No retraction, correction or concern found when last checked on Oct 27, 2026.
Metabolomic insights into the intricate gut microbial–host interaction in the development of obesity and type 2 diabetes
Magali Palau-Rodriguez, Sara Tulipani, Maria Isabel Queipo-Ortuño and 3 more
Frontiers in Microbiology · 2015 · Review
Abstract
Gut microbiota has recently been proposed as a crucial environmental factor in the development of metabolic diseases such as obesity and type 2 diabetes, mainly due to its contribution in the modulation of several processes including host energy metabolism, gut epithelial permeability, gut peptide hormone secretion, and host inflammatory state. Since the symbiotic interaction between the gut microbiota and the host is essentially reflected in specific metabolic signatures, much expectation is placed on the application of metabolomic approaches to unveil the key mechanisms linking the gut microbiota composition and activity with disease development. The present review aims to summarize the gut microbial-host co-metabolites identified so far by targeted and untargeted metabolomic studies in humans, in association with impaired glucose homeostasis and/or obesity. An alteration of the co-metabolism of bile acids, branched fatty acids, choline, vitamins (i.e., niacin), purines, and phenolic compounds has been associated so far with the obese or diabese phenotype, in respect to healthy controls. Furthermore, anti-diabetic treatments such as metformin and sulfonylurea have been observed to modulate the gut microbiota or at least their metabolic profiles, thereby potentially affecting insulin resistance through indirect mechanisms still unknown. Despite the scarcity of the metabolomic studies currently available on the microbial-host crosstalk, the data-driven results largely confirmed findings independently obtained from in vitro and animal model studies, putting forward the mechanisms underlying the implication of a dysfunctional gut microbiota in the development of metabolic disorders.
Full text
From PubMed Central, U.S. National Library of Medicine, as current on Oct 27, 2026. Paragraph numbers are ours, so an answer can point at one.
Gut Microbiota And Diabesity: Role In Energy Harvest, Gut Barrier Integrity, Endocrine Modulation, And Metabolic Inflammation
Obesity is a complex, multifactorial disease characterized by an excessive accumulation of fat due to an imbalance between energy intake and expenditure. The linear rise in the prevalence of T2D throughout the normal, overweight and obese ranges is so high that the relative risks of diabetes are 40 times higher when BMI increases above 35 kg/m2. The public concern over the obesity epidemic mostly lies in the intimate connection between obesity and T2D (so-called diabesity; ) and makes the elucidation of mechanisms underlying the co-occurrence of the two diseases a central focus of current biomedical research.
Recently, consideration has started to be given to the gastrointestinal tract as a key point in the development and progression of complex metabolic diseases, since it represents the milieu where interactions between exogenous (i.e., diet, microbiome) and endogenous (i.e., genetic) factors predisposed to disease and the body’s defenses (physical barrier, immune system response) actually take place. Increasing evidence indicates in particular the impact of changes in the composition of the human gut microbiota on host metabolism and a variety of diseases.
Firmicutes (Gram-positive), Bacteroidetes (Gram-negative) and Actinobacteria (Gram-positive) represent over 90% of the phyla and dominate the gut microbiota, but a relevant change in their relative proportion has been described in obesity and T2D. A favorable prevalence of Firmicutes bacteria toward healthy subjects has been observed in both animal models of obesity and human obesity, also reviewed in, although with some discrepancies among data. Although the potential impact of specific species on host metabolism has already been elucidated, most of the data so far available have reported observed changes at the phylum level. Furthermore, the physiological contribution of Firmicutes in the development of the obese phenotype is still being debated. In turn, some studies have observed a positive correlation between ratios of Bacteroidetes to Firmicutes and plasma glucose concentration, but not with BMI, although this was expected.
Different mechanisms have been proposed in the attempt to understand the impact of microbiota both in maintaining metabolic health and in the development of obesity and T2D. Essentially, the intestinal microbial variability has been hypothesized as an important factor in four different processes, namely: (i) the modulation of energy homeostasis by regulating the energy harvest from diet, fat storage, lipogenesis, and fatty acid oxidation (host energy metabolism;; ); (ii) the modulation of the gut barrier integrity by regulating the epithelial permeability, the intestinal motility and the transport of digestion products such as short-chain fatty acids, which are an energy source for colonocytes; (iii) the regulation of gastrointestinal peptide hormone secretion, by suppressing the secretion of the lipoprotein lipase inhibitor (fasting-induced adipose factor), determining the release of fatty acids from circulating triglycerides and lipoproteins in muscle and adipose tissue and promoting fat mass accumulation; and (iv) the modulation of the host inflammatory state by contributing to the systemic increase of lipopolysaccharide, which impairs insulin sensitivity (metabolic endotoxemia; reviewed in;,;;; ). Evidence of the role of gut microbiota in the preservation of metabolic health also comes from the effect of prebiotics, such as non-digestible carbohydrates, namely non-digestible ingredients that are fermented by specific beneficial bacterial strains, selectively promote the growth and/or activity (release of end-products of bacterial fermentation) of the gastrointestinal microbiota, affecting favorably the host health. The intake of prebiotics has in fact been described to act on host endocrine secretion, improve gut barrier integrity by increasing the release of glucagon-like peptide-2, stimulate postprandial release of peptides involved in energy homeostasis and/or pancreatic functions such as the anorexigenic glucagon-like peptide-1 and peptide YY, and the decrease of orexigenic peptides such as ghrelin in plasma which in turn modulates food intake (regulators of appetite) and energy expenditure across the entire gastrointestinal tract (;; reviewed in ). Furthermore, evidence suggests that the modulation of the host metabolic health by prebiotics intake can be mediated to specific fermentation products (i.e., short-chain fatty acids, predominantly acetate, propionate and butyrate) produced by cross-feeding between Eubacterium rectale and Bifidobacterium thetaiotaomicron; Propionibacterium sp. and Bacteroides sp.; Faecalibacterium prausnitzii and Roseburia intestinalis/Eubacterium rectale respectively.
The Metabolomic Approach
Due to the species specificity of several enzymatic machineries, the gut microbial composition and activity are likely to be characterized by the profile of small metabolites produced in the intestinal lumen, eventually absorbed through the intestinal barrier and further biotransformed by the host. Consequently, the complexity of microbial–host exchanges may be reflected in the specific chemical signature of host circulating biofluids. Metabolomics has recently attracted attention as the most suitable -omics technology for investigating complex, polygenic and multifactorial diseases with a strong metabolic etiology, such as obesity and T2D as well as the crosstalk of distinct predisposing factors in disease development and progression. Aimed at the comprehensive analysis of the low- molecular- weight compounds contained in a biological system –by definition, metabolites comprise a plethora of primary or secondary derivatives of the intermediate metabolism (molecular weight below 900 and 2000 Dalton, depending on sources;;; ) metabolomics represents a powerful tool for exploring the crosstalk between the microbial and host metabolism in a more exhaustive fashion.
The workflow applied in metabolomic studies is broadly categorized into five main steps: (1) sample collection, (2) sample preparation, (3) data acquisition, (4) data analysis, and (5) biological interpretation of the results obtained. The analytical techniques most commonly used for the characterization of the metabolome of a biological sample are MS and 1H-NMR. Both technologies have their advantages and disadvantages. 1H-NMR implies a non-destructive, non-selective, cost-effective, and relatively sensitive analysis while, compared to 1H-NMR, MS mainly offers potential advantages in terms of sensitivity and, if coupled to different separation techniques such as LC or GC, it provides a means of detecting a broader and complementary range of biomarkers. LC coupled to electrospray ionization MS is becoming the method of choice for the acquisition of profiling metabolites in complex biological samples through both targeted (i.e., triple quadrupole-driven) and non-targeted (e.g., quadrupole time-of-flight-, linear trap quadrupole orbitrap-driven) approaches.
The present review aims to summarize the gut microbial–host cometabolites identified so far in humans in relation to obesity and/or T2D by targeted and untargeted metabolomic studies. Since the potential impact of some specific species in host metabolism has already been elucidated, an attempt to associate bacterial producers of the co-metabolites with the metabolic alterations related to the obese, diabetic, or diabese phenotype was also made. A critical view of the current limitations and future directions of metabolomics will accompany the discussion.
Search Strategy
The following keywords were searched for in the PubMed and Web of Science electronic databases: (Metabolom∗ [TW] or co-metabol∗ [TW] or host-gut metabo∗ [TW] or nuclear magnetic resonance [TW] or MS [TW] or magnetic resonance spectroscopy [TW]) AND (OBES∗ [TW] OR DIABET∗ [TW] OR DIABES∗ [TW]) AND (gut micro∗ [TW]). Species (human), language (English), and publication date restrictions (2000 to date, last search on November 27th, 2014) were imposed, but there were none for gender, age or ethnicity. Relevant references cited in the selected articles were additionally reviewed. Targeted and untargeted metabolomic approaches driven by 1H-NMR or MS techniques were both included in the selection. Low-molecular-weight (<1000 Da) metabolites significantly up- or downregulated in overweight and obese subjects with/without impaired glycemic control, with respect to controls (i.e., lean, healthy subjects), were the primary outcomes of interest of the review.
Characteristics of the Studies and Metabolic Variations
Only eight human studies successfully met the eligibility criteria for inclusion in the review (details in the Supplementary Material File). As summarized in Table 1, seven observational and one interventional study have so far applied a metabolomic approach and specifically identified changes in products of the gut microbial–host co-metabolism in overweight to obese individuals (BMI > 25 kg/m2) and/or several degrees of impaired glycemic control (ranging from IGT up to T2D) compared to control individuals. Other comorbidities were not described (i.e., hypertension, renal or liver dysfunction).
Overall, the study subjects, designs and objectives were quite heterogeneous despite the small number of retrieved studies (Supplementary Material File), thereby complicating an otherwise integrated and consistent picture of the metabolomic changes observed.
Urine, fasting serum and plasma were the biological samples used in these studies. A data-driven untargeted approach was chosen in the majority of the studies while two of them provided quantitative information about known targeted metabolites. The metabolic changes observed in these studies and the related interpretations are summarized in Table 2.
Co-metabolism of Bile Acids
Two of the metabolomic studies described in this review highlighted a change in the circulating pool of BA in obese patients with insulin resistance or T2D, compared with BMI-matched healthy individuals. Alterations involved both human-derived (hepatic) structures (primary BA) and gut microbial-produced derivatives (secondary BA).
To the best of our knowledge, it is currently accepted that the bacterial enzymes involved in the biotransformation from primary to secondary BA are not shared across the whole microbial community, although they have been described so far in genera belonging to the four major phyla Firmicutes, Actinobacteria, Bacteroidetes and Proteobacteria. Furthermore, according to Actinobacteria and Firmicutes clones would be the only ones able to degrade all conjugated BA, with Bacteroidetes species being limited to tauro- conjugation activities.
After their production in the liver and the eventual glyco- and tauro-conjugation (N-acyl amidation with glycine or taurine substituents), primary BA are secreted in the small intestine through the bilis, where they are first subjected to deconjugation by a bacterial bile salt hydrolase enzyme produced by species of the four phyla, such as Clostridium, Bacteroides, Lactobacillus, Bifidobacterium, and Enterococcus. In the ileum, more than 95% of these BA undergo enterohepatic recycling, are absorbed from the intestine and returned to the liver. The remaining 5% escape the enterohepatic circulation and reach the large bowel where the bioconversion into secondary BA is completed, especially by Firmicutes phyla (Eubacterium sp. and Clostridium sp.;;; ).
A decrease of primary BA and an increase of secondary BA was observed in the fasting serum of overweight patients with T2D, compared to healthy subjects. The authors hence concluded that differences in the gut microbiota of diabetic patients may lead to higher rates of conversion from primary to secondary BA. In turn, only observed an increase of glycochenodeoxycholic acid (primary BA) in the plasma of prediabetic individuals, while no changes were noticed in urine. Despite the only partial overlapping of the results, both studies suggested that overweight and obesity may not be the predominant factor implied in the metabolomic changes observed, and thus in linking impaired glucose homeostasis to alterations in BA pool composition. As argued in those studies, the variation of the BA pool in biofluids may depend on different factors, namely a change in the prevalence or activation of the gut microbial species implied in BA bioconversion or an altered reabsorption of BA through the gut mucosa, in turn produced by the disease itself, by dietary or other external changes (i.e., induced by bariatric surgery), or by a combination of these. In any case, the results indicated a probable implication of the modulation of BA biosynthetic pathways in the relationship between gut microbiota and insulin resistance (Figure 1).
These findings are in line with independent studies that recently associated changes in BA turnover with diabesity. In turn, a reduction of the bacterial enzymatic activities involved in the conversion of primary into secondary BA was observed in diabetic patients compared to healthy controls, and linked to Firmicutes phyla. A very similar pattern was also reported in obese patients with diagnosed metabolic syndrome, treated with antibiotic agents (vancomycin and amoxicillin) and associated with both a decreased prevalence of the Firmicutes population and a reduction of peripheral insulin sensitivity. Taken together, these data suggest a possible link between BA and metabolic health. In line with these findings, BA have recently been proposed as metabolic integrators of whole-body energy homeostasis implicated in the regulation of various metabolic pathways, including their own synthesis and enterohepatic circulation, triglyceride, glucose, and energy homeostasis, by acting as signaling molecules through receptor-dependent and -independent pathways. The role of a dysregulation of this BA-mediated metabolic control in the pathogenesis of T2D and co-morbidities, such as its attractiveness as a therapeutic target, is now beginning to be elucidated (reviewed in ). Their action on energy metabolism regulation would occur via both the activation of the nuclear receptor FXR and FXR-independent pathways, such as through the membrane receptor TGR5 expressed in several tissues including gall bladder, ileum, colon, and brown and white adipose tissue.
It is worth noting that preliminary evidence has shown that not all BA activate equally, and the microbial-derived production of secondary BA could be an important mechanism in the regulation of signaling pathways involved in the development of diabesity. When gut microbial-derived secondary BA are bound to TGR5, the receptor is internalized and a series of adenylate cyclase-dependent signaling is triggered by activating distinct pathways involved in glucose and lipid energy metabolism. In tissues, such as brown adipose tissue and muscle, this would lead to an increased mitochondrial activity and oxidative phosphorylation, which has been linked to an insulin sensitization both in genetic and diet-induced models of diabesity. In enteroendocrine L-cells, in turn, these signaling pathways would enhance glucose metabolism by stimulating the production of glucagon like peptide, thereby promoting insulin secretion. Finally, recent studies have also shown that BA secretion improves insulin secretion, insulin sensitivity and whole-body glucose homeostasis (reviewed in ), improving liver and pancreatic function in obese mice.
Co-metabolism of Vitamins
Products of the gut microbiota-driven metabolic pathway of vitamins such as choline and niacin have been associated with obesity and diabetes.
Choline Metabolism
Although humans may produce choline endogenously (de novo hepatic synthesis), dietary intake (e.g., from egg yolk, liver, muscle meats, fish, nuts, legumes) is necessary to meet the demand for body health maintenance. Once dietary choline reaches the intestine, anaerobic intestinal microorganisms, mainly of Firmicutes and Proteobacteria phyla catalyze its conversion to TMA, which may be further degraded to several methylamines by the gut microbiota (sym-xenobiotic pathway; ), or is absorbed and oxidized to TMAO by the hepatic FMO3 enzyme. Choline may also be converted into betaine and further products (e.g., dimethylglycine) by mammalian mitochondrial pathways in the liver and kidney (Figure 2). The bacterial gene clusters responsible for anaerobic choline degradation started to be identified only recently. Bacterial species belonging to Firmicutes, Actinobacteria and Proteobacteria phyla have been revealed as possessing the necessary enzymatic activities, while Bacteroidetes would be apparently deprived. However, the complete diversity of species that contribute to TMAO production in humans still remains unknown.
Recent studies have shown that circulating levels of choline and TMAO are related to cardiovascular disease risk, and the gut microbiota-driven TMA-FMO3-TMAO pathway has been particularly recognized as a key regulator of lipid metabolism and inflammation. Increased levels of TMAO have been observed in a leptin-deficient murine model of obesity and T2D and revealed a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. The systemic perturbations of key metabolites of choline have also been related to the progression of T2D, suggesting that in the early stages of diabetes an attenuated conversion of choline into dimethylglycine may occur, which can be observed by the increased levels of TMAO and TMA, with a reversion of this behavior at a later stage of the disease.
published the first study linking TMAO and T2D in humans. Using a1H-NMR approach, increased levels of TMAO and dimethylamine were observed in the urine of diabetic individuals compared to a group of healthy individuals, and were present in high concentrations even in diabetics with good metabolic control (i.e., absence of glycosuria and glycohemoglobin). In the last decade, little progress has been made on the mechanisms that would directly or indirectly involve TMAO in the development of diabetes. Nevertheless, the potential role of an altered composition of the microbiota and its ability to metabolize choline in glucose homeostasis and disease development has become increasingly relevant.
To the best of our knowledge, three further metabolomic studies observed a change in choline metabolism, which was associated with impaired glycemic control in humans, within a wide range of BMI (;;; Table 1). In all of them, fasting biosamples were analyzed, thereby avoiding fluctuations in choline concentrations due to dietary intake. carried out a 1H-NMR-driven metabolomic comparison of urinary changes linked to T2D both in animals (obese mice and rats with autosomal recessive defects in the leptin receptor gene – db/db and Zucker fa/fa, respectively), and individuals who were overweight to obsese (BMI = 25–40) compared to healthy lean controls. An increased excretion of a product of choline biotransformation, namely N,N-dimethylglycine and N,N-dimethylamine, distinguished the urinary metabolome of T2D in all species in the study, suggesting a possible increase in the choline turnover. The authors assumed that the diabese phenotype may have a major demand for choline, possibly due to an altered biosynthesis of lipoproteins, an altered metabolism of methylamines – which would play an important osmoregulatory role by degrading dietary choline – or to an altered intestinal microbiota composition. Although there is a scarcity of data in this regard, Firmicutes and Proteobacteria seem to be the most implicated phyla in the conversion of choline to TMAO. For this reason, the decline of choline circulating levels and increase of choline subproducts such as TMAO and DMA in obese subjects would be in line with the increase of the Firmicutes phylum associated with obese phenotype.
The role of BMI in the observed association was partly downsized in a second study. By applying a similar untargeted and 1HNMR-driven approach, in fact, demonstrated a decrease in the serum levels of choline in normal-weight subjects with T2D (BMI = 25.9 ± 9.0) compared to non-diabetic lean individuals (normal or IGT); however, no changes in the downstream products of choline metabolism were detected. Aside from increased insulin resistance, the decrease in serum choline is linked to a specific shift in the gut microbial community in the diabetic patients (relative increase in Firmicutes) and to an increase in the prevalence of T2D complications, as NAFLD. The role of the microbial community hosted by diabese subjects in altering choline metabolism was also tested by assessing the effects of antidiabetic medication. As shown in Table 1, observed increased serum levels of TMAO in overweight diabetic subjects receiving metformin treatment versus untreated diabese controls, which may indicate an intestinal bacterial regulation function of metformin. It has already been suggested that antidiabetic treatments have a beneficial effect on gut microbiota metabolism, although the exact mechanisms are still unclear. The authors suggested a link between the deregulation of choline metabolism in T2D and a rupture of the intestinal barrier by oxidative stress. In any case, a possible two-way relationship between anti-T2D treatment and gut microbiota has been hypothesized.
Niacin Metabolism
Alterations in the niacin (vitamin B3) metabolism have also been observed in association with obesity and T2D, and due to the overlapping in the choline/niacin catabolic pathways (i.e., via betaine and glycine metabolism), may also reflect a dysregulation in choline metabolism. Through a LC–MS-driven metabolomic approach, an altered urinary excretion of nicotinuric acid (N-nicotinoyl-glycine) was recently proposed as a potential marker of metabolic syndrome diagnostic traits and of cardiometabolic risk. Similarly, an association between the presence of trigonelline (betaine nicotinate) and obese and diabetic phenotypes has been proposed. Despite having a possible exogenous (dietary) origin, trigonelline is mostly biosynthesized by the gut microbiota during the conversion of S-adenosylmethionine to S-adenosylhomocysteine (methionine cycle). By applying a 1H-NMR-based metabolomics approach, found lower levels of trigonelline in the urine of diabetic (db/db) mice and humans, associated with a change in energy and tryptophan metabolism. Further animal and human studies confirmed a decline of trigonelline in obesity and diabetes, some authors suggesting that oxidative stress possibly has a role (i.e., via glutathione store depletion) in the observed relationship. Trigonelline is known to be involved in major physiological functions including lipid and carbohydrate metabolism.
Co-metabolism of Organic Acids and Derivates
identified high levels of 2-hydroxyisobutyrate in the urine of obese people, and the change was associated with a reduced bacterial diversity in ‘obese’ gut microbiota possibly involved in nutrient and energy harvest (Tables 1 and 2). In particular, 2-hydroxyisobutyrate is a product of the microbial degradation of dietary proteins that escape digestion in the upper gastrointestinal tract, and its production has been associated with the presence of specific microbial members such as Faecalibacterium prausnitzii, butyrate-producer species with anti-inflammatory effect and to be in low levels in obese and diabese individuals compared to healthy subjects. In addition, observed that obese subjects involved in a dietary weight loss program had higher levels of tricarballylic acid after an OGTT compared with the fasting concentrations. Interestingly, tricarballylic acid is a product of gut microbial metabolism of food-derived trans-aconitate, described as an additive contained in the OGTT solution. Once again, the authors suggested a two-way relationship between the obese and gut microbial phenotype (tricarballylic acid production would in turn increase the metabolic activity of specific gut microbes associated with its production).
Co-metabolism of Phytochemicals and Purines
Hippuric acid and 3-hydroxyhippuric acid are two normal urinary components mainly derived from the degradation of plant (poly)phenols and aromatic amino acids (i.e., phenylalanine and tryptophan) by a range of gut microbes, recently found to belong to Clostridium sp.. The resulting benzoic acid is then absorbed, subjected to glycine conjugation reaction (by mitochondrial glycine N-acyltransferase) and finally excreted in urine. Decreased levels of hippuric acid and 3-hydroxyhippuric acid in urine have been related to IGT and obesity in both animal and human studies. In turn, the downregulation was reduced in T2D patients after the treatment with sulfonylurea, suggesting the drug potentially has a protector effect on gut microbiota metabolism. However, a strict dietary assessment is mandatory to dismiss any diet-dependent variation among groups, due to the wide range of phenolic compounds leading to these last-step metabolites following microbial and human biotransformations (lack of specificity; ). Moreover, the reasons for their putative associations with obesity and T2D are unknown. suggested that hippurate could be related to age progression and gender effects on metabolism in T2D, but these suppositions need to be further investigated.
observed that subjects with IGT had a reduced excretion of methyluric acid and methylxanthine, which are products of the microbial metabolism of methylxanthines contained for instance in coffee and tea. The authors tentatively interpreted the observed changes as the result of an altered gut microbiota in the presence of insulin resistance, although their putative role in glucose metabolism is still unknown.
Conclusion
Current public health burdens such as obesity and T2D are complex, polygenic, multifactorial diseases with a strong metabolic etiology. Gut microbiota have recently been proposed as a crucial environmental factor in their development, but the metabolic complexity of the symbiotic interaction between the host individual and its microbial community, as well as the impact of this crosstalk between body weight changes and glucose homeostasis, are still unclear.
However, our review highlighted how few metabolomic studies have been specifically conducted so far to identify the role of the gut microbiota in the development and progression of obesity and T2D, at least in humans.
Despite the scarcity, heterogeneity and intrinsic limitations of the metabolomic studies conducted so far aimed at identifying the role of the gut microbiota in the development and progression of obesity and T2D (i.e., wide range of BMI and/or glycemic status evaluated, important sources of variability not considered including ethnic, gender effects, and dietary assessments), the results obtained by these data-driven metabolomic approaches are in line with findings independently obtained from in vitro or animal model studies. Products of the microbial/host metabolism of BA, vitamins (choline, niacin), branched fatty acids, purines and phenolic compounds have been described as being altered in (pre-)diabetic subjects, with or without increased BMI, compared with healthy controls. Moreover, few articles show a clear relation between metabolites and their bacterial producers in terms of the complexity of the gut microbiota and the cross-feeding mechanisms that would have combined bacterial effects in the colon ecosystem.
More efforts should be directed in the future toward expanding our knowledge of the metabolic interactions of the host and the gut microbiota, particularly through a strict evaluation of the lifestyle factors (i.e., diet) strongly involved in the modulation of this crosstalk.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References 86
- 1.
Astrup A. Finer N. (2000 ). Redefining type 2 diabetes: “diabesity” or “obesity dependent diabetes mellitus”? Obes. Rev. 1 57–59.
Not in our library10.1046/j.1467-789x.2000.00013.x
- 2.
Bäckhed F. Ley R. E. Sonnenburg J. L. Peterson D. A. Gordon J. I. (2005 ). Host-bacterial mutualism in the human intestine. Science 307 1915–1920.
Not in our library10.1126/science.1104816
- 3.
Bäckhed F. Manchester J. K. Semenkovich C. F. Gordon J. I. (2007 ). Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. U.S.A. 104 979–984.
Not in our library10.1073/pnas.0605374104
- 4.
Beckonert O. Coen M. Keun H. C. Wang Y. Ebbels T. M. D. Holmes E. (2010 ). High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat. Protoc. 5 1019–1032.
Not in our library10.1038/nprot.2010.45
- 5.
Begley M. Gahan C. G. M. Hill C. (2005 ). The interaction between bacteria and bile. FEMS Microbiol. Rev. 29 625–651.
Not in our library10.1016/j.femsre.2004.09.003
- 6.
Blusztajn J. K. (1998 ). Choline, a vital amine. Science 281 794–795.
Not in our library10.1126/science.281.5378.794
- 7.
Calvani R. Miccheli A. Capuani G. Tomassini Miccheli A. Puccetti C. Delfini M. (2010 ). Gut microbiome-derived metabolites characterize a peculiar obese urinary metabotype. Int. J. Obes. (Lond.) 34 1095–1098.
Not in our library10.1038/ijo.2010.44
- 8.
Campbell C. Grapov D. Fiehn O. Chandler C. J. Burnett D. J. Souza E. C. (2014 ). Improved metabolic health alters host metabolism in parallel with changes in systemic xeno-metabolites of gut origin. PLoS ONE 9:e84260
Not in our library10.1371/journal.pone.0084260
- 9.
Cani P. D. Amar J. Iglesias M. A. Poggi M. Knauf C. Bastelica D. (2007 ). Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56 1761–1772.
Not in our library10.2337/db06-1491
- 10.
Cani P. D. Osto M. Geurts L. Everard A. (2012 ). Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity. Gut Microbes 3 279–288.
Not in our library10.4161/gmic.19625
- 11.
Craciun S. Balskus E. P. (2012 ). Microbial conversion of choline to trimethylamine requires a glycyl radical enzyme. Proc. Natl. Acad. Sci. U.S.A. 109 21307–21312.
Not in our library10.1073/pnas.1215689109
- 12.
Delzenne N. M. Cani P. D. (2011 ). Interaction between obesity and the gut microbiota: relevance in nutrition. Annu. Rev. Nutr. 31 15–31.
Not in our library10.1146/annurev-nutr-072610-145146
- 13.
Dewulf E. M. Cani P. D. Claus S. P. Fuentes S. Puylaert P. G. B. Neyrinck A. M. (2013 ). Insight into the prebiotic concept: lessons from an exploratory, double blind intervention study with inulin-type fructans in obese women. Gut 62 1112–1121.
Not in our library10.1136/gutjnl-2012-303304
- 14.
DiBaise J. K. Zhang H. Crowell M. D. Krajmalnik-Brown R. Decker G. A. Rittmann B. E. (2008 ). Gut microbiota and its possible relationship with obesity. Mayo Clin. Proc. 83 460–469.
Not in our library10.4065/83.4.460
- 15.
Du F. Virtue A. Wang H. Yang X.-F. (2013 ). Metabolomic analyses for atherosclerosis, diabetes, and obesity. Biomark. Res. 1:17
Not in our library10.1186/2050-7771-1-17
- 16.
Dumas M.-E. Barton R. H. Toye A. Cloarec O. Blancher C. Rothwell A. (2006 ). Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U.S.A. 103 12511–12516.
Not in our library10.1073/pnas.0601056103
- 17.
Duncan S. H. Holtrop G. Lobley G. E. Calder A. G. Stewart C. S. Flint H. J. (2004 ). Contribution of acetate to butyrate formation by human faecal bacteria. Br. J. Nutr. 91 915–923.
Not in our library10.1079/bjn20041150
- 18.
Faber J. H. Malmodin D. Toft H. Maher A. D. Crockford D. Holmes E. (2007 ). Metabonomics in diabetes research. J. Diabetes Sci. Technol. 1 549–557.
Not in our library10.1177/193229680700100413
- 19.
Fukiya S. Arata M. Kawashima H. Yoshida D. Kaneko M. Minamida K. (2009 ). Conversion of cholic acid and chenodeoxycholic acid into their 7-oxo derivatives by Bacteroides intestinalis AM-1 isolated from human feces. FEMS Microbiol. Lett. 293 263–270.
Not in our library10.1111/j.1574-6968.2009.01531.x
- 20.
Gibson G. R. Scott K. P. Rastall R. A. Tuohy K. M. Hotchkiss A. Dubert-Ferrandon A. (2010 ). Dietary prebiotics: current status and new definition. Food Sci. Technol. Bull. Funct. Foods 7 1–19.
Not in our library10.1616/1476-2137.15880
- 21.
Gipson G. T. Tatsuoka K. S. Ball R. J. Sokhansanj B. A. Hansen M. K. Ryan T. E. (2008 ). Multi-platform investigation of the metabolome in a leptin receptor defective murine model of type 2 diabetes. Mol. Biosyst. 4 1015–1023.
Not in our library10.1039/b807332e
- 22.
Guan M. Xie L. Diao C. Wang N. Hu W. Zheng Y. (2013 ). Systemic perturbations of key metabolites in diabetic rats during the evolution of diabetes studied by urine metabonomics. PLoS ONE 8:e60409
Not in our library10.1371/journal.pone.0060409
- 23.
Hadacek F. (2015 ). Low-molecular-weight metabolite systems chemistry. Front. Environ. Sci. 3:12
Not in our library10.3389/fenvs.2015.00012
- 24.
- 25.
Hofmann A. F. Hagey L. R. (2008 ). Bile acids: chemistry, pathochemistry, biology, pathobiology, and therapeutics. Cell. Mol. Life Sci. 65 2461–2483.
Not in our library10.1007/s00018-008-7568-6
- 26.
Hosseini E. Grootaert C. Verstraete W. Van de Wiele T. (2011 ). Propionate as a health-promoting microbial metabolite in the human gut. Nutr. Rev. 69 245–258.
Not in our library10.1111/j.1753-4887.2011.00388.x
- 27.
Hu F. B. Manson J. E. Stampfer M. J. Colditz G. Liu S. Solomon C. G. (2001 ). Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N. Engl. J. Med. 345 790–797.
Not in our library10.1056/nejmoa010492
- 28.
Huang C.-F. Cheng M.-L. Fan C.-M. Hong C.-Y. Shiao M.-S. (2012 ). Nicotinuric acid: a potential marker of metabolic syndrome through a metabolomics-based approach. Diabetes Care 36 1729–1731.
Not in our library10.2337/dc12-1067
- 29.
Huo T. Cai S. Lu X. Sha Y. Yu M. Li F. (2009 ). Metabonomic study of biochemical changes in the serum of type 2 diabetes mellitus patients after the treatment of metformin hydrochloride. J. Pharm. Biomed. Anal. 49 976–982.
Not in our library10.1016/j.jpba.2009.01.008
- 30.
Huo T. Xiong Z. Lu X. Cai S. (2015 ). Metabonomic study of biochemical changes in urinary of type 2 diabetes mellitus patients after the treatment of sulfonylurea antidiabetic drugs based on ultra-performance liquid chromatography/mass spectrometry. Biomed. Chromatogr. 29 115–122.
Not in our library10.1002/bmc.3247
- 31.
Jones M. L. Martoni C. J. Ganopolsky J. G. Labbé A. Prakash S. (2014 ). The human microbiome and bile acid metabolism: dysbiosis, dysmetabolism, disease and intervention. Exp. Opin. Biol. Ther. 14 467–482.
Not in our library10.1517/14712598.2014.880420
- 32.
Kawamata Y. Fujii R. Hosoya M. Harada M. Yoshida H. Miwa M. (2003 ). A G protein-coupled receptor responsive to bile acids. J. Biol. Chem. 278 9435–9440.
Not in our library10.1074/jbc.m209706200
- 33.
Koeth R. A. Wang Z. Levison B. S. Buffa J. A. Org E. Sheehy B. T. (2013 ). Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19 576–585.
Not in our library10.1038/nm.3145
- 34.
Kootte R. S. Vrieze A. Holleman F. Dallinga-Thie G. M. Zoetendal E. G. de Vos W. M. (2012 ). The therapeutic potential of manipulating gut microbiota in obesity and type 2 diabetes mellitus. Diabetes Obes. Metab. 14 112–120.
Not in our library10.1111/j.1463-1326.2011.01483.x
- 35.
Kurland I. J. Accili D. Burant C. Fischer S. M. Kahn B. B. Newgard C. B. (2013 ). Application of combined omics platforms to accelerate biomedical discovery in diabesity. Ann. N. Y. Acad. Sci. 1287 1–16.
Not in our library10.1111/nyas.12116
- 36.
- 37.
- 38.
Lefebvre P. Cariou B. Lien F. Kuipers F. Staels B. (2009 ). Role of bile acids and bile acid receptors in metabolic regulation. Physiol. Rev. 89 147–191.
Not in our library10.1152/physrev.00010.2008
- 39.
Lever M. Slow S. (2010 ). The clinical significance of betaine, an osmolyte with a key role in methyl group metabolism. Clin. Biochem. 43 732–744.
Not in our library10.1016/j.clinbiochem.2010.03.009
- 40.
Ley R. E. Bäckhed F. Turnbaugh P. Lozupone C. A. Knight R. D. Gordon J. I. (2005 ). Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. U.S.A. 102 11070–11075.
Not in our library10.1073/pnas.0504978102
- 41.
Ley R. E. Turnbaugh P. J. Klein S. Gordon J. I. (2006 ). Microbial ecology: human gut microbes associated with obesity. Nature 444 1022–1023.
Not in our library10.1038/4441022a
- 42.
Li M. Wang B. Zhang M. M. Rantalainen M. Wang S. Zhou H. (2008 ). Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl. Acad. Sci. U.S.A. 105 2117–2122.
Not in our library10.1073/pnas.0712038105
- 43.
Llorach R. Garcia-Aloy M. Tulipani S. Vazquez-fresno R. Andres-lacueva C. (2012 ). Nutrimetabolomic strategies to develop new biomarkers of intake and health effects. J. Agric. Food Chem. 60 8797–8808.
Not in our library10.1021/jf301142b
- 44.
Messana I. Forni F. Ferrari F. Rossi C. Giardina B. Zuppi C. (1998 ). Proton nuclear magnetic resonance spectral profiles of urine in type II diabetic patients. Clin. Chem. 44 1529–1534.
Not in our library
- 45.
Micha R. Wallace S. K. Mozaffarian D. (2010 ). Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Circulation 121 2271–2283.
Not in our library10.1161/circulationaha.109.924977
- 46.
Midtvedt T. (1974 ). Microbial bile acid. Am. J. Clin. Nutr. 27 1341–1347.
Not in our library10.1093/ajcn/27.11.1341
- 47.
Mokdad A. H. Ford E. S. Bowman B. A. Dietz W. H. Vinicor F. Bales V. S. (2003 ). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289 76–79.
Not in our library10.1001/jama.289.1.76
- 48.
- 49.
Moreno-Navarrete J. M. Ortega F. Serino M. Luche E. Waget A. Pardo G. (2012 ). Circulating lipopolysaccharide-binding protein (LBP) as a marker of obesity-related insulin resistance. Int. J. Obes. (Lond.) 36 1442–1449.
Not in our library10.1038/ijo.2011.256
- 50.
Musso G. Gambino R. Cassader M. (2010 ). Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care 33 2277–2284.
Not in our library10.2337/dc10-0556
- 51.
Nguyen A. Bouscarel B. (2008 ). Bile acids and signal transduction: role in glucose homeostasis. Cell. Signal. 20 2180–2197.
Not in our library10.1016/j.cellsig.2008.06.014
- 52.
Nicholson J. K. Holmes E. Kinross J. Burcelin R. Gibson G. Jia W. (2012 ). Host-gut microbiota metabolic interactions. Science 336 1262–1267.
Not in our library10.1126/science.1223813
- 53.
Piche T. des Varannes S. B. Sacher-Huvelin S. Holst J. J. Cuber J. C. Galmiche J. P. (2003 ). Colonic fermentation influences lower esophageal sphincter function in gastroesophageal reflux disease. Gastroenterology 124 894–902.
Not in our library10.1053/gast.2003.50159
- 54.
Poirier P. Giles T. D. Bray G. A. Hong Y. Stern J. S. Pi-Sunyer F. X. (2006 ). Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition Physical Activity, and Metabolism. Circulation 113 898–918.
Not in our library10.1161/circulationaha.106.171016
- 55.
Prawitt J. Caron S. Staels B. (2011 ). Bile acid metabolism and the pathogenesis of type 2 diabetes. Curr. Diab. Rep. 11 160–166.
Not in our library10.1007/s11892-011-0187-x
- 56.
- 57.
Qin J. Li Y. Cai Z. Li S. S. Zhu J. Zhang F. (2012 ). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490 55–60.
Not in our library10.1038/nature11450
- 58.
- 59.
Romano K. A. Vivas E. I. Amador-Noguez D. Rey F. E. (2015 ). Intestinal microbiota composition modulates choline bioavailability from diet and accumulation of the proatherogenic metabolite trimethylamine-N-oxide. MBio 6:e02481
Not in our library10.1128/mbio.02481-14
- 60.
Salek R. M. Maguire M. L. Bentley E. Rubtsov D. V. Hough T. Cheeseman M. (2007 ). A metabolomic comparison of urinary changes in type 2 diabetes in mouse, rat, and human. Physiol. Genomics 29 99–108.
Not in our library10.1152/physiolgenomics.00194.2006
- 61.
Samuel B. S. Shaito A. Motoike T. Rey F. E. Backhed F. Manchester J. K. (2008 ). Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41. Proc. Natl. Acad. Sci. U.S.A. 105 16767–16772.
Not in our library10.1073/pnas.0808567105
- 62.
Sanz Y. Rastmanesh R. Agostoni C. Agostonic C. (2013 ). Understanding the role of gut microbes and probiotics in obesity: how far are we? Pharmacol. Res. 69 144–155.
Not in our library10.1016/j.phrs.2012.10.021
- 63.
Scalbert A. Brennan L. Fiehn O. Hankemeier T. Kristal B. S. van Ommen B. (2009 ). Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics 5 435–458.
Not in our library10.1007/s11306-009-0168-0
- 64.
Schwiertz A. Taras D. Schäfer K. Beijer S. Bos N. A. Donus C. (2010 ). Microbiota and SCFA in lean and overweight healthy subjects. Obesity (Silver Spring) 18 190–195.
Not in our library10.1038/oby.2009.167
- 65.
Shen J. Obin M. S. Zhao L. (2013 ). The gut microbiota, obesity and insulin resistance. Mol. Aspects Med. 34 39–58.
Not in our library10.1016/j.mam.2012.11.001
- 66.
Shoaie S. Ghaffari P. Kovatcheva-Datchary P. Mardinoglu A. Sen P. Pujos-Guillot E. (2015 ). Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22 320–331.
Not in our library10.1016/j.cmet.2015.07.001
- 67.
- 68.
Sun L. Yu Z. Ye X. Zou S. Li H. Yu D. (2010 ). A marker of endotoxemia is associated with obesity and related metabolic disorders in apparently healthy Chinese. Diabetes Care 33 1925–1932.
Not in our library10.2337/dc10-0340
- 69.
Swann J. R. Want E. J. Geier F. M. Spagou K. Wilson I. D. Sidaway J. E. (2011 ). Systemic gut microbial modulation of bile acid metabolism in host tissue compartments. Proc. Natl. Acad. Sci. U.S.A. 108(Suppl.), 4523–4530.
Not in our library10.1073/pnas.1006734107
- 70.
Thomas C. Gioiello A. Noriega L. Strehle A. Oury J. Rizzo G. (2009 ). TGR5-mediated bile acid sensing controls glucose homeostasis. Cell Metab. 10 167–177.
Not in our library10.1016/j.cmet.2009.08.001
- 71.
Thomas C. Pellicciari R. Pruzanski M. Auwerx J. Schoonjans K. (2008 ). Targeting bile-acid signalling for metabolic diseases. Nat. Rev. Drug Discov. 7 678–693.
Not in our library10.1038/nrd2619
- 72.
Tilg H. Moschen A. R. Kaser A. (2009 ). Obesity and the microbiota. Gastroenterology 136 1476–1483.
Not in our library10.1053/j.gastro.2009.03.030
- 73.
Tremaroli V. Bäckhed F. (2012 ). Functional interactions between the gut microbiota and host metabolism. Nature 489 242–249.
Not in our library10.1038/nature11552
- 74.
Turnbaugh P. J. Gordon J. I. (2009 ). The core gut microbiome, energy balance and obesity. J. Physiol. 587 4153–4158.
Not in our library10.1113/jphysiol.2009.174136
- 75.
Venema K. (2010 ). Role of gut microbiota in the control of energy and carbohydrate metabolism. Curr. Opin. Clin. Nutr. Metab. Care 13 432–438.
Not in our library10.1097/mco.0b013e32833a8b60
- 76.
Vrieze A. Holleman F. Zoetendal E. G. de Vos W. M. Hoekstra J. B. L. Nieuwdorp M. (2010 ). The environment within: how gut microbiota may influence metabolism and body composition. Diabetologia 53 606–613.
Not in our library10.1007/s00125-010-1662-7
- 77.
Vrieze A. Out C. Fuentes S. Jonker L. Reuling I. Kootte R. S. (2014 ). Impact of oral vancomycin on gut microbiota, bile acid metabolism, and insulin sensitivity. J. Hepatol. 60 824–831.
Not in our library10.1016/j.jhep.2013.11.034
- 78.
Wang Z. Klipfell E. Bennett B. J. Koeth R. Levison B. S. Dugar B. (2011 ). Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472 57–63.
Not in our library10.1038/nature09922
- 79.
Warrier M. Shih D. M. Burrows A. C. Ferguson D. Gromovsky A. D. Brown A. L. (2015 ). The TMAO-generating enzyme flavin monooxygenase 3 is a central regulator of cholesterol balance. Cell Rep. [Epub ahead of print].
Not in our library10.1016/j.celrep.2014.12.036
- 80.
Watanabe M. Houten S. M. Mataki C. Christoffolete M. A. Kim B. W. Sato H. (2006 ). Bile acids induce energy expenditure by promoting intracellular thyroid hormone activation. Nature 439 484–489.
Not in our library10.1038/nature04330
- 81.
Wei L. Liao P. Wu H. Li X. Pei F. Li W. (2008 ). Toxicological effects of cinnabar in rats by NMR-based metabolic profiling of urine and serum. Toxicol. Appl. Pharmacol. 227 417–429.
Not in our library10.1016/j.taap.2007.11.015
- 82.
- 83.
World Health Organization [WHO] (2013 ). Obesidad y Sobrepeso, 311 Available at: http://www.who.int/mediacentre/factsheets/fs311/en/ [Accessed April 2 2014].
Not in our library
- 84.
Zeisel S. H. (2000 ). Choline: an essential nutrient for humans. Nutrition 16 669–671.
Not in our library10.1016/s0899-9007(00)00349-x
- 85.
Zhang X. Wang Y. Hao F. Zhou X. Han X. Tang H. (2009 ). Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J. Proteome Res. 8 5188–5195.
Not in our library10.1021/pr900524z
- 86.
Zhao X. Fritsche J. Wang J. Chen J. Rittig K. Schmitt-Kopplin P. (2010 ). Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics 6 362–374.
Not in our library10.1007/s11306-010-0203-1
Cited by papers we hold 5
- Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota · 2022No notice
- Beneficial effects of exercise on gut microbiota functionality and barrier integrity, and gut-liver crosstalk in an in vivo model of early obesity and non-alcoholic fatty liver disease · 2019No notice
- Abscisic Acid as Pathogen Effector and Immune Regulator · 2017No notice
- Introduction to the human gut microbiota · 2017No notice
- Detection of Increased Plasma Interleukin-6 Levels and Prevalence of Prevotella copri and Bacteroides vulgatus in the Feces of Type 2 Diabetes Patients · 2017No notice
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.