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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.
As we continue to elucidate the mechanisms underlying age-related brain diseases, the reductionist strategy in nutrition–brain function research has focused on establishing the impact of individual foods. However, the biological processes connecting diet and cognition are complex. Therefore, consideration of a combination of nutritional compounds may be most efficacious. One barrier to establishing the efficacy of multi-nutrient interventions is that the area lacks an established set of evidence-based guidelines for studying their effect on brain health. This review is an output of the International Life Sciences Institute (ILSI) Europe. A multi-disciplinary expert group was assembled with the aim of developing a set of considerations to guide research into the effects of multi-nutrient combinations on brain functions. Consensus recommendations converged on six key issues that should be considered to advance research in this area: (1) establish working mechanisms of the combination and contributions of each individual compound; (2) validate the relevance of the mechanisms for the targeted human condition; (3) include current nutrient status, intake or dietary pattern as inclusion/exclusion criteria in the study design; (4) select a participant population that is clinically and biologically appropriate for all nutritional components of the combination; (5) consider a range of cognitive outcomes; (6) consider the limits of reductionism and the ‘gold standard’ randomised controlled trial. These guiding principles will enhance our understanding of the interactive/complementary activities of dietary components, thereby strengthening the evidence base for recommendations aimed at delaying cognitive decline.
Dietary patterns (DP) rich in plant foods are associated with improved health and reduced non-communicable disease risk. In October 2021, the Nutrition Society hosted a member-led conference, held online over 2 half days, exploring the latest research findings examining plant-rich DP and health. The aim of the present paper is to summarise the content of the conference and synopses of the individual speaker presentations are included. Topics included epidemiological analysis of plant-rich DP and health outcomes, the effects of dietary interventions which have increased fruit and vegetable (FV) intake on a range of health outcomes, how adherence to plant-rich DP is assessed, the use of biomarkers to assess FV intake and a consideration of how modifying behaviour towards increased FV intake could impact environmental outcomes, planetary health and food systems. In conclusion, although there are still considerable uncertainties which require further research, which were considered as part of the conference and are summarised in this review, adopting a plant-rich DP at a population level could have a considerable impact on diet and health outcomes, as well as planetary health.
The influence of dietary habits on health/disease is well-established. Accurate dietary assessment is essential to understand metabolic pathways/processes involved in this relationship. In recent years, biomarker discovery has become a major area of interest for improving dietary assessment. Well-established nutrient intake biomarkers exist; however, there is growing interest in identifying and using biomarkers for more accurate and objective measurements of food intake. Metabolomics has emerged as a key tool used for biomarker discovery, employing techniques such as NMR spectroscopy, or MS. To date, a number of putatively identified biomarkers were discovered for foods including meat, cruciferous vegetables and legumes. However, many of the results are associations only and lack the desired validation including dose–response studies. Food intake biomarkers can be employed to classify individuals into consumers/non-consumers of specific foods, or into dietary patterns. Food intake biomarkers can also play a role in correcting self-reported measurement error, thus improving dietary intake estimates. Quantification of food intake was previously performed for citrus (proline betaine), chicken (guanidoacetate) and grape (tartaric acid) intake. However, this area still requires more investigation and expansion to a range of foods. The present review will assess the current literature of identified specific food intake biomarkers, their validation and the variety of biomarker uses. Addressing the utility of biomarkers and highlighting gaps in this area is important to advance the field in the context of nutrition research.
Osteoporosis is characterized by low bone mineral density (BMD) and increased susceptibility to low trauma fractures(1).The relationship between osteoporosis risk and general metabolic health parameters is poorly understood. The aim of this study was to investigate the relationship between anthropometric and metabolic parameters with BMD in Adults.
Materials and Methods
A total of 214 (100 male and 114 female) healthy adults were recruited. The mean age was 32 ± 10 years for males and 31 ± 11 years for females. BMD was assessed by whole body dual energy X ray- absorptiometry (Dexa scan). Dexa scores were reported as total bone mineral density, T-score and Z-score. Anthropotemetric measures included body weight, height, waist circumference. Basal metabolic rate (BMR) was assessed by indirect calorimetry. Tertiles of BMD were obtained for males and females. Assessment of parameters across BMD tertiles was performed in males and females separately using ANOVA. Relationships between parameters was assessed using Spearman correlation analysis controlling for gender and age where appropriate.
BMI, Weight and BMR increased significantly across the tertiles for both genders. The mean weight, BMI and BMR were significantly increased in the males at the highest tertile of BMD. Positive correlations (adjusted for gender and age) were observed between weight, BMI, BMR and BMD (R2 = 0.404; p = 0.001, R2 = 0.348, p = 0.001; R2 = 0.363; p = 0.001, respectively).
Overall, the results confirm the relationships between BMD and BMI and weight in a healthy cohort. Furthermore, it highlights a relationship between BMR and BMD. Targeting improvement in body composition and BMR may be a strategy for the age-related decline in BMD.
A person's dietary intake consists of multiple foods eaten as part of a meal as opposed to any one single food/nutrient. Therefore, it is important to understand the interactions between foods and how they affect diet-disease associations. As a result, dietary patterns have emerged as important tools in nutrition research. The objective of the current study is to assess the reproducibility and stability of dietary patterns across four different time-points. Anthropometric measurements were taken from a subset of participants of a free-living cohort study (n = 94), followed by the administration of a 24-hour dietary recall once a month, for four months. The dietary data was entered into dietary analysis software, Nutritics, by two researchers independently, and cross-checked. Foods were assigned to one of 33 predefined food groups, which were further collapsed to 18 food groups based on previous research. Statistical analysis was then performed on the final dataset. Intra-class correlation coefficients were derived to assess the reproducibility of each food group across the four time-points. Variables were standardized using z-scores and dietary patterns were derived using K-means cluster analysis. Stability was assessed by coding participants into one of six groups based on their dietary pattern transition between visit one and four. Analysis of this sub cohort revealed that the intake of food groups (% energy contribution) was reproducible across the time-points. The majority had good to very good agreement, with vegetables and vegetable dishes having the strongest agreement (ICC = 0.831) followed by milk and yogurts (ICC = 0.773), fruit and fruit dishes (ICC = 0.729), and breakfast cereals (ICC = 0.680). Two distinct dietary patterns were identified at each time-point; a ‘Healthy’ and an ‘Unhealthy’ dietary pattern. The ‘Healthy’ dietary pattern was characterized by a significantly higher energy contribution (p < 0.05) from the following food groups – vegetables and vegetable dishes; fruit and fruit dishes; milk and yogurts; breakfast cereals; butter, spreading fats and oils. The analysis on stability demonstrated 42% of participants remained in the same dietary pattern, while 58% transitioned from one dietary pattern to the other. Our results to date demonstrate that two distinct dietary patterns can be derived across multiple time-points using cluster analysis and the food group composition of these dietary patterns can be considered reproducible. Future work will explore these dietary patterns further incorporating the entire cohort and linking stability to health parameters.
Previous work in an Irish cross-sectional study in adults identified three metabolic subgroups (metabotypes) of individuals using k-means cluster analysis based on four fasting clinical standard parameters (triacylglycerols, total cholesterol, HDL cholesterol and glucose). We aimed to validate these metabotypes in another large population-based study. We assigned 2221 participants aged 38–88 years from the German Cooperative Health Research in the Region of Augsburg (KORA) FF4 study (2013/2014) to the three metabotype clusters identified previously by minimizing the Euclidean distances. These clusters were characterized and compared with one another by metabolic characteristics as well as by cardiometabolic disease prevalence. Further, usual dietary intake of various foods/nutrients was estimated based on a food frequency questionnaire and multiple 24-hour food lists and was investigated across clusters. We identified three metabolically distinct clusters in the KORA FF4 study. Cluster 3 represented the group of participants with the most unfavorable metabolic characteristics (e.g. parameters of glucose and lipid metabolism, inflammatory markers), followed by clusters 2 and 1. Individuals in cluster 3 had the highest prevalence of metabolic diseases. Furthermore, they were characterized by the most unfavorable diet with significantly lowest intakes of vegetables, dairy products and fibers as well as significantly highest intakes of total, red and processed meat. Our finding of distinct metabolic subgroups in the KORA FF4 study suggest a successful validation of the metabotypes originally identified based on four commonly measured clinical parameters. Based on these metabotypes, targeted dietary recommendations may be developed for metabolic disease prevention.
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.
Knowing the biological signals associated with appetite control is crucial for understanding the regulation of food intake. Biomarkers of appetite have been defined as physiological measures that relate to subjective appetite ratings, measured food intake, or both. Several metabolites including amino acids, lipids and glucose were proposed as key molecules associated with appetite control over 60 years ago, and along with bile acids are all among possible appetite biomarker candidates. Additional metabolites that have been associated with appetite include endocannabinoids, lactate, cortisol and β-hydroxybutyrate. However, although appetite is a complex integrative process, studies often investigated a limited number of markers in isolation. Metabolomics involves the study of small molecules or metabolites present in biological samples such as urine or blood, and may present a powerful approach to further the understanding of appetite control. Using multiple analytical techniques allows the characterisation of molecules, such as carbohydrates, lipids, amino acids, bile acids and fatty acids. Metabolomics has proven successful in identifying markers of consumption of certain foods and biomarkers implicated in several diseases. However, it has been underexploited in appetite control or obesity. The aim of the present narrative review is to: (1) provide an overview of existing metabolites that have been identified in human biofluids and associated with appetite control; and (2) discuss the potential of metabolomics to deepen understanding of appetite control in humans.
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.
The metabolomic profile of a biofluid can be altered by dietary intake, exercise and disease processes and, thus provides an important tool for the study of many physiological processes. However, in addition to perturbation due to disease, the metabolomic profile of urine and plasma has also been shown to vary due to many intrinsic physiological factors such as age, sex, hormonal status and diurnal variation. Characterisation of this normal degree of variation in the metabolomic profiles of human biofluids is a necessary and important step in the development of metabolomics for use in nutrition-related research. The current review focuses on the impact of sex on the metabolomic profile. A number of studies have reported that sex impacts metabolites such as amino acids, lipids, sugars and keto acids. Furthermore, we examine the effect of the menstrual cycle on the metabolomic profile. Responses to dietary interventions can also differ between the sexes and highlighting this is important for the development of the field of precision nutrition.
Dietary assessment methods including FFQ and food diaries are associated with many measurement errors including energy under-reporting and incorrect estimation of portion sizes. Such errors can lead to inconsistent results especially when investigating the relationship between food intake and disease causation. To improve the classification of a person's dietary intake and therefore clarify proposed links between diet and disease, reliable and accurate dietary assessment methods are essential. Dietary biomarkers have emerged as a complementary approach to the traditional methods, and in recent years, metabolomics has developed as a key technology for the identification of new dietary biomarkers. The objective of this review is to give an overview of the approaches used for the identification of biomarkers and potential use of the biomarkers. Over the years, a number of strategies have emerged for the discovery of dietary biomarkers including acute and medium term interventions and cross-sectional/cohort study approaches. Examples of the different approaches will be presented. Concomitant with the focus on single biomarkers of specific foods, there is an interest in the development of biomarker signatures for the identification of dietary patterns. In the present review, we present an overview of the techniques used in food intake biomarker discover, including the experimental approaches used and challenges faced in the field. While significant progress has been achieved in the field of dietary biomarkers in recent years, a number of challenges remain. Addressing these challenges will be key to ensure success in implementing use of dietary biomarkers.
FFQ, food diaries and 24 h recall methods represent the most commonly used dietary assessment tools in human studies on nutrition and health, but food intake biomarkers are assumed to provide a more objective reflection of intake. Unfortunately, very few of these biomarkers are sufficiently validated. This review provides an overview of food intake biomarker research and highlights present research efforts of the Joint Programming Initiative ‘A Healthy Diet for a Healthy Life’ (JPI-HDHL) Food Biomarkers Alliance (FoodBAll). In order to identify novel food intake biomarkers, the focus is on new food metabolomics techniques that allow the quantification of up to thousands of metabolites simultaneously, which may be applied in intervention and observational studies. As biomarkers are often influenced by various other factors than the food under investigation, FoodBAll developed a food intake biomarker quality and validity score aiming to assist the systematic evaluation of novel biomarkers. Moreover, to evaluate the applicability of nutritional biomarkers, studies are presently also focusing on associations between food intake biomarkers and diet-related disease risk. In order to be successful in these metabolomics studies, knowledge about available electronic metabolomics resources is necessary and further developments of these resources are essential. Ultimately, present efforts in this research area aim to advance quality control of traditional dietary assessment methods, advance compliance evaluation in nutritional intervention studies, and increase the significance of observational studies by investigating associations between nutrition and health.
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.
Evidence suggests that processed red meat consumption is a risk factor for CVD and type 2 diabetes (T2D). This analysis investigates the association between dietary patterns, their processed red meat contributions, and association with blood biomarkers of CVD and T2D, in 786 Irish adults (18–90 years) using cross-sectional data from a 2011 national food consumption survey. All meat-containing foods consumed were assigned to four food groups (n 502) on the basis of whether they contained red or white meat and whether they were processed or unprocessed. The remaining foods (n 2050) were assigned to twenty-nine food groups. Two-step and k-means cluster analyses were applied to derive dietary patterns. Nutrient intakes, plasma fatty acids and biomarkers of CVD and T2D were assessed. A total of four dietary patterns were derived. In comparison with the pattern with lower contributions from processed red meat, the dietary pattern with greater processed red meat intakes presented a poorer Alternate Healthy Eating Index (21·2 (sd 7·7)), a greater proportion of smokers (29 %) and lower plasma EPA (1·34 (sd 0·72) %) and DHA (2·21 (sd 0·84) %) levels (P<0·001). There were no differences in classical biomarkers of CVD and T2D, including serum cholesterol and insulin, across dietary patterns. This suggests that the consideration of processed red meat consumption as a risk factor for CVD and T2D may need to be re-assessed.
Recent technology advancements are aiding the development of scientific discoveries and changing the methods by which we perform research. In order to gain full benefits for human health, it will be important to embrace these new technologies in nutrition research while also acknowledging their limitations. The present issue covers a range of technological approaches that impact on public health nutrition and molecular nutrition. The critical appraisal of these approaches in the context of nutrition research makes this issue a timely and pertinent addition to the scientific literature.
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.