The foodomics approach for discovering biomarkers of food consumption in nutrition studies

Postprandial Plasma and Whole Blood Amino Acids Are Largely Indicative of Dietary Amino Acids in Adult Dogs Consuming Diets with Increasing Whole Pulse Ingredient Inclusion

BACKGROUND

Pulse ingredients often replace grains in grain-free dog diets owing to their high-protein content. However, research to ascertain the benefit of this modification is limited.

OBJECTIVES

This study aimed to correlate food compounds in 1 corn-inclusive control diet and 3 grain-free diets with increasing inclusions of whole pulses (≤45%; Pulse15, Pulse30, and Pulse45), formulated to meet similar macronutrient and micronutrient targets with postprandial amino acids (AAs) in healthy dogs >20 wk.

METHODS

Diets were analyzed for biochemical compounds using tandem mass spectrometry. Twenty-eight outdoor-housed, healthy, adult Siberian Huskies were allocated to diet, and meal responses were analyzed at baseline and weeks 2, 4, 8, 16, and 20 with samples collected at fasted and 15, 30, 60, 90, 120, and 180 min after meal presentation. Blood AAs were analyzed by ultra performance liquid chromatography and differences across week, treatment, and time postmeal were analyzed in SAS Studio. Partial least squares regression was performed in SAS Studio using biochemical compounds in the diet as predictor variables and blood AAs as response variables.

RESULTS

In plasma, Pulse45 had ∼32% greater postprandial Asn than Pulse15, and the control diet had ∼34% greater postprandial Leu and ∼35% greater Pro than Pulse15 (P < 0.05). In whole blood, Pulse30 had ∼23% greater postprandial Lys than the control diet, and the control diet had ∼21% greater postprandial Met and ∼18% greater Pro than Pulse45 and Pulse30, respectively (P < 0.05). Several phospholipids were correlated with postprandial AAs. Compounds in the urea cycle and glycine and serine metabolism were more enriched (P < 0.05) in plasma and whole blood, respectively.

CONCLUSIONS

In macronutrient-balanced and micronutrient-balanced canine diets that differ in their inclusion of corn-derived compared with pulse-derived ingredients, postprandial changes in circulating AAs are largely indicative of the dietary AAs. This helps further our understanding of AA metabolism in healthy dogs fed grain-free diets.

The NMR added value to the green foodomics perspective: Advances by machine learning to the holistic view on food and nutrition

Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modeling a necessity. High-throughput techniques, such as nuclear magnetic resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modeling food-human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients; detect biomarkers of intake and study how they impact the metabolism of different individuals; study the kinetics of compounds in foods or their by-products to detect pathological conditions; and improve the efficiency of in silico models of the metabolic network.

Plasma metabolites associated with healthy Nordic dietary indexes and risk of type 2 diabetes-a nested case-control study in a Swedish population

Background

Epidemiologic evidence on the association of a healthy Nordic diet and future type 2 diabetes (T2D) is limited. Exploring metabolites as biomarkers of healthy Nordic dietary patterns may facilitate investigation of associations between such patterns and T2D.

Objectives

We aimed to identify metabolites related to a priori-defined healthy Nordic dietary indexes, the Baltic Sea Diet Score (BSDS) and Healthy Nordic Food Index (HNFI), and evaluate associations with the T2D risk in a case-control study nested in a Swedish population-based prospective cohort.

Design

Plasma samples from 421 case-control pairs at baseline and samples from a subset of 151 healthy controls at a 10-y follow-up were analyzed with the use of untargeted liquid chromatography-mass spectrometry metabolomics. Index-related metabolites were identified through the use of random forest modelling followed by partial correlation analysis adjustment for lifestyle confounders. Metabolite patterns were derived via principal component analysis (PCA). ORs of T2D were estimated via conditional logistic regression. Reproducibility of metabolites was assessed by intraclass correlation (ICC) in healthy controls. Associations were also assessed for 10 metabolites previously identified as linking a healthy Nordic diet with T2D.

Results

In total, 31 metabolites were associated with BSDS and/or HNFI (-0.19 ≤ r ≤ 0.21, 0.10 ≤ ICC ≤ 0.59). Two PCs were determined from index-related metabolites: PC1 strongly correlated to the indexes (r = 0.27 for BSDS, r = 0.25 for HNFI, ICC = 0.45) but showed no association with T2D risk. PC2 was weakly associated with the indexes, but more strongly with foods not part of the indexes, e.g., pizza, sausages, and hamburgers. PC2 was also significantly associated with T2D risk. Predefined metabolites were confirmed to be reflective of consumption of whole grains, fish, or vegetables, but not related to T2D risk.

Conclusions

Our study did not support an association between healthy Nordic dietary indexes and T2D. However, foods such as hamburger, sausage, and pizza not covered by the indexes appeared to be more important for T2D risk in the current population.

1H NMR and multivariate data analysis of the differences of metabolites in five types of dry-cured hams

In order to distinguish the taste styles of dry-cured hams (Jinhua, Xuanwei, Country, Parma and Bama), we established a ¹H nuclear magnetic resonance spectroscopy method to identify metabolites. Totally, 33 charged metabolites, including amino acids, organic acids, nucleic acids and their derivatives, sugars, alkaloids and others were identified. The abundant glutamate, lysine, alanine, leucine and lactate could be the major contributors of taste. Total variables were explained by PC1 (67.7%) and PC2 (16.0%) which showed that Parma and Xuanwei styles were close to each other (similar amino acids, peptide, organic acids and alkaloids contents). Bama style showed the highest PC1 and amino acids, organic acids and alkaloids contents. Country style was located on the left-most area of PC1 (the lowest amino acids, organic acids and peptide, but the highest sugars contents). Sensory evaluation revealed that Bama ham had the highest overall taste score, followed by Jinhua, Parma, Xuanwei and American Country ham. We concluded that the proportions and combinations of taste components explained the specific taste instead of any single component. These findings provided a better understanding of different metabolomics among hams.