Bifidobacterium breve BBr60 Improves Metabolic Abnormalities by Regulating Gut Microbiota Balance in a High‐Fat Diet‐Induced Obese Mouse Model

ABSTRACT

Aim

This study aimed to evaluate the modulatory effects of Bifidobacterium breve BBr60 on gut microbiota dysbiosis and metabolic abnormalities in a high‐fat diet (HFD) ‐induced obese mouse model.

Methods

Thirty mice aged 6 weeks were divided into a normal diet group (CTL), an HFD group, and a BBr60 intervention group (HFD + BBr60). During 8 weeks, the food intake and body weight of the mice were monitored. Subsequently, an oral glucose tolerance test (OGTT) was conducted, and fasting insulin levels, lipid profiles, and the gut microbiota composition were determined. Liver lesions, inflammation levels, and tight junction protein expression were examined using Oil Red O staining, haematoxylin and eosin staining, and reverse transcription polymerase chain reaction analysis, respectively. 16s rRNA high‐throughput sequencing techniques were used to investigate the diversity and richness of the gut microbiota.

Results

BBr60 not only ameliorated HFD‐induced metabolic disorders, including weight gain (body weight, p < 0.001), hyperglycaemia (FBG, p < 0.001), insulin resistance (HOMA‐IR, p = 0.002), dyslipidaemia (TC, p < 0.001), and liver steatosis, but also mitigated systemic inflammation and impaired intestinal permeability. Furthermore, BBr60 reversed gut microbiota dysbiosis, as evidenced by a significant reduction in the relative abundance of opportunistic pathogens such as Duncaniella, Allobaculum, and Parasutterella while increasing beneficial bacteria such as Clostridium cluster XIVa, Limosilactobacillus, and Bifidobacterium. Time‐dynamic analyses showed that BBr60 regulated the relative abundance of the gut microbiota and improved HFD‐induced dysbiosis of the gut microbiota.

Conclusion

BBr60 significantly ameliorated glucose and lipid metabolic disorders and gut microbiota dysbiosis in an HFD‐induced obese mouse model, and these effects were associated with changes in gut microbiota composition, suggesting its potential as a therapeutic intervention for obesity‐related metabolic abnormalities.

1. Introduction

Obesity represents a significant global public health challenge and is strongly associated with numerous chronic diseases such as type 2 diabetes, cardiovascular disease, and certain cancers, as reported by the World Health Organization (WHO) report in 2021 [1, 2]. The aetiology of obesity is multifactorial, involving genetics, environment, and lifestyle factors [3]. Emerging evidence indicates that gut microbiota dysbiosis contributes to the pathogenesis of obesity by increasing intestinal permeability and promoting systemic inflammation, thereby exacerbating obesity and its related complications [4].

The gut microbiome, a complex and dynamic ecosystem, plays a crucial role in host health and homeostasis. In models of obesity [5], gut microbiota dysbiosis has been shown to exacerbate intestinal permeability, systemic inflammation, and metabolic dysfunction [5, 6]. Alterations in the gut microbial community are closely linked to impaired metabolic functions, directly influencing host metabolic pathways including lipid and carbohydrate metabolism [7, 8]. Furthermore, crosstalk between the gut microbiota and liver metabolism via the gut‐liver axis is essential for the regulation of host metabolic health [9].

Probiotics, which act as potential modulators of the gut microbiota, have garnered significant scientific interest in recent years due to their metabolic benefits. Supplementation with probiotics has been shown to enhance intestinal barrier integrity and modulate immune responses [10]. Accumulating evidence indicates that specific probiotic strains, particularly those belonging to the genera Bifidobacterium and Lactobacillus, can alleviate HFD‐induced obesity and related metabolic disorders [11]. Among Bifidobacterium species, the most extensively studied include B. longum , B. animalis , B. bifidum , and B. adolescentis [12]. In HFD‐fed mice, supplementation with B. longum APC1472 was associated with weight loss, reduced fat accumulation, and improved glucose tolerance. Treatment with B. animalis sp. lactis GCL2505 inhibited fat accumulation and improved glucose tolerance via the short‐chain fatty acid receptor G‐protein‐coupled receptor 43 [13, 14]. The effects of Bifidobacterium breve , a prominent species within the Bifidobacterium genus, on gut microbiota composition and hepatic glucose metabolism remain poorly understood. Although supplementation with B. breve B‐3 has been reported to reduce body weight and improve serum total cholesterol (TC), fasting blood glucose (FBG), and insulin levels [15], previous studies have not sufficiently characterized its impact on the gut microbiota or captured the dynamic changes in microbial composition in obese mouse models. The commercially available probiotic Bifidobacterium breve BBr60 (BBr60) has been shown to improve intestinal barrier integrity, ameliorate glycaemic and lipid metabolic disorders, and attenuate inflammatory responses under obesity conditions [16, 17]. However, it remains unclear whether these beneficial effects are mediated through modulation of the gut microbiota. Given its therapeutic potential in alleviating metabolic disorders, this study was designed to comprehensively investigate the efficacy of BBr60 in alleviating HFD‐induced obesity in mice, with a specific focus on its impact on gut microbiome composition, intestinal permeability, systemic inflammation, and hepatic glucose metabolism.

2. Materials and Methods

2.1. Bifidobacterium breve BBr60 Cultivation and Bacterial Suspension Preparation

The strain Bifidobacterium breve BBr60 was purchased from Wecare Probiotics Co. Ltd. The strain identity was verified by 16S rRNA gene sequencing and molecular identification, and the strain is available from the German Collection of Microorganisms and Cell Cultures (DSMZ) under the accession number DSM 35053. Bacteria were cultured under anaerobic conditions in de Man, Rogosa, and Sharpe (MRS) broth at 37°C for 18 h. Subsequently, cells were harvested via centrifugation at 4500 × g for 10 min and resuspended in saline to prepare the bacterial suspension. The final bacterial suspension concentration was adjusted to 5 × 10⁹ CFU/mL.

2.2. Mouse Model and Experimental Setup

6‐week‐old male C57BL/6J specific pathogen‐free (SPF) mice were obtained from the Shanghai Animal Research Center. After a 1‐week acclimatization period, the mice were randomly assigned into three groups (n = 10 per group). A priori power analysis for one‐way ANOVA was performed using G*Power. A minimum of 8 mice per group was required to achieve 80% statistical power at α = 0.05 with an effect size of f = 0.5 [18, 19]. Considering the high biological variation of gut microbial composition and potential sample loss in animal experiments, we finally employed 10 mice in each group, which was sufficient to reliably detect significant differences in metabolic phenotypes and gut microbial profiles. The groups were designated as a normal diet group (CTL), an HFD group, and a BBr60 intervention group. The CTL group received a standard chow diet (Shanghai Puluteng Biological Technology Co. Ltd.) containing 11.0% kcal from fat, 21.5% kcal from protein, and 67.5% kcal from carbohydrate (3.531 kcal/g), while both the HFD and BBr60 groups were fed a HFD containing 60% kcal from fat. Additionally, the BBr60 group received daily oral BBr60 administration at a dose of 1 × 10⁹ CFU/day. This dose was selected based on previous peer‐reviewed probiotic intervention studies in HFD‐induced obese mice, in which similar dosing ranges have been demonstrated to effectively ameliorate obesity‐related metabolic disorders [15]. This dose is also within the commonly used effective range for probiotic studies in murine models, ensuring appropriate efficacy and experimental feasibility [20]. The experimental period lasted for 8 weeks, and a schematic of the study design is presented in Figure 1A. All mice were housed under a 12‐h light/dark cycle and had ad libitum access to food and water. Body weight and food intake were monitored and recorded weekly. The Lee's index, a validated metric for assessing obesity in rodents, was calculated using the following formula [21]:

$${\mathrm{Lee}}^{\text{'}}\mathrm{s}\text{index}=\left(\text{body weight}{\left(\mathrm{g}\right)}^{1/3}/\text{nasal}–\text{anal length}\left(\mathrm{cm}\right)\right)\times 1000$$

FIGURE 1.

Effect of BBr60 on food intake, body weight, liver mass, oral glucose tolerance test (OGTT), hepatic glucose metabolic pathway, fasting blood glucose, and fasting insulin in high‐fat diet (HFD)‐fed mice. (A) Flow chart of the experimental design. (B) Food intake of mice. (C) HFD‐induced body weight changes, including weight gain in each group of mice. (D) Lee's index to evaluate the degree of obesity of mice. (E) Liver mass of mice. (F) Results of the OGTT were used to evaluate glucose metabolism. (G) The mRNA expression level of glucose‐6‐phosphatase (G6pc) in the liver. (H) The mRNA expression level of phosphoenolpyruvate carboxykinase (Pck1) in the liver. (I) The fasting blood glucose level. (J) The fasting insulin level. (K) Homeostatic model assessment of insulin resistance (HOMA‐IR). Data are expressed as mean ± standard deviation (SD) (n = 10). Significance levels are indicated as follows: *$p<0.05$, **$p<0.01$, ***$p<0.001$.

All experimental procedures were approved by the Animal Care and Use Committee of Shanghai Chengxi Biotech Co. LTD. (Ethics number 2023052023).

2.3. Biochemical Analysis

At the end of the experiment, mice were euthanized and blood samples were immediately taken. Blood samples were collected via cardiac puncture and placed in tubes without anticoagulants. After being allowed to clot at room temperature for 30 min, the samples were centrifuged at 3000 × g for 15 min at 4°C to separate the serum from the supernatant. The serum obtained was used for subsequent biochemical analysis, including the measurement of TC, triglycerides (TG), low‐density lipoprotein cholesterol (LDL‐C), and high‐density lipoprotein cholesterol (HDL‐C) using an automated biochemical analyser. Additionally, the concentrations of lipopolysaccharide (LPS), tumour necrosis factor α (TNFα), interleukin‐1 β (IL1β), IL6, and IL10 in serum were analysed using enzyme‐linked immunosorbent assay (ELISA) kits from Wuhan Chundu Biotech Co. Ltd.

2.4. Histological Analysis

The liver and colon tissue samples were fixed, dehydrated, and then embedded in kerosene. For the liver samples, 5‐mm‐thick sections were cut and then stained with Oil Red O and haematoxylin and eosin (H&E) to investigate the lipid deposition and changes in tissue structure in detail. After paraffin embedding, the colon tissue samples were cut into 5‐mm‐thick slices and stained with periodic acid‐Schiff (PAS). PAS staining focuses on goblet cells and is used to evaluate the condition of the intestinal mucosa [22].

2.5. Analysis of Expression of Colonic Tight Junction Proteins

To prepare a 10% (w/v) colonic tissue homogenate, a 1 g sample from the colon of each mouse was weighed and homogenized in 9 mL of sterile saline using an ice bath to maintain a low temperature throughout the process. Subsequently, zonula occludens‐1 (ZO‐1), claudins, and occludin concentrations in the colonic homogenates were determined using ELISA kits following the manufacturer's instructions (Wuhan Chundu Co. LTD., China).

2.6. Assessment of Glucose Metabolism

An oral glucose tolerance test (OGTT) was performed on all mice to assess glucose metabolism. The mice received an oral gavage of a glucose solution at a dose of 2 g/kg body weight [23]. Blood glucose levels were accurately measured at 0, 30, 60, 90, and 120 min following the glucose administration. The area under the glucose curve (AUC) was calculated based on these measurements.

After fasting overnight for 12 h, blood samples were taken from the tail vein of all mice for analysis. Fasting blood glucose (FBG) levels were measured. Fasting insulin (FI) levels were measured using an enzyme‐linked immunosorbent assay (ELISA) according to the manufacturer's instructions (Wuhan Chundu Biotechnology Co. Ltd., China). Homeostatic model assessment of insulin resistance (HOMA‐IR) was calculated using fasting blood glucose (mmol/L) and fasting insulin level (μU/mL) according to the following formula: HOMA−IR = (FBG value × FI value)/22.5.

2.7. RNA Extraction From the Liver and Reverse Transcription Polymerase Chain Reaction (RT‐PCR) Analysis

The total RNA was extracted from 100 mg of each liver tissue sample. TRIzol reagent was used for this process, and the RNA purity and concentration were accurately measured using a NanoDrop spectrophotometer. Subsequently, cDNA synthesis was performed using a reverse transcription kit. Forward (5′‐CTGCATAACGGTCTGGACTTC‐3′) and reverse (5′‐CAGCAACTGCCCGTACTCC‐3′) primers were used for phosphoenolpyruvate carboxykinase (Pck1) amplification. Similarly, forward (5′‐CGACTCGCTATCTCCAAGTGA‐3′) and reverse primers (5′‐GTTGAACCAGTCTCCGACCA‐3′) were used for glucose‐6‐phosphatase (G6pc) [24]. RT‐PCR analysis, performed using the SYBR Green method, evaluated the expression levels of glucose metabolism‐related genes.

2.8. Analysis of Gut Microbiota

Faecal samples were collected at week 0, 4, and 8 for gut microbiota analysis from all mouse groups. DNA from mouse faecal samples was extracted using the E.Z.N.A. Stool DNA Kit (Omega Bio‐tek Inc., GA, USA) according to the manufacturer's standard protocol. The methods described in the literature were used for amplification of the 16S rRNA V3–V4 region [25]. After purification, the amplified products were sequenced on the Illumina MiSeq PE300 platform to ensure the quality and depth of the data. Sequencing data were analysed using USEARCH software (version 11.0.667) (https://www.drive5.com/usearch/). A table of amplicon sequence variants (ASVs) was generated using the UNOISE3 algorithm. Representative sequences of ASVs were then aligned with the 16S rRNA database using the RDP Classifier (version 18) for accurate taxonomic assignment [26]. Finally, ASVs were clustered, and USEARCH software was used for microbial diversity analysis.

2.9. Statistical Analysis

The normality of data distribution was assessed using the Shapiro–Wilk test, and the homogeneity of variance was evaluated using Levene's test. For data with normal distribution and equal variance, one‐way analysis of variance (ANOVA) followed by Bonferroni post hoc test was applied for intergroup comparisons. For datasets that did not conform to a normal distribution, the non‐parametric Kruskal–Wallis H test was adopted, with subsequent pairwise rank comparisons. The analysis of microbial community structure used alpha diversity indices (including Chao1, ACE, and Shannon indices), and principal coordinate analysis (PCoA) was used to comprehensively assess microbial diversity. Additionally, the adonis2 function from the R package vegan [27] was used for significance testing. STAMP analysis was performed to compare significant differences at the genus level between different experimental groups. The Benjamini–Hochberg false discovery rate (FDR) correction was applied to correct for multiple testing. Taxonomic differences with FDR‐adjusted p < 0.05 were considered statistically significant. Additionally, Mantel tests [27] based on the Bray–Curtis distance were used to analyse the correlation of gut microbial composition. All plots were generated using the ggplot2 package in R [28]. Statistical analysis was performed in the R4.3 environment, and a p value of 0.05 was considered statistically significant.

2.10. Nucleotide Sequence Accession Numbers

The raw 16S rRNA sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1069785 and are publicly accessible.

3. Results

3.1. Body Weight

There were no significant differences in food intake among the three groups (Figure 1B). After an 8‐week intervention, mice in the HFD group exhibited a significant increase in body weight (p < 0.001, Figure 1C), Lee's Index (p < 0.001, Figure 1D), and liver mass (p < 0.001, Figure 1E) compared with those in the CTL group. In contrast, the BBr60 group exhibited significant decreases in body weight (p < 0.001, Figure 1C), Lee's Index (p < 0.05, Figure 1D), and liver mass (p < 0.001, Figure 1E) relative to the HFD group.

3.2. Glucose and Lipid Metabolism

Administration of BBr60 significantly reduced fasting blood glucose levels compared to the HFD group (5.63 ± 0.32 mmol/L in the HFD group versus 4.89 ± 0.35 mmol/L in the BBr60 group, p < 0.001, Figure 1I). HFD feeding significantly increased serum insulin levels compared with the control group. Although the BBr60 group showed a slight reduction in insulin concentration, no statistically significant difference was observed between the HFD and BBr60 groups (Figure 1J). Mice in the HFD group demonstrated significantly increased AUC of serum glucose, whereas BBr60 effectively reversed the change (Figure 1F). The HOMA‐IR index (Figure 1K) was significantly reduced in the BBr60 group compared to the HFD group.

Serum levels of TC, TG, and LDL‐C were increased accompanied by decreased HDL‐C levels in the HFD group (Figure 2A). BBr60 treatment significantly decreased the circulating levels of TC and TG and significantly increased HDL‐C levels, whereas no significant difference in LDL‐C was observed.

FIGURE 2.

Effects of BBr60 on liver lipid metabolism and liver tissue structure. (A) Levels of total cholesterol (TC), triglycerides (TG), low‐density lipoprotein cholesterol (LDL‐C), and high‐density lipoprotein cholesterol (HDL‐C) in serum. (B) Analysis of lipid deposits in liver tissue using Oil Red O staining. (C) Evaluation of pathological changes in liver tissue using haematoxylin and eosin (H&E) staining. Data are expressed as mean ± standard deviation (SD) (n = 10). Significance levels are indicated as follows: *$p<0.05$, **$p<0.01$, ***$p<0.001$.

3.3. Liver Lesions

Histological analysis using Oil Red O and H&E staining revealed that the HFD induced significant lipid accumulation (Figure 2B) and inflammatory infiltration (Figure 2C) in the liver. Remarkably, BBr60 treatment significantly reversed these HFD‐induced liver lesions.

RT‐PCR analysis of genes associated with glucose metabolism in the liver revealed that the HFD increased the expression of hepatic glucose metabolism genes, whereas BBr60 significantly decreased the expression of G6pc (Figure 1G) and Pck1 (Figure 1H).

3.4. Inflammation Factors and Intestinal Permeability

The TNFα, IL1β, and IL6 levels were increased, whereas the IL10 levels were significantly decreased in the HFD group (Figure 3A–D). However, BBr60 application significantly attenuated the upregulation of inflammatory markers. Additionally, we observed a significant increase in LPS levels in the serum of HFD mice, while BBr60 significantly attenuated the upregulation (Figure 3E). We further investigated the expression of tight junction proteins in the epithelial cells of the colon. The study revealed that ZO‐1, occludin, and claudin protein expressions were significantly lower in the colons of HFD mice than those in the CTL group, whereas BBr60 significantly improved the expression of these tight junction proteins (Figure 3F–H). Further histological analysis suggested that BBr60 treatment was associated with improved PAS staining of colonic goblet cells and better‐preserved colonic morphology compared with HFD mice (Figure 3I).

FIGURE 3.

Effects of BBr60 on inflammatory cytokines and the physical structure of the intestine. (A) Serum tumour necrosis factor α (TNFα) levels. (B) Serum interleukin‐1 β (IL1β) levels. (C) Serum IL6 levels. (D) Serum IL10 levels. (E) Serum lipopolysaccharide (LPS) concentrations. (F) Expression level of the tight junction protein ZO‐1 in the colon. (G–H) Expression level of the tight junction proteins occludin and claudin in the colon. (I) Pathological analysis of colon tissue by periodic acid‐Schiff (PAS) staining. Data are expressed as mean ± standard deviation (SD) (n = 10). Significance levels are indicated as follows: *$p<0.05$, **$p<0.01$, ***$p<0.001$.

3.5. Gut Microbiota Dysbiosis

At the beginning of the intervention (week 0), no significant differences in the beta diversity of the gut microbiota were found among the three mice groups (Figure 4A). However, after 4 and 8 weeks of HFD feeding, significant changes in beta diversity were observed compared with the CTL group (Adonis, p < 0.05, Figure 4B,C). PCoA revealed a significant alteration of the gut microbial community structure induced by the HFD. BBr60 intervention shifted the overall composition towards that of the CTL group. No significant differences in alpha diversity were found among the groups in Chao1, ACE, and Shannon indices at week 1 (Figure 4D). At week 4, the Chao1 index in the BBr60 group was significantly lower than that in the HFD group, whereas no significant differences were observed in the ACE or Shannon indices among the three groups (Figure 4E). At week 8, no significant difference was observed in the ACE index, whereas the Chao1 and Shannon indices were significantly higher in the HFD group than in the CTL group; the BBr60 group did not differ significantly from either the CTL or HFD group (Figure 4F). BBr60 intervention attenuated these HFD‐induced changes in the diversity and richness of the gut microbiota.

FIGURE 4.

Regulatory effects of BBr60 on the diversity and composition of gut microbiota in high‐fat diet (HFD)‐induced obese mice. (A–C) Differences in the beta diversity of the gut microbiota between the different experimental groups of mice at weeks 0, 4, and 8 of the experiment. The assessment was performed using principal coordinate analysis based on the unweighted Unifrac distance. (D–F) Differences in the alpha diversity of the gut microbiota between the different experimental groups of mice at weeks 0, 4, and 8 of the experiment. (G–I) Changes in the relative abundance of the gut microbiota at the phylum level between the different experimental groups of mice at weeks 0, 4, and 8 of the experiment. Each group consisted of 10 mice ($n=10$). Alpha diversity was analysed using Kruskal–Wallis test. Beta diversity was assessed by PCoA and tested using adonis2. Significance levels are indicated as follows: *$p<0.05$, **$p<0.01$, ***$p<0.001$.

Further analysis was conducted to determine the effects of BBr60 on the gut microbiota composition. The results revealed that the primary gut microbiota of the mice consisted of Firmicutes and Bacteroidetes, followed by Proteobacteria and Campilobacterota (Figure 4G–I). Minimal differences in the relative abundance of gut microbiota at the phylum level were found among the three groups at weeks 0 (Table S1) and 4 (Table S2), except for differences in Campilobacterota at week 0, with no significant differences in the relative abundance of Firmicutes, Bacteroidetes, Proteobacteria, and Deferribacteres. Significant gut microbiota differences of the three groups occurred at week 8 (Table S3). At week 8, compared with the CTL group, the HFD group showed a significantly higher relative abundance of Bacteroidetes and a significantly lower relative abundance of Firmicutes. Compared with the HFD group, BBr60 treatment was associated with a significantly lower relative abundance of Bacteroidetes and a significantly higher relative abundance of Firmicutes and lower abundance of Proteobacteria.

3.6. Gut Microbiota Dysbiosis and Genus‐Level Correlation Analysis

We performed STAMP analysis to gain a deeper insight into the regulatory effects of BBr60 on specific microbes. The HFD significantly increased the relative abundance of genera, such as Duncaniella, Allobaculum, and Parasutterella, while concurrently reducing the abundance of beneficial microbes at the genus level, such as Lactobacillus and Clostridium cluster XIVa (Figure 5A). However, BBr60 application significantly attenuated this trend and increased the relative abundance of beneficial microbes, such as Clostridium cluster XIVa, Limosilactobacillus, and Bifidobacterium (Figure 5B), which is consistent with PCoA results.

FIGURE 5.

Differential and correlation analysis of gut microbiota at the genus level in obese mice induced by a high‐fat diet (HFD) and treated with BBr60. (A) Differences in gut microbiota at the genus level between the control group (CTL) and the HFD group of mice at the end of the experiment (week 8). (B) Differences in the gut microbiota at the genus level between the BBr60 and HFD groups of mice at week 8 of the experiment. (C) Analysis of the correlation between gut microbiota and serum indicators. (D) Analysis of the correlation between glucose metabolism and lipid markers. (E–G) Presentation of the correlation analysis of gut microbiota between weeks 0 and 8 in the CTL (E), HFD (F), and BBr60 (G) groups of mice. n = 10 per group. Genus‐level differences were analysed using STAMP analysis. Correlations were assessed using Spearman correlation analysis, and temporal consistency was evaluated using Mantel tests. *$p<0.05$, **$p<0.01$, ***$p<0.001$. FBG, fasting blood glucose; FI, fasting insulin; G6pc, glucose‐6‐phosphatase; HDL‐C, high‐density lipoprotein cholesterol; HOMA‐IR, Homeostatic model assessment of insulin resistance; IL10, interleukin 10; IL1β, interleukin‐1 β; IL6, interleukin 6; LDL‐C, low‐density lipoprotein cholesterol; LPS, lipopolysaccharide; Pck1, phosphoenolpyruvate carboxykinase; TC, total cholesterol; TG, triglycerides; TNFα, tumour necrosis factor α.

Moreover, we used Spearman correlation analysis to investigate the association between biochemical markers and gut microbiota. Correlation analysis revealed that specific biochemical markers showed significant positive or negative correlations with several microbial genera (Figure 5C). Specifically, Duncaniella, Allobaculum, and Parasutterella, which were enriched in the HFD group, were positively correlated with lipid markers (TC, TG, and LDL‐C), proinflammatory cytokines, and markers related to gluconeogenesis, while negatively correlated with IL10, tight junction proteins, and HDL‐C. Conversely, Clostridium cluster XIVa, Limosilactobacillus, and Bifidobacterium, which were enriched in the BBr60 group, were negatively correlated with lipid markers (TC, TG, and LDL‐C), proinflammatory cytokines, and markers associated with gluconeogenesis, while positively correlated with IL10, tight junction proteins, and HDL‐C. Furthermore, significant correlations were observed between the parameters of glucose metabolism (FBG, FI, HOMAIR, G6pc and Pck1) and lipid markers (TC, TG, HDL‐C and LDL‐C) (Figure 5D).

Additionally, mantel tests were performed to investigate the correlation of gut microbiota in the different groups at the beginning (week 0) and at the end of the experiment (week 8). The gut microbiota in the CTL group exhibited significant temporal consistency from week 0 to week 8 (Figure 5E, r = 0.324, p = 0.019). In contrast, the HFD group showed no significant correlation over time (Figure 5F, p = 0.32), indicating that the HFD destabilized the microbial community structure, a finding consistent with the perturbations observed in alpha and beta diversity. Notably, a significant temporal correlation of the gut microbiota from weeks 0 to 8 in the BBr60 group (Figure 5G, r = 0.886, p = 0.001) suggests that BBr60 administration mitigated HFD‐induced dysbiosis and promoted microbial community resilience.

3.7. Temporal Dynamics of the Most Important Microbial Genera in the Gut Microbiota

We monitored the dynamic changes in the relative abundance of the major microbial genera over time (weeks 0, 4, and 8) to evaluate the HFD‐induced perturbation and the subsequent restorative effect of BBr60 intervention. The relative abundance of Duncaniella gradually decreased in the CTL group (Figure 6A) and increased in the HFD group (Figure 6B) over time, while BBr60 application significantly reversed this trend (Figure 6C). The relative abundance of Allobaculum gradually decreased in the CTL group (Figure 6A), whereas it gradually increased in the HFD and BBr60 groups (Figure 6B,C). Additionally, the relative abundance of Parasutterella gradually increased in the HFD group (Figure 6B), but no significant changes were observed in the CTL and BBr60 groups (Figure 6A,C).

FIGURE 6.

Changes in the relative abundance of the major microbial genera in the gut at different time points. (A) Changes in the relative abundance of gut microbiota in the control diet (CTL) group of mice at weeks 0, 4, and 8 of the experiment. (B) Changes in the relative abundance of gut microbiota in the high‐fat diet (HFD) group of mice at weeks 0, 4, and 8 of the experiment. (C) Changes in the relative abundance of the gut microbiota in the BBr60 intervention group at weeks 0, 4, and 8 of the experiment. These graphs illustrate the dynamic changes in the major genera of the gut microbiome over time and reflect the effects of different diets and interventions on the gut microbiome composition. The data are presented as mean ± standard deviation (SD) (n = 10). Statistical analysis was performed as described in the Statistical Analysis section. *p < 0.05, **p < 0.01, ***p < 0.001.

The relative abundance of beneficial bacteria, such as Clostridium cluster XIVa, in the HFD group remained stable over time (Figure 6B), while it significantly increased in the CTL and BBr60 groups (Figure 6A,C). The relative abundance of Limosilactobacillus remained stable in the CTL group but gradually decreased in the HFD and BBr60 groups (Figure 6). Additionally, we observed a gradual decrease in the relative abundance of Bifidobacterium in all three groups (Figure 6). Overall, these results indicate that HFD consumption disturbed the homeostasis of the gut microbial ecology by favouring putative pathogens over commensals. Remarkably, BBr60 administration showed a restorative potential, significantly reversing the HFD‐induced dysbiosis.

4. Discussion

This study revealed that BBr60 not only counteracts HFD‐induced metabolic disturbances—including weight gain, glucose/lipid dysregulation, and liver injury—but also reduces associated inflammation and intestinal permeability. Notably, the restructuring of the gut microbial community appears to be a critical mechanism through which BBr60 exerts these multifaceted protective effects.

Probiotics, acting as potential regulators of the gut microbiota, have been garnering significant attention in recent times. Although multiple Bifidobacterium breve strains including B. breve B‐3 have been documented to alleviate HFD‐induced metabolic dysfunction, strain‐specific differences remain evident. Consistent with the metabolic benefits previously reported for B‐3 [15], BBr60 effectively improved body weight gain, glucose and lipid dysregulation, as well as hepatic injury in obese mice. In addition, the present study incorporated a comprehensive evaluation of systemic inflammation, intestinal barrier integrity, and longitudinal gut microbiota dynamics during BBr60 intervention. Moreover, whereas earlier studies mainly focused on metabolic phenotypes, the present study further characterized the longitudinal changes in gut microbiota during BBr60 intervention and systematically linked microbiota alterations with metabolic, inflammatory and intestinal barrier homeostasis. Furthermore, the metabolic effects of the BBr60 strain itself have not previously been systematically investigated in an HFD‐induced obese mouse model. This integrative and longitudinal assessment provided further mechanistic insight into the probiotic effects of BBr60.

In our study, BBr60 administration not only restored the diversity of the gut microbiota, enhanced the similarity of the microbiota between the intervention group and the normal control group, but also significantly modulated the relative abundance of specific bacterial genera. Specifically, it reduced the relative abundance of Duncaniella, Allobaculum, and Parasutterella while increasing the relative abundance of Clostridium cluster XIVa, Lactococcus, and Bifidobacterium. These results indicated that BBr60 administration remodelled the microbiota structure of obese mice induced by a high‐fat diet, bringing its composition closer to a healthy state. This effect may be attributed to the selective enrichment of beneficial bacteria and the suppression of opportunistic pathogens. However, because the relative abundance of some taxa, particularly Clostridium cluster XIVa and Bifidobacterium, was low, their biological relevance should be interpreted with caution despite statistical significance.

Notably, we observed increased gut microbial richness and alpha diversity in HFD‐fed mice, which differs from the commonly reported reduction in microbial diversity associated with obesity and high‐fat diet feeding. However, accumulating evidence suggests that microbiota responses to HFD are highly context‐dependent rather than uniformly suppressive [29]. First, gut microbiota composition may undergo dynamic temporal changes during continuous HFD exposure [30, 31]. In the early or intermediate stages of HFD intervention, microbial communities may exhibit adaptive restructuring in response to dietary perturbation, which can transiently increase species richness and diversity. In contrast, reduced microbial diversity may become more evident during prolonged and stable obesity progression. Second, variations in dietary fat source, caloric composition, feeding duration, and mouse strain may contribute to inconsistent microbiota responses across studies [32, 33]. Certain HFD formulations may create distinct metabolic niches that support the expansion of specific microbial taxa, thereby increasing overall community diversity rather than causing a uniform loss of microbial richness.

In addition, the relative abundance of Bifidobacterium gradually declined over the experimental period in all groups, including the BBr60 intervention group. Several factors may contribute to this temporal trend. First, prolonged HFD exposure has been reported to adversely affect the growth and persistence of endogenous Bifidobacterium populations [34, 35]. Long‐term HFD intervention may alter the intestinal nutritional environment and reduce the availability of carbohydrate substrates favourable for bifidobacterial growth, thereby contributing to a gradual decline in relative abundance over time. Second, age‐related alterations in gut microbiota composition may also play a role [36]. As mice age, the intestinal microbial community undergoes dynamic ecological succession, which may be accompanied by a physiological reduction in Bifidobacterium abundance independent of dietary intervention. Although BBr60 supplementation transiently increased Bifidobacterium abundance during the early stage of intervention, exogenous probiotic administration may not fully overcome the long‐term effects of HFD exposure and time‐dependent microbial succession [37]. Therefore, while BBr60 may partially improve gut microbial composition and metabolic phenotypes, sustained elevation of Bifidobacterium abundance may be difficult to maintain throughout prolonged HFD feeding.

Our subsequent correlation analysis revealed that the relative abundance of these microorganisms was significantly associated with glucose metabolism parameters (FBG, FI, HOMA‐IR, G6pc and Pck1) as well as lipid markers (TC, TG, HDL‐C, LDL‐C). These findings further substantiated the close relationship between alterations in hepatic glucose metabolism, insulin sensitivity, and lipid metabolism with the gut microbiota, a connection that has been corroborated in prior research [38, 39, 40, 41, 42, 43]. Therefore, the modulation of gut microbiota composition by BBr60 may represent a critical factor contributing to its enhancement of metabolic health.

It is particularly noteworthy that the structural imbalance of gut microbiota induced by a HFD could compromise intestinal barrier function and was closely associated with increased intestinal permeability. It subsequently resulted in elevated levels of endotoxins, particularly LPS, within the circulatory system, thereby eliciting metabolic endotoxemia and contributing to insulin resistance (IR), obesity, and potentially diabetes [44, 45]. Subcutaneous injections of LPS in mice reproduced core metabolic disturbances characteristic of HFD feeding, including obesity, elevated blood glucose levels, IR, and increased weight of adipose tissue and liver [46]. These findings support the role of LPS as a critical intermediary factor linking HFD and obesity [46, 47]. Consequently, the disruption of gut microbiota was strongly associated with HFD‐induced obesity, and the reduction of LPS levels may represent an efficacious strategy for mitigating HFD‐induced obesity. A recent study had demonstrated that the novel intestinal pathogen, Duncaniella muricolitica, played a pivotal role in the dextran sulfate sodium (DSS) model by compromising the integrity of the intestinal epithelial barrier and increasing mucosal permeability [48]. Furthermore, additional studies had revealed that opportunistic pathogens, such as Allobaculum and Parasutterella, could also contribute to increased intestinal permeability and were closely linked to intestinal inflammation [49, 50]. Conversely, beneficial bacteria, including Lactococcus and Bifidobacterium, have been shown to restore the damaged intestinal barrier through the upregulation of tight junction proteins, such as ZO‐1, occludin, and claudin, thereby reducing intestinal permeability [51, 52]. Moreover, Clostridium cluster XIVa exerted its protective effects on the intestinal barrier and modulated immune responses via its metabolic products, such as butyrate and short‐chain fatty acids (SCFAs) [53]. In this study, BBr60 intervention enhanced the expression of intestinal tight junction proteins (e.g., ZO‐1, Occludin, and Claudin) while reducing systemic LPS levels, indicating a significant improvement in gut barrier function. Further correlation analysis indicated that Duncaniella, Allobaculum, and Parasutterella, which were enriched in the HFD group, exhibited negative correlations with tight junction proteins. Conversely, Clostridium cluster XIVa, Lactococcus and Bifidobacterium, which were enriched in the BBr60 group, showed positive correlations with tight junction proteins. Collectively, these results suggested that BBr60 restored intestinal barrier integrity by modulating the gut microbiota—specifically by promoting beneficial bacteria and suppressing opportunistic pathogens. This modulation, in turn, markedly reduced systemic LPS levels and alleviated peripheral endotoxemia, providing mechanistic support for the established link between gut microbiota and host inflammatory state [54].

The liver, as a pivotal metabolic organ, plays a central role in regulating blood glucose levels and lipid metabolism [55, 56]. Recent evidence suggested that intestinal barrier dysfunction and increased intestinal permeability in patients with non‐alcoholic fatty liver disease (NAFLD) were closely associated with more severe disease states, including liver function deterioration, hyperlipidemia, hepatic steatosis, and IR. This may be attributed to the translocation of LPS into the portal vein and subsequent entry into the liver, thereby inducing inflammation and damage [57]. Furthermore, elevated LPS levels in the bloodstream could activate the TLR4/MyD88/NF‐κB signalling pathway, leading to the release of pro‐inflammatory cytokines (TNFα, IL1β, and IL6) in the liver and a reduction in anti‐inflammatory cytokines (IL10), which contributed to low‐grade systemic inflammation. The secretion of these cytokines negatively impacted insulin signalling in skeletal muscle; for instance, TNFα induces the inactivation of insulin receptor substrate‐1 (IRS‐1) by enhancing serine phosphorylation, thus promoting IR. Subsequent IR exacerbated hyperinsulinemia and excessive lipid accumulation in both the liver and adipose tissue. Additionally, this study demonstrated that the liver is the primary organ affected by LPS exposure [46]. In our investigation, BBr60 administration significantly decreased circulating LPS levels by increasing the abundance of beneficial gut microbiota, further reducing inflammatory markers in HFD‐fed mice, alleviating systemic inflammation, and improving IR, hepatic lipid accumulation and inflammatory infiltration. These findings align with those reported by di Vito R et al. [51]. Importantly, although BBr60 improved inflammatory status and intestinal barrier function, the present study does not provide sufficient evidence to support a bacteria‐specific or pathway‐specific immune mechanism underlying its metabolic effects. Rather, the observed alterations in cytokine profiles and endotoxin levels are more likely secondary consequences of gut microbiota remodelling and attenuation of metabolic endotoxemia. Collectively, these results indicated that BBr60 may mitigate HFD‐induced low‐grade inflammation by modulating the composition of the gut microbiota, thereby ameliorating metabolic disorders associated with IR. Based on our findings, we propose a mechanistic model for BBr60: it restores gut microbial homeostasis in HFD‐fed mice by enriching beneficial bacteria and suppressing opportunistic pathogens. This restoration, in turn, enhances intestinal mucosal integrity, reduces systemic LPS levels, alleviates hepatic oxidative stress, and establishes a virtuous feedback loop that collectively ameliorates metabolic syndrome (Figure 7).

FIGURE 7.

Hypothesized mechanism of BBr60 in reducing obesity and metabolic changes in mice induced by a high‐fat diet (HFD) through gut microbiota modulation. This figure illustrates how BBr60 intervention could alter the composition of the gut microbiota, thereby affecting gut permeability, systemic inflammation levels, and hepatic glucose metabolic pathways.

Several limitations of the present study should be acknowledged. First, although our correlation analyses demonstrated that alterations in gut microbiota composition were associated with improvements in HFD‐induced metabolic dysfunction, inflammation, and intestinal barrier injury, the current evidence supports associative rather than causal relationships. Further mechanistic studies, including faecal microbiota transplantation and validation in germ‐free mice, are required to determine whether specific microbial alterations directly mediate the metabolic benefits of BBr60. Second, faecal and circulating short‐chain fatty acids (SCFAs) were not quantified in the present study. Given the recognized role of SCFAs as key mediators linking gut microbiota to host metabolic homeostasis, future metabolomic analyses will help clarify the downstream mechanisms involved in BBr60 intervention. Third, intestinal barrier integrity was primarily evaluated through tight junction protein expression and histological observation, whereas direct functional permeability assays were not performed. Future studies incorporating direct intestinal permeability assays, such as FITC‐dextran measurement, together with quantitative histomorphological analyses of the intestinal mucosa, including goblet cell counting per crypt and crypt depth measurement, would further strengthen the evaluation of barrier function. Despite these limitations, our findings still provide valuable evidence supporting the metabolic benefits of BBr60 and may offer clues regarding microbial taxa potentially involved in its effects, thereby providing a basis for future mechanistic and causal investigations.

5. Conclusion

In conclusion, our research demonstrates that the probiotic BBr60 alleviates HFD‐induced obesity and metabolic dysregulation by modulating gut microbiota, reinforcing intestinal barrier integrity, and reducing systemic LPS levels. These findings position BBr60 as a promising microbiota‐targeted intervention for metabolic syndrome, pending further investigation into its more detailed molecular mechanisms.

Author Contributions

Yu Liao and Xun Yuan contributed equally to this work and shared first authorship. They were responsible for conceptualization, experimental design and execution, and data curation. Yu Liao wrote the original draft, and Xun Yuan performed data analysis. An‐qi Qin and Yi‐cheng Qi contributed to animal models, visualization and validation of the results. Chang Shan and Jing Ma contributed equally to this work and shared corresponding authorship. Chang Shan was responsible for conceptualization, project supervision, manuscript review and editing, and funding acquisition. Jing Ma was responsible for project administration, validation of the final data, manuscript review and editing, and funding acquisition. All authors reviewed and approved the final version of the manuscript.

Funding

This study received support from Noncommunicable Chronic Diseases‐National Science and Technology Major Project (2024ZD0531900 and 2024ZD0531901) and National Natural Science Foundation Promotion Project within Renji Hospital, Shanghai Jiao Tong University School of Medicine (RJTJ26‐QN‐032 and RJTJ25‐QN‐005).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Relative abundance of gut microbiota at the phylum level at baseline (Week 0).

Table S2: Relative abundance of gut microbiota at the phylum level after 4 weeks of intervention.

Table S3: Relative abundance of gut microbiota at the phylum level after 8 weeks of intervention.

Liao Y., Yuan X., Qin A.‐q., Qi Y.‐c., Shan C., and Ma J., “ Bifidobacterium breve BBr60 Improves Metabolic Abnormalities by Regulating Gut Microbiota Balance in a High‐Fat Diet‐Induced Obese Mouse Model,” Diabetes, Obesity and Metabolism 28, no. 9 (2026): 7834–7848, 10.1111/dom.70972.

Data Availability Statement

The raw 16S rRNA sequencing data generated during the current study are publicly available in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1069785. Other data supporting the findings of this study are available from the corresponding author on reasonable request.