Vaginal microecological characterization of women of childbearing age at different altitudes: a multi-omics exploration.

Samples were collected in strict accordance with the inclusion and exclusion criteria, and a total of 60 vaginal secretion samples were collected in this study, and the mean age of the participants was 34.58 years old, and the mean ages of the LL, MM, MH and HH groups were 35.67, 33.73, 35.27 and 33.67 years old, and there was no significant difference between the four groups of researchers in terms of age, BMI, the number of children they had given birth to, the number of abortions they had had, their marital status, and their ethnicity ( P  > 0.05), as shown in Table  1 . There was no significant difference in age, BMI, number of children born, number of abortions, marital status, and ethnicity ( P  > 0.05), so the possible interference of the above general clinical information on vaginal flora can be excluded, and the final results of sequencing between the groups are comparable. Table 1 General clinical characteristics of participants Diagnostic trait LL( n  = 15) MM( n  = 15) MH( n  = 15) HH( n  = 15) P Age 35.67(28–40) 33.73(30–40) 35.27(21–41) 33.67(26–42) 0.21 ＋ BMI a 21.27(19.10–25.97.10.97) 22.41(20.03–26.84) 22.34(19.53–29.33) 22.56(19.81–25.10) 0.18 ＋ Number of children 1.33(1–2) 1.33(1–2) 1.40(1–2) 1.53(1–3) 0.80 ＋ Number of abortions 0.47(0–1) 0.33(0–1) 0.53(0–2) 0.27(0–1) 0.56 ＋ Marital status (married/other) 15/0 15/0 15/0 15/0 1.00 ＋＋ Ethnicity (Han/other) 15/0 15/0 15/0 15/0 1.00 ＋＋ a is expressed as the median (minimum-maximum) ＋ Independent Sample Kruskal-Wallis Test ＋＋ χ 2 test General clinical characteristics of participants a is expressed as the median (minimum-maximum) ＋ Independent Sample Kruskal-Wallis Test ＋＋ χ 2 test To investigate the vaginal flora composition of women of childbearing age at different altitudes, we performed 16 S rDNA sequencing of the vaginal secretions of 60 women of childbearing age (15 in the LL group, 15 in the MM group, 15 in the MH group, and 15 in the HH group). A total of 6,763,507 high-quality 16 S rDNA sequences, with an average of 11,270,725 per sample, were obtained in this study. A total of 3,309 ASVs were obtained from the 60 samples using the QIIME 2 software package after selecting representative sequences of individual ASVs using the Silva (version 138) database. After selecting the representative sequences of each ASV using the QIIME 2 software package, a total of 3309 ASVs were obtained from 60 samples using the Silva (version 138) database comparison. The samples had high sequencing coverage and extraction quality (Fig.  1 A and B). Fig. 1 Vaginal flora composition at different altitudes Sample dilution curves ( A ) and Shannon curves ( B ) flatten out at the tail end, indicating that the sequencing depth was qualified for each sample. C  Wayne plots show unique and shared OUTs for each group. green represents Group LL, purple represents Group MM, blue represents Group MH, and red represents Group HH. The top 10 representative species differ at the phylum level ( D ) and genus level ( E ) and their proportions in the four groups. The horizontal axis represents the different subgroups and the vertical axis represents the relative abundance of the different species. F  The bar-stacked plot of the other species after removing the genus with the highest proportion at the genus level Vaginal flora composition at different altitudes Sample dilution curves ( A ) and Shannon curves ( B ) flatten out at the tail end, indicating that the sequencing depth was qualified for each sample. C  Wayne plots show unique and shared OUTs for each group. green represents Group LL, purple represents Group MM, blue represents Group MH, and red represents Group HH. The top 10 representative species differ at the phylum level ( D ) and genus level ( E ) and their proportions in the four groups. The horizontal axis represents the different subgroups and the vertical axis represents the relative abundance of the different species. F  The bar-stacked plot of the other species after removing the genus with the highest proportion at the genus level According to the results of ASV clustering analysis, there were 499, 606, 641, and 595 unique ASVs in LL, MM, MH, and HH groups, respectively. The HH group had an increased number of ASVs compared to MH, MM, and LL groups, with a total of 423 ASVs in all four groups (Fig.  1 C). We analyzed the community structure of vaginal microorganisms, Whereas at the phylum level, Firmicutes, Actinobacteria, Bacteroidota, Fusobacteriota, and Proteobacteria dominated the vaginal flora (Fig.  1 D), and at the genus level, the top 5 bacterial genera were Lactobacillus, Gardnerella, Prevotella, Atopobium, and Sneathia. a comparison of the MM, MH, and HH groups revealed a decreasing trend in the percentage of Lactobacillus spp. in the vagina with the increase in altitude (Fig.  1 E). The bar stacked plot of other species after removing Lactobacillus spp. showed more clearly the visual comparison of the relative abundance of other species among the four groups, and it could be seen that the relative abundance of each species in the MH and HH groups was significantly higher than that in the LL group (Fig.  1 F). This also indicates that the relative abundance of vaginal flora increased with increasing altitude, which may be related to hypoxia, different hygiene practices, and other modalities. This also suggests an increase in the relative abundance of vaginal flora with increasing altitude, which may be related to hypoxia, different hygiene practices, and other modalities. To visualize the differences among the four groups in terms of species composition, the top 30 genera in terms of abundance were selected based on the species annotations and abundance information of all the samples at the genus level, and based on their abundance information, they were clustered at the species level and plotted as a heat map (Fig.  2 A). Comparative analysis of multiple groups revealed that there were differences ( P  < 0.050) among Atopobium, Sneathia, Mycoplasma, Escherichia-Shigella, Enterococcus, Porphyromonas, Anaerococcus, Peptoniphilus, Pseudomonas and Helicobacter(Fig.  2 E). Fig. 2 Differential vaginal flora at different altitudes ( A ) Clustered heat map of species abundance at the genus level for each subgroup. The horizontal axis represents different samples, the vertical axis represents different species, and the shade of the color correlates with species abundance, with darker colors increasing abundance. B  Shannon’s index and ( C ) Simpson’s index reflect vaginal microbial α-diversity at the species level. D  PCoA shows the differences in vaginal flora between the four groups. Horizontal and vertical coordinates are the two main indicators to explain the largest differences between samples, and the dots in the graph represent samples, with different colors representing different subgroups, where similar samples are clustered together. E  Comparison of multiple groups, the X-axis represents different groups, different colors represent different subgroups, and the Y-axis represents the average relative abundance of species in different groups. F  LEfSe plot. Circles radiating from inside to outside represent taxonomic levels from phylum to genus, different color nodes indicate significant enrichment of microbiota in corresponding subgroups and significant effect on intergroup differences, light yellow nodes indicate microbes with no significant difference between groups or no significant effect on intergroup differences. G  Histogram of the distribution of LDA values, the length of the histogram represents the size of the LDA values, the higher the score, the greater the contribution of microorganisms to the differences Differential vaginal flora at different altitudes ( A ) Clustered heat map of species abundance at the genus level for each subgroup. The horizontal axis represents different samples, the vertical axis represents different species, and the shade of the color correlates with species abundance, with darker colors increasing abundance. B  Shannon’s index and ( C ) Simpson’s index reflect vaginal microbial α-diversity at the species level. D  PCoA shows the differences in vaginal flora between the four groups. Horizontal and vertical coordinates are the two main indicators to explain the largest differences between samples, and the dots in the graph represent samples, with different colors representing different subgroups, where similar samples are clustered together. E  Comparison of multiple groups, the X-axis represents different groups, different colors represent different subgroups, and the Y-axis represents the average relative abundance of species in different groups. F  LEfSe plot. Circles radiating from inside to outside represent taxonomic levels from phylum to genus, different color nodes indicate significant enrichment of microbiota in corresponding subgroups and significant effect on intergroup differences, light yellow nodes indicate microbes with no significant difference between groups or no significant effect on intergroup differences. G  Histogram of the distribution of LDA values, the length of the histogram represents the size of the LDA values, the higher the score, the greater the contribution of microorganisms to the differences The differences in species richness and diversity of microbial communities among the groups were further evaluated by α-diversity analysis. chao1 index and ACE index were used to estimate the richness of microflora in the samples. The chao1 index ( P  = 0.910) and ACE index ( P  = 0.860) were elevated in the MM, MH, and HH groups compared to the LL group (Supplementary Fig. 1), and the microbial abundance was higher in the MH and HH groups than in the LL group, but no statistically significant differences were observed. Shannon’s index (Fig.  2 B) and Simpson’s index (Fig.  2 C) were used to estimate the diversity of microbial flora in the samples, and no differences were observed among the four groups ( P  > 0.050). To further analyze whether there were differences in the composition of vaginal flora among women of childbearing age at different altitudes, principal coordinate analysis (PCOA) based on the Bray-Curtis distance was performed, which showed that the populations of the LL, MM, MH, and HH groups differed in vaginal microflora community structure in terms of the first two principal component scores, which respectively which accounted for 36.81% and 18.5% of the total variation, but the difference in vaginal flora among the four groups was not significant ( P  = 0.051) (Fig.  2 D). Evolutionary branching diagrams were used to show the microbial communities that may play important roles in the four groups, and zero, one, one, and seven key genera were identified in the LL, MM, MH, and HH groups, respectively (Fig.  2 F). The LDA scores among the four groups showed that in the LL group, there were no groups that dominated in relative abundance, and in the HH group, Mycoplasma, Anaerococcus, Peptoniphilus, Sutterella, Actinomyces, and lentimicrobium were higher than the other three groups (Fig.  2 G). The differential flora can be a useful biomarker for areas with high altitudes. Moreover, the diversity and abundance of vaginal flora increased significantly with increasing altitude. Different microbial compositions are often accompanied by changes in vaginal microorganisms and their metabolites, and their metabolites have an important impact on the homeostasis and imbalance of the vaginal microenvironment. In the present study, to investigate the metabolite compositions of vaginal secretions of women of childbearing age in different groups, non-targeted metabolomics was used to detect metabolites in vaginal secretions, and a total of 1,688 metabolites were identified in both positive and negative ionization modes. A total of 1688 metabolites were identified in the positive and negative ion modes, of which 992 metabolites were identified in the positive ion mode and 696 metabolites were identified in the negative ion mode. Based on the metabolite abundance detected by metabolomics, OPLS-DA analysis was performed, and according to the scatter plot, samples from the LL, MM, MH & and HH groups were largely separable, which shows that the OPLS-DA model was able to differentiate between the four groups of samples (Fig.  3 A-C, G-I). The model was also tested using the permutation test, and it was found that there was no overfitting and the model was robust (Fig.  3 D-F, J-L). The positive ion model is shown in the Supplementary Section (Supplementary Fig. 2). Fig. 3 Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) of Negative Ion Modes ( A - C ) ( G - I ) OPLS-DA shows differences in metabolites between groups, with horizontal coordinates indicating between-group changes and vertical coordinates indicating within-group changes. D - F ,  J - L  Comparison of real model parameters with replacement model parameters in the validation test. Horizontal coordinates indicate replacement retention and vertical coordinates indicate the values of R2 and Q2. The green dots indicate R2, the blue dots indicate Q2, and the two dashed lines indicate the regression lines for R2 and Q2, respectively. R2 and Q2 in the upper right corner indicate that the replacement retention is equal to 1, i.e., the R2 and Q2 values of the original model Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) of Negative Ion Modes ( A - C ) ( G - I ) OPLS-DA shows differences in metabolites between groups, with horizontal coordinates indicating between-group changes and vertical coordinates indicating within-group changes. D - F ,  J - L  Comparison of real model parameters with replacement model parameters in the validation test. Horizontal coordinates indicate replacement retention and vertical coordinates indicate the values of R2 and Q2. The green dots indicate R2, the blue dots indicate Q2, and the two dashed lines indicate the regression lines for R2 and Q2, respectively. R2 and Q2 in the upper right corner indicate that the replacement retention is equal to 1, i.e., the R2 and Q2 values of the original model The Variable Importance for the Projection (VIP) values obtained from the OPLS-DA model can be used to measure the strength of the influence of the expression pattern of each metabolite on the categorical discrimination of each group of samples and the ability to explain it and to excavate biologically significant differential metabolite molecules. Usually, metabolites with VIP > 1 are considered to have a significant contribution to model interpretation. Metabolomics used strict OPLS-DA VIP > 1 and P  < 0.05 as the screening criteria for significant differential metabolites, and a total of 896 significant differential metabolites were screened in the LL, MM, MH & HH groups. The differential metabolites with the top 50 VIP values were clustered and analyzed to demonstrate more intuitively the relationship between samples and the differences in metabolite expression between samples, (Fig.  4 A). Fig. 4 Differential metabolic pathways and products ( A ) Heat map of differential metabolites. The horizontal coordinate indicates the name of the sample and the vertical coordinate that search is the differential metabolite, with red color representing relatively high expression and blue color representing relatively low expression. B - H  Graph of metabolic signaling pathway enrichment histograms of differential metabolites, the vertical axis in the histogram represents each KEGG metabolic pathway, and the horizontal axis indicates the number of differentially expressed metabolites contained in each KEGG metabolic pathway. The color indicates the p-value of enrichment analysis, the darker the color, the smaller the p-value, the more significant the enrichment, and the number on the bar represents the rich factor Differential metabolic pathways and products ( A ) Heat map of differential metabolites. The horizontal coordinate indicates the name of the sample and the vertical coordinate that search is the differential metabolite, with red color representing relatively high expression and blue color representing relatively low expression. B - H  Graph of metabolic signaling pathway enrichment histograms of differential metabolites, the vertical axis in the histogram represents each KEGG metabolic pathway, and the horizontal axis indicates the number of differentially expressed metabolites contained in each KEGG metabolic pathway. The color indicates the p-value of enrichment analysis, the darker the color, the smaller the p-value, the more significant the enrichment, and the number on the bar represents the rich factor To better understand the mechanisms by which altitude affects vaginal metabolites in women of childbearing age, metabolic pathway analyses of the KEGG pathway of metabolites were carried out in the present study, and the significance level of metabolite enrichment of each pathway was analyzed by Fisher’s exact test to identify the metabolic and signaling pathways that were significantly affected. The differential metabolites of the LL group, the MM group, the MH group, and the HH group were mainly involved in the metabolic pathways of Neuroactive ligand-receptor interactions, bile secretion metabolism and vitamin digestion and absorption (Fig.  4 B); LL group and HH group differential metabolites were mainly involved in ABC transporters, glycine-serine and threonine metabolism, and taurine and low taurine metabolism (Fig.  4 C); differential metabolites of LL and MH groups were mainly involved in metabolic pathways such as ABC transporter, tryptophan metabolism, glycine-serine and threonine metabolism (Fig.  4 D); LL and MM groups differential metabolites were mainly involved in metabolic pathways such as tryptophan metabolism, lysine degradation, and glycine-serine and threonine metabolism (Fig.  4 E); Differential metabolites of MH and HH groups were mainly involved in metabolic pathways such as ABC transporter, purine metabolism, digestion and absorption of carbohydrates (Fig.  4 F); MM and HH groups Differential metabolites were mainly involved in metabolic pathways such as neuroactive ligand-receptor interactions, tryptophan metabolism, and cAMP signaling pathway (Fig.  4 G); MM and MH groups differential metabolic products were mainly ABC transporter, amino acid biosynthesis, protein digestion and absorption (Fig.  4 H) and other metabolic pathways. Statistical analysis of differential metabolites showed that Ectoine, Thiamine, Taurine, D-glucono-1,5-lactone, 2-oxoadipic acid, and N-Acetylserotonin differed significantly in the distribution of the four groups, and they were significantly elevated in the HH ( P  < 0.05) (Fig.  5 ). However, regarding the mechanism of action of these substances in the vagina, we are still unclear and need to further explore the mechanism in the future. Fig. 5 Distribution of different metabolites in different groups. * p  < 0.05, ** p  < 0.01, *** p  < 0.001 Distribution of different metabolites in different groups. * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Sampling of women of childbearing age who attended gynecological outpatient clinics or physical examination clinics at the Maternal and Child Health Hospital of Wuxi City, Jiangsu Province, the Second People’s Hospital of Haidong City, Qinghai Province, the People’s Hospital of Hualong County, Qinghai Province, and the People’s Hospital of Guoluo Prefecture, Qinghai Province, during the period of August 2022-May 2023, with the following criteria for sample enrollment: (1) Residence in the last 3 years in Wuxi City, urban area of Haidong City, Hualong County, and Guoluo Prefecture; (2) Ordinary regular menstrual cycle (menstrual period 5-7d, cycle 28 ± 7 days); (3) No vaginal medication and sexual intercourse within 3 d; (4) No active vaginal bleeding on the day of sampling; (5) No antibiotics and other antimicrobial drugs within 2 months; (6) No tobacco, alcohol, and other bad habits, and single-sex partner. The exclusion criteria were as follows: (1) Women who were infertile, pregnant, menstruating, menopausal, or breastfeeding; (2) Patients who refused to take samples; (3) Oral contraceptives and other sex hormones such as birth control pills or glucocorticosteroids within 3 months; (4) Patients with inflammation of the vagina, cervical cancer, ovarian cancer. Sixty subjects were included in this study and were divided into four groups according to the altitude of the area where they were located: (1) low altitude (8 m above sea level) Healthy Han group (LL); (2) middle altitude (2000 m above sea level) Healthy Han group (MM); (3) middle-high altitude (2900 m above sea level) Healthy Han group (MH); (4) high altitude (4000 m above sea level) Healthy Han group (HH). The participants’ general information such as age, ethnicity, occupation, marriage and childbearing history, and other basic information was also collected. Participants collected vaginal secretion specimens after menstrual cleansing, used a disposable sterile dilator into the vagina to fully expose the vaginal wall and cervix, and used three dry sterile vaginal swabs to scrape an appropriate amount of vaginal secretion at the posterior vaginal vault; one was used to immediately send to the hospital’s Laboratory Department for routine leukorrhea testing, and the other two sterile vaginal swabs were immediately placed into liquid nitrogen buckets for preservation and were transferred as soon as possible to the refrigerator for preservation at −80 °C, and then later used in the determination of 16 S rDNA and nontargeted metabolomics, respectively. OMEGA Soil DNA Kit (D5625-01) (Omega Bio-Tek, Norcross, GA, USA) extracts genomic DNA from samples and tests the purity and concentration of DNA. According to the selection of sequencing region, the selected V3-V4 variable region was amplified by PCR using specific primers with Barcode and high-fidelity DNA polymerase. PCR products were detected by 2% agarose gel electrophoresis, and the target fragments were cut and recovered by Quant-iT PicoGreen dsDNA Assay Kit. Referring to the preliminary quantitative results of electrophoresis, the PCR amplification recovered products were detected and quantified with the Microplate reader (BioTek, FLx800) fluorescence quantitative system, and the corresponding proportions were mixed according to the sequencing requirements of each sample. The library was constructed using TruSeq Nano DNA LT Library Prep Kit from Illumina. The constructed library is inspected by Agilent Bioanalyzer 2100 and Promega QuantiFluor. After the library is qualified, it is sequenced. To extract metabolites from vaginal swabs, 400 µL of a cold extractant of methanol: acetonitrile: water (2:2:1) was added to 100 mg of sample and spun thoroughly. After spinning the sample was incubated on ice for 20 min, then centrifuged at 14,000 g for 20 min at 4 °C. The supernatant was collected and dried in a vacuum centrifuge at 4 °C for LC-MS analysis, and the sample was redissolved in 100 µL of acetonitrile: water (1:1) solvent and transferred to an LC vial. The samples were separated on an Agilent 1290 Infinity LC ultra-high performance liquid chromatography (UHPLC) system with a HILIC column; the column temperature was 25 °C; the flow rate was 0.5 mL/min; the injection volume was 2 µL; the mobile phases were composed of A: water + 25 mM ammonium acetate + 25 mM ammonia, and B: acetonitrile; and the gradient elution procedure was as follows: 0–0.5 min. 95% B; 0.5–7 min, B varied linearly from 95% to 65%; 7–8 min, B varied linearly from 65% to 40%; 8–9 min, B was maintained at 40%; 9–9.1 min, B varied linearly from 40% to 95%; 9.1–12 min, B was maintained at 95%; the samples were placed at 4 °C throughout the analysis. The sample was placed in the autosampler at 4 °C throughout the analysis. To avoid the effects caused by the fluctuation of the instrumental detection signal, a random order was used for the continuous analysis of the samples. QC samples were inserted in the sample queue for monitoring and evaluating the stability of the system and the reliability of the experimental data. Sequencing data were processed using QIIME2.0 software for data analysis and visualization, and P  < 0.05 indicated that the differences were statistically significant. The analysis of non-targeted metabolomics data included univariate statistical analysis, multidimensional statistical analysis, differential metabolite screening, differential metabolite correlation analysis, and KEGG pathway analysis.

A multi-omics study revealed that the diversity of vaginal flora in women of childbearing age increased with increasing altitude, while there were differences in vaginal metabolites at different altitudes, which were hypothesized to be possibly related to factors such as hypoxia, high altitude, and differences in hygiene practices at high altitude. The appearance of its specific metabolites could be a useful metabolomic marker for the HH group. Its relationship with high altitude and reproductive tract diseases will be further explored later to provide theoretical guidance for improving the reproductive health of women of childbearing age in highland areas.

The relationship between the environment and the human vaginal microbiome is still little explored. In order to gain a deeper understanding of this issue, we selected the vaginal secretions of 60 women of childbearing age living at four altitudes (8 m above sea level, 2,000 m above sea level, 2,900 m above sea level, and 4,000 m above sea level) to explore the issue, and found that the composition of the vaginal flora was different among the women of childbearing age living at the four altitudes The study found that the composition of the vaginal flora differed among women of reproductive age living at the four altitudes, but there were no significant differences in diversity and differences in vaginal metabolites among women at different altitudes. Our study demonstrates that high altitude environment affects the vaginal flora and metabolites of women of childbearing age [ 23 – 25 ]. The present study is in good agreement with previous studies on the composition of vaginal flora in healthy women of childbearing age, which is mainly composed of Phylum Thicket and Actinobacteria at the phylum level, and Lactobacillus, Gardnerella and Prevotella spp. at the genus level [ 26 – 28 ]. The correlation between altitude and vaginal flora has not been previously reported. Zhou Qian et al. [ 29 ] studied the salivary microbiota of young male subjects before, during, and after acute high altitude exposure and found that the diversity of human salivary microorganisms was higher prior to entering the plateau than upon entering and returning from the plateau region. Liu Fang et al. [ 30 ] analyzed saliva samples from Tibetans at four altitudes in Tibet and found that the alpha diversity of the oral flora decreased with increasing altitude, whereas the beta diversity increased with increasing altitude. A study of skin flora at different altitudes also showed a significant decrease in α diversity in skin samples with increasing altitude [ 12 ]. In the present study, it was found that the alpha diversity of vaginal flora increased with increasing altitude, but there was no statistically significant difference. The reason for this may be, on the one hand, because the vaginal microbiota is different from the microbiota of other body parts in that it is dominated by a single genus of Lactobacillus and that Lactobacillus produces acid, which inhibits the growth of some microorganisms [ 31 ]. On the other hand, compared to the skin, the vagina is located inside the body and is less susceptible to changes caused by alterations in the external environment [ 32 ]. Also, this study was all Han Chinese, further highlighting the importance of genetics in shaping the human vaginal flora. Although no statistical differences were observed, which may be due to the insufficient sample size and the lack of sensitivity of the study design, further studies need to expand the sample size or consider other potential confounding factors in order to more accurately assess the effect of altitude on the diversity of vaginal flora. The relatively low temperatures in the plateau region, the large temperature difference between day and night, and the low oxygen content of the air, the oxygen content of the air decreases by 30% and 50% at altitudes of 3,000 and 5,500 m above sea level, respectively [ 33 ]. Hypoxia damages cells, tissues, and organs of the human body and leads to the development of diseases [ 34 ]. Studies on hypoxia and flora have found that specialized anaerobic bacteria are positively correlated with altitude, and parthenogenetic anaerobic and aerobic bacteria are negatively correlated with altitude [ 35 ]. It was also confirmed in the present study that the percentage of specialized anaerobes such as Porphyromonas spp, Anaerococcus spp, and Peptostreptococcus spp increased in the vagina with increasing altitude. These species colonize the vaginas of a small number of normal women of childbearing age and are low abundance taxa in the vaginal flora, as well as being recognized pathogens in pregnant individuals as well as in immunocompromised populations. It is associated with bacterial vaginosis, HPV infection, preterm delivery, and gynecologic neoplasms [ 36 – 38 ]. Li Kang et al. [ 39 ] studied the intestinal flora and found that high altitude environment can increase the abundance of pathogenic bacteria in the host’s intestinal tract. Similarly in this study, it was found that the percentage of Lactobacillus spp. in the vagina showed a decreasing trend with increasing altitude, but still dominated in the vagina, and the percentage of pathogenic bacteria, such as Mycoplasma spp. in the vagina increased. The high-altitude environment shapes a unique vaginal community structure, which also leads to a higher incidence of reproductive tract diseases in the plateau than in the plains. Zhai Guolong et al. [ 40 ] compared polycystic ovary syndrome in women from the Tibetan Plateau region with those from the plains through retrospective analysis. Li Jianqi et al. [ 41 ] investigated the rate of HPV infection in rural women in the Tibetan region, which showed an overall HR-HPV infection rate of 13%, which was significantly higher than the infection rate of 7.82% in women in rural areas outside of the Tibetan Plateau (χ 2 = 635.7, P < 0.001), and found that altitude may affect the rate of HPV infection. In the future, it is important to focus on the reproductive health level of women of reproductive age in this particular region. We have found that altitudinal changes affect vaginal metabolism, particularly energy metabolism and amino acid metabolism, and previous studies have found a sustained enrichment of metabolic pathways related to energy metabolism, amino acid metabolism, and carbohydrate metabolism in the gut at high altitudes [ 42 , 43 ]. In the present study, we found enrichment of neuroactive ligand-receptor interactions, glycine-serine and threonine metabolism, and ABC transporter pathways in the HH group, which appeared to be closely related to high altitude. Neuroactive ligand-receptor interactions, a collection of all receptors and ligands on the plasma membrane associated with intra- and extracellular signaling pathways, have been associated with a wide range of disease processes [ 44 ], and it was found that by modulating the neurological ligand-receptor interaction pathway in zebrafish larvae exerts an anti-hypoxic effect, reduces inflammation, promotes angiogenesis, regulates blood pressure and blood flow, inhibiting apoptosis, and ultimately exerting anti-hypoxic effects [ 45 ]. Glycine, serine, and threonine are important components of amino acids and are involved in a variety of biochemical metabolic pathways [ 46 ]. Both glycine and serine are metabolized by intestinal flora as a source of energy and are metabolized by the liver into glycogen and uric acid to supply energy to the organism [ 47 ], while threonine is mainly involved in adenosine triphosphate (ATP) synthesis, which is closely related to cellular metabolism [ 48 ].ABC transporters are an important class of transmembrane proteins, which utilize the energy from the hydrolysis of adenosine triphosphate for transporting substances through the cell membrane [ 49 ]. The enrichment of the above metabolic pathways in the vaginal flora may reflect the fact that changes in altitude may lead to alterations in the demand and utilization of nutrients and metabolic substances by the vaginal microbial community. Statistical analysis of the differential metabolites showed that tetrahydropyrimidine, thiamine, taurine, gluconolactone, 2-oxoadipic acid, and N-acetylserotonin differed significantly in the distribution of the four groups and were significantly elevated in the vaginas of high-altitude women. High-altitude environments are usually characterized by low oxygen, low air pressure and low temperature, and these environmental factors may lead to adaptive changes in human metabolism. Substances such as tetrahydropyrimidines and thiamine are involved in energy metabolism pathways [ 49 , 50 ], and their elevation may be to adapt to the demands of energy metabolism in high-altitude environments. High altitude organisms face more intense oxidative stress, which may lead to an increase in intracellular free radical production, and taurine and gluconolactone, etc. have antioxidant effects [ 51 , 52 ].N-acetylserotonin is a precursor substance of serotonin, which plays an important role in regulating the nervous system, and the body’s serotonin level is elevated under hypoxic conditions to exert neuroprotection [ 53 ]. However, about its specific mechanism of action is still unclear, and in vitro cellular and animal experiments are needed to further explore it in the future. Although the changes and associations between vaginal flora and metabolites in women of childbearing age at different altitudes were described in our study, the present study involved a small number of subjects and these results were not validated in a larger population-based cohort.

The human microbiome is a complex ecosystem consisting of trillions of microorganisms, including bacteria, viruses, fungi, and archaea, which are present in all parts of the human body [ 1 ]. Their number exceeds that of the body’s own cells, and the study of the human microbiome has been an area of interest over the past few years because of its close connection to human health [ 2 ]. The Human Microbiome Project (HMP) has opened new horizons in microbial research, enhancing researchers’ understanding of host-microbe interactions at the four major colonization sites in the body (oral cavity, gastrointestinal tract, reproductive tract, and skin) [ 3 ]. Many conventional therapies are beginning to consider the multiple microbial causes of disease and the implications for treatment and prevention [ 4 ]. Along with the development of genome sequencing technology and bioinformatics, the study of the microbiome has evolved from culture-based studies to explorations using multi-omics techniques [ 5 ]. The female reproductive tract is divided into upper (uterus, fallopian tubes, and ovaries) and lower (cervix and vagina) according to its anatomical location, and reproductive tract microorganisms tend to congregate in the vaginal area, so the physiological status and microbiota of the lower genital tract are crucial for women’s health. Vaginal microorganisms in healthy women of reproductive age show low diversity (low abundance and homogeneity of microorganisms) [ 6 ]. The vaginal microbiota, dominated by Lactobacillus, is a marker of female reproductive tract health. The species and relative abundance of Lactobacillus species in the vagina determine the vaginal community status type (CST), Ravel et al. [ 7 ] classified the vaginal microbial community of women of childbearing age into 5 types using 16 S rRNA high-throughput sequencing, CST type I (the dominant bacterial population is Lactobacillus curvatus), CST type II (the dominant bacterial population is Lactobacillus gattii), CST type III (inert Lactobacillus as the dominant flora), CST type IV (low percentage of Lactobacillus and anaerobic bacteria as the dominant microorganisms) and CST type V (Lactobacillus Jenner as the dominant flora). Vaginal microorganisms are diverse, they are symbiotic and antagonistic to each other to maintain the dynamic balance of the vaginal microenvironment, which plays a decisive role in resisting the invasion of pathogenic microorganisms [ 8 ]. Microorganisms of the female reproductive system are influenced by behavioral factors (sexual orientation, number of sexual partners, contraceptive methods, feminine hygiene practices, smoking, alcohol consumption, obesity, etc.), socioeconomic factors (education, access to health care), racial and genetic factors (age, pregnancy, ethnicity), and environmental factors [ 9 ]. Once these influences disrupt the dynamic balance of the vaginal microenvironment, it will lead to the risk of vaginitis, miscarriage, preterm labor, HIV, and other sexually transmitted infections (STIs), which will seriously jeopardize the sexual and reproductive health of women of reproductive age [ 10 ]. The Tibetan Plateau is the highest plateau in the world, with an average elevation of 4,000 m above sea level, creating an alpine climate that is relatively cool, dry, low-pressure, and low-oxygen all year round. The unique physiological hypoxic environment of the Tibetan Plateau has produced a distinctive subpopulation of organisms evolving in this region that differs from lower altitude species in both physiological and genetic adaptations. This environment also exerts tremendous selective pressure on human microorganisms [ 11 ]. Currently, the effects of altitude on the oral, intestinal, and skin microbiota of humans and animals have been found to be altered in terms of flora composition and species diversity [ 12 – 14 ]. Still, no studies have been conducted in the literature on the effects of altitude on the human vaginal microbiota. Therefore, understanding the characteristics of the vaginal microbiome of women of childbearing age at different altitudes is the first step in identifying and correcting the microbial structure associated with the disease. High-altitude environments are often accompanied by hypoxia, and oxygen is an essential nutrient and a key substrate for cellular metabolism and bioenergetics [ 15 ]. Hypoxia affects all aspects of cellular function, including metabolism, growth, cell division, and death [ 16 ]. Acute plateau hypoxia exposure often leads to a range of disorders such as acute mountain sickness, sleep disorders, and headaches, however, most of the biological mechanisms are unknown [ 17 ]. A recent study showed that hypoxia can promote the development of endometriosis by increasing cell adhesion capacity [ 18 ]. Xiong Zhengfang et al. [ 19 ] studied young infertile women undergoing in vitro fertilization-embryo transfer who had lived for a long period in a low altitude normoxic environment ( 2500 m above sea level), and found that the rates of blastocyst formation, available blastocysts, and high-quality blastocysts in the hypoxic group were lower than those in the normoxic group. Hypoxia also has a greater impact on the female reproductive system, and it was found that female reproductive tract infections in the plateau region are higher than at the national level, which has become a major public health problem that jeopardizes the physical and mental health of women in the plateau region [ 20 ]. The physiological state of pregnancy itself is characterized by profound immunological and endocrinological adaptations that increase susceptibility to certain infections. Notably, the steadily rising levels of estrogen and progesterone throughout gestation induce significant changes in the urogenital tract. Estrogen promotes the accumulation of glycogen in vaginal epithelial cells, which can serve as a nutrient source for various microorganisms, including GBS. Furthermore, these hormonal fluctuations are known to modulate the local immune response, leading to a state of altered leukocyte function and potentially reduced capacity to clear pathogens [ 21 ]. Concurrently, the composition and stability of the vaginal microbiota can be disrupted. This hormonally-mediated shift in the vaginal microenvironment—characterized by altered nutrient availability, immune tolerance, and microbial ecology—is thought to create a niche that favors the colonization and ascension of GBS [ 22 ]. However, the precise interplay between these hormonal changes, the vaginal microbiome, and GBS colonization outcomes remains incompletely understood. In this study, we analyzed the differences in vaginal microbiota composition and vaginal metabolites among healthy Han Chinese women of reproductive age living at different altitudes based on 16 S rDNA sequencing and metabolomics testing and compared the results obtained to explore the characteristics and influencing factors of vaginal microorganisms in women of reproductive age at different altitudes. The results of this study are of great significance for the prevention of reproductive tract diseases in women of childbearing age at different altitudes, the monitoring of drug efficacy, and the improvement of prognosis in the future.

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