Two untargeted metabolomics reveals yogurt-associated metabolic alterations in women with multiple metabolic disorders from a randomized controlled study

Changes in Plasma, Urine, and Fecal Metabolome after 16 Weeks of Consuming Dairy With Different Food Matrixes - A Randomized Controlled Trial

SCOPE

Understanding the mode-of-action by which fermented dairy consumption influences health is of interest. The aim of this study is to elucidate the impact of the chemical-physical properties of the dairy matrix and postbiotic effects on the metabolomics response to fermented dairy consumption.

METHODS AND RESULTS

Hundred males (Body Mass Index (BMI) 28.0-45.0 kg m-2, waist circumference ≥ 102 cm) are included in the study. During a 16-week intervention, the study subjects are instructed to consume 400 g per day of either 1) milk, 2) yogurt, 3) heat-treated yogurt, or 4) chemically acidified milk as part of their habitual diet. Nuclear Magnetic Resonance (NMR)-based metabolomics is conducted on plasma, urine, and fecal samples collected before and after the intervention. Both consumption of acidified milk and heat-treated yogurt resulted in changes in the fecal metabolome including decreases in the level of amino acids (leucine, valine, and threonine), and the branched-chain fatty acid (BCFA) isobutyrate that indicated an altered protein putrefaction, and proteolytic metabolism in the gut. In the plasma metabolome, an increased citrate is found for yogurt consumption. No difference in the urine metabolome is found.

CONCLUSIONS

Our metabolomics analyses indicate that consumption of heat-treated yogurt and acidified milk exerted similar effects on the metabolic activity in the gut as yogurt consumption.

Physicochemical, microbiological and metabolomics changes in yogurt supplemented with lactosucrose

Lactosucrose (LS) is a known prebiotic that has gained recognition for its low caloric content and various health benefits. However, its potential in food applications remains largely unexplored. In this study the effects of adding LS to milk at concentrations (0 %, 2 %, 5 % and 8 % w/v) for yogurt production, and the relevant changes in yogurt texture, microbial composition and metabolomics were investigated. Our findings revealed that LS played a role in promoting the formation of a structured gel during fermentation, resulting in increased elasticity and viscosity while reducing fluidity. Additionally incorporating high doses of LS into yogurt led to reduced post-acidification, enhanced survival of starter bacteria, improved water retention capacity and overall texture throughout a refrigerated storage period of 21 days. Notably higher concentrations of LS (8 % w/v) exhibited effects on enhancing yogurt quality. Furthermore, untargeted metabolomics analysis using UPLC Q TOF MS/MS revealed 45 differentially expressed metabolites, including up-regulated L-arginine, L-proline and L-glutamic acid along with the down-regulated glutathione, L-tyrosine, L-phenylalanyl and L-proline. These differential metabolites were primarily associated with amino acid metabolism such as thiamine metabolism, nicotinic acid salt and nicotinamide metabolism, and pyrimidine metabolism. As a result, the inclusion of LS in yogurt had an impact on the production of various beneficial metabolites in yogurt, highlighting the importance of combining prebiotic LS with probiotics to obtain desired physiological benefits of yogurt.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

NMR foodomics in the assessment of diet and effects beyond nutrients

PURPOSE OF REVIEW

This review provides an overview of most recent research studies employing nuclear magnetic resonance (NMR)-based metabolomics in the assessment of effects of diet and food ingestion.

RECENT FINDINGS

NMR metabolomics is a useful tool in the elucidation of specific diets, for example, the Mediterranean diet, the New Nordic diet types, and also for comparing vegan, vegetarian and omnivore diets where specific diet-linked metabolite perturbations have been identified. Another core area where NMR metabolomics is employed involves research focused on examining specific food components or ingredients, including dietary fibers and other functional components. In several cases, NMR metabolomics has aided to document how specific food components exert effects on the metabolic activity of the gut microbiota. Research has also demonstrated the potential use of NMR metabolomics in assessing diet quality and interactions between specific food components such as meat and diet quality. The implications of these findings are important as they address that background diet can be decisive for if food items turn out to exert either harmful or health-promoting effects.

SUMMARY

NMR metabolomics can provide important mechanistic insight and aid to biomarker discovery with implications for compliance and food registration purposes.