The association between the physio-pathological variables of type 2 diabetes (T2D) and gut microbiota composition suggests a new avenue to track the disease and improve the outcomes of pharmacological and non-pharmacological treatments. This enterprise requires new strategies to elucidate the metabolic disturbances occurring in the gut microbiome as the disease progresses. To this end, physiological knowledge and systems biology pave the way for characterizing microbiota and identifying strategies in a move toward healthy compositions. Here, we dissect the recent associations between gut microbiota and T2D. In addition, we discuss recent advances in how drugs, diet, and exercise modulate the microbiome to favor healthy stages. Finally, we present computational approaches for disentangling the metabolic activity underlying host-microbiota codependence. Altogether, we envision that the combination of physiology and computational modeling of microbiota metabolism will drive us to optimize the diagnosis and treatment of T2D patients in a personalized way. Thus, metabolic signals produced by the gut microbiome can indirectly promote IR by altering the host’s metabolism. Among these metabolic changes, we highlight metabolic endotoxemia and the low rate of production of short-chain fatty acids (SCFAs) and secondary bile acids (BAs).1 Although these findings have helped to characterize the association between the gut microbiome profile and T2D, it remains a challenge to grasp the mechanisms that drive their codependence; it is also difficult to use this knowledge to modulate the metabolic crosstalk between the microbiome and host.4Type 2 diabetes mellitus (T2D) is a metabolic disorder characterized by hyperglycemia as a result of insulin resistance (IR) and a relative lack of insulin in the human body.5 Moreover, new technologies, such as fecal microbiota transplantation or bacteriophage intervention (phagosome), are promising technologies for enhancing patients’ wellness and treatment.7 Currently, two approaches are helping to reach these objectives. On the one hand, 16S rRNA and shotgun metagenome technologies allow us to carefully monitor the physiological state and composition of the gut microbiome in patients with different degrees of T2D.9 This massive amount of biological data contributes to characterizing the phenotype state of patients and evaluating how these phenotypes are altered as the disease progresses. On the other hand, the development of computational modeling approaches (CMAs) with the capacity to integrate high-throughput (HT) data is a pioneering effort to elucidate the ecological interactions of the bacterial community, and to postulate the metabolic mechanism by which T2D progresses. In particular, computational models based on inference microbial interactions and genome-scale metabolic reconstructions have emerged as a remarkable scheme required to understand the metabolic activity of the gut microbiome and to track the changes that follow the emergence of T2D.9 Currently, this field is nascent, but some pioneering advances have been reported in T2D and type 1 diabetes (T1D). In this narrative review, we analyze the state of the art of the association between the gut microbiome and T2D, the factors that modulate the interaction, and some in silico strategies to reveal the underlying metabolic mechanisms. First, we discuss the cutting-edge evidence of the relationship between host-microbiota metabolism and physiological alterations associated with T2D. Then, we discuss recent publications that highlight the importance of handling microbiome composition through lifestyle, diet, and promising intervention methods to modulate the gut microbiome. Finally, the last section is devoted to presenting and discussing frontiers in computational strategies to describe the complex interactions in the bacterial community, shed light on their organization, and build testable hypotheses to modulate metabolic mechanisms. Overall, our review highlights the importance of combining physiological knowledge, HT technologies, and computational modeling of microbiome metabolisms for designing microbiome interventions in favor of a healthy phenotype. To add an original contribution on the state of the art, the structure of the review was grounded in a bibliometric analysis with the Bibliometrix R library.10 This analysis was built with the terms “type 2 diabetes”, “gut microbiota”, “gut microbiome”, “systems biology” and “bioinformatics”, as keywords to find either in the documents’ titles or abstracts. Next, we filtered this search based on the review article as a type of document. Interestingly, we did not detect any document related to the content of our manuscript (Table S1-S2). Undoubtedly, the achievements around this field will have a strong impact on precision medicine for optimizing the outcome of treatments and improving patients’ quality of life.Currently, there is great interest in controlling external factors to modulate host-microbiome metabolic crosstalk and to restore patients to a healthy state. Thus, classical variables associated with lifestyle, such as diet and exercise, have increased their relevance in personalized medicine.11 Through interactive evolutionary processes, hosts and their microbiomes have established mutual benefits. With the abundant evidence of this relationship and its influence on health, humans and their gut microbiome can be considered holobionts, and the health of the host depends on the microbiota and cannot be seen as disconnected from it.12 Nevertheless, there are intrinsic  and environmental factors  that affect the composition of the gut microbiome and its structural functions. These factors, known as microbial disruptors, can alter a variety of physiological mechanisms that favor the development of pathologies such as intestinal permeability, chronic low-grade inflammation, and changes in carbohydrates metabolism and its associated signaling pathways (the insulin route).13The gut microbiome is a complex microbial ecosystem that coexists with various biological processes and metabolic capacities in the host.14 The set of metabolites derived from the gut microbiome serves as a source of signals that facilitate communication between the body’s organs via the nervous system (afferent and efferent autonomic pathways). Through these signals, the gut microbiota (GM) modulate the immune, endocrine, gastrointestinal, and nervous systems, forming the microbiota-gut-brain axis.15 When bacteria-host communication fails, vital functions of the host are interrupted, causing numerous dysfunctions associated with disease. In the case of T2D, several studies have characterized the composition of GM and have confirmed the existence of a particular dysbiosis depending on biogeographical variables.17 For instance, in European and Chinese populations, remarkable differences between T2D and healthy subjects were the low relative abundances of butyrate-producing bacteria  and the higher relative abundance of species such as Lactobacillus, as well as some opportunistic pathogens like Bacteroides caccae, Clostridium hathewayi, Clostridium ramosum, Clostridium symbiosum, and Escherichia coli.19 Previous results suggest that an interesting aspect of these findings is that GM has been associated with the immune system (IS) through immunoinflammatory signaling, which can affect insulin sensitivity .22 Fifteen grams of non-digestible carbohydrates in the colon subjected to saccharolytic fermentation produce between 400 and 600 mmol/day of SCFAs.23 The effectiveness of this biochemical transformation depends on numerous factors, including pH, which influences the growing competition of bacteria in the intestine. Bacteria such as Lactobacilli, Roseburia, Faecalibacterium prausnitzii, and Bifidobacteria promote health in the host and enhance the production of SCFAs at a pH of 5.5. Alternatively, when the non-fermentable fiber is limited in the distal large intestine, the luminal pH is raised to 6.5, eliminating butyrate-producing bacteria almost entirely.24Of the most evident associations between T2D and physiological alterations, we highlight those comprising the gut microbiome and alterations of glucose metabolism through SCFA and BA secondary reduction .14,21 GM25 SCFAs play a role in insulin sensitivity in humans through incretins, which are intestinal peptides that act as hormones produced in the gastrointestinal tract by enteroendocrine cells in response to food intake. At a functional level, incretins affect the cells of the islets of Langerhans by increasing the secretion of insulin by 70% in the postprandial state and consequently decreasing glucagon secretion depending on circulating glucose. The main incretins are glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP). SCFAs, products of saccharolytic fermentation, bind to G protein-coupled free fatty acid receptors in the colon mucosa, which are G-protein-coupled receptor type 41 (GPR41) and G-protein-coupled receptor type 43 (GPR43). Through the GPR41 receptor, the YY peptide is released, which reduces the energy extracted from the diet, increasing intestinal peristalsis. In contrast, GPR43 induces glucose-dependent insulin discharge, inhibits glucagon secretion, improves insulin sensitivity, reduces hepatic gluconeogenesis, and controls appetite  to produce SCFAs.appetite .26 Furth30 These alterations were related to liver structures (primary BAs) and derivatives produced by intestinal microbes (secondary BAs).31 BAs are signaling molecules that regulate glucose and lipid metabolism. The primary BAs are secreted in the small intestine after production and glyco-taurus conjugation (N-acyl amidation with glycine or taurine substituents) in the liver. Through a bacterial hydrolase enzyme , deconjugation is first generated.32 There is evidence pointing out that 95% of conjugated primary BAs are reabsorbed through enterohepatic circulation, while the remaining 5% escape from this mechanism, reaching the large intestine and becoming secondary BAs by the action of Firmicutes (Eubacterium spp and Clostridium spp).33On the other hand, several studies have reported changes in the concentration of circulating bile acid groups (BAs) in subjects with IR or T2D.31 This regulation of energy metabolism would be produced through nuclear receptors, identical to the farnesoid X receptor (FXR) and the G-protein-coupled bile acid receptor (Gpbar1). Specifically, Takeda G-protein–coupled receptor 5 (TGR5) is a bile acid membrane receptor expressed in the gallbladder, ileum, colon, and brown and white adipose tissue.34 This receptor internalizes and activates a series of adenylate cyclase-dependent signals through the involvement of glucose metabolism and lipid energy in brown adipose tissue and muscle, heightened mitochondrial activity, and phosphorylation. These processes stimulate insulin sensitization in murine models of diabetic and obese conditions. Likewise, in L cells, TGR5 improves glycemic metabolism by stimulating GLP-1 production and insulin secretion , and white adipose tissue (WAT). In BAT and muscle, increased mitochondrial activity and phosphorylation lead to insulin sensitization in obese and diabetic mouse models.Subjects with T2D have a lower number of secondary BAs than healthy subjects. This characteristic is related to an alteration in carbohydrate metabolism since they have an insulin-sensitizing role.Thus, BAs have been proposed as metabolic integrators of energy homeostasis involved in the regulation of numerous metabolic pathways, including their synthesis and enterohepatic circulation.ecretion .33 Furth35 The signals from the gut microbiome regulate innate immunity and influence local and systemic responses.37 In T2D, microbiome signals can promote low-grade chronic inflammation associated with IR through toll-like receptors (TLRs). Cell surface TLRs mainly recognize microbial membrane components such as lipids, lipoproteins, and proteins. Specifically, TLR4 recognizes bacterial lipopolysaccharide (LPS).38 TLRs differentially recruit members of a set of TIR domain-containing adaptors, such as MyD88, which is utilized by all TLRs (except TLR3) and activates nuclear factor kappa-light-chain-enhancer of activated β-cells (NF-κB) and mitogen-activated protein kinases (MAPKs) for the induction of inflammatory cytokine genes.37 Due to a high-fat diet, these receptors are activated in response to microbial stimuli such as LPS, which is present on the membranes of gram-negative bacteria.39 TLRs, specifically TLR-4, also interact with glycosylated serum proteins called advanced glycosylation end products (AGEs)41 to activate the transcription of MAPKs and NF-κB. In turn, this last pathway acts as an enhancer of activated β-cells after activation by LPS.39 On the other hand, proteins  are enzymatically glycosylated in lysine and arginine residues in the hyperglycemic state. As a consequence of this glycosylation, the production of AGEs is favored.41 AGEs in macrophages can bind to TLR-4, which is overexpressed in T2D. Thus, TLR activation leads to a cascade of intracellular signaling mediated by NF-κB when it is translocated to the nucleus, and activates the transcription of genes coding for cytokines and inflammatory chemokines, including tumor necrosis factor-alpha (TNF-α), interleukin 1 beta (IL-1β), and interleukin 8 (CXCL8). As a result, this process promotes the onset of the inflammatory response to hyperglycemia  are considered key players in the pathogenesis of T2D, as they can recognize microbial signals such as LPS and peptidoglycan, which are considered PAMPs. In addition, they have the ability to recognize non-microbial signals released by cellular damage or stress, such as mitochondrial DNA and ATP, which are considered DAMPs.glycemia .4243 Subsequently, an intracellular signaling cascade mediated by the insulin receptor substrate (IRS) is initiated, which activates protein kinase B (Akt), and finally induces the mobilization of two isoforms of glucose transporter (GLUT-2 and GLUT-4) to introduce glucose into the cell. When β-cells are subjected to an inflammatory process, they respond through the activation of two main pathways: 1) the activation of TLR2 and TLR4 by PAMPs or DAMPs; and 2) the assembly of the inflammasome nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain-containing (NLRP3).44 This inflammatory process causes insulitis, characterized by a continuous release of IL-6, IL-8, TNF-α, and monocyte chemotactic protein 1 (MCP-1), and by the activation of insular macrophages and the recruitment of new monocytes and peripheral macrophages.43 When the inflammatory response is improperly regulated, it produces a state of damage called chronic low-grade inflammation.36 During low-grade systemic inflammation, adipose, liver, and muscle tissue endure infiltration of inflammatory macrophages producing TNF-α; this cytokine directly interferes with the tissues and reduces their ability to respond to insulin. TNF-α reduces tyrosine phosphorylation of the insulin receptor and IRS-1, and induces serine phosphorylation of IRS1, which in conjunction reduces downstream transduction of insulin signaling.45 It has recently been postulated that the presence of gut dysbiosis favors an increase in the prolonged circulation of inflammatory markers and increased intestinal permeability.46 In turn, bacterial products (PAMPs) enter the circulatory system and promote the genesis of systemic inflammation in T2D.42 Overall, the genesis of T2D is conditioned by high levels of pro-inflammatory cytokines and the composition of the GM.48Under normal physiological conditions, insulin binds to the receptor located on the surface of myocytes, hepatocytes, and adipocytes.Given their importance, in the following sections, we describe the relationship among the gut microbiome and two variables with relevance in T2D physiopathology: IR and pancreatic β-cell dysfunction (PBD).20 An imbalance between potentially pathogenic and nonpathogenic bacteria can induce crucial metabolic alterations in the entire organism, which in turn can disturb the physiological parameters in multiple body compartments.49 This perturbation has at least two complex consequences. First, IR promotes alterations in different metabolic pathways, such as lipids, amino acids, and bile acids. Second, these alterations have substantial implications for the modulation of insulin sensitivity.14 In this way, metabolites produced by GM may regulate insulin sensitivity through several components of the insulin signaling pathway, such as insulin receptor substrates (IRS) and the enzyme kinase AKT , and an alteration in insulin signaling at the hypothalamic level.14 Additionally, BCAAs increase lipid oxidation in muscle, accumulating acylcarnitines and generating mitochondrial dysfunction. In adipose tissue, the alteration of BCAA catabolism reduces substrate flow to lipogenesis, contributing to metabolic dysfunction in IR and linking it to the progressive loss of pancreatic β-cell function.14 Ultimately, these changes generate an acute secretory response in the β-cell, resulting in a progressive dysfunction characterized by an abnormal secretory process due to the pancreatic β-cell trying to adapt and compensate for the IR, as described in the following section.On the other hand, branched-chain amino acids (BCAAs) have recently attracted significant scientific attention because elevated blood concentrations of BCAAs are associated with states of IR, such as obesity and T2D.2 This dysfunction associated with GM can be induced by two effects: 1) the increase in intestinal permeability; and 2) the chronically elevated glucose levels in the host (islet glucotoxicity).53 This physiological imbalance causes an increase in intestinal permeability, which in turn promotes the translocation of some bacteria (Proteus mirabilis and Escherichia coli) and their metabolites.54 To respond to intestinal permeability in the host, two main detection systems continuously scan for bacteria capable of translocating the intestinal mucosa or adhering to the epithelium: 1) NOD-like receptors (NLRs), which detect the presence of intracellular microbes; and 2) TLRs.38Glucose homeostasis is the product of metabolic, hormonal, neural, and microbial signals whose regulation determines the degree of glucose-dependent insulin release. The progression from normoglycemia to glucose intolerance and later to T2D occurs due to a deterioration in these signals, which gradually decreases insulin sensitivity and β-cell functionality .2 This d55 Activation of the inflammasome depends on two pathways: first, NF-κB expression upon sensing PAMPs or DAMPs by their respective receptors, e.g., TLR4 binds to LPS. This process alerts the cell to express inflammasome-related genes such as inactive NLRP3, Pro-IL1, and Pro-IL-18. The second signal involves the recognition of bacterial toxins  or host metabolites (potassium efflux) by inflammasome sensor proteins .56 This recognition system allows recruitment of the adaptor protein ASC, which binds to pro-caspase-1, leading to its self-processing and activation. Caspase-1 cleaves the inactive precursor proteins of IL-1β and IL-18 into their bioactive fragments. In addition, this protease induces an inflammatory cell death known as pyroptosis.55 The second detection system for bacterial permeability corresponds to TLRs. These are pattern recognition receptors (PRRs) capable of recognizing PAMPs and inducing the expression of pro-inflammatory cytokines , mediated by the adapter molecules MyD88 and NF-κB. Taken together, these mechanisms directly affect the function of β-cells.53 Spranger et al. reported that the combined elevation of IL-6 and IL-1β, products of the activation of the two detection systems, was related to a threefold increased risk of developing T2D.57The first detection system involves NLRs, specifically the NLR family pyrin domain containing (NLRP) subfamily, which can oligomerize into a macromolecular complex known as an inflammasome. Inflammasomes are found in the cytosol and mediate the activation of inflammatory caspases.58Concerning islet glucotoxicity, chronically elevated glucose levels have been reported to impair islet function and proliferation, and induce apoptosis. In addition, the intra-islet expression of IL-1β may contribute to T2D pathogenesis by inducing the loss of mass and β-cell function. Consequently, hyperglycemia and glucotoxicity result from pancreatic dysfunction, increase intestinal permeability, and activate the inflammatory response.59 Currently, their composition and diversity are modulated through classical factors such as diet, exercise, drugs, and more recently through interventions such as fecal microbiota transplantation (FMT), bacteriophage intervention through phageome studies, antibiotics, and bariatric surgery. All of these modulator methods can generate beneficial changes in the structure and function of the GM and restore them temporarily or permanently.60 In the next section, we will analyze the effect of these modulators on microbiome composition   increased significantly, suggesting that physical exercise for short periods of time exerts differential modulating effects on microbial composition.75 Moreover, chronic physical activity modifies GM composition and reduces intestinal permeability and systemic inflammation.76 On the other hand, Motiani et al. reported the effects of continuous training of different intensities  on the GM in subjects with T2D and pre-T2D. Remarkably, the authors concluded that the composition of the GM changed due to physical exercise in two weeks. Both forms of training decreased the Firmicutes/Bacteroidetes ratio, Blautia spp., and Clostridium spp. Faecalibacterium increased only in continuous moderate-intensity training. Notably, these changes in microbiome abundances were correlated with clinical parameters. For example, a lower abundance of Blautia was associated with better insulin sensitivity. Overall, regular physical activity provides metabolic benefits, even in short periods of time77 , suppress appetite in the brain. On the other hand, sodium-dependent glucose transport-2 inhibitors (SGLT2) block renal reabsorption of glucose. Metformin (a biguanide) reduces hepatic gluconeogenesis. Meanwhile, thiazolidinediones (TZD) agonists of PPAR-γ increase glucose uptake in skeletal muscles and adipose tissues.79 Last, sulfonylureas (SUs) increase pancreatic insulin secretion.81 The effect between the GM and anti-diabetic drugs is bidirectional; the drug influences microbiota composition, and in turn, the metabolism of the microbiota may have a positive effect in the host.80 One effect of these drugs in the host is to modify the gut microbiome composition by increasing the bacteria that produce SCFAs.82 In turn, the GM and SCFAs exert effects on anti-diabetic agents, influencing their pharmacogenetics and bioavailability.80 Understanding the bidirectional drug-microbiome interaction and how it influences clinical outcomes in T2D patients is necessary to identify possible modulating mechanisms of the GM.80 Given their current application, we discuss and present some evidence of the interactions between the GM and three anti-diabetic drugs .Patients with T2D are clinically treated with various anti-diabetic drugs83 Various studies on mouse and human models suggest that metformin increases the abundance of Akkermansia and other bacteria that produce SCFAs . Simultaneously, with this dysbiosis, metformin improves glucose concentrations in the patient.84 Forslund et al. in their study with T2D patients stratified by treatment regimens, found that the subjects treated with metformin changed their GM composition. They observed that patients treated with metformin presented with a higher production of butyrate and propionate than untreated patients due to the enrichment of bacteria producing SCFAs .85 On the other hand, Shin et al. showed statistically significant differences in the abundance of Firmicutes and Bacteroidetes between mice fed a high-fat diet and treated with and without metformin.86 At the genus level, an increase in Escherichia and a decrease in Intestinibacter were recognized in mice treated with metformin.86 In addition, Na-Ri Shin et al. explored the relationship of the anti-diabetic effect of metformin and GM composition in obese mice with T2D. They found that the effect of metformin may be mediated by a specific subset of bacterial taxa associated with an increased abundance of Anaerotruncus, Lactococcus, Akkermansia, Parabacteroides, Odribacter, Alistipes, Lawsonia, Blautia, and Lactonifacter, of which Akkermansia was responsible for the greatest phylum Verrucomicrobia whose abundance was observed in T2D-induced mice treated with metformin compared to control mice with p < .05. Similarly, the authors concluded that the number of goblet cells producing mucin and mucin-degrading bacteria was higher in the group of mice with T2D and treatment with metformin vs the control group . Another outstanding conclusion is that the modification of GM composition was associated with greater glucose tolerance in mice treated with metformin than in control mice (p < .05).86 The previous results coincide with those found by de la Cuesta-Zuluaga et al. who studied 14 subjects with a diagnosis of T2D treated with metformin, and 42 healthy individuals with similar clinical conditions.87 They concluded that Akkermansia muciniphila and Butyrivibrio increased their abundance in subjects with T2D respect to the control. In addition, they reported a non significant change in abundance for Roseburia, Subdoligranulum, and Faecalibacterium, all of which are butyrate-producing bacteria.87 Treatment with metformin, in addition to increasing Akkermansia muciniphila in the colon, also increases the abundance of Lactobacillus in the upper small intestine, which may contribute to the anti-diabetic effect of metformin.89 Furthermore, metformin increases the population of SCFA-producing bacteria  in the gut.90 Taking into account all these findings in animal models and humans, the drug’s ability to modify bacterial diversity becomes evident by selectively increasing the abundance of specific bacteria and altering multiple metabolic pathways in the GM, such as those involved in the metabolism of glucose.Among the biguanides, metformin is used as the first-line treatment in patients with T2D.37 GLP-1 receptor agonists (liraglutide) can modulate the GM.91 Zhang et al. in a murine model, determined the effect of liraglutide (0.4 mg/kg/day) on GM composition. In this study, 58 bacteria changed significantly between the normoglycemic, diabetes-induced, and liraglutide groups (p < .05). Of these microorganisms, 11 showed statistically significant differences between the treatment and control groups. The genera Flavonifractor, Lachnoclostridium, Ruminococcus_gnavus, Flavonifractor_plautii, and Bacteroides_acidi-faciens were significantly elevated in the liraglutide treated group compared with the diabetic group. The bacteria that significantly reduced their abundance were the genera Christensenellaceae_R_7_group, Ruminococcaceae_UCG_010, Ruminoclostridium_6, Prevotella_9, and the class Mollicutes. The Bifidobacterium and Lactobacillus genera were exclusively identified in the liraglutide group compared with the healthy group.92 Complementarity, Charpentier et al. evaluated the effect of liraglutide (intervention), exendin (control), and saline solution (control) in a situation of mouse model-induced hyperglycemia. Liraglutide significantly improved insulin secretion, and this effect was associated with changes in GM composition. The frequency of the Bacteroidetes/Firmicutes phyla relationship increased in response to both GLP-1 receptor agonists. Additionally, Porphyromonadaceae and Lactobacillaceae significantly increased, while Lachnospiraceae and Bacteroidaceae decreased in abundance. Exendina modified the families Lachnospiraceae and Porphyromonadaceae, the genus Odoribacter, and Lactobacillaceae and liraglutide modified Escherichia and Shigella.91A second frequent drug clinically applied in T2D is the GLP-1 analogue. These activate GLP-1 receptors and increase resistance to inactivation by the DPP-4 enzyme. Currently, six drugs are clinically approved in this category: exenatide, liraglutide, semaglutide, albiglutide, lixisenatide, and dulaglutide.83 Inhibiting DPP-4 prolongs the circulating half-life of GLP-1, thereby improving its insulinotropic and glucoregulatory capabilities.93 Five drugs have so far been approved in this class: sitagliptin, saxagliptin, vildagliptin, linagliptin, and alogliptin.93 In particular, the composition of the GM can be modulated with DPP-4 inhibitors.80 Xinleng Yan et al. in their study using an animal model, determined the effect of sitagliptin on the composition of GM and its relationship with glucose intolerance. The results obtained are conclusive; treatment with sitagliptin for 12 weeks modified the GM composition, reduced Firmicutes , and increased Bacteroidetes . Additionally, serum glucose was reduced in comparison to mice with T2D without pharmacological treatment, showing statistical significance (p < .01).94 Also, Zhan et al. determined the effect of vildagliptin on the increase in butyrate-producing bacteria in mice with T2D. Vildagliptin was administered in two doses; high (0.02 g/kg) and low (0.01 g/kg). Statistically significant differences were found in comparison to control rats in the number of operational taxonomic units (OTU), Shannon’s index, and Chao’s index, all with p < .01. Vildagliptin modified the characteristic Firmicutes/Bacteroidetes index in the GM in mice with T2D, reduced Firmicutes, and increased Bacteroidetes, butyrate-producing bacteria, and Lactobacillus. In addition, vildagliptin treatment enriched the phyla Streptococcaceae (p < .01) and Bacteroides (p < .01), but decreased Ruminococcaceae oscillibacter (p < .05), Ruminiclostridium (p < .05), Anaerotruncus (p < .01), Eubacterium (p < .05), and Prevotellaceae (p < .05). It was determined that the animal model treated with vildagliptin at any dose reduced fasting, postprandial glucose, HbA1c, HOMA-IR, and IL-6 compared to T2D and normoglycemic mice, with statistical significance of p < .01 for each variable.95Finally, dipeptidyl peptidase-4 inhibitor (DPP-4i) is a proteolytic enzyme found in the cell membrane of most cells in the body whose main function is to inactivate GLP-1.Allobacum (p = .004), and Turicibacter (p = 1.77e-8), the family Erysipelotrichaceae, specifically the genera Anaerostipes (p = 5.51e-5) and Blautia (p = .039), and the family Lachnospiraceae genus Lactobacillus (p = .013). These changes in GM composition were associated with a reduction in body weight in mice treated with liraglutide. In contrast, the GM composition in treatment with saxagliptin increased the abundance of some bacteria from the class Erysipelotrichaceae, such as Lactobacillus (p = .023), Allobaculum (p = .017), and Turicibacter (p = .001). Furthermore, the abundance of Bacteoridetes decreased, specifically the genera Bacteroides (p = .003) and Prevotella (p = .018). The random glucose concentration was lower in linagliptin-treated mice, p < .05. Liraglutide and saxagliptin were shown to enrich Lactobacillus and Turicibacter. Furthermore, Lactobacillus showed inhibitory activity against DPP-4, through increased incretins and glucose homeostasis.96On the other hand, Lin-Wang et al. compared the GM structural modulation of body weight and serum glucose with two treatments: liraglutide (GLP-1 receptor agonist) and saxagliptin (DPP-4 inhibitor). The authors found that mice treated with liraglutide enriched the genera 97 The use of antibiotics has been associated with remarkable metabolic alterations, mainly when the application comprises broad-spectrum antibiotics and their use occurs in the first years of life. It has been estimated that antibiotics impact the abundance of 30% of the GM, producing a rapid and significant decline in richness, diversity, gene expression, and protein and metabolic activity.98 Despite the GM metabolically responding under this perturbation, the initial state is not fully recovered, and antibiotic-induced microbial alterations can remain for months or even years.99Antibiotics are administered to combat pathogens. However, their use disturbs the microbial composition of some important genera participating in immune, endocrinological, and metabolic functioning.Firmicutes in B6J mice to 37% (p = .009) and in 129 T mice to 50% of untreated HFD levels (p= .003). This dysbiosis was associated with a rise in the relative abundance of Proteobacteria.100 In 129 J mice, metronidazole and vancomycin markedly diminished Verrucomicrobia from 66 to 0% (p = .002) and 23% (p = .007), respectively, favoring the predominance of Proteobacteria and Firmicutes. HFD B6J mice treated with vancomycin and metronidazole exhibited reduced blood glucose levels and improved glucose tolerance during the OGTT.100 Furthermore, Gridhar et al. evaluated the effect of antibiotic treatment and an HFD on the metabolism and function of the pancreas. The C57BL/6 J mice were divided into four groups: 1) fed a standard chow diet, 2) an HFD for six weeks, 3) fed a standard diet plus oral vancomycin, and 4) metronidazole in the last two weeks of the experiment. Vancomycin treatment significantly increased glucose tolerance and insulin sensitivity in HFD-exposed mice.101 On the other hand, Reijnders et al. investigated how GM manipulation by antibiotics  affects host metabolism in 57 obese and pre-diabetic men. This study was a double-blind controlled clinical trial in which the population was randomized into three groups: the placebo, amoxicillin, and vancomycin. Vancomycin at seven days decreased the diversity of intestinal microbiota compared to the placebo (p < .001); however, amoxicillin did not affect diversity (p = .42). Likewise, the group treated with vancomycin decreased the relative abundance of butyrate producing bacteria, such as Coprococcus eutactus, Faecalibacterium prausnitzii, and Anaerostipes caccae, as well as species involved in BA dehydroxylation, like Clostridium leptum. In contrast, patients treated with amoxicillin did not experience a change in the composition of the microbiota after seven days of treatment.102 The data obtained in this clinical trial were in contrast with several previous studies on rodents, which indicated that antibiotic treatment can improve glucose homeostasis and metabolic alterations.103 In human species, the effects of antibiotics on glycemic control or insulin sensitivity remain inconclusive.102Recently, murine model studies and human clinical trials have shown that antibiotics can modulate the GM with antibiotic treatment, which in turn, could reduce glucose intolerance, adiposity, and adipose tissue inflammation. Using a murine model, Fujisaka et al. determined the effect of antibiotics on the GM and host metabolism. In this study, cB6J, 129 T, and 129 J mice were treated with a placebo, vancomycin, or metronidazole in their drinking water. Vancomycin treatment lowered the relative abundance of 104 The two most common procedures in BS are sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), both with comparable efficacy. According to the American Diabetes Association (ADA) and the International Diabetes Federation (IDF) guidelines, BS is recommended in individuals with T2D and body max index (BMI) of at least 35 kg/m2, it can be an option for individuals with mild obesity (BMI of 30–34.9 kg/m2) who have inadequate glycemic control despite optimal medical management.104Bariatric surgery (BS) refers to surgical procedures designed to achieve weight loss and long-term glycemic control in patients with T2D and obesity. Importantly, it can achieve better outcomes than non-surgical interventions (medication and diet).104 After BS, an improvement in metabolism occurs even before weight loss begins. Although the mechanisms underlying these favorable responses are not fully understood, the gut neuroendocrine system, gut hormones, bile acids, and GM have been proposed as key mediators.105Noticeably, BS can achieve a remission of T2D in 23 to 60% of patients, defined by normalization of blood glucose (FPG below 126 mg/dL or estimated HbA1c below 6.5%) without the need for normoglycemic medications.Escherichia, Klebsiella, and Akkermansia muciniphila and a decline in the relative abundance of Faecalibacterium prausnitzii, Lactobacillus acidophilus, and Coprococcus.106 Different BS procedures affect GM differently, with the most notable change occurring in RYGB. Sanchez-Alcoholado et al. found that patients after SG experienced an increase in the relative abundance of Akkermansia, Eubacterium, Haemophilus and Blautia. In contrast, patients after RYGB saw a preferential increase in the relative abundance of Veillonella and Granucatiella.107 In addition, the microbiota are altered in individuals with mild obesity treated with RYGB, with a rise in the relative abundance of Klebsiella, Gammaproteo-bacteria, Enterobacter, Gammaproteobacteria_un-classified, and Veillonellaceae_unclassified.108Remarkable changes in GM composition were observed after BS. In general, patients treated with BS experience an early increase in the richness of the GM, which may reflect an attempt to restore intestinal homeostasis. There is also a profound shift in certain genera associated with an improvement in glucose metabolism and a reduction in systemic inflammation markers. A comparative study of GM composition in T2D patients before and after BS showed an increase in the relative abundance of Bacteroida class  before and five years after surgery compared to post-RYGB patients with adequate remission. In addition, they investigated the causal link between GM and BS by performing human-to-mouse (free mice) FMT. They found that the phenotype of IR can be induced in a recipient mouse by receiving a transplantation of fecal microbiota from patients with inadequate remission after RYGB.109The role of the GM in the prognosis of T2D remission after BS has also been studied. J. Debédat et al. demonstrated that an inadequate response to RYGB in T2D patients is associated with an increase in the 110 However, these outcomes are in contrast to those reported by Ilhan et al. who characterized the fecal metabolome of an American cohort with severe obesity and T2D after BS (the majority of subjects had resolution of diabetes and other comorbidities 12 months after treatment). They found a decline in the concentration of secondary BAs at 12 months after surgery compared to the non-surgical controls.111 Overall, these results imply that GM and BS have an important codependence in the improvement of T2D patients. Consequently, the study on the differences in GM composition is critical for understanding the pathways underlying metabolic improvement after surgery.Several microbiota-mediated mechanisms have been proposed to induce glycemic control and improve insulin sensitivity after BS. One of them is the crosstalk between the BAs and the GM. After BS, there is an increase in the blood concentrations of primary BAs and secondary BAs in T2D patients. Altogether, the overproduction of these BAs impacts lipid and glucose metabolism, and seems to be associated with the remission and improvements of T2D patients treated with BS.112 Recently, FMT has been proven to be an effective method for treating and preventing the recurrence of gastrointestinal disorders through host-microbiota interactions; for example, FMT treatment against Clostridium difficile infection.113 Furthermore, FMT has been suggested as a therapeutic approach to modulate chronic and metabolic conditions such as T2D.114 Vrieze et al. established the effects of infusing GM from lean donors to male recipients with metabolic syndrome on the recipients’ microbiota composition and glucose metabolism. Six weeks after microbiota infusion, receptor insulin sensitivity increased along with butyrate-producing bacteria levels.115 Likewise, the authors demonstrated that FMT with or without lifestyle changes increased butyrate-producing bacteria in subjects with obesity and T2D. A recent study highlighted that the combination of lifestyle changes and FMT increased Bifidobacterium and Lactobacillus compared to FMT alone.116 In addition, Kootte et al. studied the effect of allogeneic FMT (from lean donors) on metabolism in relation to GM composition at 6 and 18 weeks after treatment. Moreover, the authors obtained microbiome composition in autologous FMT as the control treatment (placebo) in the donor subjects. In their clinical trial, the authors observed a statistically significant increase in insulin sensitivity after 6 weeks of allogeneic transplantation, accompanied by an altered composition of the GM. Further, using the GM composition, it was possible to classify the status of responders versus non-responders to the allogeneic FMT (recipient operating characteristics [ROC] AUC 0.88). Metabolic responders to FMT were characterized by lower baseline GM diversity and a higher abundance of Subdoligranulum variabile and Dorea longicatena than non-responders, while the abundance of Eubacterium ventriosum and Ruminococcus torques was lower in the baseline fecal samples. Although FMT has been proposed as a candidate to treat T2D, the molecular mechanisms underlying the therapeutic benefits are not yet understood.116FMT has become an outstanding research topic with potential applications in clinical medicine and biomedicine.117 Additionally, Zhang et al. determined the effect of transplanted fecal bacteria from Kazakhs (the Kazakh Chinese ethnic group) with normal glucose tolerance on male db/db mice with T2D. In these recipient mice, the levels of Desulfovibrio and Clostridium coccoides in the intestine were significantly reduced, but the abundance of Akkermansia muciniphila and the expression of colonic protein histone deacetylase-3 (HDAC3) in the colon samples were increased. These results indicate that Akkermansia muciniphila could have affected the metabolism of the murine model by regulating the expression of HDAC3. This is because it can activate brown fat cells to oxidize lipids, increase metabolism and promote weight loss to counter T2D. Furthermore, in this study, HDAC3 was positively correlated with glycolipid levels, suggesting that Akkermansia muciniphila may be the main intestinal probiotic that improves metabolism in T2D.118 Based on previous evidence, FMT has been recognized as a therapeutic strategy that in combination with lifestyle can be potentially effective in the treatment of T2D.119 However, more evidence in humans needs to be generated, and we suggest that for future interventions, determining the baseline fecal microbiota composition may help predict treatment efficacy.112On the other hand, in a murine model with T2D, FMT increased the number of species and the alpha diversity indices (Shannon and Simpson). In addition, the level of HbA1c decreased and improved pancreatic β-cell function (measurement performed with HOMA-B) in the FMT group. Notably, this study suggests that GM transplantation reverses IR and damages islets.120Obesity and T2D are associated with changes in gut bacterial composition, but little is known about the role of the virome in T2D disease development. Yang et al. reported the results of viral-bacterial transkingdom correlation for a Chinese cohort of 101 lean controls and 128 obese subjects (74 diagnosed with T2D). The authors found a decreased number of correlations between the relative abundance of the virome and bacteriome in obese subjects compared to the lean controls. Furthermore, obese subjects with T2D displayed an increased number of negative correlations and a lower number of positive correlations compared to the lean controls.121 Their results imply that the number of phages in the intestinal tract of diabetes subjects was significantly increased, especially in the group with seven phage OTUs (pOTUs) . However, they identified pOTUs belonging to the Caudovirales order, which has several limitations of taxonomic annotation. Despite these incipient achievements providing little insight into the mechanisms by which the phageome participates in T2D, they supply evidence of their involvement in the disease and pave the way for further research to discover human gut phage functions in the development of T2D.121Given the extensive evidence that phages can shape the composition and function of bacterial communities, the phageome of the human gut has been studied in T2D patients. For example, Yingfei et al. conducted a computational study of associations  between bacterial and phage abundance in a Chinese cohort of 74 healthy patients and 71 T2D patients.122 For example, proteomic studies and 16S rRNA data suggest a lower abundance of the Lachnospiraceae family in obese diabetic mice than in the control group.123 Additionally, Reeves et al. suggested that inoculation with Lachnospiraceae in germ-free mice is associated with suppression of Clostridium difficile colonization.124 Altogether, these findings suggest that an increase in the abundance of Lachnospiraceae can be considered beneficial and is highly associated with the maintenance of gut homeostasis and health.123 On the other hand, integration of whole metagenome shotgun (WMS) with metabolomics pointed out that a high fiber diet was associated with a decrease in the abundance of sulfate reducer bacteria and a rise in SCFA producers, which in turn is related to better levels of HbA1c and increased GLP-1 in the host.125 Moreover, an integrative analysis with WMS and targeted metabolomics performed on a T2D-Chinese cohort supplies evidence that SCFA metabolism enriches its activity after metformin application. Simultaneously, this last finding was associated with higher levels of butyrate and propionate metabolites.88High-throughput technologies (HTs) are powerful tools, as they offer new insight into the functioning and behavior of microbial communities. However, data is not sufficient, and the generation of knowledge requires computational schemes with the capacity to integrate data and reach conclusions at a systemic level. As depicted in Barnesiella, and TNF-α was inversely correlated with Faecalibacterium.126 Given that the host’s IL-1 activity127 and IFN-γ128 signaling are related to Barnesiella and Faecalibacterium, they are associated with TNF-α, which might enhance IL-17 activity.124 The absence of these correlations in IR subjects suggests that IR could affect associations between GM and cytokines.126 Moreover, the genera Clostridium-XIVb and Phascolarctobacterium were significantly correlated (p < .05) with Holdemania in healthy participants, and Holdemania was correlated with Clostridium-XlVa, Clostridium-XVII and Collinsella. Thus, IR and healthy associations can indicate different patterns of GM interactions between the two groups.126 These findings support the idea that systemic analyses offer novel insight into the role of GM in the development and course of T2D disease.In terms of the development and progression of patients with T2D, the information obtained through HT is in constant growth. For example, Zhou et al. carried out deep profiling of the transcriptomes, metabolomes, cytokines, proteome, and GM of a cohort of 106 subjects . This report revealed two main conclusions about GM and individuals with prediabetes. First, GM is differ between healthy and IR subjects. Second, the GM (conceived as a holobiont) in subjects with IR responded differently than those associated with healthy subjects when the immune system was stimulated . Moreover, the global co-association analysis revealed precise host-microbe interactions that differed between healthy and IR subjects. In this way, the correlations between GM and host circulating cytokines revealed several interactions in IR subjects (p < .05). For example, IL-1β was positively correlated with 129 Due to their relevance to elucidate regulatory and metabolic mechanisms, we discuss the main computational approaches that have been used to explore the relationship between GM and T2D, which are summarized in An effort to discover new insights about the function, structure, and design of GM has been triggered by the development of qualitative and quantitative computational models.130The generation of extended comprehensive studies for GM requires massive high-quality data and bioinformatic methods to derive a proper biological interpretation. For example, one of the first and most fundamental questions belongs to the taxonomic composition of the GM and the phylogenetic connections of its members. An approach for taxonomic classification is based on the counts of specific marker genes., i.e., 16S rRNA, 18S rRNA, and internal transcribed spacer. These approaches have enabled the analysis of different and complex habitats in the GM. In particular, 16S rRNA has been frequently used due to its capacity to analyze large numbers of samples, i.e., multiple patients and longitudinal studies.131 After the primary analysis, it is possible to perform more specific analyses, , at several taxonomic levels .132At the same time, several bioinformatics tools have been developed to examine the reads obtained from 16S rRNA technologies, most of which rely on three main steps: preprocessing and quality control, taxonomic assignment, and ecological analyses.133The taxonomic assignment is a key step in the 16S rRNA sequencing data analysis pipeline. Currently, three representative tools have been successfully and widely applied in 16S analysis: Quantitative Insights Into Microbial Ecology (QIIME2), Mothur, and DADA2. In general, these tools have reliable and comparable results in terms of performance for 16S analysis under different next generation sequencing technologies.134 thus, we can predict functional genetic information through diverse bioinformatics tools, such as the phylogenetic investigation of communities by reconstructing unobserved states (PICRUSt2) and Tax4Fun.135Although 16S analysis allows us to obtain information on the taxonomic and phylogenetic community structure at the genus level, these methods cannot provide information about the functional genes in the bacterial community. Despite this limitation, there are bioinformatic strategies to fill this gap. Phylogeny is actively correlated with functional gene patterns;136 Hence, the following bioinformatic tools have been developed for statistical analysis of microbiome data, such as LEfSe and MaAsLin2.137 They provide several methods for data normalization and transformation. Additionally, SparCC, and SPEIC-EASI address the compositional problem by assuming that few species are correlated, and BAnOCC makes no assumptions about the microbial data.138 In addition, several supervised learning algorithms have been proposed to identify a subset of highly predictive taxa from the different stages of the disease. Due to the high complexity of the data, it is necessary that these algorithms be able to model the complex interactions and non-linear effects between microbial communities. As such, the most commonly used methods are support vector machine, random forest, and multilayer perceptron with variable predictive accuracy.139However, we need to consider that HT data  fall into a class of data termed “closed” or “compositional”, which includes particular geometric and statistical properties; this makes establishing microbial taxa associations between communities challenging.140 MENA,141 LSA,141 CoNet,142 SPIEC-EASI,140 MetaMIS,143 and Web-gLV,144 among others fall in this category. These bioinformatic tools integrate correlation networks and include methods for OTU pre-processing and microbial associations. The most common output from all of these tools is the inference of a microbial association network and the estimation of its robustness through their topological parameters, such as cluster coefficient, connectivity, and modularity.145Among the computational approaches available to analyze the microbiome, we highlight those associated with reconstructing the ecological structure from HT, such as 16S rRNA and WMS, SparCC,146 This geographic location was selected due to the high rate of obesity and T2D cases that emerge there each year.147 According to this report, the authors identified more reads in Lachnospiraceae and Roseburia (Firmicutes) in CCHC subjects that in the Human Microbiome Project (HMP). Moreover, the authors deduced that Lachnospiraceae plays a key role in metabolic functions in the progression of T2D in CCHC subjects.146By following these strategies, some remarkable studies about T2D and microbiome association have been recently reported . For insRhizoma Anemarrhenae, Momordica charantia, Coptis chinensis, Aloe vera, and red yeast rice).149 In the metformin-treated group, the authors noted a remarkable increase in Blautia spp, (SCFA producer), which is in line with other studies conducted on animals.90 Also, they observed a decline in Akkermansia in T2D Chinese subjects with metformin treatment. However, these findings do not agree with those of other studies on humans, where Akkermansia increases after metformin is consumed.151 This discrepancy might be explained when we take into account strain-specific functions; thus, the authors suggested further studies  to clarify this controversy.149 On the other hand, treatment with AMC increased the abundance of two genera related to butyrate production .152 Interestingly, Faecalibacterium prausnitzii has been reported as a functionally important bacterium to prevent physiological damage through the production of butyrate and anti-inflammatory metabolites.153 Moreover, AMC had a stronger modulatory effect on the GM than metformin treatment in terms of improving IR and triglyceride levels. This can be explained by the synergistic effects of the multiple phytochemicals present in AMC.148 Concerning other diseases caused by T2D, the information is limited.154 Das et al. published an interesting study on the association between GM dysbiosis in T2D and diabetic retinopathy (DR) with an Indian cohort of 30 subjects. Regarding T2D subjects, the authors found positive associations between a chronic low-grade inflammatory state and pathogenic genera such as Gardnerella, Atopobium, Fusobacterium, Gemella, Halomonas, and Vagococcus. Moreover, and with DR participants, a reduction in the abundance of anti-inflammatory and probiotic bacteria in comparison to other genera was observed. However, in terms of the GM of T2D and DR, the authors did not report significant differences at the genus level.154In another study using SparCC, it was found that alterations in GM are involved in the treatment of T2D with hyperlipidemia in a Chinese cohort of 450 subjects exposed to two clinical interventions: metformin and AMC  has been developed in recent years with the purpose of quantitatively examining metabolic activity in the microbiome. Among these methods, we highlight the COBRA Toolbox and cobrapy, computational frameworks developed to model genome-scale metabolic reconstructions through constraint-based modeling.157 Based on this approach, there have been some efforts to characterize the microbial metabolic community at small, medium, and large scales. Among the efforts to model metabolic activity and species-level interactions in GM, we list computational strategies such as OptCom,158 cFBA,159 CASINO,160 BacArena,161 Dynamic OptCom,162 bialSim,163 and MICOM.9 These schemes differ from each other in several ways: 1) the amount of genome-scale metabolic reconstruction capable of being included in the simulation, 2) the dynamic condition constraining the state of the system (steady or unsteady state), and 3) the computational procedure to define and calculate the community objective function in the microbial community.164Genome-scale metabolic reconstruction and constraint-based modeling is a paradigm in systems biology to quantitatively explore the metabolic activity of a bacterium or a group of bacteria constrained in a specific metabolic environment. This approach is characterized by three global elements: the use of genome-scale metabolic reconstruction for microorganisms, their mathematical representation to simulate their metabolic phenotypes, and their integrative description with HT technologies.165 For example, Rosario et al. used the COBRA Toolbox to model the contribution of four bacteria  to the physiology of T2D patients undergoing metformin treatment.166 To characterize the metabolic alterations produced by this dysbiosis, they applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify patterns of growth. Their results suggest that the metabolism of Escherichia sp., A. muciniphila, S. variabile, and I. bartlettii may explain the features observed in T2D-metformin patients, which are related to commensal and competing behavior through extracellular compounds, including SCFAs, H2, and amino acids.In terms of T2D and metabolic modeling of microbiota through annotated genomes, the information is scarce but continues to growth with several articles.9 an OptCom based framework capable of simulating the GM metabolism of almost 850 instances of genome-scale metabolic reconstruction simultaneously, starting from the relative abundance of bacteria obtained from 16S or metagenome technologies. Remarkably, the authors were able to derive the growth rates that correspond directly to the observed replication rates. Moreover, they integrated several constraints, such as taxon abundance and adjustable dietary input, to prepare personalized metabolic models for individual GM samples. With T2D, T1D, and the healthy control data of 186 subjects, the authors showed that the community-level production of SCFAs was heterogeneous and mostly distinctive at the individual level. Also, their model output showed complex cross-feeding associations among bacteria, which mirrors the complex community structure that is difficult to measure 9in vivo. In addition, MICOM was able to predict reduced SCFA (butyrate and propionate) production levels in T2D participants, with a consecutive restoration of these rates found in subjects with metformin treatment. In general, they reported that changes in taxon abundance or diet have highly personalized effects.9 Based on these findings, the in silico modeling of the metabolic activity of microbiota has started to be a fundamental and useful approach to obtain quantitative mechanistic explanations that complement association studies. This last point involves genome-scale metabolic reconstruction of bacterial communities in the gut microbiome and diet; all of these are placed in the context of a personalized background.From the other side, Diener et al. (2020) reported MICOM,167 Genetic components, a sedentary lifestyle, and dietary habits (low dietary fiber and high fat consumption) are etiological factors that contribute to the development of T2D.168 Recently, GM dysbiosis has been integrated as a factor associated with the rapid progression from IR to T2D. Notably, this dysbiosis can remodel functions of the intestinal barrier and metabolic pathways in the host. In particular, these alterations are closely associated with SCFA production, bile acid transformation, adipose tissue inflammation, and chronic low-grade systemic inflammation.169 In this review, we have discussed in detail how the physiological variables in T2D are associated with a disruption of microbiota in the host, as well as how lifestyle, drugs, and new promising interventions like FMT can control and reshape their profiles to benefit the health of the host. Altogether, these findings offer innovative possibilities for preventing and treating T2D. However, their implementation still faces some challenges that need to be addressed in the coming years. Let us review some of them.The prevalence of T2D has become a serious public health problem worldwide.Akkermansia muciniphila, Faecalibacterium prausnitzii and Roseburia intestinalis are relevant to the phenotype,151Escherichia-Shigella, Veillonella, Blautia and Anaerostipes were associated with T2D progression in a Mexican cohort.170 These and other reports provide evidence of the need to characterize the local microbiome, and reinforce the need to generate an individualized intervention to enhance glycemic control through the synergistic effects of lifestyle, diet, probiotics, drugs, or surgical interventions.First, inter-individuality variability is an inherent factor that underlines the need to define microbiome profiles at the ethnic, geographic, and sociocultural levels. While in Chinese treatment-naive individuals with T2D 171 However, the response to diet differs among individuals, with some being non-responders to dietary interventions,172 since what can be a good diet for one person might not work for someone else. Personalized nutrition requires frequent evaluation of anthropometric, biochemical, and clinical variables for designing optimal interventions that produce a favorable change in the patient’s metabolism. Although the idea is clear, the practical implementation of this dietary plan in conjunction with other adjuvant factors, such as drugs and changes in lifestyle, remains a challenge in many countries. Some questions with inconclusive answers should be addressed in the future. For instance, the long-term effect of diet on the patient, and how to design it in terms of socio-cultural factors. Although some contributions have been reported in this aspect,173 we consider the implementation of these strategies to represent the vanguard of personalized nutrition.Second, dietary components orchestrate and modulate GM composition and thus alter the host’s metabolism.Lactobacillus and Akkermansia muciniphila strains.87 Other drugs, such as DPP-4 inhibitors, decrease the abundance of Oscillibacter and increase the abundance of Lactobacillus. These compositional changes reduce TLR ligands and pro-inflammatory cytokines and increase propionate. Although metformin is the first-line treatment in most T2D patients, there is a spectrum of drugs that are administered according to clinical variables defining the individual . One challenge here is to define the role of the microbiome in selecting the treatment, which is unclear, because there is a specific pattern of microbiome phenotypes that can increase the success of the treatment. To this end, longitudinal studies that integrate the different omics are necessary to verify the effect on the long-term composition and help clarify the role of microbial ecology in the host. Remarkably, this information can generate valuable information to help decide on precision medical treatments that slow down the progression and long-term vascular complications of the disease, and which increase quality of life for patients with T2D.Third, antidiabetic treatments positively influence the host through changes in GM and the host’s metabolism. For example, metformin can facilitate a healthy state by increasing the abundance of the Escherichia coli, and consequently one died.174 Additionally, in March 2020, the FDA reported that two patients developed enteropathogenic Escherichia coli (EPEC) and Shiga toxin-producing Escherichia coli (STEC) infection after receiving FMT.175 Therefore, FMT as an experimental therapy requires a well-defined and standardized methodology to design properly conducted clinical trials in the next few years. Despite their practical implications, information on the mechanisms underlying its modulatory effect on microbiota is currently scarce.Fourth, FMT could be used as a favorable modulator of insulin resistance, showing potential in the treatment of T2D patients. However, the Food and Drug Administration (FDA) has issued several warnings on FMT regarding the potential risk of serious or life-threatening infections. In June 2019, approximately two immunocompromised adults developed invasive infections caused by ESBL-producing Fifth, beyond HT technologies, SB offers new approaches to mechanistically understand T2D development, physiopathology, and its relationship with GM through the constant development of integrative computational approaches. For example, in the framework of modeling based on sequence read abundance, SparCC (a tool to infer correlation networks) has proven to be an outstanding procedure for estimating correlations in microbiota composition, and it has become the gold standard for association studies related to GM.in vivo, such as Clostridium difficile infection. In this way, computational modeling of metabolism in microbiota, through software such as MICOM, offers a computational framework to infer the growth rates of selected bacteria and the metabolic interactions into GM. Moreover, it provides a high-throughput platform for generating mechanistic hypotheses and testing them in clinical assays. We suggest that the most useful application of metabolic models in bacterial communities is to offer detailed functional metabolic inferences that can serve as a means for novel hypothesis testing.170 Despite these computational themes, which have been applied in GM, their use is pioneering in exploring the metabolic consequences of T2D. The outcomes of such approaches comprise the foundations for developing more accurate models by including information about the GM and their ecological relationships with the host (as a holobiont). For example, Thiele et al. (2020) investigated a complete sex-specific model of humans that involved 26 organs and six cell types in the blood to study inter-organ metabolic fluxes and to explore host-microbe cometabolism.176 The accuracy of the hypothesis, generated with genome-scale metabolic reconstruction, the reproducibility, and reuse should be a challenge to face in the future of T2D.On the other hand, constrained-based modeling allows for the versatility needed to simulate bacterial communities in numerous conditions that cannot be performed 177 A vast amount of health-related data is now being collected and stored due to the increased use of computers, mobile devices, wearable devices, and other biosensors.178 These data have the potential to allow us to better design and conduct clinical trials and studies in health care to answer questions that were previously unfeasible. In addition, with the development of new and sophisticated analytical capabilities, we can better scrutinize data and apply the results of the analysis to the development and approval of medical products. For example, in the study of GM, it is necessary to design studies with a sufficient number of subjects of both sexes. In addition, information on lifestyle, diet, mental health, comorbidities, and metadata should be included that allow for comprehensive interpretation of the GM. To this end, it is necessary to have electronic databases at different levels  for multicenter data analysis. Likewise, the capacity of omics sciences should be increased with shotgun studies, meta-transcriptomics, meta-metabolomics, meta-proteomics, and medium-term fluxomic studies to grasp the dynamics of metabolism in the microbial community and the host.122 In conclusion, we now have more measurable variables and are able to add more “data layers” to systems biology studies. Undoubtedly, we are only at the tip of the iceberg of understanding host-microbiome interactions and their specific mechanisms of modulation, one of the frontiers in the medicine of this century.Finally, in recent years, there has been a real revolution in the field of big data. In this sense, real-world data and real-world evidence play a more relevant role in medical care decisions.Click here for additional data file.


There were higher abundances of genera Bifidobacterium, Duncaniella, and Ruminococcus in the placenta samples of women with GDM. The genera of Bacteroides, Paraprevotella, and Ruminococcus were more enriched in the placental samples of women with PROM. The authors concluded that the placenta may harbor small amounts of microbiota, and significant differences in the dominant microbiota of the placenta were observed between those pregnant women with and without adverse pregnancy outcomes.To verify whether the placenta harbors bacteria, and to explore the composition of placental microbiota (if yes) and its association with adverse pregnancy outcomes. The placental microbiota was detected by 16S rRNA gene sequencing technology. In the process of detecting placental samples, exogenous marine bacterial DNA that does not exist in the human body was artificially added to obtain a visible 16S band. At the same time, the sterile samples, such as scissors, sheets, and cotton swabs, in delivery and operating rooms were collected as the environmental control samples. As a result, a total of 2,621,009 sequences were obtained from 71 samples, 88.9% of which came from artificially added exogenous bacterial DNA, suggesting that the placenta contained fewer bacteria. After removing the operational taxonomic units (OTUs) that coexisted in environmental controls, the placenta was annotated with 11 phyla, 22 classes, 43 orders, 79 families, and 157 genera. The β diversity analysis showed that there were significant differences in the placental microbiota between 10 women with gestational diabetes mellitus (GDM) ( Firmicutes, Tenericutes, Proteobacteria, Bacteroidetes, and Fusobacteria  detect whether the placenta harbors microbiota and what were the characteristics of microbiota composition if bacteria were detectable in placentas, and (2) explore whether the characteristics of the placental microbiota were associated with adverse pregnancy outcomes.The study was based on the Fudan Preconceptional Offspring Trajectory Study (PLOTS) , and all research participants provided written informed consent before the recruitment.A total of 61 placental samples were tested in this study. The placental samples were collected by trained midwives and nurses in a sterile environment of the operating and delivery rooms from February 2019 to July 2020. Sterile scissors were used to take a 1 × 1 cm piece of the placenta from the maternal side at 3, 6, 9, and 12:00 and the center point of the placenta, respectively. These pieces were immediately put in a sterile cryopreservation tube and placed in a −80°C temperature refrigerator.In addition, 10 environmental control samples were collected, five from the delivery room and five from the operating room. In the delivery room, two sterile cotton swabs were collected after repeatedly wiping the surface with sterile scissors. Two pieces of the fabric sheet where the placenta was placed were also obtained. Furthermore, one sterile cotton swab was placed in the air for 10 min and then collected as the blank environment control sample. Other five environmental samples were also collected in the operating room using the same methods. All samples were placed in 15 ml sterile centrifuge tubes and immediately stored in an ultralow temperature refrigerator at −80°C.The QIAampPowerFecal Pro DNA Kit  was used for DNA extraction following the manufacturer's protocol. Placental tissue samples were grounded with a tissue homogenizer. Nonwoven fabric cloths and cotton swabs were placed in PowerBead Pro Tubes, soaked, and shaken with Solution CD1 for 10 min.Loktanella sp., which does not exist in humans, was artificially added to the DNA of placental and control samples (0.01 ng exogenous DNA for each sample). All amplicons were purified with a QIAquick PCR Purification Kit  and pooled with equal concentrations. Then, the pooled amplicons were sequenced on an Illumina MiSeq instrument with a 2 × 300 cycles.For the detection of the bacterial 16S rRNA gene, polymerase chain reaction (PCR) amplification of the V3-V4 region was performed using the barcode-specific primers 338F (5′-ACTCCTACGGGAGGCAGCAG) and 806R (5′-GGACTACHVGGGTWTCTAAT). PCR with nuclease free water as template was set as a negative control. Due to the extremely low bacterial content of the placental samples, most of the extracted DNA could not be amplified to produce visible electrophoresis bands. Therefore, the genomic DNA of a marine bacterium https://www.biosino.org/node/) under accession number OEP003119.The sequence data have been deposited in the National Omics Data Encyclopedia  and premature rupture of membranes (PROM) were selected for exploring the association between placenta microbiota and pregnancy outcomes. GDM was diagnosed by the Oral Glucose Tolerance Test (OGTT) performed at 24–28 gestational weeks in the morning, after at least an 8 h-overnight fast, when the fasting plasma glucose was ≥5.1 mmol/L and/or 1 h post-OGTT glycemia ≥ 10.0 mmol/L and/or 2 h post-OGTT glycemia ≥ 8.5 mmol/L, according to international criteria   years old. The median month from preconception to conception was 3.28 (SD 3.16). The mean gestation age at childbirth was 39.17 (range: 36.14–41.57) weeks. In total, nineteen women were multipara . More than half of them  had college or above degree. The majority of these participants  had an annual family income of over 100,000 yuan . Among a total of 61 pregnant women, 10 women had GDM, 19 had PROM, one woman complicated from both PROM and GDM, eleven women had other pregnancy complications , and 21 women had no adverse pregnancy outcomes.A total of 2,621,009 sequences were obtained from 71 samples, including 61 placental samples and 10 environmental control samples. Among them, 88.9% sequences  were artificially added exogenous bacterial DNA, indicating low bacterial content in the placental samples.via AMOVA analysis (p = 0.003).After excluding the exogenous bacterial DNA and non-16S DNA sequences, a total of 1,074 OTUs were revealed, with 300 OTUs shared by 61 placental samples and 10 environment control samples. Other 571 OTUs are presented in the placenta samples . The PCoFirmicutes (54.27%), Bacteroidetes (36.55%), Proteobacteria (4.39%), Actinobacteria (2.94%), etc. , a total of 11 phyla, 22 classes, 43 orders, 79 families, and 157 genera were annotated in the remaining 571 OTUs of placental samples. The main dominant phyla in the placenta were %), etc. .Alistipes, was revealed in 39 samples, while the genus Duncaniella existed in 32 placental samples.At genus level, 16 dominant genera (> 0.5%) accounted for 30.19% of all placenta microbiota , but nonpAMOVA = 0.01) and PROM (pAMOVA = 0.004) (pAMOVA = 0.407).The placental microbiota of women without adverse pregnancy outcomes showed significant difference with the combined samples of GDM (= 0.004) . While tBdellovibrionales), two families (Bacteroidaceae and Bdellovibrionaceae), and nine genera  were significantly enriched in the placenta of the group consisting of 10 women with GDM , one order , five families , and eight genera  were enriched in the placenta. Burkholderiales at the order level and Prevotella at the genus level were significantly decreased compared with placental samples from 21 women without adverse pregnancy outcomes.In the PROM group containing 19 cases , one phyBacteroides, Bifidobacterium, Ruminococcus, and Harryflintia were all significantly increased in both groups of GDM and PROM, compared with the healthy control groups.Furthermore, the genera Loktanella sp., into placental DNA to help library construction. After removing this artificially introduced DNA sequences and OTUs existed in environmental blank control samples, we obtained 571 OTUs that only existed in the placenta. Though OTUs were observed in each of the 61 placental samples, we did not find a “common/ shared” OTU in all placental samples. The two most abundant genera Alistipes and Duncaniella were separately revealed in 39 and 32 placental samples, indicating that placental microbiota were in low amount and differential among pregnant women. This may suggest that there were no common bacteria that existed in all the placental samples.To date, there are still big controversies regarding the existence of placental microbiota. In this study, during our pre-test, no visible electrophoresis bands were amplified from placental DNA, which indicated that the biomass of placenta microbiota was very low even if there was microbiota in placenta. Therefore, we added the DNA of a marine bacterium, Loktanella” that did not exist in the human body was artificially introduced to achieve detectable sequencing of placenta, through which 16S rRNA sequencing was successfully constructed and completed in our study.In 2014, Aagaard et al. used high-throughput sequencing technology to sequence placental samples and concluded that there was a unique microbiota in the placenta, challenging people's long-standing perception of a “sterile uterus”  were more abundant in placental samples of women with GDM. Faecalibaculum, Harryflintia  can be found below: National Omics Data Encyclopedia (NODE) - OEP003119.The studies involving human participants were reviewed and approved by Ethics Committee of the School of Public Health, Fudan University. The patients/participants provided their written informed consent to participate in this study.HJ conceived and obtained funding for this study. LS, FY, LZ, XC, and HJ coordinated and supervised study implementation and data collection. XL and WC participated in data collection. HZ and YW conducted lab test on the samples. XL and HZ conducted the data analysis. XL, YW, HJ, and HZ drafted the manuscript. XX and WC provided comments and revisions. All authors have approved to submit the manuscript for publication.This study was funded by the National Natural Science Foundation of China (81973057 and 82181220077), the Natural Science Foundation of Shanghai (19ZR1405900), the Shanghai Municipal Public Health Outstanding Discipline Leadership Program (GWV-10.2-XD10), and the Fifth Round of the Three-Year Public Health Action Plan of Shanghai (GWV-10.1-XK08).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.