A GC–MS-based metabonomic investigation of blood serum from irritable bowel syndrome patients undergoing intervention with acidified milk products

Dairy intake screener as web-based application is reliable and valid

The "Dairy Diary" is a user-friendly web-based dairy intake screener. The reliability and validity are unknown. We aimed to evaluate the screener in terms of test-retest reliability and comparative validity. In a diagnostic accuracy study, a purposefully recruited sample of 79 (age: 21.6 ± 3.8 years) undergraduate dietetics/nutrition students from three South African universities completed 3 non-consecutive days of weighed food records (reference standard) within a seven-day period (comparative validity), followed by two administrations, 2 weeks apart, of the screener (index test) (reliability). For the four dairy product serving scores (PSSs) and the summative dairy serving scores (DSSs) of the screener and the food records, t-tests, correlations, Bland-Altman, Kappa, McNemar's, and diagnostic accuracy were determined. For reliability, mean PSSs and DSSs did not differ significantly (p > .05) between the screener administrations. The mean PSSs were strongly correlated: milk (r = .69; p < .001), maas (fermented milk) (r = .72; p < .001), yoghurt (r = .71; p < .001), cheese (r = .74; p < .001). For DSSs, Kappa was moderate (k = 0.45; p < .001). Non-agreeing responses suggest symmetry (p = .334). For validity, the PSSs of the screener and food records were moderately correlated [milk (r = .30; p = .0129), yoghurt (r = .38; p < .001), cheese (r = .38; p < .001)], with k = 0.31 (p = .006) for DSS. Bland-Altman analyses showed acceptable agreement for DSSs (bias: -0.49; 95% CI: -0.7 to -0.3). Categorized DSSs had high sensitivity (81.4%) and positive predictive value (93.4%), yet low specificity (55.6%) and negative predictive value (27.8%). The area under the receiver operating characteristic curve (0.7) was acceptable. The "Dairy Diary" is test-retest reliable with moderate comparative validity to screen for dairy intake of nutrition-literate consumers.

Plasma Metabolites Related to the Consumption of Different Types of Dairy Products and Their Association with New-Onset Type 2 Diabetes: Analyses in the Fenland and EPIC-Norfolk Studies, United Kingdom

SCOPE

To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk.

METHODS AND RESULTS

Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers.

CONCLUSION

Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.

Dairy as a Source of Iodine and Protein in the UK: Implications for Human Health Across the Life Course, and Future Policy and Research

This narrative review summarizes key concepts in dairy nutrition for supporting human health throughout the life course. Milk and dairy products have been a staple component of our diet for thousands of years and provide a wide range of important nutrients that are otherwise difficult to obtain from dairy-free diets. In this review, we provide a broad perspective on the nutritional roles of iodine and dairy protein in supporting human health during pregnancy and early life, childhood and adolescence, mid- and later-life. New methodologies to identify biomarkers of dairy intake via high-throughput mass spectrometry are discussed, and new concepts such as the role of the food matrix in dairy nutrition are introduced. Finally, future policy and research related to the consumption of dairy and non-dairy alternatives for health are discussed with a view to improving nutritional status across the lifespan.

Electrochemical creatinine detection for advanced point-of-care sensing devices: a review

Creatinine is an amino acid derived from creatine catabolism at different steps of the body's organs, and its detection is significant because levels out of normal values are linked to some diseases like kidney failure. Normal concentration levels of creatinine in blood are from 45 to 110 μM, while in urine, typical concentrations range between 3.3 to 27 mM, and in saliva from 8.8 and 26.5 μM. Nowadays, the creatinine detection is carried through different spectroscopic-colorimetric methods; however, the resulting values present errors due to high interferences, delayed analysis, and poor stability. Electrochemical sensors have been an alternative to creatinine detection, and the electrochemical methods have been adapted to detect in enzymatic and non-enzymatic sensors, the latter being more relevant in recent years. Nanomaterials have made creatinine sensors more stable, sensitive, and selective. This review presents recent advances in creatinine electrochemical sensors for advances in point-of-care (POC) sensing devices, comprising both a materials point of view and prototypes for advanced sensing. The effect of the metal, particle size, shape and other morphological and electronic characteristics of nanomaterials are discussed in terms of their impact on the effective detection of creatinine. In addition, the application of nanomaterials in POC devices is revised pointing to practical applications and looking for more straightforward and less expensive devices to manufacture.

Creatinine is an amino acid derived from creatine catabolism at different steps of the body's organs, and its detection is significant because levels out of normal values are linked to some diseases like kidney failure.

Dairy consumption, plasma metabolites, and risk of type 2 diabetes

BACKGROUND

Epidemiologic studies have reported a modest inverse association between dairy consumption and the risk of type 2 diabetes (T2D). Whether plasma metabolite profiles associated with dairy consumption reflect this relationship remains unknown.

OBJECTIVES

We aimed to identify the plasma metabolites associated with total and specific dairy consumption, and to evaluate the association between the identified multi-metabolite profiles and T2D.

METHODS

The discovery population included 1833 participants from the Prevención con Dieta Mediterránea (PREDIMED) trial. The confirmatory cohorts included 1522 PREDIMED participants at year 1 of the trial and 4932 participants from the Nurses' Health Studies (NHS), Nurses' Health Study II (NHSII), and Health Professionals Follow-Up Study US-based cohorts. Dairy consumption was assessed using validated FFQs. Plasma metabolites (n = 385) were profiled using LC-MS. We identified the dairy-related metabolite profiles using elastic net regularized regressions with a 10-fold cross-validation procedure. We evaluated the associations between the metabolite profiles and incident T2D in the discovery and the confirmatory cohorts.

RESULTS

Total dairy intake was associated with 38 metabolites. C14:0 sphingomyelin (positive coefficient), C34:0 phosphatidylethanolamine (positive coefficient), and γ-butyrobetaine (negative coefficient) were associated in a directionally similar fashion with total and specific (milk, yogurt, cheese) dairy consumption. The Pearson correlation coefficients between self-reported total dairy intake and predicted total dairy intake based on the corresponding multi-metabolite profile were 0.37 (95% CI, 0.33-0.40) in the discovery cohort and 0.16 (95% CI, 0.13-0.19) in the US confirmatory cohort. After adjusting for T2D risk factors, a higher total dairy intake-related metabolite profile score was associated with a lower T2D risk [HR per 1 SD; discovery cohort: 0.76 (95% CI, 0.63-0.90); US confirmatory cohort: 0.88 (95% CI, 0.78-0.99)].

CONCLUSIONS

Total dairy intake was associated with 38 metabolites, including 3 consistently associated with dairy subtypes (C14:0 sphingomyelin, C34:0 phosphatidylethanolamine, γ-butyrobetaine). A score based on the 38 identified metabolites showed an inverse association with T2D risk in Spanish and US populations.

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.

Dairy Intake in 2 American Adult Cohorts Associates with Novel and Known Targeted and Nontargeted Circulating Metabolites

BACKGROUND

The role of dairy in health can be elucidated by investigating circulating metabolites associated with intake.

OBJECTIVES

We sought to identify metabolites associated with quantity and type of dairy intake in the Framingham Heart Study Offspring and Third Generation (Gen3) cohorts.

METHODS

Dairy intake (total dairy, milk, cheese, yogurt, and cream/butter) was analyzed in relation to targeted (Offspring, n = 2205, 55.1 ± 9.8 y, 52% female, 217 signals; Gen3, n = 866, 40.5 ± 8.8 y, 54.9% female, 79 signals) and nontargeted metabolites (Gen3, ∼7031 signals) in a 2-step analysis including orthogonal projections to latent structures with discriminant analysis (OPLS-DA) in discovery subsets to identify metabolites distinguishing between high and low intake; and linear regression in confirmation subsets to assess putative associations, subsequently tested in the total samples. Previously reported associations were also investigated.

RESULTS

OPLS-DA in the Offspring targeted discovery subset resulted in a variable importance in projection (VIP) >1 of 65, 60, 58, 66, and 60 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively, of which 5, 3, 1, 6, and 4 metabolites, respectively, remained after confirmation. In the Gen3 targeted discovery subset, OPLS-DA resulted in a VIP >1 of 17, 15, 13, 7, and 6 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively. In the Gen3 nontargeted discovery subset, OPLS-DA resulted in a VIP >2 of 203, 503, 78, 186, and 206 metabolites, respectively. Combining targeted and nontargeted results in Gen3, significant associations of 7 (6 unannotated), 2, 12 (11 unannotated), 0, and 61 (all unannotated) metabolites, respectively, remained. Candidate identities of unannotated signals included fatty acids and food flavorings. Results supported relations previously reported for C14:0 sphingomyelin, and marginal associations for deoxycholates.

CONCLUSIONS

Dairy in 2 American adult cohorts associated with numerous circulating metabolites. Reports about diet-metabolite relations and confirmation of previous findings might be limited by specificity of dietary intake and breadth of measured metabolites.

Biomarker of food intake for assessing the consumption of dairy and egg products

Dairy and egg products constitute an important part of Western diets as they represent an excellent source of high-quality proteins, vitamins, minerals and fats. Dairy and egg products are highly diverse and their associations with a range of nutritional and health outcomes are therefore heterogeneous. Such associations are also often weak or debated due to the difficulty in establishing correct assessments of dietary intake. Therefore, in order to better characterize associations between the consumption of these foods and health outcomes, it is important to identify reliable biomarkers of their intake. Biomarkers of food intake (BFIs) provide an accurate measure of intake, which is independent of the memory and sincerity of the subjects as well as of their knowledge about the consumed foods. We have, therefore, conducted a systematic search of the scientific literature to evaluate the current status of potential BFIs for dairy products and BFIs for egg products commonly consumed in Europe. Strikingly, only a limited number of compounds have been reported as markers for the intake of these products and none of them have been sufficiently validated. A series of challenges hinders the identification and validation of BFI for dairy and egg products, in particular, the heterogeneous composition of these foods and the lack of specificity of the markers identified so far. Further studies are, therefore, necessary to validate these compounds and to discover new candidate BFIs. Untargeted metabolomic strategies may allow the identification of novel biomarkers, which, when taken separately or in combination, could be used to assess the intake of dairy and egg products.

GC-MS Based Metabolomics and NMR Spectroscopy Investigation of Food Intake Biomarkers for Milk and Cheese in Serum of Healthy Humans

The identification and validation of food intake biomarkers (FIBs) in human biofluids is a key objective for the evaluation of dietary intake. We report here the analysis of the GC-MS and 1H-NMR metabolomes of serum samples from a randomized cross-over study in 11 healthy volunteers having consumed isocaloric amounts of milk, cheese, and a soy drink as non-dairy alternative. Serum was collected at baseline, postprandially up to 6 h, and 24 h after consumption. A multivariate analysis of the untargeted serum metabolomes, combined with a targeted analysis of candidate FIBs previously reported in urine samples from the same study, identified galactitol, galactonate, and galactono-1,5-lactone (milk), 3-phenyllactic acid (cheese), and pinitol (soy drink) as candidate FIBs for these products. Serum metabolites not previously identified in the urine samples, e.g., 3-hydroxyisobutyrate after cheese intake, were detected. Finally, an analysis of the postprandial behavior of candidate FIBs, in particular the dairy fatty acids pentadecanoic acid and heptadecanoic acid, revealed specific kinetic patterns of relevance to their detection in future validation studies. Taken together, promising candidate FIBs for dairy intake appear to be lactose and metabolites thereof, for lactose-containing products, and microbial metabolites derived from amino acids, for fermented dairy products such as cheese.

Identification of Urinary Food Intake Biomarkers for Milk, Cheese, and Soy-Based Drink by Untargeted GC-MS and NMR in Healthy Humans

The measurement of food intake biomarkers (FIBs) in biofluids represents an objective tool for dietary assessment. FIBs of milk and cheese still need more investigation due to the absence of candidate markers. Thus, an acute intervention study has been performed to sensitively and specifically identify candidate FIBs. Eleven healthy male and female volunteers participated in the randomized, controlled crossover study that tested a single intake of milk and cheese as test products, and soy-based drink as a control. Urine samples were collected at baseline and up to 24 h at distinct time intervals (0-1, 1-2, 2-4, 4-6, 6-12, and 12-24 h) and were analyzed using an untargeted multiplatform approach (GC-MS and ¹H NMR). Lactose, galactose, and galactonate were identified exclusively after milk intake while for other metabolites (allantoin, hippurate, galactitol, and galactono-1,5-lactone) a significant increase has been observed. Urinary 3-phenyllactic acid was the only compound specifically reflecting cheese intake although alanine, proline, and pyroglutamic acid were found at significantly higher levels after cheese consumption. In addition, several novel candidate markers for soy drink were identified, such as pinitol and trigonelline. Together, these candidate FIBs of dairy intake could serve as a basis for future validation studies under free-living conditions.

Whole Milk Increases Intestinal ANGPTL4 Expression and Excretion of Fatty Acids through Feces and Urine

The angiopoietin-like 4 (ANGPLT4) protein is involved in lipid metabolism and is known to inhibit lipoprotein lipase in the bloodstream. We investigated the effect of milk on intestinal ANGPTL4 and the metabolic profile of growing pigs and the effect of free fatty acids (FFAs) on ANGPTL4 in ex vivo and in vitro assays. Feeding pigs whole milk increased intestinal ANGPTL4 mRNA and increased fecal excretion of long-chain FFA compared to the control group fed soybean oil (n = 9). Furthermore, FFAs (C4-C8) induced ANGPTL4 gene expression in porcine intestinal tissue mounted in Ussing chambers and ANGPTL4 protein secretion to both the apical and basolateral sides of intestinal Caco-2 cells on permeable membranes. Altogether, these results support an ANGPTL4-induced secretion of fecal FFAs. Urinary levels of FFAs (C4-C12), 3-hydroxyadipic acid, and suberic acid were also increased by milk consumption, indicating higher energy expenditure compared to the control group.

Metabolomics to Explore Impact of Dairy Intake

Dairy products are an important component in the Western diet and represent a valuable source of nutrients for humans. However, a reliable dairy intake assessment in nutrition research is crucial to correctly elucidate the link between dairy intake and human health. Metabolomics is considered a potential tool for assessment of dietary intake instead of traditional methods, such as food frequency questionnaires, food records, and 24-h recalls. Metabolomics has been successfully applied to discriminate between consumption of different dairy products under different experimental conditions. Moreover, potential metabolites related to dairy intake were identified, although these metabolites need to be further validated in other intervention studies before they can be used as valid biomarkers of dairy consumption. Therefore, this review provides an overview of metabolomics for assessment of dairy intake in order to better clarify the role of dairy products in human nutrition and health.