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. 2025 Jul 9;39(13):e70814. doi: 10.1096/fj.202501689R

Non‐Surgical Periodontal Therapy and Metformin Improve Bone Loss in Obese Mice With Periodontitis by Modulating the Gut Microbiota

Rixin Chen 1, Wei Wei 1, Lili Li 1, Miaomiao Zhang 1, Nannan Wang 1, Ruiyang Ge 1, Yue Shen 1, Wen Zhang 1, Daiyv Lu 1, Wenzheng Liao 2, Yanfen Li 1,✉, Fuhua Yan 1,✉
PMCID: PMC12239949  PMID: 40632484

ABSTRACT

Periodontitis and obesity are chronic inflammatory diseases associated with osteoporosis. Controlling inflammation is crucial for managing periodontitis in individuals with obesity. Metformin has shown potent anti‐inflammatory effects under inflammatory conditions. However, the effects of adjunctive systemic administration of metformin alongside non‐surgical periodontal therapy (NSPT) on periodontal and systemic bone health in obesity remain unknown. In this study, a high‐fat diet (HFD)‐induced obese murine model was created with periodontitis through ligation. Periodontal treatment consisted of standard mechanical debridement via NSPT, with or without metformin treatment through oral gavage. Outcomes were evaluated based on changes in periodontal status, systemic bone resorption and inflammation, as well as the gut microbiota community and metabolism. Our results indicated that periodontitis significantly increased osteoclastic activity and resulted in alveolar and femoral bone loss in HFD‐fed mice. Compared to NSPT alone, NSPT and metformin significantly improved periodontitis, reduced systemic inflammation, and alleviated femoral bone absorption in HFD‐fed mice. Mechanistically, periodontitis promoted gut microbiota dysbiosis and disrupted microbial linoleic acid metabolism. NSPT and metformin normalized the gut microbiota, enhanced the growth of species with anti‐inflammatory properties, including Faecalibacterium prausnitzii , Akkermansia muciniphila , Lactobacillus reuteri , and Butyricicoccus pullicaecorum , and restored the balance of linoleic acid metabolism in the gut and serum. This research presents novel evidence that metformin may serve as a promising adjunct to enhance the response of obese subjects to NSPT by regulating the gut microbiota and linoleic acid metabolism, indicating that the gut microbiota could be a potential therapeutic target for multidisciplinary intervention in periodontitis and obesity.

Keywords: 16s rRNA sequencing, linoleic acid metabolism, metformin, non‐surgical periodontal therapy, obesity, osteoporosis, periodontitis

Periodontitis significantly increased osteoclastic activity and accelerated femoral bone loss in high‐fat diet (HFD)‐induced obese mice. A combined therapy of non‐surgical periodontal therapy (NSPT) and metformin improved periodontitis and alleviated femoral bone absorption in HFD‐fed mice with periodontitis. Mechanistically, periodontitis promoted gut microbiota dysbiosis and disrupted microbial linoleic acid metabolism. NSPT and metformin significantly normalized the gut microbiota, enhanced the growth of species with anti‐inflammatory properties, and restored the balance of linoleic acid metabolism in the gut and serum.

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1. Introduction

Periodontitis is a common infectious disease characterized by the progressive destruction of the tooth‐supporting apparatus, which includes alveolar bone, cementum, periodontal ligament, and gingiva, ultimately leading to tooth loss [1]. In addition, periodontitis is closely associated with several systemic disorders, such as osteoporosis and obesity [2, 3]. Obesity is a global pandemic with profound social consequences and is recognized as a low‐grade systemic inflammation that may contribute to osteoporosis and alveolar bone resorption [4, 5]. A cohort study has indicated that obese patients experience markedly greater alveolar bone loss compared to individuals of normal weight [6]. Additionally, periodontitis increases the likelihood of developing osteoporosis with an odds ratio of 2.16 [7]. Consequently, the interplay between periodontitis, obesity, and bone loss is crucial and not yet fully elucidated.

Mounting evidence suggests that periodontitis induces shifts in gut microbiota under pathological conditions, which play a crucial role in bone homeostasis [8]. A clinical study suggested that the abundance of Firmicutes and Proteobacteria phyla was significantly enriched in the gut microbiota of patients with periodontitis in comparison to that of periodontally healthy subjects [9]. Periodontitis could disrupt the intestinal flora and exacerbate systemic bone resorption in ApoE−/− mice [10]. Furthermore, gut microbiota dysbiosis in obese patients has been well‐documented. An increased ratio of Firmicutes to Bacteroidetes is an essential contributor to obesity‐related complications [11]. Metagenomic studies in humans also identify that Akkermansia muciniphila and Faecalibacterium prausnitzii are decreased by obesity [12]. Therefore, we hypothesized that periodontitis may promote intestinal flora disorder in obesity, thereby exacerbating the adverse effects of obesity on bone health.

Non‐surgical periodontal therapy (NSPT) is an effective procedure for periodontal treatment that removes dental calculus, dental plaque, and pigment from the tooth surface [13]. NSPT significantly benefits individuals with obesity and periodontitis in the short term [14]. However, clinical studies reveal that improvements are less evident in obese patients than in non‐obese individuals following NSPT [15]. Martinez‐Herrera et al. found that obese individuals had merely a 20% decrease in teeth with moderate‐deep probing depths, while lean individuals had a 34.5% reduction within 3 months post‐therapy, suggesting that obesity adversely affected NSPT outcomes, especially for deeper pocket depths [16]. Therefore, adjunctive measures are necessary to eliminate inflammation and maintain a balanced microenvironment for bone regeneration under obese conditions.

Metformin has been recommended as a first‐line therapeutic agent for type 2 diabetes [17, 18]. Previous research suggests that metformin exhibits potential anti‐inflammatory properties on human vascular smooth muscle cells [19]. In dentistry, metformin has been reported to enhance the osteogenic capacity of human periodontal ligament cells through reducing oxidative stress in experimental periodontitis [20]. Recently, it has also been proposed that the therapeutic effects of metformin stem from its interactions with the gut microbiota [21]. A notable rise in the abundance of Akkermansia muciniphila has been identified in high‐fat diet (HFD)‐fed mice administered metformin [22]. Obesity and diabetes are interrelated, and metformin has been found to enhance the abundance of Lactobacillus in obese mice [23]. However, few studies have examined whether metformin mediates the reaction of obese individuals to NSPT. Therefore, our study aimed to evaluate the effect of metformin and non‐surgical periodontal intervention on alveolar and femoral bone loss using an HFD‐induced obese murine model. Additionally, we investigated whether the regulation of the gut microbiota following the combined therapy correlated with the curative effects of metformin on bone loss in obesity, thus providing a pre‐clinical basis for combating osteoporosis in patients with obesity and periodontitis.

2. Materials and Methods

2.1. Experimental Animals

Six‐ to seven‐week‐old wild‐type C57BL/6J male mice were purchased from Beijing Vital River Laboratory. All mice were maintained under SPF conditions with a 12:12‐h light/dark cycle, consistent temperature, and humidity at Nanjing Agricultural University. Mice were fed a daily high‐fat diet consisting of 60% fat, 20% carbohydrate, and 20% protein (D12492; CAT #D12492). All studies were conducted in compliance with the protocol approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University (No. PZW2023058, China), adhering to the ARRIVE Guidelines. Following one week of acclimation, 24 mice were randomly allocated into four groups (n = 6): HFD, HFD with periodontitis (HPD), HPD with ligature removal for NSPT (HLR), and HPD with ligature removal and metformin gavage intervention (HLM). The mice were anesthetized using isoflurane and sacrificed using sodium pentobarbital overdose.

2.2. Induction of Periodontitis, NSPT, and Metformin Treatment

Experimental periodontitis was induced by ligatures as previously described [24]. In the 10th week, 4–0 silk braided non‐absorbable sutures (Jinghuan Medical Appliance, Yangzhou, China) were tied around the maxillary bilateral second molars of mice in the HPD, HLR, and HLM groups, while HFD mice underwent sham ligation to avoid any potential trauma during the procedure. If the ligatures detached, the tooth would be re‐ligated.

In the 12th week, the ligatures in the HLR and HLM groups were removed for periodontal non‐surgical therapy using micro‐operating instruments.

In the 12th week, HLM mice were administered metformin (Sigma, 100 mg/kg, CAT #317240) via oral gavage five times a week. HPD, HLR, and HFD mice were given PBS. In the 16th week, all mice were euthanized, and serum, maxillae, femur samples, and cecal content were collected using aseptic techniques.

2.3. 16S rRNA Gene Sequencing and Analysis

DNA was extracted from the cecal content with the OMEGA Soil DNA Kit (M5635‐02) (Omega Bio‐Tek, Norcross, GA, USA) in accordance with the manufacturer's guidelines. The concentration and integrity of DNA were evaluated using a NanoDrop NC2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis. The V3–V4 variable portions of the bacterial 16S rRNA genes were amplified utilizing universal primers 341F 5′‐CCTACGGGRSGCAGCAG‐3′ and 806R 5′‐GGACTACVVGGGTATCTAATC‐3′. PCR amplicons were purified using Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified with the Quant‐iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Sequencing was conducted on an Illumina NovaSeq 6000 platform using 250 bp paired‐end reads. Representative reads of each ASV were chosen using the QIIME2 package and subjected to BLAST analysis against the Silva database (Version 138). Microbial diversity was assessed by beta diversity analysis, employing Bray‐Curtis and principal coordinate analysis (PCoA) for calculation. The bacterial composition at the phylum and gene levels was visualized using “qiime taxa barplot.” Linear discriminant analysis effect size (LefSe) was utilized to compare the abundance spectrum of taxonomy. Random forest analysis was employed to distinguish samples from different groups at both the gene and species levels using QIIME2. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) was used to predict the functional capabilities of the gut microbiota.

2.4. Metabolomic Profiling

Metabolome profiling of cecal content and serum was conducted using liquid chromatography‐mass spectrometry (LC–MS). The cecal content was weighed and dissolved in a methanol and acetonitrile mixture (2/1, vol/vol), adequately vortexed, and centrifuged at 13400 × g at 4°C for 15 min. Supernatants were collected for LC–MS analysis.

An ACQUITY UPLC I‐Class (Waters Corporation, Milford, USA) coupled with a Q‐Exactive quadrupole‐Orbitrap mass spectrometer equipped with a heated electrospray ionization (ESI) source (Thermo Fisher Scientific) was utilized. The binary gradient elution system comprised (A) water with 0.1% formic acid (v/v) and (B) acetonitrile with 0.1% formic acid (v/v). Separation was accomplished utilizing the subsequent gradient: 0.01 min, 5% B; 2 min, 5% B; 4 min, 30% B; 8 min, 50% B; 10 min, 80% B; 14 min, 100% B; 15 min, 100% B; 15.1 min, 5%; and 16 min, 5% B. The flow rate was determined at 0.35 mL/min, while the column temperature was sustained at 45°C. An electrospray ionization source was utilized in both positive and negative modes for the ionization of samples. Quality control samples were regularly injected for monitoring.

Initial LC–MS data were analyzed by Progenesis QI V2.3 software (Nonlinear Dynamics, Newcastle, UK). Data were correlated with online resources, including the Human Metabolome Database (HMDB), Lipid Maps (V2.3), and Metlin. The matrix was imported into the R software. Partial Least‐Squares–Discriminant Analysis (PLS‐DA) was employed to identify the metabolites that vary between groups. Variable Importance of Projection (VIP) values were used to rank the overall contributions of each variable to group discrimination. Differential metabolites were identified with VIP ≥ 1 and p ≤ 0.05. Differential metabolites were subsequently used for enrichment analysis of the KEGG pathway (https://www.kegg.jp).

2.5. Micro‐CT Analysis

Femora and maxillae were preserved in 4% paraformaldehyde for 48 h and later analyzed via micro‐computed tomography (Skyscan. 1176 scanner, Bruker, Karlsruhe, Germany) at a voxel resolution of 18 μm. Data Viewer and CTAn software facilitated the analysis of bone parameters. Alveolar bone loss was assessed by measuring the distance from the cementoenamel junction (CEJ) to the alveolar ridge crest (ARC) at the mesial, distal, buccal, and lingual sites of the maxillary second molars with DataViewer software (version 1.5.4.0, Bruker, Germany). Bone parameters—including bone mineral density (BMD), bone volume (BV)/tissue volume (TV), trabecular thickness (Tb.Th), trabecular number (Tb.N), and trabecular separation (Tb.Sp)—were determined by CTAn software (Bruker, Belgium). The area of root trifurcation surrounding the maxillary second molars was designated as the region of interest (ROI). The ROI for the femoral trabecular was defined as 30 slides from the distal growth plate level, extending toward the proximal femur (50 slides).

2.6. Histological Analyses

The femur and maxilla were fixed in a 4% paraformaldehyde solution for 48 h and decalcified in a 10% ethylenediaminetetraacetic acid (EDTA) solution (Servicebio, Wuhan, China) for 1 month. The solution was refreshed three times every week. Subsequently, the samples were dehydrated, embedded, and sectioned to a thickness of 5 μm. Hematoxylin and eosin (H&E) staining was performed (Servicebio, CAT # 1005) to observe the morphology and degree of inflammation. Masson's trichrome and TRAP staining were used to evaluate bone resorption, osteoclastic activity, and tissue destruction. Images were obtained using a scanner (3DHISTECH Ltd., Budapest, Hungary) and the CaseViewer software. For quantification, three slices from each group were assessed to objectively evaluate osteoclast activity in the femur.

2.7. Immunohistochemical Analysis

The maxilla sections were incubated with primary antibodies (IL‐1β rabbit antibody, Servicebio, CAT # GB11113, 1:100; IL‐6 rabbit antibody, Servicebio, CAT # GB11117, 1:100; TNF‐α rabbit antibody, Servicebio, CAT # GB115702, 1:100) overnight at 4°C and secondary antibodies (Servicebio, CAT # GB23303, 1:200) for 1 h at room temperature. For quantification, three areas of each slide (mesial, distal, and root furcation areas around the maxillary second molars) were assessed using ImageJ software, with three slices analyzed in each group.

2.8. Enzyme‐Linked Immunosorbent Assay (ELISA)

The expression of serum TNF‐α, IL‐1β, and IL‐6 was measured using ELISA kits (NeoBioscience, Shenzhen, China; CAT #EMC001b, CAT #EMC102a, CAT #EMC004, respectively) following the manufacturer's instructions.

2.9. Quantitative Real‐Time Polymerase Chain Reaction (RT‐qPCR)

Total RNA from the gingiva was extracted using the RNAprep Pure Tissue Kit (Agbio, Nanjing, China) and reverse transcribed into cDNA using the HiScript III RT SuperMix (Vazyme). An ABI ViiA 7 detection system (Thermo Fisher Scientific) was utilized for qPCR. Data were normalized to the internal reference β‐actin, and relative quantification was determined using the 2−ΔΔCt method. The primer sequences are as follows: TNF‐α (F) AACTCCAGGCGGTGCCTAT, TNF‐α (R) TGCCACAAGCAGGAATGAGA; IL‐6(F) AGTTGCCTTCTTGGGACTGA, IL‐6(R) TCCACGATTTCCCAGAGAAC; IL‐1β(F) AAGGAGAACCAAGCAACGACAAAA; IL‐1β(R) TGGGGAACTCTGCAGACTCAAACT; β‐actin(F) GGTGTGATGGTGGGAATGGG, β‐actin(R) ACGGTTGGCCTTAGGGTTCAG.

2.10. Biochemical Assays

Whole blood samples were obtained via retro‐orbital sampling and centrifuged at 3000 rpm for 30 min to obtain the supernatants. Plasma TG, TC, LDL, and HDL were measured using assay kits in accordance with the manufacturer's instructions (Jiancheng, Nanjing, China, CAT #A110‐1‐1, CAT #A111‐1‐1, CAT #A112‐1‐1, CAT #A113‐1‐1).

2.11. Statistical Analysis

Data were expressed as the mean ± standard deviation using GraphPad Prism version 8.0 (GraphPad Software Inc., CA, USA). Statistical significance was assessed using one‐way analysis of variance (ANOVA) or Kruskal–Wallis tests for multiple groups, followed by Tukey's post hoc test. The value of p < 0.05 was regarded as statistically significant. Correlation analysis was conducted between bone parameters and linoleic acid metabolites in the gut and serum.

3. Results

3.1. NSPT and Metformin Suppress Ligation‐Induced Periodontitis in HPD Mice

To validate the effect of combined therapy on periodontitis in vivo, the experiment was conducted as shown in Figure 1A. After NSPT and metformin intervention, the average body weight of HLM mice decreased compared to that of the other mice in the HPD and HLR groups, although this disparity was not statistically significant (Figure 1B). Compared to the HFD and HPD groups, the expression of serum biochemical indices, including TC and TG, was markedly reduced in the HLR and HLM groups, particularly in the HLM group (Figure 1C). Conversely, the expression of serum HDL was elevated in the HLM group compared to the HLR and HPD groups, while the expression of serum LDL levels did not show significant changes among the four groups (Figure 1C). Hematoxylin & eosin (H&E) and Masson staining analysis revealed severe inflammation, collagen fiber degeneration, and degradation in the HPD group compared to the HFD group, indicating that the periodontitis model was successfully established (Figure 1D,E). After combined therapy, elastic and collagenous fibers were denser and better organized in the HLM groups (Figure 1D,E). Additionally, TRAP‐stained sections indicated that HPD and HLR mice had more osteoclasts (indicated by the black arrow) than HFD mice, which could be reversed by combined therapy in the HLM group (Figure 1F). Furthermore, NSPT significantly alleviated periodontal bone loss around the maxillary bilateral second molars, which manifested as reduced bone loss at the mesial, distal, buccal, and lingual locations in the HLR and HLM groups compared to the HPD group, while bone mineral density (BMD) and bone volume (BV)/tissue volume (TV) were elevated (Figure 1G,H). Notably, the HLM group exhibited the most significant improvement, with only slight bone destruction observed (Figure 1G,H). These results showed that metformin and NSPT effectively controlled dyslipidemia, periodontal inflammation, and alveolar osteoclastic activity in HPD mice.

FIGURE 1.

FIGURE 1

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NSPT and metformin suppress ligation‐induced periodontitis in HPD mice. (A) Schematic illustration of the animal experiment. (B) Body weight of mice in the 16th week. (C) Concentrations of plasma TC, TG, LDL, and HDL. (D) Representative image of hematoxylin and eosin (H&E) staining of the maxillary alveolar bone in the HFD, HPD, HLR, and HLM groups. (E) Representative image of Masson's trichrome staining of the maxillary alveolar bone. (F) Representative image of TRAP staining of the maxillary alveolar bone. (G) Representative micro‐CT images and 3D reconstruction of the maxillary alveolar bone. (H) BMD, BV/TV, and alveolar bone loss around the maxillary second molars at the mesial, distal, buccal, and lingual sites. Scale bar = 250 μm. *p < 0.05, **p < 0.01, ***p < 0.001. ARC, alveolar ridge crest; CEJ, cemento‐enamel junction; BMD, Bone mineral density; BV, bone volume; TV, tissue volume; TRAP, tartrate‐resistant acid phosphatase; TG, total triglycerides; TC, total cholesterol; LDL, low‐density lipoprotein cholesterol; HDL, high‐density lipoprotein cholesterol.

To better evaluate the anti‐inflammatory effects of combined therapy in vivo, we performed immunohistochemistry (IHC) to observe the expression levels of IL‐1β, TNF‐α, and IL‐6. Our results suggested that following the placement of the ligature, these factors were dramatically elevated in the periodontal tissues (indicated by black arrows, Figure 2A,B). NSPT effectively suppressed the expression of these factors, with combined therapy demonstrating the most significant inhibitory effects (Figure 2A,B). Additionally, qPCR results confirmed the same trends in the expression of IL‐1β, IL‐6, and TNF‐α across the four groups, suggesting that NSPT could alleviate periodontitis and that, when combined with metformin, it enhanced the reduction of the periodontal inflammatory response, although the improvement was not significantly different (Figure 2C).

FIGURE 2.

FIGURE 2

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NSPT and metformin inhibit inflammation in the periodontal tissues of HPD mice. (A) Immunohistochemical images for IL‐1β, TNF‐α, and IL‐6 in the periodontal tissues and (B) quantitative analysis. (C) Relative mRNA expression levels of IL‐1β, TNF‐α, and IL‐6 in the gingiva. *p < 0.05, **p < 0.01, ***p < 0.001.

3.2. NSPT and Metformin Attenuate Femoral Bone Loss and Systemic Inflammation in HPD Mice

Next, the effects of combined therapy on the trabecular microstructure of the distal femur were assessed through histological analysis and μCT. H&E staining revealed a significant reduction and sparse structure in the trabecular area of the HPD and HLR mice compared to that of HFD and HPD mice (Figure 3A). To further evaluate changes in osteoclastic activity, we employed TRAP staining to visualize the osteoclasts. As shown in Figure 3B,E, periodontitis increased the total number of TRAP‐positive cells (indicated by the black arrow) in the metaphysis, while combined therapy partially reduced excessive osteoclastogenesis in HPD mice. μCT analysis of femoral bone architecture indicated that periodontitis significantly accelerated bone absorption in obesity, which manifested as a decline in BMD, BV/TV, Tb.Th, and Tb.N, accompanied by an increase in Tb.Sp (Figure 3C,D). NSPT alone exerted mild protective effects against periodontitis‐induced bone loss in obesity conditions (Figure 3C,D). In contrast, metformin and NSPT treatment significantly reduced bone loss and micro‐architectural damage in the HLM group (Figure 3C,D). These results demonstrated that metformin and NSPT significantly protected against systemic bone loss and inhibited excessive osteoclastic activity in HPD mice. Meanwhile, the serum levels of TNF‐α and IL‐6 were elevated in the HPD and HLR groups, while metformin and NSPT treatment significantly reduced these inflammatory factors, although the expression of IL‐1β showed no significant difference, suggesting that metformin and NSPT have great potential in controlling systemic inflammation in obesity (Figure 3F).

FIGURE 3.

FIGURE 3

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NSPT and metformin reduce femoral bone loss and systemic inflammation in HPD mice. (A) Representative image of H&E staining of femoral sections across the four groups. (B) Representative image of TRAP staining of femoral sections and (E) quantitative analysis of osteoclast number to bone surface ratio (Oc.N/BS). (C) Representative micro‐CT images and 3D reconstruction of the femur for analysis. (D) BMD, BV/TV, Tb.Th, Tb.N, and Tb.Sp of the femur. Scale bar = 1 mm. (F) ELISA analysis of the inflammatory factors IL‐6, TNF‐α, and IL‐1β in serum. *p < 0.05, **p < 0.01, ***p < 0.001. BMD, Bone mineral density; BV, bone volume; TV, tissue volume; Tb.Th, trabecular thickness; Tb.N, trabecular number; Tb.Sp, trabecular separation; TRAP, tartrate‐resistant acid phosphatase.

3.3. NSPT and Metformin Modulate the Gut Microbiota in HPD Mice

Considering the significant role of the gut‐bone signaling system in bone health, we investigated the interaction between gut microbiota and bone. 16S rRNA gene sequencing analysis indicated that HPD and HLR mice exhibited a markedly altered microbial community compared to HFD mice, characterized by a dramatic upregulation of Firmicutes and Actinobacteria, along with a downregulation of Proteobacteria at the phylum level (Figure 4A,B). Supplementation with metformin strikingly normalized the structure of intestinal flora at the levels of phylum and genus (Figure 4A,B). Notably, Lactobacillus and Akkermansia, well‐known probiotic genera, were significantly decreased in the HPD group; however, they were restored by NSPT, particularly in the HLM group (Figure 4B). Principal component analysis (PCA) based on Bray‐Curtis dissimilarities suggested that HFD and HLM mice shared a similar gut microbiota structure, clustering separately from that of HFD and HPD mice (Figure 4C). Additionally, Erysipelotrichales and Allobaculum were the most significantly abundant bacteria in the HPD group compared to those in the HFD and HLM groups, according to the LEfSe results (Figure 4D). At the genus level, random forest analysis indicated that Allobaculum, Sutterella, Blautia, Arthrobacter, and Corynebacterium were markedly increased in the HPD group, which can be reversed following combined therapy (Figure 4E). In contrast, Faecalibacterium prausnitzii , Akkermansia muciniphila , Lactobacillus reuteri , Butyricicoccus pullicaecorum , and Roseburia faecis were dramatically depleted in HPD mice, while these species were replenished in HLM mice (Figure 4F). Finally, PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) was employed to predict functional pathways of microbial communities in the cecal contents. As shown in Figure 3G, the functional pathways of altered bacteria primarily pertain to metabolic‐related processes, encompassing xenobiotic biodegradation and metabolism, carbohydrate metabolism, amino acid metabolism, and lipid metabolism. These findings indicated that metformin and NSPT can effectively ameliorate the gut microbiota dysbiosis induced by periodontitis in obese conditions.

FIGURE 4.

FIGURE 4

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NSPT and metformin modulate the gut microbiota in HPD mice. Relative abundances of gut bacteria at the (A) phylum and (B) genus levels across the four groups. (C) β‐diversity index analysis based on Bray–Curtis dissimilarities. (D) Analysis of the relative abundance of taxa among the HFD, HPD, and HLM groups using LEfSe analysis. LDA ≥ 2.0 and p ≤ 0.05 are considered significant. Random forest analysis showing the importance score for the relative abundance of (E) genera and (F) species among the four groups. (G) Functional prediction of microbial communities following PICRUSt2 in the four groups. *p < 0.05, **p < 0.01. LEfSe, linear discriminant analysis effect size; PICRUSt2, phylogenetic investigation of communities by reconstruction of unobserved states.

3.4. NSPT and Metformin Regulate Gut Metabolism in HPD Mice

To investigate the relevance of microbial metabolism to bone loss in periodontitis and obesity, we performed liquid chromatography‐mass spectrometry (LC–MS) on cecal content samples. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS‐DA) were used to evaluate similarities and differences across the four groups. As shown in Figure 5A, significant differences in intestinal metabolites were observed in the HFD, HPD, and HLR groups, while the HLM group shared a similar metabolic composition to the HFD group, indicating that metabolic disorders could be ameliorated by metformin and NSPT in periodontitis and obesity. Compared to the HFD group, 75 metabolites were downregulated in the HPD group, while 44 metabolites were upregulated (Figure 5B). Additionally, 73 different metabolites were identified between the HPD and HLR groups, with 45 metabolites downregulated and 28 metabolites upregulated (Figure 5B). Metformin and NSPT downregulated 67 metabolites and upregulated 36 metabolites in the HPD mice (Figure 5B). KEGG enrichment analyses of the differential metabolites for the HPD and HFD groups, as well as for the HPD and HLM groups, revealed that linoleic acid metabolism was the prominent pathway (Figure 5C). Furthermore, the heatmap indicated a significant inhibition of linoleic acid metabolites in the HPD group compared to the HFD and HLM groups, except that PC(15:0/22:2(13Z,16Z)), PC(20:4(5Z,8Z,11Z,14Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), and PC(18:2(9Z,12Z)/22:5(4Z,7Z,10Z,13Z,16Z)) were increased by periodontitis in obesity (Figure 5D).

FIGURE 5.

FIGURE 5

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NSPT and metformin regulate gut metabolism in HPD mice. (A) PCA score plot in a negative model, and PLS‐DA score plot in a positive model. (B) Statistics for differently expressed metabolites in the HPD vs. HFD, HPD vs. HLR, HPD vs. HLM, and HFD vs. HLM groups. (C) Bubble diagram of the KEGG enrichment analysis based on DEGs between the HPD and HFD groups and between the HPD and HLM groups, respectively. (D) Heatmaps with a color gradient from red to blue indicating the relative abundance of derivatives between the HPD and HFD groups and between the HPD and HLM groups, respectively. PCA, Principal component analysis; PLS‐DA, Partial least squares discriminant analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.

3.5. NSPT and Metformin Regulate Serum Metabolism in HPD Mice

Alternatively, we further examined serum metabolomics using LC–MS to identify key metabolites that affect bone homeostasis. The results of the PCA and PLS‐DA showed that the metabolic profiling in the HPD and HLR groups was similar, clustering separately from that in the HFD and HLR groups, indicating that the combined therapy could also normalize the serum metabolism in HPD mice (Figure 6A). Based on the significantly different metabolites analysis, we found that 60 metabolites were downregulated in the HPD group, while 42 metabolites were upregulated in the HPD group compared to the HFD group (Figure 6B). Additionally, 18 metabolites were upregulated while 53 metabolites were downregulated after combined therapy in the HPD (Figure 6B). Enrichment analysis of these metabolites indicated that they were mainly enriched in choline metabolism in cancer, glycosylphosphatidylinositol‐anchor biosynthesis, and linoleic acid metabolism (Figure 6C). Consistent with the gut metabolite results, the heatmap indicated that most linoleic acid metabolites, except for PC(15:0/22:2(13Z,16Z)), PC(20:4(5Z,8Z,11Z,14Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), and PC(18:2(9Z,12Z)/22:5(4Z,7Z,10Z,13Z,16Z)), exhibited a decreasing tendency in the HPD group compared to the HFD group. This reduction can be recovered through NSPT and metformin (Figure 6D).

FIGURE 6.

FIGURE 6

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NSPT and metformin regulate serum metabolism in HPD mice. (A) PCA scores plot in a negative model, and PLS‐DA scores plot in a positive model. (B) Statistics for differently expressed metabolites in the HPD vs. HFD, HPD vs. HLR, HPD vs. HLM, and HFD vs. HLM groups. (C) Bubble diagram of the KEGG enrichment analysis based on DEGs between the HPD and HFD groups, as well as between the HPD and HLM groups, respectively. (D) Heatmaps with a color gradient from red to blue indicating the relative abundance of derivatives between the HPD and HFD groups and between the HPD and HLM groups, respectively. PCA, Principal component analysis; PLS‐DA, Partial least squares discriminant analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Linoleic acid metabolism is enriched in both the intestine and serum. Therefore, Pearson correlation analysis was performed to identify the relationship between linoleic acid metabolites and femoral and alveolar bone resorption with a threshold of p < 0.05 and a coefficient greater than 0.6. The findings in the gut and serum consistently suggested that three linoleic metabolites that were increased due to periodontitis—PC(15:0/22:2(13Z,16Z)), PC(20:4(5Z,8Z,11Z,14Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), and PC(18:2(9Z,12Z)/22:5(4Z,7Z,10Z,13Z,16Z))—were negatively correlated with BMD, BV/TV, Tb.N, and Tb.Th, while showing a positive correlation with alveolar bone loss and Tb.Sp in the femur (Figure 7A,B). In contrast, other linoleic metabolites that were replenished after NSPT and metformin therapy were positively correlated with increased bone mass and were negatively correlated with bone resorption in the femur and maxillae, indicating that linoleic acid metabolism may be the core pathway in the protective effects of NSPT and metformin on skeletal bone health (Figure 7A,B).

FIGURE 7.

FIGURE 7

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Pearson correlation analysis of bone parameters and linoleic acid metabolites in the (A) cecal content and (B) serum, with a threshold of p < 0.05 and a coefficient greater than 0.6 for the HPD and HFD groups, as well as for the HPD and HLM groups. F, femur; M, maxilla.

4. Discussion

Obesity and periodontitis have a synergistic effect on systemic inflammation, predisposing affected individuals to the challenges of maintaining desired clinical improvements, especially in cases of severe periodontitis [25]. Previous research has suggested that obesity can influence the outcome of NSPT. However, the results are inconsistent. A study by Toy et al. indicated that obese individuals exhibited higher levels of adipocytokines and oxidative stress biomarkers than normal‐weight patients three months after therapy, which might lead to the recurrence of periodontal disease [26]. On the other hand, a recent systematic review and meta‐analysis concluded that after three months of NSPT, the serum level of IL‐6 decreased in individuals with periodontitis and obesity [27]. In our study, we observed a significant reduction in serum total cholesterol, mean alveolar bone loss, and periodontal inflammation after NSPT in obese mice. Notably, the combined therapy of metformin and NSPT remarkably improved dyslipidemia and alveolar bone resorption in HPD mice, although the weight loss was insignificant, which could be explained by the duration of the experiment and the basal body weight of the mice.

Epidemiological studies have revealed that obesity is linked to reduced bone mass and osteoporosis [28]. With the rising prevalence of multimorbidity, the interrelationship among obesity, periodontitis, and osteoporosis has garnered increasing attention, particularly among the elderly. However, to date, few studies have evaluated the effect of NSPT on systemic bone loss under obese conditions. Bone remodeling is a dynamic biological process that encompasses both bone formation and resorption, orchestrated by osteoblasts and osteoclasts [29]. In obesity, hypertrophic or hyperplastic white adipocytes can recruit various inflammatory cells that release proinflammatory cytokines and chemokines, triggering insulin resistance, endothelial dysfunction, and meta‐inflammation [30]. TNF‐α and IL‐6 are classical inflammatory mediators that have a bidirectional effect on systemic bone homeostasis and participate in the pathogenesis of osteoporosis through direct and indirect pathways [31]. Murine studies have shown that TNF‐α is a maturation/activation stimulus for osteoclast precursors in rheumatoid arthritis [32]. Mice overexpressing IL‐6 exhibit increased osteoclasts and asymmetric bone resorption activity in the trabeculae [33]. Similarly, our results indicated that periodontitis exacerbated osteoporosis, osteoclastic activity, and systemic inflammation in HFD‐fed mice. A notable reduction in plasma TNF‐α and IL‐6 levels, osteoclastic activity, and femoral bone loss was observed in HPD mice following NSPT and metformin therapy. It should be noted that NSPT alone is insufficient to ameliorate periodontitis‐induced systemic bone loss in the context of obesity, likely due to the limited impact of NSPT on periodontitis and the persistent presence of systemic low‐grade inflammation.

Long‐term maintenance is critical for achieving the desired outcome of periodontal treatment. Several non‐surgical adjunctive therapies, including dietary therapy, exercise, weight loss, and antibiotics, have been proposed to enhance the response to NSPT in individuals with obesity [34, 35]. However, their effects are limited and largely depend on patient compliance. Metformin is a cost‐effective, safe, and well‐tolerated medication [36]. Previous research has indicated that the local application of metformin significantly improves periodontal clinical status [37]. Only one study has shown that systemic metformin significantly enhances blood glucose metabolism and reduces alveolar bone loss in a periodontitis animal model and a pilot trial [38]. However, the molecular mechanism is still unclear. Periodontitis is a polymicrobial oral disease that can alter the gut microbiota via the “oral‐gut” axis [39]. Alternatively, periodontitis could exacerbate gut inflammation by supplying the gut with colitogenic pathobionts [40]. Our results indicated that, compared to HFD mice, the Actinobacteria and Firmicutes taxa were more prominent in HPD mice; a similar shift was observed in obese individuals compared to their lean twin siblings, indicating that periodontitis may exacerbate gut microbiota disorders in obesity [41]. In contrast, periodontitis decreased the relative abundance of Proteobacteria in obese mice, which seems contradictory to previous findings in obesity and requires further investigation [42].

Non‐surgical periodontal therapy is considered an effective protocol for reducing the oral bacterial load, periodontal inflammation, and the translocation of periodontal pathobionts into the gut [43, 44]. Metformin has been shown to alter the composition of the gut microbiota under obese conditions in both preclinical and clinical studies [45]. Therefore, it is plausible that periodontitis‐induced gut microbiota dysbiosis could be reversed by the combined use of the two in obesity. In the present study, we found that the combination treatment reduced gut microbiota dysbiosis induced by periodontitis, promoting the growth of Akkermansia muciniphila , Lactobacillus reuteri , Butyricicoccus pullicaecorum , and Roseburia faecis species with anti‐inflammatory properties, consistent with previous findings [46, 47, 48]. Similarly, Zhu et al. found that metformin administration increased the proportion of Akkermansia muciniphila and decreased the inflammatory response in aged mice [49]. The beneficial effect of Lactobacillus on bone health has been reported previously [50]. Therefore, we speculated that the role of metformin in restructuring intestinal flora may help alleviate osteoporosis via the gut‐bone axis. However, the causal role of gut microbiota should be confirmed by further investigation, such as fecal microbiota transplantation and antibiotic treatment.

Metabolomics is an approach to evaluate the dynamic metabolic responses of living systems to pathophysiological stimuli. Linoleic acid (LA), which is closely involved in the amelioration of inflammation and osteoclastic activity, is recognized as an essential fatty acid and is the most abundant ω‐6 polyunsaturated fatty acid in the Western diet [51]. Previous research suggests that LA‐rich sunflower seed oil significantly alleviates bilirubin neurotoxicity in offspring mice [52]. By suppressing Th17 differentiation while promoting Treg cell differentiation, LA has been found to act as a ligand for the aryl hydrocarbon receptor (AHR) in alleviating colitis [53]. Furthermore, LA could strengthen the intestinal epithelial barrier and reduce pathogen translocation by regulating the NF‐κB/MLCK pathway in a GPR40‐dependent manner, which may be attributed to the alleviation of systemic inflammation observed in our model [54]. On the other hand, conjugated LA has been shown to enhance alkaline phosphatase activity and prevent bone loss by regulating the RANKL/RANK/OPG pathway [55]. Zhang et al. also found that LA‐rich seed oil could significantly suppress the expression of NF‐κB and subsequent RANKL‐induced osteoclastogenesis in vitro [56]. Multiple intestinal microorganisms are capable of metabolizing LA [57]. Notably, Akkermansia muciniphila has been reported to replenish intestinal LA levels [54]. Lactobacillus reuteri is another bacterium participating in LA production [58]. In this study, we observed a dramatic reduction in the intestinal and plasma LA levels of HPD mice, which can be restored with NSPT and metformin intervention. Therefore, we speculate that the reshaped gut microbiome and the subsequent LA metabolites may mediate protective effects. Further studies are warranted to investigate how LA affects skeletal health under obese conditions, such as through in vitro experiments and RNA sequencing.

5. Conclusion

NSPT and metformin effectively enhance treatment for periodontitis and systemic bone loss by modulating gut microbiota, recovering linoleic acid metabolism, and ameliorating systemic inflammation. With the limitations of our study, metformin appears to be a novel therapeutic adjunct to NSPT for patients with periodontitis and obesity. This study provides valuable insights into how obesity, periodontitis, and osteoporosis interconnect, highlighting the necessity for a comprehensive and personalized approach to manage individuals with morbidity.

Author Contributions

Rixin Chen conceived and conducted the research, collected the data, and wrote the manuscript. Lili Li conceived, analyzed, and interpreted the data. Wei Wei, Miaomiao Zhang, Nannan Wang, Ruiyang Ge, Yue Shen, Wen Zhang, Daiyv Lu, and Wenzheng Liao assisted with the animal experiments. Yanfen Li and Fuhua Yan conceived, designed, funded the research, and revised the manuscript. All authors have read and approved this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [No. 82270979]; the Jiangsu Province Key Research and Development Program [No. BE2022670]; and the Jiangsu Provincial Medical Key Discipline Cultivation Unit [No. JSDW202246].

Chen R., Wei W., Li L., et al., “Non‐Surgical Periodontal Therapy and Metformin Improve Bone Loss in Obese Mice With Periodontitis by Modulating the Gut Microbiota,” The FASEB Journal 39, no. 13 (2025): e70814, 10.1096/fj.202501689R.

Funding: This work was supported by National Natural Science Foundation of China (Grant 82270979), Jiangsu Province Key Research and Development Program (Grant BE2022670), Jiangsu Provincial Medical Key Discipline Cultivation Unit (Grant JSDW202246).

Rixin Chen and Wei Wei equally contribute to this work.

Contributor Information

Yanfen Li, Email: liyanfen2003@126.com.

Fuhua Yan, Email: yanfh@nju.edu.cn.

Data Availability Statement

The 16S rRNA gene sequencing data have been deposited in the GenBank Sequence Read Archive database at https://www.ncbi.nlm.nih.gov/sra under accession number PRJNA1258240.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The 16S rRNA gene sequencing data have been deposited in the GenBank Sequence Read Archive database at https://www.ncbi.nlm.nih.gov/sra under accession number PRJNA1258240.

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