Genotype-based dietary and physical activity advice can be delivered to young adults before unhealthy lifestyle behaviours or metabolic and physiological conditions have developed. The aim of the present study was to investigate the factors that influence the intention to adopt genotype-based personalised advice on diet and physical activity in young adults who perceive themselves to be a healthy weight versus those who perceive themselves to be overweight or obese. An online survey of 396 young adults (18–25 years) evaluated background factors (participant characteristics (including perception of body weight), psychological factors, belief composites) and constructs of the Theory of Planned Behaviour (TPB) related to the adoption of genotype-based personalised advice. The association between background factors and TPB constructs was assessed using multiple linear regression. The constructs of TPB predicted intention to adopt genotype-based personalised nutrition (P < 0.001, adj. R2 = 0.54; attitude: B = 0.24, subjective norm: B = 0.25, PBC: B = 0.45). Background factors including belief composites, health locus of control, gender, physical activity, and food choice motives of ‘health’, ‘price’, ‘familiarity’, ‘weight control’, and ‘convenience’ significantly added to models of TPB constructs related to the intention to adopt personalised advice (P < 0.05). The influence of background factors varied between TPB constructs and differed based on participants perception of their body weight. The study provides support for the use of the TPB in understanding the intention of young adults to adopt gene-based advice for dietary and physical activity behaviour. In addition to perceived body weight, the background factors identified should help to inform and modify the delivery of advice in behaviour change interventions that seek to use genotype-based personalised advice in young adult populations.

Indigenous peoples and ethnic minority groups often experience poor diet quality and poor health outcomes. Such inequities may be partially due to nutrition interventions not meeting the unique cultural and linguistic needs of these population groups, which could be achieved using co-creation and/or personalised approaches. Cultural adaptation or tailoring of nutrition interventions has shown promise in improving some aspects of dietary intake, but this requires careful consideration to ensure it does not inadvertently exacerbate dietary inequities. The aim of this narrative review was to examine examples of cultural adaptations and/or tailoring of public health nutrition interventions that improved the dietary intake and to consider implications for the optimal design and implementation of personalised and precision nutrition interventions. This review identified six examples of cultural adaptation and/or tailoring of public health nutrition intervention in Indigenous peoples and ethnic minority groups across Australia, Canada and the US. All studies used deep socio-cultural adaptations, such as the use of Indigenous storytelling, and many included surface-level adaptations, such as the use of culturally appropriate imagery in intervention materials. However, it was not possible to attribute any improvements in dietary intake to cultural adaptation and/or tailoring per se, and the minimal reporting on the nature of adaptations limited our ability to determine whether the interventions used true co-creation to design content or were adapted from existing interventions. Findings from this review outline opportunities for personalised nutrition interventions to use co-creation practices to design, deliver and implement interventions in collaboration with Indigenous and ethnic minority groups.

The overall aim of precision nutrition is to replace the ‘one size fits all’ approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more ‘precision’ dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene–diet or microbiota–diet interactions. Third, precision nutrition can be used for ‘personalised nutrition’ advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet–disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Three-dimensional (3D) food printing is a rapidly emerging technology offering unprecedented potential for customised food design and personalised nutrition. Here, we evaluate the technological advances in extrusion-based 3D food printing and its possibilities to promote healthy and sustainable eating. We consider the challenges in implementing the technology in real-world applications. We propose viable applications for 3D food printing in health care, health promotion and food waste upcycling. Finally, we outline future work on 3D food printing in food safety, acceptability and economics, ethics and regulations.

Diet-related diseases are the leading cause of death globally and strategies to tailor effective nutrition advice are required. Personalised nutrition advice is increasingly recognised as more effective than population-level advice to improve dietary intake and health outcomes. A potential tool to deliver personalised nutrition advice is metabotyping which groups individuals into homogeneous subgroups (metabotypes) using metabolic profiles. In summary, metabotyping has been successfully employed in human nutrition research to identify subgroups of individuals with differential responses to dietary challenges and interventions and diet–disease associations. The suitability of metabotyping to identify clinically relevant subgroups is corroborated by other fields such as diabetes research where metabolic profiling has been intensely used to identify subgroups of patients that display patterns of disease progression and complications. However, there is a paucity of studies examining the efficacy of the approach to improve dietary intake and health parameters. While the application of metabotypes to tailor and deliver nutrition advice is very promising, further evidence from randomised controlled trials is necessary for further development and acceptance of the approach.

The world is waking up to the reality of climate change and the challenge of feeding 10 billion people in a healthy and sustainable way. For population and planetary health, food systems need to change. ‘Food and nutrition: pathways to a sustainable future’ was the first face-to-face Nutrition Society Summer Conference since 2018, bringing together leading contributors from across the globe to explore six pathways to a better tomorrow. Review papers from the conference symposia cut across disciplinary divides showcasing advances in scientific methods and our cumulative understanding of the impact of the food system on climate change. The depth, breadth and advancement of research presented demonstrate the power of collaborative research that can shape industry, individual and population recommendations and create a powerful shift towards the sustainable dietary patterns and systems that are so urgently required.

Constipation can greatly impact the quality of life (QoL), which can be relieved by dietary fibres; however, preserving a higher fibre intake remains a challenge. We investigated the effects of a personalised dietary advice (PDA) on fibre intake and mild constipation complaints. A total number of twenty-five adults with mild constipation complaints were included in a 4-week observation period followed by a 4-week personalised intervention. The PDA provided high-fibre alternatives via a web tool. In weeks 1, 4 and 8, dietary intake, constipation complaints and QoL were assessed. Furthermore, participants collected a faecal sample at weeks 1, 4 and 8 to determine microbiota diversity and composition, and short-chain fatty acids (SCFA). Participants completed questions daily for 8 weeks regarding abdominal complaints, stool frequency and stool consistency. Fibre intake in week 8 was significantly higher compared to week 1 (Δ = 5·7 ± 6·7 g, P < 0·001) and week 4 (Δ = 5·2 ± 6·4 g, P < 0·001). Constipation severity and QoL significantly improved at week 8 compared to the observation period (P < 0·001). A higher fibre intake significantly reduced constipation severity (β = −0·031 (−0·05; −0·01), P = 0·001) and the QoL (β = −0·022 (−0·04; −0·01), P = 0·009). Stool consistency (P = 0·040) and abdominal pain (P = 0·030) improved significantly during the intervention period (P = 0·040), but stool frequency did not. Average microbial alpha diversity and composition and SCFA concentrations did not change over time, but indicated individual-specific dynamics. Several SCFAs were associated with constipation complaints. To conclude, a PDA effectively increased fibre intake and subsequently reduced constipation complaints, indicating that guided dietary adjustments are important and feasible in the treatment of mild constipation complaints.

Complete nutrition drinks with a low glycemic index (GI) provide nutritional support and prevent hyperglycaemia. The present study identified GI and factors predicting individual glucose response to a new complete nutrition drink. A randomised cross-over controlled trial was conducted in eighteen healthy volunteers (FPG < 100 mg/dl). Complete nutrition drinks containing retrograded starch, glucose solution and white bread were assigned in a random sequence with 14-day wash-out intervals. Plasma glucose and insulin levels were measured from baseline to 180 min after consuming each food. Results show the adjusted GIs of the drink was 48.2 ± 10.4 and 46.7 ± 12.7 with glucose and white bread as the reference, respectively. While the drink has low GI (<55), the individual glucose responses varied (GI: 7–149). Comparing characters in individual GI < 55 (n = 12) and GI ≥ 55 (n = 6) groups revealed significantly higher baseline insulin in the low GI group (14.86 ± 16.51 μIU/ml v. 4.9 ± 3.4 μIU/ml, P < 0·05). The correlation matrix confirms only two predictive factors for having individual GI <55 were baseline insulin (r = 0·5, P = 0·03) and HOMA-IR (r = 0·55, P = 0·02). ROC curve reveals fasting insulin above 1.6 μIU/ml and HOMA-IR above 1.05 as the cut-off values. The findings suggest that the complete nutrition drink has a low GI, but there was wide variability in individual responses partly explained by fasting insulin levels and HOMA-IR. Screening for fasting insulin and HOMA-IR may be encouraged to maximise the functional benefit of the drink.

The aim of this review is to provide an overview of how person-specific interactions between diet and the gut microbiota could play a role in affecting diet-induced weight loss responses. The highly person-specific gut microbiota, which is shaped by our diet, secretes digestive enzymes and molecules that affect digestion in the colon. Therefore, weight loss responses could in part depend on personal colonic fermentation responses, which affect energy extraction of food and production of microbial metabolites, such as short-chain fatty acids (SCFAs), which exert various effects on host metabolism. Colonic fermentation is the net result of the complex interplay between availability of dietary substrates, the functional capacity of the gut microbiome and environmental (abiotic) factors in the gut such as pH and transit time. While animal studies have demonstrated that the gut microbiota can causally affect obesity, causal and mechanistic evidence from human studies is still largely lacking. However, recent human studies have proposed that the baseline gut microbiota composition may predict diet-induced weight loss-responses. In particular, individuals characterised by high relative abundance of Prevotella have been found to lose more weight on diets rich in dietary fibre compared to individuals with low Prevotella abundance. Although harnessing of personal diet–microbiota interactions holds promise for more personalised nutrition and obesity management strategies to improve human health, there is currently insufficient evidence to unequivocally link the gut microbiota and weight loss in human subjects. To move the field forward, a greater understanding of the mechanistic underpinnings of personal diet–microbiota interactions is needed.

As individuals seek increasingly individualised nutrition and lifestyle guidance, numerous apps and nutrition programmes have emerged. However, complex individual variations in dietary behaviours, genotypes, gene expression and composition of the microbiome are increasingly recognised. Advances in digital tools and artificial intelligence can help individuals more easily track nutrient intakes and identify nutritional gaps. However, the influence of these nutrients on health outcomes can vary widely among individuals depending upon life stage, genetics and microbial composition. For example, folate may elicit favourable epigenetic effects on brain development during a critical developmental time window of pregnancy. Genes affecting vitamin B12 metabolism may lead to cardiometabolic traits that play an essential role in the context of obesity. Finally, an individual’s gut microbial composition can determine their response to dietary fibre interventions during weight loss. These recent advances in understanding can lead to a more complete and integrated approach to promoting optimal health through personalised nutrition, in clinical practice settings and for individuals in their daily lives. The purpose of this review is to summarise presentations made during the DSM Science and Technology Award Symposium at the 13th European Nutrition Conference, which focused on personalised nutrition and novel technologies for health in the modern world.

Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of ‘big data’ and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

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.

Personalised nutrition is at its simplest form the delivery of dietary advice at an individual level. Incorporating response to different diets has resulted in the concept of precision nutrition. Harnessing the metabolic phenotype to identify subgroups of individuals that respond differentially to dietary interventions is becoming a reality. More specifically, the classification of individuals in subgroups according to their metabolic profile is defined as metabotyping and this approach has been employed to successfully identify differential response to dietary interventions. Furthermore, the approach has been expanded to develop a framework for the delivery of targeted nutrition. The present review examines the application of the metabotype approach in nutrition research with a focus on developing personalised nutrition. Application of metabotyping in longitudinal studies demonstrates that metabotypes can be associated with cardiometabolic risk factors and diet-related diseases while application in interventions can identify metabotypes with differential responses. In general, there is strong evidence that metabolic phenotyping is a promising strategy to identify groups at risk and to potentially improve health promotion at a population level. Future work should verify if targeted nutrition can change behaviours and have an impact on health outcomes.

Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.

Non-alcoholic fatty liver disease (NAFLD) is now a major public health concern with an estimated prevalence of 25–30% of adults in many countries. Strongly associated with obesity and the metabolic syndrome, the pathogenesis of NAFLD is dependent on complex interactions between genetic and environmental factors that are not completely understood. Weight loss through diet and lifestyle modification underpins clinical management; however, the roles of individual dietary nutrients (e.g. saturated and n-3 fatty acids; fructose, vitamin D, vitamin E) in the pathogenesis or treatment of NAFLD are only partially understood. Systems biology offers valuable interdisciplinary methods that are arguably ideal for application to the studying of chronic diseases such as NAFLD, and the roles of nutrition and diet in their molecular pathogenesis. Although present in silico models are incomplete, computational tools are rapidly evolving and human metabolism can now be simulated at the genome scale. This paper will review NAFLD and its pathogenesis, including the roles of genetics and nutrition in the development and progression of disease. In addition, the paper introduces the concept of systems biology and reviews recent work utilising genome-scale metabolic networks and developing multi-scale models of liver metabolism relevant to NAFLD. A future is envisioned where individual genetic, proteomic and metabolomic information can be integrated computationally with clinical data, yielding mechanistic insight into the pathogenesis of chronic diseases such as NAFLD, and informing personalised nutrition and stratified medicine approaches for improving prognosis.

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.