
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72230
10.1038/s41598-024-72230-4
Article
Analysis of the characteristics of intestinal microbiota in patients with different severity of obstructive sleep apnea
Wang Pei-Pei 1
Wang Li-Juan 2
Fan Yong-Qiang 3
Dou Zhan-Jun 4
Han Jian-Xing 5
Wang Bei wangbei1613@163.com

6
1 grid.263452.4 0000 0004 1798 4018 Department of the Second Clinical Medicine, The Second Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, China
2 Department of Respiratory, Ninth Hospital of Xi’an, Xian, China
3 https://ror.org/02vzqaq35 grid.452461.0 0000 0004 1762 8478 Department of the General Surgery, The First Hospital of Shanxi Medical University, Taiyuan, China
4 grid.440201.3 0000 0004 1758 2596 Department of Respiratory, Shanxi Cancer Hospital, Taiyuan, China
5 https://ror.org/03tn5kh37 grid.452845.a Department of Stomatology, The Second Hospital of Shanxi Medical University, Taiyuan, China
6 https://ror.org/03tn5kh37 grid.452845.a Department of Respiratory, The Second Hospital of Shanxi Medical University, Taiyuan, China
16 9 2024
16 9 2024
2024
14 215528 6 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Intestinal microbiota imbalance plays an important role in the progression of obstructive sleep apnea (OSA), and is considered to be the main mediator that triggers metabolic comorbidities. Here, we analyzed the changes in intestinal microbiota in patients with different severities of OSA based on apnea hypopnea index (AHI) classification, and explored the role of intestinal microbiota in the severity of OSA. This study included 19 healthy volunteers and 45 patients with OSA [5 ≤ AHI < 15 (n = 14), 15 ≤ AHI < 30 (n = 13), AHI ≥ 30 (n = 18)]. Relevant sleep monitoring data and medical history data were collected, and microbial composition was analyzed using 16S rRNA high-throughput sequencing technology. The diversity analysis of intestinal microbiota among different groups of people was conducted, including alpha diversity, beta diversity, species diversity, and marker species as well as differential functional metabolic pathway prediction analysis. With the increase of AHI classification, the alpha diversity in patients with OSA significantly decreased. The results revealed that the severity of OSA is associated with differences in the structure and composition of the intestinal microbiota. The abundance of bacteria producing short-chain fatty acids (such as Bacteroides, Ruminococcacea, and Faecalibacterium) in severe OSA is significantly reduced and a higher ratio of Firmicutes to Bacteroidetes. Random forest analysis showed that Parabacteroides was a biomarker genus with important discriminatory significance. The differential metabolic pathway prediction function shows that the main function of maintaining intestinal microbiota homeostasis is biosynthetic function. Our results show that the differences in the composition of intestinal microbiota in patients with different severities of OSA are mainly related to short-chain fatty acid-producing bacteria. These changes may play a pathological role in OSA combined with metabolic comorbidities.

Keywords

Obstructive sleep apnea
Apnea hypopnea index
Intestinal microbiota
Short-chain fatty acid
Subject terms

Microbial communities
Microbiology
Diseases
Shanxi Health Commission Free Exploration ProjectYDZJSX20231A063 Han Jian-Xing Shanxi Health Commission Key Research Special Projects2022XM29 Wang Bei issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Obstructive sleep apnea (OSA) is the most common sleep apnea disorder characterized by recurrent hypoventilation or respiratory interruption caused by upper respiratory collapse during sleep, leading to chronic intermittent hypoxia (CIH) and hypercapnia1. Among them, CIH is the main pathogenesis and an important factor causing systemic multisystem damage. It has been confirmed that OSA is closely related to cardiovascular and cerebrovascular diseases, type 2 diabetes, metabolic syndrome and other multi system diseases2,3. However, its mechanism is currently unclear. Epidemiological research shows that the incidence rate of OSA is high among the elderly and obese people. In the 30–60 age group, 24% of men and 9% of women have OSA, and the incidence rate of men and women aged 65 and above has further increased, as high as 40–60%4,5. This is an extremely significant group and a significant issue that cannot be ignored by the medical community and society.

With the development of high-throughput sequencing technology, some scholars have explored the close relationship between the occurrence and development of OSA and changes in intestinal microbiota6,7. Clinical studies have found that there is a significant imbalance in intestinal microorganisms in patients with OSA, including Clostridium, Ruminococcus, Escherichia coli, and bacteria that produce short-chain fatty acid (SCFA) in large quantities6,8. The decrease in species diversity of intestinal microbiota in patients with OSA is closely related to apnea and hypoxia9. In animal models of OSA, it has been found that CIH and sleep fragmentation can lead to substantial changes in the intestinal microbial community10–12. Changes in intestinal microbiota affect body metabolism and physiological functions, thereby participating in the progression of OSA disease, including hypertension and impaired glucose metabolism13,14. CIH regulates microorganisms (such as Clostridium, Lactococcus, and Bifidobacterium) and important functional metabolites (such as free fatty acids and bile acids), promoting the occurrence of lipid metabolic disorders15. In addition, OSA induces intestinal dysbiosis, leading to neuroimmune response and intestinal inflammation16.

At present, continuous positive airway pressure (CPAP) is still the main treatment method for patients with moderate to severe OSA, but due to its low acceptance and poor long-term compliance, about 40–70% of patients do not accept or cannot adhere to CPAP treatment for a long time. However, the emergence of new strategies to improve intestinal dysbiosis through interventions such as probiotics, prebiotics, and fecal microbiota transplantation may be an effective approach to address ecological imbalance mediated by OSA.

However, there are few studies on the intestinal microbiota of patients with different severities of OSA, which is related to multiple organ damage in OSA. In order to explore the role of changes in intestinal microbiota in the progression of OSA disease, this study performed high-throughput sequencing and feature analysis on the intestinal microbiota of patients with different severities of OSA, evaluating the composition and functional differences of intestinal microorganisms in patients with different degrees of OSA. This study provides strategies for early intervention and treatment of patients with OSA.

Materials and methods

Participants

This study included 19 healthy volunteers and 45 OSA patients who underwent overnight PSG monitoring at the Sleep Medicine Center of the Second Hospital of Shanxi Medical University. According to the AASM diagnostic criteria, all participants were divided into four groups based on their apnea hypopnea index (AHI) scores: no OSA (Control, AHI < 5 events/hour), mild OSA (L-OSA, 5 ≤ AHI < 15 events/hour), moderate OSA (M-OSA, 15 ≤ AHI < 30 events/hour), and severe OSA (S-OSA, AHI ≥ 30 events/hour)17–19. Exclusion criteria: Age < 18 years old; Pregnant women; Taking drugs that affect sleep, such as benzodiazepines, barbiturates, or sedatives; Accept relevant treatments, including but not limited to surgery and mechanical ventilation; Concomitant gastrointestinal diseases, such as ulcerative colitis and irritable bowel syndrome; Patients who have taken intestinal microbiota preparations, antibiotics, or immunosuppressants within 2 months prior to enrollment; People with tumors, diabetes, liver and kidney dysfunction, rheumatic immune diseases or other diseases that may affect intestinal microbiota imbalance. The Ethics Committee of the Second Hospital of Shanxi Medical University approved the trial protocol, and all methods were conducted in accordance with relevant guidelines and regulations. All subjects provide written informed consent.

Inclusion criteria for the healthy Control group: Age > 18 years old; No history of sleep apnea, insomnia, or other sleep disorders. Exclusion criteria: Individuals who have taken probiotics, antibiotics, or immunosuppressants within the two months prior to enrollment; Pregnant women; Significant changes in dietary structure or living environment within one month prior to enrollment; People with digestive system diseases, tumors, diabetes, liver and kidney dysfunction, rheumatic immune diseases or other diseases that may affect intestinal microbiota imbalance.

OSA assessment

All subjects underwent PSG monitoring at the Sleep Medicine Center of the Second Hospital of Shanxi Medical University. The PSG was conducted by a sleep technician from 10 pm to 7 am the next day. The raw PSG data was manually scored and interpreted by an experienced sleep technician, and then reviewed by a professional sleep physician.

16S rRNA gene amplification and sequencing

Clean and collect fecal samples from patients who wake up in the morning, and transfer them to a − 80 °C ultra-low temperature freezer for freezing within 30 min. Use OMEGA Soil DNA kit (D5625-01) to extract the total genomic DNA of the bacteria in the sample, and store it at − 20 °C. Use NanoDrop ND-1000 spectrophotometer (Thermo Fisher Science, Waltham, Massachusetts, USA) for quantitative analysis and 1.2% agarose gel electrophoresis for quality inspection.

PCR amplification, purification, quantification, and sample mixing: PCR amplification was performed on the highly variable V3-V4 region of the bacterial 16S rRNA gene. The PCR amplicon was purified using Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN). The PCR product was quantified using Quant-iT PicoGreen dsDNA Assay Kit on a microplate reader (BioTek, FLx800), and then mixed according to the required data volume for each sample.

Library construction and high-throughput sequencing: The sequencing library was prepared using Illumina's TruSeq Nano DNA LT Library Prep Kit. The library system was screened and purified by magnetic beads, and the final fragments were selected and purified by 2% agarose gel electrophoresis. Quality inspection of the library was performed on the Agilent Bioanalyzer, and the Quant it PicoGreen dsDNA Assay Kit was used to quantify the library on the Promega QuantiFluor fluorescence quantification system. Finally, a NovaSeq sequencer was used for dual end sequencing.

Bioinformatics analysis: For the raw sequence data obtained on the Illumina NovaSeq platform, QIIME2 bioinformatics platform is used for DADA2 processing of raw data, including primer removal, quality filtering, denoising, splicing, and de-chimerization steps. Each de-duplicated sequence generated after DADA2 quality control is called ASV (amplicon sequence variant), or feature sequence, and high-quality sequences are finally obtained. The Greengenes database is selected for species annotation of each ASV feature sequence. At the same time, the method of rarefaction is used to sample the ASV table to ensure that each sample is processed at the same sequencing depth level. Finally, QIIME2 bioinformatics platform is used for feature sequence analysis.

Statistical analysis

The bioinformatics-related statistics were analyzed using the QIIME2 software. The measurement data were expressed as mean ± standard deviation (X¯±S). Data with homogeneity of variance is tested using one-way analysis of variance, while data without homogeneity of variance is tested using Kruskal Wallis H-test. P < 0.05 indicates statistical significance.

Results

Clinical characteristics

A total of 45 patients with OSA (L-OSA: N = 14, M-OSA: N = 13, H-OSA: N = 18) and 19 controls were recruited for this study after PSG monitoring (Fig. 1). There were no significant differences in gender, age, body weight, height, systolic pressure, and diastolic pressure among the four groups. The AHI in the OSA group was significantly higher than that in the Control group. Compared with the Control group, the SpO2min in the moderate and severe OSA groups was significantly increased. Additionally, the BMI and SpO2mean in the severe OSA group were significantly higher than those in the Control group (Table 1).Fig. 1 Patient Ęow chart.

Table 1 Characteristics of all participants.

	Control (N = 19)	L-OSA (N = 14)	M = OSA (N = 13)	S-OSA (N = 18)	
Gender (male/female)	13/6	11/3	6/7	13/5	
Age (years, mean ± SD)	41.74 ± 16.71	52.71 ± 14.67	61.23 ± 21.84**	49.44 ± 13.30	
Weight (kg)	72.79 ± 21.14	68.24 ± 9.24	75.29 ± 8.89	81.00 ± 10.43	
Height	169.53 ± 13.73	164.93 ± 9.50	166.46 ± 6.29	171.39 ± 5.09	
BMI (kg/m2)	24.74 ± 4.91	25.11 ± 2.75	27.11 ± 2.19	27.57 ± 3.34*	
Systolic pressure	122.84 ± 18.68	125.36 ± 18.66	125.92 ± 15.20	127.72 ± 14.47	
Diastolic pressure	79.16 ± 11.19	80.79 ± 13.24	82.69 ± 9.05	85.50 ± 8.00	
AHI (events/h)	2.41 ± 1.32	7.71 ± 1.44***	19.38 ± 2.24***	58.28 ± 14.57***	
SpO2 min (%)	88.32 ± 3.51	86.00 ± 3.44	82.23 ± 6.18*	70.83 ± 13.09***	
SpO2 mean (%)	94.98 ± 1.66	94.54 ± 2.04	93.12 ± 4.39	92.00 ± 2.90**	
Data are presented as mean ± standard deviation or percentage. * indicates P < 0.05 compared with the Control group, ** indicates P < 0.01, and *** indicates P < 0.001. AHI: apnea–hypopnea index; SpO2 min represents the lowest oxygen saturation; SpO2 mean represents the average oxygen saturation.

OTU differences

Stool samples from patients were collected for the analysis of the composition and function of the intestinal microbiota using 16S rRNA gene sequencing targeting the V3-V4 region. The OTU clustering analysis results showed a total of 1934 common OTUs among the four groups. The Control group included 10,743 OTUs, the L-OSA group included 8862 OTUs, the M-OSA group included 8042 OTUs, and the S-OSA group included 6889 OTUs (Fig. 2A). The species accumulation curve (Fig. 2B).Fig. 2 (A) Outward Wayne diagram; (B) species accumulation curve, which is widely used to measure and predict the increase in species richness in a community as the sample size increases, and is widely used to determine whether the sample size is sufficient and estimate community richness.

Analysis of alpha diversity

To comprehensively evaluate the alpha diversity of the microbial community, this study utilized the Chao1 and Observed Species index to characterize richness, and the Simpson and Shannon indices to represent diversity. The results indicated no significant difference in the observed Chao1 index between the Control group and OSA patients. Compared with mild OSA, severe OSA patients have a significant decrease in Chao1 index (Fig. 3A). In terms of the Simpson, Shannon and Observed species indices, there was a significant decrease in severe OSA patients compared to healthy controls. Additionally, we observed that the Simpson, Shannon and Observed species indices of severe OSA patients were significantly lower than those of mild OSA patients (Fig. 3B–D).Fig. 3 Alpha diversity index represents the diversity of species within their habitat; (A) Chao1; (B) Shannon; (C) Simpson; (D) Observed_species.

Analysis of β diversity

β diversity analysis based on PCoA and NMDS principal coordinate analysis using bray_curtis distances revealed complete separation between the control group and OSA patient samples (Fig. 4A). Permanova analysis demonstrated that the inter-group differences between severe OSA patients and healthy controls, as well as between severe and mild OSA patients, were significantly greater than the intra-group differences (Fig. 4B).Fig. 4 (A) Principal component analysis of PCoA based on bray_curtis distance; (B) Inter group difference analysis.

Composition of Intestinal microbiota

Phylum level

We further evaluated the differences in microbial communities among different groups. Analysis of the top 10 phylum level differences among groups demonstrated that the dominant phyla in each group were primarily composed of Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria. There were no significant differences in the relative abundances of Firmicutes among the groups. The abundance of Bacteroidetes in the moderate and severe OSA groups was significantly lower than that in the Control group. Compared with mild OSA, the abundance of Bacteroidetes decreases more significantly in severe OSA. In addition, the Firmicutes/Bacteroidetes (F/B) ratio in the severe OSA group was significantly higher than that in the Control and mild OSA groups (Fig. 5A–D).Fig. 5 (A–D) Relative abundance of microbial communities at the phylum level; (E–G) Relative abundance of bacterial communities at the family level, *P < 0.05, **P < 0.01, ***P < 0.001. ****P < 0.0001.

Family level

At the family level, the dominant microbial communities among each group are mainly composed of Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, and Veillonellaceae. The abundance of Lachnospiraceae in the moderate and severe OSA groups was significantly higher than that in the Control group. In addition, compared with the Control group, the relative abundance of Ruminococcaceae in OSA patients was significantly reduced. The relative abundance of Ruminococcaceae in the moderate and severe OSA groups was significantly lower than that in the mild OSA group (Fig. 5E–G).

Genus level

At the genus level, we found that the relative abundance of Roseburia significantly increased in moderate and severe OSA groups compared to the Control group. The relative abundance of Faecalibacterium in moderate and severe OSA groups was significantly lower than that in mild OSA group. In addition, compared to the Control group, the number of Blautia significantly increased and the number of Oscillospira significantly decreased in severe OSA group (Fig. 6A–D). To further express the differences between groups, we used Lefse analysis to determine the main differences between the Control group and OSA patients. The results showed that mild OSA was enriched in genus level with g–lachnobacterium, moderate OSA was enriched with g–Acidovorax, g–Acidaminococcus, f_Comamonadaceae, severe OSA was enriched with f_Sphingomonadaceae, g_Sphingomonas (Fig. 6E). Random forest analysis showed that Parabacteroides had the highest importance in identifying the four groups (Fig. 6F).Fig. 6 Differences in microbial communities at the (A–D) genus level. (E) Lefse analyzed the taxonomic units with significant differences between groups. (F) Random forest analysis. *P < 0.05, **P < 0.01.

Analysis of the factors affecting community distribution by RDA

The RDA analysis was used to explore the correlation between the influencing factors and the distribution of samples. The results showed that among the multiple factors of physical examination indicators, weight (r2 = 0.157, P = 0.007), BMI (r2 = 0.184, P = 0.003), AHI (r2 = 0.246, P = 0.002), and SpO2 min (r2 = 0.138, P = 0.014), SpO2 mean (r2 = 0.147, P = 0.005) have significant effects on the community distribution of intestinal microbiota (Table 2, Fig. 7). Table 2 RDA analysis of the influence factors of community distribution.

Factors	RDA1	RDA2	r2	P	
Age	− 0.996	0.0876	0.0764	0.092	
Weight	− 0.381	0.924	0.157	0.007	
Hight	− 0.497	0.868	0.026	0.439	
BMI	− 0.250	0.968	0.184	0.003	
Systolic pressure	− 0.657	− 0.754	0.023	0.527	
Diastolic pressure	− 0.582	0.813	0.014	0.65	
AHI	− 0.366	0.931	0.246	0.002	
SpO2 min	0.025	− 1.000	0.138	0.014	
SpO2 mean	0.414	− 0.910	0.147	0.005	
RDA1 and RDA2 represent two sorting axes, namely principal components 1 and 2.

Fig. 7 RDA analysis of the correlation between related influencing factors and intestinal microbiota.

Functional analysis

Based on the MetaCyc database, the results of predictive analysis by PICRUSt2 in this study reveal that the intestinal microbiota, which plays a major role in maintaining the stability of the intestinal microbiota in the host, is primarily involved in biosynthesis functions. The main functions are the synthesis of amino acids and nucleotides (Fig. 8).Fig. 8 Functional potential prediction based on PICRUSt.

Discussion

The intestinal microbiota is a microbial community in the human body that plays a crucial role in maintaining human health and disease development. Research has found that the intestinal microbiota plays a vital role in regulating the risk of various chronic diseases, maintaining intestinal immunity, and systemic homeostasis. For example, it is of significant importance in conditions such as obesity, cardiac metabolic abnormalities, inflammatory bowel disease, and mental disorders20. In recent years, an increasing number of studies have identified a certain association between the intestinal microbiota and OSA. OSA and its unique pathological manifestations (CIH and sleep fragmentation) contribute to intestinal dysbiosis, leading to systemic low-grade chronic inflammatory changes. Ultimately, intestinal dysregulation may trigger or exacerbate multi-organ damage caused by OSA in susceptible individuals. However, these studies are very preliminary and the specific underlying mechanisms remain unclear. In this study, we investigated the impact and difference of intestinal microbiota in different severity levels of OSA. We found alterations in the composition of intestinal microbiota in patients with different severity levels of OSA. In severe OSA patients, the relative abundance of SCFA producing bacteria such as Bacteroidetes, Ruminococcaceae and Faecalibacterium, significantly decreased, while the F/B ratio and the abundance of harmful bacteria such as Roseburia and Lachnospiraceae significantly increased. In summary, our results indicate that differences in the composition of intestinal microbiota in OSA, which are mainly related to SCFA producing bacteria. These changes may play a pathological role in the metabolic comorbidities associated with OSA.

This study utilized 16S rRNA high-throughput sequencing technology to perform diversity and microbiota analysis on healthy control subjects as well as patients with mild, moderate, and severe OSA. The results revealed significant differences in the abundance and evenness of intestinal microbiota among OSA patients with different severity levels, and characteristic intestinal genera predictive of different severity levels of OSA were identified. Alpha diversity analysis of the microbiota showed significant differences between OSA patients of different severity levels and the healthy control group. The Shannon, Simpson indices and Observed species of severe OSA patients were significantly lower than those of healthy subjects. Moreover, as the AHI increased, the diversity of OSA patients decreased gradually. Compared with mild OSA, the diversity of severe OSA is significantly reduced. β diversity analysis indicated significant differences in intestinal microbiota alterations between mild and severe OSA patients, with inter-group differences being more pronounced than intra-group differences. These findings suggest a close relationship between the severity of OSA and the composition of intestinal microbiota.

Furthermore, we found that the severity of OSA is associated with differences in the structure and composition of the intestinal microbiota. The abundance of SCFA producing bacteria, such as Bacteroidetes, Ruminococcaceae, and Faecalibacterium, significantly decreased in patients with severe OSA. Studies have found that SCFA is closely related to insulin resistance and the pathological process of type 2 diabetes21. It has been reported that the phylum Bacteroidetes can produce SCFAs such as acetate, which is closely associated with reduced production of inflammatory mediators22. Inflammation has been linked to various OSA-related conditions such as hypertension, coronary heart disease, obesity, and diabetes23–27. SCFAs are produced from dietary fiber in fermented foods of the bacteria, including acetate, propionate, and butyrate salts. They play an important role in the energy metabolism of bacteria and the physiological and biochemical stability of the intestinal tract28–30. The SCFAs-producing microbiota is believed to be closely related to human metabolism and cardiovascular disease. Reduced production of SCFAs can lead to intestinal barrier dysfunction31,32. SCFAs promote mucin synthesis, reduce bacterial translocation, maintain intestinal mucosal integrity, and thereby reduce intestinal inflammation33–35. Consumption of an unhealthy diet or intermittent hypoxia can lead to dysbiosis of the intestinal microbiota, resulting in the depletion of significant amounts of SCFAs. This depletion can cause dysfunction of colonic cells, manifested by weakened tight junctions between epithelial cells and the protective intestinal epithelial barrier. Consequently, the intestine becomes more permeable. Moreover, the process of hypoxia/reoxygenation itself exerts direct toxicity on tight junctions and increases the risk of intestinal permeability30. Previous studies have also observed alterations in SCFAs-producing microbiota and increased levels of inflammation in patients with OSA36. In this study, SCFA producing bacteria, including Bacteroidetes, Ruminococcaceae, and Faecalibacterium, were highly enriched in the control group, which helped to increase intestinal anti-inflammatory activity37. In OSA patients, the reduction of SCFA producing bacteria leads to a decrease in some anti-inflammatory activity of the body, thereby increasing inflammation. This suggests that changes in the SCFAs producing microbiota in OSA patients may be associated with the progression of OSA. Furthermore, we also observed a significant increase in the relative abundance of harmful bacteria Roseburia and Lachnospiraceae in moderate to severe OSA cases. Studies have indicated that Roseburia can induce infection38. Lachnospiraceae (belonging to the Firmicutes phylum) has been shown to be associated with an increased risk of obesity39. This indicates that alterations in SCFAs-producing microbiota in OSA patients may be linked to their pathophysiological processes. Therefore, qualitative and quantitative analysis of metabolomics related to SCFA will be a key focus of our research. Targeted analysis and modification of specific microbial communities and metabolites will further contribute to our understanding of the relationship between intestinal microbiota and the occurrence and development of OSA, potentially making the regulation of intestinal microbiota a novel alternative treatment for OSA.

Dysfunction of intestinal microbiota is related to increased permeability of the intestinal mucosal barrier, leading to increased local and systemic inflammatory responses as well as metabolic disorders40,41. The increased F/B ratio is considered a marker of intestinal dysbiosis associated with obesity and hypertension, and is widely used to evaluate the pathological state of the body42–44. Studies have suggested that manipulating the F/B ratio through interventions could potentially aid in weight loss45. Animal studies have revealed that intestinal microbiota dysbiosis in OSA is primarily characterized by an increase in Firmicutes or a decrease in Bacteroidetes, leading to an elevated F/B ratio, which subsequently results in an increase in lactobacilli and a decrease in butyrate-producing bacteria10,46. Clinical trials from OSA patients also observed an increase in the F/B ratio47. In this study, we found that severe OSA exhibited more pronounced disruption of intestinal microbiota. It is worth noting that compared to mild OSA, patients with severe OSA exhibit lower abundance of Bacteroidetes and higher F/B ratio. This suggests that there is a correlation between intestinal microbiota disorder and different degrees of OSA, and intervening in the intestinal F/B ratio may be one of the ways to reduce the risk of OSA disease.

To further identify target bacterial genera influencing intestinal microbiota homeostasis in OSA patients of varying severity, this study employed a multidimensional model analysis to explore and validate intestinal microbiota biomarkers in OSA patients. The Lefse analysis revealed that individuals with mild OSA exhibited enrichment of g–lachnobacterium at the genus level, while those with moderate OSA showed increased abundance of g–Acidovorax, g–Acidaminococcus, f_Comamonadaceae. Those with severe OSA demonstrated enrichment of f_Sphingomonadaceae and g_Sphingomonas. Random forest analysis highlighted the significance of Parabacteroides in discriminating among the four groups. Functional prediction analysis suggested that alterations in the intestinal microbiota of OSA patients may play a crucial role in the progression of OSA by affecting biosynthesis signaling pathways.

Conclusion

In summary, this study suggested that differences in the structure and composition of intestinal microbiota in patients with different severities of OSA, indicating that alterations in intestinal microbiota may contribute to metabolic comorbidities associated with OSA. These discoveries have potential implications for profiling the microbiome of specific OSA patients and OSA-related diseases, as well as for targeted therapies such as probiotics or prebiotics.

Limitations

Our study has some limitations. Firstly, the sample size in our study was small, which may limit the robustness of our findings and necessitates a cautious interpretation of the results and conclusions. Secondly, our study revealed a significant BMI difference between severe OSA and healthy control groups. Given the potential influence of BMI on intestinal microbiota, future studies should include control and OSA groups with comparable BMIs. We consider this an area for improvement in our study. Thirdly, as dietary intake can alter intestinal microbiota and lead to adverse outcomes, the absence of dietary data in our study may impact the research findings. Future studies are needed to rectify and further validate our research. Finally, as a small-scale cross-sectional study, it cannot establish a definitive causal relationship between varying degrees of OSA severity and intestinal microbiota. Thus, future studies should focus on larger-scale longitudinal research across different levels of OSA severity to corroborate our findings. Despite these limitations, the data on the composition, diversity, and functional alterations of microbial taxa from our study form a critical foundation for understanding the intestinal microbiome in patients with various degrees of OSA.

Acknowledgements

We thank all the members involved in this study for their dedication in the design, implementation, acquisition, analysis and collation of the data.

Author contributions

P.-P.W.: Data analysis, literature review and draft writing. L.-J.W. and Y.-Q.F.:Integrate results and draft writing. Z.-J.D. and J.-X.H.: Sample selection and review literature. B.W.: Experimental design guidance.

Funding

This research was funded by the Shanxi Health Commission Key Research Special Projects (2022XM29), Shanxi Health Commission Free Exploration Project (YDZJSX20231A063).

Data availability

The raw sequence data of microbiota that corroborate our study's conclusions have been added to the NCBI SRA with accession number PRJNA1121898.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed consent has been obtained from all subjects for this study.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Lévy P Obstructive sleep apnoea syndrome Nat. Rev. Dis. Primers 2015 1 15015 10.1038/nrdp.2015.15 27188535
Lévy, P. et al. Obstructive sleep apnoea syndrome. Nat. Rev. Dis. Primers 1, 15015 (2015).27188535 10.1038/nrdp.2015.15
2. Wang F The association between obstructive sleep apnea syndrome and metabolic syndrome: A confirmatory factor analysis Sleep Breath 2019 23 1011 1019 10.1007/s11325-019-01804-8 30820851
Wang, F. et al. The association between obstructive sleep apnea syndrome and metabolic syndrome: A confirmatory factor analysis. Sleep Breath 23, 1011–1019 (2019).30820851 10.1007/s11325-019-01804-8
3. Konishi T Obstructive sleep apnea is associated with increased coronary plaque instability: An optical frequency domain imaging study Heart Vessels 2019 34 1266 1279 10.1007/s00380-019-01363-8 30790035
Konishi, T. et al. Obstructive sleep apnea is associated with increased coronary plaque instability: An optical frequency domain imaging study. Heart Vessels 34, 1266–1279 (2019).30790035 10.1007/s00380-019-01363-8
4. Rosenzweig I Williams SCR Morrell MJ The impact of sleep and hypoxia on the brain: Potential mechanisms for the effects of obstructive sleep apnea Curr. Opin. Pulm. Med. 2014 20 565 571 10.1097/MCP.0000000000000099 25188719
Rosenzweig, I., Williams, S. C. R. & Morrell, M. J. The impact of sleep and hypoxia on the brain: Potential mechanisms for the effects of obstructive sleep apnea. Curr. Opin. Pulm. Med. 20, 565–571 (2014).25188719 10.1097/MCP.0000000000000099
5. Young T The occurrence of sleep-disordered breathing among middle-aged adults N. Engl. J. Med. 1993 328 1230 1235 10.1056/NEJM199304293281704 8464434
Young, T. et al. The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl. J. Med. 328, 1230–1235 (1993).8464434 10.1056/NEJM199304293281704
6. Tripathi A Intermittent hypoxia and hypercapnia, a hallmark of obstructive sleep apnea, alters the gut microbiome and metabolome mSystems 2018 3 e00020 18 10.1128/mSystems.00020-18 29896566
Tripathi, A. et al. Intermittent hypoxia and hypercapnia, a hallmark of obstructive sleep apnea, alters the gut microbiome and metabolome. mSystems 3, e00020-18 (2018).29896566 10.1128/mSystems.00020-18
7. Han M Yuan S Zhang J The interplay between sleep and gut microbiota Brain Res. Bull. 2022 180 131 146 10.1016/j.brainresbull.2021.12.016 35032622
Han, M., Yuan, S. & Zhang, J. The interplay between sleep and gut microbiota. Brain Res. Bull. 180, 131–146 (2022).35032622 10.1016/j.brainresbull.2021.12.016
8. Wang F The dysbiosis gut microbiota induces the alternation of metabolism and imbalance of Th17/Treg in OSA patients Arch. Microbiol. 2022 204 217 10.1007/s00203-022-02825-w 35322301
Wang, F. et al. The dysbiosis gut microbiota induces the alternation of metabolism and imbalance of Th17/Treg in OSA patients. Arch. Microbiol. 204, 217 (2022).35322301 10.1007/s00203-022-02825-w
9. Baldanzi G OSA Is associated with the human gut microbiota composition and functional potential in the population-based Swedish cardiopulmonary bioimage study Chest 2023 164 503 516 10.1016/j.chest.2023.03.010 36925044
Baldanzi, G. et al. OSA Is associated with the human gut microbiota composition and functional potential in the population-based Swedish cardiopulmonary bioimage study. Chest 164, 503–516 (2023).36925044 10.1016/j.chest.2023.03.010
10. Moreno-Indias I Intermittent hypoxia alters gut microbiota diversity in a mouse model of sleep apnoea Eur. Respir. J. 2015 45 1055 1065 10.1183/09031936.00184314 25537565
Moreno-Indias, I. et al. Intermittent hypoxia alters gut microbiota diversity in a mouse model of sleep apnoea. Eur. Respir. J. 45, 1055–1065 (2015).25537565 10.1183/09031936.00184314
11. Lucking EF Chronic intermittent hypoxia disrupts cardiorespiratory homeostasis and gut microbiota composition in adult male guinea-pigs EBioMedicine 2018 38 191 205 10.1016/j.ebiom.2018.11.010 30446434
Lucking, E. F. et al. Chronic intermittent hypoxia disrupts cardiorespiratory homeostasis and gut microbiota composition in adult male guinea-pigs. EBioMedicine 38, 191–205 (2018).30446434 10.1016/j.ebiom.2018.11.010
12. Durgan DJ Role of the gut microbiome in obstructive sleep apnea-induced hypertension Hypertension 2016 67 469 474 10.1161/HYPERTENSIONAHA.115.06672 26711739
Durgan, D. J. et al. Role of the gut microbiome in obstructive sleep apnea-induced hypertension. Hypertension 67, 469–474 (2016).26711739 10.1161/HYPERTENSIONAHA.115.06672
13. Khalyfa A Circulating exosomes and gut microbiome induced insulin resistance in mice exposed to intermittent hypoxia: Effects of physical activity EBioMedicine 2021 64 103208 10.1016/j.ebiom.2021.103208 33485839
Khalyfa, A. et al. Circulating exosomes and gut microbiome induced insulin resistance in mice exposed to intermittent hypoxia: Effects of physical activity. EBioMedicine 64, 103208 (2021).33485839 10.1016/j.ebiom.2021.103208
14. Badran M Khalyfa A Ericsson A Gozal D Fecal microbiota transplantation from mice exposed to chronic intermittent hypoxia elicits sleep disturbances in naïve mice Exp. Neurol. 2020 334 113439 10.1016/j.expneurol.2020.113439 32835671
Badran, M., Khalyfa, A., Ericsson, A. & Gozal, D. Fecal microbiota transplantation from mice exposed to chronic intermittent hypoxia elicits sleep disturbances in naïve mice. Exp. Neurol. 334, 113439 (2020).32835671 10.1016/j.expneurol.2020.113439
15. Wang F Effects of chronic intermittent hypoxia and chronic sleep fragmentation on gut microbiome, serum metabolome, liver and adipose tissue morphology Front. Endocrinol. 2022 13 820939 10.3389/fendo.2022.820939
Wang, F. et al. Effects of chronic intermittent hypoxia and chronic sleep fragmentation on gut microbiome, serum metabolome, liver and adipose tissue morphology. Front. Endocrinol. 13, 820939 (2022).10.3389/fendo.2022.820939
16. Ayyaswamy S Shi H Zhang B Bryan RM Durgan DJ Obstructive sleep apnea-induced hypertension is associated with increased gut and neuroinflammation J. Am. Heart Assoc. 2023 12 e029218 10.1161/JAHA.122.029218 37260032
Ayyaswamy, S., Shi, H., Zhang, B., Bryan, R. M. & Durgan, D. J. Obstructive sleep apnea-induced hypertension is associated with increased gut and neuroinflammation. J. Am. Heart Assoc. 12, e029218 (2023).37260032 10.1161/JAHA.122.029218
17. Baldanzi G OSA Is associated with the human gut microbiota composition and functional potential in the population-based Swedish cardiopulmonary bioimage study Chest 2023 164 503 516 10.1016/j.chest.2023.03.010 36925044
Baldanzi, G. et al. OSA Is associated with the human gut microbiota composition and functional potential in the population-based Swedish cardiopulmonary bioimage study. Chest 164, 503–516 (2023).36925044 10.1016/j.chest.2023.03.010
18. Kapur VK Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: An American academy of sleep medicine clinical practice guideline J. Clin. Sleep Med. 2017 13 479 504 10.5664/jcsm.6506 28162150
Kapur, V. K. et al. Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: An American academy of sleep medicine clinical practice guideline. J. Clin. Sleep Med. 13, 479–504 (2017).28162150 10.5664/jcsm.6506
19. Yeghiazarians Y Obstructive sleep apnea and cardiovascular disease: A scientific statement from the American heart association Circulation 2021 144 e56 e67 10.1161/CIR.0000000000000988 34148375
Yeghiazarians, Y. et al. Obstructive sleep apnea and cardiovascular disease: A scientific statement from the American heart association. Circulation 144, e56–e67 (2021).34148375 10.1161/CIR.0000000000000988
20. Singh RK Influence of diet on the gut microbiome and implications for human health J. Transl. Med. 2017 15 73 10.1186/s12967-017-1175-y 28388917
Singh, R. K. et al. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 15, 73 (2017).28388917 10.1186/s12967-017-1175-y
21. Abdel-Moneim A Bakery HH Allam G The potential pathogenic role of IL-17/Th17 cells in both type 1 and type 2 diabetes mellitus Biomed. Pharmacother 2018 101 287 292 10.1016/j.biopha.2018.02.103 29499402
Abdel-Moneim, A., Bakery, H. H. & Allam, G. The potential pathogenic role of IL-17/Th17 cells in both type 1 and type 2 diabetes mellitus. Biomed. Pharmacother 101, 287–292 (2018).29499402 10.1016/j.biopha.2018.02.103
22. Maslowski KM Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43 Nature 2009 461 1282 1286 10.1038/nature08530 19865172
Maslowski, K. M. et al. Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature 461, 1282–1286 (2009).19865172 10.1038/nature08530
23. Turnbull CD Transcriptomics identify a unique intermittent hypoxia-mediated profile in obstructive sleep apnea Am. J. Respir. Crit. Care Med. 2020 201 247 250 10.1164/rccm.201904-0832LE 31517507
Turnbull, C. D. et al. Transcriptomics identify a unique intermittent hypoxia-mediated profile in obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 201, 247–250 (2020).31517507 10.1164/rccm.201904-0832LE
24. Sanderson JE Fang F Wei Y Obstructive sleep apnoea and inflammation in age-dependent cardiovascular disease Eur. Heart J. 2020 41 2503 10.1093/eurheartj/ehaa332 32385494
Sanderson, J. E., Fang, F. & Wei, Y. Obstructive sleep apnoea and inflammation in age-dependent cardiovascular disease. Eur. Heart J. 41, 2503 (2020).32385494 10.1093/eurheartj/ehaa332
25. Campos-Rodriguez F Interleukin 6 as a marker of depression in women with sleep apnea J. Sleep Res. 2021 30 e13035 10.1111/jsr.13035 32212220
Campos-Rodriguez, F. et al. Interleukin 6 as a marker of depression in women with sleep apnea. J. Sleep Res. 30, e13035 (2021).32212220 10.1111/jsr.13035
26. Arnaud C Bochaton T Pépin J-L Belaidi E Obstructive sleep apnoea and cardiovascular consequences: Pathophysiological mechanisms Arch. Cardiovasc. Dis. 2020 113 350 358 10.1016/j.acvd.2020.01.003 32224049
Arnaud, C., Bochaton, T., Pépin, J.-L. & Belaidi, E. Obstructive sleep apnoea and cardiovascular consequences: Pathophysiological mechanisms. Arch. Cardiovasc. Dis. 113, 350–358 (2020).32224049 10.1016/j.acvd.2020.01.003
27. Wali SO The utility of proinflammatory markers in patients with obstructive sleep apnea Sleep Breath 2021 25 545 553 10.1007/s11325-020-02149-3 32705528
Wali, S. O. et al. The utility of proinflammatory markers in patients with obstructive sleep apnea. Sleep Breath 25, 545–553 (2021).32705528 10.1007/s11325-020-02149-3
28. Burt VL Trends in the prevalence, awareness, treatment, and control of hypertension in the adult US population. Data from the health examination surveys, 1960 to 1991 Hypertension 1995 26 60 69 10.1161/01.HYP.26.1.60 7607734
Burt, V. L. et al. Trends in the prevalence, awareness, treatment, and control of hypertension in the adult US population. Data from the health examination surveys, 1960 to 1991. Hypertension 26, 60–69 (1995).7607734 10.1161/01.HYP.26.1.60
29. Canani RB Potential beneficial effects of butyrate in intestinal and extraintestinal diseases World J. Gastroenterol. 2011 17 1519 1528 10.3748/wjg.v17.i12.1519 21472114
Canani, R. B. et al. Potential beneficial effects of butyrate in intestinal and extraintestinal diseases. World J. Gastroenterol. 17, 1519–1528 (2011).21472114 10.3748/wjg.v17.i12.1519
30. Vinolo MAR Rodrigues HG Nachbar RT Curi R Regulation of inflammation by short chain fatty acids Nutrients 2011 3 858 876 10.3390/nu3100858 22254083
Vinolo, M. A. R., Rodrigues, H. G., Nachbar, R. T. & Curi, R. Regulation of inflammation by short chain fatty acids. Nutrients 3, 858–876 (2011).22254083 10.3390/nu3100858
31. Duncan SH Louis P Flint HJ Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product Appl. Environ. Microbiol. 2004 70 5810 5817 10.1128/AEM.70.10.5810-5817.2004 15466518
Duncan, S. H., Louis, P. & Flint, H. J. Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product. Appl. Environ. Microbiol. 70, 5810–5817 (2004).15466518 10.1128/AEM.70.10.5810-5817.2004
32. Durgan DJ Obstructive sleep apnea-induced hypertension: Role of the gut microbiota Curr. Hypertens. Rep. 2017 19 35 10.1007/s11906-017-0732-3 28365886
Durgan, D. J. Obstructive sleep apnea-induced hypertension: Role of the gut microbiota. Curr. Hypertens. Rep. 19, 35 (2017).28365886 10.1007/s11906-017-0732-3
33. Moreno-Indias I Intermittent hypoxia alters gut microbiota diversity in a mouse model of sleep apnoea Eur. Respir. J. 2015 45 1055 1065 10.1183/09031936.00184314 25537565
Moreno-Indias, I. et al. Intermittent hypoxia alters gut microbiota diversity in a mouse model of sleep apnoea. Eur. Respir. J. 45, 1055–1065 (2015).25537565 10.1183/09031936.00184314
34. Sanz Y Moya-Pérez A Microbiota, inflammation and obesity Adv. Exp. Med. Biol. 2014 817 291 317 10.1007/978-1-4939-0897-4_14 24997040
Sanz, Y. & Moya-Pérez, A. Microbiota, inflammation and obesity. Adv. Exp. Med. Biol. 817, 291–317 (2014).24997040 10.1007/978-1-4939-0897-4_14
35. Moustafa A Genetic risk, dysbiosis, and treatment stratification using host genome and gut microbiome in inflammatory bowel disease Clin. Transl. Gastroenterol. 2018 9 e132 10.1038/ctg.2017.58 29345635
Moustafa, A. et al. Genetic risk, dysbiosis, and treatment stratification using host genome and gut microbiome in inflammatory bowel disease. Clin. Transl. Gastroenterol. 9, e132 (2018).29345635 10.1038/ctg.2017.58
36. Ko C-Y Gut microbiota in obstructive sleep apnea-hypopnea syndrome: Disease-related dysbiosis and metabolic comorbidities Clin. Sci. 2019 133 905 917 10.1042/CS20180891
Ko, C.-Y. et al. Gut microbiota in obstructive sleep apnea-hypopnea syndrome: Disease-related dysbiosis and metabolic comorbidities. Clin. Sci. 133, 905–917 (2019).10.1042/CS20180891
37. Sokol H Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients Proc. Natl. Acad. Sci. USA 2008 105 16731 16736 10.1073/pnas.0804812105 18936492
Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl. Acad. Sci. USA 105, 16731–16736 (2008).18936492 10.1073/pnas.0804812105
38. Yumoto T Raoultella planticola bacteremia-induced fatal septic shock following burn injury Ann. Clin. Microbiol. Antimicrob. 2018 17 19 10.1186/s12941-018-0270-0 29728100
Yumoto, T. et al. Raoultella planticola bacteremia-induced fatal septic shock following burn injury. Ann. Clin. Microbiol. Antimicrob. 17, 19 (2018).29728100 10.1186/s12941-018-0270-0
39. de la Cuesta-Zuluaga J Gut microbiota is associated with obesity and cardiometabolic disease in a population in the midst of Westernization Sci. Rep. 2018 8 11356 10.1038/s41598-018-29687-x 30054529
de la Cuesta-Zuluaga, J. et al. Gut microbiota is associated with obesity and cardiometabolic disease in a population in the midst of Westernization. Sci. Rep. 8, 11356 (2018).30054529 10.1038/s41598-018-29687-x
40. Neroni B Relationship between sleep disorders and gut dysbiosis: What affects what? Sleep Med. 2021 87 1 7 10.1016/j.sleep.2021.08.003 34479058
Neroni, B. et al. Relationship between sleep disorders and gut dysbiosis: What affects what?. Sleep Med. 87, 1–7 (2021).34479058 10.1016/j.sleep.2021.08.003
41. Barceló A Gut epithelial barrier markers in patients with obstructive sleep apnea Sleep Med. 2016 26 12 15 10.1016/j.sleep.2016.01.019 28007354
Barceló, A. et al. Gut epithelial barrier markers in patients with obstructive sleep apnea. Sleep Med. 26, 12–15 (2016).28007354 10.1016/j.sleep.2016.01.019
42. Li J Gut microbiota dysbiosis contributes to the development of hypertension Microbiome 2017 5 14 10.1186/s40168-016-0222-x 28143587
Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, 14 (2017).28143587 10.1186/s40168-016-0222-x
43. Qin J A human gut microbial gene catalogue established by metagenomic sequencing Nature 2010 464 59 65 10.1038/nature08821 20203603
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).20203603 10.1038/nature08821
44. Mariat D The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age BMC Microbiol. 2009 9 123 10.1186/1471-2180-9-123 19508720
Mariat, D. et al. The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol. 9, 123 (2009).19508720 10.1186/1471-2180-9-123
45. Ley RE Turnbaugh PJ Klein S Gordon JI Microbial ecology: Human gut microbes associated with obesity Nature 2006 444 1022 1023 10.1038/4441022a 17183309
Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).17183309 10.1038/4441022a
46. Yang T Gut dysbiosis is linked to hypertension Hypertension 2015 65 1331 1340 10.1161/HYPERTENSIONAHA.115.05315 25870193
Yang, T. et al. Gut dysbiosis is linked to hypertension. Hypertension 65, 1331–1340 (2015).25870193 10.1161/HYPERTENSIONAHA.115.05315
47. Collado MC Dysbiosis in snoring children: An interlink to comorbidities? J. Pediatr. Gastroenterol. Nutr. 2019 68 272 277 10.1097/MPG.0000000000002161 30289820
Collado, M. C. et al. Dysbiosis in snoring children: An interlink to comorbidities?. J. Pediatr. Gastroenterol. Nutr. 68, 272–277 (2019).30289820 10.1097/MPG.0000000000002161
