will focus, where possible, on internet and web-based interventions. Table 1 illustrates some of the studies which focus specifically on changes in dietary intake such as increases in fruit and vegetables⁽ Reference Alexander, McClure and Calvi ^{13 )} and reduced fat intake⁽ Reference Kroeze, Oenema and Dagnelie ^{12 )}. Alexander et al. ⁽ Reference Alexander, McClure and Calvi ^{13 )} examined changes in fruit and vegetable consumption by comparing an online untailored programme with a tailored behavioural intervention or a tailored behavioural intervention with an additional motivational programme in participants aged between 21 and 65 years (n 2540). Average servings of fruit and vegetables increased by more than two servings across all study groups (P < 0·001) with the greatest increase (+2·8 servings) observed among those participants in the tailored behavioural intervention with motivational counselling (P = 0·05)⁽ Reference Alexander, McClure and Calvi ^{13 )}. In the Self-Monitoring and Recording using Technology Trial, obese individuals were randomised to one of three groups including those who monitored their dietary intake by recording what they ate using a paper diary, those who self-monitored using a personal digital assessment device and those who used the personal digital assessment device and also received daily tailored feedback⁽ Reference Ambeba, Ye and Sereika ^{16 )}. For analysis purposes, the investigators combined those in the first two groups who received no daily feedback and compared them with those in the third group to examine the effect of daily tailored feedback on changes in energy and fat intakes⁽ Reference Ambeba, Ye and Sereika ^{16 )}. The group who received the daily feedback achieved a larger reduction in energy (−22·8 % v. −14·0 %, P = 0·02) and saturated fat (−11·3 % v. −0·5 %, P = 0·03) compared with those who did not receive the daily feedback⁽ Reference Ambeba, Ye and Sereika ^{16 )}. Similar trends were found in other studies as shown in Table 1. A recent systematic review in the area concluded that online personalised interventions were more effective in increasing fruit and vegetable intake (weighted mean differences: 0·35 servings/d, 95 % CI 0·28, 0·42; P < 0·0001) than generic advice⁽ Reference Celis-Morales, Lara and Mathers ^{11 )}. Overall, there is convincing evidence for the efficacy of a personalised approach for facilitating changes in dietary intakes.

Table 1.

Summary of studies evaluating effectiveness of personalised nutrition with a focus on changes in dietary measures

RCT, randomised controlled trial.

There is a wealth of studies examining the effect of personalised programmes on weight loss⁽ Reference Tate, Jackvony and Wing ^{18 –} Reference Collins, Morgan and Jones ^{23 )} as depicted in Table 2. Bennett et al. conducted a 12-week randomised controlled trial among obese primary care patients with hypertension (n 101)⁽ Reference Bennett, Herring and Puleo ^{20 )}. Participants randomised to the control group received a generic weight loss programme. The intervention group had access to a website which provided participants with a series of tailored obesogenic behaviour change goals as well as receiving behavioural skills training and regular health coach support⁽ Reference Bennett, Herring and Puleo ^{20 )}. Intention-to-treat analysis demonstrated greater weight loss at 3 months (−2·56 kg; 95 % CI −3·60, −1·53) in the intervention participants (−2·28 (sd 3·21) kg) compared with the usual care group (0·28 (sd 1·87) kg). Collins et al. ⁽ Reference Collins, Morgan and Jones ^{23 )} performed a 12-week randomised controlled trial which randomised subjects to one of three groups: control: basic which had free access to a commercial web based weight loss programme; enhanced group who were provided with access to an enhanced version of the website (n 309). The web-based weight loss programme was designed to target important features of behaviour change such as self-efficacy, goal setting, self-monitoring, exercise and diet and social support. Those in the enhanced group also had access to personalised, system-generated enrolment reports detailing appropriate weight-loss goals and key behaviour changes required, weekly personalised e-feedback and a reminder schedule to continue to record their dietary intake and weight as well as the basic website features as described above. Individuals with access to a commercial web-based weight loss programme had a reduction in their BMI by the end of the trial; there was no additional benefit of having access to the enhanced version of the website. In the Self-Help, Exercise and Diet using Internet Technology trial, subjects were randomised to either the resources programme (n 54), online programme (n 53) or control group (n 52)⁽ Reference Morgan, Callister and Collins ^{25 )}. Those in the resources group received a weight loss DVD, handbook and support book targeted to men, a pedometer, measuring tape, kilojoule counting book and advised to record their weight, diet and physical activity. Those randomised to the online programme received the same resources as well as instructions on how to use an online food and exercise diary instead of a paper-based diary and received feedback on their food and exercise diary entries via email. At 6 months, significantly more weight was lost in the online (−4·7 kg; 95 % CI −6·1, −3·2) and resources (−3·7 kg; 95 % CI −4·9, −2·5) groups compared with the control (−0·5 kg; 95 % CI −1·4, 0·4). Both intervention groups also had significant improvements in BMI, per cent body fat, waist circumference, blood pressure, physical activity, quality of life, alcohol risk and portion size compared with the controls. A recent meta-analysis also demonstrated that personalised interventions resulted in greater weight loss in comparison with non-personalised advice (weighted mean differences: −1·83 kg 95 % CI −2·2, −1·4; P < 0·0001)⁽ Reference Celis-Morales, Lara and Mathers ^{11 )}. Therefore there is good evidence to suggest that personalised weight loss interventions can result in greater weight loss compared with generic dietary advice.

Table 2.

Summary of studies evaluating effectiveness of personalised nutrition with a focus on changes in phenotypic measures including body weight

RCT, randomised controlled trial.

Other studies have also investigated the effect of personalised advice with respect to changes in multiple dietary and lifestyle factors such as diet, phenotypic measures, physical activity⁽ Reference Sternfeld, Block and Quesenberry ^{26 –} Reference Mouttapa, Robertson and McEligot ^{29 )} and composite lifestyle score changes⁽ Reference Jacobs, Clays and De Bacquer ^{30 )}. In an intervention which targeted both changes in physical activity and dietary intakes, the investigators reported that a tailored programme resulted in more significant lifestyle changes including increases in physical activity levels, decreases in fat and increases in fruit and vegetables intake⁽ Reference Sternfeld, Block and Quesenberry ^{26 )}. Mouttapa et al.⁽ Reference Mouttapa, Robertson and McEligot ^{29 )} reported similar results in women with respect to increases in dairy intake and decreases in weight. However, individually tailored feedback was not found to be more motivating regarding changes in multiple lifestyle behaviours in individuals with familial hypercholesterolemia⁽ Reference Broekhuizen, van Poppel and Koppes ^{28 )}. One intervention investigated the effect of a tailored behaviour change programme on a composite lifestyle change score⁽ Reference Jacobs, Clays and De Bacquer ^{30 )}. The investigators found the composite lifestyle score to be significantly higher in the high intervention group compared with the no/low intervention group (P = 0·009). Therefore the evidence is mixed with regard to the effectiveness of personalised advice in changes in multiple lifestyle behaviours.

With regard to personalised advice at genotypic level, some of the key studies in the area are shown in Table 3. Arkadianos et al. ⁽ Reference Arkadianos, Valdes and Marinos ^{31 )} demonstrated a benefit in providing a nutrigenetic based diet for weight loss compared with a traditional weight management programme in those with a history of weight loss failures. The group observed no difference between the traditional weight management diet group (controls) and nutrigenetic diet group (cases) at 3 months, where both groups had lost weight. However a year later, those in the nutrigenetic group fared much better as they were more likely to keep the weight off (P < 0·023) compared with the controls who on average, gained weight. The Risk Evaluation and Education for Alzheimer's Disease Study was a series of multicentre randomised trials which investigated the effect of APOE risk disclosure with respect to Alzheimer's disease on cognitively normal adults⁽ Reference Chao, Roberts and Marteau ^{32 )}. In the first Risk Evaluation and Education for Alzheimer's Disease Study, the investigators reported that those who were informed they had the risk APOE genotype were more likely to report making an Alzheimer's disease-specific health behaviour change compared with those with the non-risk genotype following a year after disclosure⁽ Reference Chao, Roberts and Marteau ^{32 )}. The second Risk Evaluation and Education for Alzheimer's Disease Study also showed that those with the risk APOE genotype had 4·75 times the odds of reporting a change in dietary supplement use compared with those with the non-risk variant (P < 0·0001)⁽ Reference Vernarelli, Roberts and Hiraki ^{38 )}. However, in contrast to these positive studies, Bloss et al. investigated changes in anxiety, dietary fat and exercise behaviour in individuals who received direct-to-consumer testing and reported no changes 3 months after testing⁽ Reference Bloss, Schork and Topol ^{33 )} and the group reported similar results when they assessed the participants again a year later⁽ Reference Bloss, Wineinger and Darst ^{34 )}. Grant et al.⁽ Reference Grant, O'Brien and Waxler ^{35 )} investigated the effect of genetic testing in individuals at phenotypic risk of diabetes. Individuals were randomised to either undergo genetic testing or not undergo testing (n 108). Both groups then participated in a 12-week diabetes prevention programme. The investigators reported no differences between the groups in relation to self-reported motivation, programme attendance or weight loss⁽ Reference Grant, O'Brien and Waxler ^{35 )}.

Table 3.

Summary of studies evaluating the effectiveness of gene based personalised nutrition advice

RCT, randomised controlled trial.

One of the most recent trials in the area demonstrated a benefit of genetic based dietary advice compared with general dietary recommendations. In this double-blind placebo controlled trial, participants were randomised to receive either genetic based dietary advice on five nutrition related SNP (CYP1A2, GSTT1, GSTM1, TAS1R2 and ACE) for 12 months or general dietary recommendations with no genetic advice⁽ Reference Nielsen and El-Sohemy ^{36 )}. The authors reported no differences between the groups in relation to caffeine, vitamin C, sugar and sodium intakes between baseline and month 3. However, there was a significant difference observed between the risk group and control group at 12 months for the ACE gene with respect to reductions in sodium where mean change in sodium intake was −287·3 (sem 114·1) mg/d in the risk group compared with 129·8 (sem118·2) mg/d in the control group (P = 0·008). In the MOVE! programme, individuals with BMI ≥30·0 kg/m² were randomly assigned to either the genetics-guided therapy group or standard therapy group⁽ Reference Frankwich, Egnatios and Kenyon ^{37 )}. Guided therapy participants were then matched to one of four possible diet types (balanced, low-carbohydrate, low-fat or Mediterranean) based on their risk of seven obesity related SNP (APOA2, ADIPOQ, FTO, KCTD10, LIPC, MMAB and PPARG). The investigators reported no significant differences between the guided therapy  and standard therapy  groups in the percentage of those achieving a 5 % weight loss at 8 weeks or at 24 weeks⁽ Reference Frankwich, Egnatios and Kenyon ^{37 )}. However, it is possible that the sample size (n 51) of this feasibility study may have been too small to detect any significant changes between the groups.

Overall, the evidence with respect to the effectiveness of personalised nutrition based on genetic information is mixed with some studies demonstrating positive effects and others reporting no benefits. Furthermore, a Cochrane review in the area, concluded that there was not enough evidence to support the claim that genetic testing motivates individuals to change their behaviour and larger and better-quality randomised controlled trials were needed to further test this hypothesis⁽ Reference Marteau, French and Griffin ^{39 )}. To date, no study has examined the effect of varying levels of personalised advice in comparison with generic advice. Food4Me is an EU funded project which aimed to examine all aspects of personalised nutrition⁽ Reference Celis-Morales, Livingstone and Marsaux ^{40 )}. As part of Food4Me, a proof-of-principle study was conducted to test the effects of varying levels of personalised nutrition in motivating behaviour change compared with general healthy eating guidelines⁽ Reference Celis-Morales, Livingstone and Marsaux ^{40 )}. This was an internet-based intervention trial conducted across seven centres in Europe (n 1607). Individuals were randomised to receive either healthy eating guidelines, level 1 personalised advice based on diet alone, level 2 personalised advice based on diet and phenotype or level 3 personalised advice based on diet, phenotype and genotype. Details of the study have been described elsewhere⁽ Reference Celis-Morales, Livingstone and Marsaux ^{40 )}. Findings from this intervention trial will decipher whether personalised advice delivered via the internet is effective in motivating behaviour change compared with generic healthy eating advice and which level of personalisation is most effective.

Moving from individuals to groups: a novel use of phenotyping/metabotyping

In recent years the concept of targeted nutrition has emerged which involves delivering tailored dietary advice at a group level rather than at an individual level. The process of grouping similar individuals based on their metabolic or phenotypic profiles is referred to as metabotyping⁽ Reference Brennan ^{41 –} Reference Morris, O'Grada and Ryan ^{44 )}. A number of statistical tools can be employed to identify these metabotypes; some of the more common types include k-means analysis and hierarchical cluster analysis. In the field of personalised medicine, metabotyping may play a role by stratifying patients for the development of tailored healthcare solutions⁽ Reference Holmes, Wilson and Nicholson ^{45 )}.

Examples of this concept are also available from the medical literature where cluster analysis has been used to identify different phenotypes for a range of diseases including chronic obstructive pulmonary disease⁽ Reference Castaldi, Dy and Ross ^{46 )} and Parkinsons disease⁽ Reference Erro, Vitale and Amboni ^{47 )}. This concept has been particularly useful for multisymtomatic and multifactorial diseases where presentation and severity of the disease can differ from one individual to the next. An example of such a disease is asthma; metabotyping has been successful in the identification of varying asthma phenotypes that will in turn allow development of more tailored disease management approaches⁽ Reference Moore, Meyers and Wenzel ^{48 )}. Similar methods have been applied in other respiratory diseases heterogeneous in nature including chronic obstructive pulmonary disease⁽ Reference Castaldi, Dy and Ross ^{46 ,} Reference Lee, Cho and McDonald ^{49 )} and emphysema⁽ Reference Cho, Washko and Hoffmann ^{50 )}. Other examples of the use of cluster analysis to identify sub-groups of patients include Parkinson's disease⁽ Reference Erro, Vitale and Amboni ^{47 ,} Reference Ma, Chan and Gu ^{51 )}, gout sufferers⁽ Reference Richette, Clerson and Perissin ^{52 )}, patients with back pain⁽ Reference Viniol, Jegan and Hirsch ^{53 )} and patients with hip fractures⁽ Reference Penrod, Litke and Hawkes ^{54 ,} Reference Eastwood, Magaziner and Wang ^{55 )}.

Another interesting application of metabotyping is the identification of differential response to intervention studies including responses to drug treatments⁽ Reference van Bochove, van Schalkwijk and Parnell ^{56 )} and dietary interventions⁽ Reference O'Sullivan, Gibney and Connor ^{57 )}. In the Genetics of Lipid Lowering Drug and Diet Network, family members from three generations were recruited and given fenofibrate therapy, in order to assess the variable response of TAG lowering in a genetically similar sample with heterogenous degree of dyslipidaemia⁽ Reference van Bochove, van Schalkwijk and Parnell ^{56 )}. In this sample of 775 participants, cluster analysis was applied to the baseline lipoprotein profiles and three distinct lipid-metabolic phenotypes/metabotypes were identified. Each of the clusters displayed varying responses to fenofibrate therapy. While both cluster 2 and cluster 3 responded positively to the therapy, little benefit was observed for cluster 1 due to the decrease in LDL particle rather than an increase which is usually associated with treatment⁽ Reference van Bochove, van Schalkwijk and Parnell ^{56 )}. This is a clear example of how this approach could be used to match the patient with the appropriate treatment. This concept has previously been applied to a nutritional intervention study where individuals were supplemented with vitamin D for 6 months to improve markers of the metabolic syndrome⁽ Reference O'Sullivan, Gibney and Connor ^{57 )}. In this double-blind placebo controlled study, there was a significant increase in vitamin D status in the intervention group compared with the placebo group (P < 0·001) but no improvements observed in any of the metabolic syndrome markers. However, following application of a clustering approach, a vitamin D responsive cluster was identified which had significant decreases in fasting insulin (P = 0·011), homeostatic model assessment score (P = 0·006) and C-reactive protein (P = 0·011)⁽ Reference O'Sullivan, Gibney and Connor ^{57 )}. These clear examples from the literature demonstrate the potential benefit of grouping individuals based on similar characteristics, either for diagnosis and/or treatment. There is a great potential in this technique for the development of more personalised healthcare practices. However, while there are many examples of the use of cluster analysis to identify these metabolic sub-groups, there is a dearth in the literature with regard to the development of personalised health solutions for these groups.

A proposed framework for the delivery of targeted nutrition

Work from our centre has proposed a framework for the delivery of targeted nutrition based on a metabotype approach. The framework used a cluster analysis approach to define metabotypes and targeted nutrition advice is delivered by decision trees specific for each cluster. Using this approach, metabotyping was performed with total cholesterol, TAG, direct HDL cholesterol and glucose in a population of healthy Irish subjects⁽ Reference O'Donovan, Walsh and Nugent ^{58 )}. The use of these commonly measured markers of metabolic health ensures future applicability across different studies. Three distinctly different clusters or metabotypes were identified across the population. Cluster 1 (n 274) was characterised by high total cholesterol. Subjects in cluster 2 (n 423) had low total cholesterol, TAG and glucose values and were the youngest group. Cluster 3 subjects (n 178) were found to have the most metabolically unfavourable profile including high total cholesterol, TAG and glucose values, highest BMI and subsequently, greatest prevalence of the metabolic syndrome.

Targeted dietary advice was developed based on the characteristics of each cluster where cluster 1 subjects were given cholesterol lowering advice, cluster 2 were relatively healthy and encouraged to continue with their current healthy lifestyle and cluster 3 were given advice on how to lower their cholesterol, TAG and glucose levels. Using a decision tree method, branches of anthropometric (BMI and waist circumference) and blood pressure information were added which resulted in twelve targeted dietary advice messages per cluster. To test the ability of the targeted approach to deliver personalised nutrition, individualised dietary advice was compiled for a random selection of the participants (n 99) and compared with the targeted advice. Overall good agreement was found between the methods with a mean match of 89·1 % (range 20–100 %) in terms of the dietary advice which would be given to a participant at an individual level compared with their targeted dietary advice based on their cluster membership.

To the best of our knowledge this is the first example of this type of approach or framework. The key advantages of such an approach are (1) use of routine biochemical measurements, (2) limited number of decision trees required and (3) easy automation which could enable the rapid delivery of targeted dietary advice in a variety of clinical settings. An example of where this approach could have major implications is the use in general practice clinics. In this scenario, patients are undergoing routine checks and the framework could be implemented with minimal effort. Recent data demonstrates that patients would like to receive dietary information from their general practitioner⁽ Reference Brotons, Drenthen and Durrer ^{59 )} but general practitioners feel they are unequipped to do this due to reported barriers such as heavy workload and lack of skills or training⁽ Reference Brotons, Bjorkelund and Bulc ^{60 )}. However, by adopting this framework, delivery of personalised nutrition could become a reality in general practice clinics. Future work will be directed towards demonstrating the applicability in a more diverse population.