Trimethylamine N-oxide (TMAO) is a diet- and microbiome-derived metabolite and a proposed biomarker of adverse cardiometabolic outcomes. TMAO studies have mainly been conducted in individuals with cardiometabolic disease, and studies in population-derived samples are limited. We aimed to investigate the associations between plasma TMAO concentrations and its precursors [carnitine, choline, betaine, and dimethylglycine (DMG)] with metabolic syndrome (MetS) scores, preclinical cardiovascular phenotypes, and inflammatory biomarkers (i.e. high-sensitivity C-reactive protein and serum glycoprotein acetyls) in a population-derived cohort of children and their parents. The concentrations of TMAO and its precursors were quantified using UHPLC coupled with tandem MS (UHPLC/MS-MS) in 1166 children (mean age 11 y ± 0.5 y, 51% female) and 1324 adults (44 y ± 5.1 y, 87% female) participating in The Growing Up in Australia's Child Health CheckPoint Study. We developed multivariable fractional polynomial models to analyze associations between TMAO, its precursors, MetS (adjusted for sex and age), and cardiovascular phenotypes (adjusted for sex, age, BMI, household income, and the urinary albumin to creatinine ratio). Pearson's correlations were computed to identify associations between TMAO, its precursors, and inflammatory biomarkers. The concentrations of TMAO precursors, but not TMAO itself, were associated with MetS, cardiovascular phenotypes, and inflammatory biomarkers in children and adults. TMAO precursors, but not TMAO itself, were associated with adverse cardiometabolic and inflammatory phenotypes in children and adults. TMAO precursor concentrations may better reflect cardiovascular health and inflammatory status within the wider population. Replication in other population settings and mechanistic studies are warranted.

Trimethylamine N-oxide (TMAO) is a microbiome- and diet-derived metabolite implicated in adverse cardiovascular outcomes. To date, studies of plasma TMAO concentrations have largely focused on individuals with metabolic disease. As such, data on TMAO concentrations in population settings and parent–child dyads are lacking. This study aimed to investigate parent–child concordance, age, and sex effects on plasma concentrations of TMAO and its precursors [l-carnitine, choline, betaine, and dimethylglycine (DMG)]. Associations between concentrations of TMAO and its precursors and self-reported dietary intakes of animal protein (i.e., red meat, meat products, chicken, fish, milk products, and cheese) and fast-food meals were also investigated. A total of 1166 children (mean ± SD age: 11 ± 0.5 y, 51% female) and 1324 parents (mean ± SD age: 44 ± 5.1 y, 87% female) had a biomedical assessment as part of Growing Up in Australia's Child Health Checkpoint. Plasma TMAO and precursor concentrations were quantified using ultra-high-pressure LC coupled with tandem MS. Familial dyads significantly contributed to plasma TMAO and precursor concentrations (P < 0.0001), explaining 37% of variance for TMAO concentrations. Least-square mean ± SE plasma TMAO was lower in children (0.79 ± 0.02 µM on the log-scale) than in adults (1.22 ± 0.02 µM). By contrast, children's betaine (40.30 ± 0.34 µM) and DMG concentrations (1.02 ± 0.01 µM on the log-scale) were higher than adults’ betaine (37.50 ± 0.32 µM) and DMG concentrations (0.80 ± 0.01 µM) (P < 0.0001). Mean values of all metabolites, except adult TMAO, were higher in males than in females (P < 0.001). Greater reported intake of red meat and fish was associated with higher TMAO concentrations in both children [estimates (95% CIs) for red meat: 0.06 (0.01, 0.10); fish: 0.11 (0.06, 0.17)] and adults [red meat: 0.13 (0.08, 0.17); meat products: 0.07 (0.03, 0.12); and fish: 0.09 (0.04, 0.14)]. Age, sex, and shared family factors, including diet, contribute to variation in plasma concentrations of TMAO and its precursors.

Context: Establishing diet–disease associations requires reliable assessment of dietary intake. With the rapid advancement of metabolomics, its use in identifying objective biomarkers of dietary exposure has substantially increased.Objective: The aim of our review was to systematically combine all observational studies linking dietary intake patterns with metabolomic profiles of human biospecimens.Data Sources: Five databases were searched – MEDLINE, Embase, Scopus, Web of Science, and Cochrane CENTRAL – to March 2020.Data Extraction: Of the 14 328 studies initially screened, 35 observational studies that met the specified inclusion criteria were included.Data analysis: All reviewed studies indicated that metabolomic measures were significantly correlated with dietary patterns, demonstrating the potential for using objective metabolomic measures to characterize individuals’ dietary intake. However, similar dietary patterns did not always result in similar metabolomic profiles across different study populations.Conclusion: Metabolomic profiles reflect a multitude of factors, including diet, genetic, phenotypic, and environmental influences, thereby providing a more comprehensive picture of the impact of diet on metabolism and health outcomes. Further exploration of dietary patterns and metabolomic profiles across different population groups is warranted.

Diet and lifestyle are vital to population health, but their true contribution is difficult to quantify using traditional methods. Nutrient-health relations are typically based on epidemiological associations that are assessed at the population level, traditionally using self-reported dietary and lifestyle data. Unfortunately, such measures are inherently inaccurate. New technologies such as metabolomics can measure nutritional and micronutrient profiles in body fluids, providing objective evaluation of nutritional status. A critical step toward accurate health prediction models would be the building of integrated repositories of nutritional measures combining subjective methods of reporting with objective metabolomics profiles and precise phenotypic data. Here we outline a roadmap to achieve this goal and discuss both the advantages and risks of this approach. We also highlight the uncertain associations between the complexity of high-dimensional data generated in 'omics research (along with the public confusion this may engender) and the rapid adoption of 'omics approaches by nutrition and health companies to develop nutritional products and services.