To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Little is known about who would benefit from Internet-based personalised nutrition (PN) interventions. This study aimed to evaluate the characteristics of participants who achieved greatest improvements (i.e. benefit) in diet, adiposity and biomarkers following an Internet-based PN intervention. Adults (n 1607) from seven European countries were recruited into a 6-month, randomised controlled trial (Food4Me) and randomised to receive conventional dietary advice (control) or PN advice. Information on dietary intake, adiposity, physical activity (PA), blood biomarkers and participant characteristics was collected at baseline and month 6. Benefit from the intervention was defined as ≥5 % change in the primary outcome (Healthy Eating Index) and secondary outcomes (waist circumference and BMI, PA, sedentary time and plasma concentrations of cholesterol, carotenoids and omega-3 index) at month 6. For our primary outcome, benefit from the intervention was greater in older participants, women and participants with lower HEI scores at baseline. Benefit was greater for individuals reporting greater self-efficacy for ‘sticking to healthful foods’ and who ‘felt weird if [they] didn’t eat healthily’. Participants benefited more if they reported wanting to improve their health and well-being. The characteristics of individuals benefiting did not differ by other demographic, health-related, anthropometric or genotypic characteristics. Findings were similar for secondary outcomes. These findings have implications for the design of more effective future PN intervention studies and for tailored nutritional advice in public health and clinical settings.
Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.
It is postulated that knowledge of genotype may be more powerful than other types of personalised information in terms of motivating behaviour change. However, there is also a danger that disclosure of genetic risk may promote a fatalistic attitude and demotivate individuals. The original concept of personalised nutrition (PN) focused on genotype-based tailored dietary advice; however, PN can also be delivered based on assessment of dietary intake and phenotypic measures. Whilst dietitians currently provide PN advice based on diet and phenotype, genotype-based PN advice is not so readily available. The aim of this review is to examine the evidence for genotype-based personalised information on motivating behaviour change, and factors which may affect the impact of genotype-based personalised advice. Recent findings in PN will also be discussed, with respect to a large European study, Food4Me, which investigated the impact of varying levels of PN advice on motivating behaviour change. The researchers reported that PN advice resulted in greater dietary changes compared with general healthy eating advice, but no additional benefit was observed for PN advice based on phenotype and genotype information. Within Food4Me, work from our group revealed that knowledge of MTHFR genotype did not significantly improve intakes of dietary folate. In general, evidence is weak with regard to genotype-based PN advice. For future work, studies should test the impact of PN advice developed on a strong nutrigenetic evidence base, ensure an appropriate study design for the research question asked, and incorporate behaviour change techniques into the intervention.
Individual response to dietary interventions can be highly variable. The phenotypic characteristics of those who will respond positively to personalised dietary advice are largely unknown. The objective of this study was to compare the phenotypic profiles of differential responders to personalised dietary intervention, with a focus on total circulating cholesterol. Subjects from the Food4Me multi-centre study were classified as responders or non-responders to dietary advice on the basis of the change in cholesterol level from baseline to month 6, with lower and upper quartiles defined as responder and non-responder groups, respectively. There were no significant differences between demographic and anthropometric profiles of the groups. Furthermore, with the exception of alcohol, there was no significant difference in reported dietary intake, at baseline. However, there were marked differences in baseline fatty acid profiles. The responder group had significantly higher levels of stearic acid (18 : 0, P=0·034) and lower levels of palmitic acid (16 : 0, P=0·009). Total MUFA (P=0·016) and total PUFA (P=0·008) also differed between the groups. In a step-wise logistic regression model, age, baseline total cholesterol, glucose, five fatty acids and alcohol intakes were selected as factors that successfully discriminated responders from non-responders, with sensitivity of 82 % and specificity of 83 %. The successful delivery of personalised dietary advice may depend on our ability to identify phenotypes that are responsive. The results demonstrate the potential use of metabolic profiles in identifying response to an intervention and could play an important role in the development of precision nutrition.
To characterise clusters of individuals based on adherence to dietary recommendations and to determine whether changes in Healthy Eating Index (HEI) scores in response to a personalised nutrition (PN) intervention varied between clusters.
Food4Me study participants were clustered according to whether their baseline dietary intakes met European dietary recommendations. Changes in HEI scores between baseline and month 6 were compared between clusters and stratified by whether individuals received generalised or PN advice.
Individuals in cluster 1 (C1) met all recommended intakes except for red meat, those in cluster 2 (C2) met two recommendations, and those in cluster 3 (C3) and cluster 4 (C4) met one recommendation each. C1 had higher intakes of white fish, beans and lentils and low-fat dairy products and lower percentage energy intake from SFA (P<0·05). C2 consumed less chips and pizza and fried foods than C3 and C4 (P<0·05). C1 were lighter, had lower BMI and waist circumference than C3 and were more physically active than C4 (P<0·05). More individuals in C4 were smokers and wanted to lose weight than in C1 (P<0·05). Individuals who received PN advice in C4 reported greater improvements in HEI compared with C3 and C1 (P<0·05).
The cluster where the fewest recommendations were met (C4) reported greater improvements in HEI following a 6-month trial of PN whereas there was no difference between clusters for those randomised to the Control, non-personalised dietary intervention.
To characterise participants who dropped out of the Food4Me Proof-of-Principle study.
The Food4Me study was an Internet-based, 6-month, four-arm, randomised controlled trial. The control group received generalised dietary and lifestyle recommendations, whereas participants randomised to three different levels of personalised nutrition (PN) received advice based on dietary, phenotypic and/or genotypic data, respectively (with either more or less frequent feedback).
Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.
Adults aged 18–79 years (n 1607).
A total of 337 (21 %) participants dropped out during the intervention. At baseline, dropouts had higher BMI (0·5 kg/m2; P<0·001). Attrition did not differ significantly between individuals receiving generalised dietary guidelines (Control) and those randomised to PN. Participants were more likely to drop out (OR; 95 % CI) if they received more frequent feedback (1·81; 1·36, 2·41; P<0·001), were female (1·38; 1·06, 1·78; P=0·015), less than 45 years old (2·57; 1·95, 3·39; P<0·001) and obese (2·25; 1·47, 3·43; P<0·001). Attrition was more likely in participants who reported an interest in losing weight (1·53; 1·19, 1·97; P<0·001) or skipping meals (1·75; 1·16, 2·65; P=0·008), and less likely if participants claimed to eat healthily frequently (0·62; 0·45, 0·86; P=0·003).
Attrition did not differ between participants receiving generalised or PN advice but more frequent feedback was related to attrition for those randomised to PN interventions. Better strategies are required to minimise dropouts among younger and obese individuals participating in PN interventions and more frequent feedback may be an unnecessary burden.
The interplay between the fat mass- and obesity-associated (FTO) gene variants and diet has been implicated in the development of obesity. The aim of the present analysis was to investigate associations between FTO genotype, dietary intakes and anthropometrics among European adults. Participants in the Food4Me randomised controlled trial were genotyped for FTO genotype (rs9939609) and their dietary intakes, and diet quality scores (Healthy Eating Index and PREDIMED-based Mediterranean diet score) were estimated from FFQ. Relationships between FTO genotype, diet and anthropometrics (weight, waist circumference (WC) and BMI) were evaluated at baseline. European adults with the FTO risk genotype had greater WC (AAv. TT: +1·4 cm; P=0·003) and BMI (+0·9 kg/m2; P=0·001) than individuals with no risk alleles. Subjects with the lowest fried food consumption and two copies of the FTO risk variant had on average 1·4 kg/m2 greater BMI (Ptrend=0·028) and 3·1 cm greater WC (Ptrend=0·045) compared with individuals with no copies of the risk allele and with the lowest fried food consumption. However, there was no evidence of interactions between FTO genotype and dietary intakes on BMI and WC, and thus further research is required to confirm or refute these findings.
An efficient and robust method to measure vitamin D (25-hydroxy vitamin D3 (25(OH)D3) and 25-hydroxy vitamin D2 in dried blood spots (DBS) has been developed and applied in the pan-European multi-centre, internet-based, personalised nutrition intervention study Food4Me. The method includes calibration with blood containing endogenous 25(OH)D3, spotted as DBS and corrected for haematocrit content. The methodology was validated following international standards. The performance characteristics did not reach those of the current gold standard liquid chromatography-MS/MS in plasma for all parameters, but were found to be very suitable for status-level determination under field conditions. DBS sample quality was very high, and 3778 measurements of 25(OH)D3 were obtained from 1465 participants. The study centre and the season within the study centre were very good predictors of 25(OH)D3 levels (P<0·001 for each case). Seasonal effects were modelled by fitting a sine function with a minimum 25(OH)D3 level on 20 January and a maximum on 21 July. The seasonal amplitude varied from centre to centre. The largest difference between winter and summer levels was found in Germany and the smallest in Poland. The model was cross-validated to determine the consistency of the predictions and the performance of the DBS method. The Pearson’s correlation between the measured values and the predicted values was r 0·65, and the sd of their differences was 21·2 nmol/l. This includes the analytical variation and the biological variation within subjects. Overall, DBS obtained by unsupervised sampling of the participants at home was a viable methodology for obtaining vitamin D status information in a large nutritional study.
Over a decade since the completion of the human genome sequence, the promise of personalised nutrition available to all has yet to become a reality. While the definition was originally very gene-focused, in recent years, a model of personalised nutrition has emerged with the incorporation of dietary, phenotypic and genotypic information at various levels. Developing on from the idea of personalised nutrition, the concept of targeted nutrition has evolved which refers to the delivery of tailored dietary advice at a group level rather than at an individual level. Central to this concept is metabotyping or metabolic phenotyping, which is the ability to group similar individuals together based on their metabolic or phenotypic profiles. Applications of the metabotyping concept extend from the nutrition to the medical literature. While there are many examples of the metabotype approach, there is a dearth in the literature with regard to the development of tailored interventions for groups of individuals. This review will first explore the effectiveness of personalised nutrition in motivating behaviour change and secondly, examine potential novel ways for the delivery of personalised advice at a population level through a metabotyping approach. Based on recent findings from our work, we will demonstrate a novel strategy for the delivery of tailored dietary advice at a group level using this concept. In general, there is a strong emerging evidence to support the effectiveness of personalised nutrition; future work should ascertain if targeted nutrition can motivate behaviour change in a similar manner.
Food records or diaries, dietary recalls and FFQ are methods traditionally used to measure dietary intake; however, advancing technologies and growing awareness in personalised health have heightened interest in the application of new technologies to assess dietary intake. Dietary intake data can be used in epidemiology, dietary interventions and in the delivery of personalised nutrition advice. Compared with traditional dietary assessment methods, new technologies have many advantages, including their ability to automatically process data and provide personalised dietary feedback advice. This review examines the new technologies presently under development for the assessment of dietary intakes, and their utilisation and efficacy for personalising dietary advice. New technology-based methods of dietary assessment can broadly be categorised into three key areas: online (web-based) methods, mobile methods and sensor technologies. Several studies have demonstrated that utilising new technologies to provide tailored advice can result in positive dietary changes and have a significant impact on selected nutrient and food group intakes. However, comparison across studies indicates that the magnitude of change is variable and may be influenced by several factors, including the frequency and type of feedback provided. Future work should establish the most effective combinations of these factors in facilitating dietary changes across different population groups.
Personalised nutrition (PN) has the potential to reduce disease risk and optimise health and performance. Although previous research has shown good acceptance of the concept of PN in the UK, preferences regarding the delivery of a PN service (e.g. online v. face-to-face) are not fully understood. It is anticipated that the presence of a free at point of delivery healthcare system, the National Health Service (NHS), in the UK may have an impact on end-user preferences for deliverances. To determine this, supplementary analysis of qualitative data obtained from focus group discussions on PN service delivery, collected as part of the Food4Me project in the UK and Ireland, was undertaken. Irish data provided comparative analysis of a healthcare system that is not provided free of charge at the point of delivery to the entire population. Analyses were conducted using the ‘framework approach’ described by Rabiee (Focus-group interview and data analysis. Proc Nutr Soc 63, 655-660). There was a preference for services to be led by the government and delivered face-to-face, which was perceived to increase trust and transparency, and add value. Both countries associated paying for nutritional advice with increased commitment and motivation to follow guidelines. Contrary to Ireland, however, and despite the perceived benefit of paying, UK discussants still expected PN services to be delivered free of charge by the NHS. Consideration of this unique challenge of free healthcare that is embedded in the NHS culture will be crucial when introducing PN to the UK.
To evaluate the accuracy of the most commonly used anthropometric-based equations in the estimation of percentage body fat (%BF) in both normal-weight and overweight women using air-displacement plethysmography (ADP) as the criterion measure.
A comparative study in which the equations of Durnin and Womersley (1974; DW) and Jackson, Pollock and Ward (1980) at three, four and seven sites (JPW3, JPW4 and JPW7) were validated against ADP in three groups. Group 1 included all participants, group 2 included participants with a BMI<25·0 kg/m2 and group 3 included participants with a BMI≥25·0 kg/m2.
Human Performance Laboratory, Institute for Sport and Health, University College Dublin, Republic of Ireland.
Forty-three female participants aged between 18 and 55 years.
In all three groups, the %BF values estimated from the DW equation were closer to the criterion measure (i.e. ADP) than those estimated from the other equations. Of the three JPW equations, JPW3 provided the most accurate estimation of %BF when compared with ADP in all three groups.
In comparison to ADP, these findings suggest that the DW equation is the most accurate anthropometric method for the estimation of %BF in both normal-weight and overweight females.
The application of metabolomics in multi-centre studies is increasing. The aim of the present study was to assess the effects of geographical location on the metabolic profiles of individuals with the metabolic syndrome. Blood and urine samples were collected from 219 adults from seven European centres participating in the LIPGENE project (Diet, genomics and the metabolic syndrome: an integrated nutrition, agro-food, social and economic analysis). Nutrient intakes, BMI, waist:hip ratio, blood pressure, and plasma glucose, insulin and blood lipid levels were assessed. Plasma fatty acid levels and urine were assessed using a metabolomic technique. The separation of three European geographical groups (NW, northwest; NE, northeast; SW, southwest) was identified using partial least-squares discriminant analysis models for urine (R2X: 0·33, Q2: 0·39) and plasma fatty acid (R2X: 0·32, Q2: 0·60) data. The NW group was characterised by higher levels of urinary hippurate and N-methylnicotinate. The NE group was characterised by higher levels of urinary creatine and citrate and plasma EPA (20 : 5 n-3). The SW group was characterised by higher levels of urinary trimethylamine oxide and lower levels of plasma EPA. The indicators of metabolic health appeared to be consistent across the groups. The SW group had higher intakes of total fat and MUFA compared with both the NW and NE groups (P≤ 0·001). The NE group had higher intakes of fibre and n-3 and n-6 fatty acids compared with both the NW and SW groups (all P< 0·001). It is likely that differences in dietary intakes contributed to the separation of the three groups. Evaluation of geographical factors including diet should be considered in the interpretation of metabolomic data from multi-centre studies.
Although personalised nutrition is frequently considered in the context of diet–gene interactions, increasingly, personalised nutrition is seen to exist at three levels. The first is personalised dietary advice using Internet-delivered services, which ultimately will become automated and which will also draw on mobile phone technology. The second level of personalised dietary advice will include phenotypic information on anthropometry, physical activity, clinical parameters and biochemical markers of nutritional status. It remains possible that in addition to personalised dietary advice based on phenotypic data, advice at that group or metabotype level may be offered where metabotypes are defined by a common metabolic profile. The third level of personalised nutrition will involve the use of genomic data. While the genomic aspect of personalised nutrition is often considered as its main driver, there are significant challenges to translation of data on SNP and diet into personalised advice. The majority of the published data on SNP and diet emanate from observational studies and as such do not offer any cause–effect associations. To achieve this, purpose-designed dietary intervention studies will be needed with subjects recruited according to their genotype. Extensive research indicates that consumers would welcome personalised dietary advice including dietary advice based on their genotype. Unlike personalised medicine where genotype data are linked to the risk of developing a disease, in personalised nutrition the genetic data relate to the optimal diet for a given genotype to reduce disease risk factors and thus there are few ethical and legal issues in personalised nutrition.
Email your librarian or administrator to recommend adding this to your organisation's collection.