The microbial-mammalian metabolic axis has become recognized as an important component governing the overall homeostatic balance of the mammalian host. Disruption of the state of homeostasis among the gut microbiota has been shown to be causally linked to the development of host metabolic diseases including obesity, cardiovascular, diabetes, and fatty liver disease. This disruption is often referred to as gut dysbiosis. Gut dysbiosis leads to altered metabolic products derived from the microbiota and these in turn, typically shift the homeostatic metabolic balance of the host towards a low-grade chronic inflammation, a hallmark of metabolic syndrome. The primary objective of this review is to examine and discuss some very current research that has been done to study the effect of bacterial metabolites on host metabolism, sometimes referred to as microbiota-host co-metabolism. The metabolic conditions reviewed here include obesity, a known risk factor for all of the other metabolic conditions, as well as, cardiovascular disease, diabetes and nonalcoholic fatty liver disease. Only by further understanding the cause and result of gut dysbiosis will an adequate solution be found for metabolic disease, a viewpoint shared by many.

Introduction

The microbiota consists of diverse populations of bacteria, archaea, viruses and fungi that are found in multiple sites of the body but achieve their greatest abundance in the gastrointestinal tract, especially in the large intestine [1]. In humans, the gut microbiota community is dominated by bacteria with ∼90% of the species belonging to the phyla Firmicutes (contains genera, Ruminococcus, Clostridium, Lactobacillus, Eubacterium, Faecalibacterium, Roseburia) and Bacteriodetes (contains genera, Bacteroides, Prevotella, Xylanibacter). Other dominant bacterial phyla in humans include, Actinobacteria (contains the genera, Collinsella, Bifidobacterium), Proteobacteria (contains genera, Eschericia, Desulfovibrio) and Verrocumicrobia (contains the mucus degrading genera, Akkermansia). The gut microbiota directly impact the metabolic status of their hosts by regulating the energy yield from food and therefore, modulate the levels of host and diet-derived products which, in turn, directly control many metabolic pathways [2]. It is thus logical to hypothesize that disruption of the gut microbiota contributes to the development of metabolic diseases in the host.

One of the most studied risk factors for the development of metabolic disease is obesity. The gut microbiota affect host adiposity through their ability to hydrolyze and ferment otherwise host-indigestible dietary polysaccharides to short-chain fatty acids (SCFA) and increase the energy harvest for the host. The ability of this energy harvest to affect adiposity and weight gain has been demonstrated in germ-free (GF) mice relative to conventionally raised mice. GF mice have been shown to develop less body fat than conventionally raised mice despite higher food intake and lower metabolic rates [3]. They are also more resistant to diet-induced obesity (DIO) [4] and when colonized with microbiota from ob/ob mice, they showed a higher percentage increase in body weight relative to GF mice conventionalized with microbiota from lean mice [5]. The production of SCFAs by the gut microbiota contributes ∼10% to energy needs of the host. SCFAs are also important signaling molecules that bind to the intestinal epithelial cell (IEC) expressed G-protein coupled receptors, GPR43 and GPR41. When GPR43 is activated by the SCFA, acetate or propionate, there is increased secretion of the hormone glucagon-like peptide-1 (GLP-1) which acts to decrease GI motility resulting in increased energy harvest and feelings of satiety. When GPR41 is activated by the SCFA, butyrate or propionate, there is an increase in levels of peptide YY (PYY) that also causes decreased GI motility and increased energy harvest. In the context of overfeeding, however, the consequences of IEC GPR41, 43 activation can lead to obesity [6,7].

A second, important risk factor for the development of metabolic disease is disruption of the circadian rhythm. Individuals with altered sleep cycles such as those engaged in shift work or frequently flying across time zones were shown to exhibit a propensity for metabolic disease [8,9]. Loss of gut microbiota diurnal oscillations in time-shifted mice was found to be caused by dysbiosis and led to obesity and glucose intolerance which were subsequently rescued by antibiotic treatment. GF mice given fecal material from the time-shifted mice became obese and glucose intolerant relative to controls indicating gut microbiota involvement in both the weight gain and development of metabolic disease [10].

Chronic inflammation that exists where there is no acute threat and which never becomes resolved is another hallmark of metabolic disease. Instead there is a shift in the host’s homeostatic balance that results in altered gut microbiota colonization or “dysbiosis” [11]. The altered gut microbiota drive the inflammation via their ability to produce luminal antigens such as lipopolysaccharide (LPS) which increase intestinal permeability [12], alter the intestinal bile acid composition [13,14], decrease levels of intestinal alkaline phosphatase which is involved in LPS detoxification [15], produce lymphotoxin [16], and activate both toll-like receptors [17], and inflammasomes [18].

In this review we will examine current research directed at the involvement of the gut microbiota in the development and progression of metabolic diseases such as obesity, nonalcoholic fatty liver disease (NAFLD), type 2 diabetes (T2D), cardiovascular disease (CVD), and cancer. An emphasis will be placed on the adverse effects of microbial metabolites on host metabolic health, their use as biomarkers with prognostic value and the possibility of being able to remodel the gut microbiota to alleviate or prevent metabolic disease. We have elected to cover a broad range of metabolic diseases and highlight some select current research for each rather than do a comprehensive literature review for a given condition. The papers discussed here relate to a specific metabolic disease, are current (published in the last 2–3 years) and relate to the risk factors and the selected emphasis topics as described above. Novelty of approach to the study of a particular condition and the potential for clinical translation were also considered.

The link between gut microbiota and obesity

Obesity is one of the greatest risk factors for the development of metabolic disease and cancer [19]. In this section we highlight the involvement of the gut microbiota, (1) in their response to different types of fat in the HFD model and their alteration of cholesterol metabolism to induce DIO, (2) the microbiota response to high sugar diets causing loss of intestinal innervation, (3) the possibility of producing a lean gut microbiota phenotype at low temperature that could potentially be used to transplant thermoneutral individuals as an anti-obesity therapy.

The link between obesity and the gut microbiota has been studied extensively using GF mouse models, however, some of the results have been conflicting. For example, it was shown that the resistance of GF mice to diet-induced obesity depended on the obesogenic diets that were employed [4,20]. It is therefore important to carefully define the diet composition for these types of obesity studies. This protocol was followed in a 2016 study on the interaction of diet and gut microbiota in DIO, employing conventional, specific pathogen-free (SPF) and GF male C57BL/6N mice to assess host energy balance in response to different dietary fat sources [21]. Two different high-fat diets (HFD) were used, 20 wt% palm oil (PHFD) vs. 20 wt% lard (LHFD) with other constituents constant. The conventional diet (CD) used for control had neither of these two fats but contained 5 wt% soy oil which was present in the same amounts in both HFDs as well. The results of HFD were different based on the type of fat fed to the GF mice. Only the LHFD GF mice were found to be resistant to DIO whereas those fed PHFD exhibited significantly increased body mass gain that was not significantly different from SPF PHFD controls, indicating that presence or absence of gut microbiota had no effect on weight gain for PHFD mice. Additionally, the basal metabolic rate (BMR) was found to be significantly affected by microbiota status for LHFD but not PHFD for GF vs. SPF mice. The BMR was highest in LHFD GF mice relative to all other groups and 13.1% higher than the SPF controls. In order to assess whether the GF status and its differential susceptibility to DIO were associated with metabolic substrate utilization, respiratory exchange rates (RER) were measured. GF and SPF mice fed CD had distinct day-night rhythm in RER with a rise during the nocturnal activity phase, indicating preferential carbohydrate oxidation, and a decrease during the resting day when fat oxidation occurs. This diurnal rhythm was attenuated in both GF and SPF HFD mice and was completely abolished in GF PHFD mice which had constantly low RER values and thus preference for fat oxidation throughout the day. Comparison of GF mice on LHFD vs. PHFD indicated significantly more fecal fat content in LHFD. Thus an increased BMR plus higher energy loss via higher fecal fat excretion was proposed to explain the diminished body fat accumulation in LHFD vs. PHFD GF mice. Notably, increased fat absorption and weight gain was associated with the increased amounts of the primary bile acids (BAs), taurocholic acid (TCA), and tauromuricholic acid (TMCA) in PHFD GF mice. The difference in energy balance between lean (LHFD) and obese GF (PHFD) mice was diminished upon microbiota colonization and a few LHFD-specific bacteria were identified which may have contributed to the obese phenotype in LHFD SPF mice. These included bacteria from the genus Acetatifactor which showed a positive correlation with BA levels of lithocholic acid (LCA) and ursodeoxycholic acid (UDCA) and also the species, Eubacterium coprostanoligenes. Acetatifactor muris has been previously isolated from the cecum of obese mice [22] and Eubacterium coprastanoligenes is able to produce coprostanol from cholesterol [23]. Overall, SPF mice fed either LHFD or PHFD exhibited increased abundances of Clostridiales spp. (Firmicutes phylum) and decreased Bacteriodes spp. (Bacteriodetes phylum). Metabolomic analysis revealed increased levels of 17β-estradiol in lean GF mice fed CD or LHFD in agreement with other studies which have provided evidence for the role of 17β-estradiol in energy balance regulation. The authors proposed that increased levels of 17β-estradiol, a cholesterol derivative, could interfere with BA metabolism to decrease fat absorption and promote DIO resistance in LHFD GF mice [24]. Finally, it was concluded that the metabolic fate of dietary cholesterol is different between GF and SPF mice and was associated with the change from DIO resistance to obesity upon gut microbiota colonization in LHFD mice [21]. The finding that the host without input from microbiota metabolizes cholesterol differently, i.e., to 17β-estradiol which causes DIO resistance provides additional strong evidence and further explanation for the causality of obesity via gut microbiota. It also highlighted the importance of choice of fat when performing animal studies involving HFD. Further studies are needed to identify and assess the functional role of sterol metabolites such as 17β-estradiol in resistance to DIO and to the identification of the microbes which may subvert the metabolism of cholesterol down a different pathway to cause DIO.

Obesity research traditionally has used the HFD protocol and there have been fewer investigations into the effects of high sugar (HSD) in combination with high- or low-fat diets (LFD). In a 2017 study, utilizing Sprague–Dawley rats, the effects of HSD in combination with either HFD or LFD were examined using low sugar LSD/LFD controls on microbiota composition, gut inflammation, gut-brain vagal communication and body fat accumulation [25]. Both LFD/HSD and HFD/HSD diets led to a significant increase in body fat and weight relative to the LFD/LSD controls leading to the conclusion that HSD can promote obesity. However, a HFD/LSD group was not included in the study so it could not be determined if there was any synergy between HFD and HSD. Both LFD/HSD and HFD/HSD groups had to consume fewer calories to gain a gram of body fat than the LFD/LSD group indicating that the LFD/HSD diet modified body fat metabolism and storage similar to the HFD/HSD diet. Liver fat accumulation was significantly higher for both HFD/HSD and LFD/HSD rats relative to the LFD/LSD group but was not different between HFD/HSD and LFD/HSD groups. Microbiota were characterized at baseline, 1 day, 6 days, and 4 weeks after 4 weeks of diet intervention. By week 4, both LFD/HSD and HFD/HSD microbiota were clustered together via PCA analysis indicating that regardless of fat content, diets rich in sugar induced rapid dysbiosis. The dysbiosis was characterized by a loss of diversity. Both HSDs led to a significant increase in Firmicutes and a decrease in Bacteriodetes phyla abundances with more marked changes seen for the HFD group. Clostridia class abundance increased under HSD especially, the families, Ruminococcaceae and Lachnospiraceae but with a decrease in the probiotic class Lactobacillus. Specific increases in abundance for the HFD/HSD group included increases in the Actinobacteria phylum, especially genus Micrococcacae and also increases in the family, Anaeroplasmatales, while specific increases in abundance for the LFD/HSD group included those in phylum Proteobacteria, especially the genera, Sutterella and Bilophila. HSD consumption regardless of fat consumption also led to significant increases in cecal and circulating lipopolysacharide (LPS), promoted distal gut inflammation, and decreased cecal IB4 (used to detect vagal afferents) binding and β3-tubulin protein expression (neuron-specific marker) suggesting that HSD compromised gut innervation. From the combined results of this study, the authors propose that HSD, regardless of fat content, induced gut dysbiosis which in turn, induced gut inflammation that altered the vagal gut-brain communication axis via withdrawal of vagal innervations of the gut [25].

Exposure to cold can induce thermogenesis and alter the management of fuel for energy [26]. In a 2016 study, mice housed at 12°C showed attenuated DIO that was associated with acute (after 1 day of exposure to cold) changes in gut microbiota, increased iBAT thermogenesis and changes in the BA pool composition which was similar to that of GF mice [27]. The cold-exposed mice were compared with mice housed at 29°C. Two diets were tested, conventional chow (CD) and HFD. At 12°C, there was increased induction of BA synthetic enzymes, CYP7A1, CYP8B1, and CYP27A1 as well as increased production of taurine and enhanced taurine conjugation of BAs which led to a BA composition that consisted of mainly primary, conjugated BAs (TCA, TMCAs) regardless of diet, while mice housed at 29°C had a BA composition that was dominated by unconjugated BAs, CA and MCA. Increased expression of fibroblast growth factor-21 (FGF-21) and TGR5 were observed in iBAT at reduced temperature. To examine the changes that occurred in the gut microbiota upon cold exposure, mice previously housed at 29°C were transferred to 12°C. Within 1 day, at the phylum level, Deferribacteres increased and Verrucomicrobia decreased. Within 6 days, increased abundance of the phylum Bacteroidetes and decreased abundance of the phylum Firmicutes were observed. A bloom of the phylum Proteobacteria ( especially, family Desulfovibrionaceae) was also observed during long-term cold exposure. Within the phylum Actinobacter, the genus Adlercreutzia exhibited a significant increase in abundance at 12°C. At the family level, microbiota that increased due to cold exposure included, Bacteroidacaea (especially, genus, Bacteroides), Rikenellaceae, Porphyromonadaceae, and Deferribacteraceae. In contrast, the Firmicutes families, Lactobacilliaceae, Clostridiaceae, Dehalobacterium, Peptostreptococcaceae and Mogibacteriaceae were decreased in abundance at 12°C. GF mice were then colonized at ambient temperature (23°C) with the microbiota from the mice kept at 12°C and compared with GF mice colonized with microbiota from mice kept at 29°C, they had significantly reduced fat mass, lower adiposity, higher expression of uncoupling protein-1 (Ucp1), CYP7A1, Cyp8B1 mRNA, increased proportion of conjugated and reduced proportion of unconjugated and secondary BAs in plasma. Both glucose tolerance and increased hepatic β-oxidation was also observed for the 12°C microbiota transplants. Figure 1 summarizes the findings based on this work and others for the changes in the gut microbiota in response to cold and their mediation of BA synthesis and other metabolic factors to regulate thermogenesis and cause resistance to DIO [27–31]. Of particular note, is the fact that even at 23°C, the transplanted microbiota from cold-treated mice were capable of sustaining the improved metabolic status and resistance to DIO observed in cold-treated mice. It was proposed that the microbiota changes in response to cold exposure caused altered BA metabolism through changes in AMPK and FXR signaling that complimented sympathetic signaling in the regulation of iBAT thermogenesis. This offers hope for establishing a gut microbiota at normal physiological temperature that would similarly affect these pathways could flourish and be anti-obesogenic in humans. The findings of this study highlights the duality of the host-microbiota interactions with BAs, as another 2017 publication showed that BAs were a significant host factor for shaping the composition of the gut microbiota, especially in DIO mice. [32].

The effects of exposure to cold on the gut microbiota, metabolism and phenotype of HFD mice

Figure 1
The effects of exposure to cold on the gut microbiota, metabolism and phenotype of HFD mice

The effect of exposure to cold (12°C) on HFD mice. Exposure to cold leads to changes in the gut microbiota which results in altered host metabolism such that treatment with HFD has little effect as the mice are resistant to DIO.

Figure 1
The effects of exposure to cold on the gut microbiota, metabolism and phenotype of HFD mice

The effect of exposure to cold (12°C) on HFD mice. Exposure to cold leads to changes in the gut microbiota which results in altered host metabolism such that treatment with HFD has little effect as the mice are resistant to DIO.

The link between gut microbiota and circadian rhythm

Intestinal microbiota in both mice and humans have been documented to exhibit diurnal rhythms that are controlled by feeding/fasting cycles which correspond to the light/dark entrained central clock in the suprachiasmatic nucleus (SCN). When this circadian rhythm is disrupted by gut dysbiosis, glucose intolerance and obesity often result leading to metabolic disease. This effect of this induced “jet-lag” gut dysbiosis has also been shown to be transferrable from SPF to GF mice [10].

A 2016 study examined in great detail, the link between the microbiome and hepatic circadian clock oscillators [33]. In order to address the hypothesis as to whether the microbiome impacts liver physiology via specifically, the liver core clock, liver and blood samples were collected at many time points around the clock from GF and also SPF C57BL/6 mice. In GF mice, it was found that Cry1 gene expression was shifted toward the dark (active) period while Rev-erbβ, Per1, 2 expressions were shifted to the light (inactive) period relative to SPF mice. The expression pattern of the liver clock output transcription factors, albumin D-box binding protein (Dbp), thyrotroph embryonic factor (Tef), and basic helix-loop-helix factor Dec42 (Bhlhb42) showed modified gene expression patterns in GF relative to SPF mice. In GF mice, Cyp3a11 (xenobiotic metabolizing enzyme), Fasn (FA synthesis) which are target genes of LXRα and Cyp2b10 (drug/steroid metabolizing enzyme), Cyp4a14 (FA hydroxylation) along with FGF21 which are all target genes of peroxisome-proliferation-activated receptor alpha (PPARα) exhibited low expression with dampened daily oscillations relative to SPF mice. Plasma concentrations of HDL (↓), cholesterol (↑), bilirubin (↓),free fatty acids (FFAs) (↓), FGF21 (↓) and lactate (↑) showed significant changes at ZT0 (end of dark period) for GF relative to SPF mice [33]. This work thus demonstrated that the gut microbiome was important in establishing physiological oscillations in the liver, that it regulated core clock genes and their transcription factor effectors, influencing the nuclear receptors, LXRα and PPARα and their target genes. Figure 2 is a diagrammatic summary of this work. It should be mentioned that this study could not exclude hormonal or humoral mechanisms that could be affected by the gut microbiota. Female hormones and the estrus cycle, in particular, have been shown to affect intestinal microbiota circadian rhythms and were excluded from this study as only male mice were used [34].

The gut microbiota has also been shown to exhibit their own circadian patterns (CC) which result in the production of key metabolic mediators that are then integrated into the host’s metabolic CC for metabolic homeostasis [35]. In a 2015 study [35] of the effects of HFD on the CC of GF vs. SPF mice, both the mediobasal hypothalamus (MBH) and the liver were examined for changes in host CC gene expression (Figure 2). HFD was shown to induce the expression of aryl hydrocarbon receptor nuclear translocater-like protein-1 (Bmal1) and circadian locomotor output cycle kaput (Clock) in the MBH of SPF but not GF mice during the dark phase which suggested that microbes mediated this induction. However, HFD suppressed hepatic Bmal1 and thus affected hepatic CC in both GF and SPF mice but GF mice showed a lower expression of hepatic CC genes regardless of diet. In the MBH of SPF mice, HFD was also shown to increase period circadian clock-2 (Per2) and cryptochrome circadian clock-1 (Cry1) but decreased Per2 in the liver. In GF mice there was loss of hepatic expression of Per2 regardless of diet which again implicates the gut microbiota as mediators in the HFD induced CC gene expression. Having established a definite role for the microbiota in mediating the host’s CC patterns, the impact of HFD was examined on the gut microbiota structure/function. HFD was shown to significantly alter the microbial community composition. There was a loss of diversity and also blunted diurnal rhythms of microbiota abundance. Under CD, the majority of significant oscillating operational taxonomic units (OTUs) in both fecal and cecal contents belonged to the family Lachnospiraceae, which were mostly absent from HFD SPF mice. There was a significant HFD-induced increase in relative abundance of Ruminococcus, although diurnal oscillations were absent which indicated that the oscillatory nature of specific microbes may be more important than their relative abundance. HFD also induced significant oscillation in Lactococcus that was not evident in CD mice. Since Lachnospiraceae has been well documented to synthesize butyrate [36], it was hypothesized that there would be diurnal variations in SCFAs in CD mice. Cecal levels of butyrate did show distinct diurnal patterns in CD mice as did propionate but acetate, however, did not. The direct impact of SCFAs on the cycling of liver CC genes was then tested on hepatic organoids where treatment with butyrate caused significant shifts in the rhythm and increased amplitude of the CC genes, Per2 and Bmal1. Treatment with NaHS, a source of H2S was used to treat the organoids, there was loss of all phasic changes in both Bmal1 and Per2 gene expression. Mice that were constantly fed parenterally showed significant changes in gut microbiota composition but still maintained diurnal variations thus indicating the importance of host factors in microbial chronobiology. Furthermore, GF mice, when intraperitoneally treated with either saline of butyrate (1000 mg/kg) twice a day, showed no impact on the Per2:Bmal1 m RNA ratio in the MBH but that there was an increase observed in the liver [35]. These combined results therefore established not only the effect of diet and microbial shifts in composition/abundance on CC patterns but also that there was a direct link between microbe-derived metabolites and expression of CC genes that could partially explain HFD metabolomic shifts and was thus direct evidence for microbiota-host co-metabolism. A drawback for this study was that they only examined effects on male mice and the influence of hormones and the female estrus cycle on the CC were not elucidated. Although they refer to the idea of altering gut microbiota as a therapy for CC dysfunction/obesity, no recovery study of the gut microbiota induced CC in GF mice was done using the microbiota from the normal WT or the DIO mice was performed.

The hepatic circadian core clock and differences between the CC of PPARα and LXRα target gene expression for SPF vs. GF mice

Figure 2
The hepatic circadian core clock and differences between the CC of PPARα and LXRα target gene expression for SPF vs. GF mice

The circadian control of PPARα and LXRα target genes in GF vs. SPF mice. In hepatocytes, there exists a circadian core clock that consists of an autoregulated feedback loop of rhythmically expressed genes that oscillate within a 24 h period. Two important genes, Clock and Bmal1 comprise the forward segment of the clock loop. CLOCK and BMAL1 proteins form a heterodimer which then bind to the DNA response element, E-box, to cause the transcription of Per and Cry genes. PER and CRY proteins then re-enter the nucleus and inhibit CLOCK:BMAL1 protein activity to reduce their own transcription. There is further regulation of the feedback loop by the nuclear hormone receptors, Rev-erbα which negatively regulates and RORα that positively regulates Bmal1 transcription [37]. PPARα, LXRα and the co-activator, peroxisome proliferator-activated receptor gamma co-activator 1-α (PGC1α) modulate Bmal1 transcription [37,38]. In addition, certain nutrient-sensitive signaling pathways such as SIRT1 and AMPK couple metabolic flux to the circadian cycle [37]. Comparison of hepatic circadian rhythms in GF mice revealed that there were shifts in the clock gene expression with respect to phase which resulted in phase shift in transcription of PPARα and LXRα. The downstream effects of the phase shifts were a significant dampening in the transcription of both PPARα and LXRα target genes at ZT0. Abbreviations: AMPK, adenosine monophosphate-activated protein kinase; Bmal1, brain and muscle Arnt-like 1; CK1, casein kinase 1; Cry, cryptochrome; FBLX3, FBLX3, F-box/LRR repeat protein 3; NAD, nicotinamide adenine dinucleotide; Per, period; PGC1α, peroxisome proliferator-activated receptor gamma co-activator 1-α; Rev-erbα, reverse-erythroblastosis α; RORα, retinoic acid-related orphan receptor α; SIRT1, sirtuin 1.

Figure 2
The hepatic circadian core clock and differences between the CC of PPARα and LXRα target gene expression for SPF vs. GF mice

The circadian control of PPARα and LXRα target genes in GF vs. SPF mice. In hepatocytes, there exists a circadian core clock that consists of an autoregulated feedback loop of rhythmically expressed genes that oscillate within a 24 h period. Two important genes, Clock and Bmal1 comprise the forward segment of the clock loop. CLOCK and BMAL1 proteins form a heterodimer which then bind to the DNA response element, E-box, to cause the transcription of Per and Cry genes. PER and CRY proteins then re-enter the nucleus and inhibit CLOCK:BMAL1 protein activity to reduce their own transcription. There is further regulation of the feedback loop by the nuclear hormone receptors, Rev-erbα which negatively regulates and RORα that positively regulates Bmal1 transcription [37]. PPARα, LXRα and the co-activator, peroxisome proliferator-activated receptor gamma co-activator 1-α (PGC1α) modulate Bmal1 transcription [37,38]. In addition, certain nutrient-sensitive signaling pathways such as SIRT1 and AMPK couple metabolic flux to the circadian cycle [37]. Comparison of hepatic circadian rhythms in GF mice revealed that there were shifts in the clock gene expression with respect to phase which resulted in phase shift in transcription of PPARα and LXRα. The downstream effects of the phase shifts were a significant dampening in the transcription of both PPARα and LXRα target genes at ZT0. Abbreviations: AMPK, adenosine monophosphate-activated protein kinase; Bmal1, brain and muscle Arnt-like 1; CK1, casein kinase 1; Cry, cryptochrome; FBLX3, FBLX3, F-box/LRR repeat protein 3; NAD, nicotinamide adenine dinucleotide; Per, period; PGC1α, peroxisome proliferator-activated receptor gamma co-activator 1-α; Rev-erbα, reverse-erythroblastosis α; RORα, retinoic acid-related orphan receptor α; SIRT1, sirtuin 1.

The link between gut microbiota and diabetes

It has been previously shown in rodents that LPSs derived from Gram-negative bacteria were a cause of metabolic endotoxemia, triggered low-grade chronic inflammation, increased fat deposition, insulin resistance and thus increased the risk of diabetes. LPS is a ligand for the Toll-like receptor 4 (TLR4) and LPS binding initiates inflammation-causing nuclear factor kappa-B (NF-κB) signaling pathways and oxidative stress via reactive oxygen (ROS) production [39,40]. Many types of lipoproteins including, very low density (VLDL), low-density (LDL and high-density lipoproteins (HDL) bind LPS and when bound to lipoprotein, LPS-induced toxicity has been shown to be reduced in mice by reducing the biological inflammatory response to LPS [41]. What is not well characterized is the distribution of LPS among lipoproteins or its impact on lipoprotein metabolism in humans. Thus a 2014 lipoprotein kinetics study of 30 individuals of which 16 had T2D was performed [42]. What was found was that T2D patients had a very different distribution of LPS binding to various lipoproteins relative to controls with a lower proportion of LDL–LPS (30% vs. 52%) and a higher proportion of VLDL–LPS (31% vs. 22%), HDL–LPS (29% vs. 19%), and more protein-free LPS (10% vs. 7%) compared with controls. Their other important findings were (1) VLDL–LPS was associated with HDL–LPS and LDL–LPS, (2) LDL–LPS was associated with VLDL and VLDL catabolism and that transfer of LPS from VLDL to LDL was via catabolism of VLDL within the VLDL–IDL–LDL pathway, (3) HDL-LPS was associated with VLDL–LPS and free LPS but not LDL suggesting that transfer of LPS from HDL to HDL was minor and that (4) free LPS was associated with HDL-LPS. Examination of a patient with a functional deficiency in lipoprotein lipase (LPL) which was a cause of decreased catabolism of VLDL revealed that this patient had a significantly decreased ratio of LDL–LPS: VLDL–LPS and hence it was proposed that the lower LDL–LPS levels seen in patients with T2D was due to a compromised catabolism of VLDL. Furthermore, the LDL–LPS fraction in healthy controls was quite high (52%) and this suggested that the LDL catabolic pathway may be important for catabolism of LPS and reduction of LPS inflammatory potential and that T2D-related LPS endotoxemia may be related to impaired VLDL catabolism [42]. Further studies on impaired VLDL catabolism and inflammation in T2D patients are warranted. Although the effect of statin therapy in diabetes has been shown to be due to decreased de novo cholesterol biosynthesis and consequent alleviation of dyslipidemia [43], an argument can be made based on clinical studies of T2D and non-diabetic patients with bacteremia deriving benefits from statin therapy [44,45] that the LPS–LDL catabolic pathway is a significant catabolic pathway for plasma LPS and that statins are beneficial in a cholesterol-lowering independent way for lowering LPS-induced inflammation [42].

Microbiota are also capable of producing metabolites that are beneficial in controlling energy homeostasis and preventing hyperglycemia. The most widely studied bacterial metabolites have been the SCFAs, butyrate and propionate [46], but certain bacteria such as Prevotello copri [47] and Bacteroides [48] are able to ferment dietary fiber and produce succinate, especially in the cecum [49]. Improved management of metabolic diseases has been shown previously to be facilitated by increased consumption of dietary fiber [50]. In a previous study [51], it was shown that the SCFAs butyrate and propionate were able to activate a gut-brain neural circuit that involved the induction of intestinal gluconeogenesis (IGN) and resulted in metabolic benefits for the host. Propionate, in particular, was first metabolized to propionyl-CoA, then it was carboxylated to methylmalonyl-CoA and finally transformed to succinyl-CoA which allowed it to become incorporated into the Krebs cycle in the intestinal gluconeogenic pathway [52,53]. Therefore, in a 2016 study it was hypothesized that succinate production by commensal bacteria could also improve glucose metabolism by increasing IGN [54]. Figure 3 illustrates this metabolic pathway. In order to evaluate the effect of succinate on IGN, mice with a specific knockout of intestinal glucose-6-phosphatase catalytic subunit (I-G6pc−/−) were utilized. Control WT and I-G6pc−/− mice fed a HFD/HSD supplemented with fructo-oligosaccharides (FOS) showed a marked increase in cecal succinate with no dramatic changes observed in the portal vein or vena cava after FOS feeding which suggested that most of the succinate was metabolized in the intestine. FOS feeding resulted in enrichment of Bacteroidetes, a phylum known to contain propionate/succinate producing species and a decrease in the overall Firmicutes/Bacteroidetes ratio. In particular, the genus Bacteroides had the largest abundance increase in response to FOS feeding. Previous results had shown that metabolic benefits due to FOS feeding were absent from the G6pc−/− mice (mice incapable of IGN) [51]. Therefore, to determine whether it was the effect of succinate produced from fermentation of FOS, both mouse genotypes were fed succinate. Improved glucose and insulin tolerance accompanied by resistance to weight gain were observed in the succinate-fed WT but not in I-G6pc−/− mice relative to controls that were not fed succinate indicating the metabolic improvements required IGN. The levels of SCFAs were not altered in the cecum which was additional evidence that succinate had a direct effect on IGN [54]. By acting as a substrate for IGN, succinate can reduce hepatic gluconeogenesis which is a known causal factor of insulin resistance and T2D [55]. Therefore, a bacterial metabolite, succinate, plays a role in glucose homeostasis and disruption of the microbiota, particularly the balance between the phyla Bacteriodetes and Firmicutes potentially could lead to decreased IGN and thus disrupted glucose metabolism that leads to diabetes. This study highlights yet another example of how disrupted host-microbiota co-metabolism could lead to metabolic disease and that understanding the underlying mechanism reveals potential therapeutic interventions, i.e., reduced patient succinate levels can lead to possible therapeutic approaches to improve energy metabolism via a probiotic containing succinate producing bacteria such as Prevotella copri or even the use of succinate dietary supplements.

The importance of adequate succinate for maintenance of glucose/insulin homeostasis.

Figure 3
The importance of adequate succinate for maintenance of glucose/insulin homeostasis.

Intestinal gluconeogenesis has been shown to be an important factor in glucose/insulin homeostasis. Bacteroides sp. are enhanced in the gut on a high fiber diet or when the diet is supplemented with FOS. Succinate is a direct substrate for the TCA cycle where it can be biotransformed to oxaloacetate which then enters the gluconeogenic pathway. Abbreviation: IEC, intestinal epithelium cell.

Figure 3
The importance of adequate succinate for maintenance of glucose/insulin homeostasis.

Intestinal gluconeogenesis has been shown to be an important factor in glucose/insulin homeostasis. Bacteroides sp. are enhanced in the gut on a high fiber diet or when the diet is supplemented with FOS. Succinate is a direct substrate for the TCA cycle where it can be biotransformed to oxaloacetate which then enters the gluconeogenic pathway. Abbreviation: IEC, intestinal epithelium cell.

The link between gut microbiota and NAFLD

Non-alcoholic fatty liver disease (NAFLD) has been described as the hepatic manifestation of metabolic syndrome [56]. The risk factors for developing NAFLD include male gender, increasing age, obesity, insulin resistance, diabetes, and hyperlipidemia. All of these risk factors have been linked to gut microbiota dysbiosis [57,58]. Volatile organic compounds (VOCs) are known to be by-products of gut microbial metabolism and may enter the portal circulation to cause hepatoxic effects that may lead to NAFLD. Table 1 lists the differences in VOCs present in NAFLD vs. healthy control patients.

Table 1
Fecal VOCs that distinguished NAFLD from healthy patients [59]
Increased in NAFLD Decreased in NAFLD 
Butanoic acid, propyl ester 2-Butanone 
Propanoic acid, propyl ester Furan, 2-methylheptanal 
Acetic acid, ethyl ester Heptanal 
Acetic acid, pentyl ester 2-Heptanone, 6-methyl 
Cyclohexene, 4-ethenyl-4methyl-3-(1-methylenyl)-1-(1-methylyethyl)-(3R-trans) 2(3H)-Furanone, dihydro-5-methyl 
Butanoic acid, 3-methyl-, butyl ester 2,3 Pentanedione 
n-Propyl acetate 1,6-Octadien-3-ol, 3,7-dimethyl, 
Butanoic acid, butyl ester 2-Aminobenzoate 
Phellandrene Cyclohexanol, 5-methyl-2-(1-methylethyl) 
Propanoic acid, ethyl ester 2-Octene, 3,7-dimethyl 
1,6-Octadien-3-ol, 3,7-dimethyl Acetic acid, (1,2,3,4,5,6,7,8-octahydro-3,8,8-trimethylnaphth-2-yl)methyl ester 
Myrcene Cyclohexane, hexyl 
Pentanoic acid, methyl ester  
Acetic acid methyl ester  
Acetic acid, methyl ester  
2-Propynoic acid methyl ester  
Butanoic acid, 3-methyl, ethyl ester  
1-Propanol  
Propanoic acid, 2- methyl, propyl ester  
Increased in NAFLD Decreased in NAFLD 
Butanoic acid, propyl ester 2-Butanone 
Propanoic acid, propyl ester Furan, 2-methylheptanal 
Acetic acid, ethyl ester Heptanal 
Acetic acid, pentyl ester 2-Heptanone, 6-methyl 
Cyclohexene, 4-ethenyl-4methyl-3-(1-methylenyl)-1-(1-methylyethyl)-(3R-trans) 2(3H)-Furanone, dihydro-5-methyl 
Butanoic acid, 3-methyl-, butyl ester 2,3 Pentanedione 
n-Propyl acetate 1,6-Octadien-3-ol, 3,7-dimethyl, 
Butanoic acid, butyl ester 2-Aminobenzoate 
Phellandrene Cyclohexanol, 5-methyl-2-(1-methylethyl) 
Propanoic acid, ethyl ester 2-Octene, 3,7-dimethyl 
1,6-Octadien-3-ol, 3,7-dimethyl Acetic acid, (1,2,3,4,5,6,7,8-octahydro-3,8,8-trimethylnaphth-2-yl)methyl ester 
Myrcene Cyclohexane, hexyl 
Pentanoic acid, methyl ester  
Acetic acid methyl ester  
Acetic acid, methyl ester  
2-Propynoic acid methyl ester  
Butanoic acid, 3-methyl, ethyl ester  
1-Propanol  
Propanoic acid, 2- methyl, propyl ester  

In this previous 2013 study, there were also distinct differences in the fecal microbiome as well as VOCs relative to healthy control patients. Over-representation of Lactobacillus species and selected members of the phylum Firmicutes, in particular from the family Lachnospiraceae: genera, Dorea, Robinsoniella, and Roseburia along with one member of the phylum Firmicutes, Ruminococcaceae, genus Oscillibacter, were detected in NAFLD patients. Table 2 is a list of microbes that were found to be changed in NAFLD patients. The observed significant increased levels of fecal ester VOCs was associated with and thus attributed to the compositional shifts in the microbiome of obese NAFLD patients. Similar to ethanol, ester VOC may have deleterious effects on the intestinal barrier and/or local immune functions. Finally, it was pointed out that diet was not controlled in the patient cohorts and that therefore, regardless of diet, the production of certain VOCs may contribute to the pathogenesis of NAFLD [59].

Table 2
Gut microbiota that were found to be significantly altered in abundance in NAFLD patients
Phylum Class Order Family Relative abundance 
Proteobacteria Alphaproteobacteria Kiloniellales Kiloniellaceae ↑ 
Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae ↑ 
Firmicutes Bacilli Lactobacillales Lactobacillaceae ↑ 
Firmicutes Clostridia Clostridiales Lachnospiraceae ↑ 
Firmicutes Clostridia Clostridiales Ruminoccaceae ↓ 
Bacteroidetes Bacteroidia Bacteroidales Porphyromonadacea ↓ 
Firmicutes Clostridia Clostridiales Veillonellaceae ↑ 
Family Genus    
Lactobacillaceae Lactobacillus   ↑ 
Ruminococcaceae Oscillibacter   ↓ 
Lachnospiraceae Robinsoniella   ↑ 
Lachnospiraceae Roseburia   ↑ 
Lachnospiraceae Dorea   ↑ 
Phylum Class Order Family Relative abundance 
Proteobacteria Alphaproteobacteria Kiloniellales Kiloniellaceae ↑ 
Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae ↑ 
Firmicutes Bacilli Lactobacillales Lactobacillaceae ↑ 
Firmicutes Clostridia Clostridiales Lachnospiraceae ↑ 
Firmicutes Clostridia Clostridiales Ruminoccaceae ↓ 
Bacteroidetes Bacteroidia Bacteroidales Porphyromonadacea ↓ 
Firmicutes Clostridia Clostridiales Veillonellaceae ↑ 
Family Genus    
Lactobacillaceae Lactobacillus   ↑ 
Ruminococcaceae Oscillibacter   ↓ 
Lachnospiraceae Robinsoniella   ↑ 
Lachnospiraceae Roseburia   ↑ 
Lachnospiraceae Dorea   ↑ 

Ranked from highest to lowest significance [59].

A second, more recent, 2016 study, endeavored to assess whether gut-derived VOCs enter the portal venous circulation and whether they were a source of inflammation and liver injury in a murine model of nonalcoholic steatohepatitis (NASH) induced by feeding mice a methione/choline deficient diet (MCD) [60]. The VOC, 2,3-pentanedione was tested because it was present only in the portal vein and cecal contents of MCD-fed NASH mice and had been previously reported to have a pro-inflammatory and pro-fibrotic role in alcoholic liver disease and lung fibrosis [61,62]. This experiment represents the first time that VOCs derived from the luminal cecal contents could be detected in the portal vein of a NASH mouse model. Primary Kupfer cells were then isolated from the mice and were cultured with 2,3-pentandione. Cell death in the presence of the VOC was dose dependent as measured by lactate dehydrogenase release (LDH). Kupfer cell treatment also resulted in a significant release of IL-1β, IL-3, IL-12, tumor necrosis factor (TNF-α), and monocyte chemo-attractant protein-1 (MCP-1) pro-inflammatory cytokines thus confirming the potential of VOCs such as 2,3-pentandione to cause liver injury. The conclusion for this study was that diet induced liver disease which causes gut dysbiosis, can lead to overproduction of VOCs that in turn can enter the portal vein and cause damage to the liver [60]. One drawback encountered in this study was the inability to distinguish certain short-chain FAs (SCFAs) that were present in the chow from those produced by gut microbiota fermentation leading to their discard from the study. Furthermore, there is a need for continued evaluation of the role of specific VOCs in the progression of liver disease.

Helicobacter pylori, a urease-producing Gram-negative bacterium usually found in the gastric epithelium has been implicated in the development of insulin resistance and has recently been linked to NAFLD [63]. A 2016 meta-analysis was performed on 18 different studies including 27544 participants regarding the association of infection with Helicobacter pylori and metabolic syndrome. The conclusions based on this analysis were that H. pylori infected individuals were associated not only with metabolic syndrome (MS) but also with higher triglycerides (TGs), fasting blood sugar (FBS), homeostatic model assessment of insulin resistance (HOMO-IR), and body mass index (BMI), higher systolic blood pressure (SBP), as well as lower high-density lipoprotein (HDL-C) than non-infected individuals. The limitations of this analysis was that it was purely observational and therefore nothing could be proven as to causality. The results did indicate the possibility that by treating H. pylori infection, one could improve metabolic syndrome parameters [64]. However, a 2014 study showed that only patients with diabetes have higher insulin resistance when infected with H. pylori relative to non-diabetics [65]. On the other hand, another 2010 investigation provided supporting evidence for the improvement of metabolic parameters with H. pylori eradication in nondiabetic subjects [66]. Therefore, there remains some controversy as to the benefits of H. pylori eradication as a treatment option for MS.

In a 2015 clinical study of 130 patients with biopsy-proven NAFLD (43 with NAFLD and 87 with NASH) results of blood tests showed that H. pylori immunoglobin G (IgG) seropositivity was found in 40% of the patients with higher prevalence found in the patients diagnosed with NASH. Histological examination of the liver biopsies revealed a higher grade of hepatocyte ballooning, but not with steatosis or fibrosis. The increase in hepatocyte ballooning is a key feature of NASH [67] and thus these findings suggested that infection with H. pylori may be a contributing factor in the progression from benign NAFLD to NASH. There were limitations to this study in that it was a cross-sectional study in a Japanese center which meant selection bias and results would need to be confirmed in other ethnic groups as well as in a larger number of cohorts from different centers. Furthermore, adipokines and inflammatory cytokines were not examined in relation to infection [68]. This study, however, raised the possibility that antibiotic treatment to eradicate H. pylori may help prevent the progression of NAFLD to the more serious NASH condition.

Some of the most compelling evidence that gut dysbiosis is a contributing factor to NAFLD is the successful use of probiotics and prebiotics or a synbiotic combination to adjust the gut microbiota and reduce TG levels, improve glucose/insulin homeostasis and reduce inflammation. Table 3 is a summary of clinical studies in humans that have provided evidence for the efficacy of probiotics/prebiotics for the treatment of NAFLD.

Table 3
Summary of clinical intervention studies of probiotics and synbiotics in NAFLD
Subjects Strain/prebiotic Weeks Outcome Ref. 
20 obese children Lactobacillus rhamnous GG ↓ALT [69
28 adults Lactobacillus bulgaris 12 ↓ALT and γ-GTP [70
Streptococcus thermophilus 
72 adults Lactobacillus acidophilus ↓ALT, ASP, TC, LDL-C [71
Bifidobacterium breve 
44 obese children Bifidobacterium lactobacillus 16 ↓fatty liver index, ↓BMI, ↑GLP1 [72
Streptococcus thermophilus  
22 adults VSL#3 12 ↑MDA, 4-HNE, S-NO [73
66 adults Bifidobacterium longum, FOS 24 ↓liver TGs, AST [74
52 adults L. casei, L Rhamnous, S. thermophilus, B. breve, L. acidophilus, B. Longum, L. bulgaricus, and FOS 30 ↓NF-κB, TNFα [75
50 adults Synbiotic protexin 28 ↓FBS, TGs, ALT, GGT, LDL cholesterol [76
Subjects Strain/prebiotic Weeks Outcome Ref. 
20 obese children Lactobacillus rhamnous GG ↓ALT [69
28 adults Lactobacillus bulgaris 12 ↓ALT and γ-GTP [70
Streptococcus thermophilus 
72 adults Lactobacillus acidophilus ↓ALT, ASP, TC, LDL-C [71
Bifidobacterium breve 
44 obese children Bifidobacterium lactobacillus 16 ↓fatty liver index, ↓BMI, ↑GLP1 [72
Streptococcus thermophilus  
22 adults VSL#3 12 ↑MDA, 4-HNE, S-NO [73
66 adults Bifidobacterium longum, FOS 24 ↓liver TGs, AST [74
52 adults L. casei, L Rhamnous, S. thermophilus, B. breve, L. acidophilus, B. Longum, L. bulgaricus, and FOS 30 ↓NF-κB, TNFα [75
50 adults Synbiotic protexin 28 ↓FBS, TGs, ALT, GGT, LDL cholesterol [76

Abbreviations: VSL3#, combination of B. breve, B. infantis, L. casei, L. planetarium, L. acidophilus, L. delbrueckii ssp., S. thermophilus.

Dietary choline deficiency has also been associated with NAFLD development in both humans and animals [77]. Choline is considered an essential dietary nutrient and it functions as a major methyl group donor for the biosynthesis of the important cell membrane lipids, phosphatidylcholine (PC), lysophosphatidylcholine (LPC) and sphingomyelin (SM) (Figure 4 diagrams the three pathways for choline catabolism, two are mammalian and one is bacterial) [78,79]. Choline is also essential for the biosynthesis of the neurotransmitter, acetylcholine [79]. PC deficiency increases de novo hepatic lipogenesis which leads to an increase in hepatic TGs. Lack of PC in hepatic lipid droplets reduces their surfactant properties and larger lipid droplets are formed that are less likely to undergo lipolysis. PC is required for both VLDL synthesis and their secretion from the liver [58,80]. PC has also been identified as a cell wall component of ∼10–15% of all bacteria [81].

The metabolism of choline and the effect of TMA producing microbiota on host choline levels

Figure 4
The metabolism of choline and the effect of TMA producing microbiota on host choline levels

TMA producing microbiota were isolated from human feces and gavaged into GF mice which had been colonized specifically with a non-TMA producing core of microbiota. When the mice were fed a diet supplement with choline before and after gavage, a decrease in both fecal and plasma choline levels were observed after gavage with the TMA producing bacteria. The possible fate of dietary PC and choline are also depicted. Dietary PC can be metabolized to choline in the gut. All choline in the gut can then be metabolized to TMA by certain species of gut microbiota. Diversion of choline into this metabolic pathway results in diminished synthesis of PC in the liver that occurs in the liver via the mammalian Kennedy pathway and PEMT pathways. In the liver, TMA can be demethylated by CYP enzymes to DMA and MMA or it can be N-oxidized by FMO3 enzymes to produce TMAO, a toxic substance that can be secreted to other tissues such as macrophages and arterial epithelium where it causes inflammation and atherosclerosis, respectively. If the microbiota cause choline deficiency in the liver via excess TMAO synthesis, then not enough PC can be produced to export VLDL and TGs accumulate in the liver resulting in NAFLD. A polymorphism in the PEMT gene causes the PEMT pathway to shut down and mammalian synthesis of PC decrease by ∼30%. The combination of a PEMT polymorphism and high abundance of gut microbiota that produce TMAO is a risk factor for the development of NAFLD [79,80,82]. Abbreviations: DMA, dimethylamine; MMA, monomethylamine; PE, phosphoethanolamine; PEMT, phosphatidylethanolamine-N-methyltransferase; TMAO, trimethylamine-N-oxide.

Figure 4
The metabolism of choline and the effect of TMA producing microbiota on host choline levels

TMA producing microbiota were isolated from human feces and gavaged into GF mice which had been colonized specifically with a non-TMA producing core of microbiota. When the mice were fed a diet supplement with choline before and after gavage, a decrease in both fecal and plasma choline levels were observed after gavage with the TMA producing bacteria. The possible fate of dietary PC and choline are also depicted. Dietary PC can be metabolized to choline in the gut. All choline in the gut can then be metabolized to TMA by certain species of gut microbiota. Diversion of choline into this metabolic pathway results in diminished synthesis of PC in the liver that occurs in the liver via the mammalian Kennedy pathway and PEMT pathways. In the liver, TMA can be demethylated by CYP enzymes to DMA and MMA or it can be N-oxidized by FMO3 enzymes to produce TMAO, a toxic substance that can be secreted to other tissues such as macrophages and arterial epithelium where it causes inflammation and atherosclerosis, respectively. If the microbiota cause choline deficiency in the liver via excess TMAO synthesis, then not enough PC can be produced to export VLDL and TGs accumulate in the liver resulting in NAFLD. A polymorphism in the PEMT gene causes the PEMT pathway to shut down and mammalian synthesis of PC decrease by ∼30%. The combination of a PEMT polymorphism and high abundance of gut microbiota that produce TMAO is a risk factor for the development of NAFLD [79,80,82]. Abbreviations: DMA, dimethylamine; MMA, monomethylamine; PE, phosphoethanolamine; PEMT, phosphatidylethanolamine-N-methyltransferase; TMAO, trimethylamine-N-oxide.

In a 2015 metabolomic study, human gut isolates were used to identify six different genera and eight different species from two different phyla, Firmicutes and Proteobacteria that could be cultured ex vivo in media containing deuterated choline where they consumed 60% of it. The isolates were identified as, (Firmicutes) Anaerococcus hydrogenalis, Clostridium asparagiforme, Clostridium hathewayi, Clostridium sporogenes, (Proteobacteria) Escherichia gergusonii, Proteus penneri, Providencia rettgeri, and Edwardsiella tarda. All contained the component group of genes for choline metabolism. When the isolates were gavaged into GF mice which were previously colonized with a core on non-trimethylamine (TMA) producing bacteria, there was a significant decrease in the abundance of choline both in feces and serum of the mice [83].

Therefore, bioavailability of choline for the host was shown to be affected by the presence of choline consuming, TMA producing gut microbiota. The TMA produced by the microbiota has been implicated in CVD, the next topic to be discussed.

The link between gut microbiota and CVD

There is growing evidence to support the role of gut microbiota in CVD and a hypothesis, known as the “gut hypothesis of heart failure” has emerged. The hypothesis states that decreased cardiac output and redistribution of systemic circulation can lead to a decrease in intestinal perfusion, mucosal, and gut dysbiosis. Furthermore, disruption of the intestinal barrier will facilitate increased gut permeability, increased bacterial translocation, increased circulation of endotoxins such as LPS and result in chronic inflammation [84]. The gut microbiota are also responsible for metabolizing dietary choline to TMA from which the liver then biosynthesizes the highly toxic and pro-atherosclerotic compound, trimethylamine-N-oxide (TMAO) (Figure 5). Another source of TMA is L-carnitine, an abundant nutrient found in red meat (Figure 5) [85].

The effects of gut microbiota remodeling induced by reservatrol.

Figure 5
The effects of gut microbiota remodeling induced by reservatrol.

RSV acts in a beneficial way to prevent AS by remodeling the gut microbiota so as to reduce or block the production of TMA from dietary PC or choline. This means less TMAO production by the liver. The adjusted microbiota are more efficient at deconjugation (increased BSH activity) and this allows for increased amount of hydrophobic unconjugated BAs to be converted to secondary BAs. There is an increase in fecal BAs observed with RSV treatment. Increased BA excretion leads to a decrease in ileal BA and therefore decreased FXR activation and thus decreased FGF15 production. When fgf15 is down-regulated the rate-limiting enzyme for BA synthesis in the liver is up-regulated causing increased BA synthesis in the liver. The increased fecal excretion of BAs and the increased de novo synthesis of BAs in the liver combine to lower plasma cholesterol levels and reduce plaque formation and AS. Abbreviations: βMCA, beta muricholic acid; TβMCA, tauro beta muricholic acid.

Figure 5
The effects of gut microbiota remodeling induced by reservatrol.

RSV acts in a beneficial way to prevent AS by remodeling the gut microbiota so as to reduce or block the production of TMA from dietary PC or choline. This means less TMAO production by the liver. The adjusted microbiota are more efficient at deconjugation (increased BSH activity) and this allows for increased amount of hydrophobic unconjugated BAs to be converted to secondary BAs. There is an increase in fecal BAs observed with RSV treatment. Increased BA excretion leads to a decrease in ileal BA and therefore decreased FXR activation and thus decreased FGF15 production. When fgf15 is down-regulated the rate-limiting enzyme for BA synthesis in the liver is up-regulated causing increased BA synthesis in the liver. The increased fecal excretion of BAs and the increased de novo synthesis of BAs in the liver combine to lower plasma cholesterol levels and reduce plaque formation and AS. Abbreviations: βMCA, beta muricholic acid; TβMCA, tauro beta muricholic acid.

The following discussion will focus on selected recent articles addressing three major areas of CVD research with respect to metabolism of choline by gut microbiota to produce the toxic TMAO: (1) clinical studies of the relationship between levels of the microbial metabolite, TMAO, and coronary artery disease, (2) the use of dietary components to remodel the gut microbiota to reduce TMAO production, and (3) the role of TMAO in platelet hyperreactivity which leads to thrombosis and MI or stroke.

In a recent, 2016 study, TMAO was identified as an important risk factor for promotion of atherosclerosis (AS) [86,87]. The relationship between fasting plasma TMAO and all-cause mortality over a 5-year follow-up in sequential patients with stable coronary artery disease (CAD) with ≥70% stenosis (n-2235) was investigated. The conclusions based on this study were that elevated plasma TMAO levels predicted higher long-term mortality risk among patients managed with optimal care for stable coronary artery disease. Elevated plasma TMAO levels were prognostic for higher long-term mortality risk independent of traditional risk factors, inflammation markers, B-type natiuretic peptide levels, and renal function. Therefore the use of TMAO levels may aid in identifying those CAD patients who have significant future elevated CVD risks [86]. Another study (2016), this time in a cohort of 220 Caucasian subjects, examined the association of plasma TMAO levels with an early marker of atherosclerosis, carotid intima-media thickness (cIMT) and their most significant finding was that elevated TMAO levels was positively associated with elevated cIMT levels independently of established CVD risk markers such as visceral obesity, fatty liver and insulin resistance. A longitudinal study that involved a lifestyle intervention for the same cohort revealed that there was a large change in the differences of TMAO levels with the intervention ranging from an 82% decrease to a 730% increase and this was proposed to be the result of individual gut microbiota differences. A drawback of this investigation was that no evaluations of gut microbiota were performed and this would have provided information about the abundance of TMA producing bacteria and whether adjustment of the microbiota via transplant or probiotics could be of use in lowering TMAO levels and reducing the risk of future CVD enhancement. As the cIMT decreased only in those subjects who had the greatest amount of decrease in TMAO level, alteration of the gut microbiota could potentially serve as an important therapeutic intervention. The lifestyle intervention was, however, successful in prevent progression of cIMT [88]. These two studies illustrate the feasibility of using TMAO as a biomarker to assess, for both early and late CAD, as well as, the future risk of increased CVD events. They also emphasized the fact that early detection of CAD and intervention via either lifestyle changes or potentially alteration of the microbiota can reverse CAD. Finally, a meta-analysis of 11 different cohort studies encompassing 10,245 participants supported the above studies in that higher circulating TMAO was independently associated with a 23% increased risk of subsequent cardiovascular events (CVE) and 55% increased risk for all-cause mortality [89]. It should also be noted that another recent 2016 paper has proposed that that the carnitine metabolite, γ-butyrobetaine which can be formed either from carnitine via the gut microbiota or endogenously in the liver from trimethyl-lysine in a gut microbiota independent pathway, also associated positively with cardiovascular death and CAD [90].

A 2016 investigation that addressed the mechanism of TMAO toxicity was done in an investigation of the potential of reservatrol (RSV), a known anti-atherosclerosis (AS) agent on the alteration of gut flora and TMAO production [91,92]. RSV is a natural phytoalexin with prebiotic benefits and poor bioavailability. The hypothesis for this study was that RSV’s anti-AS effects were via gut microbiota remodeling [93]. RSV was found to inhibit TMAO synthesis in C57BL/6 mice after choline administration. RSV was also found to increase flavin monooxygenase 3 (FMO3) in the liver indicating that the decrease in TMAO by RSV was not due to diminished expression of the enzyme which converts TMA to TMAO. Comparison of the microbiota of vehicle vs. RSV treated mice showed that phylum Bacteroidetes was increased at the expense of phylum Firmicutes. Genus level analysis showed that RSV induced an increase in the relative abundances of Bacteroides, Lactobacillus, Bifidobacterium, and Akkermansia and a relative decrease in abundances of Prevotella, Anaerotruncus, Alistipes, Helicobacter, and the two families, Ruminococcaceae and Peptococcaceae. Thus RSV administration was able to remodel the gut microbiota. Next the effects of RSV administration on fecal BA excretion and composition were determined. The results were that RSV enhanced BA excretion with a decrease in the in fecal conjugated/unconjugated ratio, there was no change in the cholic acid(CA)/deoxycholic acid (DCA) ratio and there was an increase in bile salt hydrolase activity (BSH). RSV also affected the small intestine (SI) where BA levels were decreased. Notably decreased were taurocholic acid (TCA), tauro beta-muricholic acid (TβMCA), CA, chenodeoxycholic acid (CDCA) and DCA. There was no change in mRNA or protein levels for the apical sodium BA transporter (ASBT) and a slight reduction in the protein/mRNA for the organic solute transporters (OSTα,β). RSV treatment also had the effect of decreasing liver cholesterol levels, increasing the gallbladder and SI luminal BA contents along with the induction of increased CYP7A1 mRNA and protein in the liver.

Regarding other components in BA metabolism, RSV treatment had no effect on expression levels of the hepatic farnesoid X receptor (FXR), small heterodimer partner (SHP) or ileal FXR. However, RSV down-regulated the intestinal FXR–FGF15 axis significantly with decreased FGF15 mRNA expression and protein levels in RSV treated, choline supplemented mice. Therefore, RSV enhanced hepatic BA synthesis via down-regulation of the FXR–FGF15 axis (Figure 5). Other studies have shown that the gut microbiota regulates secondary BA metabolism and de novo BA synthesis in the liver via down-regulation of the FXR–FGF15 axis [94,95]. Finally, RSV was tested in an AS prone mouse model and the results were similar to that determined in the C57BL/6 mice but with attenuation of AS [93]. In conclusion, RSV caused gut microbiota remodeling which led to decreased production of TMA–TMAO and which also profoundly impacted BA pool size and composition. Figure 5 is a visual representation of the mechanism of RSV action that was proposed in this study. The results presented here are consistent with other studies of gut microbiota remodeling occurring upon ingestion of RSV [96–99]. What is novel here is that RSV attenuated TMAO-induced AS by regulating the synthesis of both TMAO and BAs via remodeling of the gut microbiota and furthermore, that RSV-mediated hepatic de novo BA synthesis was partially modulated by down-regulation of the ileal FXR–FGF15 axis which would be expected to have an effect on lowering cholesterol as was previously shown in other studies [100].

TMAO has also been implicated in enhanced platelet hyper-reactivity and risk for thrombosis which can also result in heart failure or stroke [101]. In this 2016 study, the first task was examination of the association of plasma TMAO levels and thrombotic events (n = 4007). TMAO levels were found to have a dose-dependent association with thrombotic events (myocardial infarction or stroke). Next, platelets obtained from healthy volunteers with low TMAO levels were isolated and tested by treating them with exogenous TMAO and a platelet aggregometer. The results supported the hypothesis that there was direct enhanced platelet function under the influence of TMAO. The effect of TMAO on platelet adhesion to collagen was then determined to also be enhanced. After these in vitro studies were completed, a carotid artery wounding experiment was done on mice fed a diet rich in choline and the time for blood cessation was measured. Mice high in TMAO had shorter bleeding times. To test whether thrombosis potential is transmissible via the microbiota, two strains of mice were identified as high TMAO producers (C57BL/6J) and low TMAO producers (NZW/LacJ), were fed defined chow, low choline (0.08%0 and high choline (1.0%) for 6 weeks. The C57BL/6J mice had twice the TMAO regardless of diet relative to the NZW/Lac mice. Upon wounding, the high TMAO mice had a shorter wound closing time. Both of these strains of mice were then used for transplantation of their feces into GF mice with the final result that the GF mice who had received high TMAO feces also demonstrated the pro-thrombotic phenotype indicating involvement of the microbiota in this phenotype. Therefore, gut microbiota dysbiosis to a higher TMAO producing population has the capability via the microbiota metabolite, TMA, to induce major CVD events such as MI or stroke. The proportions of three taxa, Allobaculum, Candidatus arthromitus, and Lachospiraceae, were determined to be significant with both increased TMAO and decreased wound occlusion time.

At a more cell mechanistic level, it was shown that multiple agonists, ADP, thrombin, collagenand arachidonic acid all generated TMAO-dependent enhanced Ca2+ release from platelet intracellular stores. Further, TMAO-dependent enhancement of inositol triphosphate (IP3) signaling in platelets which also leads to Ca2+ mobilization and platelet activation, was also observed. However, the receptor for TMAO has yet to be identified. This paper, to my knowledge was the first to examine the role of gut microbiota produced TMAO in platelet activation and thrombosis [102]. Further studies in this field are needed to both validate the current findings and identify the TMAO receptor as a potential therapeutic target for the treatment of thrombotic risk.

The final endpoint of many metabolic diseases is cancer and cancer itself, as it is often driven by low-grade inflammation and obesity may be considered another manifestation of metabolic disease that is directly involved with alterations of the gut microbiota. The next section will be a discussion of the link between the gut microbiota and cancer.

The link between gut microbiota and cancer

Current studies (2015–2018) on the link between the microbiota and cancer have focused on investigating (1) the role of bacteria in tumor pathogenesis [19,103–106], (2) the potential for microbiota mediation of anti-tumor immune responses [107–113], and (3) the contribution of bacterial metabolism to the efficacy of chemotherapy [114–117] In the discussion below we will discuss an example of each one of these approaches.

Obesity is known to increase the risk of several types of cancer and is thought to be responsible for 20% of cancer-related deaths in adults [118,119]. With respect to liver cancer, both NAFLD and nonalcoholic steatohepatitis (NASH) have been implicated as a risk factor for HCC [120]. As an example of the role of bacteria in tumor pathogenesis, a 2018 study [19] of the gut microbiota–liver axis was undertaken using a 7,12-dimethylbenz(α)anthracene (DMBA)/HFD induced HCC mouse model to determine the influence of the gut microbiota on carcinogenesis. The first important observation was that the gut microbial profile of DMBA/HFD mice showed a dramatic increase in Gram-positive bacteria which led to the investigation of the role of lipoteichoic acid (LTA), a cell wall component of Gram-positive bacteria, and its receptor, toll-like receptor 2 (TLR2), in the development of obesity-related HCC. This was consistent with previous reports [121]. Their major findings included (1) increased amounts of LTA in liver tumors, (2) numbers and sizes of tumors were reduced in TLR2-deficient DMBA/HFD mice along with senescence-associated secretory phenotypic (SASP) markers such as IL-1β, p21, and IL-6 in hepatic stellate cells (HSCs), (3) the gut microbiota metabolite deoxycholic acid (DCA) acted in a synergistic way to induce the SASP in HSCs, (4) LTA was found to up-regulate cyclooxygenase 2 (COX2) in the SASP HSCs to promote increased amounts of prostaglandins (PGs), especially PGE2, 5) the use of an antagonist to the PGE2 receptor in HSCs, PTGER4, demonstrated significant suppression of obesity-related HCC development along with reactivation of anti-tumor immunity. This paper demonstrated a clear connection between obesity-related increased amounts of Gram-positive gut microbiota, their metabolites, DCA and LTA and the development of obesity-related HCC in mice. Examination of human tissue from patients with nonfibrotic NASH-associated HCC also revealed up-regulation of COX2 and overproduction of PGE2. Their final conclusion was that targeting the PGE2/PTGER4 pathway may potentially be useful for nonfibrotic NASH-associated HCC [19]. It should be noted here that TLR4-mediated signaling induced by lipopolysacharide (LPS) produced by Gram-negative bacteria has been shown to be important for promoting liver fibrosis and fibrosis-associated HCC [122]. Therefore, a distinction may have to be made between obesity-related nonfibrotic HCC and a predominantly Gram positive gut microbiota profile and fibrosis driven HCC for determining therapy options.

Another aspect of the role of microbiota in cancer is that of conferring chemotherapy drug resistance particularly in tumors. A 2017 study looked at the ability of microbiota to metabolize the nucleoside analog drug, gemcitabine, commonly used to treat patients with pancreatic ductal adenocarcinoma (PDAC) [114]. Bacteria can metabolize gemcitabine to its inactive form, and the bacterial enzyme which had previously been shown to mediate this was cytidine deaminase (CDD) [123]. A database search revealed that 98.4% of the bacteria containing the 880 nucleotide long form of the CDD gene (CCDL) belonged to the class Gammaproteobacteria while other types of bacteria contained a shorter 400 nucleotide CDDs gene. Several bacterial stains containing either CCDL (Klebsiella pneumonia, Escherichia coli K-12), CCDs (Streptococcus pneumonia, Enterococcus faecalis), or no CCD gene (Achromobacter xylosoxidans, Burkholderia capacia) were incubated with 4 µM gemcitabine and bacteria were filtered off at different time points spanning 8 h. The remaining gemcitabine was measured via HPLC–MS/MS and only those bacteria containing CCDL were able to metabolize gemcitabine. A subcutaneous model of colon carcinoma (MC-26 cells) was then established in immunocompetent mice and after tumor formation, E. coli (CCDL) cells were injected via the tail vein. Injection of the antibiotic, ciprofloxacin along with gemcitabine increased the efficacy of the chemotherapy. Injection of gemcitabine alone gave a much lower apoptotic index according to histological staining for caspase 3 of the tumor tissue. These results provided a clear link between microbial metabolism of the chemotherapeutic drug gemcitabine and tumor development in vivo.

To establish that this was occurring in patients, 113 PDAC samples were obtained during pancreatic cancer surgery, 76% of the samples contained bacterial DNA, 52% of the 76% belonged to the class Gammaproteobacteria (families Enterobacteriaceae and Psuedomonadaceae) and were CCDL positive. 93% of the bacteria cultured from fresh human PDAC tumors were found to make RKO and HCT116 human colon carcinoma cell lines fully resistant to gemcitabine. It should be mentioned that Proteobacteria are abundant in the duodenum, to which the pancreatic duct opens, suggesting that retrograde microbe migration into the duct is a potential source of PDAC-associated bacteria [124,125].

Notably, although this study examined the effect of Gammaproteobacteria on gemcitabine in cell, mouse and human tumor tissue, much of the preclinical studies utilized colon carcinoma cell lines instead of a pancreatic duct cancer cell line such as PANC-1. The clinical study of human samples, on the other hand, used primary pancreatic duct tumor tissue and primary PDAC cancer cells. Perhaps in the future, a PDAC mouse model or the use of PANC-1 cells to produce a tumor in a mouse may be used to test the effect of Gammaproteobacteria on gemcitabine. This type of study would be expected to yield results that more closely parallel what was found in human PDAC samples.

This last article summary is from a 2018 research article in Science that addresses the gut microbiome influence on the efficacy PD-1 based immunotherapy against epithelial tumors such as advanced melanoma, non-small cell lung cancer (NSCLC), and renal cell carcinoma (RCC) [109]. Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that target mainly programed cell death protein-1 (PD-1) and it ligand, PD-L1. ICIs up-regulate a T-lymphocyte-mediated immune response by suppressing the interaction of T-cell inhibitory receptors (PD-1) with their ligands (PD-L1) on tumor or stromal cells, thus resensitizing the tumor to the action of the immune system [126]. Mice with established MCA-205 sarcoma were raised in specific pathogen-free (SPF) conditions and one group was then treated for 14 days with a combination of broad-spectrum antibiotics (ATB) (ampicillin + colistin + streptomycin) and one group was untreated. ATB treatment severely diminished the efficacy of anti-PD-1 therapy, thus providing a link between efficacy of the immunotherapy and the gut microbiota. In a clinical study, of 249 patients (140 NSCLC, 67 RCC, 42 urothelial carcinoma), 69 received ATB within 2 months before or 1 month after anti-PD-1 treatment and had significantly shorter progression-free survival (PFS) or overall survival (OS) than the other 180 patients who did not take any ATBs. Therefore, it was hypothesized that gut dysbiosis affected the efficacy of ICIs. Quantitative metagenomics of the gut microbiota from 100 patients, 60 with NSCLC and 40 with RCC was then performed before anti-PD-1 therapy and serially afterwards. Patients were then segregated into responders (R) to anti-PD-1 and non-responders (NR) according to clinical responses that were assessed via RECIST1.1. It was observed that the R group had higher abundances of both classified and unclassified Firmicutes as well as definite increases in distinct genera such as Akkermansia and Alistipes. The commensal bacteria that was most significantly associated with favorable clinical outcomes was Akkermansia mucinophila with 69% and 58% of patients exhibiting a partial response or stable disease, respectively. A. mucinophila was only detected in 34% of the patients who died. A second validation cohort of 53 patients, 27 NSCLC and 26 RCC confirmed enrichment of A. mucinophila with PFS longer than 3 months. Other over-represented commensals included Ruminococcus spp., Alistipes spp., and Eubacterium spp. and under-represented were Bifidobacterium adolescentis and longum as well as, Parabacteroides distasonis. Stool samples transplanted into ATB treated SPF mice from R patients conferred sensitivity to anti-PD-1 treatment for MCA-205 sarcoma while stool from NR patients did not. ATB sterilization of mice followed by natural recolonization and five oral gavages of A. mucinophila reinstated sensitivity to PD-1 blockade which had been eliminated by ATB treatment. Several other mouse models were also tested including Lewis lung carcinoma, melanoma and also a recovery of NR stool implanted mouse.

The actual mechanism for the immunomodulatory effects of A. mucinophila was not completely elucidated in this article but it was proposed that reduction of inflammation and reinforcement of the intestinal barrier would reduce systemic immunosuppression [109]. The importance of this study is that it opened the possibility that treatment with a single species of bacteria has the potential to enhance the effects of cancer immunotherapy. The actual mechanism of PD-1 blockade enhancement still needs to be determined and also whether PD-1 blockade enhancement would extend to other types of cancer.

Conclusions

In this review, selective studies were summarized that highlighted the involvement of the gut microbiota in host metabolism which, in some cases either contributed to or ameliorated the development of metabolic disease. The fact that many of the metabolic disease phenotypes such as obesity, fatty liver, insulin resistance, hyperglycemia, and increased atheroschlerosis can be transmitted from an afflicted animal via their feces into a GF mouse conclusively implicates the gut microbiota as culpable agents in both the development and progression of metabolic conditions. This ability to transmit a particular phenotype, however, provides some hope that there exists a cure or at least alleviation of metabolic disease via manipulation of the gut microbiota. This was found to be true for the use of pro/prebiotics in the treatment of NAFLD. The concept of using dietary supplements such as choline and succinate to ward off CVD and diabetes, respectively, emphasizes the fact that further understanding of just what the microbiota provides for the benefit of the host will open up possible ways to replenish the host that is deficient in an essential microbial metabolite. Additionally, we have demonstrated in this discussion that metabolites of the microbiota can be either beneficial or harmful depending on the context. For example, SCFAs can reduce intestinal inflammation but on the other hand, can also reduce GI motility and contribute to weight gain. The use of microbial metabolites such as plasma TMAO, have also proven to be useful in predicting patient’s outcomes for CVD events. It is therefore, imperative that the work goes on to fully understand the impact of the gut microbiota on host metabolism for both the alleviation and prevention of metabolic disease.

Summary

  • Gut microbiota-host mammalian co-metabolism can have a causative effect in the development of metabolic diseases such obesity, diabetes, cardiovascular disease and nonalcoholic fatty liver disease.

  • By further investigation into the types of microbial metabolites that occur in both normal physiological and dysbiotic states, potential therapies may be determined.

  • Microbial metabolites identified in conjunction with disease states may provide useful prognostic information for the patient.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Abbreviations

     
  • ALT

    alanine aminotransferase

  •  
  • AS

    atheroschlerosis

  •  
  • ASBT

    apical sodium bile acid transporter

  •  
  • AST

    aspartate aminotransferase

  •  
  • BA

    bile acid

  •  
  • iBAT

    interscapular brown adipose tissue

  •  
  • Bhlhb42

    basic helix-loop-helix factor DEC42

  •  
  • BMI

    body mass index

  •  
  • BMR

    basal metabolic rate

  •  
  • CA

    cholic acid

  •  
  • CD

    conventional diet

  •  
  • CDCA

    chenodeoxycholic acid

  •  
  • CLOCK

    circadian locomotor output cycle kaput

  •  
  • Cry1,2

    cryptochrome circadian clock 1,2

  •  
  • CVD

    cardiovascular disease

  •  
  • Dbβp

    albumin D-box binding protein

  •  
  • DCA

    deoxycholic acid

  •  
  • DIO

    diet induced obesity

  •  
  • FA

    fatty acid

  •  
  • FBS

    fasting blood sugar

  •  
  • FMO3

    flavin monooxygenase-3

  •  
  • FGF15, 21

    fibroblast growth factor 15,21

  •  
  • FOS

    fructo-oligosaccharides

  •  
  • FXR

    farnesoid X receptor

  •  
  • GF

    germ free

  •  
  • GGT

    γ-glutamyltranspeptidase

  •  
  • GI

    gastrointestinal

  •  
  • GLP-1

    glucagon-like protein receptor-1

  •  
  • GPR41,43

    G-protein coupled receptor 41,43

  •  
  • HDL

    high-density lipoprotein

  •  
  • HFD

    high-fat diet

  •  
  • HOMO-IR

    homeostatic model assessment of insulin resistance

  •  
  • HSD

    high sugar diet

  •  
  • 4-HNE

    4-hydroxynonenal

  •  
  • IEC

    intestinal epithelial cell

  •  
  • IgG

    immunoglobulin G

  •  
  • IGN

    intestinal gluconeogenesis

  •  
  • IG6pc

    intestinal glucose-6-phosphatase catalytic subunit

  •  
  • LCA

    lithocholic acid

  •  
  • LDH

    lactate dehydrogenase

  •  
  • LDL

    low-density lipoprotein

  •  
  • LFD

    low-fat diet

  •  
  • LHFD

    lard high-fat diet

  •  
  • LPC

    lysophophatidylcholine

  •  
  • LPS

    lipopolysaccharides

  •  
  • LXRα

    liver x receptor-α

  •  
  • MBH

    medial basal hypothalamus

  •  
  • MCD

    methionine choline deficient diet

  •  
  • MCP-1

    monocyte chemoattractant protein-1

  •  
  • MDA

    malondialdehyde

  •  
  • MI

    myocardial infarction

  •  
  • NAFLD

    nonalcoholic fatty liver disease

  •  
  • NASH

    nonalcoholic steatohepatitis

  •  
  • NF-κB

    nuclear factor-kappaB

  •  
  • S-NO

    S-nitrothiol

  •  
  • OSTα,β

    organic solute transporter α, β

  •  
  • OUT

    operational taxonomic unit

  •  
  • PC

    phosphatidylcholine

  •  
  • Per 1,2

    period circadian clock 1,2

  •  
  • PHFD

    palm oil high fat diet

  •  
  • PPARα

    peroxisome proliferator-activated receptor α

  •  
  • PYY

    peptide YY

  •  
  • ROS

    reactive oxygen species

  •  
  • RSV

    reservatrol

  •  
  • SCFA

    short chain fatty acid

  •  
  • SCN

    suprachariasmatic nucleus

  •  
  • SI

    small intestine

  •  
  • SHP

    small heterodimer partner

  •  
  • SM

    sphingomyelin

  •  
  • SPF

    specific pathogen-free

  •  
  • TCA

    taurocholic acid

  •  
  • Tg

    triglyceride

  •  
  • TLR4

    Toll-like receptor 4

  •  
  • TMA

    trimethylamine

  •  
  • TMAO

    trimethylamine-N-oxide

  •  
  • TMCA

    tauromuricholic acid

  •  
  • TNFα

    tumor necrosis factor-α

  •  
  • T2D

    type 2 diabetes

  •  
  • UCP1

    uncoupling protein1

  •  
  • UDCA

    ursodeoxycholic acid

  •  
  • VLDL

    very low density lipoprotein

  •  
  • VOC

    volatile organic compound

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