Metabolomics as a tool in the identification of dietary biomarkers

Abstract

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

“…Similarly, from the point of view of metabolic network reconstructions, the latest version of Recon2, Recon2.2 [33], contains 2652 unique chemical species, some 60% more than in Recon1 [31, 133], implying that we are far from discovering them all, and some are known still to be absent [9]. Thirdly, many of the metabolites may not be entirely the result of the host’s biosynthesis, being derived from dietary sources [134, 135] and including biotransformations in the gut. At an elementary level this is clearly true, since essential amino acids, fatty acids and vitamins are (by definition) not synthesised by the host.…”

Investigation of Variations in the Human Urine Metabolome amongst European Populations: An Exploratory Search for Biomarkers of People at Risk‐of‐Poverty

“…An inclusion of such heterogeneous populations may be an advantage, revealing reliable findings, or a disadvantage, hiding an effect of economic status due to large variations derived from the above‐mentioned factors. In fact, the establishment of a unique metabolic pattern representing urine from subjects at ROP can be difficult due to 1) a significant difference in dietary habits of individuals across the different countries and with different ethnic background and 2) the fact that the last meal and drink before biofluid sample collection have a significant impact on the metabolic status …”

“…In fact, the establishment of a unique metabolic pattern representing urine from subjects at ROP can be difficult due to 1) a significant difference in dietary habits of individuals across the different countries and with different ethnic background [8] and 2) the fact that the last meal and drink before biofluid sample collection have a significant impact on the metabolic status. [9,10] This study employs an untargeted metabolomics approach to screen urine samples using 1D 1 H-NMR spectroscopy in combination with advanced multivariate data analysis. The combination of 1D 1 H-NMR data and multivariate data analysis has been proven of great potential for investigations on human biofluids [11] and 1 H-NMR spectroscopy is capable of profiling around 50 most abundant metabolites in human urine.…”

1H NMR-Based Metabolic Profiling of Urine from Mice Fed Lentinula edodes-Derived Polysaccharides

“…in biological samples [Monterio et al, 2013]. It is now widely used to identify various metabolic features associated with diseases, drug treatments or diet intervention [Gibbons & Brennan, 2017]. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the primary techniques applied in metabolomics.…”

“…Representative 600 MHz 1 H NMR spectra of urinary samples obtained from normal mouse (A) and L2-treated mouse (B). Key for spectra: 1, isoleucine; 2, fucose; 3, arginine;4, 2-oxoglutarate;5, dimethylamine;6, citrulline; 7, proline; 8, phenylacetylglutamine; 9, leucine;10, adenosine; 11, trigonelline; 12, niacin;13, benzoate; 14, lysine; 15, N-acetylglutamic acid; 16, propionate; 17, valine; 18, glutamate; 19, succinate; 20, citrate; 21, malate; 22, trimethylamine; 23, creatinine;24, malonic acid; 25, betaine; 26, trimethylamine oxide (TMAO); 27, glycine; 28, glucose; 29, phenylacetylglycine; 30, methionine; 31, creatine;32, phenylalanine; 33, choline; 34, hippurate; 35, lactate.…”