Brain-gut-microbiota interactions in sleep disorders
Sleep is a fundamental physiological process essential for maintaining both physical and mental health. While significant advances have been made in understanding the central nervous system mechanisms that regulate sleep-wake cycles, emerging evidence reveals the crucial role of peripheral organs, particularly the digestive system, in modulating brain function and behavior through the microbiota-gut-brain axis. This bidirectional communication network between the gut and the central nervous system directly and indirectly impacts sleep regulation. Disruptions in gut microbiota composition are closely linked to sleep disturbances, and alterations in the microbiota-gut-brain axis have been observed in several sleep disorders and illnesses with comorbid sleep disturbances. This review synthesizes current insights into the interplay between gut microbiota and sleep-wake regulation, highlighting potential routes of the microbiota-gut-brain axis in sleep and gut microbiome interactions and their health implications. Modulating gut microbiota may present a promising strategy for developing novel therapeutic approaches to address sleep disorders.
Introduction
Sleep is a physiological cornerstone of life playing a pivotal role in maintaining physical health, cognitive function, and emotional well-being (1). Recent data have revealed the bidirectional association between sleep and the gut, amid considerable breakthroughs in understanding the central nervous system mechanisms that regulate sleep-wake cycles (2). The gut microbiota, a diverse assemblage of bacteria in the gastrointestinal (GI) tract, interacts with the central nervous system through the gut-brain axis, influencing sleep patterns both directly and indirectly (2). The gut microbiota is implicated in the pathophysiology of neurological disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), autism spectrum disorder (ASD), schizophrenia, and depression (3–5). Alterations in gut microbiota composition are linked to multiple sleep disorders, such as insomnia, obstructive sleep apnea (OSA), and circadian rhythm disturbances (6–10).
Early research into the human microbiome focused on correlational studies, identifying microorganisms associated with either healthy sleep patterns or sleep disorders (11). Recently, hypothesis-driven investigations have begun to uncover molecular-level connections between microbiome and sleep-related conditions (12, 13). These advances are essential for understanding how microbiota influence sleep and for developing targeted therapies to treat sleep disorders. However, the rapid expansion of studies in this area poses challenges in assessing how individual study findings contribute to the broader evidence supporting causal relationships between the microbiome and specific host phenotypes.
In this review, we explore the microbiota-gut-brain axis and its role in regulating sleep. We summarize the potential mechanisms linking gut microbiota composition and function to sleep disturbances. To help guide future research on microbiota-targeted interventions for improving sleep quality, we discuss the opportunities this framework provides, focusing on methodologies to strengthen the evidence base, uncover underlying mechanisms, and establish causality. A deeper understanding of the relationships between gut microbiota and sleep may pave the way for innovative approaches to managing sleep disorders and enhancing overall brain health.
Sleep disorders and their relation to the brain-gut microbiota axis
Studies have revealed significant alterations in gut microbiota composition and their metabolites in individuals with sleep disorders (8), a phenomenon that has been consistently replicated in animal models (Figure 1) (14). These changes are not only observed in primary sleep disturbances but are also increasingly recognized in patients with sleep disturbances comorbid with various neuropsychiatric conditions. It is therefore likely that gut microbiota may contribute to the pathogenesis of sleep disorders and that these gut dysbioses may provide substrates for novel sleep disorders treatment strategies (Table 1). Here we review the evidence for the role of microbiota in sleep disorders.


Citation: Brain Medicine 2025; 10.61373/bm025i.0128
| Author and year of Publication | Models | Species | Sample size | Dysbiosis of microbiota | Other outcomes |
|---|---|---|---|---|---|
| Insomnia | |||||
| Jiang et al., 2022 (8) | Insomnia | Humans | 6398 | Significant differences in the microbial β-diversity of the chronic insomnia group compared to the long-term healthy group Decrease of α-diversity in chronic insomnia group compared to the long-term healthy group (Observed species, Chao 1 index, ACE index and Shannon index) Chronic insomnia group was associated with lower levels of Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 |
Higher levels of muro cholic acid (MCA) and nor cholic acid (NorCA) Lower levels of isolithocholic acid (IsoLCA), lithocholic acid (LCA) and ursodeoxycholic acid (UDCA) Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 were positively associated with secondary bile acids (IsoLCA, LCA, and UDCA), and inversely associated with primary bile acids (MCA and NorCA) Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 were significantly inversely associated with CMD Inverse association of the chronic insomnia-related gut microbial biomarkers with CMD were mediated by MCA, NorCA and IsoLCA |
| Zhou et al., 2022 (6) | Insomnia | Humans | 24 | Decrease of α-diversity Increase of family Prevotellaceae and genus Prevotella Decrease of family Bacteroidaceae, family Ruminococcaceae, and genus Bacteroides Genus Gemmiger and genus Fusicatenibacter were dominant in patients with insomnia disorder |
Decrease of androsterone sulfate and chenodeoxycholate Increase of 1,2-dioleoyl-sn-glycero 3-phosphatidylcholine and 1-Methyladenosine Genus Clostridium XI negatively correlated with potential insomnia alleviator 3phosphocholine |
| Haimov et al., 2022 (16) | Insomnia | Humans | 72 (73.19 ± 5.73 years) | Sleep quality and cognitive performance explained a variation of 7.5%–7.9% in gut microbiota composition in older adults with insomnia Lachnoclostridium (genus) correlates positively with sleep efficiency |
|
| Barone et al., 2024 (7) | Insomnia | Humans (postmenopausal women) | O-IN 18 (53–58 years) P-IN 36 (54-71 years) |
Significant differences in β-diversity of the insomnia group compared to the control group O-IN Significant enrichment in Collinsella and Clostridium compare to control group Depletion in Lachnospira compare to control group P-IN Significant enrichment in Collinsella and Clostridium compare to control group Depletion in Lachnospira compare to control group Significant enrichment of Bacteroides |
Clostridium showed negative correlations with age, vitamin B5, and manganese while displaying a positive correlation with diastolic blood pressure |
| Even et al., 2024 (17) | Insomnia | Humans | INSD 15 (74.2 ± 8.42 years) ISSD 10 (75.90 ± 4.01 years) |
ISSD Increase of benzophenone, pyrogallol, 5-aminopental, butyl acrylate, kojic acid, deoxycholic acid (DCA), trans-anethole, and 5-carboxyvanillic acid in comparison to the INSD |
Short sleepers consumed relatively lower levels of the aromatic amino acids (AAAs) phenylalanine, branch-chain amino acid (BCAA) and tyrosine compared to the normal group |
| Yao et al., 2022 (14) | Insomnia (PCPA-induced model) | Male Sprague-Dawley rats | N = 5/group | Increase of α-diversity (Chao index, Shannon index) Significant differences in β-diversity of the insomnia group compared to the control group (Bray–Curtis differences) Increase of Bacteroidetes and Actinobacteria Decrease of Firmicutes and Patescibacteria Decreased ratio of F/B |
Decrease of the 5- HT levels and 5- HT/5- HIAA ratios in the hippocampus Decrease of GSH-Px and SOD Increase of IL- 6,TNF- α, and IL-1β Ruminococcaceae UCG- 014, Lachnospiraceae NK4A136 group, unclassified Lachnospiraceae, [Eubacterium] xylanophilum group, and [Eubacterium] coprostanoli-genes group were significantly and positively correlated with insomnia indices (sleep duration) Actobacillus, Romboutsia, Turicibacter, Alloprevotella,Clostridium sensu stricto 1, and Prevotellaceae UCG-001 were significantly and negatively associated with insomnia indices |
| Du et al., 2024 (20) | Insomnia (PCPA + CUMS-induced model) | Male Sprague-Dawley rats | N = 6/group (3–8 weeks) | Significant differences in β-diversity of the insomnia group compared to the control Increase of Actinobacteriota Coriobacteriaceae_UCG-002, Dubosiella, Bifidobacterium, Prevotellaceae_NK3B31_group, and Anaerostipes Decrease of Cyanobacteria |
Serum metabolites that differed between groups involved in histidine metabolism; phenylalanine metabolism; leucine, valine, and isoleucine biosynthesis; tryptophan metabolism; and phenylalanine, tyrosine, tryptophan biosynthesis and other amino acid metabolic pathways Coriobacteraceae UCG-002, Dubosiella, and Bifidobacterium are positively correlated with bile excretion Dubosiella, Bifidobacterium are positively correlated with L-DOPA and negatively correlated with 5-HT Anaerostipes are negatively correlated with L-Tryptophan and DL Tryptophan |
| Shi et al., 2024 (19) | Insomnia (PCPA-induced model) | Male Sprague-Dawley rats | N = 3/group (6–8 weeks) | Decrease of α-diversity (Simpson and Shannon index) Significant differences in β-diversity of the insomnia group compared to the control 8 SCFAs were significantly reduced in the model group compared with the control group | Increase of TNF-α, IL-6 Decrease of IL-10 and Foxp3 Decreased protein expression levels of BDNF and TrkB in the hippocampus of insomnia rats |
| OSA | |||||
| Valentini et al., 2020 (23) | OSA | Children | 16 | Increase of Bacteroides fragilis Decrease of Chao1 |
F/B ratio was directly correlated to sleep clinical record Bacteria applied in the gut barrier integrity (Desulfovibrionaceae, Bacteroides fragilis and Faecalibacterium prausnitzii) were correlated with sleep parameters |
| Li et al., 2023 (9) | OSA | Humans | 48 | Compared with the control group, the overall OSA group was enriched in Fusobacterium and Lachnoclostridium, and at the same time decreased in Ruminococcaceae_UCG_013 Enriched Fusobacterium, Megamonas, Lachnospiraceae_UCG_006, and reduced Anaerostipes was found in patients with severe OSA |
Plasma D-LA and I-FABP were significantly elevated in patients with OSA The severity of OSA was related to differences in the structure and composition of the fecal microbiome Lachnoclostridium and Lachnospiraceae_UCG_006 were the common dominant bacteria of OSA and intestinal barrier damage Fusobacterium and Peptoclostridium independently associated with AHI Changes in gut microbiota are associated with gut barrier biomarkers in patients with OSA Different levels of OSA severity are associated with specific changes in the gut microbiome |
| Wu et al., 2022 (24) | OSAHS | Children | 88 45hc 43osahs | Increase of Faecalibacterium and Roseburia Decrease of Ruminococcaceae |
The intestinal flora screening model ROC-AUC has certain potential diagnostic value OSAHS may affect children's cognitive function through intestinal flora |
| Wang et al., 2024 (25) | OSA | Humans | 64 19hc 45osa | Decrease of α-diversity The abundance of short-chain fatty acid-producing bacteria (such as Bacteroides, Ruminococcacea, Faecalibacterium) is significantly reduced in severe OSA Increase of the F/B ratio |
The severity of OSA is related to the differences in the structure and composition of the intestinal microbiota Parabacteroides play a key role in distinguishing between patients with OSA of different severity |
| Feng et al., 2024 (26) | OSAHS | Humans | 105 | Decrease of α-diversity (richness, diversity and evenness), but no significant difference | The abundance of Shigella and Brucella were negatively correlated with BMI, while the abundance of Actinomyces and Akkermansia were positively correlated with BMI Thermus Anoxybacillus, Anaerofustis, Blautia, Sediminibacterium, Ralstonia, Pelomonas, Ochrobactrum, Thermus Sediminibacterium, Ralstonia, Coccidia, Cyanobacteria, Anoxic bacilli and Anaerobes were negatively correlated with AHI and positively correlated with SpO2min |
| Baldanzi et al., 2023 (27) | OSA | Humans | 3,570 | AHI, T90, and ODI are all associated with a decrease in intestinal flora α diversity | T90 and ODI are jointly associated with 128 species (Blautia obeum, Collinsella aerofaciens, etc) T90-related species enrich nine metabolic pathways (Threonine degradation, Propionic acid production, etc) The relative abundance of the combination of T90/ODI-related species is associated with systolic blood pressure and diastolic blood pressure |
| Y. Zhang et al., 2022 (28) | OSA | Male C57BL/6J mice | N = 6/group | Decrease of Bifidobacterium Increase of Bacteroides, Desulfovibrionaceae, Ruminococcaceae and Lachnospiraceae |
Increased serum LBP, IL-6 and TNF-α levels Reduced synthesis of secondary bile acids and increased biosynthesis of fatty acids |
| Badran et al., 2023 (30) | OSA | Male C57BL/6J mice | N = 10/group | Increase of Lachnospiraceae, Ruminococcaceae, Prevotellaceae and Muribaculaceae | Increased abundance of Lactococcus and Bifidobacterium species after treatment with VSL3 Changes in intestinal flora are closely related to changes in blood pressure, and probiotic intervention can regulate abnormal blood pressure caused by IH-FMT |
| Circadian Rhythm Sleep-Wake Disorders | |||||
| Mortaş et al., 2020 (33) | Shift work | Humans | 10 Day for 4 weeks/night for 2 weeks rotation (25–40 years) | Night shift (23:00–07:00 h) vs. Day shift work (07:00–15:00 h) Phylum level: Decrease of Bacteroidetes Increase of Actinobacteria and Firmicutes Genus level: Increase of Dorea, [Ruminococcus] torques group, Coprococcus 3, and [Ruminococcus] gauvreauii Species level: Increase of Dorea longicatena and Dorea formicigenerans |
|
| Rogers et al., 2021 (32) | Shift work | Humans | 51 Day shift 24 Night shift 27 (21–59 years) | Night shift vs. Day shift work No differences in the richness and diversity of species End of the night shifts vs. beginning of the night shift Decrease of α-diversity (Chao1 and Shannon index) |
|
| Hu et al., 2022 (34) | Shift work (8 weeks of 6-h phase delay in the light-dark cycle) | Male C57BL/6J mice | Decrease of α-diversity (chao1, ACE, and Shannon) Significant differences in β-diversity compared to control group Phylum level: Decrease of Firmicutes Increase of Proteobacteria Family level: Increase of Burkholderiaceae, Decrease of Erysipelotrichaceae, Lachnospiraceae, and Ruminococcaceae Genus level: Increase of Prevotellaceae_UCG-001, Parasutterella, Alloprevotella, and Ileibacterium Decrease of Allobaculum, Lachnospiraceae_NK4A136_group, un_f_Lachnospiraceae, and Ruminococcaceae_UCG-014 |
g_Prevotellaceae_UCG_001, g_Ileibacterium, and g_Akkermansia were the predominant taxa and were considered biomarkers of the phase shift group Phase shift increases intestinal permeability (permeability of colonic mucosa to FD4) related to the disrupted epithelial barrier (reduced expression of ccludin and claudin1) Increased mRNA expression of IL-17α, IL-1β, and IL-6 in colonic tissue |
|
| Zheng et al., 2023 (35) | Chronic jet lag (16 weeks of 8-h phase advance in light-dark cycle) | Male C57BL/6J mice | N = 12/group (5 weeks) | Significant differences in β-diversity between the chronic jet lag (CJ) group and the normal group (NC) chronic jet lag caused rhythmic oscillation of Bacteroidetes and Verrucomicrobia, with Bacteroidetes peaking at night and Verrucomicrobia reaching a trough at night HFHFD-CJ group vs. the HFHFD-NC Decrease in α-diversity (Chao1 and observed features index) Lower abundances of Akkermansia, Lactococcus, Prevotella, Clostridium, and Bifidobacterium genera ND-CJ group vs. ND-NC group Increase of Turicibacter and Gemmiger genera Decrease of Anaerotruncus and Sutterella genera |
CJ upregulated pathways related to chemical carcinogenesis, hepatocellular carcinoma, and steroid biosynthesis in HFHFD-fed mice |
| Anderson et al., 2023 (36) | Chronic jet lag (8-h phase advance in light-dark cycle) | C57BL/6 mice | N = 7/group 8–16 weeks |
β-diversity (Weighted UniFrac distances) were significantly different between control and misaligned mice Decreased the abundance of the Bacteroides genus Increase of Clostridiaceae 02d06 and Erysipelotrichaceae Allobaculum in male mice after misalignment |
The superpathway for BCAA metabolism was significantly up-regulated in male mice after misalignment Increase of LDL:HDL ratios Glucose intolerance was observed at ZT12 in male mice after misalignment |
| Narcolepsy | |||||
| Lecomte et al., 2020 (41) | Narcolepsy (NT1) | Humans | 35 (38.29 ± 19.98 years) | β-diversity significant differences between patients and controls (Bray-Curtis dissimilarity) Decrease of Bacteroides Otu00012, Bacteria unclassified Otu00078 at taxa level Increase of Flavonifractor Otu00099 and Bacteroidetes unclassified Otu00102 at taxa level |
|
| Zhang et al., 2021 (43) | Narcolepsy (NT1) | Humans | 20 (14.3–26.8 years) | Increase of Klebsiella Decrease of Blautia, Barnesiellaceae, Barnesiella, Phocea, Lactococcus, Coriobacteriia, Coriobacteriales, Ruminiclostridium_5, and Bilophila |
The relative abundance of Coriobacteriales, Coriobacteriia, and Blautia were negatively correlated with total sleep time The relative abundance of Coriobacteriales and Coriobacteriia were negatively correlated with sleep efficiency The relative abundance of Lactococcus was positively correlated with stage 1 sleep and negatively correlated with arousal index The abundance of Klebsiella was positively correlated with sleep latency |
| Jezkova et al., 2024 (42) | Narcolepsy (NT1) Narcolepsy (NT2) |
Humans | 92 (30–35 years) 28 (32–43 years) |
β-diversity significant differences between NT1 patients and controls (Jaccard dissimilarities) | |
| REM Sleep Behavior Disorder | |||||
| Huang et al., 2023 (47) | RBD | Humans | 170 (68.6 ± 7.6 years) | β-diversity of RBD was significantly differed from control(Bray–Curtis distance matrix at the genus level) Decrease of butyrateproducing bacteria (Roseburia, Lachnospiraceae_ND3007_group, Lachnospira, [Eubacterium]_ventriosum_group, Butyricicoccus, Faecalibacterium, and family Lachnospiraceae) Increase of Desulfovibrio, Akkermansia, Collinsella, Oscillospiraceae_UCG-002 and − 005 |
Blautia, Collinsella, Gordonibacter, Hungatella, and Lachnospiraceae UCG010 may be associated with a decreased risk of NT1 An increased relative abundance of class Betaproteobacteria, genus Alloprevotella, and genus Ruminiclostridium6 may heighten the risk of NT1 |
| Zhang et al., 2024 (50) | iRBD | Humans | 35 (64.63 ± 9.69 years) | β-diversity of iRBD was significantly differed from control(UniFrac analysis) Increase of Aerococcus, Eubacterium, Gordonibacter and Stenotrophomonas Decreased of Butyricicoccus, Faecalibacterium and Haemophilus |
Microbial markers (including Butyricicoccus, UBA1819, Lachnoclostridium, Oscillospiraceae_UCG002,Uncultured_Oscillospiraceae_g061, [Ruminococcus]_torques_group, and [Eubacterium]_ventriosum_group) could differentiate RBD from control with a mean area under the receiver operating characteristic curve (AUC) of 0.79 Pathways related to short-chain fatty acids metabolism (e.g.,Bifidobacterium shunt and heterolactic fermentation) and were increased in RBD Pathways related to PreQ0 biosynthesis were decreased in RBD |
| Sleep disorders comorbid with neuropsychiatric disorders | |||||
| Li et al., 2024 (5) | Depression patients SAS patients |
Humans | depression patients: 23,424 SAS patients: 13,818 |
the genera Roseburia and Slackia, may be positively associated with the risk of depression | The bidirectional causal relationship between SAS and depression Various gut microbiota, blood metabolites, and inflammatory variables are correlated with both |
| Zhang et al., 2021 (53) | MDD patients with sleep disorder | Humans | 81 | At the genus level, Blautia, Coprococcus, Dorea, and Intestinibacter were negatively correlated with PSQI Intestinibacter was negatively correlated with PSQI and ISI | Acidaminococcus was associated with better sleep quality |
| Q. Zhang et al., 2022 (55) | MDD patients | Humans | 81 | Flavonifractor positively correlated with fatigue in patients with MDD and all individuals simultaneously | The OTUs OTU255, OUT363 were positively correlated with HAMD and HAMA. OTU244, OTU542, and OTU221 were positively correlated with ESS, HAMD, and HAMA The correlation between gut microbiome and daytime function was different in MDD and HCs |
| Ma et al., 2019 (56) | Rat with depression-like behavior | 7d-PSD male Rats | N = 10/group | Significant reduction in Akkermansia and significant enrichment of Oscillospira, Parabacteroides, Ruminococcus, Phascolarctobacterium, and Aggregatibacter Decreased α-diversity |
Serum levels of pro-inflammatory cytokines such as IL-6, TNF-α, and CRP are elevated Alterations in gut microbiota correlate with depressive behavior, proinflammatory cytokines, and varying urine metabolites |
| Tanaka et al., 2023 (54) | Depression patients anxiety disorders | Humans | 40 | Insomnia group has lower α-diversity of gut microbiota than the non-insomnia group The noninsomnia group exhibits a favorable correlation with Bacteroides and sleep quality |
Bacteroides and PSQI score are correlated in the insomnia group The concentration of glucosamine and N-methylglutamate in the insomnia group is higher than that in the non-insomnia group N-methylglutamic acid and glycolic acid are significantly associated with the PSQI scores of all patients |
| Zhang et al., 2024 (57) | Rat with anxiety-like behavior | 6-week-old male Sprague-Dawley rats | N = 15/group | Chao1 index increases | Sleep deprivation in rats induces anxiety-like behaviors, correlating with elevated blood LPS levels, metabolic abnormalities, and modifications in gut microbiota Probiotic supplementation enhances anxiety-like behaviors and diminishes blood LPS, gut microbiota, and metabolite alterations linked to anxiety-like behaviors |
| Yao et al., 2024 (4) | Geriatric syndromes patients | Humans | human gut microbiota: 19,000 PD: 449,056 |
PD was associated with a higher genetically predicted abundance of phylum Lentisphaerae, order Victivallales, class Lentisphaeria, and genus Anaerostipes The genetically predicted abundance of the family Oxalobacteraceae, genus Clostridium sensu stricto1, and order Bacillales showed a positive correlation with the risk of PD |
Multiple gut microbiota causes geriatric syndrome, according to two-sample Mendelian randomization analysis |
| Zhang et al., 2024 (50) | PD patients | Humans | 189 | Decreased Butyricicoccus and Faecalibacterium might be potential hallmarks of phenoconversion of RBD to PD | RBD has similar gut microbial changes to PD |
| Park et al., 2024 (61) | PD patients | Humans | 36 | Body-priority PD patients have lower abundance of the Firmicutes and Bacteroidetes phyla in their intestines, while the Actinobacteria, Proteobacteria, and Verrucomicrobia phyla have higher abundance The gut microbiome in body-first PD showed a distinct profile, characterized by an increased presence of Escherichia coli and Akkermansia muciniphila, and a decreased abundance of short-chain fatty acid-producing commensal bacteria |
|
| Heintz-Buschart et al., 2018 (49) | PD with Idiopathic RBD | Humans | 175 | Increase of Akkermansia sp.and Prevotella sp. | |
| Hua et al., 2020 (58) | ASD patients with sleep disorder | Children | 120 | Increase of the ACE, Chao, and Sobs diversity indices for the sleep disorder group Decrease of Faecalibacterium and Agathobacter | 3-hydroxybutyric acid and melatonin levels were decreased and serotonin levels were increased There was a significantly negative correlation between CSHQ scores and the abundances of Faecalibacterium |
| Liu et al., 2023 (59) | ASD patients | Children | 24 | WMT stably and continuously downregulated Bacteroides/Flavonifractor/Parasutterella while upregulated Prevotella_9 to decrease toxic metabolic production | Improve detoxification by regulating glycolysis/myo-inositol/D-glucuronide/ D-glucarate degradation, L-1,2-propanediol degradation, fatty acid β-oxidation WMT moderated gut microbiome to improve the behavioral and sleeping disorder symptoms of ASD via decrease toxic metabolic production and improve detoxification |
Abbreviations: AHI, apnea–hypopnea index; ASD, autism spectrum disorder; BMI, body mass index; CJ, chronic jet lag; CMD, cardiometabolic diseases; CRP, C-reactive protein; CSHQ, children sleep habits questionnaire; CUMS, chronic unpredictable mild stress stimulation; D-LA, D-lactic acid; F/B, Firmicutes/Bacteroidetes; HFHFD-CJ, high fat and high fructose diet and chronic jet lag; HFHFD-NC, high fat and high fructose diet and normal circadian cycle; I-FABP, intestinal fatty acid-binding protein; IH-FMT, intermittent hypoxia-fecal microbiota transplantation; ISI, insomnia severity index; LBP, lipopolysaccharide-binding protein; LPS, lipopolysaccharide; MDD, major depressive disorder; ND-CJ, normal diet and chronic jet lag; ND-NC, normal diet and normal circadian cycle; ODI, oxygen desaturation index; OSA, obstructive sleep apnoea; OSAHS, obstructive sleep apnea–hypopnea syndrome; OTU, operational taxonomic unit; PCPA, para-chlorophenylalanine; PD, Parkinson's disease; PSD, paradoxical sleep deprivation; RBD, REM sleep behavior disorder; SAS, sleep apnea syndrome; T90, total sleep time spent with oxygen saturation < 90%.
Insomnia
Insomnia is a highly prevalent sleep disorder that can lead to significant physical and psychological health concerns (15). Recent studies have begun to illuminate the evidence linking insomnia and gut microbiota. Several studies have found that patients with insomnia have a decrease in microbial α-diversity (a summary of species richness and species evenness in individual samples) and altered β-diversity (a metric comparing microbial community differences between individuals) compared to healthy controls (6–8), suggesting these may be relatively robust features of insomnia-related dysbiosis. Other studies have reported that the richness of some microbiota is significantly different from that of controls and is associated with the severity of insomnia (16). However, findings regarding specific bacterial taxa remain less consistent. For instance, a large-cohort study involving 6398 participants with chronic insomnia revealed decreased abundances of Ruminococcaceae UCG-002 and UCG-003 (8). This finding, while notable for being the first report of this association and partially validated in an independent cohort by the same team, has not yet been replicated by independent research groups, and its generalizability therefore awaits further confirmation. Other microbiota “signatures” of insomnia have been reported in cross-sectional case-control studies with much smaller sample sizes, such as increases of family Prevotellaceae, genus Prevotella (6), Collinsella, Clostridium (7) and decreases of family Bacteroidaceae and genus Lachnospira (7). These findings have not been widely replicated and remain to be causally validated. Moreover, bacteria-related metabolites such as butyl acrylate, deoxycholic acid, and trans-anethole are also significantly changed in patients with insomnia compared to controls (17). Specifically, in a longitudinal cohort with 1809 participants, Jiang et al. reported that patients with chronic insomnia have higher levels of primary bile acids (BAs) [muro cholic acid (MCA) and nor cholic acid (NorCA)] and lower levels of secondary BAs [isolithocholic acid (IsoLCA), lithocholic acid (LCA), and ursodeoxycholic acid (UDCA)], which were correlated with the microbiota Ruminococcaceae UCG-002 and Ruminococcaceae UCG-003 (8). Notably, MCA, NorCA, and IsoLCA have been reported to mediate the inverse association between insomnia-related gut microbial biomarkers and the risk of cardiometabolic diseases (CMD) (8). These findings were validated in an independent cross-sectional cohort (n = 6122), strengthening the evidence for a gut microbiota-BA axis in insomnia-related CMD pathophysiology (8).
The gut microbiota may also affect the brain physiology of patients with insomnia. A recent study found that brain pathology (enhanced cerebral blood flow similarities) of patients with chronic insomnia could be explained by the gut microbiota Negativicutes and Lactobacillales (18). In rodents, the disturbances of neurotransmitters in the brains of rats could also result from gut microbiota dysbiosis as evidenced by the observation that increased levels of Dubosiella and Bifidobacterium observed in an insomnia rat model were significantly associated with dopamine increases and serotonin (5-HT) metabolism decreases (14, 19–21).
Obstructive sleep apnea
Another highly prevalent sleep disorder is OSA, which is defined by consistent episodes of complete (apneas) or partial (hypopneas) obstructions of the upper airway during sleep (22). Community-level measures of gut microbiota composition (α-and β-diversity) have been reported to be altered in patients with OSA, and the taxonomic richness (relative abundance at the phylum, family, or genus levels) varied across individuals with differing severities of OSA. A relatively consistent observation across studies is the alteration in community-level microbial composition. Both children and adults with OSA have been reported to exhibit reduced α-diversity compared to healthy controls (9, 23), a finding replicated in an independent case-control study (24). The evidence for alterations in specific bacterial taxa remains preliminary and heterogeneous. Nevertheless, a reduction in Ruminococcaceae has been noted across two independent case studies in both adults (24) and children (25), suggesting it may be a recurrent feature. In contrast, shifts in other genera, such as Faecalibacterium, are conflicting—with reports of both increase (25) and decrease (24). Similarly, other reported changes, including decreases in short-chain fatty acid (SCFA)-producing bacteria (24), await confirmation in larger cohorts. Certain bacteria (Thermus Anoxybacillus, Anaerofustis, etc.) exhibit a substantial correlation with clinical sleep monitoring data, such as apnea-hypopnea index (AHI) (26). Utilizing deep shotgun metagenomics in the population-based Swedish CardioPulmonary bioImage Study (n = 3570), Baldanzi et al. reported that hypoxic parameters of OSA were correlated with alterations in gut microbiota α-diversity, particular species, and functional metabolic pathways (27). Besides, experimental models provide mechanistic insights into the association between OSA and gut microbiota. Six weeks of intervention for chronic intermittent hypoxia (IH) resulted in lowered body weight, diminished fasting blood glucose, increased systemic inflammation, and significant alterations in gut flora (Bifidobacterium, Bacteroides, etc.) (28). These data indicate elevated levels of gut inflammation in OSA. Additionally, fecal microbiota transplantation (FMT) from OSA model mice altered normal sleep patterns by augmenting sleep during the dark (wake) phase in naïve mice (29). The FMT observation of modified gut microbiota resulting from chronic IH is, in part, the foundation of the characteristic cardiovascular anomalies associated with sleep apnea, which can be mitigated with the simultaneous use of probiotics (30). Collectively, these findings indicate that disturbances in gut microbiota may significantly contribute to the development of OSA.
Circadian rhythm sleep-wake disorders
Circadian rhythm sleep-wake disorders (CRSWDs) are defined by misalignment between an individual's internal circadian clock and the external environment, resulting in altered sleep patterns, excessive daytime sleepiness, and impaired functioning (31). These disorders are frequently associated with metabolic syndrome and GI symptoms, indicating that alterations in gut microbiota composition and diversity may play a role in CRSWD pathophysiology (10). Due to limited basic and human studies, little is known about the link between CRSWDS and gut microbiota. Shift work and chronic jet lag are the most researched sleep disorders examining CRSWD and gut microbiota. Preliminary studies involving small cohorts of human night-shift workers have reported notable microbiota shifts (32, 33). At the phylum level, Actinobacteria and Firmicutes are found in higher abundance (33). At the species level, Dorea longicatena and Dorea formicigenerans, linked to heightened intestinal permeability and inflammatory indicators, exhibited an increase after 2 weeks of night-shift employment (33). The findings indicate that gut microbes may contribute to the metabolic disorders frequently seen in shift workers.
In animal models, mice subjected to 6-h phase delay in light-dark cycles (a model for shift work) exhibit decreases in beneficial families, such as Lachnospiraceae and Ruminococcaceae, and increases in potentially pathogenic families like Burkholderiaceae and Proteobacteria (34). Interestingly, FMT from phase-shifted mice into antibiotic-treated mice replicates the increased intestinal permeability and visceral hypersensitivity in phase-shifted mice, further suggesting the role of gut dysbiosis in the pathophysiology of circadian disruption (34).
Chronic jet lag models provide additional evidence of the association between gut microbiota and circadian misalignment. Chronic jet lag caused rhythmic oscillations of Bacteroidetes and Verrucomicrobia (35). Since this rhythmic oscillation was absent in control mice, it was presumed to be a microbiota adaptation to the misalignment of the circadian rhythm to the environment. Furthermore, metabolic pathways correlated with glucose intolerance were upregulated in misaligned mice (36).
Narcolepsy
Narcolepsy, a chronic neurological disorder marked by excessive daytime sleepiness, is clinically classified into two subtypes: type 1 narcolepsy (NT1) with cataplexy and cerebrospinal hypocretin-1 deficiency, and type 2 narcolepsy (NT2) lacking cataplexy with preserved hypocretin-1 levels (37). The pathogenesis of NT1 is hypothesized to be the autoimmune destruction of the hypocretin-producing neurons in the hypothalamus, which is supported by the evidence that infections such as H1N1 influenza and Streptococcus pyogenes are associated with triggering the disease (38, 39). These infections are thought to trigger narcolepsy by molecular mimicry. The gut microbiota, involved in immune regulation, may also be involved in the pathogenesis of narcolepsy (40). A study employing Bray-Curtis dissimilarity revealed significant β-diversity differences in NT1 patients compared to healthy controls (41). This finding has recently been replicated by a larger-scale investigation, which confirmed the divergence in microbial community structure using Jaccard dissimilarities (42). At the genus level, NT1 patients show increased abundance of Klebsiella and decreased beneficial genera such as Blautia, Barnesiella, and Lactococcus (43). Meanwhile, the abundance of Coriobacteriales and Coriobacteriia exhibited a negative correlation with total sleep duration and sleep efficiency, suggesting that reduced levels of these bacteria may contribute to poorer sleep quality. Lactococcus, known for its potential role in regulating immune response (44), has been reported to negatively correlate with the arousal index, a marker of fragmented sleep (43). These data suggest that the disrupted sleep of patients with narcolepsy may be associated with the imbalanced diversity between immunosuppressive and immunostimulatory microbiotas.
Rapid eye movement sleep behavior disorder
Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by a loss of muscle atonia during REM sleep, which results in individuals acting out their hallucinations (45). This behavior may pose a risk of injury to themselves or their bed partners (45). RBD is a risk factor for developing neurodegenerative diseases, particularly PD and other synucleinopathies such as dementia with Lewy bodies and multiple system atrophy (46). The etiology of RBD may be linked to the gut microbiota, as patients exhibit an increased prevalence of constipation and enteric α-syn histopathology (47). Moreover, recent findings have also reported that the onset of RBD is associated with gut microbiota dysbiosis (48, 49). Notably, a study conducted by Huang et al highlighted significant differences in gut microbiota composition between control, first-degree relatives of RBD, RBD, and early PD with premotor RBD features. This research identified microbial communities that correlate with a continuum of disease progression. For instance, the abundance of Desulfovibrio, Akkermansia, Collinsella, Oscillospiraceae_UCG-002, and Oscillospiraceae_UCG-005 increased with disease progression. In contrast, the abundance of Roseburia, Lachnospiraceae_ND3007_group, Lachnospira, Eubacterium_ventriosum_group, Butyricicoccus, Faecalibacterium, and Lachnospiraceae decreased as the disease advanced. These results indicate that gut dysbiosis is already present before the clinical onset of RBD and underscore the potential role of gut microbiota in the pathogenesis of α-synucleinopathies. Additionally, the application of a random forest model demonstrated that gut microbiota can effectively differentiate between healthy individuals and patients with RBD with an area under curve (AUC) of 0.79 (47), providing evidence for gut microbiota as a potential biomarker. Consistent with this possibility, a recent study identified Butyricicoccus as a potential biomarker for idiopathic RBD (iRBD), reporting an inverse correlation between its abundance and disease severity (50). These findings collectively emphasize the likely role of gut microbiota in the development and progression of RBD. Future research should focus on elucidating the causal link between gut microbiota and RBD, as well as the underlying neurometabolic mechanisms.
Restless leg syndrome
Restless leg syndrome (RLS) is a neurological and sensorimotor illness marked by an intense urge to move the legs, accompanied by frequent discomfort, significantly affecting sleep quality (51). RLS is a common sleep disease with an incompletely understood pathophysiology, potentially related to iron deficiency in the brain, which may arise from dietary factors or intestinal inflammation, likely involving the gut microbiome (52). Blum et al. demonstrated that small intestinal bacterial overgrowth may be more prevalent in patients with RLS, establishing a preliminary foundation for investigating the involvement of intestinal microorganisms in the pathophysiology of RLS (52). Nonetheless, limited research exists about the relationship between intestinal flora and RLS, necessitating more investigation to enhance comprehension of the illness process and to potentially inform novel therapeutic strategies.
Sleep disorders comorbid with neuropsychiatric disorders
Depression disorder and anxiety disorder
Numerous studies indicate that the comorbidity of sleep disturbances with neuropsychiatric diseases has unique microbiological properties in patients (5, 53, 54). The role of gut microbiota as a mechanistically involved factor in comorbidity warrants further exploration. Prior research has demonstrated a causal relationship between some microorganisms (Roseburia, Veillonellaceae, etc.) and depression (5). Zhang et al. discovered that the makeup of gut microbiota correlates with sleep disturbances in major depressive disorder (MDD), with four genera associated with sleep quality and Intestinibacter linked to sleep severity (53). The composition of specific gut bacteria, including Flavonifractor and Lachnoclostridium, showed a strong association with daytime functional performance (54). Prolonged sleep deprivation (SD) can result in both depression and alterations in gut microbiota (55). α-diversity is diminished in individuals with depression and anxiety accompanied by insomnia, while Bacteroides exhibited a positive connection with Pittsburgh Sleep Quality Index (PSQI) levels in the noninsomnia cohort (56). SD induces anxiety-like behaviors in rats, along with alterations in the gut flora (57). These results suggest that alterations in gut microbiota may serve as an indicator for comorbid sleeplessness associated with depression and anxiety disorders.
Autism spectrum disorder
Individuals with ASD frequently experience sleep problems. Hua et al. identified metabolites in fecal samples from patients with ASD, revealing that those with sleep abnormalities exhibited diminished melatonin levels and increased tryptophan levels, suggesting a potential correlation between neurotransmitter alterations and sleep disturbances (58). In recent years, the significance of the microbiota-gut-brain axis in the development of ASD has garnered increasing attention. Fresh washed microbiota transplantation (WMT) consistently downregulates Bacteroides/Aspergillus flavus/Parasutterella while upregulating Prevotella_9, thereby ameliorating behavioral and sleep disorder symptoms of ASD through the regulation of glycolysis, inositol, D-glucuronic acid, D-gluconic acid degradation, L-1,2-propanediol degradation, and fatty acid beta-oxidation to diminish toxic metabolites and enhance detoxification (59). Melatonin supplementation can ameliorate sleep disorders in the ASD animal model. Animal studies demonstrate that melatonin can mitigate social difficulties in mice with ASD (60). Future research may investigate the therapeutic efficacy of melatonin precursor-producing strains in people with ASD experiencing sleep disturbances.
Parkinson's disease and Alzheimer's disease
PD is characterized by motor symptoms (including tremor, rigidity, and bradykinesia) and an array of nonmotor symptoms (such as constipation, anosmia, and sleep disturbances). A recent two-sample bidirectional Mendelian randomization (MR) analysis, utilizing genome-wide association study (GWAS) data, has demonstrated a putative causal association between PD and specific gut microbiota, including Lentisphaerae, Victivallales, and Lentisphaeria. This inference was robust across multiple MR methods and survived sensitivity analyses for pleiotropy and heterogeneity (4). RBD is intricately associated with PD. Zhang et al. demonstrate that the decrease of Butyricoccus and Faecium may serve as indicators of the change from REM sleep behavior disorder to PD phenotype (50). Patients with body-first PD with nonmotor symptoms are the first, often accompanied by RBD. Additionally, these patients typically present with a disruption in the gut flora (61). Secondary idiopathic REM sleep behavior disorder (iRBD) often occurs in PD. Heintz-Buschart et al. proposed that 41 prevalent operational taxonomic units (OTUs) varied between iRBD subjects and healthy individuals, with Akkermansia sp. and Prevotella sp. demonstrating heightened abundance in patients with PD with iRBD compared to those without iRBD (49). These data suggest that alterations in intestinal flora may serve as a potential biomarker for PD, offering a foundational basis for diagnosis and therapy; nevertheless, the specific mechanisms require additional investigation. Research involving both humans and animals has indicated that sleep disturbances in AD may be directly associated with the accumulation of Aβ (62). Simultaneously, modifications in bacterial flora may also transpire in AD (3).
In summary, while specific microbial signatures vary across individual sleep disorders, a comparative analysis reveals several convergent alterations that are shared by at least two conditions (Figure 2). These shared features include an increased Firmicutes/Bacteroidetes (F/B) ratio, elevated levels of Actinobacteria and Collinsella, alongside decreased abundances of beneficial genera like Bacteroides, Bifidobacterium, and Faecalibacterium. The consistency of these changes across different sleep disorders suggests they may represent a common microbial underpinning or consequence of disturbed sleep, potentially contributing to systemic inflammation and metabolic dysregulation often observed in these patients. Additional study is required to elucidate the connection between gut microbiota and various sleep disorders, including parasomnias, delayed sleep-wake phase disorder, and advanced sleep-wake phase disorder.


Citation: Brain Medicine 2025; 10.61373/bm025i.0128
Potential mechanisms underlying the bidirectional relationship between sleep and gut microbiota
The microbiota-gut-brain axis coordinates the sleep process in humans and rodents through several bidirectional communication routes, including metabolic, neurological, and immunological pathways (Figure 3). The vagus nerve detects and relays signals related to metabolic and immune functions through molecules such as hormones, fatty acids, and cytokines. Furthermore, metabolites and other bioactive substances from the gut can pass through the intestinal and blood-brain barriers, providing an additional mechanism for gut-brain communication. This section explores the potential pathways through which the microbiota-gut-brain axis influences sleep and the gut microbiome.


Citation: Brain Medicine 2025; 10.61373/bm025i.0128
Metabolic signals from the gut involved in sleep regulation
Bile acids
BAs are cholesterol-derived metabolites essential for solubilizing dietary lipids and fat-soluble vitamins in the small intestine, facilitating their absorption or excretion. Synthesized in the liver as primary bile acids and stored in the gallbladder, they are released into the gut, where bacterial metabolism transforms them into secondary bile acids. These secondary bile acids act as signaling molecules, regulating metabolic and immune processes while shaping gut microbiota composition.
Circadian disruption has been shown to affect BA metabolism by altering BA homeostasis and enterohepatic circulation (63). Studies indicate that circadian disturbances influence BA metabolism, with sleep disruption significantly increasing the BA pool size—comprising bile acids in the gallbladder, intestine, and liver—particularly observed at ZT2 in sleep-disturbed mice (64). These findings highlight a strong link between sleep and BA regulation, suggesting potential impacts on metabolic processes. A prebiotic diet has been shown to modulate bile acid metabolism by reducing the levels of certain secondary and conjugated bile acids, while increasing cholic/taurocholic acid in feces. Prebiotic intake represents a promising strategy for enhancing gut health and mitigating the effects of circadian disturbances on sleep (65). Chronic insomnia is associated with alterations in gut microbiota composition and specific bile acid profiles, including elevated levels of MCA and NorCA and reduced levels of IsoLCA, LCA, and UDCA (8). These findings suggest that the microbiota-bile acid axis plays a critical role in the impact of chronic insomnia on cardiometabolic health, offering a potential target for therapeutic intervention.
Short-chain fatty acid
SCFAs, including butyrate, acetate, and propionate, are metabolites produced primarily by gut microbiota fermentation of dietary fibers. Sodium butyrate has demonstrated efficacy in enhancing sleep quality among patients with active ulcerative colitis, as evidenced by a double-blind randomized controlled trial, underscoring its dual function in promoting gut health and improving sleep (66). In animal studies, butyrate supplementation alleviated the inflammatory response and memory impairment induced by SD, pointing to its protective effects on cognitive and physiological health (67). Furthermore, oral melatonin supplementation, by modulating gut microbiota and its propionic acid metabolite, improved skin damage linked to circadian rhythm disruptions (68). Butyrate supplementation improved sleep restriction–induced obesity in mice (69). These studies suggest that butyrate has extensive ameliorative effects on various abnormalities caused by sleep disorders. However, the relationship between SCFAs and sleep efficiency appears complex. Enhanced physical activity lowers SCFA levels in older individuals suffering from insomnia. Older adults with insomnia demonstrated a negative association between sleep efficiency and fecal SCFA concentrations, including acetate and butyrate, possibly due to altered SCFA metabolism with aging (70). Sleep time has been positively correlated with fecal concentrations of total SCFAs, including acetate and propionate (71). Additionally, children with lower wake times after sleep onset exhibit higher fecal propionate levels. This suggests that SCFAs may influence sleep continuity (72). These studies suggest that SCFAs are associated with sleep across different age groups. A deficit in short-chain acyl-coenzyme A dehydrogenase (encoded by acids) in mice resulted in a distinct reduction in theta frequency during paradoxical (REM) sleep, thus indicating a role for SCFAs in the modulation of brain activity during sleep (73). Sleep fragmentation can indeed influence the levels of SCFAs. Increased acetate levels have been observed in patients with OSA, especially when coexisting with type 2 diabetes, a chronic sleep fragmentation mouse model exhibited elevated acetate levels in hypothalamic astrocytes as a protective mechanism (74). These findings suggest that SCFAs like acetate may mitigate metabolic and cognitive impairments associated with disrupted sleep.
Gamma-aminobutyric acid
Several GI bacteria, including strains from Lactobacillus and Bifidobacterium, possess the gene encoding glutamate decarboxylase, which facilitates the production of gamma-aminobutyric acid (GABA) (75, 76). These bacteria can elevate GABA levels in the enteric nervous system, highlighting their potential significance in gut-brain communication. Vancomycin treatment in mice led to a significant decrease in cecal GABA levels (77), suggesting that antibiotic disruption of gut microbiota composition directly affects GABA production and its availability in the GI tract. Studies using electroencephalography (EEG) have shown that oral GABA intake induces changes in brain responses compared to control substances such as water, L-theanine, or a dextrin placebo. This indicates that GABA produced or supplemented via the gut may influence central nervous system activity (78). Further research is essential to clarify how gut microbiota and GABA-homocarnosine metabolism play a role in sleep behaviors.
Serotonin and tryptophan
Over 90% of the body's 5-HT is synthesized in the gut, with gut bacteria serving as major producers, especially in the neonatal intestine (79, 80). Serotonin concentrations peak during awake, diminish throughout slow-wave sleep, and reach their nadir during REM sleep across several brain areas, including the hippocampus and cortex (81). This rhythmic fluctuation highlights serotonin's regulatory role in the sleep-wake cycle. Tryptophan, the unique precursor for serotonin and melatonin biosynthesis, is crucial for sleep regulation.
Microbiota-depleted mice have elevated tryptophan levels but reduced serotonin, indicating that gut microbiota are essential for converting tryptophan into serotonin (13). Stress-induced changes in tryptophan metabolites are microbiome-dependent and localized to the gut, further linking gut health to serotonin production (82). Microbial metabolites of tryptophan, such as ligands binding to receptors like AhR (83, 84), TRPA1 (85), and PXR (86), modulate gut barrier integrity and host tryptophan metabolism. These interactions underscore the microbiota's role in maintaining gut and systemic health. Tryptophan is the unique precursor for the biosynthesis of serotonin and melatonin, which are crucial regulators in the sleep-wake cycle. 5-hydroxytryptophan supplementation can improve certain sleep quality components and the gut microbiota composition, particularly in older adults with poor sleep (87). This suggests a bidirectional relationship between gut health and sleep quality.
Melatonin
The GI tract is the most significant extrapineal source of melatonin, with concentrations reaching up to 400 times those found in plasma (88). SD reduces melatonin levels in both the gut and plasma, altering the composition of the gut microbiota (89, 90). In sleep-deprived mice, lower plasma melatonin levels were associated with decreased colonic Card9 expression, leading to colitis and gut microbiota dysbiosis (91). Exogenous melatonin also ameliorated neuropsychiatric symptoms caused by chronic SD (92), highlighting its potential for treating sleep-related mental health issues. Melatonin restores circadian rhythm homeostasis in the gut microbiota disrupted by sleep restriction. This suggests a protective role against the systemic effects of poor sleep (93). These findings emphasize the critical role of melatonin in maintaining gut health and microbiota balance, which are linked to sleep and circadian rhythm regulation.
Neuronal pathway involved in sleep regulation
The autonomic nervous system: Vagal pathways
Recent research indicates that gut microbes may affect sleep via the vagus nerve pathway (94). Using tracing techniques, research revealed that gut microbiota can regulate the activation of brainstem regions via the gut-sympathetic-vagus nerve pathway, with a key area affected being the REM sleep-promoting region, the lateral paragigantocellular nucleus (LPGi) (95). This study indicates that gut microbiota may impact sleep-related brain regions via the vagus nerve, potentially playing a role in sleep regulation and the pathogenesis of sleep disorders. Similarly, Fan et al. discovered that reduced levels of the luminal metabolite kynurenic acid lead to the hyperactivity of glutamatergic neurons through the gut-vagus-nucleus of the solitary tract nucleus (NTS)-paraventricular nucleus of the thalamus (PVT) pathway, driving binge-eating behavior (96). Interestingly, the PVT and its neurons are also critically involved in sleep-wake regulation (97), suggesting that the PVT could be an important target through which the vagus nerve influences sleep. Furthermore, research shows that Streptococcus salivarius subsp. thermophilus CCFM1312 can modulate NTS activation via the vagus nerve, enhancing resistance to anorexia (98). Given that the medullary nucleus of NTS and its vagal afferents have long been implicated in the regulation of sleep promotion (99), further studies are needed to investigate whether gut microbiota can modulate the NTS or its downstream brain regions, such as the PVT, to regulate sleep and contribute to sleep disorders.
On the other hand, vagal pathways also play key roles in modulating gut microbiota dysbiosis induced by SD. Postseptic SD significantly increased the relative abundance of Proteobacteria, Gammaproteobacteria, Enterobacteriales and aggravated systemic inflammation, while the altered composition of the gut microbiota and inflammatory injury was abrogated by subdiaphragmatic vagotomy (100). These results emphasize the importance of vagal pathways in mediating the harmful multisystem outcomes in SD. Therefore, expanding to the increasing application of vagus nerve stimulation (VNS) in treating sleep disorders (101, 102), methods to manipulating the function of vagal pathways may also be potentially utilized in recovering the homeostasis of gut microbiota and curing gastrointestinal disease induced by sleep-wake disturbance (103, 104).
Translating these mechanistic insights to human sleep disorders remains a crucial step. While direct measurement of vagal afferent signaling in patients is challenging, heart rate variability (HRV) serves as a noninvasive proxy for vagal tone. Notably, reduced HRV, indicating impaired vagal function, is a consistent finding in patients with insomnia (105) and obstructive sleep apnea (106). This clinical observation aligns with the animal models, suggesting a convergent pathophysiology (107). The therapeutic application of VNS in sleep disorders (101, 102) also indicated the clinical relevance of vagal pathways. Collectively, the evidence positions the vagus nerve as an important mediator in the gut-brain axis, integrating microbial signals with sleep-wake regulation.
The stress system: Hypothalamic-pituitary-adrenal axis
The hypothalamic-pituitary-adrenal (HPA) axis is crucial for regulating wakefulness and sleep patterns. Any disruption in this system, whether at the level of the corticotropin releasing hormone (CRH) receptor, glucocorticoid receptor (GR), or mineralocorticoid receptor, can lead to sleep disturbances (108). This is exemplified by a study showing that chronic corticosterone treatment in rats induced sleep disturbances through a defined neural circuit: downregulation of GR in the locus coeruleus (LC) led to activation of its noradrenergic neurons, which in turn inhibited GABAergic neurons in the ventrolateral preoptic area (VLPO). These results suggest that GR in LC may play a key role in sleep disorders (109). Conversely, sleep disruption can also impair the HPA axis function. A comprehensive study in rats demonstrated that chronic sleep restriction (CSR) induces decreased basal corticosterone and alters the timing of the corticosterone response peak (110), creating a potential feedback loop wherein sleep loss perpetuates neuroendocrine dysfunction. Compelling evidence has further illustrated that gut microbiota is a potential modulator of the HPA axis. Wu et al. found that serum corticosterone levels were more robustly increased after a transient social encounter in germ-free and antibiotic cocktail (ABX)-treated mice than in specific pathogen-free controls, and injection of the corticosterone synthesis blocker metyrapone (MET) or adrenalectomy (ADX) can abolish this phenomenon. The study demonstrated that gut bacteria can modulate the HPA stress response in mice and affect corticosterone production by the HPA axis (111). Strikingly, another recent study further found that the circadian rhythm of gut microbiota can modulate the suprachiasmatic nucleus (SCN) transcriptome and identified that the oscillation of Limosilactobacillus reuteri may modulate the rhythmicity of corticosterone (112). The rhythmicity of corticosterone is an important output of endogenous circadian signals which send signals to the brain to maintain the balance of sleep-wake cycle (31). Therefore, the gut microbiota may regulate the sleep-wake circadian rhythm through the HPA axis.
Notably, a recent clinical study gives complementary evidence to the role of the HPA axis in gut microbiota-brain communication in sleep disorders. A randomized controlled trial (RCT) reported that Bifidobacterium breve CCFM1025 can improve sleep quality by regulating the activity of the HPA axis (113). This provides direct interventional evidence that the gut microbiota-HPA axis—sleep pathway is operative in human sleep disorders.
Collectively, these studies implicate the potential role of the HPA axis in mediating the association between gut microbiota and sleep. The gut dysbiosis observed in sleep disorders may drive HPA axis dysregulation (e.g., GR overactivation), which can disrupt the balance of key sleep-wake circuits by increasing excitability of the LC and suppressing the VLPO, ultimately promoting a state of hyperarousal and the manifestation of sleep disturbances. Future research may include exploration of markers including salivary/serum cortisol (as a marker of the HPA axis), and heart rate variability (as a marker of the sympathovagal balance) to illustrate the underlying mechanism of gut-microbiota–brain interaction.
Immune pathways for gut microbiota and sleep interactions
The intestine is recognized as the body's largest immunological organ and is crucial for sustaining health. The intestinal vascular barrier serves a crucial protective function by obstructing the entry of harmful substances from the intestinal lumen into the bloodstream (114). Compromised barrier function is intricately associated with numerous intestinal and extraintestinal disorders (115). The gut microbiota is essential for sustaining immunological equilibrium by modulating metabolic and immune functions. Collectively, these mechanisms govern intestinal immune activity and sustain overall immunological homeostasis. Sang et al. have constructed a high-efficiency sleep deprivation model, elucidating for the first time the molecular mechanism by which sleep deprivation triggers cytokine storms and systemic inflammation via the PGD2/DP1 signaling axis (116). This discovery is significant for comprehending the relationship between sleep and immunity (116). The gut microbiota plays a crucial role in regulating immune responses, and its disruption can lead to systemic inflammation, potentially exacerbating conditions such as cytokine storms.
Gut microbiota can affect the permeability of the blood–brain and intestinal barriers, allowing intestinal immune cells and pathogens to access the central nervous system (117). The disruption of normal physiological functions in the gut microbiota causes damage to the intestinal lining and weakens the intestinal barrier. This disruption subsequently leads to immune system dysregulation, fostering the emergence of inflammatory diseases that can impact various organ systems (118). Simultaneously, a bidirectional connection exists between sleep and immune system function (119). Our prior research indicates that SD markedly elevates blood concentrations of proinflammatory cytokines (including TNF-α, IL-1β, and IL-6), while concurrently reducing levels of anti-inflammatory cytokines (such as IL-10) (120). Subsequent research indicates that insufficient sleep not only heightens the response to proinflammatory cytokines but also results in learning and memory impairments, which may be strongly associated with disturbances in the gut microbiota (121). Huynh et al. demonstrated that monocyte-derived TNF engages with Tnfrsf1a-expressing glutamatergic neurons in the thalamic lateral posterior nucleus to facilitate sleep, hence aiding in the restoration of heart function postmyocardial infarction and diminishing the likelihood of cardiovascular events (122). Moreover, increased TNF-α levels correlate with the severity of OSA (123). Animal studies indicate that OSA can elevate serum levels of LBP, IL-6, and TNF-α, while also modifying the composition of gut microbiota, such as decreasing Bifidobacterium levels (28). The intestinal microbiota of patients with obstructive sleep apnea experiences significant alterations, perhaps resulting in cognitive impairment through the activation of a systemic inflammatory response (124).
Our prior research indicates that sleep deprivation can induce local and systemic inflammatory responses in the gut by disrupting the gut microbiota ecosystem. Furthermore, FMT from sleep-deprived donors into germ-free mice resulted in hippocampal inflammation in the recipient mice (120). Further investigation indicates that acute sleep deprivation intensifies systemic inflammation and psychological disorders by disrupting gut microbiota and circadian rhythms (125). Probiotic fermentation products derived from germinated grains can markedly enhance sleep quality in mice, potentially linked to the modulation of neurotransmitter and inflammatory factor levels, the improvement of intestinal flora composition, and the elevation of SCFA concentrations (126). The intricate relationships among gut microbiota, the immune system, and sleep underscore their significance in health maintenance and offer novel perspectives for the treatment of associated disorders. Consequently, future research needs to investigate whether inflammatory substances in the peripheral gut might influence specific brain regions to regulate sleep.
Microbiota-targeted interventions for improving sleep
Gut microbiota is essential in regulating sleep and the gut-brain axis. Recent studies have highlighted the potential of microbiota-targeted interventions to offer therapeutic benefits in sleep and circadian homeostasis. Traditional treatments for sleep disorders often come with significant side effects, underscoring the need for alternative approaches. Microbiota-targeted therapies, including probiotics, prebiotics, synbiotics, and FMT (Figure 4), present promising strategies for improving sleep quality and managing sleep disorders may be promising novel strategies for the management of sleep disorders (Table 2).


Citation: Brain Medicine 2025; 10.61373/bm025i.0128
| Author and year of Publication |
Supplementation | Routes of administration |
Models | Sample Size | Results |
|---|---|---|---|---|---|
| Ho et al., 2021 (129) | Lactobacillus plantarum PS128 | Oral administration of PS128 after dinner for 30 consecutive days | Chronic insomnia patients | N = 40 (probiotic = 21, placebo = 19) | PS128 group awoke significantly fewer times during N3, compared with control group Delta power percentage was higher in the PS128 group during each stage of sleep |
| Sun et al., 2022 (131) | Bifidobacterium animalis subsp. lactis Probio-M8 | Two grams of Probio-M8 powder daily for 3 months | Patients with Parkinson's disease | N = 40 (probiotic = 48, placebo = 34) | A larger magnitude of improvement in PDSS scores was observed in the M8 group than in the placebo group |
| Lan et al, 2023 (113) | Bifidobacterium breve CCFM1025 |
A daily sachet for 4 consecutive weeks | Insomnia patients | N = 40 (probiotic = 20, placebo = 20) | A more significant reduction in the participants’ PSQI score and saliva cortisol concentration compared to the placebo group |
| Zhu et al., 2023 (130) | Lactobacillus plantarum JYLP-326 | Twice per day for 3 consecutive weeks | Senior college students under stress induced by exam | N = 90 (healthy control = 30; placebo = 30; probiotic = 30) | Insomnia symptoms measured by AIS-8 was significantly reduced compare to baseline in students taking probiotics |
| Li et al., 2024 (134) | Lacticaseibacillus paracasei 207-27 | Oral administration for 4 consecutive weeks | Healthy participants | N = 40 (probiotic = 70, placebo = 34) | Sleep duration measured by wearable device was significantly increased compared to placebo group |
| Murakami et al., 2024 (133) | Bifidobacterium adolescentis SBT2786 | Four capsules daily for 4 consecutive weeks | Healthy participants who were dissatisfied with their sleep quality | N = 126 (probiotic = 61, placebo = 65) | An elongation in sleep time (measured by EEG) compared to placebo group |
| Badrfam et al., 2024 (132) | Lactobacillus acidophilus | Once daily for 8 consecutive weeks | Patients with ATS use disorder | N = 60 (probiotic = 30, placebo = 30) | Greater decreases in PSQI scores compared to placebo group at the end of the trial |
| Freitas et al., 2024 (136) | Bifidobacterium animalis BB-12 | Gavage dose of 2 mL/day, 5 days a week for 30 days | 60-day-old Wistar-Kyoto rats | N = 8/group (probiotic = 8, placebo = 8) | A decrease in anxiety-related behaviors was observed in the supplemented animals and an increase in sleep efficiency |
| Cheng et al., 2024 (126) | A probiotic-fermented germinated grain complex | Free drinking, with an average daily intake of 30–35 mL per group for 14 days | SD mice model | N = 5/group (normal = 5; model = 5; probiotic = 5; diazepam = 5) | Sleep duration markedly increased, and worried behavior showed improvement |
| Murack et al., 2024 (135) | Lacidofil or Cerebiome | Probiotic solutions and water continued for 18 days until euthanasia | Three-week-old male and female CD-1 mice | N = 10/group | Lacidofil increased NREM duration in the latter half of the light phase |
| Lee et al., 2024 (137) | P72 | Oral administration of different doses of P72 or heat-killed P72 | C57BL/6 mice (male, 18–21 g, 6 weeks old) | N = 6–8/group | P72 and its combination with HO can alleviate DA and insomnia by upregulating serotonergic and GABAergic systems through the suppression of NF-κB signaling |
| Chen et al., 2024 (138) | L. brevis SG031 | Low, medium, and high dose groups were administered SG031 at the corresponding concentration orally daily, while the placebo group was given water, for more than 6 weeks | Male Wistar–Kyoto rats | N = 8–14/group | High-dose SG031 has a protective impact on sleep disturbance caused by stress, indicated as a reduction in waking time and an increase in sleep time High-dose SG031 administration yielded reduced depression-like responses and enhanced social interaction in behavioral tests |
| Pang et al., 2024 (139) | LGG | Gavage administration of LGG (2 × 109 cfu/day) for 5 weeks | SD model in C57BL/6 male mice | N = 5–12/group | Alleviate intestinal barrier dysfunction and neural inflammation caused by SD, and improve the motor activity and intestinal microbiota in mice |
| Zheng et al., 2023 (140) | SLAB51 | Oral administration of SLAB51 200 billion bacteria /kg/day for 9 weeks | Eight-week-old wild-type male B6128SF2 mice | N = 8–14/group | SLAB51 oral administration boosted the antioxidant capacity of the brain, thus limiting the oxidative damage provoked by the loss of sleep It positively regulated gut–brain axis hormones and reduced peripheral and brain inflammation induced by CSR |
| Li et al., 2023 (141) | Lactiplantibacillus plantarum (L. plantarum) 124 FMT from normal-sleeping mice |
Oral administration of 2 × 10 9 CFU/day for 5 weeks FMT treatment |
C57BL/6 mice | N = 8–10/group | The regulation of intestinal flora ameliorated SD-induced intestinal oxidative stress, inflammation, and malfunction of the intestinal barrier FMT can also achieve a similar effect, suggesting that the gut microbiome plays a mediating role in SD-induced gut damage |
| Wang et al., 2024 (142) | F. prausnitzii | 10⁸ CFU of F. prausnitzii (dissolved in 0.2 mL PBS) administered via gavage, for a continuous period of 14 days | 72 h SD mice model | N = 12/group | Enhance intestinal barrier integrity, mitigate inflammatory responses, rectify dysbiosis, and reduce apoptosis induced by SD in mice, elevate fecal butyrate concentrations, and modulate the expression of intestinal tight junction proteins, goblet cells, and associated genes |
| Abe et al., 2024 (144) | PHGG | Dissolved in water every day with breakfast for 12 consecutive weeks | Healthy elderly participants | N = 61 (prebiotic = 30, placebo = 30) | The score of the PHGG group for OSA-MA was significantly superior compared to the placebo |
| Tanihiro et al., 2023 (146) | YM | Five tablets (1.1 g of YM) once a day for 4 consecutive weeks | Participants with discomfort in defecation | N = 37 (prebiotic = 18, placebo = 19) | the N3 duration was significantly longer than that in the placebo group; Significantly lengthened total time in bed (TIB) and significantly shortened N3 latency compared to placebo |
| Saleh-Ghadimi et al.,2022 (145) | RD | 10 g day-1 of resistant dextrin for 8 consecutive weeks | Females with T2DM | N = 63 (prebiotic = 33, placebo = 30) | Intervention group presented favorable changes in PSQI compared to the placebo group |
| Thompson et al., 2020 (147) | Dietary prebiotics | Prebiotics diets for 10 weeks | Acute stressor (100, 1.5 mA, tail shocks) on rats | N = 7–8/group or N = 14–15/group | Prolong NREM sleep facilitated REM sleep rebound after stressor exposure |
| Thompson et al., 2021 (65) | Prebiotics | Prebiotics diets for 13 weeks | Chronic disruption of rhythms on rats | N = 17–21/group | Quickly realigned NREM sleep |
| Bowers et al., 2022 (148) | Prebiotic Diet (PDX/GOS) | Prebiotic diet for 4 weeks | Sleep disruption on rats | N = 10–12/group | Prolong NREM sleep and REM sleep during 5 days of sleep disruption and increase total sleep time during 24 hours of recovery from sleep disruption |
| Chung et al., 2023 (149) | scGOS and llcFOS (9:1 ratio) | Prebiotics were given by oral gavage for 9 weeks | C57BL/6J male mice SD model | N = 7/group | cGOS–lcFOS prebiotic supplementation significantly ameliorated the SD-associated inflammation, circadian gene dysregulation, and emotional disorders; increased permeability of the gut, hypothalamus, and hippocampus; |
| Chan et al.,2023 (151) | Bifidobacterium and Lactobacillus, along with prebiotic inulin and OS, postbiotics extract from Lactobacillus plantarum | Oral administration once daily for 8 consecutive weeks | Participants with sleep disturbance and mood symptoms | N = 68 (responder = 46, non-responder = 22) | The PSQI score decreased significantly from baseline to the end of week 8 |
| Lau et al., 2024a (152) | The synbiotic preparation (SIM01, B adolescentis, Bifidobacterium bifidum, and Bifidobacterium longum with three prebiotic compounds including GOS XOS, and RD) |
Twice daily for 6 months | PACS patients | N = 463 (symbiotic = 232, placebo = 231) | More patients in the SIM01 group compared with the placebo group had alleviation in insomnia (58% vs. 44%) |
| Palepu et al., 2024 (153) | F. prausnitzii and FOS(fructooligosaccharides) + GOS(galactooligosaccharides)-loaded synbiotic | A probiotic treatment consisting of F. prausnitzii at a dose of 1 × 108 CFU/mL per 100 g body weight and a prebiotic therapy of 8 % FOS and GOS administered at 1 mL per 100 g body weight were given daily by oral gavage over 6 weeks | TRD, rat model | N = 10/group | F. prausnitzii and FOS + GOS-loaded synbiotic may reverse the TRD-like symptoms in rats by positively impacting gut health, neuroinflammation, neurotransmitters, and gut microbial composition. |
| Fang et al., 2023 (156) | FMT from healthy donors | FMT treatment | Patients comorbid with chronic diseases and chronic insomnia | N = 33 | FMT significantly ameliorated the ISI, PSQI score and quality of life of chronic insomnia patients Increase in the relative abundance of Lactobacillus and Bifidobacterium, which exhibited negative correlations with ISI scores, PQSI total scores, PQSI subitem scores |
| Fang et al., 2024 (157) | FMT from healthy donors | 6 months of FMT treatment | Patients with a diagnosis of FM | N = 45 (FMT = 22, control = 23) | PSQI scores were significantly lower in the FMT group compared with the control group |
| Lau et al., 2024b (155) | FMT from healthy donors | FMT treatment | PACS patients with insomnia defined as ISI ≥ 8 | N = 59 (FMT = 29, control = 30) | More patients in the FMT than the control group had insomnia remission (37.9% vs. 10%) Significant improvements in ISI, PSQI, ESS following treatment with FMT |
| Li et al., 2024 (158) | FMT from healthy donors | FMT treatment (once every 4 weeks for a total of 12 weeks) | Children with ASD | N = 68 (FMT = 38, control = 30) | A 10% reduction in scores on the SDSC |
Abbreviations: ASD, autism spectrum disorder; ATS, amphetamine-type stimulants; EEG, electroencephalogram; ESS, Epworth sleepiness scale; FM, fibromyalgia; FMT; fecal microbiota transplantation; GOS, galactooligosaccharides; ISI, insomnia severity index; OS, oligosaccharides; OSA-MA, Oguri–Shirakawa–Azumi Sleep Inventory, Middle-Aged version; PACS, post-acute COVID-19 syndrome; PDSS, Parkinson's disease sleep scale; Probio-M8, Bifidobacterium animalis subsp. lactis Probio-M8; PS128, Lactobacillus plantarum PS128; PSQI, pittsburgh sleep quality index; RD, resistant dextrin; SDSC, sleep disturbance scale for children; TIB, total time in bed; T2DM, type 2 diabetic mellitus; XOS, xylo-oligosaccharides; YM, yeast mannan; SD, sleep deprivation; Lacidofil , mixture of Lacticaseibacillus rhamnosus strain R0011 and Lactobacillus helveticus strain R0052 in a ratio of 95:5; Cerebiome, mixture of L. helveticus strain R0052 and Bifidobacterium longum R0175 in a ratio of 90:10; P72, Lactobacillus (Lactiplantibacillus) plantarum P72; L. brevis, Levilactobacillus brevis; LGG, Lacticaseibacillus rhamnosus GG; SLAB51, multi-strain probiotic formulation; CSR, chronic sleep restriction; F. prausnitzii, Faecalibacterium prausnitzii; scGOS, short-chain galactooligosaccharides; lcFOS , long-chain fructooligosaccharides; FOS, fructooligosaccharides; GOS, galactooligosaccharides; TRD, treatment-resistant depression.
Probiotics
Probiotics are described as living microorganisms that give a health benefit when taken in suitable concentrations (127). The beneficial effects of probiotics in sleep occur through diverse mechanisms, including the regulation of neurochemical pathways such as the enhancement of GABA production, reduction of stress hormones like cortisol, and the regulation of serotonin pathways (128). Emerging clinical studies have demonstrated the role of specific probiotic strains (e.g., Lactobacillus and Bifidobacterium) in improving sleep across various populations. For example, Lactobacillus plantarum PS128 has been shown to improve sleep quality in chronic insomnia patients by enhancing delta power during N3 sleep, reflecting deeper and more restorative sleep (129). Additionally, Bifidobacterium breve CCFM1025 significantly reduced cortisol levels and improved subjective sleep quality in individuals with insomnia, pointing to the ability of probiotics to attenuate HPA axis hyperactivity (113). In parallel, college students under stress exhibited reduced insomnia symptoms and improved their sleep quality during exam periods after taking Lactobacillus plantarum JYLP-326 (130), further supporting the role of probiotics in mitigating stress-induced sleep disruptions.
Probiotics have also demonstrated sleep benefits in populations with neuropsychiatric disorders. Bifidobacterium animalis subsp. lactis Probio-M8 demonstrated improvements in sleep disturbances in patients with PD (131). Notably, Lactobacillus acidophilus has shown promise in patients with amphetamine-type stimulant use disorder, with greater reductions in PSQI scores observed in the probiotic group compared to the placebo group (132). This suggests that probiotics could have therapeutic potential for improving sleep disturbances related to substance use and withdrawal. Additionally, probiotics including Lacticaseibacillus paracasei 207-27 and Bifidobacterium adolescentis SBT2786 lengthened the objectively measured sleep duration in healthy participants (133, 134), underscoring the potential for probiotics to benefit both clinical and nonclinical populations.
Probiotics have demonstrated a considerable influence in enhancing sleep and regulating gut microbiota in experiments on animals. Specifically, supplementary Lacidofil demonstrated an enhancement in the length of nonrapid eye movement (NREM) sleep during the latter half of the photoperiod, contributing to improved sleep quality (135). Supplementation with Bifidobacterium animalis BB-12 can enhance sleep efficiency and diminish anxious behavior in rats (136). Supplementation with the Probiotic Fermented Germinated complex enhances sleep duration and diminishes anxious behavior in mice (126). Meanwhile, by blocking NF-κB signaling, Lactobacillus plantarum P72 and hemp seed oil efficiently increased 5-HTergic and GABAergic systems, reducing the symptoms of depression, anxiety, and insomnia in rats (137). Elevated doses of SG031 have demonstrated a protective effect in alleviating stress-induced sleep disorders, evidenced by a reduction in wakefulness and an increase in total sleep duration, alongside a decrease in depression-like behaviors and an enhancement of social interaction, which is crucial for improving sleep quality and social functionality (138).
Further study indicates that probiotics can mitigate intestinal barrier failure and neuroinflammation induced by SD while enhancing the intestinal microbiota in mice (139). The oral treatment of SLAB51 improves brain antioxidant capacity, modulates gut-brain axis hormones, and diminishes peripheral and cerebral inflammation resulting from CSR (140). Furthermore, treatment with Lactiplantibacillus plantarum 124 mitigated SD-induced intestinal oxidative stress, inflammation, and intestinal barrier dysfunction by modulating intestinal microbiota (141). Wang et al. showed that Faecalibacterium prausnitzi supplementation also enhances gut barrier integrity, reduces inflammation, and corrects biological disorders (142). These findings provide strong scientific support for the potential use of probiotics in regulating the gut-brain axis and improving sleep disorders. They also highlight promising avenues for future clinical research and the advancement of therapeutic strategies.
In conclusion, probiotics show promise as a potential intervention for improving sleep. However, most clinical evidence is derived from small-scale studies with significant heterogeneity in design. Future studies should focus on large-scale, double-blind, randomized trials to validate the therapeutic effects and incorporate animal models to uncover precise mechanisms, paving the way for targeted interventions.
Prebiotics
Prebiotics are substrates that are selectively utilized by host microorganisms conferring a health benefit (143). Recent clinical studies provide supportive evidence for the association between prebiotics and sleep regulation. In a randomized, double-blind, placebo-controlled study, partially hydrolyzed guar gum (PHGG) supplementation over 12 weeks has been revealed to significantly improve OSA-MA (Oguri–Shirakawa–Azumi Sleep Inventory, Middle-Aged version) scores in healthy elderly individuals (144). Similarly, resistant dextrin (RD) administered to females with type 2 diabetes mellitus led to favorable improvements in PSQI scores compared to placebo, highlighting its efficacy in populations with metabolic dysfunctions (145). In addition to the improvements in subjective sleep scales, prebiotics can also affect objective sleep indicators; for instance, yeast mannan supplementation significantly extended N3 sleep duration, shortened N3 latency, and increased total time in bed among individuals with defecation discomfort (146). Further work is needed to assess the therapeutic effect of prebiotics on sleep disorders.
Animal studies indicate that prebiotics may enhance sleep quality and ameliorate intestinal inflammation and barrier function. Dietary prebiotics enhanced NREM sleep by influencing particular metabolites of gut microbiota in rats (147). The relative abundances of Parabacteroides distasonis and Ruminiclostridium 5 were likewise elevated; moreover, Ruminiclostridium 5 and cholic acid were associated with core body temperature realignment cycles during light/dark reversal in rats (65). Bowers et al. discovered that a prebiotic diet extended NREM and REM sleep in rats, with the relative abundance of Parabacteroides distasonis potentially playing a significant role in facilitating the sleep-enhancing effects of the prebiotic diet (148). Chung et al. conducted further studies demonstrating that supplementation with particular prebiotics may enhance gut physiology, cognitive behavior, and motor performance affected by sleep loss through the modulation of inflammation and circadian rhythms (149). Collectively, the findings provide empirical evidence for the utilization of prebiotics as a potential approach to ameliorate sleep.
Synbiotics
The definition of a synbiotic is a mixture comprising live microorganisms and substrate(s) selectively utilized by host microorganisms that confers a health benefit on the host (150). Clinical studies highlight the potential of synbiotics in sleep improvement. Chan et al. (151) demonstrated that an intervention combining probiotics including Bifidobacterium and Lactobacillus, prebiotics such as inulin and oligosaccharides (OS), and postbiotic extracts significantly reduced PSQI scores after eight weeks in participants with sleep disturbances. Similarly, Lau et al. (2024a) showed that SIM01, a synbiotic formulation containing Bifidobacterium strains, alongside prebiotics like galactooligosaccharides (GOS), xylo-oligosaccharides (XOS), and resistant dextrin, alleviated insomnia symptoms in post-acute COVID-19 syndrome (PACS) patients (152). These studies demonstrate that synbiotics can effectively improve sleep quality. The study involving animals has demonstrated that the synbiotic combination of Faecalibacterium prausnitzii (ATCC-27766) with fructooligosaccharides (FOS) and galactooligosaccharides (GOS) can ameliorate treatment-resistant depression (TRD) symptoms in rats by enhancing intestinal health, mitigating neuroinflammation, modulating neurotransmitters, and altering intestinal microbial composition (153). Future efforts should focus on developing synbiotics tailored to specific sleep disorders, optimizing the synergistic and complementary interactions between probiotics and their substrates to maximize efficacy.
Fecal microbiota transplantation
FMT, the transfer of minimally treated feces from healthy donors to a recipient's gut to address disorders linked to abnormalities in gut microbiota (154), has emerged as a novel therapeutic strategy for sleep disturbances. Lau et al. conducted a landmark study on PACS patients with insomnia, demonstrating that FMT resulted in significantly higher insomnia remission rates compared with the control group (37.9% vs. 10%) (155). Besides, other subjective indicators including PSQI and Epworth sleepiness scale (ESS) scores, also decreased notably after the FMT treatment. These findings highlight the potential of FMT to address complex, multifactorial sleep disorders by targeting the gut microbiome.
Complementary evidence has also shown the efficacy of FMT for alleviating sleep complaints in patients with chronic insomnia and fibromyalgia (156, 157). Moreover, these benefits were associated with increased abundance of Lactobacillus and Bifidobacterium taxa (156). In pediatric populations, FMT led to a 10% reduction in sleep disturbance scale for children scores in children with ASD (158), emphasizing its potential across age groups and diverse sleep-related conditions. Future research should focus on optimizing donor selection, standardizing FMT protocols, and understanding the long-term effects to refine its application in sleep medicine.
Comparative evidence and the viability of microbiota-targeted therapies
As of yet, no direct head-to-head randomized trials have evaluated microbiota-targeted therapies regarding sleep effects, such as probiotics compared to synbiotics or probiotics compared to FMT. The majority of research has assessed a singular intervention in comparison to placebo or standard treatment. Among these methodologies, probiotics have been the subject of substantial research, with numerous randomized controlled studies indicating enhancements in sleep quality and architecture, alongside favorable safety and accessibility, although individual responses may differ (113, 129). In terms of invasiveness, regulatory compliance, and standardization, probiotics and prebiotics seem most suitable for widespread clinical use, with synbiotics following closely after. FMT, in contrast, encounters significant obstacles such as donor screening, processing standardization, infection risk, and regulatory limitations, making it more suitable for research environments or specific refractory situations. Subsequent investigations ought to emphasize direct comparison trials and cost-effectiveness studies to elucidate the relative advantages, hazards, and therapeutic relevance of these microbiota-targeted therapies.
Investigating the sleep-microbiota-gut-brain axis: toward mechanisms and causality
The brain-gut axis's influence on sleep disorders has yet to be comprehensively explained (2). Early studies in the microbiome field were primarily correlative, identifying microbial communities associated with either healthy sleep patterns or sleep disorders. While these associations provided foundational insights, understanding how microbiota causally influences sleep and developing microbiome-based therapies requires systematic evaluation of a comprehensive evidence base. Given the growing number of studies in this area, it is challenging to assess each study's individual contributions to the broader understanding of microbiome-sleep interactions. Here, we propose a framework to evaluate the connection between microbiome and sleep disorders through a 4-tiered “funnel” approach, progressing from associative studies to molecular mechanistic investigations. This strategy facilitates a clearer understanding of how the microbiota drives sleep-related phenotypes and informs the development of targeted therapeutic interventions (Figure 5).


Citation: Brain Medicine 2025; 10.61373/bm025i.0128
Level 1: Initial associations identified between gut microorganisms and sleep. Associations between the gut microbiome and sleep disorders can be investigated through multimodal approaches. Neuroimaging techniques, such as functional magnetic resonance imaging and EEG, combined with sleep evaluations, cognitive assessments, actigraphy, and polysomnography (PSG), provide valuable insights into the physiological mechanisms underlying sleep and its impact on cognitive performance (159, 160). Concurrently, the collection of biological samples—including blood, saliva, urine, and feces—is essential for elucidating potential mechanistic pathways. Fecal samples serve as a reliable resource for examining the diversity and dynamic changes in the gut microbiota, laying a critical foundation for uncovering potential associations between alterations in the intestinal microbiome and sleep disorders (41). Level 2: Discovery of potential biomarkers linked to sleep disorders through the gut-brain axis. To identify potential biomarkers linking the gut microbiota to sleep disorders, advanced multi-omics approaches and computational tools are employed. Machine learning algorithms integrate 16S rRNA sequencing, metagenomic analyses, metabolomics, and clinical data to analyze large-scale datasets, enabling the classification of microbial signatures and functional pathways associated with sleep disturbances. Identified biomarkers, such as specific microbial strains, microbial-derived metabolites, or altered metabolic pathways, provide valuable targets for understanding sleep disorders and lay the groundwork for the development of personalized diagnostic and therapeutic strategies. Level 3: Establishing causality between gut microbiota and sleep disorders. To establish a causal relationship between gut microbiota and sleep disorders, rigorous experimental approaches are required. FMT serves as a pivotal tool, enabling the transfer of gut microbial communities from individuals with sleep disorders to germ-free or antibiotic-treated animal models, with the goal of identifying causative microbial strains that produce sleep phenotypes. Interventions based on the microbiome, demonstrated through RCTs and crossover studies, exhibit considerable potential for enhancing sleep quality and addressing sleep disorders. A longitudinal intervention study design, coupled with multiple sampling of the gut microbiome and machine learning methods, can yield crucial time series data to elucidate the effects of sleep disorders on microbial composition and function (161). Multimodal longitudinal design can be utilized in follow-up cohort studies to investigate the function of the brain-gut axis in the etiology of sleep disorders and the severity of their symptoms. Such studies can inform novel avenues of treatment for sleep disorders and disorders comorbid with sleep disorders. Level 4: Developing microbiome-based interventions to establish novel strategies for addressing sleep disorders. At this level, RCTs and crossover studies are employed to rigorously assess the therapeutic efficacy of microbiome-based interventions in ameliorating sleep disorders. These interventions may include specific microorganisms (e.g., probiotics) or their bioactive metabolites, such as SCFAs and other microbial-derived compounds. RCTs provide robust evidence by comparing intervention groups with placebo controls, while crossover designs allow within-subject comparisons, enhancing statistical power and minimizing variability. Sleep parameters, including architecture and quality, are evaluated using PSG, actigraphy, and subjective assessments. Additionally, neuroinflammatory markers, neurotransmitter levels, and gut microbial composition are analyzed to elucidate mechanisms underlying therapeutic effects. These studies are critical for translating microbiome research into effective, evidence-based interventions for sleep disorders. The funnel framework presented here offers a systematic approach to evaluate emerging microbiome-associated diseases, enabling the identification of microorganisms and microbial metabolites with consistent and reproducible effects on host physiology. These insights will drive the development of novel small-molecule therapies and cell-based interventions, advancing treatment strategies for microbiome-associated human diseases, including sleep disorders.
Conclusion
The relationship between gut microbiota and sleep regulation is a promising area of research within the microbiota-gut-brain axis framework. While existing studies provide valuable insights, there remain significant gaps and challenges that need to be addressed to translate these findings into clinical applications.
Evidence suggests that the gut microbiota influences sleep and wakefulness through the microbiota-gut-brain axis. The gut interacts with the central nervous system via direct (vagal) and indirect (immune and endocrine) pathways, impacting sleep quality and circadian rhythms. Critically, research has moved beyond correlation to demonstrate causal relationships through FMT in animal models and MR studies in humans, which show how microbial signatures can directly influence sleep physiology. While pathways such as neurotransmitter modulation and immune signaling have been proposed, the exact mechanisms linking gut microbiota to sleep remain unclear and require detailed investigation.
Increasing evidence from research suggests that the gut microbiota may function as a valuable biomarker for future diagnosis, prediction of treatment effectiveness, and personalized intervention. Nevertheless, clinical advancement in this domain continues to encounter numerous challenges including the technical variability, interindividual differences in response to probiotics or synbiotics, and limited data on long-term use. To advance toward clinical applications, future research must address key methodological challenges and develop a clear translational pathway. This includes: (1) Prioritizing interventional trials for disorders with the strongest mechanistic links, such as chronic insomnia and OSA; (2) Standardizing key biomarkers across studies, including microbial sequencing data (e.g., via shotgun metagenomics), sleep metrics (combining objective polysomnography with subjective reports), and relevant metabolic profiles (e.g., SCFAs and bile acids); and (3) Harmonizing methodologies—from DNA extraction kits to sleep assessment tools—to enable valid cross-study comparisons.
Future studies should also focus on large-scale, multicenter trials to validate the efficacy of microbiota-based therapies for sleep disorders. Advances in microbial sequencing and functional studies will enable the identification of key bacterial strains that specifically influence sleep and wakefulness. Such discoveries could lead to the development of precision probiotics. A deeper understanding of the gut-sleep axis will pave the way for innovative strategies to combat sleep disorders and improve overall health.
Author contributions
LL, JS, and QQY conceptualized the manuscript. ZW, TTW, and JL prepared the initial draft and designed the figure. YFY, ZCG, ZYL, YMS, and GHY participated in the preparation of the manuscript. LL, MVV, ZW, WY, TSL, and XXL reviewed the manuscript and prepared the final version.
Acknowledgments
This work was supported by the STI2030-Major Projects (no. 2021ZD0200800) and the National Natural Science Foundation of China (no. 82288101).
Conflicts of interest
The authors declare that they have no conflict of interest.

Gut microbiota characteristics in sleep disorders. The figure shows representative characteristics of gut microbiota changes in patients with sleep disorders and those with sleep disorders combined with psychiatric disorders. F/B: the ratio of Firmicutes/Bacteroidetes.

Convergent alterations in gut microbiota across multiple sleep disorders. This schematic summarizes the direction of changes in key bacterial taxa that have been consistently reported in at least two major sleep disorders. F/B: the ratio of Firmicutes/Bacteroidetes.

Schematic representation of sleep-microbiome interactions through the microbiota-gut-brain axis. Signals originating from the gut microbiome influence the sleep-wake cycle by modulating the flip-flop switch that governs these states. Sleep-promoting signals, such as butyrate, GABA, and melatonin, are received by sleep-related nuclei, while wakefulness-related signals, including 5-HT, orexin (ORX), and histamine (His), are detected by the sleep-related nuclei. The microbiota-gut-brain axis regulates sleep and wakefulness via three key pathways: Immune pathways, gut-derived immune factors are transmitted via the bloodstream and vagal afferents to modulate immune responses and microglial activation, affecting sleep regulation; Neural pathways, where gut microbes and their metabolites impact the enteric nervous system (ENS) and interact with afferent vagal pathways to influence sleep-related brain regions and circuits. Besides, the gut microbiota and their metabolites are also able to send signals to sleep-related brain regions through hypothalamic-pituitary-adrenal (HPA) axis. Metabolic and endocrine pathways, gut-derived neurotransmitters, and metabolites, such as bile acids (BAs) and short-chain fatty acids (SCFAs), can influence sleep through systemic circulation. Additionally, stress-induced activation of the HPA axis can alter sleep and gut microbiota composition. The regulation of sleep by central and peripheral signals maintains a dynamic balance, with bidirectional interactions between sleep and gut microbiota supporting optimal function. GABA, γ-aminobutyric acid; MT, melatonin; His, histamine; 5-HT, 5-hydroxytryptamine; ORX, orexin; HPA, hypothalamic-pituitary-adrenal axis; ACTH, adrenocorticotrophic hormone; BAs, bile acids; SCFAs, short-chain fatty acids.

Microbiota-targeted strategies for improving sleep. Microbiota-targeted strategies including probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT) influence gut microbiota to regulate sleep through the microbiota-gut-brain axis. These interventions improve sleep quality offering potential therapeutic options for sleep disorders.

Investigate sleep-brain-gut-axis. Methods for investigating brain-gut axis mechanisms in sleep and potential therapeutic approaches. We propose a 4-tiered funnel approach to establish the link between microbiota and sleep disorders. First, correlations derived from human studies indicate alterations in the gut microbiome composition in individuals with sleep disorders compared to healthy controls. Second, machine learning algorithms integrate 16S rRNA sequencing, metagenomic analyses, and clinical data to classify extensive sample datasets and identify potential biomarkers associated with sleep disorders, laying a theoretical foundation for personalized treatment strategies. Third, fecal microbiota transplantation (FMT) is employed to transfer phenotypic traits from donor to recipient, providing causal evidence. Finally, randomized controlled trials (RCTs) and crossover studies are conducted to evaluate the therapeutic efficacy of microbiome-based interventions, including specific microorganisms and their bioactive metabolites, in ameliorating sleep disorders.
Contributor Notes
#These authors contributed equally.
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