Marianne C. Walsh

Metabolomics in human nutrition: opportunities and challenges

Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

Online Dietary Intake Estimation: Reproducibility and Validity of the Food4Me Food Frequency Questionnaire Against a 4-Day Weighed Food Record

Background Advances in nutritional assessment are continuing to embrace developments in computer technology. The online Food4Me food frequency questionnaire (FFQ) was created as an electronic system for the collection of nutrient intake data. To ensure its accuracy in assessing both nutrient and food group intake, further validation against data obtained using a reliable, but independent, instrument and assessment of its reproducibility are required. Objective The aim was to assess the reproducibility and validity of the Food4Me FFQ against a 4-day weighed food record (WFR). Methods Reproducibility of the Food4Me FFQ was assessed using test-retest methodology by asking participants to complete the FFQ on 2 occasions 4 weeks apart. To assess the validity of the Food4Me FFQ against the 4-day WFR, half the participants were also asked to complete a 4-day WFR 1 week after the first administration of the Food4Me FFQ. Level of agreement between nutrient and food group intakes estimated by the repeated Food4Me FFQ and the Food4Me FFQ and 4-day WFR were evaluated using Bland-Altman methodology and classification into quartiles of daily intake. Crude unadjusted correlation coefficients were also calculated for nutrient and food group intakes. Results In total, 100 people participated in the assessment of reproducibility (mean age 32, SD 12 years), and 49 of these (mean age 27, SD 8 years) also took part in the assessment of validity. Crude unadjusted correlations for repeated Food4Me FFQ ranged from .65 (vitamin D) to .90 (alcohol). The mean cross-classification into “exact agreement plus adjacent” was 92% for both nutrient and food group intakes, and Bland-Altman plots showed good agreement for energy-adjusted macronutrient intakes. Agreement between the Food4Me FFQ and 4-day WFR varied, with crude unadjusted correlations ranging from .23 (vitamin D) to .65 (protein, % total energy) for nutrient intakes and .11 (soups, sauces and miscellaneous foods) to .73 (yogurts) for food group intake. The mean cross-classification into “exact agreement plus adjacent” was 80% and 78% for nutrient and food group intake, respectively. There were no significant differences between energy intakes estimated using the Food4Me FFQ and 4-day WFR, and Bland-Altman plots showed good agreement for both energy and energy-controlled nutrient intakes. Conclusions The results demonstrate that the online Food4Me FFQ is reproducible for assessing nutrient and food group intake and has moderate agreement with the 4-day WFR for assessing energy and energy-adjusted nutrient intakes. The Food4Me FFQ is a suitable online tool for assessing dietary intake in healthy adults.

Online dietary intake estimation: The Food4Me food frequency questionnaire

Background Dietary assessment methods are important tools for nutrition research. Online dietary assessment tools have the potential to become invaluable methods of assessing dietary intake because, compared with traditional methods, they have many advantages including the automatic storage of input data and the immediate generation of nutritional outputs. Objective The aim of this study was to develop an online food frequency questionnaire (FFQ) for dietary data collection in the “Food4Me” study and to compare this with the validated European Prospective Investigation of Cancer (EPIC) Norfolk printed FFQ. Methods The Food4Me FFQ used in this analysis was developed to consist of 157 food items. Standardized color photographs were incorporated in the development of the Food4Me FFQ to facilitate accurate quantification of the portion size of each food item. Participants were recruited in two centers (Dublin, Ireland and Reading, United Kingdom) and each received the online Food4Me FFQ and the printed EPIC-Norfolk FFQ in random order. Participants completed the Food4Me FFQ online and, for most food items, participants were requested to choose their usual serving size among seven possibilities from a range of portion size pictures. The level of agreement between the two methods was evaluated for both nutrient and food group intakes using the Bland and Altman method and classification into quartiles of daily intake. Correlations were calculated for nutrient and food group intakes. Results A total of 113 participants were recruited with a mean age of 30 (SD 10) years (40.7% male, 46/113; 59.3%, 67/113 female). Cross-classification into exact plus adjacent quartiles ranged from 77% to 97% at the nutrient level and 77% to 99% at the food group level. Agreement at the nutrient level was highest for alcohol (97%) and lowest for percent energy from polyunsaturated fatty acids (77%). Crude unadjusted correlations for nutrients ranged between .43 and .86. Agreement at the food group level was highest for “other fruits” (eg, apples, pears, oranges) and lowest for “cakes, pastries, and buns”. For food groups, correlations ranged between .41 and .90. Conclusions The results demonstrate that the online Food4Me FFQ has good agreement with the validated printed EPIC-Norfolk FFQ for assessing both nutrient and food group intakes, rendering it a useful tool for ranking individuals based on nutrient and food group intakes.

The future direction of personalised nutrition: my diet, my phenotype, my genes.

Although personalised nutrition is frequently considered in the context of diet-gene interactions, increasingly, personalised nutrition is seen to exist at three levels. The first is personalised dietary advice using Internet-delivered services, which ultimately will become automated and which will also draw on mobile phone technology. The second level of personalised dietary advice will include phenotypic information on anthropometry, physical activity, clinical parameters and biochemical markers of nutritional status. It remains possible that in addition to personalised dietary advice based on phenotypic data, advice at that group or metabotype level may be offered where metabotypes are defined by a common metabolic profile. The third level of personalised nutrition will involve the use of genomic data. While the genomic aspect of personalised nutrition is often considered as its main driver, there are significant challenges to translation of data on SNP and diet into personalised advice. The majority of the published data on SNP and diet emanate from observational studies and as such do not offer any cause-effect associations. To achieve this, purpose-designed dietary intervention studies will be needed with subjects recruited according to their genotype. Extensive research indicates that consumers would welcome personalised dietary advice including dietary advice based on their genotype. Unlike personalised medicine where genotype data are linked to the risk of developing a disease, in personalised nutrition the genetic data relate to the optimal diet for a given genotype to reduce disease risk factors and thus there are few ethical and legal issues in personalised nutrition.

Effect of an Internet-based, personalized nutrition randomized trial on dietary changes associated with the Mediterranean diet: the Food4Me Study

BACKGROUND Little is known about the efficacy of personalized nutrition (PN) interventions for improving consumption of a Mediterranean diet (MedDiet). OBJECTIVE The objective was to evaluate the effect of a PN intervention on dietary changes associated with the MedDiet. DESIGN Participants (n = 1607) were recruited into a 6-mo, Internet-based, PN randomized controlled trial (Food4Me) designed to evaluate the effect of PN on dietary change. Participants were randomly assigned to receive conventional dietary advice [control; level 0 (L0)] or PN advice on the basis of current diet [level 1 (L1)], diet and phenotype [level 2 (L2)], or diet, phenotype, and genotype [level 3 (L3)]. Dietary intakes from food-frequency questionnaires at baseline and at 6 mo were converted to a MedDiet score. Linear regression compared participant characteristics between high (>5) and low (≤5) MedDiet scores. Differences in MedDiet scores between treatment arms at month 6 were evaluated by using contrast analyses. RESULTS At baseline, high MedDiet scorers had a 0.5 lower body mass index (in kg/m(2); P = 0.007) and a 0.03 higher physical activity level (P = 0.003) than did low scorers. MedDiet scores at month 6 were greater in individuals randomly assigned to receive PN (L1, L2, and L3) than in controls (PN compared with controls: 5.20 ± 0.05 and 5.48 ± 0.07, respectively; P = 0.002). There was no significant difference in MedDiet scores at month 6 between PN advice on the basis of L1 compared with L2 and L3. However, differences in MedDiet scores at month 6 were greater in L3 than in L2 (L3 compared with L2: 5.63 ± 0.10 and 5.38 ± 0.10, respectively; P = 0.029). CONCLUSIONS Higher MedDiet scores at baseline were associated with healthier lifestyles and lower adiposity. After the intervention, MedDiet scores were greater in individuals randomly assigned to receive PN than in controls, with the addition of DNA-based dietary advice resulting in the largest differences in MedDiet scores. Although differences were significant, their clinical relevance is modest. This trial was registered at clinicaltrials.gov as NCT01530139.

Within-person variation in the postprandial lipemic response of healthy adults

BACKGROUND The response to dietary fat plays a key role in metabolic health. Although this can vary widely between individuals, variation within an individual and the associated contribution of phenotypic and genotypic factors to this variation are less defined. OBJECTIVES The objectives were to quantify within-person variation in triacylglycerol response by means of a novel variation score (S(v)) and to explore the phenotypic and genotypic factors associated with this score. DESIGN Two consecutive 5-h oral-lipid-tolerance tests (OLTTs) were conducted in 51 healthy adults aged 18-60 y with a BMI (in kg/m²) of 18.5 to 49.8. Detailed body composition, physical function, biochemistry, and genotype data were gathered. RESULTS The postprandial triacylglycerol response profile did not differ (P = 0.64) across OLTTs for the group; nor did average concentrations of functional markers apolipoprotein C2 (P = 0.73) and apolipoprotein C3 (P = 0.74). S(v) was low in most (82%) of the adults and was significantly (P < 0.05) associated with age, fasting triacylglycerol, triacylglycerol AUC, and fasting nonessential fatty acids. Significant associations were also observed between S(v) and single nucleotide polymorphisms in 7 genes (APOA1, IL1α, IL1β, TLR4, TCF7L2, CCK1Rec, and STAT3) after correction for phenotypic differences. CONCLUSIONS This work showed that the within-person variability in postprandial lipemic response is low in most healthy adults. It also showed that variability in this response is associated with a defined set of phenotypic and genotypic characteristics.

Use of metabotyping for the delivery of personalised nutrition

SCOPE Personalised nutrition can be defined as dietary advice that is tailored to an individual. In recent years, the concept of targeted nutrition has evolved, which involves delivering specific dietary advice to a group of phenotypically similar individuals or metabotypes. This study examined whether cluster analysis could be used to define metabotypes and developed a strategy for the delivery of targeted dietary advice. METHOD AND RESULTS K-means clustering was employed to identify clusters based on four markers of metabolic health (triacylglycerols, total cholesterol, direct HDL cholesterol and glucose) (n = 896) using data from the National Adult Nutrition Survey. A decision tree approach was developed for the delivery of targeted dietary advice per cluster based on biochemical characteristics, anthropometry and blood pressure. The appropriateness of the advice was tested by comparison with individualised dietary advice manually compiled (n = 99). A mean match of 89.1% between the methods was demonstrated with a 100% match for two-thirds of participants. CONCLUSION Good agreement was found between the individualised and targeted methods demonstrating the ability of this framework to deliver targeted dietary advice. This approach has the potential to be a fast and novel method for the delivery of targeted nutrition in clinical settings.

PBMCs reflect the immune component of the WAT transcriptome--implications as biomarkers of metabolic health in the postprandial state.

SCOPE Food and nutrition studies often require accessing metabolically active tissues, including adipose tissue. This can involve invasive biopsy procedures that can be a limiting factor in study design. In contrast, peripheral blood mononuclear cells (PBMCs) are a population of circulating immune cells that are easily accessible through venipuncture. As transcriptomics is of growing importance in food and metabolism research, understanding the transcriptomic relationship between these tissue types can provide insight into the utility of PBMCs in this field. METHODS AND RESULTS We examine this relationship within eight subjects, in two postprandial states (following oral lipid tolerance test and oral glucose tolerance test). Multivariate analysis techniques were used to examine variation between tissues, samples, and subjects in order to define which genes havecommon/disparate expression profiles associated with highly defined metabolic phenotypes. We demonstrate global similarities in gene expression between PBMCs and white adipose tissue, irrespective of the metabolic challenge type. Closer examination of individual genes revealed this similarity to be strongest in pathways related to immune response/inflammation. Notably, the expression of metabolism-related nuclear receptors, including PPARs, LXR, etc. was discordant between tissues CONCLUSION The PBMC transcriptome may therefore provide a unique insight into the inflammatory component of metabolic health, as opposed to directly reflecting the metabolic component of the adipose tissue transcriptome.

Personalised nutrition: the role of new dietary assessment methods

Food records or diaries, dietary recalls and FFQ are methods traditionally used to measure dietary intake; however, advancing technologies and growing awareness in personalised health have heightened interest in the application of new technologies to assess dietary intake. Dietary intake data can be used in epidemiology, dietary interventions and in the delivery of personalised nutrition advice. Compared with traditional dietary assessment methods, new technologies have many advantages, including their ability to automatically process data and provide personalised dietary feedback advice. This review examines the new technologies presently under development for the assessment of dietary intakes, and their utilisation and efficacy for personalising dietary advice. New technology-based methods of dietary assessment can broadly be categorised into three key areas: online (web-based) methods, mobile methods and sensor technologies. Several studies have demonstrated that utilising new technologies to provide tailored advice can result in positive dietary changes and have a significant impact on selected nutrient and food group intakes. However, comparison across studies indicates that the magnitude of change is variable and may be influenced by several factors, including the frequency and type of feedback provided. Future work should establish the most effective combinations of these factors in facilitating dietary changes across different population groups.

Changes in Physical Activity Following a Genetic-Based Internet-Delivered Personalized Intervention: Randomized Controlled Trial (Food4Me)

Background There is evidence that physical activity (PA) can attenuate the influence of the fat mass- and obesity-associated (FTO) genotype on the risk to develop obesity. However, whether providing personalized information on FTO genotype leads to changes in PA is unknown. Objective The purpose of this study was to determine if disclosing FTO risk had an impact on change in PA following a 6-month intervention. Methods The single nucleotide polymorphism (SNP) rs9939609 in the FTO gene was genotyped in 1279 participants of the Food4Me study, a four-arm, Web-based randomized controlled trial (RCT) in 7 European countries on the effects of personalized advice on nutrition and PA. PA was measured objectively using a TracmorD accelerometer and was self-reported using the Baecke questionnaire at baseline and 6 months. Differences in baseline PA variables between risk (AA and AT genotypes) and nonrisk (TT genotype) carriers were tested using multiple linear regression. Impact of FTO risk disclosure on PA change at 6 months was assessed among participants with inadequate PA, by including an interaction term in the model: disclosure (yes/no) × FTO risk (yes/no). Results At baseline, data on PA were available for 874 and 405 participants with the risk and nonrisk FTO genotypes, respectively. There were no significant differences in objectively measured or self-reported baseline PA between risk and nonrisk carriers. A total of 807 (72.05%) of the participants out of 1120 in the personalized groups were encouraged to increase PA at baseline. Knowledge of FTO risk had no impact on PA in either risk or nonrisk carriers after the 6-month intervention. Attrition was higher in nonrisk participants for whom genotype was disclosed (P=.01) compared with their at-risk counterparts. Conclusions No association between baseline PA and FTO risk genotype was observed. There was no added benefit of disclosing FTO risk on changes in PA in this personalized intervention. Further RCT studies are warranted to confirm whether disclosure of nonrisk genetic test results has adverse effects on engagement in behavior change. Trial Registration ClinicalTrials.gov NCT01530139; http://clinicaltrials.gov/show/NCT01530139 (Archived by WebCite at: http://www.webcitation.org/6XII1QwHz)