Structural difference analysis of gut microbiota in different bone mass populations and relevant probiotic (lactobacilli) supplementation strategies

Abstract

Disruption of the bone metabolic balance with advancing age leads to an escalating prevalence of bone‐related diseases, significantly compromising individuals’ quality of life. The gut microbiota actively participates in the regulation of bone metabolism, and perturbations in the gut microbiota can exacerbate bone diseases by compromising gut barrier integrity. Determining the microbial taxa involved in bone loss could offer valuable insights into the development of alternative therapies and nutritional interventions for disease management. Therefore, based on metagenomic and 16S ribosomal RNA data, this study analyzed the gut microbiota structure of 488 individuals with different bone masses (NC, normal; ON, osteopenia; OP, osteoporosis) to identify significant associations between the gut microbiota and bone loss. The results showed that at the genus and species levels, the microbiota diversity of the ON population increased, whereas that of the OP population decreased. Bacteroides were significantly enriched in the OP population, whereas the beneficial bacteria Bifidobacterium, Akkermansia, and lactobacilli decreased. Subsequent analyses revealed no significant variation in different bone populations in terms of Bifidobacterium levels, whereas lactobacilli exhibited diverse responses across distinct bone populations. The administration of lactobacilli effectively enhanced lumbar spine bone mineral density and modulated the gut microbiota structure in a population with unhealthy bone mass. This study contributes to the validation of the association between the gut microbiota and bone mass, enhances our understanding of the potential impact of probiotics (lactobacilli) on bone mass, and establishes a robust scientific basis for the application of probiotics in the regulation of bone mass.

INTRODUCTION

Bone is a highly organized tissue consisting of intricately structured, rigid, honeycomb-like, three-dimensional formations that provide support and safeguard various organs within the human body. The skeletal system undergoes continuous remodeling and regeneration throughout an individual's lifespan, facilitated by the resorption of old bone by osteoclasts and the synthesis of new bone by osteoblasts (Wawrzyniak & Balawender, 2022). As individuals age, their bone regenerative capacity diminishes, rendering them more susceptible to injury. The dysregulation of osteoblastogenesis and osteoclastogenesis may contribute to the onset of bone disorders (Chen et al., 2018; Kim et al., 2020). Osteoporosis (OP) is a prevalent bone disease, particularly among middle-aged and older adult women, and is characterized by an increased risk of fracture, loss of bone trabeculae, and significant bone mass reduction (Walker & Shane, 2023). The disease also involves bone microstructure degradation and heightened fragility (Chen et al., 2010; Wang et al., 2023). OP is a significant public health challenge worldwide. Epidemiological surveys have indicated that the prevalence of OP in China is progressively increasing. It is anticipated that, by 2050, the prevalence of OP among individuals aged 50 years and older will reach approximately 150 million, with one in every three postmenopausal women over the age of 50 facing osteoporotic challenges. Therefore, risk assessment and early intervention for OP are necessary.

In recent years, extensive research on gut microbiota has revealed a strong correlation between bone loss and both host immunity and gut microbes, highlighting the active role of the gut microbiota in regulating bone metabolism (Guan et al., 2023; Villa et al., 2017). The gut microbiota modulate bone remodeling by altering the immune status of the bone in mice (Li et al., 2016; Sjogren et al., 2012). The absence of the gut microbiota results in alterations in mouse bones. Disorders of the gut microbiota exacerbate bone disease through gut barrier impairment, and these changes can be reversed and bone mass restored by reestablishing a healthy gut microbiota (i.e., transplanting normal [NC] microbiota into an animal with a disturbed microbiota) (Wang et al., 2022). Furthermore, gut microbiota can exert a direct or indirect influence on bone metabolism by modulating energy absorption through specific metabolites (Yan et al., 2018; Zhang et al., 2024). Determining the microbial taxa involved in bone loss could offer valuable insights into the development of alternative therapies and nutritional interventions for disease management. Supplementation with probiotics or prebiotics modulates bone metabolism by altering the gut microbiota composition, thereby exerting a profound effect on adjunctive therapy for postmenopausal OP resulting from estrogen decline (Jansson et al., 2019; Jafarnejad et al., 2017; Lambert et al., 2017). The preferred genus of bacteria showing these beneficial effects in bone is lactobacilli, which is also one of the best candidates for clinical intervention trials (Villa et al., 2017). In animal models, lactobacilli have the potential to mitigate gut and bone inflammation, ameliorate gut permeability, prevent bone loss, and alleviate OP (Guo et al., 2023; Lee & Kim, 2020; Yu et al., 2021). After the administration of Limosilactobacillus reuteri NCIMB 30242, healthy subjects exhibited a significant increase in serum vitamin D levels and a notable improvement in calcium absorption function (Jones et al., 2013). Continuous supplementation of L. reuteri ATCC PTA 6475 for 1 year proves beneficial in rectifying gut microbiota disturbance and reversing the deterioration of gut inflammatory status among older women with low bone mineral density (BMD), thereby exerting a positive impact on bone metabolism (Li et al., 2022). Simultaneously, both L. reuteri ATCC PTA 6475 and Lacticaseibacillus rhamnosus GG demonstrated efficacy in mitigating trabecular bone loss induced by glucocorticoid-induced gut microbiota dysbiosis and barrier dysfunction in osteoporotic mice (Schepper et al., 2019). Consequently, incorporating lactobacilli into daily dietary intake may serve as a viable strategy for preventing or ameliorating OP.

Despite numerous experimental and observational studies confirming the association between alterations in gut microecology and the pathophysiology of bone loss, there remains a limited understanding of the specific gut microbial dysfunctions that serve as key factors contributing to both BMD decline and associated inflammatory responses. The effect of probiotic supplementation on OP remains a topic of ongoing debate, and there is a lack of consensus among researchers. Therefore, this study aimed to investigate diverse bone mass populations (NC; osteopenia [ON]; OP) by using metagenomic and 16S ribosomal RNA (rRNA) data to identify crucial targets concealed within the intricate interplay between gut microecology and bone loss. Additionally, the protective effects of varying types and quantities of bacteria during bone loss were explored, with the ultimate goal of providing a theoretical foundation for probiotics (lactobacilli) as potential alleviators of OP.

METHODS

Data acquisition and inclusion criteria

The gut microbiome meta-analysis incorporated 16S rRNA gene and metagenome datasets sourced from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) and PubMed databases. The search was conducted using keywords, such as “16S,” “microbiome,” or “metagenome,” combined with any of these phrases: “osteoporosis,” “osteopenia,” “osteoarthrosis,” “bone fragility,” or “bone density.” Only original articles indexed in January 2024 were selected.

The literature utilized in the analysis of lactobacilli regulation of bone health in middle-aged and older adult individuals was retrieved from reputable databases, including PubMed, Web of Science, and ScienceDirect. A comprehensive search strategy incorporating keywords, such as “probiotics,” “probiotic supplements,” or “lactobacilli” alongside relevant terms like “osteoporosis,” “osteopenia,” “bone,” and “bone density” was employed. Dual-energy X-ray absorptiometry (DXA) measurements of BMD were considered the primary outcomes, whereas changes in the gut microbiota served as the secondary outcomes. Only original articles indexed in January 2024 were selected.

Data source and extraction

The metadata and raw sequence data were obtained from the European Nucleotide Archive database and the SRA database, respectively. In cases where data were unavailable, the corresponding samples were excluded from the analysis. The selected studies included demographic information and research methodologies, such as data type, geographical location, sample size, and original database sources. Prior to downstream analysis, the samples were categorized into distinct bone mass populations based on available information or T-score calculations. The T-score was calculated as follows: T-score = (measured value − peak bone value of NC adults of the same sex and race)/peak bone value of NC adults.

Data processing

QIIME2 version 2022-2 was utilized to process the raw data obtained from 16S rRNA gene sequencing. The SRA toolkit tool was employed to acquire raw sequencing SRA files from NCBI, which were then imported into qiime2 as FASTQ sequences. To ensure quality control, DADA2 was used to eliminate phiX and chimeric sequences detected in the sequencing data (Callahan et al., 2016). Each dataset was iteratively pruned against a mass interaction plot until the first position was reached, when the median mass score fell below 20. If the length of the trimmed reads was less than 100 bp, they were trimmed based on the parameters outlined in the original literature. Subsequently, we compared the resulting representative sequences with the Greengenes 13.8 database to annotate the species (Bokulich et al., 2018; Bolyen et al., 2019). The amplicon sequence variants (ASVs) of each dataset were analyzed following the method proposed by Yin and Chen (Chen et al., 2023; Yin et al., 2023). To determine the proportionate occurrence of ASVs in each sample, we aggregated the identified ASVs from every sample and divided them by the overall count of ASVs in that particular sample (Akinsuyi & Roesch, 2023; Yin et al., 2023).

The original metagenomic sequencing data were processed according to Yin et al. (2023). Low-quality sequences were removed using Trimmomatic (V.0.39), host contamination was eliminated using the Bowtie2 tool integrated into KneadData (V.2.3.4.1), and the quality of the processed metagenome was assessed using the FastQC toolkit (V.0.11.9). MetaPhlAn software (V.3.0.13) was used for the classification and quantification of the relative bioabundance of all metagenomic samples.

Statistical analysis

Statistical analyses were performed using GraphPad Prism (version 9.5) and R software (version R 3.4). Relevant analysis was conducted using principal coordinates analysis (PCoA) on the distance matrix, employing the Bray–Curtis dissimilarity calculation. Differential strains of populations with varying bone masses were mapped using the mapping capabilities provided by the Aggregated Top Taxa and Microbiome packages. Linear discriminant analysis effect size (LEfSe) was used to assess differences in the relative abundance of microbial features. The nonparametric Kruskal–Wallis test was used for the univariate analysis of the groups. The Wilcoxon test was used for pairwise comparisons between two groups. p Values greater than.05, were not considered statistically significant unless otherwise specified: *p < .05, **p < .01, ***p < .001, ****p < .0001.

RESULTS

Study selection and characteristics

A total of 2786 studies were identified using our search methodology. After a thorough review and elimination of duplicates, we selected 1831 potentially relevant articles for further examination. After an in-depth analysis of the complete texts, we excluded 143 unrelated articles, 289 animal studies, 415 reviews or meta-analyses, and 614 books and related literature. Additionally, we eliminated 20 instances of duplicate data to analyze a total of 350 articles. The exclusion criteria are shown in Figure 1. Of the 350 studies, 305 studies within the database lacked details regarding bone mass and gut microbiota. Eventually, eight articles (comprising nine population cohorts) satisfied the eligibility requirements and were included in the final meta-analytical investigation. For the remaining 24 studies without raw sequencing data, diligent efforts were made to establish contact with both the primary and corresponding authors through e-mail and telephone communication; however, no responses were received. Fecal sequencing results were obtained from nine studies, and the 16S gene sequences and metagenomic data were annotated with species information. The details and accompanying information for each study are presented in Table 1 and Table S1. Published data were included in our meta-analysis because of their clear bone information and access to gut microbiota metadata. Among the included studies, four datasets utilized 16S rRNA sequencing, and five datasets used metagenomic analysis. In total, 488 participants were included in this meta-analysis: 146 in the NC group, 149 in the ON group, and 193 in the OP group. Genomic data were obtained from the metagenomic analysis of a subset of 289 individuals, including 88 NC, 82 ON, and 119 OP.

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TABLE 1 Summary included in the meta-analysis of gut microbiota.

Data type	Reference	Geography	Total sample size	NC group	ON group	OP group	BioProject
Metagenome	Wang et al. (2022)	Xi'an, China	57	18	21	18	PRJNA565546
Metagenome	Rettedal et al. (2021)	Palmerston North, New Zealand	86	26	42	18	PRJNA672125
Metagenome	Zhao et al. (2022)	Inner Mongolia, China	28	0	0	28	PRJNA773596
Metagenome	Li et al. (2022)	Gothenburg, Sweden	19	0	19	0	PRJEB52923
Metagenome	–	Shanghai, China	99	44	0	55	PRJNA946183
16S	Wang et al. (2022)	Xi’ an, China	113	28	51	34	PRJNA565497
16S	Wang et al. (2017)	Xi’ an, China	18	6	6	6	SRP095870
16S	Wang et al. (2021)	Wenzhou, China	42	18	0	24	PRJNA631117
16S	Liang et al. (2023)	Northwest, China	26	6	10	10	PRJNA916764

Analysis of gut microbiota diversity and differential bacteria in bone loss population

To investigate the hypothesis that the species composition of the human gut microbiota is associated with bone mass, we annotated 16S rRNA and metagenomic data at the species level. Based on the gene species annotation results, 488 samples were analyzed at the phylum-to-genus level, whereas 289 metagenomic samples were analyzed specifically at the species level. Shannon and Simpson diversity indices revealed that, compared with that of the NC group, the gut microbiota diversity in the ON group decreased from the phylum level to the order level but increased at the genus level and species level (Figure 2a and Figures S1 and S2). The OP group showed a significant decrease compared with the NC group at the genus level, but there was no significant difference at the other levels. PCoA analysis demonstrated significant alterations in the gut microbiota composition in the ON group at both the genus and species levels when compared with that of the NC group (Figure 2b), whereas the OP group tended toward a newly established stability in their gut microbiota.

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To further investigate variations in the gut microbiota composition across different populations, we conducted a qualitative analysis to identify distinct categories at the phylum, genus, and species levels. At the phylum level (Figure 3a), the ON group exhibited a high abundance of Firmicutes and a low abundance of Bacteroidetes. Moreover, the abundance of Proteobacteria and Verrucomicrobia was low in the NC group. Conversely, the OP group exhibited a high abundance of Bacteroidetes, whereas no significant differences were observed for the other microbes compared with those in the NC group. However, it should be noted that variations at the phylum level may not fully reflect the overall trends across different bone populations. To gain a more comprehensive understanding, further analysis is required to assess the relative abundance differences at the genus and species levels. At the genus level (Figure 3b), Blautia, Faecalibacterium, Ruminococcus, Roseburia, Coprococcus, and Streptococcus were most abundant in the ON group, whereas Phocaeicola, Mediterraneibacter, Akkermansia, and Phascolarctobacterium showed decreasing trends. Compared with those in the NC group, Bacteroides and Phocaeicola were enriched in the OP group; however, beneficial bacteria, such as Bifidobacterium, Akkermansia, Dorea, and Collinsella, were reduced. The species composition in the three groups exhibited similar patterns at the genus level, with significant differences observed at the species level (Figure 3c). Compared with those in the NC group, Bacteroides xylanisolvens, Eubacterium rectale, Clostridia bacterium, Fusicatenibacter saccharivorans, Coprococcus eutactus, Phocaeicola dorei, and Blautia wexlerae showed increased abundance in the ON group, whereas Bifidobacterium adolescentis, Lactobacillus mucosae, and Akkermansia muciniphila were less abundant in the ON group. In addition, the abundances of B. xylanisolvens, Bacteroides thetaiotaomicron, Phocaeicola plebeius, and Megamonas funiformis significantly increased in the OP group, whereas C. bacterium, Ruminococcus bromii, Dorea longicatena, and A. muciniphila significantly decreased.

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Analysis of changes in characteristic gut bacteria of people with bone loss

By comparing the changes in the number of species and relative abundance of strains across different bone populations, we can investigate the potential correlations between microbial composition and bone health. Linear discriminant analysis combined with LEfSe was employed to perform differential abundance tests to identify characteristic microbial taxa associated with OP (Figure 4). Consistent with the aforementioned diversity results, both ON and NC groups exhibited more pronounced alterations in the microbiota than the OP group did. The characteristics of the gut microbiota observed in this population may serve as predictive indicators of OP risk.

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Beneficial bacteria play pivotal roles in the gastrointestinal tract by facilitating food digestion, synthesizing essential vitamins, and protecting against pathogenic microorganisms. In the context of bone loss, there has been a gradual decline in beneficial gut bacterial populations. Therefore, this study focused on investigating the alterations specifically related to lactobacilli and Bifidobacterium in the gut microbiota of individuals with bone loss. Based on the analysis of 289 cases of metagenomics data, as depicted in Figure 5a, there was a significant reduction in the relative abundance of Bifidobacterium in the OP group, whereas lactobacilli exhibited a significant decrease in the ON group. The diversity of lactobacilli and Bifidobacterium within the gut tracts of different bone populations was compared based on their biological classifications. As illustrated in Figure 5b,c, there was a substantial variation in the diversity of lactobacilli among the different groups; however, no significant difference was found for Bifidobacterium (p > .05). Subsequently, multiple logistic regression analyses and ternary analyses were performed at specific species levels for Bifidobacterium and lactobacilli. According to the model fitting information, both Bifidobacterium and lactobacilli exhibited statistically significant results (p < .05). Specifically, Bifidobacterium longum showed a significant negative correlation with the severity of bone damage (Table S2). As shown in Figure 5d, variations of similarity were observed for different species of Bifidobacterium within the ternary graph, whereas lactobacilli displayed varying patterns across different bone populations. Compared to the NC group, there was a notable reduction in the abundance of B. adolescentis in the ON group. In the OP group, there was a significant decrease observed in the abundance of B. longum. For lactobacilli, Lactobacillus amylovorus, Lactobacillus crispatus, Limosilactobacillus fermentum, and Lactiplantibacillus plantarum were found to be enriched in the NC group. Lactobacillus acidophilus exhibited a tendency toward the OP group. Ligilactobacillus salivarius was significantly less abundant in the ON group, whereas Ligilactobacillus ruminis showed reduced presence in the OP group.

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Strategy analysis of supplementing probiotics to alleviate bone loss

To maintain a healthy gut microecological balance, it is imperative to increase the abundance of beneficial bacteria in the gut in response to bone loss. As demonstrated in the preceding analysis, lactobacilli exhibit a significant variation among individuals with varying bone masses. Therefore, we aimed to further investigate the effect of exogenous supplementation with different Lactobacillus species on enhancing bone mass. Through a meticulous conditional search, we identified four studies that focused on the improvement of BMD through lactobacilli supplementation. Details are presented in Table 2. Among these studies, three were randomized and double-blind trials involving a total enrollment of 365 individuals with ON who were divided into placebo and probiotic groups. All three studies provided lumbar and total hip DXA data for BMD assessment. Another test was used only for the pre- and post-control microbiota analyses. Each trial specified the type of probiotic supplement used along with detailed dosage information.

TABLE 2 Summary of probiotic back-up analyses included in the study.

Reference	Geography	Probiotic species	Queue list	Probiotic group	Placebo group	Intervention time	Data usage
Nilsson et al. (2018)	Sweden	Limosilactobacillus reuteri 6475, 1 × 1010 CFU per day	−2.5 < T-score ≤−1, Female	32	36	12 months	Lumbar BMD, spine hip BMD
Jansson et al. (2019)	Sweden	Lacticaseibacillus paracasei DSM 13434, Lactiplantibacillus plantarum DSM 15312, L. plantarum DSM 15313;1 × 1010 CFU per capsule per day	In healthy women in the early postmenopausal stages, −2.5 < T-score	116	116	12 months	Lumbar BMD, spine hip BMD
Jafarnejad et al. (2017)	Iran	Lacticaseibacillus casei 1.3 × 1010 CFU, Bifidobacterium longum 5 × 1010 CFU, Lactobacillus acidophilus 1.5 × 1010 CFU, Lacticaseibacillus rhamnosus 3.5 × 109 CFU, Lactobacillus bulgaricus 2.5 × 108 CFU, Bifidobacterium breve 1 × 1010 CFU, and Streptococcus thermophilus 1.5 × 108 CFU per 500 mg per day	Patients aged 50–72 years with osteopenia	20	21	6 months	Lumbar BMD, spine hip BMD
Li et al. (2022)	Sweden	L. reuteri 6475, 1 × 1010 CFU per day	−2.5 < T-score ≤−1, female	19	/	12 months	Gut microbiota (PRJEB52923)

This study examined the rates of BMD growth before and after administration in the probiotic and placebo groups, as depicted in Figure 6a. Lumbar BMD levels in the probiotic supplement group were higher than those in the placebo group, with different probiotic supplements exhibiting heterogeneity in lumbar BMD (p = .04). No significant improvement was observed in the overall hip BMD growth with either probiotics or placebo (Figure 6b). Subsequently, we conducted further analysis of the impact of probiotic supplementation on the gut microbiota structure. As illustrated in Figure 6c, supplementation with L. reuteri led to a tendency for the osteopenic population's gut microbiota to resemble that of the NC group at both phylum level (p = 1, R = −.2148), genus level (p = 1, R = −.2229), and species level (p = 1, R = −.1948). These findings indicate that daily lactobacilli supplementation for 6–12 months enhances bone mass and promotes healthy development of the gut microbiota.

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DISCUSSION

Bone loss is a significant health concern in older adults, particularly postmenopausal women. Severe bone loss can lead to various bone disorders, including OP, fractures, and structural deformities, thereby impacting individuals’ daily lives and exacerbating financial burdens on families, as well as posing potential threats to safety. Consequently, the early detection and prevention of this condition are imperative.

The interaction between the gut microbiota and host exerts a positive influence on maintaining NC skeletal metabolic pathways in the host. The stability of the gut microbiota plays a crucial role in influencing the bone metabolism equilibrium. An imbalance in the microbiome impairs the gut immune response and triggers osteoclast factor production, leading to diseases, such as OP (Liao et al., 2021; Zhang et al., 2021). During the onset and progression of OP, there is a significant disruption in the composition and metabolic function of the gut microbiota (Chen et al., 2022). Yang et al. (2022) demonstrated a significant reduction in Collinsella and Romboutsia, which are associated with the production of short-chain fatty acids, in the gut microbiota of postmenopausal women with OP in Henan Province, China. The study conducted by Li et al. (2019) revealed that Bifidobacterium and Lactobacillus were positively correlated with BMD and T-scores in Wuhan, Hubei, China, whereas Bacteroides was negatively correlated with BMD. Conversely, in Ireland, Lactobacillus was most abundant in the gut microbiota of patients with OP (Das et al., 2019). The abundance of Prevotellaceae and Prevotella in the gut microbiota of middle-aged women with low BMD was significantly reduced in the Mexican population (Palacios-Gonzalez et al., 2020). In Japan, individuals with low Bacteroides exhibit a high prevalence of fractures, whereas the abundance of the Rikenellaceae family is elevated in individuals with reduced BMD and increased tartrate-resistant acid phosphatase-5b levels (Ozaki et al., 2021). Lachnospira in the gut microbiota of Koreans exhibit significant disparities between healthy controls and individuals with OP (Ul-Haq et al., 2022). These studies have consistently associated alterations in gut microbiota composition with an elevated susceptibility to OP; however, it is important to acknowledge that the gut microbiota itself exhibits substantial variability due to geographical and dietary factors, which may account for the observed inconsistencies across these studies.

Therefore, a standardized meta-analysis of published clinical studies is imperative to obtain universally applicable results. In this study, we conducted a comprehensive analysis of nine publicly available gut microbiome datasets, comprising five metagenomic and four 16S rRNA datasets. These datasets contained the gut microbiome of stool samples from 488 individuals across three countries (China, New Zealand, and Sweden) to minimize the impact of environmental factors. By comparing the differences in gut microbiota structure among populations with varying bone mass, with a focus on variations at the genus and species levels of microbiota, and by considering the pivotal role of gut microbiota in regulating host immunity and metabolism, our strategy aimed to modulate the gut microbiota balance through probiotic intervention to mitigate the risk of OP. The findings of this study offer novel insights into the gut microecology during bone loss. In comparison to that of the NC group, the α diversity of gut microbiota in the ON group exhibited a decrease at the phylum level while showing an increase at both genus and species levels. Conversely, the OP group exhibited a contrasting pattern, which is consistent with the findings of He et al. (2020). However, the differences in α diversity index among NC, ON, and OP subgroups in some studies may not be as significant (Kuo et al., 2023; Wang et al., 2022), whereas others have shown an increase in bacterial diversity within ON and OP populations (Wang et al., 2017). The application of hierarchical clustering based on β diversity and PCoA effectively distinguished the NC group from both the OP and ON groups, indicating a potential association between the diverse gut microbiota and bone mass reduction. As mentioned by Li et al. (2019) and Ma et al. (2020), we found that the ON group had a high abundance of Firmicutes and a low abundance of Bacteroidetes, whereas the OP group had a high abundance of Bacteroidetes. Akinsuyi and Roesch (2023) employed 16S rRNA gene sequencing to characterize five investigations of the human gut microbiota and conducted a comprehensive summary and re-analysis; however, our re-analysis contradicts their findings. Using metagenomics, we further observed that the increase in gut microbiota diversity in the ON group primarily stemmed from the augmentation of pathogenic bacteria, whereas the OP group exhibited an increase in harmful bacteria and a reduction in beneficial bacteria. This disparity may account for the absence of significant differences in α diversity and a lack of clustering in β diversity between the OP and NC groups. In general, metagenomics provides accurate predictions of dynamic changes in the gut microbiota during disease development and offers novel insights for the prevention, diagnosis, and treatment of related diseases (Lin et al., 2021; Liu et al., 2023). Monitoring and analyzing trends in the gut microbiota of individuals in the early pre-cancer stages or at high risk facilitates timely interventions by medical professionals to prevent malignant transformation (Chiu & Miller, 2019; Zaidi et al., 2022). Moreover, sharing raw data and relevant patient metadata significantly contributes to future comprehensive disease analyses. As this field continues to advance and data-processing algorithms become increasingly sophisticated, precise prediction and intervention across a wide range of health-related issues will be enabled.

While controlling dysbiosis, specific antibacterial drugs may inadvertently disrupt beneficial bacteria, whereas probiotics, as indigenous residents of the human gut tract, exhibit adhesive properties and colonize the gut to regulate the microecological balance and enhance gut barrier function. Currently, probiotics such as Bifidobacterium lactis BB-12 and L. rhamnosus GG are extensively utilized in dietary supplements, exhibiting significant potential as adjunctive therapies for gastrointestinal disorders, immune dysregulation, and metabolic disorders (Cabana et al., 2017; Mego et al., 2023; Mohan et al., 2006; Sanders et al., 2019). Probiotic supplementation enhances bone health and alleviates OP. Numerous studies have consistently reported the beneficial effects of specific strains of lactobacilli (such as L. acidophilus, L. plantarum, and Lactiplantibacillus fabifermentans) and B. longum in reducing both gut and skeletal inflammation, improving gut barrier function, preventing bone loss, and alleviating OP (Ding et al., 2024; Lee & Kim, 2020; Pugazhendhi et al., 2023). Furthermore, the combination of probiotics and prebiotics exhibits comparable efficacy (Montazeri-Najafabady et al., 2019). Based on metagenomics, this study identified differential responses of lactobacilli in individuals with varying bone masses, potentially attributable to the stability of lactobacilli within the gut (Xiao et al., 2021). Notably, influenced by dietary intake, lactobacilli are more susceptible to changes in dietary habits than Bifidobacterium spp. are (Maldonado-Gomez et al., 2016; Yi et al., 2023). This characteristic also implies that modulation of lactobacilli levels in the gut can be achieved through adjustments in diet structure. Our study demonstrates that a reduction in the consumption of a diet containing lactobacilli may contribute to bone loss and the development of OP. The relative abundance of lactobacilli in the gut microbiota of the OP group was slightly higher than that of the ON group yet lower than that of the NC group; however, no significant difference was observed between the two groups, possibly due to regional variations in dietary habits.

Additionally, we conducted a comprehensive meta-analysis to investigate the effect of exogenous supplementation with various Lactobacillus strains on enhancing bone mass. Similar to the findings of Zhao et al. (2022), lactobacilli effectively improved lumbar BMD and regulated the development of the gut microbiota structure in a population with unhealthy bone mass compared with that of the placebo. At present, the research on the clinical intervention of probiotics to alleviate the gut microbiota structure and the abundance of key species in OP patients is still in its infancy. Metagenomics can enhance the precision of predicting the variation of gut microbiota alterations during OP development. In future analyses, researchers who provide both raw gut microbiota data and patient metadata will significantly contribute to deeper insights. Furthermore, as lactobacilli research advances and data-processing algorithms improve continuously, it will offer robust support for more accurate and effective prediction and enhancement of bone health.

CONCLUSION

Targeted regulation of the gut microbiota should be based on the identification of the characteristics of the gut microbiota that are significantly altered during bone loss. Our study identified the characteristic microbiomes of different bone populations through meta-analysis and further clarified that exogenous lactobacilli supplementation effectively improves lumbar BMD and regulates the development of the gut microbiota structure in unhealthy bone mass populations. These studies are based on collective data from multiple laboratories and have objective significance. These studies not only serve to validate the association between gut microbiota and bone mass but also contribute to a deepened understanding of the potential impact of probiotics (especially lactobacilli) on bone mass regulation, thereby establishing a robust scientific basis for the application of probiotics in modulating bone health. Future research should focus on conducting additional clinical trials to comprehensively summarize and validate the feasibility of targeting gut microbiota as a therapeutic approach to address bone mass-related issues.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China (32372296), the Natural Science Foundation of Jiangsu Province (BK20220155, BE2021623), and the Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province.

CONFLICT OF INTEREST STATEMENT

The authors confirm that they have no conflicts of interest to declare for this publicatiom.

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