Evidence Database
The peer-reviewed research studies presented here are relevant in describing the financial impacts of chronic disease, the potential financial returns of lifestyle medicine interventions or health improvements, and methods by which to measure and analyze such data. Use the Search and Filter functions to narrow your results.
Dietary and health biomarkers-time for an update.
In the dietary and health research area, biomarkers are extensively used for multiple purposes. These include biomarkers of dietary intake and nutrient status, biomarkers used to measure the biological effects of specific dietary components, and biomarkers to assess the effects of diet on health. The implementation of biomarkers in nutritional research will be important to improve measurements of dietary intake, exposure to specific dietary components, and of compliance to dietary interventions. Biomarkers could also help with improved characterization of nutritional status in study volunteers and to provide much mechanistic insight into the effects of food components and diets. Although hundreds of papers in nutrition are published annually, there is no current ontology for the area, no generally accepted classification terminology for biomarkers in nutrition and health, no systematic validation scheme for these biomarker classes, and no recent systematic review of all proposed biomarkers for food intake. While advanced databases exist for the human and food metabolomes, additional tools are needed to curate and evaluate current data on dietary and health biomarkers. The Food Biomarkers Alliance (FoodBAll) under the Joint Programming Initiative-A Healthy Diet for a Healthy Life (JPI-HDHL)-is aimed at meeting some of these challenges, identifying new dietary biomarkers, and producing new databases and review papers on biomarkers for nutritional research. This current paper outlines the needs and serves as an introduction to this thematic issue of Genes & Nutrition on dietary and health biomarkers.
Author(s):
Dragsted, L. O., et al.
Year Published:
2017
The impact of Type 2 diabetes prevention programmes based on risk-identification and lifestyle intervention intensity strategies: a cost-effectiveness analysis.
AIMS: To develop a cost-effectiveness model to compare Type 2 diabetes prevention programmes targeting different at-risk population subgroups with a lifestyle intervention of varying intensity. METHODS: An individual patient simulation model was constructed to simulate the development of diabetes in a representative sample of adults without diabetes from the UK population. The model incorporates trajectories for HbA1c , 2-h glucose, fasting plasma glucose, BMI, systolic blood pressure, total cholesterol and HDL cholesterol. Patients can be diagnosed with diabetes, cardiovascular disease, microvascular complications of diabetes, cancer, osteoarthritis and depression, or can die. The model collects costs and utilities over a lifetime horizon. The perspective is the UK National Health Service and personal social services. We used the model to evaluate the population-wide impact of targeting a lifestyle intervention of varying intensity to six population subgroups defined as high risk for diabetes. RESULTS: The intervention produces 0.0003 to 0.0009 incremental quality-adjusted life years and saves up to pound1.04 per person in the general population, depending upon the subgroup targeted. Cost-effectiveness increases with intervention intensity. The most cost-effective options are to target individuals with HbA1c > 42 mmol/mol (6%) or with a high Finnish Diabetes Risk (FINDRISC) probability score (> 0.1). CONCLUSION: The model indicates that diabetes prevention interventions are likely to be cost-effective and may be cost-saving over a lifetime. In the model, the criteria for selecting at-risk individuals differentially impact upon diabetes and cardiovascular disease outcomes, and on the timing of benefits. These findings have implications for deciding who should be targeted for diabetes prevention interventions.
Author(s):
Breeze, P. R., et al.
Year Published:
2017
Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam.
BACKGROUND/OBJECTIVE: Serum metabolites have been linked to higher risk of chronic diseases but determinants of serum metabolites are not clear. We aimed to investigate the association between habitual diet as a modifiable risk factor and relevant serum metabolites. SUBJECTS/METHODS: This cross-sectional study comprised 2380 EPIC-Potsdam participants. Intake of 45 food groups was assessed by food frequency questionnaire and concentrations of 127 serum metabolites were measured by targeted metabolomics. Reduced rank regression was used to find dietary patterns that explain the maximum variation of metabolites. RESULTS: In the multivariable-adjusted model, the proportion of explained variation by habitual diet was ranked as follows: acyl-alkyl-phosphatidylcholines (5.7%), sphingomyelins (5.1%), diacyl-phosphatidylcholines (4.4%), lyso-phosphatidylcholines (4.1%), acylcarnitines (3.5%), amino acids (2.2%) and hexose (1.6%). A pattern with high intake of butter and low intake of margarine was related to acylcarnitines, acyl-alkyl-phosphatidylcholines, lyso-phosphatidylcholines and hydroxy-sphingomyelins, particularly with saturated and monounsaturated fatty acid side chains. A pattern with high intake of red meat and fish and low intake of whole-grain bread and tea was related to hexose and phosphatidylcholines. A pattern consisting of high intake of potatoes, dairy products and cornflakes particularly explained methionine and branched chain amino acids. Dietary patterns related to type 2 diabetes-relevant metabolites included high intake of red meat and low intake of whole-grain bread, tea, coffee, cake and cookies, canned fruits and fish. CONCLUSIONS: Dietary patterns characterized by intakes of red meat, whole-grain bread, tea and coffee were linked to relevant metabolites and could be potential targets for chronic disease prevention.
Author(s):
Floegel, A., et al.
Year Published:
2013
Use of Metabolomics in Improving Assessment of Dietary Intake.
BACKGROUND: Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status. CONTENT: The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns. SUMMARY: A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.
Author(s):
Guasch-Ferre, M., et al.
Year Published:
2017
Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations.
BACKGROUND: Metabolomics is an emerging field with the potential to advance nutritional epidemiology; however, it has not yet been applied to large cohort studies. OBJECTIVES: Our first aim was to identify metabolites that are biomarkers of usual dietary intake. Second, among serum metabolites correlated with diet, we evaluated metabolite reproducibility and required sample sizes to determine the potential for metabolomics in epidemiologic studies. DESIGN: Baseline serum from 502 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was analyzed by using ultra-high-performance liquid-phase chromatography with tandem mass spectrometry and gas chromatography-mass spectrometry. Usual intakes of 36 dietary groups were estimated by using a food-frequency questionnaire. Dietary biomarkers were identified by using partial Pearson's correlations with Bonferroni correction for multiple comparisons. Intraclass correlation coefficients (ICCs) between samples collected 1 y apart in a subset of 30 individuals were calculated to evaluate intraindividual metabolite variability. RESULTS: We detected 412 known metabolites. Citrus, green vegetables, red meat, shellfish, fish, peanuts, rice, butter, coffee, beer, liquor, total alcohol, and multivitamins were each correlated with at least one metabolite (P < 1.093 x 10(-6); r = -0.312 to 0.398); in total, 39 dietary biomarkers were identified. Some correlations (citrus intake with stachydrine) replicated previous studies; others, such as peanuts and tryptophan betaine, were novel findings. Other strong associations included coffee (with trigonelline-N-methylnicotinate and quinate) and alcohol (with ethyl glucuronide). Intraindividual variability in metabolite levels (1-y ICCs) ranged from 0.27 to 0.89. Large, but attainable, sample sizes are required to detect associations between metabolites and disease in epidemiologic studies, further emphasizing the usefulness of metabolomics in nutritional epidemiology. CONCLUSIONS: We identified dietary biomarkers by using metabolomics in an epidemiologic data set. Given the strength of the associations observed, we expect that some of these metabolites will be validated in future studies and later used as biomarkers in large cohorts to study diet-disease associations. The PLCO trial was registered at clinicaltrials.gov as NCT00002540.
Author(s):
Guertin, K. A., et al.
Year Published:
2014
The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies.
Most chronic diseases have been demonstrated to have a link to nutrition. Within food and nutritional research there is a major driver to understand the relationship between diet and disease in order to improve health of individuals. However, the lack of accurate dietary intake assessment in free-living populations, makes accurate estimation of how diet is associated with disease risk difficulty. Thus, there is a pressing need to find solutions to the inaccuracy of dietary reporting. Metabolic profiling of urine or plasma can provide an unbiased approach to characterizing dietary intake and various high-throughput analytical platforms have been used in order to implement targeted and nontargeted assays in nutritional clinical trials and nutritional epidemiology studies. This review describes first the challenges presented in interpreting the relationship between diet and health within individual and epidemiological frameworks. Second, we aim to explore how metabonomics can benefit different types of nutritional studies and discuss the critical importance of selecting appropriate analytical techniques in these studies. Third, we propose a strategy capable of providing accurate assessment of food intake within an epidemiological framework in order establish accurate associations between diet and health.
Author(s):
Ismail, N. A., et al.
Year Published:
2013
Effects of whole grain, fish and bilberries on serum metabolic profile and lipid transfer protein activities: a randomized trial
OBJECTIVE: We studied the combined effects of wholegrain, fish and bilberries on serum metabolic profile and lipid transfer protein activities in subjects with the metabolic syndrome. METHODS: Altogether 131 subjects (40-70 y, BMI 26-39 kg/m(2)) with impaired glucose metabolism and features of the metabolic syndrome were randomized into three groups with 12-week periods according to a parallel study design. They consumed either: a) wholegrain and low postprandial insulin response grain products, fatty fish 3 times a week, and bilberries 3 portions per day (HealthyDiet), b) wholegrain and low postprandial insulin response grain products (WGED), or c) refined wheat breads as cereal products (Control). Altogether 106 subjects completed the study. Serum metabolic profile was studied using an NMR-based platform providing information on lipoprotein subclasses and lipids as well as low-molecular-weight metabolites. RESULTS: There were no significant differences in clinical characteristics between the groups at baseline or at the end of the intervention. Mixed model analyses revealed significant changes in lipid metabolites in the HealthyDiet group during the intervention compared to the Control group. All changes reflected increased polyunsaturation in plasma fatty acids, especially in n-3 PUFAs, while n-6 and n-7 fatty acids decreased. According to tertiles of changes in fish intake, a greater increase of fish intake was associated with increased concentration of large HDL particles, larger average diameter of HDL particles, and increased concentrations of large HDL lipid components, even though total levels of HDL cholesterol remained stable. CONCLUSIONS: The results suggest that consumption of diet rich in whole grain, bilberries and especially fatty fish causes changes in HDL particles shifting their subclass distribution toward larger particles. These changes may be related to known protective functions of HDL such as reverse cholesterol transport and could partly explain the known protective effects of fish consumption against atherosclerosis. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov NCT00573781.
Author(s):
Lankinen, M., et al.
Year Published:
2014
Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts.
BACKGROUND: Detection of type 2 diabetes (T2D) is routinely based on the presence of dysglycemia. Although disturbed lipid metabolism is a hallmark of T2D, the potential of plasma lipidomics as a biomarker of future T2D is unknown. Our objective was to develop and validate a plasma lipidomic risk score (LRS) as a biomarker of future type 2 diabetes and to evaluate its cost-effectiveness for T2D screening. METHODS: Plasma LRS, based on significantly associated lipid species from an array of 319 lipid species, was developed in a cohort of initially T2D-free individuals from the San Antonio Family Heart Study (SAFHS). The LRS derived from SAFHS as well as its recalibrated version were validated in an independent cohort from Australia--the AusDiab cohort. The participants were T2D-free at baseline and followed for 9197 person-years in the SAFHS cohort (n = 771) and 5930 person-years in the AusDiab cohort (n = 644). Statistically and clinically improved T2D prediction was evaluated with established statistical parameters in both cohorts. Modeling studies were conducted to determine whether the use of LRS would be cost-effective for T2D screening. The main outcome measures included accuracy and incremental value of the LRS over routinely used clinical predictors of T2D risk; validation of these results in an independent cohort and cost-effectiveness of including LRS in screening/intervention programs for T2D. RESULTS: The LRS was based on plasma concentration of dihydroceramide 18:0, lysoalkylphosphatidylcholine 22:1 and triacyglycerol 16:0/18:0/18:1. The score predicted future T2D independently of prediabetes with an accuracy of 76%. Even in the subset of initially euglycemic individuals, the LRS improved T2D prediction. In the AusDiab cohort, the LRS continued to predict T2D significantly and independently. When combined with risk-stratification methods currently used in clinical practice, the LRS significantly improved the model fit (p < 0.001), information content (p < 0.001), discrimination (p < 0.001) and reclassification (p < 0.001) in both cohorts. Modeling studies demonstrated that LRS-based risk-stratification combined with metformin supplementation for high-risk individuals was the most cost-effective strategy for T2D prevention. CONCLUSIONS: Considering the novelty, incremental value and cost-effectiveness of LRS it should be used for risk-stratification of future T2D.
Author(s):
Mamtani, M., et al.
Year Published:
2016
Targeted metabolomics profiles are strongly correlated with nutritional patterns in women.
Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a "traditional English" diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 x 10(-5)) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 x 10(-9)) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 x 10(-13)). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research.
Author(s):
Menni, C., et al.
Year Published:
2013
Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies.
BACKGROUND: It has been suggested that metabolomics could play a role in dietary assessment and identification of novel biomarkers of dietary intake. OBJECTIVE: This study examined the link between habitual dietary patterns and metabolomic profiles. DESIGN: A total of 160 volunteers participated in a double-blind, randomized, placebo-controlled dietary intervention. We collected biofluids and recorded 3-d food diaries. Food data were reduced to 33 food groups, and a k-means cluster analysis was performed to identify dietary patterns. (1)H Nuclear magnetic resonance (NMR) spectra were acquired for plasma and urine samples, and gas chromatography was used for plasma fatty acid profiling. RESULTS: Cluster analysis identified 3 distinct dietary patterns on the basis of the energy contribution of different food groups. Dietary clusters were reflected in plasma fatty acid profiles and in metabolomic data. (1)H NMR spectra of urine allowed the identification of metabolites associated with different dietary patterns. Several of the metabolites identified were linked to the intake of specific food groups; in particular, there was a positive association between O-acetylcarnitine and phenylacetylglutamine and red-meat and vegetable intakes, respectively. CONCLUSIONS: Habitual dietary patterns are shown in metabolomic data. This approach successfully identified potential biomarkers of red-meat and vegetable intakes.
Author(s):
O'Sullivan, A., et al.
Year Published:
2011
Characterizing Blood Metabolomics Profiles Associated with Self-Reported Food Intakes in Female Twins.
Using dietary biomarkers in nutritional epidemiological studies may better capture exposure and improve the level at which diet-disease associations can be established and explored. Here, we aimed to identify and evaluate reproducibility of novel biomarkers of reported habitual food intake using targeted and non-targeted metabolomic blood profiling in a large twin cohort. Reported intakes of 71 food groups, determined by FFQ, were assessed against 601 fasting blood metabolites in over 3500 adult female twins from the TwinsUK cohort. For each metabolite, linear regression analysis was undertaken in the discovery group (excluding MZ twin pairs discordant [>/=1 SD apart] for food group intake) with each food group as a predictor adjusting for age, batch effects, BMI, family relatedness and multiple testing (1.17x10-6 = 0.05/[71 food groups x 601 detected metabolites]). Significant results were then replicated (non-targeted: P<0.05; targeted: same direction) in the MZ discordant twin group and results from both analyses meta-analyzed. We identified and replicated 180 significant associations with 39 food groups (P<1.17x10-6), overall consisting of 106 different metabolites (74 known and 32 unknown), including 73 novel associations. In particular we identified trans-4-hydroxyproline as a potential marker of red meat intake (0.075[0.009]; P = 1.08x10-17), ergothioneine as a marker of mushroom consumption (0.181[0.019]; P = 5.93x10-22), and three potential markers of fruit consumption (top association: apple and pears): including metabolites derived from gut bacterial transformation of phenolic compounds, 3-phenylpropionate (0.024[0.004]; P = 1.24x10-8) and indolepropionate (0.026[0.004]; P = 2.39x10-9), and threitol (0.033[0.003]; P = 1.69x10-21). With the largest nutritional metabolomics dataset to date, we have identified 73 novel candidate biomarkers of food intake for potential use in nutritional epidemiological studies. We compiled our findings into the DietMetab database (http://www.twinsuk.ac.uk/dietmetab-data/), an online tool to investigate our top associations.
Author(s):
Pallister, T., et al.
Year Published:
2016
Identifying biomarkers of dietary patterns by using metabolomics.
BACKGROUND: Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE: The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN: We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS: The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 x 10(-15) to 8 x 10(-6)). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS: Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.
Author(s):
Playdon, M. C., et al.
Year Published:
2017
Comparing metabolite profiles of habitual diet in serum and urine.
BACKGROUND: Diet plays an important role in chronic disease etiology, but some diet-disease associations remain inconclusive because of methodologic limitations in dietary assessment. Metabolomics is a novel method for identifying objective dietary biomarkers, although it is unclear what dietary information is captured from metabolites found in serum compared with urine. OBJECTIVE: We compared metabolite profiles of habitual diet measured from serum with those measured from urine. DESIGN: We first estimated correlations between consumption of 56 foods, beverages, and supplements assessed by a food-frequency questionnaire, with 676 serum and 848 urine metabolites identified by untargeted liquid chromatography mass spectrometry, ultra-high performance liquid chromatography tandem mass spectrometry, and gas chromatography mass spectrometry in a colon adenoma case-control study (n = 125 cases and 128 controls) while adjusting for age, sex, smoking, fasting, case-control status, body mass index, physical activity, education, and caloric intake. We controlled for multiple comparisons with the use of a false discovery rate of <0.1. Next, we created serum and urine multiple-metabolite models to predict food intake with the use of 10-fold crossvalidation least absolute shrinkage and selection operator regression for 80% of the data; predicted values were created in the remaining 20%. Finally, we compared predicted values with estimates obtained from self-reported intake for metabolites measured in serum and urine. RESULTS: We identified metabolites associated with 46 of 56 dietary items; 417 urine and 105 serum metabolites were correlated with >/=1 food, beverage, or supplement. More metabolites in urine (n = 154) than in serum (n = 39) were associated uniquely with one food. We found previously unreported metabolite associations with leafy green vegetables, sugar-sweetened beverages, citrus, added sugar, red meat, shellfish, desserts, and wine. Prediction of dietary intake from multiple-metabolite profiles was similar between biofluids. CONCLUSIONS: Candidate metabolite biomarkers of habitual diet are identifiable in both serum and urine. Urine samples offer a valid alternative or complement to serum for metabolite biomarkers of diet in large-scale clinical or epidemiologic studies.
Author(s):
Playdon, M. C., et al.
Year Published:
2016
Herring and Beef Meals Lead to Differences in Plasma 2-Aminoadipic Acid, beta-Alanine, 4-Hydroxyproline, Cetoleic Acid, and Docosahexaenoic Acid Concentrations in Overweight Men.
BACKGROUND: Dietary guidelines generally recommend increasing fish intake and reducing red meat intake for better long-term health. Few studies have compared the metabolic differences between eating meat and fish. OBJECTIVE: The objective of this study was to determine whether there are differences in the postprandial plasma metabolic response to meals containing baked beef, baked herring, and pickled herring. METHODS: Seventeen overweight men (BMI 25-30 kg/m(2), 41-67 y of age) were included in a randomized crossover intervention study. Subjects ate baked herring-, pickled herring-, and baked beef-based meals in a randomized order and postprandial blood plasma samples were taken over 7 h. Plasma metabolomics were measured with the use of gas chromatography-mass spectrometry and areas under the curve for detected metabolites were compared between meals. RESULTS: The plasma postprandial response of 2-aminoadipic acid, a suggested marker of diabetes risk, was 1.6 times higher after the beef meal than after the baked herring meal (P < 0.001). Plasma beta-alanine and 4-hydroxyproline both were markedly greater after beef intake than after herring intake (16 and 3.4 times the response of baked herring, respectively; P < 0.001). Herring intake led to a greater plasma postprandial response from docosahexaenoic acid (DHA) and cetoleic acid compared with beef (17.6 and 150 times greater, respectively; P < 0.001), whereas hippuric acid and benzoic acid were elevated after pickled herring compared with baked herring (5.4 and 43 times higher; P < 0.001). CONCLUSIONS: These results in overweight men confirm that DHA and cetoleic acid reflect herring intake, whereas beta-alanine and 4-hydroxyproline are potential biomarkers for beef intake. The greater postprandial rise in 2-aminoadipic acid after the beef meal, coupled to its proposed role in stimulating insulin secretion, may have importance in the context of red meat intake and increased diabetes risk. This trial was registered at clinicaltrials.gov as NCT02381613.
Author(s):
Ross, A. B., et al.
Year Published:
2015
Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes.
BACKGROUND: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE: This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN: Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS: After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION: In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.
Author(s):
Wittenbecher, C., et al.
Year Published:
2015
Estimation of Chicken Intake by Adults Using Metabolomics-Derived Markers.
Background: Improved assessment of meat intake with the use of metabolomics-derived markers can provide objective data and could be helpful in clarifying proposed associations between meat intake and health.Objective: The objective of this study was to identify novel markers of chicken intake using a metabolomics approach and use markers to determine intake in an independent cohort.Methods: Ten participants [age: 62 y; body mass index (in kg/m(2)): 28.25] in the NutriTech food intake study consumed increasing amounts of chicken, from 88 to 290 g/d, in a 3-wk span. Urine and blood samples were analyzed by nuclear magnetic resonance and mass spectrometry, respectively. A multivariate data analysis was performed to identify markers associated with chicken intake. A calibration curve was built based on dose-response association using NutriTech data. A Bland-Altman analysis evaluated the agreement between reported and calculated chicken intake in a National Adult Nutrition Survey cohort.Results: Multivariate data analysis of postprandial and fasting urine samples collected in participants in the NutriTech study revealed good discrimination between high (290 g/d) and low (88 g/d) chicken intakes. Urinary metabolite profiles showed differences in metabolite levels between low and high chicken intakes. Examining metabolite profiles revealed that guanidoacetate increased from 1.47 to 3.66 mmol/L following increasing chicken intakes from 88 to 290 g/d (P < 0.01). Using a calibration curve developed from the NutriTech study, chicken intake was calculated through the use of data from the National Adult Nutrition Survey, in which consumers of chicken had a higher guanidoacetate excretion (0.70 mmol/L) than did nonconsumers (0.47 mmol/L; P < 0.01). A Bland-Altman analysis revealed good agreement between reported and calculated intakes, with a bias of -30.2 g/d. Plasma metabolite analysis demonstrated that 3-methylhistidine was a more suitable indicator of chicken intake than 1-methylhistidine.Conclusions: Guanidoacetate was successfully identified and confirmed as a marker of chicken intake, and its measurement in fasting urine samples could be used to determine chicken intake in a free-living population. This trial was registered at clinicaltrials.gov as NCT01684917.
Author(s):
Yin, X., et al.
Year Published:
2017