A sleep disorder (SD) is characterized by difficulty in falling asleep, lack of sleep, excessive sleep, and poor sleep quality, which can affect the personal physical status and inherent mental health. Overwhelming evidence indicates that sleep plays an irreplaceable role in protecting human health, eliminating fatigue, restoring body function, enhancing immunity, protecting brain health, and others (Irwin 2015). However, previous studies show that inadequate sleep was found in 40 ~ 65% of American students (Carney et al. 2006; Lund et al. 2010; Kenney et al. 2012; Becker et al. 2014). Correspondingly, according to a recent study investigating 11,954 students from 50 universities in China, relatively severe sleep disorders were found among students attending high-level universities (Yang et al. 2018). These studies also suggested that academic and job strain and adverse lifestyle habits may be the main reason for sleep problems. One study of 1,125 participants from four countries provided novel evidence for the deterioration of problematic sleep among adolescents that at least one insomnia symptom was reported by 32.7% of adolescent workers and 35.2% of non-employed adolescents. Additionally, this study also indicated that insomnia symptoms were persistent in individuals with anxiety (73.2 ~ 76.5%) or depression (67.6 ~ 77.4%) disorders (Ohayon et al. 2000), which suggested that comorbidity led to the development and worsening of sleep disorder to a certain extent. Given the high prevalence of sleep problems in adolescents and the expanded influence on health outcomes, it has become a severe public health concern, which is needed to be solved urgently.
Recent studies have generally discovered the linkages between gut microbiota and sleep disturbance with the development of genome sequencing techniques. There is currently some evidence in the mice model that sleep disturbance can alter the composition of the gut microbiome (Poroyko et al. 2016). Subsequently, some findings that fecal microbiota transplantation could result in sleep disturbances supported the bidirectional linkages between the two (Badran et al. 2020). Additionally, a significant change in some bacterial taxa was reported by recent studies related to sleep, such as an increase in the Firmicutes to Bacteroidetes ratio (Bowers et al. 2020) and an alteration of the relative abundance of the
To this end, we conducted a study in which the differences in the diversity and composition of the gut microbiota between the individuals with poor subjective sleep quality and normal were analyzed. We tried to explore possible alteration of functional pathways and identify the significant gut bacteria for further studies.
Demographic characteristics of the participants.
Characteristics | SD (n = 17) | HC (n = 17) | ||
---|---|---|---|---|
Male/Female | 10/7 | 7/10 | 0.159 | 0.303 |
Age (years) | 20.059 ± 0.785 | 19.662 ± 0.611 | 1.646 | 0.109 |
BMI (kg/m2) | 21.229 ± 2.194 | 20.167 ± 2.172 | 1.419 | 0.166 |
PSQI score | 9.059 ± 1.144 | 5.118 ± 1.576 | 8.342 | < 0.001 |
All OTUs were classified based on the Ribosomal Database Project (RDP) database (version 11.5), and the microbial community abundance and diversity were calculated by alpha-diversity indices, including observed species, Chao1, and ACE for microbial richness, and the Shannon index, Simpson index, and Coverage index for microbial diversity. Beta-diversity was calculated to determine the difference between the SD and HC groups, including Principal Coordinate Analysis (PCoA) based on Bray-Curtis distance, Jaccard, and unweighted and weighted uniFrac metrics. Moreover, the ADONIS analysis with a nonparametric implementation of a Permutational Multivariate Analysis of Variance (PERMANOVA) and the Partial Least Squares Discriminant Analysis (PLS-DA) that has a better effect when the difference between groups is not significant was used. The metastats analysis was used to identify the changes in microbiota composition. The linear discriminant analysis (LDA) effect size (LEfSe) was performed to detect the difference in species abundance between two groups, and further, the species were obtained with a significant difference. LDA analysis was used to estimate the impact of these species on the differences between the two groups. The larger the LDA score, the greater the influence of species abundance on the difference effect, and only species with an LDA score > 2.0 were kept (
Comparison of gut microbiota’s alpha diversity indices between the SD and HC groups, including Observed, Chao 1, ACE, Shannon, Simpson, and Coverage. Plotted in the graphics are the interquartile ranges and boxes, medians (lines in the box), and the lowest and highest values for the first and third quartiles. The abscissa represents a different group; the ordinate represents the value of each diversity index. Different colors distinguish different groups.
The boxplot chart shows the beta diversity of the bacterial communities from the two groups, based on the Jaccard (A) and Bray-Curtis (B) distances, respectively. The interquartile ranges and boxes, medians (lines in the box), and the lowest and highest values for the first and third quartiles are plotted in the graph. Colors identify each group, and a black dot represents each sample.
Different color dots represent different groups; the horizontal and vertical axis scale is the relative distance without a practical significance; X-variable 1 and variable 2 represent the putative factors influencing changes in the microbial composition of two groups of samples, respectively. The plot is based on the weighted Unifrac distance.
The bacterial taxa’s linear discriminant analysis (LDA) effect size (LEfSe). LEfSe plot shows the top ten species with the smallest
Each point represents a sample and the 95% confidence intervals of the correlation coefficients are shown by grey areas. The relative abundances of genus
Correlation analysis of the selected bacterial species with the PSQI score.
Level | Taxon name | Relative abundance (%) | ||
---|---|---|---|---|
Phylum | Elusimicrobia | 0.171 ± 0.995 | 0.007* | –0.108 |
Phylum | Tenericutes | 0.123 ± 0.631 | 0.030* | –0.183 |
Class | Elusimicrobia | 0.171 ± 0.995 | 0.023* | –0.108 |
Class | Mollicutes | 0.123 ± 0.631 | 0.069 | –0.183 |
Class | Erysipelotrichia | 2.606 ± 2.604 | 0.276 | 0.384* |
Order | Elusimicrobiales | 0.171 ± 0.995 | 0.014* | –0.108 |
Order | Anaeroplasmatales | 0.123 ± 0.631 | 0.058 | –0.183 |
Order | Desulfovibrionales | 0.303 ± 0.259 | 0.188 | 0.328 |
Order | Erysipelotrichales | 2.606 ± 2.604 | 0.208 | 0.384 |
Family | 0.171 ± 0.995 | 0.027* | –0.108 | |
Family | 0.123 ± 0.631 | 0.110 | –0.183 | |
Family | 0.303 ± 0.259 | 0.286 | 0.328 | |
Family | 2.606 ± 2.604 | 0.339 | 0.384* | |
Genus | 0.647 ± 1.490 | 0.033* | 0.601** | |
Genus | 0.171 ± 0.995 | 0.033* | –0.108 | |
Genus | 0.123 ± 0.631 | 0.159 | –0.183 | |
Species | 0.128 ± 0.748 | 0.035* | –0.183 | |
Species | 3.700 ± 2.810 | 0.707 | 0.323 |
Functional prediction analysis of the gut microbiota in the SD and HC groups. Each color represents one group. The bar graph represents the pathways with a significant difference in relative abundance between the two groups. The figure on the right shows 95.0% confidence intervals and
Given that previous studies used mice models to verify the association between sleep disturbance and the gut microbiota and their interactions, we carried out the current study to determine whether poor sleep quality is associated with the gut microbiota in humans and, if so, whether more biomarkers can be determined for further study in the area. In the current study, a better balance of baseline characteristics between the SD and HC groups was reached after the screening of participants.
Although previous studies have suggested significant differences in the alpha diversity of gut microbiota between the sleep disorders and healthy populations, we failed to find any statistically significant differences in several parameters used to assess the alpha diversity of the gut microbiota. Moreover, the current results are consistent with those of Zhang et al. (2017), who showed that short-term sleep restriction did not significantly alter the OTU abundance, β diversity, and population shift of the gut microbiota in humans. In addition, the latest evidence has indicated that short-term sleep restriction and circadian misalignment do not appear to impair the stability of the alpha diversity of the human microbiome (Withrow et al. 2021). On the other hand, some researchers studied these relationships from a new perspective. They observed the changes in gut microbiota following short-term (two weeks) sleep extension in patients with chronic sleep deprivation, but no significant changes were found in this study (Reutrakul et al. 2020). Notably, the participants self-reported their current sleep conditions in our study, which may lead to the overestimated duration of SD in short-term partial subjects. However, recent results have shown a significant decrease in the abundance and alpha diversity of the gut microbiota in sleep disorders compared to healthy individuals (Liu et al. 2019). When there was disagreement between our results and some findings in previous studies, it is important to note the limitations of the present study and why some results should be interpreted with caution.
However, our study indeed found subtle significant differences in the structure of the gut microbiota in people with poor sleep quality compared to the sleep normal humans and finally identified eight taxa (
The PICRUSt prediction of the gut bacterial functions showed that several metabolic pathways differed significantly from healthy controls compared with individuals with poor sleep quality. However, some of them are challenging to interpret by current and previous evidence, and their clinical significance may be questionable, for instance, polycyclic aromatic hydrocarbon degradation. However, the predicted functional compositions of butanoate metabolism, C5‑branched dibasic acid metabolism, and propanoate metabolism pathways were significantly down-regulated in the SD group. It is worth mentioning that sleep fragmentation could lead to a decreased abundance of putative butyrate-producing bacteria and further affect the butanoate metabolism (Maki et al. 2020). As the members of specific short-chain fatty acids (SCFAs) (Magzal et al. 2021), butanoate has also been demonstrated to play a role in inducing sleep in mice models (Szentirmai et al. 2019). Additionally, butanoate has some functions similar to SCFAs, such as energy metabolism and inflammation of the host (Hamer et al. 2008), and pieces of previous evidence have suggested that changes in microbiota composition induced by the circadian rhythm misalignment might have implications for inflammatory diseases (Voigt et al. 2014; Parkar et al. 2019; Mashaqi and Gozal 2020). C5‑Branched dibasic acid metabolism and propanoate metabolism in patients with obstructive sleep apnea-hypopnea syndrome has also been found with significant differences compared to healthy humans (Li et al. 2017; Ko et al. 2019). Correspondingly, the increased concentration of propanoate in human infant fecal samples was associated with the prolongation of sleep duration (Heath et al. 2020). Significant differences in the gut microbiota composition and function were observed in our study, which further demonstrated the relationship between sleep problems and the gut microbiota in humans, but the causality remains to be fully explored.
This study’s findings must be considered along with limitations. Initially, the observational characteristic of the cross-sectional study cannot provide enough evidence for supporting the potential causal links between poor sleep quality and changes in the gut microbiota. Secondly, further study on larger samples is also needed to identify the other key bacteria, reported in a previous study but failed to be found in this study. Moreover, in studies with small sample size, the change of statistical methods may need to be considered. As Weiss et al. (2017) mentioned, DESeq2 could increase sensitivity on smaller datasets (< 20 samples per group). In addition, the lack of multiple objective measures of sleep (e.g., electroencephalogram, polysomnography) could reduce the accuracy and timeliness of the data and induce recall bias due to the self-reported approach.
On the other hand, the gut microbiota composition may be influenced by diet. So, in future research, the dietary survey of the participants should be added and included as a control variable for reducing the influence on gut microbiota. Finally, as a preliminary exploration, we failed to find robust evidence from the current research to establish the association between the bacteria that changed and the metabolic pathways identified. Therefore, future studies are warranted to further examine the causal relationship between sleep and gut microbiota by considering more other factors.
In conclusion, our study demonstrated an association between the composition of the gut microbiome and poor sleep quality. Moreover, we identified several specific phyla and taxa (e.g., Elusimicrobia and Tenericutes) that may be used to distinguish sleep-disordered patients from healthy individuals. Our results also revealed the difference in functional pathways, such as butanoate metabolism and propanoate metabolism, which raise the possibility that the gut microbiota could be a potential contributor to diagnosing and treating sleep problems in the future.