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.

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.

Exploring the Links between Diet and Health in an Irish Cohort: A Lipidomic Approach.

Epidemiology and clinical studies provide clear evidence of the complex links between diet and health. To understand these links, reliable dietary assessment methods are pivotal. Biomarkers have emerged as more objective measures of intake compared with traditional dietary assessment methods. However, there are only a limited number of putative biomarkers of intake successfully identified and validated. The use of biomarkers that reflect food intake to examine diet related diseases represents the next step in biomarker research. Therefore, the aim of this study was to (1) identify and confirm biomarkers associated with dietary fat intake and (2) examine the relationship between those biomarkers with health parameters. Heatmap analysis identified a panel of 22 lipid biomarkers associated with total dietary fat intake in the Metabolic Challenge (MECHE) Study. Confirmation of four of these biomarkers demonstrated responsiveness to different levels of fat intake in a separate intervention study (NutriTech study). Linear regression identified a significant relationship between the panel of dietary fat biomarkers and HOMA-IR, with three lipid biomarkers (C16, PCaaC36:2, and PCae36:4) demonstrating significant associations. Identifying such links allows us to explore the relationship between diet and health to determine whether these biomarkers can be modulated through diet to improve health outcomes.

Sexual Dimorphism, Age, and Fat Mass Are Key Phenotypic Drivers of Proteomic Signatures.

Validated protein biomarkers are needed for assessing health trajectories, predicting and subclassifying disease, and optimizing diagnostic and therapeutic clinical decision-making. The sensitivity, specificity, accuracy, and precision of single or combinations of protein biomarkers may be altered by differences in physiological states limiting the ability to translate research results to clinically useful diagnostic tests. Aptamer based affinity assays were used to test whether low abundant serum proteins differed based on age, sex, and fat mass in a healthy population of 94 males and 102 females from the MECHE cohort. The findings were replicated in 217 healthy male and 377 healthy female participants in the DiOGenes consortium. Of the 1129 proteins in the panel, 141, 51, and 112 proteins (adjusted p < 0.1) were identified in the MECHE cohort and significantly replicated in DiOGenes for sexual dimorphism, age, and fat mass, respectively. Pathway analysis classified a subset of proteins from the 3 phenotypes to the complement and coagulation cascades pathways and to immune and coagulation processes. These results demonstrated that specific proteins were statistically associated with dichotomous (male vs female) and continuous phenotypes (age, fat mass), which may influence the identification and use of biomarkers of clinical utility for health diagnosis and therapeutic strategies.

Association of the tumor necrosis factor-alpha promoter polymorphism with change in triacylglycerol response to sequential meals.

BACKGROUND: Reported associations between Tumor Necrosis Factor-alpha (TNFA) and the postprandial triacylglycerol (TAG) response have been inconsistent, which could be due to variations in the TNFA gene, meal fat composition or participant's body weight. Hence, we investigated the association of TNFA polymorphism (-308G → A) with body mass index (BMI) and postprandial lipaemia and also determined the impact of BMI on the association of the polymorphism with postprandial lipaemia. METHODS: The study participants (n = 230) underwent a sequential meal postprandial study. Blood samples were taken at regular intervals after a test breakfast (t = 0, 49 g fat) and lunch (t =330 min, 29 g fat) to measure fasting and postprandial lipids, glucose and insulin. The Metabolic Challenge Study (MECHE) comprising 67 Irish participants who underwent a 54 g fat oral lipid tolerance test was used as a replication cohort. The impact of genotype on postprandial responses was determined using general linear model with adjustment for potential confounders. RESULTS: The -308G → A polymorphism showed a significant association with BMI (P = 0.03) and fasting glucose (P = 0.006), where the polymorphism explained 13 % of the variation in the fasting glucose. A 30 % higher incremental area under the curve (IAUC) was observed for the postprandial TAG response in the GG homozygotes than A-allele carriers (P = 0.004) and the genotype explained 19 % of the variation in the IAUC. There was a non-significant trend in the impact of BMI on the association of the genotype with TAG IAUC (P = 0.09). These results were not statistically significant in the MECHE cohort, which could be due to the differences in the sample size, meal composition, baseline lipid profile, allelic diversity and postprandial characterisation of participants across the two cohorts. CONCLUSIONS: Our findings suggest that TNFA -308G → A polymorphism may be an important candidate for BMI, fasting glucose and postprandial TAG response. Further studies are required to investigate the mechanistic effects of the polymorphism on glucose and TAG metabolism, and determine whether BMI is an important variable which should be considered in the design of future studies. TRIAL REGISTRATION: NCT01172951 .

Modulation of the lipidomic profile due to a lipid challenge and fitness level: a postprandial study.

BACKGROUND: The lipid composition of plasma is known to vary due to both phenotypic factors such as age, gender and BMI as well as with various diseases including cancer and neurological disorders. However, there is little investigation into the variation in the lipidome due to exercise and/ or metabolic challenges. The objectives of this present study were (i) To identify the glycerophospholipid, sphingolipids and ceramide changes in response to an oral lipid tolerance test (OLTT) in healthy adults and (ii) To identify the effect of aerobic fitness level on lipidomic profiles. METHODS: 214 healthy adults aged 18-60 years were recruited as part of a metabolic challenge study. A sub-group of 40 volunteers were selected for lipidomic analysis based on their aerobic fitness level. Ceramides, glycerophospholipids and sphingomyelins were quantified in baseline fasting plasma samples as well as at 60, 120, 180, 240 and 300 min following a lipid challenge using high-throughput flow injection ESI-MS/MS. RESULTS: Mixed model repeated measures analysis identified lipids which were significantly changing over the time course of the lipid challenge. Included in these lipids were lysophosphoethanolamines (LPE), phosphoethanolamines (PE), phosphoglycerides (PG) and ceramides (Cer). Five lipids (LPE a C18:2, LPE a C18:1, PE aa C36:2, PE aa C36:3 and N-C16:1-Cer) had a fold change > 1.5 at 120 min following the challenge and these lipids remained elevated. Furthermore, three of these lipids (LPE a C18:2, PE aa C36:2 and PE aa C36:3) were predictive of fasting and peak plasma TAG concentrations following the OLTT. Further analysis revealed that fitness level has a significant impact on the response to the OLTT: in particular significant differences between fitness groups were observed for phosphatidylcholines (PC), sphingomyelins (SM) and ceramides. CONCLUSION: This study identified specific lipids which were modulated by an acute lipid challenge. Furthermore, it identified a series of lipids which were modulated by fitness level. Future lipidomic studies should take into account environmental factors such as diet and fitness level during biomarker discovery work. TRIAL REGISTRATION: Data, clinicaltrials.gov, NCT01172951.

Genetic and environmental influences on covariation in reproducible diet-metabolite associations.

BACKGROUND: Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet-metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points. OBJECTIVE: This research aims to examine the genetic and environmental factors influencing covariation in diet-metabolite associations that are reproducible over time in healthy twins. METHODS: The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites. RESULTS: The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15-0.49). For 8 diet-metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (ß: 0.40-0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (ß: -0.50, P < 0.001) and ß-hydroxybutyrate (ß: -0.46, P < 0.001), as well as intakes of saturated fat and acetoacetate (ß: 0.47, P < 0.001) and ß-hydroxybutyrate (ß: 0.52, P < 0.001). For these diet-metabolite associations a common shared environmental factor explained 66-69% of variance in the metabolites. CONCLUSIONS: This study shows that diet-metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.

Optimisation of a metabotype approach to deliver targeted dietary advice.

BACKGROUND: Targeted nutrition is defined as dietary advice tailored at a group level. Groups known as metabotypes can be identified based on individual metabolic profiles. Metabotypes have been associated with differential responses to diet, which support their use to deliver dietary advice. We aimed to optimise a metabotype approach to deliver targeted dietary advice by encompassing more specific recommendations on nutrient and food intakes and dietary behaviours. METHODS: Participants (n = 207) were classified into three metabotypes based on four biomarkers (triacylglycerol, total cholesterol, HDL-cholesterol and glucose) and using a k-means cluster model. Participants in metabotype-1 had the highest average HDL-cholesterol, in metabotype-2 the lowest triacylglycerol and total cholesterol, and in metabotype-3 the highest triacylglycerol and total cholesterol. For each participant, dietary advice was assigned using decision trees for both metabotype (group level) and personalised (individual level) approaches. Agreement between methods was compared at the message level and the metabotype approach was optimised to incorporate messages exclusively assigned by the personalised approach and current dietary guidelines. The optimised metabotype approach was subsequently compared with individualised advice manually compiled. RESULTS: The metabotype approach comprised advice for improving the intake of saturated fat (69% of participants), fibre (66%) and salt (18%), while the personalised approach assigned advice for improving the intake of folate (63%), fibre (63%), saturated fat (61%), calcium (34%), monounsaturated fat (24%) and salt (14%). Following the optimisation of the metabotype approach, the most frequent messages assigned to address intake of key nutrients were to increase the intake of fruit and vegetables, beans and pulses, dark green vegetables, and oily fish, to limit processed meats and high-fat food products and to choose fibre-rich carbohydrates, low-fat dairy and lean meats (60-69%). An average agreement of 82.8% between metabotype and manual approaches was revealed, with excellent agreements in metabotype-1 (94.4%) and metabotype-3 (92.3%). CONCLUSIONS: The optimised metabotype approach proved capable of delivering targeted dietary advice for healthy adults, being highly comparable with individualised advice. The next step is to ascertain whether the optimised metabotype approach is effective in changing diet quality.

A proteomic signature that reflects pancreatic beta-cell function.

AIM: Proteomics has the potential to enhance early identification of beta-cell dysfunction, in conjunction with monitoring the various stages of type 2 diabetes onset. The most routine method of assessing pancreatic beta-cell function is an oral glucose tolerance test, however this method is time consuming and carries a participant burden. The objectives of this research were to identify protein signatures and pathways related to pancreatic beta-cell function in fasting blood samples. METHODS: Beta-cell function measures were calculated for MECHE study participants who completed an oral glucose tolerance test and had proteomic data (n = 100). Information on 1,129 protein levels was obtained using the SOMAscan assay. Receiver operating characteristic curves were used to assess discriminatory ability of proteins of interest. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Replication of findings were achieved in a second human cohort where possible. RESULTS: Twenty-two proteins measured by aptamer technology were significantly associated with beta-cell function/HOMA-IR while 17 proteins were significantly associated with the disposition index (p ≤ 0.01). Receiver operator characteristic curves determined the protein panels to have excellent discrimination between low and high beta-cell function. Linear regression analysis determined that beta-endorphin and IL-17F have strong associations with beta-cell function/HOMA-IR, ß = 0.039 (p = 0.005) and ß = -0.027 (p = 0.013) respectively. Calcineurin and CRTAM were strongly associated with the disposition index (ß = 0.005 and ß = 0.005 respectively, p = 0.012). In vitro experiments confirmed that IL-17F modulated insulin secretion in the BRIN-BD11 cell line, with the lower concentration of 10 ng/mL significantly increasing glucose stimulated insulin secretion (p = 0.043). CONCLUSIONS: Early detection of compromised beta-cell function could allow for implementation of nutritional and lifestyle interventions before progression to type 2 diabetes.

Twin metabolomics: the key to unlocking complex phenotypes in nutrition research.

Twin studies are a valuable resource for studying phenotypes and their underlying biology. Heritability estimates based on the classic twin model show that genes influence human traits including health, diet, and food choice. Metabolomics is a promising tool in nutrition and health research where complex metabolite profiles reflect the metabolic effects of foods and diets as well as the biological pathways associated with diet-related diseases. In recent years, publications arising from twin research have incorporated metabolomic analysis, providing insights into genetic and environmental influences on metabolomic profiles. This article reviews the application of metabolomics in twin research with a particular focus on nutrition and diet-related diseases. The review begins by describing the classic twin study design, followed by a look at its application in nutrition research. Indeed, there is clear evidence for a genetic influence on dietary intake, regardless of the outcome measure: energy, macronutrients, dietary patterns, or food choice. The latter part of the review introduces metabolomic research showing how twin studies can separate aspects of the metabolome that are strongly influenced by genetics vs those that are more influenced by environment. The combination of metabolomics and twin research brings the promise of untangling gene-environment effects on complex phenotypes such as the metabolome, obesity, and diet-related diseases. For example, metabolomics is used in nutrition research to identify metabolites associated with particular dietary patterns. When combined within a twin study design, heritability of metabolite-dietary pattern associations can be established allowing further insight into complex gene-environment interactions that shape individual metabolomes.

Uncovering Factors Related to Pancreatic Beta-Cell Function.

AIM: The incidence of type 2 diabetes has increased rapidly on a global scale. Beta-cell dysfunction contributes to the overall pathogenesis of type 2 diabetes. However, factors contributing to beta-cell function are not clear. The aims of this study were (i) to identify factors related to pancreatic beta-cell function and (ii) to perform mechanistic studies in vitro. METHODS: Three specific measures of beta-cell function were assessed for 110 participants who completed an oral glucose tolerance test as part of the Metabolic Challenge Study. Anthropometric and biochemical parameters were assessed as potential modulators of beta-cell function. Subsequent in vitro experiments were performed using the BRIN-BD11 pancreatic beta-cell line. Validation of findings were performed in a second human cohort. RESULTS: Waist-to-hip ratio was the strongest anthropometric modulator of beta-cell function, with beta-coefficients of -0.33 (p = 0.001) and -0.30 (p = 0.002) for beta-cell function/homeostatic model assessment of insulin resistance (HOMA-IR), and disposition index respectively. Additionally, the resistin-to-adiponectin ratio (RA index) emerged as being strongly associated with beta-cell function, with beta-coefficients of -0.24 (p = 0.038) and -0.25 (p = 0.028) for beta-cell function/HOMA-IR, and disposition index respectively. Similar results were obtained using a third measure for beta-cell function. In vitro experiments revealed that the RA index was a potent regulator of acute insulin secretion where a high RA index (20ng ml-1 resistin, 5nmol l-1 g-adiponectin) significantly decreased insulin secretion whereas a low RA index (10ng ml-1 resistin, 10nmol l-1 g-adiponectin) significantly increased insulin secretion. The RA index was successfully validated in a second human cohort with beta-coefficients of -0.40 (p = 0.006) and -0.38 (p = 0.008) for beta-cell function/ HOMA-IR, and disposition index respectively. CONCLUSIONS: Waist-to-hip ratio and RA index were identified as significant modulators of beta-cell function. The ability of the RA index to modulate insulin secretion was confirmed in mechanistic studies. Future work should identify strategies to alter the RA index.

Body mass index mediates inflammatory response to acute dietary challenges.

SCOPE: Acute metabolic challenges provide an opportunity to identify mechanisms of metabolic and nutritional health. In this study, we assessed the transcriptomic response to oral glucose and lipid challenges in a cohort of individuals ranging in age and BMI. The main goal is to identify whether BMI can mediate the metabolic and transcriptional response to dietary challenges, and the differences between lipid and glucose tests. METHODS AND RESULTS: Two hundred fourteen healthy adults were assigned to the challenges and twenty-three individuals were selected for further transcriptomic proofing, using microarray analysis of peripheral blood mononuclear cells. Through linear-mixed models and network analysis, different sets of transcripts and pathways were identified that responded to the challenges depending on BMI. Different transcripts that responded to the lipid and glucose tests, independently of BMI, were also identified. In the network analysis, inflammatory and adhesion processes were strongly represented for both challenges. CONCLUSION: Our results indicate that BMI is strongly linked to the transcriptomic and metabolic response to acute challenges. The emerging biological processes are mainly inflammation-related pathways, highlighting an interconnection between obesity, inflammation/adhesion, and response to nutritional challenge. The comparison between lipid and glucose challenges shows how these trigger a substantially different molecular response.

Nutritional phenotype databases and integrated nutrition: from molecules to populations.

In recent years, there has been a great expansion in the nature of new technologies for the study of all biologic subjects at the molecular and genomic level and these have been applied to the field of human nutrition. The latter has traditionally relied on a mix of epidemiologic studies to generate hypotheses, dietary intervention studies to test these hypotheses, and a variety of experimental approaches to understand the underlying explanatory mechanisms. Both the novel and traditional approaches have begun to carve out separate identities vís-a-vís their own journals, their own international societies, and their own national and international symposia. The present review draws on the advent of large national nutritional phenotype databases and related technological developments to argue the case that there needs to be far more integration of molecular and public health nutrition. This is required to address new joint approaches to such areas as the measurement of food intake, biomarker discovery, and the genetic determinants of nutrient-sensitive genotypes and other areas such as personalized nutrition and the use of new technologies with mass application, such as in dried blood spots to replace venipuncture or portable electronic devices to monitor food intake and phenotype. Future development requires the full integration of these 2 disciplines, which will provide a challenge to both funding agencies and to university training of nutritionists.

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, IL1ß, 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.