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Alterations in the gut microbiota community are associated with childhood obesity and precocious puberty

BMC Microbiology volume 24, Article number: 311 (2024) Cite this article

Abstract

Objective

To explore the distribution and differences in the intestinal microbiota in girls with obesity-related precocious puberty and the relationship between intestinal microbiota and obesity-related precocious puberty.

Methods

16 S rRNA gene amplicons from fecal samples from girls with precocious puberty and obesity-complicated precocious puberty and healthy children were sequenced to define microbial taxa.

Results

The α- and β-diversity indices of the microbiome significantly differed among the three groups. At the phylum level, the proportions of Firmicutes, Actinobacteriota, Bacteroidota, Bacteria, Campylobacterota, and Acidobacteriota were different. At the genus level, there were differences in Bifidobacterium, Bacteroides, Anaerostipes, Fusicatenibacter, Klebsiella, Lachnospiraceae, ErysipelotrichaceaeUCG-003, Prevotella9, Ruminococcus gnavus group, and Lachnoclostridium. Additionally, Bifidobacterium, Anaerostipes, Bacteroides, Candidatus Microthrix, Eubacterium hallii group, Klebsiella, and Erysipelotrichaceae UCG-003 were identified as bacterial biomarkers by LEfSe. Furthermore, Sellimonas, Intestinibacter, Anaerostipes, Ruminococcus gnavus group, and Oscillibacter were identified as the differential biomarkers by random forest. A receiver operating characteristic (ROC) curve was used to evaluate the biomarkers with high predictive value for obesity-related precocious puberty. Spearman correlation analysis confirmed that Anaerostipes levels were negatively correlated with body weight, body mass index (BMI), bone age, luteinizing hormone, follicle-stimulating hormone, and estradiol.

Conclusions

There was a significant correlation between obesity-associated precocious puberty and gut microbiota, especially the functional characteristics of the microbiome and its interactions, which can provide a theoretical basis for the clinical intervention of obesity and precocious puberty through the microbiome.

Peer Review reports

Introduction

The rate of childhood obesity has increased significantly in recent years, and obesity has emerged as one of the most alarming public health problems worldwide [1]. The consumption of a high-fat diet and overeating are often the main reasons given for the increase in the number of children with obesity [2]. Childhood obesity is accompanied by precocious puberty, which refers to the appearance of secondary sexual characteristics in girls at the age of 8 and boys before the age of 9 [3, 4]. Epidemiological studies suggest that a high body mass index (BMI) is significantly associated with advanced precocious puberty [5, 6]. Obesity and precocious puberty are known to have deleterious impact on somatic growth and psychological maturation, increase the risk of diabetes in children, and promote the occurrence of reproductive system disease in adulthood [7, 8].

The incidence of precocious puberty is higher in girls with overweight and obesity [9]. However, the exact relationship between the high incidence of central precocious puberty and obesity is unclear [10]. Several mechanisms, including the critical weight hypothesis, leptin and insulin resistance, and the two promoting each other have been implicated in the relationship between obesity and precocious puberty [11, 12]. The intestinal microbiome is crucial for endocrine regulation, and gut microbiota abundance is associated with hormone levels, implying that the gut microbiota may promote precocious puberty by influencing hormone levels [13]. The intestinal microbiota has coevolved with the dynamic balance of the host intestinal environment to maintain functional and metabolic stability [14, 15]. The intestinal microbiota of children with central precocious puberty (CPP) and obesity differs from that of children with a normal weight. The abundance of Alistipes, Klebsiella and Sutterella in the microbiota of children with CPP is high, and an increase in the abundance of Firmicutes and a decrease in of Bacteroides characterizes the microbiota of children with obesity [16]. The intestinal microbiota is involved in NO (Nitrogen Monoxide) and serotonin synthesis pathways, which may trigger the initiation of puberty [17]. Its regulatory mechanism involves changes in the gut microbiota and alterations in its metabolites, including short-chain fatty acids (SCFAs) [18]. It has been reported that Bifidobacterium, Lactobacillus, and SCFAs can reverse the symptoms of obesity-induced precocious puberty in female rats [19, 20].

Dysbiosis of the gut microbiome is a regulator of metabolic disorders and fat storage [21]. In girls with CPP, being overweight can promote fat storage and increase the secretion of leptin. Leptin signals to GnRH neurons via interneurons to act on the neuroendocrine reproductive axis [22]. SCFAs produced by Ruminococcus and Roseburia in the intestine affect leptin gene expression by activating the endogenous free fatty acid receptor (FFAR) [23, 24]. Given the role of the gut microbiota in precocious puberty, the gut microbiota characteristics of precocious puberty in girls with obesity are unclear. Therefore, we focused on the characteristics of the gut microbiome in girls with obesity-related precocious puberty and its correlation with obesity and hormones. These findings provide a reference for the exploration of the relationship between obesity and precocious puberty, the pathogenesis of the condition, and the prevention of obesity-related through the evaluation of differences in the gut microbiota.

Methods

Ethics statement

Children who were diagnosed with precocious puberty and obesity in the Department of Pediatric Genetics and Endocrinology in Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China from December 2021 to September 2023 were included in this study. This study was approved by the ethics committee of the Women and Children Affiliated Hospital of the Medical College of the University of Electronic Science and Technology, and informed consent was obtained from all the participants and their parents in this study. This study was carried out in accordance with the guidelines of the Helsinki Declaration.

Participant recruitment

According to the diagnostic criteria for precocious puberty, the children were correctly included and examined by an experienced endocrinologist. Children wore a single layer of clothes, removed their shoes and socks, and were weighed twice to obtain an average weight to determine BMI. Normal, overweight, and obese individuals were defined on the basis of the WGOC diagnostic criteria of Chinese children on the basis of their BMI according to ages [4, 25]. Finally, 125 girls aged 6–9 years, including 41 children with the normal physical examination (CTR group), 42 normal–weight children with precocious puberty (PP group), and 42 overweight children with obesity–complicated precocious puberty (OPP group), were enrolled. The diagnostic criteria for precocious puberty were as follows [4]: (1) the secondary sexual characteristics appeared in advance. In girls, these appeared before 8 years, including breast development; (2) accelerated linear growth, and an annual growth rate higher than that of normal children; (3) advanced bone age, that is, a bone age that was at least one year higher than the actual age; (4) gonadal enlargement, that is, increased uterine and ovarian volume, with follicles ≥ 4 mm in the ovary, on pelvic ultrasonography; (5) HPGA activation and serum gonadotropin and sex hormone levels reaching those typical of puberty, with a peak value of LH/FSH ≥ 0.6 and a peak LH value ≥ 5.0 U/L. The exclusion criteria were as follows: (1) diagnosis as peripheral precocious puberty; (2) history of an obvious underlying disease; or (3) central precocious puberty secondary to hypothalamic hamartoma, post-trauma, history of radiotherapy and chemotherapy, congenital adrenocortical hyperplasia and McCune–Albright syndrome; (4) use of drugs affecting the reproductive axis; (5) use of antibiotics, probiotics, anti-inflammatory or antioxidant drugs, or antifungal drugs within the last three months.

Sample collection

Fecal samples obtained from each participant were collected in sterile tubes with a sterile spoon at the hospital and transfered to the laboratory within 30 min. Fecal samples were stored in the laboratory at -80 °C until DNA extraction. Venous blood samples were drawn from the subjects in vacuum pipettes after fasting for 12 h, and serum samples were collected into standard gel separation tubes and centrifuged at 3000 rpm for 15 min for hormone tests. Estradiol (E2), follicle-stimulating hormone (FSH) and luteinizing hormone (LH) levels were measured following a standard laboratory technique using on an Atellica IM1600 automatic analyzer (Siemens, Germany).

Library construction and sequencing

Total genomic DNA from samples was extracted using the E.Z.N.A.® Stool DNA Kit (Omega, Norcross, GA, US) following the manufacturer’s instructions. Universal primers [26]: (338_F: ACTCCTACGGGAGGCAGCA ang 806_R: GGACTACHVGGGTWTCTAAT) were used to amplify the V3–V4 regions of the 16 S rDNA (TransGen AP221-02). Sequencing libraries were constructed using primer sets modified with Illumina adapter regions (NEXTFLEX® Rapid DNA-Seq Kit, Bioo Scientific, USA). The library was sequenced with 300-bp paired-end reads on the MiSeq PE300 platform (Illumina, USA).

Sequencing data analysis

Paired-end reads from the original DNA fragments were merged using FLASH. QIIME was used to separate each sample sequence from the raw reads. Sequence analysis was performed by the UPARSE software package. Sequences with ≥ 97% similarity were assigned to the same operational taxonomic units (OTUs) through the Usearch algorithm, and the sequence with the highest frequency in each OTU is selected as the representative sequence. To correct the difference in sequencing depth, we subsampled the OTU table to the depth of 52,991 sequences per sample ten times before computing the alpha and beta diversity. Cluster analysis was preceded by principal coordinate analysis (PCoA), which was applied to reduce the dimensionality of the original variables using the QIIME software package. Linear discriminant analysis effect size (LefSe) and random forest analysis were used for the quantitative analysis of biomarkers within different groups. A correlation between two nodes was considered statistically robust at a Spearman’s correlation coefficient of |r|≥0.6 and P < 0.05. Other diagrams were implemented using the R package.

Statistical analysis

Data analyses were performed using SPSS 22.0 (IBM, Chicago, USA). Normally distribution data are presented as the mean ± standard deviation. A t-test was used for two-group comparisons, and one-way analysis of variance (ANOVA) was used for multiple-group comparisons. Data that did not conform to a normal distribution are expressed as the median (P25, P75). The Mann‒Whitny U test was used for the analysis of two-group comparisons, and the Kruskal‒Walls H test was used for the analysis of multiple-group comparisons. The Wilcoxon rank-sum test was used to analyze the differences in flora. Spearman correlation analysis was used to analyze the correlations. A significance level of P < 0.05 was considered for determining statistical significance.

Results

Study population

There was no significant difference in age among the CTR, PP and OPP groups (p > 0.05, Table 1). However, body weight, BMI and bone age significantly differed among the three groups (p < 0.001, Table 1). The body weight, and BMI of the OPP group were higher than those of CTR and PP groups, and the bone age of the PP and OPP groups was significantly higher than that of the CTR group (p < 0.05, Table 1). Compared with those in the control group, the levels of E2, FSH and LH in the PP and OPP groups were significantly higher (p < 0.05, Table 1).

Table 1 The baseline characteristics of children
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The diversity of the gut microbiome among the three groups

Based on the analysis of the 125 fecal samples, the rarefaction curves of the three groups of fecal samples tended to be flat, indicating that the number of sequences covered all the data (Supplementary Fig. 1A). A total of 10,836,442 reads were produced with an Illumina MiSeq, with an average of 86,691 reads per sample (shown in Supplementary Table 1). There were significant differences among the three groups in the Chao1 (p = 0.008), Shannon (p = 0.014) and Simpson (p = 0.045) indices, which were used to analyze α-diversity (Fig. 1A - C). Additionally, we calculated the Bray‒Curtis dissimilarity statistic, unweighted Uni-Frac distance and weighted Uni-Frac distance among the three groups. Analysis of similarities (ANOSIM) revealed that there was a significant difference in the β-diversity of the microbial structures among the three groups in terms of the Bray‒Curtis statistic (R = 0.049, p = 0.005), unweighted Uni-Frac distance (R = 0.074, p = 0.001) and weighted Uni-Frac distance (R = 0.039, p = 0.008) in β-diversity among the three groups (Fig. 1D - F). PCoA based on the Bray‒Curtis statistic (R = 0.087, p = 0.002), unweighted Uni-Frac (R = 0.128, p = 0.001) distance and weighted Uni-Frac distance (R = 0.081, p = 0.002) between the CTR and OPP groups also revealed significant differences in bacterial composition.

Fig. 1
figure 1

Analysia of the diversity of the gut microbiome among the three groups. (A) Chao1’s α-diversity. (B) Shannon’s α-diversity. (C) Simpson’s α-diversity. (D) PCoA plot of the Bray–Curtis distance. (E) PCoA plot of the unweighted Uni-Frac distance. (F) PCoA plot of the weighted Uni-Frac distance. PP, precocious puberty group; CTR, control group. OPP: Obesity-complicated precocious puberty group. Significance was tested using the Wilcoxon rank-sum test. *p < 0.05; **p < 0.01

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Obesity and precocious puberty affect the abundance of gut microbiota

At the phylum level, Firmicutes, Actinobacteriota, Proteobacteria and Bacteroidota were the dominant bacterial phyla across the three groups (Fig. 2A). However, there were significant differences in the abundances of Firmicutes, Actinobacteriota and Bacteroidota among the three groups (Fig. 2D). We further analyzed and found that the relative abundances of Bacteria, Fusobacteriota and Deinococcota were significantly different between the CTR and PP groups. The abundance of the Fusobacteriota in the PP group was significantly higher than that in the other groups (Fig. 2B). However, the abundances of Bacteria and Deinococcota decreased significantly in both the PP and OPP groups. In the OPP group, the abundance of Actinobacteriota decreased significantly (Fig. 2C). At the genus level, Bifidobacterium, Blautia, Subdoligranulum and Faecalibacterium were the dominant genera across the groups (Fig. 3A). We analyzed the relative abundances of different genera and identified 10 important genera that differed among the three groups. The abundances of Bifidobacterium, Bacteroides, Anaerostipes and Fusicatenibacter were significantly lower in the PP and OPP groups (Fig. 3D). Compared with those in the CTR group, the abundances of Anaerostipes, Fusicatenibacter, Lachnoclostridium and Acinetobacter were significantly lower in the PP group (Fig. 3B). In the OPP group, the abundances of Bifidobacterium, Bacteroides, and Anaerospites were lower. However, the abundances of the Eubacterium hallii group and Klebsiella were significantly higher than those of the PP and CTR groups (Fig. 3C).

Fig. 2
figure 2

Relative abundance of the gut microbiota at the phylum level among the three groups. (A) Relative abundances of the gut microbiota at the phylum level. (B) The most differential abundances between the CTR and PP groups at the phylum level. (C) The most differential abundances between the CTR and OPP groups at the phylum level. (D) The most differential abundances among the three groups at the phylum level. PP, precocious puberty group; CTR, control group; OPP: Obesity-complicated precocious puberty group. Significance was tested using the Wilcoxon rank-sum test. *p < 0.05; **p < 0.01

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Fig. 3
figure 3

Relative abundance of the gut microbiota at the genus level among the three groups. (A) Relative abundances of the gut microbiota at the genus level. (B) The most differential abundances between the CTR and PP groups at the genus level. (C) The most differential abundances between the CTR and OPP groups at the genus level. (D) The most differential abundances among the three groups at the genus level. PP, precocious puberty group; CTR, control group; OPP: Obesity-complicated precocious puberty group. Significance was tested using the Wilcoxon rank-sum test. *p < 0.05; **p < 0.01; ***p < 0.001

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Specific microbial communities showing significant differences

To determine whether specific gut bacteria are associated with precocious puberty and obesity, we used LEfSe. At the genus level, the LDA value was 4.0 for the identification of specific microbial communities in the CTR and OPP groups. LEfSe identified 5 bacterial biomarkers, included Bifidobacterium, Anaerostipes, Bacteroides, Eubacterium hallii group, and Klebsiella. In the CTR group, Bifidobacterium, Anaerostipes, and Bacteroides were the dominant genera. In the OPP group, Eubacterium hallii group and Klebsiella were the dominant genera (Fig. 4A). Compared with the PP group, Anaerostipes was the dominant genus in the CTR group (Fig. 4B).

Fig. 4
figure 4

Operational taxonomic unit (OTU)-based markers among the three groups. (A) LEfSe analysis showing the enriched taxa between the CTR and OPP groups. (B) LEfSe analysis showing the enriched taxa among the CTR and PP groups. PP, precocious puberty group; CTR, control group; OPP: Obesity-complicated precocious puberty group

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Relationships between bacterial genera and hormones levels and obesity

We performed Spearman’s analysis of the correlations between the abundance of intestinal microbes and the levels of body weight, BMI and bone age in the three groups. We found that Fusicatenibacter abundance was positively correlated with BMI and that Anaerostipes abundance was negatively correlated with body weight, BMI and bone age (Fig. 5A). Additionally, Spearman’s correlation analysis revealed that Anaerostipes abundance was negatively correlated with the levels of E2, LH and FSH. (Fig. 5B).

Fig. 5
figure 5

Relationship between bacterial genera and basic indicators and hormones. (A) Heatmap of the bacterial genera and body weight, BMI and bone age among the three groups. (B) Heatmap of the bacterial genera and hormone levels among the three groups. E2, estradiol; FSH, follicular estrogen; LH, luteinizing hormone. *p < 0.05; **p < 0.01; ***p < 0.001

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Auxiliary diagnostic model for precocious puberty based on gut microbiota

To determine whether specific gut bacteria are associated with precocious puberty and obesity, we used a random forest algorithm to select potential biomarkers for the predictive model of precocious puberty and obesity-complicated precocious puberty. The random forest algorithm identified 5 bacterial biomarkers between the CTR and OPP groups. These biomarkers included Sellimonas, Intestinibacter, Anaerostipes, Ruminococcus gnavus group and Oscillibacter (Fig. 6A). Receiver operating characteristic (ROC) curves were used to evaluate the predictive value of the target genus. The area under the curve (AUC) generated from the levels of Sellimonas, Intestinibacter, Anaerostipes, and Ruminococcus gnavus group revealed that these biomarkers had high predictive value, with an AUC of 0.85 (Fig. 6B). We used the same method to screen for microbial biomarkers by comparing the PP and CTR groups. The biomarkers included Acinetobacter, Clostridium innocuum, Glutamicibacter, Prevotella 7 and Aquabacterium (Fig. 6C). The AUCs generated by these biomarkers indicated predictive value (AUC = 0.82) for precocious puberty (Fig. 6D).

Fig. 6
figure 6

(A) Random forest analysis showing the enriched taxa between the CTR and OPP groups. (B) Receiver operating characteristic (ROC) curve for predicting obesity and precocious puberty. (C) Random forest analysis showing the enriched taxa between the CTR and PP groups. (D) Receiver operating characteristic (ROC) curve for the prediction of precocious puberty. PP, precocious puberty group; CTR, control group; OPP: Obesity complicated precocious puberty group

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Discussion

In this study, we found that an increase in body weight, BMI, and hormone levels, and a significant increase in bone age in girls with obesity and precocious puberty caused an imbalance in the growth and development of girls to some extent. This findings is consistent with previous studies [27]. The intestinal microbiome, as a community aggregate of intestinal function, affects children’s endocrine and metabolic activities. In children with obesity and precocious puberty, an imbalance in the composition and diversity of the intestinal microbiome, may change the community structure of key metabolic bacteria and accelerate the process of precocious puberty [23, 28]. The intestinal flora structure of precocious girls with concomitant obesity differed from that of children with simple obesity and precocious puberty, with specific differences in bacterial genera and the intestinal flora structure [13, 29]. Compared with the control group, the PP and OPP groups differed, in terms of intestinal diversity, with decreases in community diversity, species evenness, and richness of the intestinal microbiota. Similarly, the microbiota composition of the OPP group and the abundance of Actinobacteriota and Bacteroidetes decreased significantly, whereas that of Firmicutes increased. An imbalance in the intestinal microbiota is consistent with observations in children with obesity and is, characterized by an increase in Firmicutes and a decrease in Bacteroidetes [30, 31]. In the OPP group, the beneficial genera, including Anaerostipes, Bacteroides, and Bifidobacterium, decreased significantly, and the above bacteria were dominant in the control group. The above three bacterial genera were mostly considered beneficial bacteria in previous studies, and the abundance of the above bacterial genera was also significantly reduced during inflammation [32]. Anaerostipes, Bacteroides, and Bifidobacterium in the intestine regulate mainly the conversion of inositol stereoisomers and dietary fiber to SCFAs, such as acetate, propionate and butyrate, and thus affect metabolic diseases [33, 34]. An imbalance in the proportions of key bacterial groups in the community structure may play a role in obesity and precocious puberty. The underlying mechanisms have not yet been fully elucidated.

The effect of changes in the intestinal flora on hormone and endocrine disorders has become a new option for intervention in several hormone-related metabolic diseases. The regulation of the intestinal flora during precocious puberty is mutual. The intestinal flora can regulate the abundance of specific flora to control the initiation of puberty, and changes in hormone levels can affect the intestinal flora [35]. In the analysis of the correlations among the gut microbiota, body weight and hormones, consistent with previous obesity studies, the abundance of Fusicatenibacter was positively correlated with BMI [36]. Simultaneously, Fusicatenibacter positively correlated with E2, LH, and FSH. However, the abundance of Anaerostipes correlated negatively with BMI, body weight, bone age, and the levels of E2, LH, and FSH. The GnRH hormone secreted by the hypothalamus critically regulates the HPG axis. It promotes the secretion of LH and FSH and is important for the development of sexual organs [37]. These hormones are crucial in the pathogenesis of precocious puberty. Anaerostipes is the main bacteria that produce SCFAs in the intestinal tract. Its abundance decreases in girls with obesity-related precocious puberty. In metabolic diseases, when obese patients lose weight, the abundance of Anaerostipes bacteria increases. The bacteria can metabolize inositol to produce SCFAs, such as propionic acid and butyric acid, which improve blood glucose and metabolism [33, 38]. A decrease in the abundance of Anaerostipes bacteria leads to a reduction in SCFAs in girls with obesity and precocious puberty, which may underlie abnormal diagnostic indices in obesity and precocious puberty. Studies on the relationships between the abundances of Fusicatenibacter and Anaerostipes and E2, LH, and FSH levels have not been reported, and further research is needed.

The gut microbiome is an important ecosystem, as it regulates the balance of the gut-brain axis and the gonads [39]. The composition and proportion of the intestinal flora in HFD-induced precocious puberty mice may be related to estrogen, and the transplantation of microorganisms from animals fed a HFD promoted precocious puberty incidence in mice [40]. These findings suggest that the intestinal microflora may affect the occurrence of precocious puberty. However, there may be a gap in knowledge regarding intestinal flora alterations in children with clinical conditions. Thus, we used random forest analysis to analyze the differential microbiome between the OPP and CTR groups and to predict the occurrence of obesity-associated precocious puberty through ROC analysis. Interestingly, the predictive value of these biomarkers for obesity-related precocious puberty was higher. Random forest analysis and ROC analysis also revealed that the microbiota biomarkers had high predictive value for precocious puberty. In the future, alterations in flora can be used to diagnose precocious puberty and obesity.

Our findings suggest that the intestinal microbiota of girls with precocious puberty and obesity differs from that of normal girls and girls with precocious puberty alone, exhibiting unique community composition characteristics. Changes in the composition of the main beneficial bacteria and dominant bacteria in the intestinal tract suggest a correlation between the intestinal flora and obesity-complicated precocious puberty. The relationship between the intestinal flora and obese-complicated precocious puberty warrants further verification.

Conclusion

The increasing incidence of obesity and precocious puberty has several adverse consequences. Given the evidence on the crucial role of the microbiota in obesity and precocious puberty, we infer that obesity tends to predispose individuals to precocious puberty. Our results showed that obesity-associated precocious puberty was associated with alterations in the structure and diversity of the gut microbiota. Longer-term analysis with a larger sample size is needed to explore the effect of the gut microbiome on obesity and precocious puberty and provide a theoretical basis for clinical intervention through the microbiome.

Data availability

The datasets generated for this study are included in the article. Datasets presented in the study are deposited in the NCBI repository, submission number: PRJNA1066884.

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Funding

This work was supported by the Chengdu Medical Research Project [2022064] and Scientific and technological Innovation of Maternal and Child Medicine in Sichuan Province [22FXZD06].

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Author notes

  1. Li Wang and Qin Yi are contributed equally to this paper and should be considered as joint first authors.

Authors and Affiliations

  1. Department of Pediatric Research Institute, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics Metabolism and Inflammatory Disease, Children’s Hospital of Chongqing Medical University, Chongqing, China

    Li Wang, Qin Yi, Hao Xu, Huiwen Liu, Bin Tan, Hongrong Deng, Yunxia Chen, Rui Wang & Jing Zhu

  2. Department of Endocrine Genetics and Metabolism, School of Medicine, Chengdu Women’s and Children’s Center Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Fang Tang & Xinran Cheng

  3. Department of Clinical Laboratory, Children’s Hospital of Chongqing Medical University, Chongqing, China

    Hao Xu

Authors
  1. Li Wang
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  2. Qin Yi
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  3. Hao Xu
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  9. Fang Tang
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  10. Xinran Cheng
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  11. Jing Zhu
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Contributions

LW, HX and QY carried out all experiments and data analysis and prepared the manuscripts. JZ, XRC and BT designed the experiments and review and revised the manuscripts. QY and RW contributed to reagent management. HWL, YXC, HRD and FT edit the chart. All authors approved the final manuscript.

Corresponding authors

Correspondence to Xinran Cheng or Jing Zhu.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Chengdu Women and Children’s Central Hospital (No.2021(140)). Informed consent was obtained from all the participates and their parents and/or legal guardian in this study.

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The authors declare no conflicts of interests.

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The authors declare no competing interests.

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Wang, L., Yi, Q., Xu, H. et al. Alterations in the gut microbiota community are associated with childhood obesity and precocious puberty. BMC Microbiol 24, 311 (2024). https://doi.org/10.1186/s12866-024-03461-8

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Keywords

BMC Microbiology

ISSN: 1471-2180

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