Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.

Symbiotic gut microorganisms (microbiome) interact closely with the mammalian host's metabolism and are important determinants of human health. Here, we decipher the complex metabolic effects of microbial manipulation, by comparing germfree mice colonized by a human baby flora (HBF) or a normal flora to conventional mice. We perform parallel microbiological profiling, metabolic profiling by 1 H nuclear magnetic resonance of liver, plasma, urine and ileal flushes, and targeted profiling of bile acids by ultra performance liquid chromatography-mass spectrometry and short-chain fatty acids in cecum by GC-FID. Top-down multivariate analysis of metabolic profiles reveals a significant association of specific metabotypes with the resident microbiome. We derive a transgenomic graph model showing that HBF flora has a remarkably simple microbiome/metabolome correlation network, impacting directly on the host's ability to metabolize lipids: HBF mice present higher ileal concentrations of tauro-conjugated bile acids, reduced plasma levels of lipoproteins but higher hepatic triglyceride content associated with depletion of glutathione. These data indicate that the microbiome modulates absorption, storage and the energy harvest from the diet at the systems level.

The transgenomic metabolic effects of exposure to either Lactobacillus paracasei or Lactobacillus rhamnosus probiotics have been measured and mapped in humanized extended genome mice (germ-free mice colonized with human baby flora). Statistical analysis of the compartmental fluctuations in diverse metabolic compartments, including biofluids, tissue and cecal short-chain fatty acids (SCFAs) in relation to microbial population modulation generated a novel top-down systems biology view of the host response to probiotic intervention. Probiotic exposure exerted microbiome modification and resulted in altered hepatic lipid metabolism coupled with lowered plasma lipoprotein levels and apparent stimulated glycolysis. Probiotic treatments also altered a diverse range of pathways outcomes, including amino-acid metabolism, methylamines and SCFAs. The novel application of hierarchical-principal component analysis allowed visualization of multicompartmental transgenomic metabolic interactions that could also be resolved at the compartment and pathway level. These integrated system investigations demonstrate the potential of metabolic profiling as a top-down systems biology driver for investigating the mechanistic basis of probiotic action and the therapeutic surveillance of the gut microbial activity related to dietary supplementation of probiotics.

Autism is an early-onset developmental disorder with a severe life-long impact on behavior and social functioning that has associated metabolic abnormalities. The urinary metabolic phenotypes of individuals (age range=3-9 years old) diagnosed with autism using the DSM-IV-TR criteria (n=39; male=35; female=4), together with their non-autistic siblings (n=28; male=14; female=14) and age-matched healthy volunteers (n=34, male=17; female=17) have been characterized for the first time using Additionally, metabolic phenotype (metabotype) differences were observed between autistic and control children, which were associated with perturbations in the relative patterns of urinary mammalian-microbial co-metabolites including dimethylamine, hippurate and phenyacetylglutamine. These biochemical changes are consistent with the known abnormalities of gut microbiota found in autistic individuals and the associated gastrointestinal dysfunction and may be of value in monitoring the success of therapeutic interventions.3

The effects of the antibiotic vancomycin (2 × 100 mg/kg/day) on the gut microbiota of female mice (outbred NMRI strain) were studied, in order to assess the relative contribution of the gut microbiome to host metabolism. The host's metabolic phenotype was characterized using 1 H NMR spectroscopy of urine and fecal extract samples. Time-course changes in the gut microbiotal community after administration of vancomycin were monitored using 16S rRNA gene PCR and denaturing gradient gel electrophoresis (PCR-DGGE) analysis and showed a strong effect on several species, mostly within the Firmicutes. Vancomycin treatment was associated with fecal excretion of uracil, amino acids and short chain fatty acids (SCFAs), highlighting the contribution of the gut microbiota to the production and metabolism of these dietary compounds. Clear differences in gut microbial communities between control and antibiotic-treated mice were observed in the current study. Reduced urinary excretion of gut microbial co-metabolites phenylacetylglycine and hippurate was also observed. Regression of urinary hippurate and phenylacetylglycine concentrations against the fecal metabolite profile showed a strong association between these urinary metabolites and a wide range of fecal metabolites, including amino acids and SCFAs. Fecal choline was inversely correlated with urinary hippurate. Metabolic profiling, coupled with the metagenomic study of this antibiotic model, illustrates the close inter-relationship between the host and microbial "metabotypes", and will provide a basis for further experiments probing the understanding of the microbial-mammalian metabolic axis.

Coevolution shapes interorganismal crosstalk leading to profound and diverse cellular and metabolic changes as observed in gut dysbiosis in human diseases. Here, we modulated a simplified gut microbiota using pro-, pre-, and synbiotics to assess the depth of systemic metabolic exchanges in mice, using a multicompartmental modeling approach with metabolic signatures from 10 tissue/fluid compartments. The nutritionally induced microbial changes modulated host lipid, carbohydrate, and amino acid metabolism at a panorganismal scale. Galactosyl-oligosaccharides reduced lipogenesis, triacylglycerol incorporation into lipoproteins and triglyceride concentration in the liver and the kidney. Those changes were not correlated with decreased plasma lipoproteins that were specifically induced by L. rhamnosus supplementation. Additional alteration of transmethylation metabolic pathways (homocysteine-betaine) was observed in the liver and the pancreas following pre-and synbiotic microbial modulation, which may be of interest for control of glucose metabolism and insulin sensitivity. Probiotics also reduced hepatic glycogen and glutamine and adrenal ascorbate with inferred effects on energy homeostasis, antioxidation, and steroidogenesis. These studies show the breadth and the depth of gut microbiome modulations of host biochemistry and reveal that major mammalian metabolic processes are under symbiotic homeostatic control.

Objective Gut microbiome alterations in Parkinson disease (PD) have been reported repeatedly, but their functional relevance remains unclear. Fecal metabolomics, which provide a functional readout of microbial activity, have scarcely been investigated. We investigated fecal microbiome and metabolome alterations in PD, and their clinical relevance. Methods Two hundred subjects (104 patients, 96 controls) underwent extensive clinical phenotyping. Stool samples were analyzed using 16S rRNA gene sequencing. Fecal metabolomics were performed using two platforms, nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography–mass spectrometry. Results Fecal microbiome and metabolome composition in PD was significantly different from controls, with the largest effect size seen in NMR‐based metabolome. Microbiome and NMR‐based metabolome compositional differences remained significant after comprehensive confounder analyses. Differentially abundant fecal metabolite features and predicted functional changes in PD versus controls included bioactive molecules with putative neuroprotective effects (eg, short chain fatty acids [SCFAs], ubiquinones, and salicylate) and other compounds increasingly implicated in neurodegeneration (eg, ceramides, sphingosine, and trimethylamine N‐oxide). In the PD group, cognitive impairment, low body mass index (BMI), frailty, constipation, and low physical activity were associated with fecal metabolome compositional differences. Notably, low SCFAs in PD were significantly associated with poorer cognition and low BMI. Lower butyrate levels correlated with worse postural instability–gait disorder scores. Interpretation Gut microbial function is altered in PD, characterized by differentially abundant metabolic features that provide important biological insights into gut–brain pathophysiology. Their clinical relevance further supports a role for microbial metabolites as potential targets for the development of new biomarkers and therapies in PD. ANN NEUROL 2021;89:546–559

Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic and other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels. With recent developments in high-throughput technologies using advanced spectroscopic methods, metabolic profiling is now being applied to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification. Metabolic profiling at epidemiologic scale requires optimisation of experimental protocol to maximise reproducibility, sensitivity, and quantitative reliability, and to reduce analytic drift. Customised multivariate statistical modelling approaches are essential for effective data visualisation and biomarker discovery (controlled for false positive associations) when hundreds or thousands of complex metabolic spectra are being processed. We describe here the main procedures in large scale metabolic phenotyping and its application to MWA studies, for the discovery of new disease risk biomarkers, diagnostics, and to provide novel insights into etiology, biologic mechanisms and pathways.