Effect of food intake on 92 oncological biomarkers by the Proseek Oncology II panel.
Ontology highlight
ABSTRACT:
SUBMITTER: Dencker M
PROVIDER: S-EPMC6446403 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Effect of food intake on 92 oncological biomarkers by the Proseek Oncology II panel.
Dencker Magnus M Björgell Ola O Hlebowicz Joanna J
BMC research notes 20190402 1
<h4>Objective</h4>To evaluates the effect of food intake on 92 oncological biomarkers to evaluate whether the timing of blood sampling could be relevant. Twenty-two healthy subjects were investigated. A total of 92 biomarkers were measured before a standardised meal as well as 30 and 120 min afterwards with the Proseek Multiplex Oncology II kit.<h4>Results</h4>The levels of 6 biomarkers decreased significantly (P < 0.001) 30 min after food intake, and 4 biomarkers remained decreased (P < 0.001) ...[more]
Similar Datasets
Project description:OBJECTIVE:This study evaluates the effect of food intake on 92 neurological biomarkers in plasma. Moreover, it investigated if any of the biomarkers were correlated with body mass index. MATERIALS AND METHODS:Twenty-two healthy subjects (11 male and 11 female aged 25.9 ± 4.2 years) were investigated. A total of 92 biomarkers were measured before a standardized meal as well as 30 and 120 min afterward with the Proseek Multiplex Neurology I kit. RESULTS:The levels for 13 biomarkers decreased significantly (p < .001) 30 min after food intake. The levels for four biomarkers remained significantly decreased (p < .001) 120 min after food intake. One biomarker increased significantly (p < .001) 30 min after food intake. The changes were between 1% and 12%, with an average difference of about 5%. Only one biomarker showed a difference over 10% due to food intake. The biggest difference was observed for Plexin-B3 120 min after food intake (12%). Of all the 92 neurological biomarkers, only one was correlated with BMI, Kynureninase r = .46, p < .05. CONCLUSIONS:This study shows that food intake has a very modest effect on 92 different neurological biomarkers. Timing of blood sampling in relation to food intake, therefore, appears not to be a major concern. Only Kynureninase was correlated with BMI. Further studies are warranted in older healthy subjects and in patients with various neurological diseases to determine whether the findings are reproducible in such populations.
| S-EPMC5607537 | biostudies-literature
Project description:ObjectiveThe present study evaluates the effect of food intake on 92 biomarkers for cardiovascular disease (CVD).MethodsTwenty two healthy subjects (11 male and 11 female aged 25.9±4.2 years) were investigated. A total of 92 biomarkers were measured before a standardized meal as well as 30 and 120 minutes afterwards with the Proseek Multiplex CVD III kit.ResultsThe levels for eight biomarkers decreased significantly (P<0.05) 30 minutes after food intake. The levels for seven biomarkers remained significantly decreased 120 minutes after food intake. Nine biomarker decreased significantly at 120 minutes after food intake. The changes were between 4-30%, most commonly around 5%. Only six biomarkers showed a difference of 10% or more due to food intake. The biggest differences were observed for Insulin-like growth factor-binding protein 1 (30%); Azurocidin, Cystatin-B, and Myeloperoxidase (13%); Monocyte chemotactic protein 1 (11%); and Myeloblastin (10%), all 120 minutes after food intake.ConclusionsThis study shows that food intake affects several different CVD biomarkers, but the effect is predominantly modest. Timing of blood sampling in relation to food intake, therefore, appears not to be a major concern. Further studies are warranted in older healthy subjects and in patients with various cardiac diseases to determine whether the findings are reproducible.
| S-EPMC5460853 | biostudies-literature
Project description:Allium vegetables are widely consumed around the world and are known for their potential bioactive components improving human health. These effects have been extensively investigated; however, the results were inconsistent in human studies. Biomarkers of food intake (BFIs) could provide objective measurements of food intake in observational studies and assess compliance in intervention studies. Therefore, the discovery and application of BFIs for Allium vegetables would facilitate the exploring and understanding of the health benefit of Allium vegetables. In this manuscript, we reviewed the currently used and potential candidate BFIs for Allium vegetables and evaluated their levels of validation. S-Allylmercapturic acid (ALMA), allyl methyl sulfide (AMS), allyl methyl sulfoxide (AMSO), allyl methyl sulfone (AMSO2), and S-allylcysteine (SAC), which are derived from organosulfur compounds, were shown to be promising candidate BFIs for garlic consumption. Further validation is needed to assess their robustness and concordance with other measures. Their applicability for the whole food group should be evaluated as well. N-Acetyl-S-(2-carboxypropyl)cysteine (CPMA) was detected in high levels in urine after both garlic and onion intake, suggesting that it may be used for the assessment of intake of Allium food group. The available information regarding its kinetics, robustness, and analytical performance is limited and needs to be assessed in further studies. No candidate BFIs specific to intake of onion, leek, chives, shallots, or ramsons were found. Untargeted metabolomics studies and further validation studies should be performed to discover more reliable BFIs for individual Allium vegetables and the whole food group. This paper serves as an example of Biomarker of Food Intake Reviews (BFIRev) and biomarker of food intake validation procedures.
| S-EPMC6309086 | biostudies-other
Project description:Grapes and berries are two types of widely consumed fruits characterized by a high content in different phytochemicals. However, their accurate dietary assessment is particularly arduous, because of the already wide recognized bias associated with self-reporting methods, combined with the large range of species and cultivars and the fact that these fruits are popularly consumed not only in fresh and frozen forms but also as processed and derived products, including dried and canned fruits, beverages, jams, and jellies. Reporting precise type and/or quantity of grape and berries in FFQ or diaries can obviously be affected by errors. Recently, biomarkers of food intake (BFIs) rose as a promising tool to provide accurate information indicating consumption of certain food items. Protocols for performing systematic reviews in this field, as well as for assessing the validity of candidate BFIs have been developed within the Food Biomarker Alliance (FoodBAll) Project. This paper aims to evaluate the putative BIFs for blueberries, strawberries, raspberries, blackberries, cranberries, blackcurrant, and grapes. Candidate BFIs for grapes were resveratrol metabolites and tartaric acid. The metabolites considered as putative BFI for berries consumption were mostly anthocyanins derivatives together with several metabolites of ellagitannins and some aroma compounds. However, identification of BFIs for single berry types encountered more difficulties. In the absence of highly specific metabolites reported to date, we suggested some multi-metabolite panels that may be further investigated as putative biomarkers for some berry fruits.
| S-EPMC7509942 | biostudies-literature
Project description:Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.
| S-EPMC7698441 | biostudies-literature
Project description:BackgroundDiet affects the human gastrointestinal microbiota. Blood and urine samples have been used to determine nutritional biomarkers. However, there is a dearth of knowledge on the utility of fecal biomarkers, including microbes, as biomarkers of food intake.ObjectivesThis study aimed to identify a compact set of fecal microbial biomarkers of food intake with high predictive accuracy.MethodsData were aggregated from 5 controlled feeding studies in metabolically healthy adults (n = 285; 21-75 y; BMI 19-59 kg/m2; 340 data observations) that studied the impact of specific foods (almonds, avocados, broccoli, walnuts, and whole-grain barley and whole-grain oats) on the human gastrointestinal microbiota. Fecal DNA was sequenced using 16S ribosomal RNA gene sequencing. Marginal screening was performed on all species-level taxa to examine the differences between the 6 foods and their respective controls. The top 20 species were selected and pooled together to predict study food consumption using a random forest model and out-of-bag estimation. The number of taxa was further decreased based on variable importance scores to determine the most compact, yet accurate feature set.ResultsUsing the change in relative abundance of the 22 taxa remaining after feature selection, the overall model classification accuracy of all 6 foods was 70%. Collapsing barley and oats into 1 grains category increased the model accuracy to 77% with 23 unique taxa. Overall model accuracy was 85% using 15 unique taxa when classifying almonds (76% accurate), avocados (88% accurate), walnuts (72% accurate), and whole grains (96% accurate). Additional statistical validation was conducted to confirm that the model was predictive of specific food intake and not the studies themselves.ConclusionsFood consumption by healthy adults can be predicted using fecal bacteria as biomarkers. The fecal microbiota may provide useful fidelity measures to ascertain nutrition study compliance.
| S-EPMC7849973 | biostudies-literature
Project description:Food choices affect the isotopic composition of the body with each food item leaving its distinct isotopic imprint. The common view is that the natural abundance of the stable isotopes of nitrogen (expressed as δ15N) is higher in animals than in plants that constitute our contemporary diets. Higher δ15N is thus increasingly viewed as a biomarker for meat and fish intake. Here we show that organic compared to conventional farming increases plant δ15N to an extent that can appreciably impact the performance of δ15N as a biomarker. The error that can arise when organic plants are consumed was modelled for the entire range of proportions of plant versus animal protein intake, and accounting for various intakes of organic and conventionally grown crops. This mass balance model allows the interpretation of differences in δ15N in light of organic food consumption. Our approach shows that the relationship between δ15N and meat and fish intake is highly contextual and susceptible to variation at the population, community or group level. We recommend that fertilization practices and organic plant consumption must not be overlooked when using δ15N as a biomarker for meat and fish intake or to assess compliance to nutritional interventions.
| S-EPMC7601395 | biostudies-literature
Project description:BackgroundTo unravel true links between diet and health, it is important that dietary exposure is accurately measured. Currently, mainly self-reporting methods (e.g. food frequency questionnaires and 24-h recalls) are used to assess food intake in epidemiological studies. However, these traditional instruments are subjective measures and contain well-known biases. Especially, estimating the intake of the group of confectionary products, such as products containing cocoa and liquorice, remains a challenge. The use biomarkers of food intake (BFIs) may provide a more objective measurement. However, an overview of current candidate biomarkers and their validity is missing for both cocoa- and liquorice-containing foods.ObjectiveThe purpose of the current study was to (1) identify currently described candidate BFIs for cocoa (products) and liquorice, (2) to evaluate the validity of these identified candidate BFIs and (3) to address further validation and/or identification work to be done.MethodsThis systematic review was based on a comprehensive literature search of three databases (PubMed, Scopus and ISI web of Science), to identify candidate BFIs. Via a second search step in the Human Metabolome Database (HMDB), the Food Database (FooDB) and Phenol-Explorer, the specificity of the candidate BFIs was evaluated, followed by an evaluation of the validity of the specific candidate BFIs, via pre-defined criteria.ResultsIn total, 37 papers were included for cocoa and 8 papers for liquorice. For cocoa, 164 unique candidate BFIs were obtained, and for liquorice, four were identified in total. Despite the high number of identified BFIs for cocoa, none of the metabolites was specific. Therefore, the validity of these compounds was not further examined. For liquorice intake, 18-glycyrrhetinic acid (18-GA) was found to have the highest assumed validity.ConclusionsFor cocoa, specific BFIs were missing, mainly because the individual BFIs were also found in foods having a similar composition, such as tea (polyphenols) or coffee (caffeine). However, a combination of individual BFIs might lead to discriminating profiles between cocoa (products) and foods with a similar composition. Therefore, studies directly comparing the consumption of cocoa to these similar products are needed, enabling efforts to find a unique profile per product. For liquorice, we identified 18-GA as a promising BFI; however, important information on its validity is missing; thus, more research is necessary. Our findings indicate a need for more studies to determine acceptable BFIs for both cocoa and liquorice.
| S-EPMC6062926 | biostudies-literature
Project description:BackgroundFermented foods are ubiquitous in human diets and often lauded for their sensory, nutritious, and health-promoting qualities. However, precise associations between the intake of fermented foods and health have not been well-established. This is in part due to the limitations of current dietary assessment tools that rely on subjective reporting, making them prone to memory-related errors and reporting bias. The identification of food intake biomarkers (FIBs) bypasses this challenge by providing an objective measure of intake. Despite numerous studies reporting on FIBs for various types of fermented foods and drinks, unique biomarkers associated with the fermentation process ("fermentation-dependent" biomarkers) have not been well documented. We therefore conducted a comprehensive, systematic review of the literature to identify biomarkers of fermented foods commonly consumed in diets across the world.ResultsAfter title, abstract, and full-text screening, extraction of data from 301 articles resulted in an extensive list of compounds that were detected in human biofluids following the consumption of various fermented foods, with the majority of articles focusing on coffee (69), wine (69 articles), cocoa (62), beer (34), and bread (29). The identified compounds from all included papers were consolidated and sorted into FIBs proposed for a specific food, for a food group, or for the fermentation process. Alongside food-specific markers (e.g., trigonelline for coffee), and food-group markers (e.g., pentadecanoic acid for dairy intake), several fermentation-dependent markers were revealed. These comprised compounds related to the fermentation process of a particular food, such as mannitol (wine), 2-ethylmalate (beer), methionine (sourdough bread, cheese), theabrownins (tea), and gallic acid (tea, wine), while others were indicative of more general fermentation processes (e.g., ethanol from alcoholic fermentation, 3-phenyllactic acid from lactic fermentation).ConclusionsFermented foods comprise a heterogeneous group of foods. While many of the candidate FIBs identified were found to be non-specific, greater specificity may be observed when considering a combination of compounds identified for individual fermented foods, food groups, and from fermentation processes. Future studies that focus on how fermentation impacts the composition and nutritional quality of food substrates could help to identify novel biomarkers of fermented food intake.
| S-EPMC8058972 | biostudies-literature
Project description:Nuts and vegetable oils are important sources of fat and of a wide variety of micronutrients and phytochemicals. Following their intake, several of their constituents, as well as their derived metabolites, are found in blood circulation and in urine. As a consequence, these could be used to assess the compliance to a dietary intervention or to determine habitual intake of nuts and vegetable oils. However, before these metabolites can be widely used as biomarkers of food intake (BFIs), several characteristics have to be considered, including specificity, dose response, time response, stability, and analytical performance. We have, therefore, conducted an extensive literature search to evaluate current knowledge about potential BFIs of nuts and vegetable oils. Once identified, the strengths and weaknesses of the most promising candidate BFIs have been summarized. Results from selected studies have provided a variety of compounds mainly derived from the fatty fraction of these foods, but also other components and derived metabolites related to their nutritional composition. In particular, α-linolenic acid, urolithins, and 5-hydroxyindole-3-acetic acid seem to be the most plausible candidate BFIs for walnuts, whereas for almonds they could be α-tocopherol and some catechin-derived metabolites. Similarly, several studies have reported a strong association between selenium levels and consumption of Brazil nuts. Intake of vegetable oils has been mainly assessed through the measurement of specific fatty acids in different blood fractions, such as oleic acid for olive oil, α-linolenic acid for flaxseed (linseed) and rapeseed (canola) oils, and linoleic acid for sunflower oil. Additionally, hydroxytyrosol and its metabolites were the most promising distinctive BFIs for (extra) virgin olive oil. However, most of these components lack sufficient specificity to serve as BFIs. Therefore, additional studies are necessary to discover new candidate BFIs, as well as to further evaluate the specificity, sensitivity, dose-response relationships, and reproducibility of these candidate biomarkers and to eventually validate them in other populations. For the discovery of new candidate BFIs, an untargeted metabolomics approach may be the most effective strategy, whereas for increasing the specificity of the evaluation of food consumption, this could be a combination of different metabolites.
| S-EPMC6423890 | biostudies-literature