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Gut microbiome links obesity to type 2 diabetes: insights from Mendelian randomization

BMC Microbiology volume 25, Article number: 253 (2025) Cite this article

Abstract

Background

Research has established links between the gut microbiome (GM) and both obesity and type 2 diabetes (T2D), which is much discussed, but underexplored. This study employed body mass index (BMI) as the measurement of obesity to delve deeper into the correlations from a genetic perspective.

Methods

We performed the Mendelian randomization (MR) analysis to examine the causal effects of GM on T2D and BMI, and vice versa. Genome-wide association study (GWAS) summary datasets were utilized for the analysis, including T2D (N = 933,970), BMI (N = 806,834), and two GM datasets from the international consortium MiBioGen (211 taxa, N = 18,340) and the Dutch Microbiome Project (DMP) (207 taxa, N = 7,738). These datasets mainly cover European populations, with additional cohorts from Asia and other regions. To further explore the potential mediating role of GM in the connections between BMI and T2D, their interaction patterns were summarized into a network.

Results

MR analysis identified 9 taxa that showed protective properties against T2D. Seven species were within the Firmicutes and Bacteroidales phyla in the DMP, and two were from the MiBioGen (Odds Ratio (OR): 0.94–0.95). Conversely, genetic components contributing to the abundance of 12 taxa were associated with increased risks of T2D (OR: 1.04–1.12). Furthermore, T2D may elevate the abundance of seven taxa (OR: 1.03–1.08) and reduce the abundance of six taxa (OR: 0.93–0.97). In the analysis of the influence of the genetic component of BMI on GM composition, BMI affected 52 bacterial taxa, with 28 decreasing (OR: 0.75–0.92) and 24 increasing (OR: 1.08–1.27). Besides, abundances of 25 taxa were negatively correlated with BMI (OR: 0.95–0.99), while positive correlations were detected for 14 taxa (OR: 1.01–1.05). Notably, we uncovered 11 taxa genetically associated with both BMI and T2D, which formed an interactive network.

Conclusions

Our findings provide evidence for the GM-mediated links between obesity and T2D. The identification of relevant GM taxa offers valuable insights into the potential role of the microbiome in these diseases.

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Background

According to predictions of the International Diabetes Federation, the global diabetic population is anticipated to reach 783 million by 2045, posing a significant challenge to public health worldwide [1, 2]. Type 2 diabetes (T2D) encompasses 90% of all global diabetes cases and is characterized by insulin resistance and a relative insufficiency of insulin in the body, resulting in hyperglycemia [3]. Being a multifactorial disease, T2D is contributed to by various factors including infection, genetics, lifestyle choices, and socioeconomic circumstances [4, 5, 6]. Among them, obesity, induced by unhealthy dietary patterns enriched in sugar and fat and coupled with a sedentary lifestyle devoid of physical activity, serves as a primary catalyst for the continual progression of T2D [7, 8].

Recent research has shown that probiotic supplementation and fecal microbiota transplantation can improve the composition of the gut microbiome (GM), which can significantly impact obesity, diabetes, and neurological disorders by modulating inflammation and endocrine pathways [9, 10]. Studies also suggested that obesity and T2D can influence the composition of GM [11], pointing towards a complex interaction between GM, obesity, and T2D.

The human microbiome dynamically populates various organs, with the gut being particularly rich in microbial species due to its unique anatomy and function [12, 13]. Comprised of approximately 1500 different species [14], the GM is predominantly composed of five major bacterial phyla: Firmicutes, Bacteroidetes, Proteobacteria, Verrucomicrobia, and Actinobacteria [15]. These bacteria undertake diverse functions within the gut and collectively form an endocrine metabolic organ [16, 17]. Studies have shown that GM and their metabolic products significantly influence various aspects of the hosts’ physiology, including vitamin synthesis, production of intestinal hormones, maintenance of intestinal barrier function, and nutrient digestion and absorption, thereby establishing a close connection to numerous diseases [5, 18, 19].

Not only does dysbiosis in GM affect the levels of hormones related to satiety, thus, contributing to an increase in food intake and obesity [17], but individuals with obesity also typically exhibit changes in GM composition and reduced microbial diversity [15, 20]. For instance, obesity can lead to a decrease in the abundance of microbes that produce SCFAs (short-chain fatty acids) associated with glucose metabolism, which affects the development of T2D [21]. Reduction of Akkermansia and Bifidobacterium in people with obesity can cause impairment of intestinal barrier function and increased systemic exposure to pro-inflammatory bacterial toxins, which promote insulin resistance in T2D [22, 23]. In this context, GM may influence the relationship between obesity and T2D.

Recent studies that explored the association between GM and both obesity and T2D are not free of some limitations. Observational studies are vulnerable to interference from environmental factors and reverse causality, and experimental studies typically rely on animal models or in vitro investigation of fecal microbiomes. Mendelian randomization (MR) analysis can assess the causal relationship between exposure factors and outcomes by employing exposure-related genetic variants as instrumental variables (IVs) [24, 25]. This method capitalizes on the random allocation of genetic variants during transmission from parents to offspring, inherently shielding these variables from confounding factors, such as environmental influences and lifestyle habits. The causal relationships involving GM and T2D have been widely explored, significantly strengthening the persuasiveness of MR analysis in inferring causality between GM, BMI, and T2D [26, 27, 28].

Although previous genetic studies identified a causal impact of some microbiological taxa on obesity and T2D [29] species-level conclusions are far from certainty due to the limitations of genome-wide association studies (GWAS) methodologies for GM [30, 31]. In this study, we used the most extensive and recent GWAS summary data on human GM to thoroughly investigate the genetic relationships between GM taxa, T2D, and obesity, with body mass index (BMI) as a primary indicator [32].

Methods

Data source

The GWAS summary data used in this study were all sourced from publicly available datasets. The T2D dataset was obtained from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) Consortium, which includes 80,154 cases and 853,816 controls of European descent [33]. For BMI, data were derived from the GIANT consortium (N = 806,834) [34]. The GM GWAS data came from the international consortium MiBioGen and The Dutch Microbiome Project (DMP). The MiBioGen dataset is presently the largest GWAS study of GM, covering 24 cohorts from Denmark, the USA, South Korea, and other countries, totaling 18,340 participants. Among these, the European cohort is the most extensive, involving 16 cohorts and 13,266 participants [35]. The MiBioGen project utilizes 16 S rRNA sequencing technology for microbiome composition analysis, providing results that include 211 taxa (131 genera, 35 families, 20 orders, 16 classes, and 9 phyla). In our study, after excluding unknown gut microbiome taxa, a total of 195 taxa (118 genera, 32 families, 20 orders, 16 classes, and 9 phyla) were analyzed. While MiBioGen offers comprehensive data, its species-level classifications are limited. Therefore, we utilized the DMP dataset to further enrich the depth of our analysis. The DMP project encompasses 207 taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) and involves 7,738 individuals of European ancestry [36].

MR analysis

Our study used the TwoSampleMR package in R studio (version 4.0.5) for MR analysis with GM as the exposure factor and BMI and T2D as the outcomes. The inverse variance weighting (IVW) was used as the primary method, which assumes that all IVs can only affect the outcome through exposure and combines each SNP’s Wald estimate for a meta-analysis to calculate the overall estimate [37]. The MR-Egger and weighted median (WM) methods enhanced the robustness of results in the presence of horizontal pleiotropy or invalid IVs [38, 39].

To identify a wider range of potential associations, we chose significant SNPs based on a threshold P < 1 × 10− 5 in GM datasets, which has been set as the optimal threshold in many gut microbiome-related MR studies [40]. Furthermore, we clustered SNPs within a 10 Mb window based on the European 1000 Genomes Project reference panel and pruned them with an r2 set to 0.001, ensuring the independence of the SNPs and eliminating the effects of linkage disequilibrium (LD). Finally, we calculated the F-statistic to assess the strength of the IVs, with values greater than 10 indicating sufficient strength and reduced risk of weak instrument bias.

Reverse MR analysis

To further evaluate whether genetic determinants of BMI and T2D could affect GM, we conducted reverse MR analysis with BMI and T2D as exposures and GM as the outcome. The IVW, MR-Egger, and WM methods were used to estimate the SNPs in the BMI and T2D datasets, in which SNPs with genome-wide significant levels (P < 5 × 10− 8) were selected as IVs (r2 < 0.001 within a 10,000 kb window). Quality control of SNPs was performed for all datasets. We removed palindromic sequences with intermediate allele frequencies and harmonized allele orientations, followed by MR analysis for each SNP.

In the sensitivity analysis, Cochrane’s Q test and the I2 test were used to assess the heterogeneity of the IVs. The intercept of the MR-Egger regression was employed to evaluate the presence of horizontal pleiotropy, and a p-value greater than 0.05 indicates no horizontal pleiotropy.

The interactive network between BMI and T2D

Given the significant impact of obesity on T2D, we summarized taxa that were significantly associated within both results to explore the potential mediating role of GM in the relationship between BMI and T2D. Then, we categorized the dominant taxa into three patterns of interactions between GM, BMI, and T2D, including (1) taxa that influence T2D and are also affected by BMI; (2) taxa that simultaneously affect both BMI and T2D; (3) taxa influenced jointly by these two factors. Finally, to further clarify the relationship between BMI and T2D, we constructed an interactive network featuring GM as a mediator between these two characteristics.

Results

The causality between gut microbiome and T2D

Through MR analysis, we identified 9 taxa with protective effects against T2D. In the DMP data, the taxa included class Bacilli (OR: 0.97, 95%CI: 0.93-1.00), species Eubacterium eligens (OR: 0.94, 95%CI: 0.88-1.00), and species Veillonella (OR: 0.96, 95%CI: 0.93-1.00) of Firmicutes; as well as family Porphyromonadaceae (OR: 0.93–0.94, 95%CI: 0.89–0.99) and species Bacteroides caccae (OR: 0.95, 95%CI: 0.90-1.00) of Bacteroidetes. In the MiBioGen dataset, the relevant groups included order Bacillales (OR: 0.95, 95%CI: 0.90-1.00) and genus LachnospiraceaeNC2004 group (OR: 0.94, 95%CI: 0.89–0.99). We also found 12 taxa potentially increasing the risk of T2D. The groups included family Oscillospiraceae (OR: 1.08–1.09, 95%CI: 1.01–1.18), genus Roseburia (OR: 1.05, 95%CI: 1.01–1.10), genus Faecalibacterium (OR: 1.07, 95%CI: 1.00-1.13), species Eubacterium hallii (OR: 1.04, 95%CI: 1.00-1.08), and Bacteroidaceae (OR: 1.05, 95%CI: 1.00-1.10) in the DMP dataset. In the MiBioGen, these microbiomes included order Desulfovibrionales (OR: 1.08, 95%CI: 1.01–1.15), family Porphyromonadaceae (OR: 1.09, 95%CI: 1.00-1.19), genus Actinomyces (OR: 1.12, 95%CI: 1.06–1.19) and so on (Table 1; Figs. 1 and 2, and Supplementary Tables 12–13).

Table 1 Causal effects of gut microbiome on type 2 diabetes (T2D)
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Fig. 1
figure 1

The bidirectional causal effects between the gut microbiome (GM) and type 2 diabetes (T2D) and body mass index (BMI) are illustrated by the forest plot. (A) Causal effects of the GM on T2D; (B) Causal effects of T2D on the GM; (C) Causal effects of the GM on BMI; (D) Causal effects of BMI on the GM. DMP: the Dutch Microbiome Project; OR: odds ratio; CI: confidence interval

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Fig. 2
figure 2

The bidirectional causal effects between the gut microbiome, body mass index (BMI), and type 2 diabetes (T2D). (A) Causal effects of the GM on T2D; (B) Causal effects of T2D on the GM; (C) Causal effects of the GM on BMI (OR < 1); (D) Causal effects of the GM on BMI (OR > 1); (E) Causal effects of BMI on the GM (OR < 1); (F) Causal effects of BMI on the GM (OR > 1). The blue lines represent negative correlation, the red lines represent positive correlation, and the arrows represent the direction of effects. DMP: the Dutch Microbiome Project; OR: odds ratio

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The results of reverse MR analysis revealed that genetically predicted T2D has a causal impact on the abundance of certain taxa. In particular, T2D may increase the abundance of seven microbiomes, including phylum Proteobacteria (OR: 1.06–1.08, 95%CI: 1.00-1.16), species Veillonella_unclassified (OR: 1.08, 95%CI: 1.00-1.17), genus Lachnoclostridium (OR: 1.03, 95%CI: 1.00-1.06), Clostridiuminnocuum group (OR: 1.07, 95%CI: 1.01–1.14), Alistipes (OR: 1.03, 95%CI: 1.00-1.07), and Sutterella (OR: 1.04, 95%CI: 1.00-1.08). T2D also can decrease the abundance of six taxa including class Verrucomicrobiae (OR: 0.95, 95%CI: 0.90-1.00), genus Tyzzerella3 (OR: 0.93, 95%CI: 0.88–0.98) and FamilyXIIIAD3011 group (OR: 0.97, 95%CI: 0.93-1.00) (Supplementary Table 1, Supplementary Tables 14–15, Figs. 1 and 2, and Supplementary Fig. 1).

The causality between BMI and gut microbiome

It was found that BMI could decrease the abundance of 28 taxa and increase the abundance of 24 taxa (Supplementary Table 2, Supplementary Tables 16–17, and Figs. 1 and 2). Among them, the relationship between BMI and genus Allisonella (OR: 1.27, 95%CI: 1.07–1.50), family Oscillospiraceae, genus Oscillibacter, species Oscillibacter_unclassified (OR: 1.14, 95%CI: 1.03–1.27), and species Ruminococcus_callidus (OR: 0.75, 95%CI: 0.63–0.89), family Oxalobacteraceae, genus Oxalobacter, species Oxalobacter_formigenes (OR: 0.80, 95%CI: 0.68–0.95) were most remarkable. Furthermore, BMI was negatively associated with order Enterobacteriales, family Enterobacteriaceae (OR: 0.92, 95%CI: 0.85-1.00), genus LachnospiraceaeNK4A136 group (OR: 0.90, 95%CI: 0.84–0.97), and positively associated with genus Eubacteriumhallii group (OR: 1.08, 95%CI: 1.00-1.15), genus Veillonella (OR: 1.24, 95%CI: 1.04–1.47), species Veillonella_unclassified (OR: 1.25, 95%CI: 1.04–1.49), phylum Actinobacteria, class Actinobacteria (OR: 1.09, 95%CI: 1.01–1.17) and so on. Multiple of these taxa were functional for T2D.

We identified 25 taxa negatively correlated with BMI. These included species Bifidobacterium longum (OR: 0.97, 95%CI: 0.95-1.00) from the phylum Actinobacteria, genus Coprobacter (OR: 0.97, 95%CI: 0.95–0.99) from the phylum Bacteroides, genus RuminococcaceaeUCG002 (OR: 0.98, 95%CI: 0.96–0.99) from the phylum Firmicutes, species Desulfovibrio_piger (OR: 0.98, 95%CI: 0.97-1.00) from the phylum Proteobacteria, etc. The order Methanobacteriales (OR: 0.98, 95%CI: 0.97-1.00) from the Archaea was also addressed in the MiBioGen project. Further, 14 taxa were positively correlated with BMI, including genus Eubacterium hallii group (OR: 1.03, 95%CI: 1.01–1.05), order Gastranaerophilales (OR: 1.02, 95%CI: 1.00-1.03), family Porphyromonadaceae (OR: 1.03, 95%CI: 1.00-1.05), genus Coprobacter (OR: 1.02, 95%CI: 1.00-1.04) (Supplementary Table 3, Supplementary Tables 18–19, and Figs. 1 and 2). The diverse roles of Coprobacter across different microbiome projects may reflect underlying population differences.

We removed IVs with an F-statistic below 10 to mitigate weak instrument bias and enhance the reliability of the results (Supplementary Tables 4–11). The MR-Egger intercept analysis shows no evidence of horizontal pleiotropy (p-value > 0.05 for all intercept terms), suggesting our study results are reliable. While Cochrane’s Q test and the I2 test indicate some heterogeneity among the IVs (p < 0.05, I2 > 0.25), the application of the random-effects IVW method effectively mitigated the impact of heterogeneity, ensuring the robustness of our findings.

The interactive network between BMI and T2D

By comparing the results of MR and reverse MR, we identified 11 taxa that met the three patterns of interaction, which constituted an interaction network with the GM as the mediator (Table 2; Fig. 3). Intriguingly, the abundance of the family Oscillospiraceae, genus Oscillibacter, and species Oscillibacter_unclassified increases with higher BMI, while that of the genus Faecalibacterium decreases. Both of them are associated with an increased risk of T2D. The genus Coprobacter and species Coprobacter fastidiosus contributed to the reduction of both T2D and BMI.

Table 2 The shared taxa between type 2 diabetes (T2D) and body mass index (BMI)
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Fig. 3
figure 3

The interactive network between type 2 diabetes (T2D) and body mass index (BMI) with the gut microbiome as the mediator. (A) Three patterns of interactions between the gut microbiome, BMI, and T2D. (B) The interactive network with blue lines representing negative correlation, red lines representing positive correlation, and arrows representing the direction of effect

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Moreover, both BMI and T2D can positively influence the abundance of the family Sutterellaceae and genus Veillonella, while the latter may reduce the risk of T2D, highlighting its potential significance in the development of obesity and T2D. These taxa unveiled the potential mediating role of GM in the pathway from BMI to T2D.

Discussion

Some earlier studies have investigated the influence of GM on the development of obesity and T2D [30, 41, 42]. We utilized MR analysis to further substantiate the causal relationships between these two conditions and the composition of GM from a genetic perspective. Indeed, we recovered MR evidence of mutual influences between GM and both BMI and T2D. Bidirectional interaction between GM and host metabolic status provides an emphasis on a possible mediating role of GM in the impact of obesity on T2D.

It is noteworthy that Oscillospiraceae, Veillonella, and Coprobacter, along with eight other genera showed significant correlations with both T2D and BMI, providing key clues to the interactions between obesity and T2D are indeed microbiome-dependent. Among them, Veillonella was found to be associated with a reduced risk of T2D, and its abundance positively correlates with both BMI and T2D. This finding suggests that Veillonella may play a significant role in regulating metabolic pathways and insulin sensitivity [43].

The modulation of BMI by GM may involve a variety of mechanisms. Microbial metabolites can impact obesity by modifying epigenetic markers [44], exemplified by the influence of Ruminococcus on BMI through methylation at the MACROD2/SEL1L2 differentially methylated region (DMR) [45]. GM can alter energy intake efficiency and energy utilization pathways. Certain microbes can break down indigestible dietary fibers to produce short-chain fatty acids (SCFAs). The SCFAs can not only enhance intestinal barrier function and improve insulin sensitivity [46], but also influence the gut-brain axis and stimulate the release of gut hormones that regulate satiety [42], indirectly controlling food intake and weight [47, 48].

Similarly, the impact of GM on T2D largely stems from its regulation of the gut environment, particularly affecting the metabolism of SCFAs and bile acids (BAs) [5]. Acetate, propionate, and butyrate are the most predominant SCFAs in the body [49], and can be produced through multiple metabolic pathways such as pentose phosphate [41, 50]. In particular, butyrate is an in vivo metabolite critical for maintaining intestinal integrity and glucose metabolism [51, 52]. On the one hand, SCFAs can bind to G-protein coupled receptor 43 (GPR43) in the colonic mucosa, prompting glucose-dependent insulin release and inhibiting glucagon secretion, thereby enhancing insulin sensitivity [53]. On the other hand, SCFAs influence pancreatic cells by affecting incretins like GLP-1 and gastric inhibitory polypeptide (GIP), which can mitigate inflammation [54, 55]. In addition, SCFAs can redirect lipid synthesis in the liver and adipose tissue towards fatty acid oxidation, reducing fat mass and preventing insulin resistance [56]. This regulatory mechanism affects not only fat storage and mobilization but also insulin sensitivity and glucose metabolism.

Our findings suggest that Coprobacter in general, and Coprobacter fastidiosus in particular, may aid in reducing both BMI and the risks of T2D. Coprobacter is capable of producing SCFAs, which enhance the intestinal barrier and reduce the flow of inflammatory substances and bacterial toxins into the bloodstream [43]. In addition, Coprobacter may also influence energy homeostasis by affecting both the BAs, which regulate fat metabolism, and the activity of appetite-controlling hormones. BAs contribute to the regulation of glucose balance through their involvement in cholesterol metabolism by regulating the loads in enterohepatic circulation [57]. Bifidobacteria and Bacteroidia produce bile salt hydrolases, which deconjugate primary BAs into secondary BAs [58], which enhance insulin sensitivity and promote the secretion of GLP-1 through activation of G-protein coupled receptor (GPCR) TGR5, thus, supporting the glucose homeostasis [59, 60].

Some representative species of gut microbiota may also alleviate inflammation and prevent the progression of T2D by modulating soluble inflammatory factors. For instance, Akkermansia is capable of inhibiting TNF-α [61]. Moreover, GM collectives may suppress the release of pro-inflammatory cytokines such as IL-2 and IFN-γ by increasing the production of SCFAs, while promoting the growth of colonic Treg cells and other anti-inflammatory responses [62]. Butyrate can regulate the balance between pro-inflammatory and anti-inflammatory by affecting immune cell migration and adhesion, and inhibiting T-cell proliferation, thus mitigating low-grade inflammation [63]. In obese individuals, associated increases in the risk of T2D may be mediated by obesity-induced changes in GM, resulting in an increase in inflammatory signals.

Desulfovibrionaceae, a sulfate-reducing microbiome, can convert sulfate to hydrogen sulfide (H2S) in the human and animal gut, which may damage intestinal cells and the barrier, causing inflammation [64]. Moreover, Desulfovibrionaceae abundance was enriched in individuals with obesity and T2D compared to the normal population [65, 66]. Its production of LPS can further promote the development of T2D by activating inflammatory pathways.

Furthermore, it was pointed out that fiber-based interventions could effectively inhibit harmful bacteria such as Desulfovibrionaceae, reduce systemic inflammation, and positively impact the restoration of gut microbiota balance [67]. This indicates that regulating dietary fiber intake can directly improve the gut environment and offer health benefits by influencing the composition of GM, especially for obesity and T2D patients.

Of note, there are differences in the results of studies analyzing the role of microbiota. Our findings showed that the genus Roseburia may increase the risk of T2D. Existing literature identifies Roseburia as a strain of the family Lachnospiraceae, known for producing protective SCFAs in the gut, such as butyrate [68]. Moreover, experimental studies have demonstrated that Roseburia can facilitate the synthesis of melatonin and uphold intestinal integrity through SCFA production [69, 70]. Nevertheless, a previous MR analysis involving GM and T2D also reported findings consistent with ours [30]. Thus, even though Roseburia is commonly associated with a reduced risk of T2D, its role may be influenced by diet, environment, and host genetic background, leading to inconsistent findings.

Similarly, although Faecalibacterium prausnitzii is commonly regarded as a probiotic capable of regulating immune responses and inflammation [71], our study observed that the genus Faecalibacterium might heighten the risk of T2D. In contrast, a German study reported a reduced abundance of Faecalibacterium in patients with obesity [72], which is consistent with the negative correlation between BMI and Faecalibacterium in our result. These discrepancies could stem from variations in environmental factors, the host’s genetic makeup, or the presence of unrecognized strains [73]. Consequently, the effects of different species within the same genus may vary significantly, underscoring the necessity for more comprehensive studies to elucidate the complex relationships between GM and diseases.

The results of our study align with that of previous MR research on GM and T2D, with overlapping findings primarily related to the following genera: Actinomyces, Alistipes, Ruminococcaceae, and Lachnoclostridium. These shared findings emphasize the robustness of the current study’s results. We found that the genetic signature for the risk of T2D promotes the abundance of genus Lachnoclostridium, in support of previous findings linking Lachnoclostridium to increased T2D risk, likely through its role in blood glucose regulation [74, 75]. It is also likely that elevated abundance of Lachnoclostridium in T2D may be associated with accumulation of abdominal fat, thus, supporting a vicious cycle that promotes obesity and T2D development. RuminococcaceaeUCG002, which is associated with decreased BMI, is reduced in abundance in individuals with higher BMI. This suggests that Ruminococcaceae as a group may play a role in metabolic regulation. Another study done with the MiBioGen dataset has shown that species Ruminococcaceae UCG003 and UCG010 are associated with reduced risks of T2D, potentially due to Ruminococcaceae’s correlation with both insulin and glucose levels in serum [74, 76]. Although different analytic approaches uncovered different Ruminococcaceae species as T2D/obesity players, these findings collectively point at Ruminococcaceae as probable mediators of obesity and T2D.

Additionally, our study observed that the genetic component of BMI is associated with an increased abundance of Phylum Actinobacteria and Class Actinobacteria. Notably, different orders within this phylum exhibit significant heterogeneity in their effects. Several similar studies have shown that the genus Actinomyces is enriched in the gut of T2D patients and influences glucose and lipid metabolism by converting steroids into secondary bile acids, potentially contributing to increased risks of T2D, which is consistent with our findings [74, 77]. Conversely, order Bifidobacteriales, family Bifidobacteriaceae, and species Bifidobacterium longum show protective effects, which are realized through bacteria-driven reduction of body fat and the resultant decrease in the risks of T2D [78, 79]. Randomized controlled trials suggested that B. longum may decrease body fatness and BMI through its anti-inflammatory actions and regulation of lipid metabolism [80]. Moreover, the reduction of Bifidobacterium in populations with obesity can lead to decreased production of GLP-2, and higher intestinal permeability, thus, exacerbating metabolic issues [81]. Moreover, the diminished capacity of ethanolamine metabolism in the GM of obese individuals may further weaken the intestinal barrier [82]. All of these obesity-induced changes in GM can increase the risk of T2D, indicating that GM can mediate the relationship between BMI and T2D and points at the dual mediating role of Actinobacteria in obesity and T2D.

We also noted that the genetic signature predisposing to T2D caused an increase in the abundance of genus Alistipes of the phylum Bacteroidetes, a finding repeatedly described in several international studies. For example, an Indian and Danish study found that Alistipes were enriched in individuals with prediabetes [83], and another study in Han and Mongolian populations showed higher abundances of Alistipes in T2D patients [84]. These results suggest that Alistipes may be involved in the development of diabetes. It is noteworthy that some other MR studies have suggested that the genetic signature that supports the abundance of genus Alistipes may reduce the risk of T2D [77]. Although the directions of these effects differ, these findings reflect the dynamic complexity of microbiota-host milieaux, and the potential role of Alistipes at different stages of T2D development. It is even more intriguing that the involvement of Alistipes in chronic human diseases patients it as a dual player. On one hand, this bacteria is associated with intestinal dysfunction and its abundance increases the risk of colorectal cancer and depression [85]. On the other hand, some studies have shown that Alistipes may exert protective effects against atrial fibrillation and liver fibrosis [85, 86]. The specific mechanism of Alistipe in human pathophysiology should be further investigated.

The chronic systemic inflammatory state induced by obesity may be a major factor affecting GM [87]. Increased secretion of pro-inflammatory cytokines by macrophages embedded in adipose tissue have effects on the composition and activity of GM [16]. Studies have shown that the increases in serum lipopolysaccharide (LPS) and LPS-binding protein (LBP) are also associated with obesity [88], and may be related to altered intestinal permeability and the GM-dependent leakage of LPS [11].

Obesity influences the Firmicutes and Bacteroidetes phyla, as well as their ratio. Some studies indicate that BMI is positively correlated with the abundance of Firmicutes, while Bacteroidetes are negatively correlated, with the F/B ratio increasing alongside BMI [89, 90]. A study on 3-year-old children also demonstrated that the abundance of genus Parabacteroidetes from Bacteroidetes is negatively associated with obesity, while Dorea of Firmicutes is positively correlated to the same [91], aligning with our results.

Among these taxa, the Bacteroidetes stands out as one of the most prevalent and beneficial genera, exerting a protective effect on T2D [92]. Animal studies have shown that Bacteroidetes can ameliorate insulin resistance and immune function in diabetic mice [93, 94], while observational studies have likewise highlighted its favorable role in improving glucose metabolism in T2D patients [92]. Thus, the reduction of Bacteroidetes may impact both the body composition and the control of glucose, illustrating the mediating role of GM.

Additionally, our result indicated higher BMI can reduce the abundance of the family Christensenellaceae and genus ChristensenellaceaeR.7group. Christensenellaceae has been shown to be negatively correlated with visceral fat, as indicated by waist circumference and waist-hip ratio. This taxon is also associated with glucose metabolism, the decrease of its abundance may weaken the metabolic benefit of the host to dietary fiber and high protein diet [95]. Additionally, Christensenellaceae have been found as negatively associated with diabetic retinopathy, its protective mechanism may involve inhibiting systemic inflammatory response [96]. Together, these findings reveal that abnormal BMI may lead to multiple pathological mechanisms of metabolic disorder and microangiopathy by changing the abundance of Christensenellaceae.

In the studies, several archaeal microbes were also found to be negatively correlated with BMI, particularly the Methanobacteria within the phylum Archaea. The Methanobacteriaceae groups, residing in anaerobic environments like the gut, play a key role in the microbial community by utilizing hydrogen to reduce substances like acetate to methane [97, 98]. Methanogenic bacteria are also associated with reduced visceral fat [99]. A positive correlation between Christensenellaceae and Methanobacteriaceae has been reported, showing a symbiotic relationship where Christensenellaceae provide hydrogen to methanogens aiding in methane production. This symbiotic metabolic activity helps maintain a lower BMI [100], and these taxa are associated with low-level inflammation, which contributes to reducing the impact on T2D [101].

Furthermore, a metagenome-wide association study (MGWAS) reported a lower prevalence of Verrucomicrobia in T2D patients compared to healthy controls, consistent with our findings [21]. In particular, Akkermansia muciniphila, a well-known probiotic species, may exacerbate intestinal permeability and inflammation when its numbers are diminished [102]. It is plausible to hypothesize that T2D may trigger alterations in gut microbial abundance [103], and these changes of GM, in turn, would further promote T2D, establishing a detrimental feed-forward loop.

Experimental studies have shown that improving the abundance of Akkermansia muciniphila by increasing dietary fiber intake may help to control obesity [104], Metformin, which serves as primary treatment in T2D, also promotes an abundance of A. mucinophila, thus, improving glucose homeostasis in mouse models [105]. Patients with T2D should be instructed to work on improving their intestinal environment and, through that, achieving both weight loss and blood sugar control.

Our study suggests that the genetic predisposition to T2D may drive the propagation of certain gut microbiota species, possibly, due to a gradual shift in the host’s adaptation to higher exposure to glucose. The bidirectional relationship between microbiota and disease is likely defined by the interaction of host immune and metabolic systems. For example, changes in the host’s immune system may selectively promote the proliferation of particular microbial species, which, in turn, facilitate further progression of the disease. Our study highlights the potential interaction between the microbiota, T2D, and BMI, indicating that BMI may direct the metabolic milieu of the microbiota, and, therefore, contribute to the progression of T2D either by modulating immune function or by promoting metabolic inflammation, or both.

Our research has a few advantages. Firstly, MR analysis helps overcome common confounding biases which are common in traditional studies, with three different MR methods used for providing a more accurate evaluation of potential causality. Moreover, we integrated two independent microbial GWAS cohorts, MiBioGen (N = 18,340) and the DMP (N = 7,738), thus, harnessing stronger statistical power and broader ethnical coverage, while enabling a more comprehensive assessment of the impact of GM on human diseases. In addition, we conducted a bidirectional causal study on microbiota, T2D, and BMI, posing that the microbiota may serve as a mediator between T2D and BMI, and summarized their interaction patterns using the network analysis.

However, there are some limitations to consider. As our analysis was limited to a genetic component of each trait, the presented results should be interpreted with caution and understanding that the traits of the human host and its microbiome result from a complex web of interactions among environmental factors, which are difficult to collect and dissect. Participants included in the summary data were predominantly from European populations, minimizing population heterogeneity. This indicates that future research is necessary to determine whether these results are applicable to other populations. While these genetic variants represent stable genotypes and, in that, provide long-term influences directed at microbial taxa, the composition of the microbiota is highly dynamic, meaning that, in certain cases, influences of genotypes may be clouded by short-term, transient fluctuation in the microbiota. Due to limited access to additional datasets, additional validation analyses were not conducted. Confirming the causal relationship between the microbiota and diseases remains the task for the follow-on studies, which should allow more precise experimental designs, with the negative control and colocalization analyses, along with exploration of longitudinal microbiome data, which are yet to be collected.

Conclusions

Our study reveals bidirectional causal links between GM and both obesity and T2D, highlighting GM’s mediating role in the obesity-T2D pathway. Maintaining a balanced GM may be critical for the prevention and management of obesity and T2D.

Data availability

All GWAS summary datasets in this study are publicly available for download by qualified researchers.

Abbreviations

T2D:

Type 2 diabetes

GM:

Gut microbiome

SCFAs:

Short-chain fatty acids

IVs:

Instrumental variables

MR:

Mendelian randomization

GWAS:

Genome-wide association studies

BMI:

Body mass index

IVW:

Inverse variance weighting

LD:

Linkage disequilibrium

WM:

Weighted median

GIP:

Gastric inhibitory polypeptide

LPS:

Lipopolysaccharide

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Acknowledgements

We thank all investigators and participants from the DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) Consortium, the GIANT, the international consortium MiBioGen, and the Dutch Microbiome Project (DMP) for sharing these data.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Li Fu & Fuquan Zhang

  2. School of Systems Biology, George Mason University, Manassas, VA, 20110, USA

    Ancha Baranova & Hongbao Cao

  3. Research Centre for Medical Genetics, Moscow, 115478, Russia

    Ancha Baranova

  4. Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 264 Guangzhou Road, Nanjing, 210029, Jiangsu, China

    Fuquan Zhang

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  1. Li Fu
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Contributions

F.Z. conceived the project, supervised the study, collected and analyzed the data. L.F. interpretated data and prepared figures. L.F., A.B., H.C., and F.Z. wrote and reviewed the manuscript. All the authors have read and approved the manuscript.

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Correspondence to Fuquan Zhang.

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Fu, L., Baranova, A., Cao, H. et al. Gut microbiome links obesity to type 2 diabetes: insights from Mendelian randomization. BMC Microbiol 25, 253 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03968-8

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12866-025-03968-8

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BMC Microbiology

ISSN: 1471-2180

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