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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.
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.
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.
The interplay between the fat mass- and obesity-associated (FTO) gene variants and diet has been implicated in the development of obesity. The aim of the present analysis was to investigate associations between FTO genotype, dietary intakes and anthropometrics among European adults. Participants in the Food4Me randomised controlled trial were genotyped for FTO genotype (rs9939609) and their dietary intakes, and diet quality scores (Healthy Eating Index and PREDIMED-based Mediterranean diet score) were estimated from FFQ. Relationships between FTO genotype, diet and anthropometrics (weight, waist circumference (WC) and BMI) were evaluated at baseline. European adults with the FTO risk genotype had greater WC (AAv. TT: +1·4 cm; P=0·003) and BMI (+0·9 kg/m2; P=0·001) than individuals with no risk alleles. Subjects with the lowest fried food consumption and two copies of the FTO risk variant had on average 1·4 kg/m2 greater BMI (Ptrend=0·028) and 3·1 cm greater WC (Ptrend=0·045) compared with individuals with no copies of the risk allele and with the lowest fried food consumption. However, there was no evidence of interactions between FTO genotype and dietary intakes on BMI and WC, and thus further research is required to confirm or refute these findings.
An efficient and robust method to measure vitamin D (25-hydroxy vitamin D3 (25(OH)D3) and 25-hydroxy vitamin D2 in dried blood spots (DBS) has been developed and applied in the pan-European multi-centre, internet-based, personalised nutrition intervention study Food4Me. The method includes calibration with blood containing endogenous 25(OH)D3, spotted as DBS and corrected for haematocrit content. The methodology was validated following international standards. The performance characteristics did not reach those of the current gold standard liquid chromatography-MS/MS in plasma for all parameters, but were found to be very suitable for status-level determination under field conditions. DBS sample quality was very high, and 3778 measurements of 25(OH)D3 were obtained from 1465 participants. The study centre and the season within the study centre were very good predictors of 25(OH)D3 levels (P<0·001 for each case). Seasonal effects were modelled by fitting a sine function with a minimum 25(OH)D3 level on 20 January and a maximum on 21 July. The seasonal amplitude varied from centre to centre. The largest difference between winter and summer levels was found in Germany and the smallest in Poland. The model was cross-validated to determine the consistency of the predictions and the performance of the DBS method. The Pearson’s correlation between the measured values and the predicted values was r 0·65, and the sd of their differences was 21·2 nmol/l. This includes the analytical variation and the biological variation within subjects. Overall, DBS obtained by unsupervised sampling of the participants at home was a viable methodology for obtaining vitamin D status information in a large nutritional study.
For decades, it has been debated whether high protein intake compromises bone mineralisation, but no long-term randomised trial has investigated this in children. In the family-based, randomised controlled trial DiOGenes (Diet, Obesity and Genes), we examined the effects of dietary protein and glycaemic index (GI) on biomarkers of bone turnover and height in children aged 5–18 years. In two study centres, families with overweight parents were randomly assigned to one of five ad libitum-energy, low-fat (25–30 % energy (E%)) diets for 6 months: low protein/low GI; low protein/high GI; high protein/low GI; high protein/high GI; control. They received dietary instructions and were provided all foods for free. Children, who were eligible and willing to participate, were included in the study. In the present analyses, we included children with data on plasma osteocalcin or urinary N-terminal telopeptide of collagen type I (U-NTx) from baseline and at least one later visit (month 1 or month 6) (n 191 in total, n 67 with data on osteocalcin and n 180 with data on U-NTx). The level of osteocalcin was lower (29·1 ng/ml) in the high-protein/high-GI dietary group than in the low-protein/high-GI dietary group after 6 months of intervention (95 % CI 2·2, 56·1 ng/ml, P= 0·034). The dietary intervention did not affect U-NTx (P= 0·96) or height (P= 0·80). Baseline levels of U-NTx and osteocalcin correlated with changes in height at month 6 across the dietary groups (P< 0·001 and P= 0·001, respectively). The present study does not show any effect of increased protein intake on height or bone resorption in children. However, the difference in the change in the level of osteocalcin between the high-protein/high-GI group and the low-protein/high-GI group warrants further investigation and should be confirmed in other studies.
Dairy products have previously been reported to be associated with beneficial effects on body weight and metabolic risk markers. Moreover, primary data from the Diet, Obesity and Genes (DiOGenes) study indicate a weight-maintaining effect of a high-protein–low-glycaemic index diet. The objective of the present study was to examine putative associations between consumption of dairy proteins and changes in body weight and metabolic risk markers after weight loss in obese and overweight adults. Results were based on secondary analyses of data obtained from overweight and obese adults who completed the DiOGenes study. The study consisted of an 8-week weight-loss phase and a 6-month weight-maintenance (WM) phase, where the subjects were given five different diets varying in protein content and glycaemic index. In the present study, data obtained from all the subjects were pooled. Dairy protein intake was estimated from 3 d dietary records at two time points (week 4 and week 26) during the WM phase. Body weight and metabolic risk markers were determined at baseline (week − 9 to − 11) and before and at the end of the WM phase (week 0 and week 26). Overall, no significant associations were found between consumption of dairy proteins and changes in body weight and metabolic risk markers. However, dairy protein intake tended to be negatively associated with body weight gain (P= 0·08; β = − 0·17), but this was not persistent when controlled for total protein intake, which indicates that dairy protein adds no additional effect to the effect of total protein. Therefore, the present study does not report that dairy proteins are more favourable than other proteins for body weight regulation.
The application of metabolomics in multi-centre studies is increasing. The aim of the present study was to assess the effects of geographical location on the metabolic profiles of individuals with the metabolic syndrome. Blood and urine samples were collected from 219 adults from seven European centres participating in the LIPGENE project (Diet, genomics and the metabolic syndrome: an integrated nutrition, agro-food, social and economic analysis). Nutrient intakes, BMI, waist:hip ratio, blood pressure, and plasma glucose, insulin and blood lipid levels were assessed. Plasma fatty acid levels and urine were assessed using a metabolomic technique. The separation of three European geographical groups (NW, northwest; NE, northeast; SW, southwest) was identified using partial least-squares discriminant analysis models for urine (R2X: 0·33, Q2: 0·39) and plasma fatty acid (R2X: 0·32, Q2: 0·60) data. The NW group was characterised by higher levels of urinary hippurate and N-methylnicotinate. The NE group was characterised by higher levels of urinary creatine and citrate and plasma EPA (20 : 5 n-3). The SW group was characterised by higher levels of urinary trimethylamine oxide and lower levels of plasma EPA. The indicators of metabolic health appeared to be consistent across the groups. The SW group had higher intakes of total fat and MUFA compared with both the NW and NE groups (P≤ 0·001). The NE group had higher intakes of fibre and n-3 and n-6 fatty acids compared with both the NW and SW groups (all P< 0·001). It is likely that differences in dietary intakes contributed to the separation of the three groups. Evaluation of geographical factors including diet should be considered in the interpretation of metabolomic data from multi-centre studies.
Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene–diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile after weight loss and in response to a given diet, among overweight European adults participating in the Diet Obesity and Genes study. By multiple linear regressions, 240 SNPs in twenty-four candidate genes were investigated for SNP main and SNP–diet interaction effects on total cholesterol, LDL-cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect), and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP–dietary protein interaction effect on TAG was identified for lipin 1 (LPIN1) rs4315495, with a decrease in TAG of − 0·26 mmol/l per A-allele/protein unit (95 % CI − 0·38, − 0·14, P= 0·000043). In conclusion, we investigated SNP–diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration.
Weight regain after weight loss is common. In the Diogenes dietary intervention study, a high-protein and low-glycaemic index (GI) diet improved weight maintenance. The objective of the present study was to identify (1) blood profiles associated with continued weight loss and weight regain (2) blood biomarkers of dietary protein and GI levels during the weight-maintenance phase. Blood samples were collected at baseline, after 8 weeks of low-energy diet-induced weight loss and after a 6-month dietary intervention period from female continued weight losers (n 48) and weight regainers (n 48), evenly selected from four dietary groups that varied in protein and GI levels. The blood concentrations of twenty-nine proteins and three steroid hormones were measured. The changes in analytes during weight maintenance largely correlated negatively with the changes during weight loss, with some differences between continued weight losers and weight regainers. Increases in leptin (LEP) and C-reactive protein (CRP) were significantly associated with weight regain (P < 0·001 and P = 0·005, respectively), and these relationships were influenced by the diet. Consuming a high-protein and high-GI diet dissociated the positive relationship between the change in LEP concentration and weight regain. CRP increased during the weight-maintenance period only in weight regainers with a high-protein diet (P < 0·001). In addition, testosterone, luteinising hormone, angiotensinogen, plasminogen activator inhibitor-1, resistin, retinol-binding protein 4, insulin, glucagon, haptoglobin and growth hormone were also affected by the dietary intervention. The blood profile reflects not only the weight change during the maintenance period, but also the macronutrient composition of the dietary intervention, especially the protein level.
Subjects with the metabolic syndrome (MetS) have enhanced oxidative stress and inflammation. Dietary fat quality has been proposed to be implicated in these conditions. We investigated the impact of four diets distinct in fat quantity and quality on 8-iso-PGF2α (a major F2-isoprostane and oxidative stress indicator), 15-keto-13,14-dihydro-PGF2α (15-keto-dihydro-PGF2α, a major PGF2α metabolite and marker of cyclooxygenase-mediated inflammation) and C-reactive protein (CRP). In a 12-week parallel multicentre dietary intervention study (LIPGENE), 417 volunteers with the MetS were randomly assigned to one of the four diets: two high-fat diets (38 % energy (%E)) rich in SFA or MUFA and two low-fat high-complex carbohydrate diets (28 %E) with (LFHCC n-3) or without (LFHCC) 1·24 g/d of very long chain n-3 fatty acid supplementation. Urinary levels of 8-iso-PGF2α and 15-keto-dihydro-PGF2α were determined by RIA and adjusted for urinary creatinine levels. Serum concentration of CRP was measured by ELISA. Neither concentrations of 8-iso-PGF2α and 15-keto-dihydro-PGF2α nor those of CRP differed between diet groups at baseline (P>0·07) or at the end of the study (P>0·44). Also, no differences in changes of the markers were observed between the diet groups (8-iso-PGF2α, P = 0·83; 15-keto-dihydro-PGF2α, P = 0·45; and CRP, P = 0·97). In conclusion, a 12-week dietary fat modification did not affect the investigated markers of oxidative stress and inflammation among subjects with the MetS in the LIPGENE study.
Controlled human intervention trials are required to confirm the hypothesis that dietary fat quality may influence insulin action. The aim was to develop a food-exchange model, suitable for use in free-living volunteers, to investigate the effects of four experimental diets distinct in fat quantity and quality: high SFA (HSFA); high MUFA (HMUFA) and two low-fat (LF) diets, one supplemented with 1·24 g EPA and DHA/d (LFn-3). A theoretical food-exchange model was developed. The average quantity of exchangeable fat was calculated as the sum of fat provided by added fats (spreads and oils), milk, cheese, biscuits, cakes, buns and pastries using data from the National Diet and Nutrition Survey of UK adults. Most of the exchangeable fat was replaced by specifically designed study foods. Also critical to the model was the use of carbohydrate exchanges to ensure the diets were isoenergetic. Volunteers from eight centres across Europe completed the dietary intervention. Results indicated that compositional targets were largely achieved with significant differences in fat quantity between the high-fat diets (39·9 (sem 0·6) and 38·9 (sem 0·51) percentage energy (%E) from fat for the HSFA and HMUFA diets respectively) and the low-fat diets (29·6 (sem 0·6) and 29·1 (sem 0·5) %E from fat for the LF and LFn-3 diets respectively) and fat quality (17·5 (sem 0·3) and 10·4 (sem 0·2) %E from SFA and 12·7 (sem 0·3) and 18·7 (sem 0·4) %E MUFA for the HSFA and HMUFA diets respectively). In conclusion, a robust, flexible food-exchange model was developed and implemented successfully in the LIPGENE dietary intervention trial.
Ingestion of dietary protein is known to induce both insulin and glucagon secretion. These responses may be affected by the dose and the form (intact or hydrolysed) in which protein is ingested. The aim of the study was to investigate the effect of different amounts of intact protein and protein hydrolysate of a vegetable (soya) and animal (whey) protein on insulin and glucagon responses and to study the effect of increasing protein loads for both intact protein and protein hydrolysate in man. The study employed a repeated-measures design with Latin-square randomisation and single-blind trials. Twelve healthy non-obese males ingested three doses (0·3, 0·4 and 0·6 g/kg body weight) of intact soya protein (SPI) and soya protein hydrolysate (SPH). Another group of twelve healthy male subjects ingested three doses (0·3, 0·4 and 0·6 g/kg body weight) of intact whey protein (WPI) and whey protein hydrolysate (WPH). Blood was sampled before (t = 0) and 15, 30, 60, 90 and 120 min after protein ingestion for insulin, glucagon and glucose determination. SPI induced a higher total area under the curve for insulin and glucagon than SPH while no difference between WPI and WPH was found. Insulin and glucagon responses increased with increasing protein load for SPI, SPH, WPI and WPH, but the effect was more pronounced for glucagon. A higher dose of protein or its hydrolysate will result in a lower insulin:glucagon ratio, an important parameter for the control of postprandial substrate metabolism. In conclusion, insulin and glucagon responses were protein and hydrolysate specific.
The distribution of the four macronutrients is associated with energy intake and body fatness according to short-term interventions. The present study involves macronutrient distribution in relation to energy intake and body fatness over a period of 23 years in individuals who have ad libitum access to food. Eight follow-up measurements have been performed in 168 men and 182 women who participate in the Amsterdam Growth and Health Longitudinal Study. From the age of 13 years onwards, dietary intake, physical activity and the thickness of four skinfolds have been assessed. Body fatness was assessed using dual-energy X-ray absorptiometry at the age of 36 years. Generalised estimating equation regression analyses showed that energy percentages (En%) from protein and (in men) carbohydrates were inversely related to energy intake, while the En% from fat was positively related with energy intake. The men and women with high body fatness at the age of 36 years had a 1 En% higher protein intake, and the women with high body fatness had a 2 En% lower alcohol intake at the age of 32 and 36 years. The apparent inconsistent relationships between protein and energy intake and protein and body fatness can in women be explained by reverse causation and underreporting, as in women, low energy intake could not be explained by low physical activity. In conclusion, high intake of protein and (in men) carbohydrate, and low intake of fat are inversely related to total energy intake. High body fatness at the age of 36 years is related to a higher protein intake and, in women, to a lower alcohol intake.
Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene–nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient–genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countries.
In a randomised, single blind, placebo-controlled crossover design study, we investigated whether healthy, non-smoking, dietary unrestrained women (n 24), divided into linoleic acid tasters (LAT, n 14) and linoleic acid non-tasters (LANT, n 10), differed in food intake regulation when linoleic acid was added to ice creams. The determination of subjects as LAT or LANT was done using a 10μM-linoleic acid solution. The ice creams were characterised by the subjects and a taste perception test using the triangle test was conducted three times. Food intake and appetite were measured using the universal eating monitor. LAT and LANT did not differ in characterisation or in taste perception of the ice creams, even though LAT were able to increase their ability to discriminate between the ice cream with linoleic acid from the one containing oleic acid. No effect of LAT status or type of ice cream was found for hedonic value of the ice creams. Linoleic acid taster status did affect food intake regulation. For LAT, but not LANT, the amount eaten was a function of Δsatiety. Subjects ate by weight of food and not by energy content. In conclusion, differences in food intake regulation were seen between LAT and LANT, in that the amount eaten by LAT was a function of Δsatiety, but was not for LANT.
Our knowledge on the absorption of folate is incomplete. The deconjugation process as a possible limiting factor in the absorption of folates was investigated. The study also attempted to validate the use of the area under the serum response curve (AUC) from food compared with folic acid as a proxy variable for food folate bioavailability. Folate absorption was determined in healthy ileostomy volunteers (n 11) using a single-dose short-term protocol. In a randomised crossover design, volunteers received spinach meals and a supplement. Based on analysis of test meals and ileostomy effluents, there was no difference in folate absorption between spinach with a mono-:polyglutamate ratio 40:60 and the same spinach with a 100:0 ratio. The absolute absorption of spinach folate (79 %) calculated from the difference between folate intake and folate content of ileostomy effluents was approximately equal to the relative absorption (81 %) calculated from the AUC after consumption of spinach meals in relation to the AUC after consumption of the folic acid supplement. We conclude that the deconjugation process is not a limiting factor in the absorption of spinach folates. Comparison of AUC of food folate v. folic acid in a short-term protocol may be suitable for assessing food folate bioavailability.
The aim of the present study was to investigate associations between spontaneous meal initiations and blood glucose dynamics in overweight male subjects in negative energy balance. In a randomized crossover design, fifteen overweight male subjects (BMI 28·6 (SD 1·8 kg/m2) participated in three treatments, each of which consisted of 2 weeks consuming a low-energy diet followed by a test of voluntary food ingestion in the absence of time-related cues. The low-energy diet consisted of three daily meals (947 kJ) which were either semi-solid with or without 2·5 g guar gum, or solid, and a dinner of subject's own choice. During the time-blinded test, on the first, second, and third meal initiation subjects ingested a low-energy meal corresponding to that used during the preceding weeks. Changes in blood glucose were monitored on-line. Associations between spontaneous meal initiations and blood glucose dynamics were determined using the χ2 test. No difference was found between treatments in the occurrence of postabsorptive and postprandial declines in blood glucose or in associations between meal initiations and blood glucose dynamics. Postprandial dynamic blood glucose declines were associated with meal initiation (χ2 26·8, P<0·001), but postabsorptive and postprandial transient declines were not. In overweight subjects, the usual association between transient declines and spontaneous meal initiation was completely absent in negative energy balance.
In a study of the impact of aspartame, fat, and carbohydrate on appetite, we monitored blood glucose continuously for 431 (se 16) min. Ten healthy males (19–31 years) participated in three time-blinded visits. As blood glucose was monitored, appetite ratings were scored at randomized times. On the first meal initiation, volunteers consumed one of three isovolumetric drinks (aspartame, 1 MJ simple carbohydrate, and 1 MJ high-fat; randomized order). High-fat and high-carbohydrate foods were available ad libitum subsequently. Blood glucose patterns following the carbohydrate drink (+1·78 (se 0·28) mmol/l in 38 (se 3) min) and high-fat drink (+0·83 (se 0·28) mmol/l in 49 (se 6) min) were predictive of the next intermeal interval (R 0·64 and R 0·97 respectively). Aspartame ingestion was followed by blood glucose declines (40 % of subjects), increases (20 %), or stability (40 %). These patterns were related to the volunteers' perception of sweetness of the drink (R 0·81, P = 0·014), and were predictive of subsequent intakes (R -0·71, P = 0·048). For all drinks combined, declines in blood glucose and meal initiation were significantly associated (χ2 16·8, P < 0·001), the duration of blood glucose responses and intermeal intervals correlated significantly (R 0·715, P = 0·0001), and sweetness perception correlated negatively with hunger suppression (R -0·471, P = 0·015). Effects of fat, carbohydrate, and aspartame on meal initiation, meal size, and intermeal interval relate to blood glucose patterns. Varied blood glucose responses after aspartame support the controversy over its effects, and may relate to sweetness perception.