Impact of dietary regime on the metabolomic profile of bovine buttermilk and whole milk powder.

INTRODUCTION: Bovine milk contains a rich matrix of nutrients such as carbohydrates, fat, protein and various vitamins and minerals, the composition of which is altered by factors including dietary regime. OBJECTIVES: The objective of this research was to investigate the impact of dietary regime on the metabolite composition of bovine whole milk powder and buttermilk. METHODS: Bovine whole milk powder and buttermilk samples were obtained from spring-calving cows, consuming one of three diets. Group 1 grazed outdoors on perennial ryegrass which was supplemented with 5% concentrates; group 2 were maintained indoors and consumed a total mixed ration diet; and group 3 consumed a partial mixed ration diet consisting of perennial ryegrass during the day and total mixed ration maintained indoors at night. RESULTS: Metabolomic analysis of the whole milk powder (N = 27) and buttermilk (N = 29) samples was preformed using liquid chromatography-tandem mass spectrometry, with 504 and 134 metabolites identified in the samples respectively. In whole milk powder samples, a total of 174 metabolites from various compound classes were significantly different across dietary regimes (FDR adjusted p-value ≤ 0.05), including triglycerides, of which 66% had their highest levels in pasture-fed samples. Triglycerides with highest levels in pasture-fed samples were predominantly polyunsaturated with high total carbon number. Regarding buttermilk samples, metabolites significantly different across dietary regimes included phospholipids, sphingomyelins and an acylcarnitine. CONCLUSION: In conclusion the results reveal a significant impact of a pasture-fed dietary regime on the metabolite composition of bovine dairy products, with a particular impact on lipid compound classes.

Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

INTRODUCTION: Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates. OBJECTIVE: This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health. MATERIALS AND METHODS: A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR). RESULTS: Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population. CONCLUSION: A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Impact of increased protein intake in older adults: a 12-week double-blind randomised controlled trial.

BACKGROUND: Emerging evidence suggests health-promoting properties of increased protein intake. There is increased interest in plant protein but a dearth of information in relation to its impact on muscle function. The objective of the present work was to examine the impact of intake of different types of proteins on muscle functional parameters including handgrip strength, biomarkers of metabolic health, sleep quality and quality of life in a group of older adults. METHODS: Healthy men and women aged 50 years and older entered a double-blinded, randomised, controlled nutritional intervention study with three parallel arms: high plant protein, high dairy protein and low protein. Participants consumed once daily a ready-to-mix shake (containing 20 g of protein in high protein groups) for 12 weeks. Changes in handgrip and leg strength, body composition, metabolic health, quality of life and sleep quality were analysed by linear mixed models in an intention-to-treat approach. RESULTS: Eligible participants (n = 171) were randomly assigned to the groups (plant: n = 60, dairy: n = 56, low protein: n = 55) and 141 completed the study. Handgrip strength increased after the intervention (Ptime = 0.038), with no significant difference between the groups. There was no significant difference between groups for any other health outcomes. CONCLUSIONS: In a population of older adults, increasing protein intake by 20 g daily for 12 weeks (whether plant-based or dairy-based) did not result in significant differences in muscle function, body composition, metabolic health, sleep quality or quality of life, compared with the low protein group.

Prepartum supplementation of dairy cows with inorganic selenium, organic selenium or rumen-protected choline does not affect carotenoid composition or colour characteristics of bovine colostrum or transition milk.

Minerals are supplemented routinely to dairy cows during the dry period to prevent metabolic issues postpartum. However, limited information exists on the impacts of mineral supplementation on colostrum carotenoids. This study aimed to determine the effects of prepartum supplementation with three micro-nutrients; inorganic selenium (INORG), organic selenium (ORG) or rumen-protected choline (RPC) on the carotenoid content of bovine colostrum and transition milk (TM) from pasture-based dairy cows. A total of 57 (12 primiparous and 45 multiparous) Holstein-Friesian (HF) and HF × Jersey (JEX) cows were supplemented daily for 49 ± 12.9 d before calving. Colostrum samples were collected from all cows immediately postpartum and TM one to five (TM1-TM5) were collected from a sub-set of 15 cows (five per treatment group) at each consecutive milking postpartum. Carotenoid concentration was determined using ultra-high performance liquid chromatography - diode array detection (UHPLC-DAD). With the use of transmittance, the colour index and colour parameters a*, b* and L* were used to determine colour variations over this period. Prepartum supplementation did not have a significant effect on colostrum ß-carotene concentration or colour. Positive correlations between ß-carotene and colour parameter b* (R2 = 0.671; P < 0.001) and ß-carotene and colour index (R2 = 0.560; P < 0.001) were observed. Concentrations of ß-carotene were highest in colostrum (1.34 µg/g) and decreased significantly with each milking postpartum (TM5 0.31 µg/g). Breed had a significant effect on colostrum colour with JEX animals producing a greater b* colostrum than HF animals (P = 0.030). Primiparous animals produced colostrum with the weakest colour compared to second or ≥third parity animals (P = 0.042). Despite statistical increases in the b* parameter in colostrum from JEX cows and multiparous cows, ß-carotene concentrations did not significantly increase suggesting that other factors may influence colostrum colour. The b* parameter may be used as an indicator for estimating carotenoid concentrations in colostrum and TM, particularly when assessed via transmittance spectroscopy.

Hypercapnia alters mitochondrial gene expression and acylcarnitine production in monocytes.

CO2 is produced during aerobic respiration. Normally, levels of CO2 in the blood are tightly regulated but pCO2 can rise (hypercapnia, pCO2 > 45 mmHg) in patients with lung diseases, for example, chronic obstructive pulmonary disease (COPD). Hypercapnia is a risk factor in COPD but may be of benefit in the context of destructive inflammation. The effects of CO2 per se, on transcription, independent of pH change are poorly understood and warrant further investigation. Here we elucidate the influence of hypercapnia on monocytes and macrophages through integration of state-of-the-art RNA-sequencing, metabolic and metabolomic approaches. THP-1 monocytes and interleukin 4-polarized primary murine macrophages were exposed to 5% CO2 versus 10% CO2 for up to 24 h in pH-buffered conditions. In hypercapnia, we identified around 370 differentially expressed genes (DEGs) under basal and about 1889 DEGs under lipopolysaccharide-stimulated conditions in monocytes. Transcripts relating to both mitochondrial and nuclear-encoded gene expression were enhanced in hypercapnia in basal and lipopolysaccharide-stimulated cells. Mitochondrial DNA content was not enhanced, but acylcarnitine species and genes associated with fatty acid metabolism were increased in hypercapnia. Primary macrophages exposed to hypercapnia also increased activation of genes associated with fatty acid metabolism and reduced activation of genes associated with glycolysis. Thus, hypercapnia elicits metabolic shifts in lipid metabolism in monocytes and macrophages under pH-buffered conditions. These data indicate that CO2 is an important modulator of monocyte transcription that can influence immunometabolic signaling in immune cells in hypercapnia. These immunometabolic insights may be of benefit in the treatment of patients experiencing hypercapnia.

Prepregnancy Body Mass Index and Lipoprotein Fractions are Associated with Changes in Women's Serum Metabolome from Late Pregnancy to the First Months of Postpartum.

BACKGROUND: Pregnancy and postpartum are periods of intense changes in women's metabolism. The knowledge of the metabolites and maternal factors underlying these changes is limited. OBJECTIVES: We aimed to investigate the maternal factors that could influence serum metabolome changes from late pregnancy to the first months of postpartum. METHODS: Sixty-eight healthy women from a Brazilian prospective cohort were included. Maternal blood and general characteristics were collected during pregnancy (28-35 wk) and postpartum (27-45 d). A targeted metabolomics approach was applied to quantify 132 serum metabolites, including amino acids, biogenic amines, acylcarnitines, lysophosphatidylcholines (LPC), diacyl phosphatidylcholines (PC), alkyl:acyl phosphatidylcholines (PC-O), sphingomyelins with (SM) and without hydroxylation [SM(OH)], and hexoses. Metabolome changes from pregnancy to postpartum were measured as log2 fold change (log2FC), and simple linear regressions were employed to evaluate associations between maternal variables and metabolite log2FC. Multiple comparison-adjusted P values of < 0.05 were considered significant. RESULTS: Of 132 metabolites quantified in serum, 90 changed from pregnancy to postpartum. Most metabolites belonging to PC and PC-O classes decreased, whereas most LPC, acylcarnitines, biogenic amines, and a few amino acids increased in postpartum. Maternal prepregnancy body mass index (ppBMI) showed positive associations with leucine and proline. A clear opposite change pattern was observed for most metabolites across ppBMI categories. Few phosphatidylcholines were decreased in women with normal ppBMI, while an increase was observed in women with obesity. Similarly, women with high postpartum levels of total cholesterol, LDL cholesterol, and non-HDL cholesterol showed increased sphingomyelins, whereas a decrease was observed for women with lower levels of those lipoproteins. CONCLUSIONS: The results revealed several maternal serum metabolomic changes from pregnancy to postpartum, and the maternal ppBMI and plasma lipoproteins were associated with these changes. We highlight the importance of the nutritional care of women prepregnancy to improve their metabolic risk profile.

Multi-nutrient interventions and cognitive ageing: are we barking up the right tree?

As we continue to elucidate the mechanisms underlying age-related brain diseases, the reductionist strategy in nutrition­brain 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.

Energy Metabolism Is Altered in Radioresistant Rectal Cancer.

Resistance to neoadjuvant chemoradiation therapy is a significant clinical challenge in the management of rectal cancer. There is an unmet need to identify the underlying mechanisms of treatment resistance to enable the development of biomarkers predictive of response and novel treatment strategies to improve therapeutic response. In this study, an in vitro model of inherently radioresistant rectal cancer was identified and characterized to identify mechanisms underlying radioresistance in rectal cancer. Transcriptomic and functional analysis demonstrated significant alterations in multiple molecular pathways, including the cell cycle, DNA repair efficiency and upregulation of oxidative phosphorylation-related genes in radioresistant SW837 rectal cancer cells. Real-time metabolic profiling demonstrated decreased reliance on glycolysis and enhanced mitochondrial spare respiratory capacity in radioresistant SW837 cells when compared to radiosensitive HCT116 cells. Metabolomic profiling of pre-treatment serum samples from rectal cancer patients (n = 52) identified 16 metabolites significantly associated with subsequent pathological response to neoadjuvant chemoradiation therapy. Thirteen of these metabolites were also significantly associated with overall survival. This study demonstrates, for the first time, a role for metabolic reprograming in the radioresistance of rectal cancer in vitro and highlights a potential role for altered metabolites as novel circulating predictive markers of treatment response in rectal cancer patients.

Impact of Lactation Stage on the Metabolite Composition of Bovine Milk.

Bovine milk is a nutrient-dense food and a major component of the human diet. Therefore, understanding the factors that impact its composition is of great importance. Applications of metabolomics provide in-depth analysis of the metabolite composition of milk. The objective of this research was to examine the impact of lactation stage on bovine milk metabolite levels. Metabolomic analysis of bovine milk powder samples across lactation (N = 18) was performed using nuclear magnetic resonance (1H-NMR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Forty-one metabolites were identified and quantified in the 1H-NMR spectra. Statistical analysis revealed that 17 metabolites were significantly different across lactation stages (FDR < 0.05), of which the majority had higher levels in early lactation. In total, 491 metabolites were measured using LC-MS/MS, of which 269 had significantly different levels across lactation (FDR < 0.05). Compound classes significantly affected by lactation stage included phosphatidylcholines (59%) and triglycerides (64%), of which 100% of phosphatidylcholines and 61% of triglycerides increased from early lactation onwards. Our study demonstrates significant differences in metabolites across the stages of lactation, with early-lactation milk having a distinct metabolomic profile. More research is warranted to further explore these compositional differences to inform animal feeding practice.

Targeted Metabolomic Approach to Assess the Reproducibility of Plasma Metabolites over a Four Month Period in a Free-Living Population.

Metabolomics is increasingly applied to investigate diet-disease associations in nutrition research. However, studies of metabolite reproducibility are limited, which could hamper their use within epidemiologic studies. The objective of this study was to evaluate the metabolite reproducibility during 4 months in a free-living population. In the A-DIET Confirm study, fasting plasma and dietary data were collected once a month for 4 months. Metabolites were measured using liquid chromatography tandem mass spectrometry, and their reproducibility was estimated using the intraclass correlation coefficient (ICC). Regularized canonical correlation analysis (rCCA) was employed to examine the diet-metabolite associations. In total, 138 metabolites were measured, and median ICC values of 0.49 and 0.65 were found for amino acids and biogenic amines, respectively. Acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, and sphingomyelins had median ICC values of 0.69, 0.66, 0.63, and 0.63, respectively. The median ICC for all metabolites was 0.62, and 54% of metabolites had ICC values ≥0.60. Additionally, the rCCA heat map revealed positive correlations between dairy/meat intake and specific lipids. In conclusion, more than half of the metabolites demonstrated good to excellent reproducibility. A single measurement per subject could appropriately reflect the metabolites' long-term concentration levels and may also be sufficient for assessing disease risk in epidemiologic studies. The study data are deposited in MetaboLights (MTBLS3428 (www.ebi.ac.uk/metabolights)).

Associations between dietary patterns, FTO genotype and obesity in adults from seven European countries.

PURPOSE: High-fat and low-fibre discretionary food intake and FTO genotype are each associated independently with higher risk of obesity. However, few studies have investigated links between obesity and dietary patterns based on discretionary food intake, and the interaction effect of FTO genotype are unknown. Thus, this study aimed to derive dietary patterns based on intake of discretionary foods, saturated fatty acids (SFA) and fibre, and examine cross-sectional associations with BMI and waist circumference (WC), and interaction effects of FTO genotype. METHODS: Baseline data on 1280 adults from seven European countries were included (the Food4Me study). Dietary intake was estimated from a Food Frequency Questionnaire. Reduced rank regression was used to derive three dietary patterns using response variables of discretionary foods, SFA and fibre density. DNA was extracted from buccal swabs. Anthropometrics were self-measured. Linear regression analyses were used to examine associations between dietary patterns and BMI and WC, with an interaction for FTO genotype. RESULTS: Dietary pattern 1 (positively correlated with discretionary foods and SFA, and inversely correlated with fibre) was associated with higher BMI (ß:0.64; 95% CI 0.44, 0.84) and WC (ß:1.58; 95% CI 1.08, 2.07). There was limited evidence dietary pattern 2 (positively correlated with discretionary foods and SFA) and dietary pattern 3 (positively correlated with SFA and fibre) were associated with anthropometrics. FTO risk genotype was associated with higher BMI and WC, with no evidence of a dietary interaction. CONCLUSIONS: Consuming a dietary pattern low in discretionary foods and high-SFA and low-fibre foods is likely to be important for maintaining a healthy weight, regardless of FTO predisposition to obesity. TRIAL REGISTRATION: Clinicaltrials.gov NCT01530139. Registered 9 February 2012 https://clinicaltrials.gov/ct2/show/NCT01530139.

multiMarker: software for modelling and prediction of continuous food intake using multiple biomarkers measurements.

BACKGROUND: Metabolomic biomarkers offer potential for objective and reliable food intake assessment, and there is growing interest in using biomarkers in place of or with traditional self-reported approaches. Ongoing research suggests that multiple biomarkers are associated with single foods, offering great sensitivity and specificity. However, currently there is a dearth of methods to model the relationship between multiple biomarkers and single food intake measurements. RESULTS: Here, we introduce multiMarker, a web-based application based on the homonymous R package, that enables one to infer the relationship between food intake and two or more metabolomic biomarkers. Furthermore, multiMarker allows prediction of food intake from biomarker data alone. multiMarker differs from previous approaches by providing distributions of predicted intakes, directly accounting for uncertainty in food intake quantification. Usage of both the R package and the web application is demonstrated using real data concerning three biomarkers for orange intake. Further, example data is pre-loaded in the web application to enable users to examine multiMarker's functionality. CONCLUSION: The proposed software advance the field of Food Intake Biomarkers providing researchers with a novel tool to perform continuous food intake quantification, and to assess its associated uncertainty, from multiple biomarkers. To facilitate widespread use of the framework, multiMarker has been implemented as an R package and a Shiny web application.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study.

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, 1H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual's stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51-0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual's urinary NMR metabolome.

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.

The Role of Protein Intake and its Timing on Body Composition and Muscle Function in Healthy Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Increased protein intake has been suggested to improve gains in muscle mass and strength in adults. Furthermore, the timing of protein intake has been discussed as a margin of opportunity for improved prevention measures. OBJECTIVE: This systematic review investigated the effect of protein supplementation on body composition and muscle function (strength and synthesis) in healthy adults, with an emphasis on the timing of protein intake. METHODS: Randomized controlled trials were identified using PubMed, Web of Science, CINAHL, and Embase, up to March 2019. For meta-analyses, data on lean body mass (LBM), handgrip strength, and leg press strength were pooled by age group (mean age 18-55 or >55 y) and timing of protein intake. The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations approach. RESULTS: Data from 65 studies with 2907 participants (1514 men and 1380 women, 13 unknown sex) were included in the review. Twenty-six, 8, and 24 studies were used for meta-analysis on LBM, handgrip strength, and leg press strength, respectively. The protein supplementation was effective in improving (mean difference; 95% CI) LBM in adults (0.62 kg; 0.36, 0.88) and older adults (0.46 kg; 0.23, 0.70), but not handgrip strength (older adults: 0.26 kg; -0.51, 1.04) and leg press strength (adults: 5.80 kg; -0.33, 11.93; older adults: 1.97 kg; -2.78, 6.72). Sensitivity analyses removing studies without exercise training had no impact on the outcomes. Data regarding muscle synthesis were scarce and inconclusive. Subgroup analyses showed no beneficial effect of a specific timing of protein intake on LBM, handgrip strength, and leg press strength. CONCLUSION: Overall, the results support the positive impact of protein supplementation on LBM of adults and older adults, independently of intake timing. Effects on muscle strength and synthesis are less clear and need further investigation. This systematic review was registered on PROSPERO as CRD42019126742.

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.

Metabotyping and its role in nutrition research.

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.

Biomarkers of appetite: is there a potential role for metabolomics?

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 ß-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.

Exploring Covariation between Traditional Markers of Metabolic Health and the Plasma Metabolomic Profile: A Classic Twin Design.

Novel metabolomic profiling techniques combined with traditional biomarkers provide knowledge of mechanisms underlying metabolic health. Twin studies describe the impact of genes and environment on variation in traits. This study aims to identify relationships between traditional markers of metabolic health and the plasma metabolomic profile using a twin modeling approach and determine whether covariation is caused by shared genetic and environmental factors. Using a classic twin design, this study examined covariation between anthropometric, clinical chemistry, and metabolomic profiles. Cholesky decomposition modeling was used to determine the genetic and environmental path coefficients through successive anthropometric and clinical chemistry traits onto metabolomic derived metabolites. This study shows that WC, TAG, and a metabolomic signature composed of 7 metabolites are inter-related, and that covariation can be attributed to common genetic, shared and unique environmental factors as well as unique environmental factors specific to the metabolite. This quantitative modeling connecting the traditional anthropometry and clinical chemistry traits with the more recent and potentially more sensitive metabolomic profile approach may provide further insight on the pleiotropic genes or modifiable environmental factors influencing variation in metabolic health.

Variable Glycemic Responses to Intact and Hydrolyzed Milk Proteins in Overweight and Obese Adults Reveal the Need for Precision Nutrition.

Background: Dietary modifications can contribute to improved pancreatic ß cell function and enhance glycemic control. Objectives: The objectives of this study were as follows: 1) to investigate the potential of milk protein hydrolysates to modulate postprandial glucose response; 2) to assess individual responses; and 3) to explore the inter- and intraindividual reproducibility of the response. Methods: A 14-d randomized crossover study investigated interstitial glucose levels of participants in response to 12% w/v milk protein drinks (intact caseinate and casein hydrolysate A and B) consumed in random order with a 2-d washout between treatments. Milk protein drinks were consumed immediately prior to study breakfast and evening meals. Twenty participants (11 men, 9 women) aged 50 ± 8 y with a body mass index (in kg/m2) of 30.2 ± 3.1 were recruited. Primary outcome was glucose levels assessed at 15-min intervals with the use of glucose monitors. Results: Repeated-measures ANOVA revealed that for breakfast there was a significant difference across the 3 treatment groups (P = 0.037). The ability to reduce postprandial glucose was specific to casein hydrolysate B in comparison with intact caseinate (P = 0.039). However, despite this significant difference, further examination revealed that only 3 out of 18 individuals were classified as responders (P < 0.05). High intraclass correlation coefficients were obtained for glucose response to study meals (intraclass correlation coefficient: 0.892 for breakfast with intact caseinate). The interindividual CVs were higher than the intraindividual CVs. Mean inter- and intraindividual CVs were 19.4% and 5.7%, respectively, for breakfast with intact caseinate. Conclusion: Ingestion of a specific casein hydrolysate successfully reduced the postprandial glucose response; however, at an individual level only 3 participants were classified as responders, highlighting the need for precision nutrition. Exploration of high interindividual responses to nutrition interventions is needed, in combination with the development of precision nutrition, potentially through an n-of-1 approach. This clinical trial was registered as ISRCTN61079365 (https://www.isrctn.com/).