A Dietary Feedback System for the Delivery of Consistent Personalized Dietary Advice in the Web-Based Multicenter Food4Me Study

A Scoping Review of Artificial Intelligence for Precision Nutrition

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors; 2) common patterns in AI-driven precision nutrition studies; and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) process, we extracted 198 articles from major databases using search keywords in 3 categories: precision nutrition, AI, and natural language processing. The extracted literature reveals a surge in AI-driven precision nutrition research, with ∼75% (n = 148) published since 2020. It also showcases a diverse publication landscape, with the majority of studies focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets used in the literature and summarize methodologies and evaluation metrics to guide future studies. We also emphasize the importance of minority and cultural perspectives in promoting equity for precision nutrition using AI. Future research should further integrate these factors to fully harness AI's potential in precision nutrition.

Developing and testing personalised nutrition feedback for more sustainable healthy diets: the MyPlanetDiet randomised controlled trial protocol

PURPOSE

Agriculture and food production contribute to climate change. There is mounting pressure to transition to diets with less environmental impact while maintaining nutritional adequacy. MyPlanetDiet aimed to reduce diet-related greenhouse gas emissions (GHGE) in a safe, nutritionally adequate, and acceptable manner. This paper describes the trial protocol, development, and testing of personalised nutrition feedback in the MyPlanetDiet randomised controlled trial (RCT).

METHODS

MyPlanetDiet was a 12-week RCT that provided standardised personalised nutrition feedback to participants based on new sustainable healthy eating guidelines (intervention) or existing healthy eating guidelines (control) using decision trees and corresponding feedback messages. To test the personalised nutrition feedback, we modelled a sample of 20 of the MyPlanetDiet participants baseline diets. Diets were modelled to adhere to control and intervention decision trees and feedback messages. Modelled nutrient intakes and environmental metrics were compared using repeated measure one-way analysis of covariance.

RESULTS

Intervention diets had significantly lower (p < 0.001) diet-related GHGE per 2500 kilocalories (kcal) (4.7 kg CO2-eq) relative to control (6.6 kg CO2-eq) and baseline (7.1 kg CO2-eq). Modelled control and intervention diets had higher mean daily intakes of macronutrients (carbohydrates, fibre, and protein) and micronutrients (calcium, iron, zinc, and iodine). Modelled control and intervention diets had lower percent energy from fat and saturated fat relative to baseline.

CONCLUSIONS

Adherence to the MyPlanetDiet personalised nutrition feedback would be expected to lead to better nutrient intakes and reduced diet-related GHGE. The MyPlanetDiet RCT will test the effectiveness and safety of personalised feedback for a more sustainable diet.

TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION

Clinical trials registration number: NCT05253547, 23 February 2022.

Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study

BACKGROUND

China's older population is facing serious health challenges, including malnutrition and multiple chronic conditions. There is a critical need for tailored food recommendation systems. Knowledge graph-based food recommendations offer considerable promise in delivering personalized nutritional support. However, the integration of disease-based nutritional principles and preference-related requirements needs to be optimized in current recommendation processes.

OBJECTIVE

This study aims to develop a knowledge graph-based personalized meal recommendation system for community-dwelling older adults and to conduct preliminary effectiveness testing.

METHODS

We developed ElCombo, a personalized meal recommendation system driven by user profiles and food knowledge graphs. User profiles were established from a survey of 96 community-dwelling older adults. Food knowledge graphs were supported by data from websites of Chinese cuisine recipes and eating history, consisting of 5 entity classes: dishes, ingredients, category of ingredients, nutrients, and diseases, along with their attributes and interrelations. A personalized meal recommendation algorithm was then developed to synthesize this information to generate packaged meals as outputs, considering disease-related nutritional constraints and personal dietary preferences. Furthermore, a validation study using a real-world data set collected from 96 community-dwelling older adults was conducted to assess ElCombo's effectiveness in modifying their dietary habits over a 1-month intervention, using simulated data for impact analysis.

RESULTS

Our recommendation system, ElCombo, was evaluated by comparing the dietary diversity and diet quality of its recommended meals with those of the autonomous choices of 96 eligible community-dwelling older adults. Participants were grouped based on whether they had a recorded eating history, with 34 (35%) having and 62 (65%) lacking such data. Simulation experiments based on retrospective data over a 30-day evaluation revealed that ElCombo's meal recommendations consistently had significantly higher diet quality and dietary diversity compared to the older adults' own selections (P<.001). In addition, case studies of 2 older adults, 1 with and 1 without prior eating records, showcased ElCombo's ability to fulfill complex nutritional requirements associated with multiple morbidities, personalized to each individual's health profile and dietary requirements.

CONCLUSIONS

ElCombo has shown enhanced potential for improving dietary quality and diversity among community-dwelling older adults in simulation tests. The evaluation metrics suggest that the food choices supported by the personalized meal recommendation system surpass autonomous selections. Future research will focus on validating and refining ElCombo's performance in real-world settings, emphasizing the robust management of complex health data. The system's scalability and adaptability pinpoint its potential for making a meaningful impact on the nutritional health of older adults.

A Short Digital Food Frequency Questionnaire (DIGIKOST-FFQ) Assessing Dietary Intake and Other Lifestyle Factors Among Norwegians: Qualitative Evaluation With Focus Group Interviews and Usability Testing

Background

In-person dietary counseling and interventions have shown promising results in changing habits toward healthier lifestyles, but they are costly to implement in large populations. Developing digital tools to assess individual dietary intake and lifestyle with integrated personalized feedback systems may help overcome this challenge. We developed a short digital food frequency questionnaire, known as the DIGIKOST-FFQ, to assess diet and other lifestyle factors based on the Norwegian Food-Based Dietary Guidelines. The DIGIKOST-FFQ includes a personalized feedback system, the DIGIKOST report, that benchmarks diet and lifestyle habits. We used qualitative focus group interviews and usability tests to test the feasibility and usability of the DIGIKOST application.

Objective

We aimed to explore attitudes, perceptions, and challenges in completing the DIGIKOST-FFQ. We also investigated perceptions and understanding of the personalized feedback in the DIGIKOST report and the technical flow and usability of the DIGIKOST-FFQ and the DIGIKOST report.

Methods

Healthy individuals and cancer survivors were invited to participate in the focus group interviews. The transcripts were analyzed using thematic analysis. Another group of healthy individuals completed the usability testing, which was administered individually by a moderator and 2 observers. The results were analyzed based on predefined assignments and discussion with the participants about the interpretation of the DIGIKOST report and technical flow of the DIGIKOST-FFQ.

Results

A total of 20 individuals participated in the focus group interviews, divided into 3 groups of healthy individuals and 3 groups of cancer survivors. Each group consisted of 3 to 4 individuals. Five main themes were investigated: (1) completion time (on average 19.1, SD 8.3, minutes, an acceptable duration), (2) layout (participants reported the DIGIKOST-FFQ was easy to navigate and had clear questions but presented challenges in reporting dietary intake, sedentary time, and physical activity in the last year), (3) questions (the introductory questions on habitual intake worked well), (4) pictures (the pictures were very helpful, but some portion sizes were difficult to differentiate and adding weight in grams would have been helpful), and (5) motivation (users were motivated to obtain personalized feedback). Four individuals participated in the usability testing. The results showed that the users could seamlessly log in, give consent, fill in the DIGIKOST-FFQ, and receive, print, and read the DIGIKOST report. However, parts of the report were perceived as difficult to interpret.

Conclusions

The DIGIKOST-FFQ was overall well received by participants, who found it feasible to use; however, some adjustments with regard to reporting dietary intake and lifestyle habits were suggested. The DIGIKOST report with personalized feedback was the main motivation to complete the questionnaire. The results from the usability testing revealed a need for adjustments and updates to make the report easier to read.

A Clustering Approach to Meal-Based Analysis of Dietary Intakes Applied to Population and Individual Data

BACKGROUND

Examination of meal intakes can elucidate the role of individual meals or meal patterns in health not evident by examining nutrient and food intakes. To date, meal-based research has been limited to focus on population rather than individual intakes, without considering portions or nutrient content when characterizing meals.

OBJECTIVES

We aimed to characterize meals commonly consumed, incorporating portions and nutritional content, and to determine the accuracy of nutrient intake estimates using these meals at both population and individual levels.

METHODS

The 2008-2010 Irish National Adult Nutrition Survey (NANS) data were used. A total of 1500 participants, with a mean ± SD age of 44.5 ± 17.0 y and BMI of 27.1 ± 5.0 kg/m2, recorded their intake using a 4-d weighed food diary. Food groups were identified using k-means clustering. Partitioning around the medoids clustering was used to categorize similar meals into groups (generic meals) based on their Nutrient Rich Foods Index (NRF9.3) score and the food groups that they contained. The nutrient content for each generic meal was defined as the mean content of the grouped meals. Seven standard portion sizes were defined for each generic meal. Mean daily nutrient intakes were estimated using the original and the generic data.

RESULTS

The 27,336 meals consumed were aggregated to 63 generic meals. Effect sizes from the comparisons of mean daily nutrient intakes (from the original compared with generic meals) were negligible or small, with P values ranging from <0.001 to 0.941. When participants were classified according to nutrient-based guidelines (high, adequate, or low), the proportion of individuals who were classified into the same category ranged from 55.3% to 91.5%.

CONCLUSIONS

A generic meal-based method can estimate nutrient intakes based on meal rather than food intake at the sample population and individual levels. Future work will focus on incorporating this concept into a meal-based dietary intake assessment tool.

Effect of Personalized Nutrition on Dietary, Physical Activity, and Health Outcomes: A Systematic Review of Randomized Trials

Personalized nutrition is an approach that tailors nutrition advice to individuals based on an individual's genetic information. Despite interest among scholars, the impact of this approach on lifestyle habits and health has not been adequately explored. Hence, a systematic review of randomized trials reporting on the effects of personalized nutrition on dietary, physical activity, and health outcomes was conducted. A systematic search of seven electronic databases and a manual search resulted in identifying nine relevant trials. Cochrane's Risk of Bias was used to determine the trials' methodological quality. Although the trials were of moderate to high quality, the findings did not show consistent benefits of personalized nutrition in improving dietary, behavioral, or health outcomes. There was also a lack of evidence from regions other than North America and Europe or among individuals with diseases, affecting the generalizability of the results. Furthermore, the complex relationship between genes, interventions, and outcomes may also have contributed to the scarcity of positive findings. We have suggested several areas for improvement for future trials regarding personalized nutrition.

The Assessment of a Personalized Nutrition Tool (eNutri) in Germany: Pilot Study on Usability Metrics and Users’ Experiences

Background

To address the epidemic burden of diet-related diseases, adequate dietary intake assessments are needed to determine the actual nutrition intake of a population. In this context, the eNutri web app has been developed, providing online automated personalized dietary advice, based on nutritional information recorded via an integrated and validated food frequency questionnaire (FFQ). Originally developed for a British population and their dietary habits, the eNutri tool has specifically been adapted to the German population, taking into account national eating habits and dietary recommendations.

Objective

The primary aim of this study is to evaluate the system usability and users’ experience and feedback on the eNutri app in a small-scale preliminary study. The secondary aim is to investigate the efficacy of personalized nutrition (PN) recommendations versus general dietary advice in altering eating habits.

Methods

The app was piloted for 4 weeks by 106 participants from across Germany divided into a PN group and a control group. The groups differed according to the degree of personalization of dietary recommendations obtained.

Results

An overall System Usability Scale (SUS) score of 78.4 (SD 12.2) was yielded, indicating an above average user experience. Mean completion time of the FFQ was 26.7 minutes (SD 10.6 minutes). Across subgroups (age, sex, device screen sizes) no differences in SUS or completion time were found, indicating an equal performance for all users independent of the assigned experimental group. Participants’ feedback highlighted the need for more personalized dietary advice for controls, while personalized nutritional recommendations improved the awareness of healthy eating behavior. Further improvements to the eNutri app were suggested by the app users.

Conclusions

In total, the eNutri app has proven to be a suitable instrument to capture the dietary habits of a German population sample. Regarding functionality, system usability, and handling, direct user feedback was quite positive. Nutritional advice given was rated ambivalent, pointing to several weaknesses in the eNutri app, minimizing the system’s full potential. A higher level of personalization within nutritional advice subjectively improved the app’s usability. The insights gained will be used as a basis to further develop and improve this digital diet assessment tool.

Machine and cognitive intelligence for human health: systematic review

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Web-Based Personalized Nutrition System for Delivering Dietary Feedback Based on Behavior Change Techniques: Development and Pilot Study among Dietitians

Given the complex and varied nature of individual characteristics influencing dietary behaviors, personalized dietary advice may be more effective than generalized “one-size-fits-all” advice. In this paper, we describe a web-based personalized nutrition system for improving the quality of overall diet in the general adult population. The development process included identification of appropriate behavior change techniques, modification of dietary assessment method (Meal-based Diet History Questionnaire; MDHQ), selection of dietary components, and a personalized dietary feedback tool. A pilot study was conducted online among 255 dietitians. Each completed the MDHQ, received his/her own dietary feedback report, and evaluated the relevance of the report based on 12 questions using a 5-point Likert scale from “totally disagree” (score 1) to “totally agree” (score 5). The mean value of overall acceptability score of dietary feedback report was 4.2. The acceptability score was, on average, higher in plausible energy reporters (compared with implausible energy reporters), participants who printed out the report (compared with those who did not), and those spending ≥20 min to read the report (compared with those spending <20 min). This is the first attempt to develop a web-based personalized nutrition system in Japan, where dietitians were broadly supportive of the dietary feedback report.

Exploring the Potential of Personalized Dietary Advice for Health Improvement in Motivated Individuals With Premetabolic Syndrome: Pretest-Posttest Study

Background

Dietary quality plays an essential role in the prevention and management of metabolic syndrome (MetS).

Objective

The aim of this pilot study is to organize personalized dietary advice in a real-life setting and to explore the effects on dietary intake, metabolic health, and perceived health.

Methods

We followed a one-group pretest-posttest design and included 37 individuals at risk of MetS, who indicated motivation to change dietary behavior. For a period of 16 weeks, participants received personalized advice (t=0 and t=8) and feedback (t=0, t=4, t=8, t=12 and t=16) on dietary quality and metabolic health (ie, waist circumference, BMI, blood pressure, lipid profile, fasting glucose levels, and C-peptide). Personalized advice was generated in a two-stage process. In stage 1, an automated algorithm generated advice per food group, integrating data on individual dietary quality (Dutch Healthy Diet Index; total score 8-80) and metabolic health parameters. Stage 2 included a telephone consultation with a trained dietitian to define a personal dietary behavior change strategy and to discuss individual preferences. Dietary quality and metabolic health markers were assessed at t=0, t=8, and t=16. Self-perceived health was evaluated on 7-point Likert scales at t=0 and t=16.

Results

At the end of the study period, dietary quality was significantly improved compared with the baseline (Dutch Healthy Diet Index +4.3; P<.001). In addition, lipid profile (triglycerides, P=.02; total cholesterol, P=.01; high-density lipoprotein, P<.001; and low-density lipoprotein, P<.001), BMI (P<.001), waist circumference (P=.01), and C-peptide (P=.01) were all significantly improved, whereas plasma glucose increased by 0.23 nmol/L (P=.04). In line with these results, self-perceived health scores were higher at t=16 weeks than at baseline (+0.67; P=.005).

Conclusions

This exploratory study showed that personalized dietary advice resulted in positive effects on dietary behavior, metabolic health, and self-perceived health in motivated pre-MetS adults. The study was performed in a do-it-yourself setting, highlighting the potential of at-home health improvement through dietary changes.

Trial Registration

ClinicalTrials.gov NCT04595669; https://clinicaltrials.gov/ct2/show/NCT04595669

Personalised nutrition advice reduces intake of discretionary foods and beverages: findings from the Food4Me randomised controlled trial

BACKGROUND

The effect of personalised nutrition advice on discretionary foods intake is unknown. To date, two national classifications for discretionary foods have been derived. This study examined changes in intake of discretionary foods and beverages following a personalised nutrition intervention using these two classifications.

METHODS

Participants were recruited into a 6-month RCT across seven European countries (Food4Me) and were randomised to receive generalised dietary advice (control) or one of three levels of personalised nutrition advice (based on diet [L1], phenotype [L2] and genotype [L3]). Dietary intake was derived from an FFQ. An analysis of covariance was used to determine intervention effects at month 6 between personalised nutrition (overall and by levels) and control on i) percentage energy from discretionary items and ii) percentage contribution of total fat, SFA, total sugars and salt to discretionary intake, defined by Food Standards Scotland (FSS) and Australian Dietary Guidelines (ADG) classifications.

RESULTS

Of the 1607 adults at baseline, n = 1270 (57% female) completed the intervention. Percentage sugars from FSS discretionary items was lower in personalised nutrition vs control (19.0 ± 0.37 vs 21.1 ± 0.65; P = 0.005). Percentage energy (31.2 ± 0.59 vs 32.7 ± 0.59; P = 0.031), percentage total fat (31.5 ± 0.37 vs 33.3 ± 0.65; P = 0.021), SFA (36.0 ± 0.43 vs 37.8 ± 0.75; P = 0.034) and sugars (31.7 ± 0.44 vs 34.7 ± 0.78; P < 0.001) from ADG discretionary items were lower in personalised nutrition vs control. There were greater reductions in ADG percentage energy and percentage total fat, SFA and salt for those randomised to L3 vs L2.

CONCLUSIONS

Compared with generalised dietary advice, personalised nutrition advice achieved greater reductions in discretionary foods intake when the classification included all foods high in fat, added sugars and salt. Future personalised nutrition approaches may be used to target intake of discretionary foods.

TRIAL REGISTRATION

Clinicaltrials.gov NCT01530139 . Registered 9 February 2012.

MCR: Open-Source Software to Automate Compilation of Health Study Report-Back

Sharing individualized results with health study participants, a practice we and others refer to as "report-back," ensures participant access to exposure and health information and may promote health equity. However, the practice of report-back and the content shared is often limited by the time-intensive process of personalizing reports. Software tools that automate creation of individualized reports have been built for specific studies, but are largely not open-source or broadly modifiable. We created an open-source and generalizable tool, called the Macro for the Compilation of Report-backs (MCR), to automate compilation of health study reports. We piloted MCR in two environmental exposure studies in Massachusetts, USA, and interviewed research team members (n = 7) about the impact of MCR on the report-back process. Researchers using MCR created more detailed reports than during manual report-back, including more individualized numerical, text, and graphical results. Using MCR, researchers saved time producing draft and final reports. Researchers also reported feeling more creative in the design process and more confident in report-back quality control. While MCR does not expedite the entire report-back process, we hope that this open-source tool reduces the barriers to personalizing health study reports, promotes more equitable access to individualized data, and advances self-determination among participants.

Nutrition Module design in Maintain Your Brain: an internet-based randomised controlled trial to prevent cognitive decline and dementia

The Maintain Your Brain (MYB) trial is one of the largest internet-delivered multidomain randomised controlled trial designed to target modifiable risk factors for dementia. It comprises four intervention modules: physical activity, nutrition, mental health and cognitive training. This paper explains the MYB Nutrition Module, which is a fully online intervention promoting the adoption of the 'traditional' Mediterranean Diet (MedDiet) pattern for those participants reporting dietary intake that does not indicate adherence to a Mediterranean-type cuisine or those who have chronic diseases/risk factors for dementia known to benefit from this type of diet. Participants who were eligible for the Nutrition Module were assigned to one of the three diet streams: Main, Malnutrition and Alcohol group, according to their medical history and adherence to the MedDiet at baseline. A short dietary questionnaire was administered weekly during the first 10 weeks and then monthly during the 3-year follow-up to monitor whether participants adopted or maintained the MedDiet pattern during the intervention. As the Nutrition Module is a fully online intervention, resources that promoted self-efficacy, self-management and process of change were important elements to be included in the module development. The Nutrition Module is unique in that it is able to individualise the dietary advice according to both the medical and dietary history of each participant; the results from this unique intervention will contribute substantively to the evidence that links the Mediterranean-type diet with cognitive function and the prevention of dementia and will increase our understanding of the benefits of a MedDiet in a Western country.

Gut Colonization Mechanisms of Lactobacillus and Bifidobacterium: An Argument for Personalized Designs

Lactobacillus and Bifidobacterium spp. are best understood for their applications as probiotics, which are often transient, but as commensals it is probable that stable colonization in the gut is important for their beneficial roles. Recent research suggests that the establishment and persistence of strains of Lactobacillus and Bifidobacterium in the gut are species- and strain-specific and affected by natural history, genomic adaptability, and metabolic interactions of the bacteria and the microbiome and immune aspects of the host but also regulated by diet. This provides new perspectives on the underlying molecular mechanisms. With an emphasis on host–microbe interaction, this review outlines how the characteristics of individual Lactobacillus and Bifidobacterium bacteria, the host genotype and microbiome structure,diet, and host–microbe coadaptation during bacterial gut transition determine and influence the colonization process. The diet-tuned and personally tailored colonization can be achieved via a machine learning prediction model proposed here.

Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial

Advances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report generated using the Australian Eating Survey® (AES) food frequency questionnaire data; and (2) high personalization, involving structured video calls with a dietitian using the AES report plus dietary self-monitoring with text message feedback. Intake was measured at baseline and 12 weeks using the AES and diet quality using the Australian Recommended Food Score (ARFS). Fifty participants (aged 39.2 ± 12.5 years; Body Mass Index 26.4 ± 6.0 kg/m2; 86.0% female) completed baseline measures. Significant (p < 0.05) between-group differences in dietary changes favored the high personalization group for total ARFS (5.6 points (95% CI 1.3 to 10.0)) and ARFS sub-scales of meat (0.9 points (0.4 to 1.6)), vegetarian alternatives (0.8 points (0.1 to 1.4)), and dairy (1.3 points (0.3 to 2.3)). Additional significant changes in favor of the high personalization group occurred for proportion of energy intake derived from energy-dense, nutrient-poor foods (-7.2% (-13.8% to -0.5%)) and takeaway foods sub-group (-3.4% (-6.5% to 0.3%). Significant within-group changes were observed for 12 dietary variables in the high personalization group vs one variable for low personalization. A higher level of personalized support combining the AES report with one-on-one dietitian video calls and dietary self-monitoring resulted in greater dietary change compared to the AES report alone. These findings suggest nutrition-related web and mobile technologies in combination with personalized dietitian delivered advice have a greater impact compared to when used alone.

Optimisation of a metabotype approach to deliver targeted dietary advice

BACKGROUND

Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours.

METHODS

Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled.

RESULTS

The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60-69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%).

CONCLUSIONS

The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

Future Directions for Integrative Objective Assessment of Eating Using Wearable Sensing Technology

Established methods for nutritional assessment suffer from a number of important limitations. Diaries are burdensome to complete, food frequency questionnaires only capture average food intake, and both suffer from difficulties in self estimation of portion size and biases resulting from misreporting. Online and app versions of these methods have been developed, but issues with misreporting and portion size estimation remain. New methods utilizing passive data capture are required that address reporting bias, extend timescales for data collection, and transform what is possible for measuring habitual intakes. Digital and sensing technologies are enabling the development of innovative and transformative new methods in this area that will provide a better understanding of eating behavior and associations with health. In this article we describe how wrist-worn wearables, on-body cameras, and body-mounted biosensors can be used to capture data about when, what, and how much people eat and drink. We illustrate how these new techniques can be integrated to provide complete solutions for the passive, objective assessment of a wide range of traditional dietary factors, as well as novel measures of eating architecture, within person variation in intakes, and food/nutrient combinations within meals. We also discuss some of the challenges these new approaches will bring.

Characteristics of participants who benefit most from personalised nutrition: findings from the pan-European Food4Me randomised controlled trial

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.

Optimisation of a targeted approach to the delivery of personalised dietary advice

Targeted nutrition is defined as personalised nutrition tailored to groups of individuals. Such groups can be identified based on metabolic profiles and are named metabotypes. Metabotypes have been identified in a range of populations and offer the potential to deliver personalised nutrition advice. The objective of the current study was to optimise a targeted approach to deliver dietary advice through comparison with an individualised approach. Study participants (n = 160) were classified into metabotypes previously defined by four markers (triacylglycerols (TAG), total cholesterol (TC), HDL-c, and glucose) in a cross-sectional study with Irish adults. Targeted advice was designed using a decision tree approach. A personalised approach was achieved through the use of the Food4Me decision trees(1). Agreement between methods was compared and the metabotype approach was optimised to incorporate the most prevalent advice exclusively given by the Food4Me decision trees. The optimised metabotype approach was subsequently tested by comparison with individualised advice manually compiled by a dietitian. Individuals in metabotype-1 had high TC (median 5.0 mmol/L, interquartile range 4.2–5.4 mmol/L); individuals in metabotype-2 had normal concentrations of the four biomarkers; and individuals in metabotype-3 had high TAG (1.8, mmol/L 1.4–2.6 mmol/L) and TC (5.4 mmol/L, 4.8–5.9 mmol/L), with the highest BMI and diastolic blood pressure, and the most unfavourable profile for glycaemia (highest fasting insulin and HOMA-IR). Using the metabotype approach, advice for lowering TC, weight, waist circumference, TAG, and blood pressure was given to 79.4%, 46.9%, 28.1%, 20.6%, and 11.9% of the individuals, respectively. Considering the personalised approach, the most frequent advice was given to improve the intake of saturated fatty acids (56.5%), fibre (56.0%), and folate (55.0%). The total agreement between the methods was 64.0%. The optimised metabotype approach revealed a good total agreement of 80.3% with the individualised manual approach, especially in metabotype-1 (93.8%) and metabotype-3 (94.3%). Agreement was higher in females (84.8% vs. 76.4%, p = 0.02) and in older (≥ 45 years old) people (92.5% vs. 78.1%, p = 0.02). These results confirm metabotypes as a promising approach to deliver targeted dietary advice. Future work should ascertain if targeted nutrition is effective in changing behaviours that will affect health outcomes.

Frequent Nutritional Feedback, Personalized Advice, and Behavioral Changes: Findings from the European Food4Me Internet-Based RCT

INTRODUCTION

This study tested the hypothesis that providing personalized nutritional advice and feedback more frequently would promote larger, more appropriate, and sustained changes in dietary behavior as well as greater reduction in adiposity.

STUDY DESIGN

A 6-month RCT (Food4Me) was conducted in seven European countries between 2012 and 2013.

SETTING/PARTICIPANTS

A total of 1,125 participants were randomized to Lower- (n=562) or Higher- (n=563) Frequency Feedback groups.

INTERVENTION

Participants in the Lower-Frequency group received personalized nutritional advice at baseline and at Months 3 and 6 of the intervention, whereas the Higher-Frequency group received personalized nutritional advice at baseline and at Months 1, 2, 3 and 6.

MAIN OUTCOME MEASURES

The primary outcomes were change in dietary intake (at food and nutrient levels) and obesity-related traits (body weight, BMI, and waist circumference). Participants completed an online Food Frequency Questionnaire to estimate usual dietary intake at baseline and at Months 3 and 6 of the intervention. Overall diet quality was evaluated using the 2010 Healthy Eating Index. Obesity-related traits were self-measured and reported by participants via the Internet. Statistical analyses were performed during the first quarter of 2018.

RESULTS

At 3 months, participants in the Lower- and Higher-Frequency Feedback groups showed improvements in Healthy Eating Index score; this improvement was larger in the Higher-Frequency group than the Lower-Frequency group (Δ=1.84 points, 95% CI=0.79, 2.89, p=0.0001). Similarly, there were greater improvements for the Higher- versus Lower-Frequency group for body weight (Δ= -0.73 kg, 95% CI= -1.07, -0.38, p<0.0001), BMI (Δ= -0.24 kg/m², 95% CI= -0.36, -0.13, p<0.0001), and waist circumference (Δ= -1.20 cm, 95% CI= -2.36, -0.04, p=0.039). However, only body weight and BMI remained significant at 6 months.

CONCLUSIONS

At 3 months, higher-frequency feedback produced larger improvements in overall diet quality as well as in body weight and waist circumference than lower-frequency feedback. However, only body weight and BMI remained significant at 6 months.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.gov NCT01530139.

Comparison of Nutrigenomics Technology Interface Tools for Consumers and Health Professionals: A Sequential Explanatory Mixed Methods Investigation

Background

Nutrigenomics forms the basis of personalized nutrition by customizing an individual’s dietary plan based on the integration of life stage, current health status, and genome information. Some common genes that are included in nutrition-based multigene test panels include CYP1A2 (rate of caffeine break down), MTHFR (folate usage), NOS3 (risk of elevated triglyceride levels related to omega-3 fat intake), and ACE (blood pressure response in related to sodium intake). The complexity of gene test–based personalized nutrition presents barriers to its implementation.

Objective

This study aimed to compare a self-driven approach to gene test–based nutrition education versus an integrated practitioner-facilitated method to help develop improved interface tools for personalized nutrition practice.

Methods

A sequential, explanatory mixed methods investigation of 55 healthy adults (35 to 55 years) was conducted that included (1) a 9-week randomized controlled trial where participants were randomized to receive a standard nutrition-based gene test report (control; n=19) or a practitioner-facilitated personalized nutrition intervention (intervention; n=36) and (2) an interpretative thematic analysis of focus group interview data. Outcome measures included differences in the diet quality score (Healthy Eating Index–Canadian [HEI-C]; proportion [%] of calories from total fat, saturated fat, and sugar; omega 3 fatty acid intake [grams]; sodium intake [milligrams]); as well as health-related quality of life (HRQoL) scale score.

Results

Of the 55 (55/58 enrolled, 95%) participants who completed the study, most were aged between 40 and 51 years (n=37, 67%), were female (n=41, 75%), and earned a high household income (n=32, 58%). Compared with baseline measures, group differences were found for the percentage of calories from total fat (mean difference [MD]=−5.1%; Wilks lambda (λ)=0.817, F_1,53=11.68; P=.001; eta-squared [η²]=0.183) and saturated fat (MD=−1.7%; λ=0.816; F_1,53=11.71; P=.001; η²=0.18) as well as HRQoL scores (MD=8.1 points; λ=0.914; F_1,53=4.92; P=.03; η²=0.086) compared with week 9 postintervention measures. Interactions of time-by-group assignment were found for sodium intakes (λ=0.846; F_1,53=9.47; P=.003; η²=0.15) and HEI-C scores (λ=0.660; F_1,53=27.43; P<.001; η²=0.35). An analysis of phenotypic and genotypic information by group assignment found improved total fat (MD=−5%; λ=0.815; F_1,51=11.36; P=.001; η²=0.19) and saturated fat (MD=−1.3%; λ=0.822; F_1,51=10.86; P=.002; η²=0.18) intakes. Time-by-group interactions were found for sodium (λ=0.844; F_3,51=3.09; P=.04; η²=0.16); a post hoc analysis showed pre/post differences for those in the intervention group that did (preintervention mean 3611 mg, 95% CI 3039-4182; postintervention mean 2135 mg, 95% CI 1564-2705) and did not have the gene risk variant (preintervention mean 3722 mg, 95% CI 2949-4496; postintervention mean 2071 mg, 95% CI 1299-2843). Pre- and postdifferences related to the Dietary Reference Intakes showed increases in the proportion of intervention participants within the acceptable macronutrient distribution ranges for fat (pre/post mean difference=41.2%; P=.02). Analysis of textual data revealed 3 categories of feedback: (1) translation of nutrition-related gene test information to action; (2) facilitation of eating behavior change, particularly for the macronutrients and sodium; and (3) directives for future personalized nutrition practice.

Conclusions

Although improvements were observed in both groups, healthy adults appear to derive more health benefits from practitioner-led personalized nutrition interventions. Further work is needed to better facilitate positive changes in micronutrient intakes.

Trial Registration

ClinicalTrials.gov NCT03310814; http://clinicaltrials.gov/ct2/show/NCT03310814

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.9846

Strategies for online personalised nutrition advice employed in the development of the eNutri web app

The internet has considerable potential to improve health-related food choice at low-cost. Online solutions in this field can be deployed quickly and at very low cost, especially if they are not dependent on bespoke devices or offline processes such as the provision and analysis of biological samples. One key challenge is the automated delivery of personalised dietary advice in a replicable, scalable and inexpensive way, using valid nutrition assessment methods and effective recommendations. We have developed a web-based personalised nutrition system (eNutri) which assesses dietary intake using a validated graphical FFQ and provides personalised food-based dietary advice automatically. Its effectiveness was evaluated during an online randomised controlled trial dietary intervention (EatWellUK study) in which personalised dietary advice was compared with general population recommendations (control) delivered online. The present paper presents a review of literature relevant to this work, and describes the strategies used during the development of the eNutri app. Its design and source code have been made publicly available under a permissive open source license, so that other researchers and organisations can benefit from this work. In a context where personalised diet advice has great potential for health promotion and disease prevention at-scale and yet is not currently being offered in the most popular mobile apps, the strategies and approaches described in the present paper can help to inform and advance the design and development of technologies for personalised nutrition.

Personalised nutrition and health

Jose Ordovas and colleagues consider that nutrition interventions tailored to individual characteristics and behaviours have promise but more work is needed before they can deliver

Application of Behavior Change Techniques in a Personalized Nutrition Electronic Health Intervention Study: Protocol for the Web-Based Food4Me Randomized Controlled Trial

BACKGROUND

To determine the efficacy of behavior change techniques applied in dietary and physical activity intervention studies, it is first necessary to record and describe techniques that have been used during such interventions. Published frameworks used in dietary and smoking cessation interventions undergo continuous development, and most are not adapted for Web-based delivery. The Food4Me study (N=1607) provided the opportunity to use existing frameworks to describe standardized Web-based techniques employed in a large-scale, internet-based intervention to change dietary behavior and physical activity.

OBJECTIVE

The aims of this study were (1) to describe techniques embedded in the Food4Me study design and explain the selection rationale and (2) to demonstrate the use of behavior change technique taxonomies, develop standard operating procedures for training, and identify strengths and limitations of the Food4Me framework that will inform its use in future studies.

METHODS

The 6-month randomized controlled trial took place simultaneously in seven European countries, with participants receiving one of four levels of personalized advice (generalized, intake-based, intake+phenotype-based, and intake+phenotype+gene-based). A three-phase approach was taken: (1) existing taxonomies were reviewed and techniques were identified a priori for possible inclusion in the Food4Me study, (2) a standard operating procedure was developed to maintain consistency in the use of methods and techniques across research centers, and (3) the Food4Me behavior change technique framework was reviewed and updated post intervention. An analysis of excluded techniques was also conducted.

RESULTS

Of 46 techniques identified a priori as being applicable to Food4Me, 17 were embedded in the intervention design; 11 were from a dietary taxonomy, and 6 from a smoking cessation taxonomy. In addition, the four-category smoking cessation framework structure was adopted for clarity of communication. Smoking cessation texts were adapted for dietary use where necessary. A posteriori, a further 9 techniques were included. Examination of excluded items highlighted the distinction between techniques considered appropriate for face-to-face versus internet-based delivery.

CONCLUSIONS

The use of existing taxonomies facilitated the description and standardization of techniques used in Food4Me. We recommend that for complex studies of this nature, technique analysis should be conducted a priori to develop standardized procedures and training and reviewed a posteriori to audit the techniques actually adopted. The present framework description makes a valuable contribution to future systematic reviews and meta-analyses that explore technique efficacy and underlying psychological constructs. This was a novel application of the behavior change taxonomies and was the first internet-based personalized nutrition intervention to use such a framework remotely.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01530139; https://clinicaltrials.gov/ct2/show/NCT01530139 (Archived by WebCite at http://www.webcitation.org/6y8XYUft1).

Association between Diet-Quality Scores, Adiposity, Total Cholesterol and Markers of Nutritional Status in European Adults: Findings from the Food4Me Study

Diet-quality scores (DQS), which are developed across the globe, are used to define adherence to specific eating patterns and have been associated with risk of coronary heart disease and type-II diabetes. We explored the association between five diet-quality scores (Healthy Eating Index, HEI; Alternate Healthy Eating Index, AHEI; MedDietScore, MDS; PREDIMED Mediterranean Diet Score, P-MDS; Dutch Healthy Diet-Index, DHDI) and markers of metabolic health (anthropometry, objective physical activity levels (PAL), and dried blood spot total cholesterol (TC), total carotenoids, and omega-3 index) in the Food4Me cohort, using regression analysis. Dietary intake was assessed using a validated Food Frequency Questionnaire. Participants (n = 1480) were adults recruited from seven European Union (EU) countries. Overall, women had higher HEI and AHEI than men (p < 0.05), and scores varied significantly between countries. For all DQS, higher scores were associated with lower body mass index, lower waist-to-height ratio and waist circumference, and higher total carotenoids and omega-3-index (p trends < 0.05). Higher HEI, AHEI, DHDI, and P-MDS scores were associated with increased daily PAL, moderate and vigorous activity, and reduced sedentary behaviour (p trend < 0.05). We observed no association between DQS and TC. To conclude, higher DQS, which reflect better dietary patterns, were associated with markers of better nutritional status and metabolic health.

Capturing health and eating status through a nutritional perception screening questionnaire (NPSQ9) in a randomised internet-based personalised nutrition intervention: the Food4Me study

BACKGROUND

National guidelines emphasize healthy eating to promote wellbeing and prevention of non-communicable diseases. The perceived healthiness of food is determined by many factors affecting food intake. A positive perception of healthy eating has been shown to be associated with greater diet quality. Internet-based methodologies allow contact with large populations. Our present study aims to design and evaluate a short nutritional perception questionnaire, to be used as a screening tool for assessing nutritional status, and to predict an optimal level of personalisation in nutritional advice delivered via the Internet.

METHODS

Data from all participants who were screened and then enrolled into the Food4Me proof-of-principle study (n = 2369) were used to determine the optimal items for inclusion in a novel screening tool, the Nutritional Perception Screening Questionnaire-9 (NPSQ9). Exploratory and confirmatory factor analyses were performed on anthropometric and biochemical data and on dietary indices acquired from participants who had completed the Food4Me dietary intervention (n = 1153). Baseline and intervention data were analysed using linear regression and linear mixed regression, respectively.

RESULTS

A final model with 9 NPSQ items was validated against the dietary intervention data. NPSQ9 scores were inversely associated with BMI (β = -0.181, p < 0.001) and waist circumference (Β = -0.155, p < 0.001), and positively associated with total carotenoids (β = 0.198, p < 0.001), omega-3 fatty acid index (β = 0.155, p < 0.001), Healthy Eating Index (HEI) (β = 0.299, p < 0.001) and Mediterranean Diet Score (MDS) (β = 0. 279, p < 0.001). Findings from the longitudinal intervention study showed a greater reduction in BMI and improved dietary indices among participants with lower NPSQ9 scores.

CONCLUSIONS

Healthy eating perceptions and dietary habits captured by the NPSQ9 score, based on nine questionnaire items, were associated with reduced body weight and improved diet quality. Likewise, participants with a lower score achieved greater health improvements than those with higher scores, in response to personalised advice, suggesting that NPSQ9 may be used for early evaluation of nutritional status and to tailor nutritional advice.

TRIAL REGISTRATION

NCT01530139 .

Metabotyping for the development of tailored dietary advice solutions in a European population: the Food4Me study

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.

Systems biology of personalized nutrition

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person's health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology-based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of "systems flexibility" is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed.

The impact of MTHFR 677C → T risk knowledge on changes in folate intake: findings from the Food4Me study

BACKGROUND

It is hypothesised that individuals with knowledge of their genetic risk are more likely to make health-promoting dietary and lifestyle changes. The present study aims to test this hypothesis using data from the Food4Me study. This was a 6-month Internet-based randomised controlled trial conducted across seven centres in Europe where individuals received either general healthy eating advice or varying levels of personalised nutrition advice. Participants who received genotype-based personalised advice were informed whether they had the risk (CT/TT) (n = 178) or non-risk (CC) (n = 141) alleles of the methylenetetrahydrofolate reductase (MTHFR) gene in relation to cardiovascular health and the importance of a sufficient intake of folate. General linear model analysis was used to assess changes in folate intake between the MTHFR risk, MTHFR non-risk and control groups from baseline to month 6 of the intervention.

RESULTS

There were no differences between the groups for age, gender or BMI. However, there was a significant difference in country distribution between the groups (p = 0.010). Baseline folate intakes were 412 ± 172, 391 ± 190 and 410 ± 186 μg per 10 MJ for the risk, non-risk and control groups, respectively. There were no significant differences between the three groups in terms of changes in folate intakes from baseline to month 6. Similarly, there were no changes in reported intake of food groups high in folate.

CONCLUSIONS

These results suggest that knowledge of MTHFR 677C → T genotype did not improve folate intake in participants with the risk variant compared with those with the non-risk variant.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01530139.

The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: findings from the Food4Me randomized controlled trial

BACKGROUND

The apolipoprotein E (APOE) risk allele (ɛ4) is associated with higher total cholesterol (TC), amplified response to saturated fatty acid (SFA) reduction, and increased cardiovascular disease. Although knowledge of gene risk may enhance dietary change, it is unclear whether ɛ4 carriers would benefit from gene-based personalized nutrition (PN).

OBJECTIVES

The aims of this study were to 1) investigate interactions between APOE genotype and habitual dietary fat intake and modulations of fat intake on metabolic outcomes; 2) determine whether gene-based PN results in greater dietary change than do standard dietary advice (level 0) and nongene-based PN (levels 1-2); and 3) assess the impact of knowledge of APOE risk (risk: E4+, nonrisk: E4-) on dietary change after gene-based PN (level 3).

DESIGN

Individuals (n = 1466) recruited into the Food4Me pan-European PN dietary intervention study were randomly assigned to 4 treatment arms and genotyped for APOE (rs429358 and rs7412). Diet and dried blood spot TC and ω-3 (n-3) index were determined at baseline and after a 6-mo intervention. Data were analyzed with the use of adjusted general linear models.

RESULTS

Significantly higher TC concentrations were observed in E4+ participants than in E4- (P < 0.05). Although there were no significant differences in APOE response to gene-based PN (E4+ compared with E4-), both groups had a greater reduction in SFA (percentage of total energy) intake than at level 0 (mean ± SD: E4+, -0.72% ± 0.35% compared with -1.95% ± 0.45%, P = 0.035; E4-, -0.31% ± 0.20% compared with -1.68% ± 0.35%, P = 0.029). Gene-based PN was associated with a smaller reduction in SFA intake than in nongene-based PN (level 2) for E4- participants (-1.68% ± 0.35% compared with -2.56% ± 0.27%, P = 0.025).

CONCLUSIONS

The APOE ɛ4 allele was associated with higher TC. Although gene-based PN targeted to APOE was more effective in reducing SFA intake than standard dietary advice, there was no difference between APOE "risk" and "nonrisk" groups. Furthermore, disclosure of APOE nonrisk may have weakened dietary response to PN. This trial was registered at clinicaltrials.gov as NCT01530139.