Background: There are a number of biological pathways that affect our ingestive behaviours, including energy homeostasis, food reward, and taste. Given that carbohydrates such as sugars, provide energy and a sweet taste, examining candidate genes in each pathway may help explain differences in carbohydrate consumption behaviours. Objective: To determine whether variations in genes encoding a glucose transporter (GLUT2), a dopamine receptor (DRD2), and sweet taste receptor (TAS1R2) are associated with differences in sugar consumption in two distinct populations. Methods: Population 1 included diabetes-free young adults where dietary intake was assessed using a one month 196-item food frequency questionnaire (FFQ). Population 2 consisted of individuals with type 2 diabetes. Dietary intake was assessed using 3-day food records administered 2 weeks apart; food record 1 (FR1) and 2 (FR2). Subjects were genotyped for the Thr110Ile variation in GLUT2 (n1=587; n2=100), the C957T variation in DRD2 (n1=313; n2=100), and the Ser9Cys and Ile191Val variations in TAS1R2 (n1=1037; n2=100) using real-time PCR. Results: In comparison to individuals homozygous for the GLUT2 Thr allele, consumption of sugars was higher among Ile carriers in population 1 (133 ± 5 vs 118 ± 3 g/d, p=0.006) and population 2 on two separate food records (FR1: 112 ± 9 vs 87 ± 5 g/d, p=0.02; FR2: 105 ± 8 vs 78 ± 4 g/d, p=0.002). For the C957T variation in population 1, we detected a significant DRD2xSex interaction with the consumption of sucrose decreasing with each T allele among men (p=0.03) and a heterosis mode of inheritance among women where heterozygotes consumed the most (p=0.01). For TAS1R2, we detected a significant TAS1R2xBMI interaction and among overweight individuals, carriers of the Val allele consumed less sugars than those with the Ile/Ile genotype (103 ± 6 vs122 ± 6 g/d, p=0.01). In population 2, carriers of the Val allele consumed less sugars than individuals with the Ile/Ile genotype (83 ± 6 vs 99 ± 6 g/d, p=0.04) on FR2. Conclusions: Our findings demonstrate that genetic variation in GLUT2, DRD2 and TAS1R2 affect habitual sugar consumption and suggest that selection of dietary sugars can be influenced by different biological pathways.

The role of variation in the TAS2R50 bitter taste receptor gene is unknown, but may influence taste perception and dietary habits. Individuals (n=1171) aged 20 to 29, from the Toronto Nutrigenomics and Health Study, completed a food preference checklist and a semi-quantitative food frequency questionnaire to assess their preference and intake of potentially bitter foods and beverages. DNA was isolated from blood and genotyped for 3 polymorphisms in the TAS2R50 gene (rs2900554 A>C; rs10772397 A>G; rs1376251 A>G). Taste intensity was examined using taste strips infused with 3µg of naringin. The rs2900554 SNP was associated with naringin taste intensity, grapefruit preference and grapefruit intake in females. Homozygotes for the C allele reported the highest frequency of experiencing a high naringin taste intensity, disliking grapefruit and not consuming grapefruit. The rs10772397 and rs1376251 SNPs were associated with disliking grapefruit. These results suggest that naringin may be a ligand for the T2R50 receptor.

The role of variation in the TAS2R50 bitter taste receptor gene is unknown, but may influence taste perception and dietary habits. Individuals (n=1171) aged 20 to 29, from the Toronto Nutrigenomics and Health Study, completed a food preference checklist and a semi-quantitative food frequency questionnaire to assess their preference and intake of potentially bitter foods and beverages. DNA was isolated from blood and genotyped for 3 polymorphisms in the TAS2R50 gene (rs2900554 A>C; rs10772397 A>G; rs1376251 A>G). Taste intensity was examined using taste strips infused with 3µg of naringin. The rs2900554 SNP was associated with naringin taste intensity, grapefruit preference and grapefruit intake in females. Homozygotes for the C allele reported the highest frequency of experiencing a high naringin taste intensity, disliking grapefruit and not consuming grapefruit. The rs10772397 and rs1376251 SNPs were associated with disliking grapefruit. These results suggest that naringin may be a ligand for the T2R50 receptor.

Within nutrigenomics, clustering using data generated by microarray gene expression profiles can be used to identify sub-populations of subjects that respond differently to a given diet intervention. The use of clustering analyses is promising in obesity-related research as personalized nutrition is gaining popularity. This thesis focuses on clustering a human subcutaneous adipose tissue gene expression data set obtained during a low-calorie diet intervention to aid in the prediction of 6-month weight loss maintenance. The aims of the study were (1) to identify the best performing clustering method for clustering samples, (2) to identify differential responders to the low-calorie diet, and (3) to identify the biological pathways affected during the low-calorie diet by weight maintainers and weight regainers. MCLUST performed the best when clustering samples using relative weight change and either fasting insulin or insulin resistance change. Furthermore, it identified differences in the regulation of pathways between weight maintainers and regainers.

Background: There are a number of biological pathways that affect our ingestive behaviours, including energy homeostasis, food reward, and taste. Given that carbohydrates such as sugars, provide energy and a sweet taste, examining candidate genes in each pathway may help explain differences in carbohydrate consumption behaviours. Objective: To determine whether variations in genes encoding a glucose transporter (GLUT2), a dopamine receptor (DRD2), and sweet taste receptor (TAS1R2) are associated with differences in sugar consumption in two distinct populations. Methods: Population 1 included diabetes-free young adults where dietary intake was assessed using a one month 196-item food frequency questionnaire (FFQ). Population 2 consisted of individuals with type 2 diabetes. Dietary intake was assessed using 3-day food records administered 2 weeks apart; food record 1 (FR1) and 2 (FR2). Subjects were genotyped for the Thr110Ile variation in GLUT2 (n1=587; n2=100), the C957T variation in DRD2 (n1=313; n2=100), and the Ser9Cys and Ile191Val variations in TAS1R2 (n1=1037; n2=100) using real-time PCR. Results: In comparison to individuals homozygous for the GLUT2 Thr allele, consumption of sugars was higher among Ile carriers in population 1 (133 ± 5 vs 118 ± 3 g/d, p=0.006) and population 2 on two separate food records (FR1: 112 ± 9 vs 87 ± 5 g/d, p=0.02; FR2: 105 ± 8 vs 78 ± 4 g/d, p=0.002). For the C957T variation in population 1, we detected a significant DRD2xSex interaction with the consumption of sucrose decreasing with each T allele among men (p=0.03) and a heterosis mode of inheritance among women where heterozygotes consumed the most (p=0.01). For TAS1R2, we detected a significant TAS1R2xBMI interaction and among overweight individuals, carriers of the Val allele consumed less sugars than those with the Ile/Ile genotype (103 ± 6 vs122 ± 6 g/d, p=0.01). In population 2, carriers of the Val allele consumed less sugars than individuals with the Ile/Ile genotype (83 ± 6 vs 99 ± 6 g/d, p=0.04) on FR2. Conclusions: Our findings demonstrate that genetic variation in GLUT2, DRD2 and TAS1R2 affect habitual sugar consumption and suggest that selection of dietary sugars can be influenced by different biological pathways.

No description available.

Yes / Personalised diets based on people’s existing food choices, and/or phenotypic, and/or genetic information hold potential to improve public dietary-related health. The aim of this analysis, therefore, has been to examine the degree to which factors which determine uptake of personalised nutrition vary between EU countries to better target policies to encourage uptake, and optimise the health benefits of personalised nutrition technology. A questionnaire developed from previous qualitative research was used to survey nationally representative samples from 9 EU countries (N = 9381). Perceived barriers to the uptake of personalised nutrition comprised three factors (data protection; the eating context; and, societal acceptance). Trust in sources of information comprised four factors (commerce and media; practitioners; government; family and, friends). Benefits comprised a single factor. Analysis of Variance (ANOVA) was employed to compare differences in responses between the United Kingdom; Ireland; Portugal; Poland; Norway; the Netherlands; Germany; and, Spain. The results indicated that respondents in Greece, Poland, Ireland, Portugal and Spain, rated the benefits of personalised nutrition highest, suggesting a particular readiness in these countries to adopt personalised nutrition interventions. Greek participants were more likely to perceive the social context of eating as a barrier to adoption of personalised nutrition, implying a need for support in negotiating social situations while on a prescribed diet. Those in Spain, Germany, Portugal and Poland scored highest on perceived barriers related to data protection. Government was more trusted than commerce to deliver and provide information on personalised nutrition overall. This was particularly the case in Ireland, Portugal and Greece, indicating an imperative to build trust, particularly in the ability of commercial service providers to deliver personalised dietary regimes effectively in these countries. These findings, obtained from a nationally representative sample of EU citizens, imply that a parallel, integrated, public-private delivery system would capture the needs of most potential consumers. / Food4me is the acronym of the EU FP7 Project ‘‘Personalised nutrition: an integrated analysis of opportunities and challenges” (Contract No. KBBE.2010.2.3-02, ProjectNo.265494), http:// www.food4me.org/.

Yes / Purpose - This research explores the perceptions and experiences of early adopters of the technology. Design/Method/Approach - Registered Dietitians (RD´s) (N=14) were recruited from the UK, Canada, South-Africa, Australia, Mexico and Israel. Six qualitative interviews and two focus groups were conducted online using a conference calling platform. Data were recorded, transcribed and thematically analysed. Findings - Early adopters of Nutrigenomics (NGx) were experienced, self-efficacious RD’s who actively sought knowledge of NGx through communication with one another and the broader scientific community. They considered NGx an extension of current practice and believed RD’s had the skills to deliver it. Perceived barriers to widening the application of NGx were linked to skepticism among the wider dietetics community. Proliferation of unregulated websites offering tests and diets was considered ‘pseudoscience’ and detrimental to dietetics fully embracing NGx. The lack of a sustainable public health model for the delivery of NGx was also perceived to hinder progress. Results are discussed with reference to ‘diffusion of innovation theory’. Originality/Value - The views of RD’s who practice NGx have not been previously studied. These data highlight requirements for future dietetic training provision and more inclusive service delivery models. Regulation of NGx services and formal recognition by professional bodies is needed to address the research/practice translation gap.

Introdução: Conhecer as respostas proteômicas de diferentes grupos metabólicos após uma intervenção nutricional poderia ajudar a identificar biomarcadores e o tratamento dietético mais apropriado para diferentes perfis de indivíduos. Objetivos: Descrever dois diferentes grupos metabólicos em um estudo de intervenção nutricional; descrever o perfil lipídico, os níveis de glicose e os dados proteômicos ao longo do estudo nesses dois grupos. Metodologia: Foi realizado um estudo de intervenção \"N-of-1\", com indivíduos de 9 a 13 anos, no qual ocorreu avaliação antropométrica, de composição corporal, de ingestão alimentar, bioquímica e proteômica em três momentos: no início do estudo (momento 1), após 6 semanas de suplementação diária de micronutrientes (momento 2), e após outras 6 semanas sem nenhuma intervenção (momento 3). A técnica estatística \"k-means clustering\" foi utilizada para alocar os participantes em dois grupos metabólicos distintos (cluster 1 e cluster 2), de acordo com os perfis glicídicos e lipídicos (glicemia, triglicerídeos, colesterol total, LDL, HDL e VLDL colesterol) que os indivíduos apresentaram nos três momentos do estudo. Resultados: O cluster 1 (n = 111) teve melhor perfil glico-lipídico e também apresentou menores valores para índice de massa corporal, circunferência de cintura e porcentagem de gordura corporal nos três momentos do estudo, comparado ao cluster 2 (n = 25). A ingestão alimentar não diferiu entre os clusters em nenhum momento do estudo. Com a suplementação de micronutrientes, o cluster 1 apresentou redução de glicemia, LDL e colesterol total, além de diminuir sua ingestão de energia, carboidrato, lipídeo e proteína, enquanto o cluster 2 reduziu LDL, colesterol total e HDL e não alterou sua ingestão de energia e macronutrientes. Foi identificada a expressão de 20 proteínas no plasma dos indivíduos dos clusters 1 e 2, sendo que a maioria delas evoluiu de maneira diferente entre os dois grupos após a intervenção. Com a suplementação, o cluster 1 apresentou aumento na expressão de alpha-1-acid glycoprotein 1, alpha- 2-HS-glycoprotein, ceruloplasmin e de fibrinogen alpha, beta e gamma chain, bem como redução na expressão de apolipoprotein A-IV, haptoglobin, Ig alpha-1 chain C region, serotransferrin e vitamin D-binding protein. No mesmo período, o cluster 2 mostrou aumento de alpha-1-antitrypsin, ceruloplasmin, haptoglobin, Ig alpha-1 chain C region e plasma protease C1 inhibitor, além de diminuição na expressão de alpha-1-acid glycoprotein 1 e de fibrinogen alpha, beta e gamma chain. Conclusões: É possível que tenha ocorrido aumento de expressão de proteínas que podem ter auxiliado na melhora do perfil glico-lipídico dos participantes. O cluster de pior perfil parece ter se beneficiado mais com a intervenção em relação à expressão de proteínas do que o cluster de melhor perfil. O estudo do perfil genético poderia ajudar no entendimento da resposta metabólica dos indivíduos. / Introduction: Knowing the proteomic responses from different metabolic groups after a nutritional intervention could help to identify biomarkers and the most appropriate dietary treatment for different profiles of subjects. Aims: To describe two different metabolic groups in a nutritional intervention study; To describe the lipid profile, glucose levels and proteomic data throughout the study in these two groups. Methodology: An \"N-of-1\" intervention study was carried out with subjects from 9 to 13 years of age, in which anthropometric, body composition, food intake, biochemical and proteomics evaluation was performed in three moments: at the beginning of the study (moment 1), after 6 weeks of daily micronutrient supplementation (moment 2), and after another 6 weeks without any intervention (moment 3). The \"k-means clustering\" technique was used to allocate the participants to two distinct metabolic groups (cluster 1 and cluster 2) according to the glucose and lipid profiles (glycemia, triglycerides, total cholesterol, LDL, HDL and VLDL cholesterol) that these subjects presented at the three moments of the study. Results: Cluster 1 (n = 111) had a better glycemic and lipid profile and also presented lower values for body mass index, waist circumference and body fat percentage in the three moments of the study, compared to cluster 2 (n = 25). Food intake did not differ between the clusters in any moment of the study. With supplementation, cluster 1 showed a decrease in glycemia, LDL and total cholesterol, as well as decreased energy, carbohydrate, lipid and protein intake, while cluster 2 reduced LDL, total cholesterol and HDL and did not alter its energy and macronutrients intake. It was identified the expression of 20 proteins in the plasma of the subjects from clusters 1 and 2, and most of them evolved differently between the two groups after the intervention. Cluster 1 showed increase in expression of alpha-1-acid glycoprotein 1, alpha-2-HS-glycoprotein, ceruloplasmin and alpha, beta and gamma chain fibrinogen, as well as reduction in expression of apolipoprotein A-IV, haptoglobin, Ig alpha-1 chain C region, serotransferrin and vitamin D-binding protein. In the same period, cluster 2 showed an increase of alpha-1-antitrypsin, ceruloplasmin, haptoglobin, Ig alpha-1 chain C region and plasma protease C1 inhibitor, as well as decrease in the expression of alpha-1- acid glycoprotein 1 and alpha fibrinogen, beta and gamma chain. Conclusions: It is possible that there was occurred an increase in the expression of proteins that may have helped to improve glycemic and lipid profiles of the participants. The worst-profile cluster seems to have benefited more from the intervention in relation to protein expression than the cluster with the best profile. The study of the genetic profile could help in the understanding of the individuals metabolic response.

Introdução: Conhecer as respostas proteômicas de diferentes grupos metabólicos após uma intervenção nutricional poderia ajudar a identificar biomarcadores e o tratamento dietético mais apropriado para diferentes perfis de indivíduos. Objetivos: Descrever dois diferentes grupos metabólicos em um estudo de intervenção nutricional; descrever o perfil lipídico, os níveis de glicose e os dados proteômicos ao longo do estudo nesses dois grupos. Metodologia: Foi realizado um estudo de intervenção \"N-of-1\", com indivíduos de 9 a 13 anos, no qual ocorreu avaliação antropométrica, de composição corporal, de ingestão alimentar, bioquímica e proteômica em três momentos: no início do estudo (momento 1), após 6 semanas de suplementação diária de micronutrientes (momento 2), e após outras 6 semanas sem nenhuma intervenção (momento 3). A técnica estatística \"k-means clustering\" foi utilizada para alocar os participantes em dois grupos metabólicos distintos (cluster 1 e cluster 2), de acordo com os perfis glicídicos e lipídicos (glicemia, triglicerídeos, colesterol total, LDL, HDL e VLDL colesterol) que os indivíduos apresentaram nos três momentos do estudo. Resultados: O cluster 1 (n = 111) teve melhor perfil glico-lipídico e também apresentou menores valores para índice de massa corporal, circunferência de cintura e porcentagem de gordura corporal nos três momentos do estudo, comparado ao cluster 2 (n = 25). A ingestão alimentar não diferiu entre os clusters em nenhum momento do estudo. Com a suplementação de micronutrientes, o cluster 1 apresentou redução de glicemia, LDL e colesterol total, além de diminuir sua ingestão de energia, carboidrato, lipídeo e proteína, enquanto o cluster 2 reduziu LDL, colesterol total e HDL e não alterou sua ingestão de energia e macronutrientes. Foi identificada a expressão de 20 proteínas no plasma dos indivíduos dos clusters 1 e 2, sendo que a maioria delas evoluiu de maneira diferente entre os dois grupos após a intervenção. Com a suplementação, o cluster 1 apresentou aumento na expressão de alpha-1-acid glycoprotein 1, alpha- 2-HS-glycoprotein, ceruloplasmin e de fibrinogen alpha, beta e gamma chain, bem como redução na expressão de apolipoprotein A-IV, haptoglobin, Ig alpha-1 chain C region, serotransferrin e vitamin D-binding protein. No mesmo período, o cluster 2 mostrou aumento de alpha-1-antitrypsin, ceruloplasmin, haptoglobin, Ig alpha-1 chain C region e plasma protease C1 inhibitor, além de diminuição na expressão de alpha-1-acid glycoprotein 1 e de fibrinogen alpha, beta e gamma chain. Conclusões: É possível que tenha ocorrido aumento de expressão de proteínas que podem ter auxiliado na melhora do perfil glico-lipídico dos participantes. O cluster de pior perfil parece ter se beneficiado mais com a intervenção em relação à expressão de proteínas do que o cluster de melhor perfil. O estudo do perfil genético poderia ajudar no entendimento da resposta metabólica dos indivíduos. / Introduction: Knowing the proteomic responses from different metabolic groups after a nutritional intervention could help to identify biomarkers and the most appropriate dietary treatment for different profiles of subjects. Aims: To describe two different metabolic groups in a nutritional intervention study; To describe the lipid profile, glucose levels and proteomic data throughout the study in these two groups. Methodology: An \"N-of-1\" intervention study was carried out with subjects from 9 to 13 years of age, in which anthropometric, body composition, food intake, biochemical and proteomics evaluation was performed in three moments: at the beginning of the study (moment 1), after 6 weeks of daily micronutrient supplementation (moment 2), and after another 6 weeks without any intervention (moment 3). The \"k-means clustering\" technique was used to allocate the participants to two distinct metabolic groups (cluster 1 and cluster 2) according to the glucose and lipid profiles (glycemia, triglycerides, total cholesterol, LDL, HDL and VLDL cholesterol) that these subjects presented at the three moments of the study. Results: Cluster 1 (n = 111) had a better glycemic and lipid profile and also presented lower values for body mass index, waist circumference and body fat percentage in the three moments of the study, compared to cluster 2 (n = 25). Food intake did not differ between the clusters in any moment of the study. With supplementation, cluster 1 showed a decrease in glycemia, LDL and total cholesterol, as well as decreased energy, carbohydrate, lipid and protein intake, while cluster 2 reduced LDL, total cholesterol and HDL and did not alter its energy and macronutrients intake. It was identified the expression of 20 proteins in the plasma of the subjects from clusters 1 and 2, and most of them evolved differently between the two groups after the intervention. Cluster 1 showed increase in expression of alpha-1-acid glycoprotein 1, alpha-2-HS-glycoprotein, ceruloplasmin and alpha, beta and gamma chain fibrinogen, as well as reduction in expression of apolipoprotein A-IV, haptoglobin, Ig alpha-1 chain C region, serotransferrin and vitamin D-binding protein. In the same period, cluster 2 showed an increase of alpha-1-antitrypsin, ceruloplasmin, haptoglobin, Ig alpha-1 chain C region and plasma protease C1 inhibitor, as well as decrease in the expression of alpha-1- acid glycoprotein 1 and alpha fibrinogen, beta and gamma chain. Conclusions: It is possible that there was occurred an increase in the expression of proteins that may have helped to improve glycemic and lipid profiles of the participants. The worst-profile cluster seems to have benefited more from the intervention in relation to protein expression than the cluster with the best profile. The study of the genetic profile could help in the understanding of the individuals metabolic response.