Akkermansia muciniphila Ameliorates Chronic Sleep Deprivation-Induced Glucose Intolerance and Inflammatory Cytokine Activation

Abstract

Objective:

Emerging evidence indicates that Akkermansia muciniphila (A. muciniphila or AKK) regulates host glucose metabolism through multiple pathways. In this study, we examined the therapeutic effects of A. muciniphila on chronic sleep deprivation (CSD)-induced glucose dysregulation and the underlying mechanisms.

Methods:

A modified multiplatform water environment method was used to generate a mouse model of CSD. The mice were divided into three groups: the control (CON) group (ad libitum sleep), the CSD group (subjected to sleep deprivation), and the CSD+AKK group (CSD mice were supplemented with A. muciniphila at 3 × 10⁸ CFU per mouse, three times per week). After an 8-week intervention, glucose metabolism was assessed. Serum concentrations of lipopolysaccharide (LPS), interleukin-6 (IL-6), interleukin-1β (IL-1β) and tumor necrosis factor α (TNF-α) were measured. The mRNA expression and protein expression of mucin 2 (MUC2) and zonula occludens-1 (ZO-1) in the colon tissue were analyzed. Goblet cells in colon tissues were quantified using Alcian Blue–Periodic Acid-Schiff (AB–PAS) staining. Additionally, changes in gut microbiome diversity and composition among groups were compared.

Results:

CSD induced significant glucose intolerance and insulin resistance, evidenced by increased area under the curve (AUC) of the oral glucose tolerance test (OGTT), homeostatic model assessment of insulin resistance (HOMA-IR), and fasting insulin levels compared to the CON group (all p < 0.05). This was accompanied by a marked impairment of the colonic mucosal barrier, characterized by a profound loss of goblet cells and downregulation of key barrier components, MUC2 and ZO-1, at both the mRNA and protein levels (all p < 0.05). Intervention with A. muciniphila significantly ameliorated CSD-induced glucose intolerance, insulin resistance and colonic barrier damage. Furthermore, CSD elevated serum levels of LPS, IL-6, TNF-α, and IL-1β (all p < 0.05), which were effectively mitigated by A. muciniphila intervention. 16S rDNA sequencing confirmed the successful colonization of A. muciniphila, as its absolute abundance was significantly greater in the CSD+AKK group than in the CSD group. In addition, A. muciniphila intervention affected the abundance of Burkholderiales bacterium, Lactococcus garvieae, and other bacterial strains in the intestine.

Conclusion:

A. muciniphila supplementation effectively ameliorated CSD-induced glucose intolerance, reduced the serum levels of LPS and proinflammatory cytokines (IL-6, TNF-α, and IL-1β), and restored intestinal barrier integrity by upregulating MUC2 and ZO-1 expression in colon tissues.

Graphical abstract

Keywords

glucose metabolism / sleep deprivation / Akkermansia muciniphila / inflammation / intestinal barrier functions

1. Introduction

A growing body of clinical and epidemiological evidence suggests that chronic sleep deprivation (CSD) is strongly associated with an increased risk of metabolic disorders such as insulin resistance and type 2 diabetes mellitus (T2DM) [1, 2, 3], posing potential challenges to public health [4, 5]. While traditional research has focused on neurohormonal pathways [6, 7, 8], the precise mechanisms underlying CSD-induced glucose metabolic dysregulation remain incompletely elucidated, prompting exploration into novel pathological mediators.

Emerging insights point to gut microbiota dysbiosis as a critical intermediary in the metabolic disorder of sleep loss [9, 10]. Sleep disorders may alter both the composition of the intestinal flora and its microenvironment, which in turn may disrupt host metabolic homeostasis [9, 11, 12]. Notably, studies have demonstrated that sleep deprivation compromises intestinal barrier integrity [13, 14, 15], which may lead to increased systemic translocation of endotoxins (e.g., lipopolysaccharide, LPS) and subsequent activation of inflammatory pathways [16, 17, 18]. These inflammatory responses are known to interfere with insulin signaling, thereby promoting glucose intolerance [19, 20]. This cascade highlights the gut microbiome as a promising therapeutic target for mitigating sleep deprivation-related metabolic complications.

Among the various gut symbionts, Akkermansia muciniphila (A. muciniphila or AKK) has garnered significant attention because of its potential beneficial effects on metabolic health [21]. A. muciniphila is an anaerobic bacterium renowned for its ability to fortify the intestinal mucosal barrier, exert anti-inflammatory effects, and regulate host glucose metabolism [22]. Clinical observations revealed a marked reduction in A. muciniphila abundance in individuals with prediabetes and diabetes compared with healthy controls [23]. Furthermore, multiple studies have indicated an association between decreased A. muciniphila levels and the occurrence of sleep disorders [24, 25], suggesting its potential involvement in the sleep–metabolism axis. Mechanistically, A. muciniphila may enhance barrier function via extracellular vesicle secretion, reduce endotoxemia, and ameliorate insulin resistance [21, 22, 26]. These properties position A. muciniphila supplementation as a viable strategy to counteract metabolic disturbances.

In our prior investigation using a CSD mouse model (unpublished data), we observed a significant negative correlation between the abundance of A. muciniphila and glycemic parameters, indicating that it is a key modulator of CSD-induced glucose dysregulation. Building on this foundation and consistent with existing evidence on its barrier-preserving and metabolic-stabilizing functions, we hypothesize that A. muciniphila intervention could alleviate CSD-induced glucose intolerance and inflammatory activation. In this study, we therefore administered live A. muciniphila via oral gavage to CSD mice to evaluate its therapeutic effects on glucose metabolism and to preliminarily explore the underlying mechanisms, thereby addressing a critical knowledge gap in this evolving field.

2. Methods and Materials

2.1 Study Design and Experimental Protocol

Thirty male C57BL/6J mice (6 weeks old, weighing 20 \pm 1 g; C57BL/6J mice were supplied by the Laboratory Animal Center of Peking University Health Science Center) were randomly assigned to three groups (10 mice per group): the control (CON) group, CSD group, and CSD+AKK group (CSD mice with A. muciniphila oral gavage). The mice were housed under a 12:12-hour light–dark cycle (lights on from 7:00 to 19:00) and had free access to food and water.

We subjected the mice to CSD using a modified multiple-platform water environment method (Supplementary Fig. 1). CSD mice underwent 16 hours of daily sleep deprivation (17:00 to 9:00 the next day), with the remaining 8 hours (9:00–17:00) spent in standard cages for recovery, over an 8-week period [27, 28]. CON group mice were housed in identical cages without sleep restriction. The CSD experiment began at 8 weeks of age (designated as week 0) and lasted for 8 weeks. At week 8, oral glucose tolerance tests (OGTTs) and insulin tolerance tests (ITTs) were performed. After the metabolic assessments were completed, blood was collected via the retro-orbital venous plexus under 3% isoflurane anesthesia, followed by euthanasia via cervical dislocation. For the CSD+AKK group, A. muciniphila suspension (10⁹ CFU/mL) was administered via oral gavage (0.3 mL per time) three times per week during the CSD period. The CON and CSD groups received equal volumes of sterile phosphate-buffered saline (PBS, pH 7.2–7.4) as a control (Fig. 1A). All animal husbandry and experimental procedures were approved by the Institutional Animal Care and Use Committee of Peking University Health Science Center (Animal protocol approval number: DLASBE0510).

2.2 Assessment of Glucose Metabolism

2.2.1 OGTTs

After fasting overnight, the mice received an oral gavage of glucose solution (2 g/kg body weight). Blood glucose levels were measured from the tail vein immediately before (0 min) and at 15, 30, 60, 90, and 120 minutes after the administration.

2.2.2 ITTs

The mice were placed in fresh bedding and fasted for 4 hours (water allowed), followed by intraperitoneal insulin injection (0.3 IU/kg body weight). Blood glucose was measured at 0, 15, 30, 60, 90, and 120 minutes using the same tail-tip blood sampling method (The detailed procedure for the emergency management of mouse hypoglycemia is described in Appendix A1).

2.2.3 Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) Index and Insulin Sensitivity (HOMA-IS) Index

Fasting serum insulin (INS) levels were quantified by enzyme-linked immunosorbent assay (ELISA); HOMA-IR was calculated as (FBG \times INS)/22.5 and HOMA-IS was 22.5/(FBG \times INS), with FBG representing fasting blood glucose concentration.

2.3 Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)

The tissue samples were homogenized in liquid nitrogen, followed by total RNA extraction using RNAiso Plus (9109; Takara Biomedical Technology, Beijing, China), chloroform phase separation, isopropanol precipitation, and 75% ethanol washing. cDNA was reverse transcribed under standardized conditions using the commercial kit (RR036A, Takara Biomedical Technology Co., Beijing, China). RT-qPCR analysis was performed with SYBR Green (RR820A, Takara Biomedical Technology Co., Beijing, China) chemistry with prevalidated primers. GAPDH was used as the internal control and gene expression was quantified using the \mathrm{\Delta }\mathrm{\Delta }Ct method. The detailed reaction systems, thermal cycling parameters, and primer sequences are provided in Supplementary Tables 1–4.

2.4 Western Blot (WB) Analysis

The tissue samples were homogenized in liquid nitrogen and subsequently lysed in RIPA buffer (AQ521; Aoqing Biotechnology, Beijing, China) with protease/phosphatase inhibitors (AQ551/AQ552; Aoqing Biotechnology, Beijing, China) for 30 minutes on ice. The protein concentration was quantified via a bicinchoninic acid assay (WB6501; NCM Biotech, Suzhou, China), as detailed in Appendix A2. The samples were subjected to sodium dodecyl sulfate–polyacrylamide gel electrophoresis after which the proteins were transferred to activated PVDF membranes (ISEQ00010; Merck Millipore, Darmstadt, Germany). The membranes were blocked in 5% skim milk, incubated with primary antibody (detailed antibody information is listed in Appendix A3) (4 °C overnight) and horseradish peroxidase (HRP)-conjugated secondary antibody (room temperature, 1 hour), and washed with TBST (137 Mm NaCl, 2.7 mM KCL, 25 mM Tris, 0.05% Tween-20) between steps (5 minutes \times 5 times). The protein bands were quantified (densitometry measured by ImageJ 1.54k, National Institutes of Health, USA; https://imagej.nih.gov/ij/) and normalized relative to that of the housekeeping protein (\beta-tubulin) after enhanced chemiluminescence visualization (P10300; NCM Biotech, China).

2.5 Pathology Staining of Colon Tissue

After the mice were sacrificed, the colon tissues were collected and fixed in 4% paraformaldehyde for 24 h, followed by paraffin embedding and sectioning.

2.5.1 Hematoxylin and Eosin (H&E) Staining

Following dewaxing and rehydration, the paraffin sections were subjected to hematoxylin staining for 5 minutes, differentiated in 1% hydrochloric acid ethanol until they turned pink, and then blued in running water until the nuclei appeared clear and bright blue under microscopic examination. The sections were subsequently stained with eosin for 5 minutes, dehydrated through a graded series of ethanol solutions and xylene, and finally mounted with neutral gum. Colon tissues from different groups were observed under an optical microscope. A pathological evaluation was performed on H&E-stained colon sections according to a published scoring system [29], with the total histopathological score derived from the sum of the epithelium and infiltration scores.

2.5.2 Immunohistochemistry (IHC) Staining

Paraffin-embedded sections were sequentially processed as follows: after dewaxing and rehydration, antigen retrieval was performed in citrate buffer (pH 6.0) using a microwave heating protocol (medium heat to boiling, maintained for 8 minutes, then medium–low heat for 7 minutes). After cooling, the sections were washed three times with PBS (pH 7.4). Endogenous peroxidase activity was then blocked by incubation in 3% hydrogen peroxide for 25 minutes at room temperature. Following a PBS wash, nonspecific binding was blocked with 3% bovine serum albumin for 30 minutes, prior to incubation with primary antibodies overnight at 4 °C. The sections were then incubated with an HRP-conjugated secondary antibody (ab205718/ab205719; Abcam, Cambridge, UK) for 50 minutes at room temperature, followed by diaminobenzidine development, hematoxylin counterstaining, dehydration, and mounting with neutral gum.

2.5.3 Alcian Blue-Periodic Acid-Schiff (AB-PAS) Staining

Paraffin-embedded sections were dewaxed, rehydrated, and processed for AB–PAS staining. The procedure involved sequential staining with Alcian Blue (8 min), periodic acid (15 min), and Schiff’s reagent (30 min in the dark), with a wash step after each staining step. After microscopic examination, the sections were dehydrated through an ethanol-butanol-xylene series and mounted in neutral gum. Quantitative analysis of goblet cells was carried out via ImageJ software.

2.6 ELISA

We measured LPS, tumor necrosis factor-\alpha (TNF-\alpha), interleukin-1\beta (IL-1\beta), and interleukin-6 (IL-6) levels in mouse serum using a sandwich ELISA (CLOUD-CLONE CORP., Katy, TX, USA), while fasting serum insulin levels were quantified via a competitive inhibition assay, as described in Appendix A4.

2.7 In Vitro Cultures

A. muciniphila (purchased from American Type Culture Collection, Catalog number: ATCC-BAA-835) was cultured anaerobically in CMC broth at 37 °C for 48 h (marked as P1). A 0.5 mL aliquot was transferred to fresh medium for P2 expansion. After 48 h, the bacterial cells were pelleted by centrifugation, resuspended in deoxygenated PBS, and adjusted to the target concentration (1 \times 10⁹ CFU/mL, determined by the McFarland standard method).

2.8 16S rDNA Sequencing

Absolute and relative quantification approaches for gut microbial 16S rDNA amplicon sequencing were integrated in this study. Absolute quantification was achieved by spiking synthetic DNA standards into samples during PCR amplification, enabling calibration curves to determine gene fragment copy numbers at both the DNA (copies/ng DNA) and sample levels (copies/g or mL). Relative quantification was performed following conventional amplicon sequencing workflows. Experimental procedures included genomic DNA extraction with quality control (gel electrophoresis validation), incorporation of spike-in standards for normalization, high-fidelity PCR library preparation with indexed primers, and Illumina sequencing. Bioinformatics analysis via QIIME2 involved adapter trimming (cutadapt), denoising, amplicon sequence variant (ASV)/operational taxonomic unit (OTU) generation, and taxonomic classification (rdp-classifier). Absolute abundance was calculated using spike-in-derived equations, followed by cellular abundance normalization (cell/g or mL). Community diversity was assessed through \alpha-diversity indices (Shannon, Chao1), \beta-diversity ordination (principal coordinate analysis, PCoA; nonmetric multidimensional scaling, NMDS), and statistical analyses (Wilcoxon; linear discriminant analysis effect size, LEfSe). Correlation analyses were explored using redundancy analysis (RDA), Mantel tests, and heatmaps, while compositional patterns were illustrated via phylogenetic trees, Venn diagrams, and stacked abundance plots.

2.9 Statistical Methods

Continuous data are presented as the mean \pm standard deviation (SD) or median (interquartile range, IQR). Comparisons between two groups were performed using Student’s t test or Mann–Whitney U test. For multigroup comparisons, one-way analysis of variance (ANOVA) was applied, followed by post hoc pairwise tests (Tukey’s test or Dunnett’s test). A threshold of p 0.05 was considered to indicate statistical significance. All the statistical analyses and graphing were conducted using SPSS 26 (IBM, Chicago, IL, USA) and GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA).

3. Results

3.1 A. muciniphila Ameliorates Glucose Intolerance and Insulin Resistance in CSD Mice

Mice were subjected to 8-week CSD with concurrent A. muciniphila intervention to investigate subsequent changes in glucose metabolism (Fig. 1A). Compared with those in the CON group, a marked decrease in body weight (p 0.001) and elevated daily food intake were observed in CSD-exposed mice. However, supplementation with A. muciniphila did not alter these parameters (Fig. 1B,C). Crucially, CSD-impaired glucose tolerance was demonstrated by higher blood glucose levels during an OGTT and a corresponding increase in the area under the curve (AUC) (p 0.01). This metabolic disturbance was effectively mitigated by A. muciniphila intervention, which normalized both the glucose curve and the AUC value to those of the CON group (Fig. 1D,E). To further elucidate the mechanism underlying the increase in glucose tolerance, we measured fasting insulin levels and calculated the HOMA-IR and HOMA-IS indices. The CSD group exhibited hyperinsulinemia (p 0.05) and significantly elevated HOMA-IR (p 0.05), indicating a state of insulin resistance. A. muciniphila supplementation successfully counteracted these effects, significantly lowering fasting insulin (p 0.05) and HOMA-IR (p 0.05) levels and increasing HOMA-IS (p 0.05), thereby restoring insulin sensitivity (Fig. 1F–H). However, the ITT assessment revealed no notable variation in insulin sensitivity among the groups (Supplementary Fig. 2).

3.2 A. muciniphila Restores CSD-Induced Colonic Mucosal Barrier Integrity

Building upon the associations between metabolic health and gut barrier function, we assessed the key components of the colonic mucosal barrier. Our analyses revealed that CSD significantly impaired barrier integrity, an effect that was consistently reversed by A. muciniphila intervention. Specifically, CSD induced significant downregulation of the key barrier components MUC2 and ZO-1 at both the mRNA and protein levels (all p 0.01; Fig. 2A,B,E–G). Notably, A. muciniphila intervention markedly attenuated these deficits by upregulating the expression of MUC2 and ZO-1. However, no significant changes in the mRNA expression of Ocln or Cldn1 were detected among the three groups (Fig. 2C,D). Furthermore, the results of IHC also demonstrated a severe decrease in MUC2 and ZO-1-positive areas in the CSD group (p 0.001), which was significantly mitigated by A. muciniphila treatment (p 0.001 for MUC2; p 0.05 for ZO-1; Fig. 2H).

This molecular-level disruption and recovery were further corroborated by tissue-level examinations. Histological assessment revealed significant CSD-induced inflammatory cell infiltration (assessed by histopathological score, CON vs. CSD p 0.001) and a pronounced loss of goblet cells, which were markedly attenuated by A. muciniphila supplementation (p 0.01; Fig. 2I–L).

3.3 A. muciniphila Mitigates Systemic Inflammation and Endotoxemia

A compromised gut barrier often permits the translocation of microbial products, triggering systemic inflammation. We therefore measured serum markers of endotoxemia and inflammation. As hypothesized, the CSD group exhibited significant increases in the levels of circulating LPS (p 0.01) and the proinflammatory cytokines IL-6, TNF-\alpha, and IL-1\beta (all p 0.001). Notably, A. muciniphila intervention significantly attenuated this systemic inflammatory response, reducing the serum levels of LPS (p 0.05), IL-6 (p 0.001), TNF-\alpha (p 0.01), and IL-1\beta (p 0.01) to levels comparable to those in the CON group (Fig. 3A–D).

3.4 A. muciniphila Modulates the Gut Microbiota Composition and its Correlation With Systemic Inflammation

Finally, we explored whether the protective effects of A. muciniphila were associated with broader changes in the gut microbial ecosystem. While alpha diversity indices remained unchanged across groups (Fig. 4A), PCoA revealed distinct microbial communities among the CON, CSD, and CSD+AKK groups (Fig. 4B,C), indicating a shift in microbial composition.

Absolute and relative abundance analyses revealed specific taxonomic differences (Fig. 4D,E). The CSD+AKK group was characterized by enrichments of Dubosiella and Tyzzeralla at the genus level and of Adlercerutzia equolifaciens and Bacteroides vulgatus at the species level (Fig. 5). Notably, the absolute abundance of A. muciniphila itself was greater in the CSD+AKK group than in the CSD group, confirming successful colonization. Furthermore, the abundance of Lactococcus garvieae was significantly decreased after A. muciniphila supplementation (Fig. 6A).

Most importantly, correlation analysis revealed a direct link between these microbial changes and the phenotype of CSD mice treated with A. muciniphila. The absolute abundance of A. muciniphila was strongly and negatively correlated with serum levels of LPS, IL-6, IL-1\beta, and TNF-\alpha. A similar beneficial correlation was observed for Burkholderiales bacterium, while Lactococcus garvieae was strongly positively correlated with the levels of all these inflammatory markers (Fig. 6B,C). These findings suggest that A. muciniphila-mediated amelioration of glucose intolerance and inflammation is closely associated with specific, beneficial remodeling of the gut microbiota.

4. Discussion

CSD has emerged as a pervasive environmental trigger for glucose metabolism disorders [30, 31]. Consequently, identifying strategies to counteract CSD-induced metabolic abnormalities has become a focus of research. Given the established links between the gut microbiota, sleep, and glucose homeostasis, targeting the gut microbial ecosystem represents a promising therapeutic method. In this study, we demonstrate that supplementation with A. muciniphila effectively ameliorates CSD-induced glucose intolerance. The protective effects appear to involve intestinal barrier repair, with a consequent reduction in systemic inflammation.

On the basis of previous studies, A. muciniphila appears to be a bacterial strain particularly susceptible to environmental factors. Its abundance often decreases under various pathological conditions, including diabetes [32, 33], hypertension [34], obesity [35], stress [36], anxiety [37], and sleep deprivation [24]. Consistent with prior findings [24, 25, 38, 39], our data confirm that CSD significantly depletes this beneficial symbiont. From the perspective of the local microenvironment for bacterial colonization, intestinal epithelial inflammation and oxidative stress are likely significant factors influencing the abundance of A. muciniphila [12, 24]. With respect to systemic factors, sleep deprivation could disrupt the rhythmic secretion of hypothalamic–pituitary–adrenal (HPA)–axis-related hormones [40, 41] and melatonin [42, 43, 44], which may consequently impact the colonization of A. muciniphila [13, 45]. Furthermore, relevant research has indicated that the abundance of A. muciniphila fluctuates with a certain circadian rhythmic fluctuation in vivo [46]. Sleep deprivation-induced disruption of circadian rhythms [47] and alterations in dietary patterns [46, 48, 49, 50] could be other important factors influencing changes in A. muciniphila abundance.

Furthermore, a compromised intestinal mucosal barrier is likely a key mediator linking CSD to systemic inflammation activation and glucose intolerance. Multiple studies have indicated that CSD can lead to intestinal mucosal barrier damage [13, 51, 52]. Potential mechanisms underlying CSD-induced intestinal mucosal injury include inappropriate activation of intestinal inflammation [13, 51], increased oxidative stress [52], and alterations in circadian rhythm regulators [53]. However, the aforementioned conclusions are primarily based on observational findings; they have not been mechanistically proven to establish causality, nor have they been validated in large-scale clinical studies. In a recent investigation, Zhang et al. [54] demonstrated by using a hypoxia-inducible factor 1-alpha (HIF-1\alpha) stabilizer in mice that HIF-1\alpha inhibition might mediate sleep deprivation-induced intestinal mucosal barrier damage, further emphasizing the role of oxidative stress in this process. Notably, however, that Zhang et al. [54] utilized a 5-day sleep deprivation protocol, which differs somewhat from the CSD model used in our study. Therefore, whether these findings can be directly extrapolated to our experimental context requires further investigation.

A compromised intestinal barrier, a key consequence of CSD, facilitates the translocation of gut-derived endotoxins such as LPS into circulation [55]. Although we did not directly quantify LPS levels within the colonic tissue, the coincidence of structural barrier defects (loss of MUC2 and ZO-1) and functional systemic endotoxemia (elevated serum LPS) strongly supports the “leaky gut” hypothesis in our model. This interpretation is consistent with the concept of “metabolic endotoxemia” established by Cani et al. [17], in which serum LPS is widely accepted as a surrogate marker for intestinal permeability and bacterial translocation. Consequently, the elevated proinflammatory cytokines observed in the serum likely originate from this gut-derived LPS triggering toll-like receptor 4 (TLR4) signaling, leading to the release of proinflammatory cytokines including TNF-\alpha and IL-6 [17, 56], and effectively linking the local barrier damage with the systemic metabolic phenotype. Our findings confirmed that A. muciniphila intervention robustly reduced serum LPS and inflammatory cytokine levels and concomitantly increased insulin sensitivity.

On the basis of existing research, A. muciniphila may modulate the host inflammatory factors through the following mechanisms: by degrading mucins while stimulating their production, thereby expanding the mucus layer [57]; by secreting extracellular vesicles that activate AMP-activated protein kinase (AMPK) in intestinal epithelial cells to increase tight junction protein expression [22]; and by producing anti-inflammatory metabolites that dampen immune activation [58]. The results presented here demonstrate the efficacy of A. muciniphila in restoring the expression of key barrier proteins, preventing goblet cell depletion in the colons, and mitigating systemic inflammation of CSD mice. Our study thus identifies A. muciniphila not only as a promising probiotic therapeutic but also as a key node in the complex pathophysiology linking sleep, the gut microbiota, and glucose metabolism.

Notably, we administered the intervention to mice via oral gavage of live A. muciniphila. Although this method has been widely used in previous studies and has been confirmed to effectively increase the colonization of A. muciniphila in the gut [58, 59, 60], how the bacteria survive the harsh gastric environment and successfully colonize the intestine remains incompletely understood. We speculate that repeated gavage and the dilution of gastric juice by the gavage vehicle (PBS, pH 7.2–7.4) may partially protect A. muciniphila, allowing a fraction of the bacteria to transit through the highly acidic stomach. On the other hand, it should be noted that the ameliorative effect of A. muciniphila on CSD-induced glucose metabolic abnormalities may not completely depend on successful colonization. A. muciniphila-derived proteins and extracellular vesicles [22, 26] could also play a role in this process, although this study does not confirm whether these A. muciniphila-derived proteins or extracellular vesicles contributed to the observed effects.

As research on the metabolic regulatory mechanisms of A. muciniphila has advanced [61], its potential for clinical application has attracted increasing attention. A randomized controlled trial conducted by Depommier et al. [21] in overweight/obese volunteers demonstrated that A. muciniphila supplementation effectively improved insulin resistance and hyperinsulinemia and reduced total cholesterol and serum LPS levels. Another clinical study in overweight/obese individuals with T2DM revealed that the efficacy of A. muciniphila in reducing body weight and improving glucose metabolism was largely depended on the baseline abundance in the gut, with metabolic benefits observed only in those with low baseline A. muciniphila levels [62]. However, whether A. muciniphila confers metabolic benefits in humans under conditions of sleep disorder remains unclear, as no related clinical studies have been conducted. Some clinical reports suggest that probiotic supplementation may moderately improve sleep disturbances and related complications [63, 64], however, the efficacy and safety of probiotic supplementation require further validation in large-scale trials. This study lays a preliminary groundwork for future research in this area.

This study has several limitations. First, the multiplatform water environment limits the use of metabolic cage systems for comprehensive energy expenditure measurements (e.g., VO₂ and VCO₂), thereby restricting our metabolic profiling to body weight and food intake. Second, although IL-6 is a known mediator of insulin resistance [65, 66], our study did not investigate its downstream signaling pathways, such as the upregulation of suppressor of cytokine signaling 3 (SOCS-3), the phosphorylation status of insulin receptor \beta (IR-\beta) and insulin receptor substrate-1 (IRS-1), or the activation of protein kinase B (PKB) and extracellular signal-regulated kinases 1 and 2 (ERK1/2) signaling [65, 66]. Third, we acknowledge a significant limitation regarding the verification of local colonic inflammation. Owing to experimental constraints, we were unable to measure local inflammatory cytokine or LPS levels in the colonic tissue, nor did we perform in vivo permeability assays (e.g., FITC-dextran). We recognize that providing such local data would have strengthened the direct causal link between barrier injury and systemic inflammation. However, to address this gap, we integrated the histological evidence of goblet cells depletion and tight junction loss with the functional outcome of systemic endotoxemia. While this strongly implies a gut-origin mechanism, we caution that our conclusions regarding the “sequence” of pathological events are inferred from these combined systemic and structural markers, and future studies are warranted to directly validate the local inflammatory milieu. Fourth, while our findings support a beneficial role for A. muciniphila, the underlying mechanisms have not been fully elucidated, particularly concerning the involvement of microbial metabolites and host lipid metabolism. Finally, the exclusive use of a murine model and the absence of human samples represent a primary translational constraint.

This study provides a theoretical foundation for development of A. muciniphila-based interventions against CSD-induced metabolic dysregulation. Future investigations should aim to elucidate the precise underlying mechanisms of A. muciniphila, integrating multiomics approaches such as metagenomics, metabolomics (including short-chain fatty acids), and lipidomics to comprehensively explore its interactions with host metabolism. Furthermore, direct assessments of gut barrier function, oxidative stress, and detailed lipid metabolic profiles are warranted to elucidate the proposed pathways. Finally, clinical studies are essential to validate these findings in human populations and to explore the therapeutic potential of A. muciniphila for sleep-related metabolic disorders.

5. Conclusion

In conclusion, A. muciniphila intervention can effectively improve glucose intolerance induced by CSD. It can also significantly ameliorate the increase in serum endotoxin LPS and the increase in serum levels of the inflammatory factors IL-6, TNF-\alpha, and IL-1\beta in CSD mice. Moreover, A. muciniphila intervention significantly abrogated the downregulation of the intestinal mucosal barrier proteins MUC2 and ZO-1 induced by CSD, partially alleviating damage to the intestinal mucosal barrier. Additionally, A. muciniphila intervention effectively increased the absolute abundance of certain bacterial strains, such as Burkholderiales_bacterium in the guts of CSD mice.

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Funding

National High Level Hospital Clinical Research Funding(BJ-2022-145)

National Natural Science Foundation of China(82200928)

China Endocrinology and Metabolism Young Scientific Talent Research Project(2021-N-03)