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Systematic Review

Microbiota Composition and Probiotics Supplementations on Sleep Quality—A Systematic Review and Meta-Analysis

by
Daniele Santi
1,2,3,
Valentina Debbi
1,
Francesco Costantino
1,2,
Giorgia Spaggiari
2,3,
Manuela Simoni
1,2,3,4,
Carla Greco
1,2,* and
Livio Casarini
1,4
1
Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, 41126 Modena, Italy
2
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, 41126 Modena, Italy
3
Unit of Andrology and Sexual Medicine of the Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria of Modena, 41126 Modena, Italy
4
Center for Genomic Research, University of Modena and Reggio Emilia, 41126 Modena, Italy
*
Author to whom correspondence should be addressed.
Clocks & Sleep 2023, 5(4), 770-792; https://doi.org/10.3390/clockssleep5040050
Submission received: 13 November 2023 / Revised: 29 November 2023 / Accepted: 5 December 2023 / Published: 13 December 2023
(This article belongs to the Section Disorders)
Browse Figures
Review Reports Versions Notes

Abstract

:
The gut microbiota (GM) plays a crucial role in human health. The bidirectional interaction between GM and the central nervous system may occur via the microbiota–gut–brain axis, possibly regulating the sleep/wake cycle. Recent reports highlight associations between intestinal dysbiosis and sleep disorders, suggesting that probiotics could ameliorate this condition. However, data are poor and inconsistent. The aim of this quantitative metanalytic study is to assess the GM composition in sleep disturbances and evaluate probiotics’ effectiveness for managing sleep disorders. A systematic review was carried out until July 2022 in online databases, limiting the literature research to human studies and English language articles. No significant GM diversity between patients with sleep disturbances versus healthy controls was found, revealed by α-diversity, while β-diversity is missing due to lack of proper reporting. However, probiotics supplementation significantly reduced the self-assessed parameter of sleep quality and disturbances Pittsburgh Sleep Quality Index (PSQI) score compared with the placebo. No difference in the Epworth Sleepiness Scale (ESS) score was found. While available data suggest that GM diversity is not related to sleep disturbances, probiotics administration strongly improves sleep quality as a subjective perception. However, heterogeneity of data reporting in the scientific literature should be considered as a limitation.

1. Introduction

The gut microbiota (GM) is a community of intestinal microorganisms, including bacteria, archaea, and eukarya, constituting the intestinal flora [1]. Physiologically, GM shows (i) metabolic properties, being able to produce essential nutrients, (ii) protective functions through the regulation of mucus production, (iii) structural actions, mediating the expression of tight junction proteins, and (iv) neurological properties, interacting with the peripheral and the central nervous system [2]. It is accepted that GM has broad impacts on human health, impacting the colonization and the resistance to pathogens, maintaining the intestinal epithelium, metabolizing dietary and pharmaceutical compounds, and controlling immune function [3]. The GM composition is heterogeneous and represents an individual signature, reflecting dietary habits [4]. In humans, more than two thousand prokaryotic species distributed in 11 different phyla have been recognized [5,6], among which the large dominant phyla are Firmicutes and Bacteroidetes [7,8]. Derangement of microbiota may impact its metabolic activities, leading to dysbiosis, which, in turn, could lead to several dysfunctions. Therefore, the evaluation of GM composition gathered increasing relevance [9,10,11,12,13]. GM heterogeneity is described by the α- and β-diversity parameters as measures of species diversity within a community at a local scale and between different communities, respectively [14]. In particular, α-diversity is a measure of microbiome diversity applicable to a single sample, whereas β-diversity quantifies the similarity or dissimilarity between two communities.
In the last decade, the link between GM composition and pathological conditions has gathered increasing interest, suggesting possible connections with infectious and autoimmune diseases, metabolic dysfunctions, neurocognitive disorders, and cancer [15,16,17,18,19,20,21]. The mutual relationship between GM and the host is expected to modulate additional physiological processes, such as sleep, which could be perturbed in the case of dysbiosis [22]. Sleep disturbances are classified into major categories, such as insomnia, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep–wake disorders, sleep-related movement disorders, and parasomnias [23]. Sleep could be described both quantitatively and qualitatively using multiple tools [24]. Polysomnography (PSG) remains the gold standard approach to quality and quantitatively assess sleep, providing detailed information about brain activity, sleep stages, patterns, oxygen saturation, and eye and leg movements [25]. However, PSG is scarcely used in large-scale investigations due to its cost and invasiveness. Alternatively, actigraphy is based on small wrist-watch digital devices monitoring motor activity data for extended periods [26] and is frequently employed in population-based epidemiologic studies [27]. However, it is not recommended for the routine management of sleep disturbances. Thus, several alternative subjective tools, such as direct observations, sleep diaries, and questionnaires, are largely used as well [28]. In particular, validated questionnaires are common tools because of the advantageous cost/benefit ratio despite the less accurate data collection [29]. Among these, the Pittsburgh Sleep Quality Index (PSQI) is a 19-items questionnaire assessing sleep quality [30], while the Epworth Sleepiness Scale (ESS) is an 8-item self-administered questionnaire evaluating the perceived propensity to fall asleep [31,32]. Finally, the nature and severity of insomnia may be assessed by the Insomnia Severity Index (ISI), determined by a 7-item questionnaire [29].
Several pieces of evidence suggest that GM is essential for the maintenance of normal sleep physiology, modulating the production of metabolites involved in sleep homeostasis, such as interleukin (IL)-1 β , short-chain fatty acids (SCFAs), serotonin (5-HT), γ -aminobutyric acid (GABA), and melatonin [1,33,34,35]. Experiments in mouse models demonstrated that the SCFA butyric acid modulates the expression of the clock genes Per2 and Bmal1, potentially disrupting circadian rhythms [36,37]. A recent study identified the GM signature of good sleepers, which would be abundant in Firmicutes, low in Prevotella, and with a high α-diversity index [38]. These data suggest that sleep disorders may be treated by acting on the GM composition using probiotics. They are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host” [39,40,41] and may be used to treat dysbiosis [39]. Probiotic preparations are largely heterogeneous, and their effects depend, for instance, on dosage, supplementation duration, and bacterial strains [42,43,44]. However, the possible beneficial impact of probiotics on sleep disorders is a matter of debate. In particular, few pieces of evidence are available so far to apply these compounds in clinical practice.
This systematic review and meta-analytic study has two aims: (i) to evaluate differences in GM composition between subjects with sleep disturbances versus healthy controls and (ii) to investigate the effectiveness of probiotics in treating sleep disorders.

2. Materials and Methods

The systematic review and meta-analysis were performed according to criteria described in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2015 Statement. Two separate literature searches were carried out from the first available study on probiotics until July 2022, querying the online databases PubMed (MEDLINE), Embase, the Cochrane Library, and Scopus. Then, two different meta-analyses were performed in sequence.

2.1. Study Selection and Inclusion Criteria

The first literature search evaluated all published articles concerning the relationship between sleep disorders and GM composition, using the following keywords: microbiota OR microbiome OR (gut microbiota) OR (gut microbiome) OR (gut microflora) OR dysbiosis AND sleep OR (sleep disorder) OR (sleep disturbance) OR (sleep problem). Studies were considered eligible when investigating patients with sleep disorders and reporting GM composition. No restrictions on age, sex, study design (i.e., observational or interventional), year of publication, or tool used to evaluate sleep dysfunctions were applied.
The second literature search was designed to identify all studies in which oral daily supplements containing probiotics (live bacteria) or para-probiotics (heat-inactivated/killed probiotic bacteria) were administered to human subjects with sleep disorders. The following search strategy was applied: probiotic AND sleep OR (sleep disorder) OR (sleep disturbance) OR (sleep problem). Interventional studies in which probiotics or para-probiotics were administered together with other substances were excluded to avoid potential biases. A first screening collected all the interventional studies available, but subsequently, only placebo-controlled studies (probiotic treatment versus placebo) were selected. The randomization was not considered an inclusion criterion.
For both literature searches, two authors (FC and VD) independently collected the eligible articles, and conflicts were resolved by a third investigator (DS). A preliminary screening identified potentially relevant studies based on their titles and/or abstracts. The selected studies were systematically screened for inclusion by full text, according to the aforementioned criteria.

2.2. Data Extraction and Analysis

In the first literature search, all extracted data were included in a single dataset, considering authors, year of publication, journal, study design, subjects’ age, patient’s number, microbiota profiling method, sleep assessment methods, criteria for group subdivision according to sleep disorder, α–diversity, β–diversity, and Firmicutes to Bacteroidetes ratio, representing a clinical biomarker of dysbiosis [45]. The α-diversity index was considered as the primary endpoint. The main outcome was the comparison of the α-diversity index between subjects with vs. without sleep disorders, as it was the parameter reported across almost all studies, while it was not for β–diversity. When studies reported the median and the interquartile range (IQR) of the index, the corresponding mean ± standard deviation (SD) was calculated [46,47].
In the second literature search, the following data types were extracted: authors, year of publication, journal, study design, subjects’ age and body mass index (BMI), patients’ number, microbiota profiling method, sleep assessment methods, intervention(s), probiotic bacteria used and colony forming unit (CFU), placebo treatment, treatment duration, and sleep data (both pre- and post-treatment). Since the most used tool to assess sleep quality was PSQI, it was considered the primary endpoint. The PSQI mean ± SD before and after treatment was used to quantify the probiotics’ effect on sleep quality. When articles reported the standard error of mean (σ), SD was calculated using the following equation:
SEM = σ/√n; σ = SEM × √n
where n indicates the number of subjects.
Meta-analyses were performed only when more than three studies were available. The parameter indicating heterogeneity among studies (I2) was considered as “low,” “moderate,” and “high” for values of 25, 50, and 75%, respectively [48]. Considering the high heterogeneity expected for the outcomes selected, the random effect model was applied to evaluate the mean difference (MD) among continuous data when available. The Review Manager (RevMan) 5.3 software (Version 5.3.1 Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) was used to perform meta-analyses. When a significant difference was detected by meta-analysis, meta-regression analyses were performed using “Statistical Package for the Social Science” software for Windows (version 28.0; SPSS Inc., Chicago, IL, USA). Statistical significance was considered for p values < 0.05.

3. Results

3.1. Overview of Included Studies

The first literature search evaluated the relationship between sleep disorders and GM composition. Among 16846 studies detected, fifty-eight potentially relevant studies were extracted, and eighteen articles were finally included (Figure 1). Table 1 summarizes data from studies analyzed during the first literature search.
While not statistically quantifiable, visual assessment of the number of GM components reported in studies shows high variability across studies. The mean age of patients considered was 35.7 ± 22.1 years, ranging from a minimum of 2-year-old children [54] to adults of 68.0 years [62] (Table 1). Similarly, high variability in sleep disorders was detected among studies. Indeed, sleep disturbance pattern includes several conditions of different nature, such as insomnia, reduced sleep quantity, obstructive sleep hypopnea/apnoea syndrome (OSHAS) [54,62], and narcolepsy type 1 [55], not allowing clear conclusions about associations between GM and sleep disturbances (Table 1). Relatively high study heterogeneity is also characterized by the tool used to evaluate sleep quality, which includes PSQI [65], sleep clinical record (SCR) [54], PSG [54,55], ESS [55], and multiple sleep latency test (MSLT) [55], while all studies considered the 16S ribosomal subunit mRNA to profile the microbiota (Table 1).
Eighteen eligible studies [38,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65] were included in the systematic review, accounting for 236 patients with sleep disorders compared with 233 healthy controls. The α-diversity index was assessed in sixteen of them, while the β-diversity index was assessed in thirteen. However, three different α-diversity indexes were used: the “Shannon” index was evaluated in all the studies, while “Simpson” [55,62,65] and “Chao” [54,55,65] indexes were not. Therefore, α-diversity indexes were used for quantitative analyses. The Firmicutes/Bacteroidetes ratio is generally reported, although this is not enough to establish clear differences in sleep patterns in all the populations [54,66,67].
The second literature collection included studies describing oral daily supplements of probiotics or para-probiotics administered to individuals with sleep disorders. Among 5684 studies, thirty were evaluated and twenty-four finally included [68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91] (Figure 1). Finally, eight studies were eligible to be quantitatively analyzed for PSQI score [68,71,80,87,88,90,91] and three for ESS [87,88,89]. Table 2 summarizes data from studies extracted during the second literature search.
Overall, 274 subjects treated with probiotics were compared with 261 subjects receiving a placebo. Only one study showed a cross-over design, whereas others employed a randomized approach. Five studies recruited healthy individuals [68,71,78,82], one study [87] enrolled cirrhosis patients recovered from hepatic encephalopathy (HE), one employed [88] patients with irritable bowel syndrome (IBS) and sleep complaints, and one [90] recruited post-menopausal women. Moreover, three studies [71,87,88] reported baseline BMI, and one study enrolled patients with overweight (BMI > 25 kg/m2). Regarding the use of probiotics, three studies employed single strains of bacteria [68,78,82] and five multiple strains [71,87,88,90,91] (Table 2). Typically, probiotics were administered within the 108–1011 CFU dose range for a variable duration of 3 [71] to 24 weeks [68,87]. Different media were used to convey the treatment, such as capsules [78,88,91], tablets [68], powder sachets [71,87], fermented milk [82], or yogurt [90], revealing a certain grade of variability among studies for probiotic administration. Compliance with supplementation protocols, if shown, was higher than 95% [82,87,91], except for one study [68], reporting 92%. Thus, as occurred in the first literature search, a high heterogeneity among studies was expected for the aforementioned reasons. Among these, the patient’s compliance with the probiotic consumption protocol should be considered as a further source of heterogeneity. However, none of the studies reported side effects or adverse events, suggesting general good tolerance to the treatment.

3.2. Meta-Analyses

Meta-analysis to compare GM composition between sleep disorders vs. healthy individuals was performed using α-diversity data (Figure 2). α-diversity was not different between patients with and without sleep disorders, considering the Shannon (p = 0.580, Figure 2A), Simpson (p = 0.160, Figure 2B), and Chao (p = 0.410, Figure 2C) indexes. Instead, β-diversity was not considered due to lack of reporting in more than three (i.e., 13) studies.
For the meta-analytic investigation of probiotics effectiveness in sleep disorders and health, only PSQI and ESS data were accessible in more than three articles. Other sleep measures were not suitable for meta-analysis. Probiotic consumption significantly reduced PSQI score compared with placebo administration (p = 0.04, Figure 3).
Meta-regression analysis was performed using patients’ age as a covariate and the PSQI as the dependent variable. The difference between the study and control group was significantly related to patients’ age (Chi-squared 10.9, p = 0.012).
The use of probiotics was linked to ESS score reduction vs. placebo, although it was not statistically significant (p = 0.070, Figure 4). Finally, the mean age of subjects did not differ between the study and control groups (−0.06; 95%CI: −0.05, 0.54 years, p = 0.850), excluding the age as a potential source for differences in PSQI score.

4. Discussion

This meta-analysis demonstrates that the use of probiotics improves sleep quality, as established by self-assessment questionnaires. However, the self-assessed propensity to fall asleep does not change after probiotics consumption, suggesting that the GM–sleep quality relationship could be only slightly influenced by this therapeutic intervention. Indeed, here we demonstrate that the GM diversity is not different between patients with vs. without sleep disorders, suggesting that there is not a stable substrate on which probiotics could act to improve sleep quality. Results were obtained using α-diversity data, which does not provide a measure of the specific abundance of taxa or species in the gut. Moreover, β-diversity is missing due to a lack of reporting in more than three studies. This is a limitation depending on the heterogeneity of the current literature and precludes the meta-analysis of individual microbiome taxa and/or species. Further, we have considered GM modulation by therapeutic measures on sleep quality as a topic recently explored in a meta-analytics study [92], where authors evaluated the efficacy of probiotic or prebiotic intervention on sleep characteristics. In this study, people with type 2 diabetes mellitus, dementia, and bone fracture were included, and no improvements in sleep quality (eighteen trials analyzed) and sleep duration (five trials analyzed) were found [92]. These results contrast with our results since we found improvement in sleep quality parameters with probiotics administration versus placebo. This discrepancy could be due to the subject analyzed since only healthy subjects or subjects with specific diseases, such as inflammatory bowel disease and fibromyalgia, were included in the present study. Moreover, the authors considered sleep quality parameters using both standardized questionnaires and self-reported information. In the present work, only PSQI reported significant improvement of sleep disturbances compared with the placebo. ESS score seems to be not significantly different, although is shows a trend similar to what observed by PSQI analysis. Unfortunately, heterogeneity of patient’s characteristics with sleep disorders, and wide variability in study experimental settings, make it difficult to generalize further about these results. The role of GM in sleep disorder was recently discussed and converging findings indicated that microbiota could be targeted by interventions aimed at improving sleep [93]. However, we found that sleep disorders could not be associated with GM composition, irrespective to the index used to measure its diversity. On the contrary, PSQI offers the subjective perspective that probiotics administration, obviously influencing GM, improves sleep quality. We may assume that GM could impact sleep behavior, even if possibly weakly, and that this issue requires specific clinical studies with well-selected populations to be addressed.
The scientific interest in the relationship between GM and human functions’ homeostasis has been increasing in recent decades [94,95]. In fact, several lines of evidence indicate the existence of a connection between GM and the central nervous system, leading to the definition of the gut–brain axis (GBA) [96]. The bidirectional flow of information between the GM and the central nervous system is poorly understood and probably works through different mechanisms, such as the hypothalamic pituitary adrenal (HPA) axis [2], the immune system [33,97,98], the intestinal neuroendocrine cells [99], the vagus nerve [100] and the enteric nervous system [2]. The GBA influences sleep status in close interaction with emotions, physiological stress, and circadian rhythms [96]. While the existence of these connections is overall clear, the potential impact of GM on sleep physiology has not been completely unraveled. Sleep patterns could be modulated by changes in intestinal permeability, inflammation, immune system activation, energy harvest, and bacterial diversity [101,102]. However, the relationship between either GM composition or the effect of GM changes after probiotics consumption and sleep patterns remains questionable [103]. Our meta-analysis was not able to quantify the relationship between GM composition and sleep disturbance pattern since no differences in α-diversity were found by any indexes used, such as Shannon, Simpson, and Chao. However, taken individually, authors found reduced α- and β-diversity of GM in specific groups with sleep disturbances [38,51,53,54,57,61]. For example, total GM diversity is positively correlated with increased sleep efficiency and total sleep time when otherwise healthy subjects were considered [104]. Thus, we could speculate that the relationship between GM composition and sleep must be considered, evaluating each group of sleep disturbances separately.
The beneficial effect of probiotics supplementation on sleep disturbance seems to be weak but relatively well established, taking the present study into account with previous analyses [105]. In fact, our findings confirmed that probiotics consumption improves the subjective measure of sleep quality in patients with sleep disturbances when compared with placebo groups. Also, in this case, both sleep disturbance patterns and probiotic composition are extremely variable among studies. Despite this heterogeneity, PSQI is statistically different between the two groups considered, suggesting that the beneficial effect of probiotics could be stronger overall than expected, overcoming potential biases, such as selection and detection biases. Moreover, the mean age of participants is similar between the two groups, excluding the role of age in the observed differential composition of microbial community under probiotics administration. Other subjective sleep questionnaires could be less indicative than PSQI of effects obtained upon probiotics administration. For instance, ESS investigates general parameters such as day time sleepiness, instead of straightly the sleep quality [106]. These considerations might explain why we found no significant improvement in ESS after probiotics administration. However, it is worthy of note that only three studies evaluated ESS [87,88,89], limiting the statistical power of this subgroup meta-analysis. This is a drawback that may be improved by increasing the number of clinical trials on this specific topic. Interestingly, one study was performed on subjects with bipolar disorder, reporting a negative correlation between Lactobacillus count and sleep [61]. This finding, obtained in a specific and limited set of subjects, is suggestive of a possible effect of this bacterial strain on sleep health.
It is intriguing that major benefits on sleep were observed among university students exposed to stressful conditions (e.g., academic examinations or courses), as indicated by some of the studies evaluated [68,71,82]. We may speculate that, in these groups, probiotics mitigate markedly the physiological response to stress exposure, via modulation of glucocorticoid action. A perceived environmental threat, such as a stressful event, triggers the HPA axis to secrete glucocorticoids and the sympathetic nervous system, leading to catecholamine release [107]. Moreover, increasing glucocorticoid levels were detected even in short sleep conditions [103]. Some studies indicated that probiotics administration attenuates the physiological increase in salivary glucocorticoids [82,106], potentially promoting sleep enhancement. This hypothesis suggests a beneficial role of probiotics for the general management of sleep disturbances, although subgroup analyses would be useful to establish firm conclusions.
Gender should be considered among potential determinants for high heterogeneity. Women were enrolled in only one study of those considered [90], whereas other trials included mixed groups of both males and females. Since sex-related differences in the effect of probiotics on sleep were suggested [74,84], we may assume that the heterogeneous gender composition of studies meta-analyzed could be a limitation. According to these studies, we could speculate that better outcomes occurred in men than women after Lactobacillus gasseri supplementation, although the limited availability of data prevented more accurate evaluations. Second, the participant’s health status varied among trials, including either healthy subjects or individuals affected by different pathological conditions [87,88]. Last, the experimental intervention differed for the type of probiotic bacteria, i.e., species, single vs. multiple strains, dosage, duration of the supplementation, and delivery method. In conclusion, methodological biases should be carefully considered by other studies on this topic.
The present study shows several limitations. The number of trials using the same methodological approach to evaluate sleep disturbance, GM composition, and probiotics type is low. Moreover, studies are highly heterogeneous, including participants with different ages and health statuses and assessment methods to evaluate sleep quality (i.e., subjective or objective measures). In this context, we were not able to collect data useful to adjust the analyses performed. The only available parameter was the patient’s age, which, as demonstrated by meta-regression analysis, potentially influenced the final result of the meta-analysis. Moreover, we could collect only α-diversity data, while β-diversity is missing due to lack of proper reporting, limiting the analysis of how individual microbiome taxa or species may contribute to mitigate sleep disturbances. In particular, the variability in Systematic Reviewpatients’ ages should be carefully considered since it is well-known that sleep patterns undergo physiological changes with aging [108,109]. In our study, the limited number of studies detected does not allow subgroup analyses based on age and gender. Third, the evaluation of sleep quality by questionnaires is obviously influenced by the subjectivity of these instruments. Although self-reported questionnaires are broadly used for clinical and research purposes, they do not provide objective parameters. Thus, data collected could be less accurate than those obtained by objective sleep assessment tools, such as PSG or actigraphy. Finally, only gut microbial genera/species significantly associated with the outcome are likely to be reported in the literature. This would introduce publication bias and limit the number of included studies. Further clinical trials with randomized placebo-controlled designs and objective measurements are required to achieve solid conclusions.
In conclusion, the connection between sleep disturbances and GM homeostasis remains to be established since major drawbacks prevent definitive conclusions about this issue. Promising results based on subjective assessment of sleep quality suggested that probiotics could represent an effective clinical intervention to manage sleep disorders targeting GM. The understanding of variables impacting GM, such as diet, physical activity, stress, and pathological conditions, will be crucial to clarify the relationship between intestinal bacteria and sleep.

Author Contributions

All authors included in this article made substantial contributions to the data included, as well as assisted with critical revisions of the writing, and approved the final version for submission for publication. Conceptualization, D.S. and L.C.; Methodology, D.S., V.D., F.C., G.S. and C.G.; Formal Analysis, D.S.; Resources, V.D.; Data Curation, V.D. and G.S.; Writing—Original Draft Preparation, V.D., F.C. and C.G.; Writing—Review and Editing, D.S., V.D., F.C., G.S., C.G. and L.C.; Supervision, M.S. and L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data reviewed and analyzed during this meta-analysis is available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

List of Abbreviations

BMI, body mass index; CFU, colony forming unit; ESS, Epworth Sleepiness Scale; GABA, γ -aminobutyric acid; GBA, gut–brain axis; GM, gut microbiota; HE, hepatic encephalopathy; HPA, hypothalamic pituitary adrenal; 5-HT, serotonin; IBS, irritable bowel syndrome; IL, interleukin; IQR, interquartile range; MD, mean difference; MSLT, multiple sleep latency test; OSHAS, obstructive sleep hypopnea/apnoea syndrome; PSG, polysomnography; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analysis; PSQI, Pittsburgh Sleep Quality Index; SCFAs, short chain fatty acids; SCR, sleep clinical record; SD, standard deviation.

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Figure 1. Study flow chart. The relationship between GM composition and sleep disorders (metanalysis 1) and the effect of probiotics on sleep disturbance (metanalysis 2) were assessed.
Figure 1. Study flow chart. The relationship between GM composition and sleep disorders (metanalysis 1) and the effect of probiotics on sleep disturbance (metanalysis 2) were assessed.
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Figure 2. Forest plot for the mean difference of α-diversity. Patients with sleep disorders and healthy controls were compared, considering Shannon (A), Simpson (B), and Chao indexes (C). References in figure: Valentini 2020 [54]; Cai 2021 [65]; Zhang 2021 [52]; Tang 2022 [66].
Figure 2. Forest plot for the mean difference of α-diversity. Patients with sleep disorders and healthy controls were compared, considering Shannon (A), Simpson (B), and Chao indexes (C). References in figure: Valentini 2020 [54]; Cai 2021 [65]; Zhang 2021 [52]; Tang 2022 [66].
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Figure 3. Forrest plot for standard mean difference of PSQI between study (probiotic) and control (placebo) groups. References in figure: Dhiman 2014 [87]; Wong 2015 [88]; Kato-Kataoka 2016 [82]; Kelly 2017 [78]; Nishida 2019 [68]; Marotta 2019 [71]; Lee 2021 [91]; Shafie 2022 [90].
Figure 3. Forrest plot for standard mean difference of PSQI between study (probiotic) and control (placebo) groups. References in figure: Dhiman 2014 [87]; Wong 2015 [88]; Kato-Kataoka 2016 [82]; Kelly 2017 [78]; Nishida 2019 [68]; Marotta 2019 [71]; Lee 2021 [91]; Shafie 2022 [90].
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Figure 4. Forrest plot for the standard mean difference of ESS in study vs. control groups. References in figure: Dhiman 2014 [87]; Wong 2015 [88]; Majeed 2018 [89].
Figure 4. Forrest plot for the standard mean difference of ESS in study vs. control groups. References in figure: Dhiman 2014 [87]; Wong 2015 [88]; Majeed 2018 [89].
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Table 1. Studies detected during the first literature search.
Table 1. Studies detected during the first literature search.
Common FeaturesStudy Group (Altered)Control Group (Healthy)
AuthorsYearType of StudyMicrobiota Profiling MethodMicrobiota DetectedMethod (Sleep)Inclusion CriteriaNumber Age (Years ± SD)Relative AbundanceNumber Age (Years ± SD)Relative Abundance
Evans 2017 [49]CCS16S rRNA amplicon sequencing (V4 region) using Illumina MiSeqPhyla Bacteroidetes. Firmicutes. Verrucomicrobia. Actinobacteria.
Genera Bacteroides. Facealibacterium. Prevotella. Roswburia. Akkermansia. Alistipes. Bifidobacterium. Parabacteroides. Blautia. Phascolarctobacterium. Alistipes		PSQIBD11550.2 ± 12.8Phylum:
Faecalibacterium 5.1 ± 4.3%
unclassified Firmicutes 0.6 ± 1%		6448.6 ± 16.6Phylum: Faecalibacterium 7.7 ± 5.0%; unclassified Firmicutes 1.1 ± 1.2%
Ko2019 [50]CCS 16S rRNA pyrosequencing (V3–V4 regions) using Illumina MiseqGenera Bacteroides. Ruminococcus. PrevotellaPSGAHI score > 5 health controls52NANA61NANA
Collado2019 [51]CCS16S rRNA amplicon sequencing (V3–V4 region) using MiSeqIllumina protocolsPhylum Tenericutes. Firmicutes. TM7. Lentisphaerae. Fusobacteria. Proteobacteria. Verrucomicrobacteria. Actinobacteria. Bacteroidetes.
Porphyromonadaceae. Peptospreptococcaceae and other clostriales		Snoring assessed by interviewSnoring frequency (< or ≥3/week)272.0 ± 0.0Proteobacteria 1.1% 162.0 ± 0.0Proteobacteria 0.4%
Zhang2021 [52]CCS16S rRNA amplicon sequencing (V4–V5 region) using qIllumina Phyla Bacteroidetes. Firmicutes
Orders Pasteurellales and Actinomycetes
Families Bacteroidaceae. Prevotellaceae. Porphyromonadaceae. Rikenellaceae
Genera Bacteroides. Prevotella. Parabacteroides. Escherichia. Flavonifractora. Alloprevotella. Parabacteroides. Hungatella		PSQIMDD diagnosis3636.81 ± 13.5GENUS: Bacteroides 40.0% Prevotella 5.9% Parabacteroides 2.8% Escherichia 2.5% Alistipes 2.2% Alloprevotella 0.5% Tyzzerella 0.3%
Paraprevotella 0.2% Haemophilus 0.1% Flavonifractor 0.2% Anaerotruncus 0.1%		4539.29 ± 11.44GENUS: Bacteroides 25.0% Prevotella 24.3% Parabacteroides 1.7% Escherichia 0.7%
Alistipes 0.8%. Alloprevotella 0.18%
Tyzzerella 0.2%. Paraprevotella 0.2%
Haemophilus 0.2% Flavonifractor 0.1% Anaerotruncus 0.1% Weissella 0.02. Eisenbergiella 0.01
Fei2021 [53]CCS 16S rRNA pyrosequencing (V4 regions) using Illumina MiseqFamily Ruminococcaceae Erysipelotrichaceae
Genera Bacteroides. Oscillospira. Catenibacterium. Prevotella. Dialister		QuestionnaireSleep lenght (short ≤ 7 h. normal 7–9 h long ≥ 9 h)Short 154
Long 248 		Short 35.6 ± 6.2
Long 33.6 ± 6.3		GENUS: Streptococcus 0.7% Coprococcus 1.0%
Dorea 0.3 Bamasiella 0.9% Intestinibacter 0.1%
SPECIES: Blautia_obeum 0.7% Streptococcus_salivarius 0.7% Clostridium_sp 0.1% Dorea_formicigenerans 0.2% Coprococcus_sp 0.2% Ruminococcus_lactaris 0.7%		25035.7 ± 6.4NA
Valentini2020 [54]CCS16S rRNA amplicon sequencing Phyla Bacteroidetes Actinobacteria Firmicutes Bacteroidetes Proteobacteria Families Clostridiaceae Lactobacillaceae Lachnospiraceae Oscillospiraceae Erysipelotrichaceae Coriobacteriaceae Desulfovibrionaceae Enterobacteriaceae Erwiniaceae Enterobacteriaceae. Erwiniaceae Bacteroidaceae Prevotellaceae Lactobacillaceae Prevotellaceae Sutterellaceae Flavobacteriaceae
Genera Colinsella. Eubacterium. Faecalibacterium Colinsella Escherichia Klebsiella Clostridium Lactobacillus Oscillobacter Clostridium Ruminococcus Oscillospira Veillonella Klebsiella		Sleep Clinical Record
PSG		OSAHS 75.0 ± 1.9NA88.7 ± 3.6NA
Zhang2021 [55]CCS16S rRNA amplicon sequencing (V3–V4 region) using Illumina Order Coriobacteriales
Class Coriobacteria
Family Barnesiellaceae.
Genera Klebsiella. Barnesiella. Ruminiclostridium. Phocea. Blautia. Lactococcus. Bilophila 		PSG
ESS
MSLT		NT1 2019.0NA1626.0LDA score:
More abundant in HC individuals
Class Coriobacteriia 2.26%. Order Coriobacteriales 2.26%. Family Barnesiellaceae 2.52%. Genus Lactococcus 2.37%. Genus Phocea 2.39%. Genus Ruminiclostridium 2.00%.
Genus Barnesiella 2.46%. Genus Blautia 3.25%. Genus Bilophila 2.00%
Mercado 2021 [56]CCS16s rDNA amplificon sequencing (V3–V4 region) using the MiSeq IlluminaEzakiella. Clostridium sensu stricto. Porphyromonas and Barnesiella (family Porphyromonadaceae). Coriobacteriales Incertae Sedis. Synergistaceae/Synergistales/Synergistia/Synergistestes. Escherichia-Shigella. Turicibacter PROMIS-SD PROMIS-SD. T-score > 55 19 high-occurring symptoms 60.9 ± 16.0LDA score:
More abundant in NT1 individuals
Genus Klebsiella 3.19 		22 low-occurring symptoms56.4 ± 7.9NA
Agrawal2021 [57]CCS16S rRNA amplicon sequencing (V4 region) using Illumina MiSeq Phyla Firmicutes. Bacteroidetes
Order Rhodospirillales
Families Acidaminococcaceae. Rikenellaceae. Sutterellaceae.Acidaminococcaceae. Rikenellaceae. Alcaligenaceae.Desulfovibrionaceae. Pseudomonadaceae. Pasteurellaceae
Genera Lachnoclostridium. Sutterella. Bilophila. Phascolarctobacterium. Alistipes. Pseudomonas		Sleep length (self-reported)Sleep length
< 6 h short sleepers, 6–8 normal sleepers		1659.4 ± 7.5NA4762.7 ± 5.8 FIRMICUTES 40% BACTEROIDOTA 36%
 Lachnoclostridium 1.5%
Sutterella 1.25%
Alistipes 1.3%
Bilophila 0.61%
 Phascolarctobacterium 0.5%
UBA1819 0.13%
Paraprevotella 0. 29%
Pseudomonas 0.06%
Eubacterium_siraeum 0.006%
Hua2020 [58]CCS16S rRNA amplicon sequencing using Illumina MiSeq Phyla Firmicutes. Actinobacteria. Bacteroidetes. Proteobacteria.Verrucomicrobia
Genera Faecalibacterium. Agathobacter		CSHQCSHQ < 41604.0 ± 0.2 FIRMICUTES 34% BACTEROIDOTA 39%. Lachnoclostridium 0.40% Sutterella 0.38% Alistipes 0.48%
Bilophila 0.25%. Phascolarctobacterium 0.20%
UBA1819 0.03%
Paraprevotella 0.11%
Pseudomonas 0.08%
Eubacterium_siraeum 0.13%		603.9 ± 0.1Predominant phyla:
Firmicutes 43.3%. Actinobacteria 28.3%. Bacteroidetes 20.7%. Proteobacteria 5.6%. Verrucomicrobia 1.3%
Buschart2018 [59]CCS16S and 18S rRNA amplicon sequencing (V4 regions) using Illumina HiSeqFamilies
Corynobacteriaceae. Lachnospiraceae. Rumnococcaceae. Bacteroidaceae. Prevotellaceae. Porphyromonadaceae. Enterobacteriaceae. Phylobacteriaceae. Streptococcaceae. Comamonadaceae. Moraxellaceae.		ESS
PSG		PD or iRBD97 (76 PD and 21 iRBD)PD: 68.0 ± 9.7
iRBD: 66.1 ± 7.9		Predominant phyla:
Firmicutes 43.15% Actinobacteria 25.88% Bacteroidetes 22.57%
Proteobacteria 6.34% Verrucomicrobia1.62%		7868.4 ± 6.7
Zhang2020 [60]CCS16S rRNA gene sequencing (V4 region)Phyla Actinobacteria. Proteobacteria. Firmicutes. Bacteroidetes
Orders Coriobacteriales. Sphingobacteriales
Genera Vagococcus. Adlercreutzia. Bifidobacterium. Parascardovia. Metascardovia. Ruminococcus
Species Anaerostipes caccae		OSHASOSHAS/OSHAS + cerebral infarction diagnosisCerebral infarction group: 28
OSAHS + cerebral infarction group: 28		NANA30NANA
Aizawa2019 [61]CCS16S or 23S rRNA-targeted RT-qPCRGenera Bifidobacterium. Lactobacillus HAM-D subscaleBD3940.3 ± 9.2NA5843.1 ± 12.9NP
Tang2022 [62]CCS16S rRNA gene sequencing (V3–V4 region)Phyla Firmicutes. Proteobacteria
Genera Escherichia-Shigella. Faecalibacterium. Streptococcus. Haemophilus. Phascolarctobacterium. Oscillibacter 		AHI OSHAS + T2DM 27 47.6 ± 5.2NP2645.6 ± 8.8NA
Masyutina2021 [63]CCS16S rRNA gene sequencingPhyla Actinobacteria
Genera Faecalibacterium. Prevotella 9. Lachnospira. Blautia. Faecalibacterium. Lachnospira
Species Eubacterium hallii 		PSQI
ISI		CI diagnosis5531.6 ± 7.4NA5033.2 ± 6.6NA
Grosicki2020 [38]CCS16S rRNA gene sequencing (V3–V4 region)Phyla Firmicutes. Bacteroidetes. Proteobacteria
Classes Clostridia. Clostridia. Negativicutes
Orders Clostridiales. Bacteroidales
Families Bacteroidales. Lachnospiraceae. Ruminococcaceae
Genera Blautia. Prevotella. Faecalibacterium. Ruminococcus. Bacteroides		PSQI PSQI > 5928.8 ± 10Euryachaeota 2.41 × 104 
Actinobacteria 9.06 × 103 
Bacteriodetes 3.16 × 101 
Chloroflexi 1.47 × 106 
Cyanobacteria 1.84 × 103 
Elusimicrobia 1.66 × 104 
Firmicutes 5.99 × 101 
Fusobacteria 1.36 × 104 
Lentisphaerae 6.21 × 105 
Proteobacteria 4.19 × 102 
Spirochaetes 7.35 × 107 
Synergistetes 6.25 × 105 
TM7 1.23 × 105 
Tenericutes 9.26 × 104 
Verrucomicrobia 3.10 × 102 
Thermi 2.20 × 106		1930.3 ± 10.8Phylum Firmicutes 38.0 ± 10.3 Bacteroidetes 34.6 ± 11.8
Proteobacteria 2.8 ± 1.8
Class Clostridia 32.0 ± 9.9 Bacteroidia 34.6 ± 11.8
Negativicutes 2.2 ± 1.5
Order Clostridiales 32.0 ± 9.9 Bacteroidales 34.6 ± 11.8
Family Bacteroidaceae 13.9 ± 9.3 Lachnospiraceae 9.6 ± 4.2 Ruminococcaceae 12.2 ± 5.5
Genus Blautia 2.2 ± 1.1 Prevotella 16.0 ± 19.1 Faecalibacterium 8.7 ± 4.2
Bacteroides 13.9 ± 9.3 Ruminococcus 2.3 ± 2.6
Bikov2022 [64]CCS16S rRNA gene sequencing (V3–V4 region)Phyla Actinobacteria. Proteobacteria
Class Gammaproteobacteria
Families Prevotellaceae. Lactobacillae
Genera Porphyromonas. Lachnosporaceae. Lactobacillus. Roseburia		PSGOSAHS1955 ± 12Phylum Firmicutes 38.0 ± 10.3 Bacteroidetes 34.6 ± 11.8 Proteobacteria 2.8 ± 1.8
Class Clostridia 32.0 ± 9.9 Bacteroidia 34.6 ± 11.8 Negativicutes 2.2 ± 1.5
Order Clostridiales 32.0 ± 9.9 Bacteroidales 34.6 ± 11.8
Family Bacteroidaceae 13.9 ± 9.3 Lachnospiraceae 9.6 ± 4.2 Ruminococcaceae 12.2 ± 5.5
Genus Blautia 2.2 ± 1.1 Prevotella 16.0 ± 19.1 Faecalibacterium 8.7 ± 4.2 Bacteroides 13.9 ± 9.3 Ruminococcus 2.3 ± 2.6		2043 ± 16NA
Cai2021 [65] rRNA gene sequencing PSQIHealthy controls15722.3 ± 2.4NP
AHI: Apnea-Hypopnea index; ASD: Autism Spectrum Disorder; BD: Bipolar disorder; CSHQ: Children’s Sleep Habits Questionnaire; CCS: case-control study; CI: chronic insomnia; ESS: Epworth Sleepiness Scale; HAM-D: Hamilton Depression Rating scale; ISI: Insomnia Severity Index; MDD: Major Depressive Disorder; MSLT: Multiple Sleep Latency Test; NA: not available; NR: not reported; NT1: Narcolepsy type 1 OSAHS: Obstructive Sleep Apnea/Hypopnea Syndrome; PD: Parkinson disease; PROMIS-SD: PROMIS Sleep Disturbance; PSG: polysomnography; PSQI: Pittsburgh Sleep Quality Index; T2DM: type 2 diabetes mellitus.
Table 2. Studies detected during the second literature search.
Table 2. Studies detected during the second literature search.
Common FeaturesStudy Group (Probiotic)Control Group (Placebo)
AuthorsYearStudy DesignMicrobiota Profiling MethodSleep Assessment MethodInclusion CriteriaTreatment Duration Participants (n)Age (y ± SD)Intervention TreatmentProbiotic Bacteria CFUParticipants (n)Age (y ± SD)Placebo Treatment
Nishida et al. [81]2017 R/DB/PC/PNPPSQI
EEG 		Healthy 6th-year Japanese medical students 12 weeks34 25.1 ± 2.37200 mL Fermented Milk/DayHeat—inactivated L. gasseri CP23051 × 10103525.1 ± 2.4Milk: high-fructose corn syrup, powdered skim milk, lactic acid, soybean polysaccharide, pectin, sodium citrate, flavors, sweeteners
Kato-Kataoka et al. [82]2016R/DB/PC/PNPPSQIHealthy 4th-year medical students undertaking an examination for promotion6 or 8 weeks2423.0 ± 0.4 100 mL Fermented Milk/DayL. casei strain Shirota1 × 109/mL 23 22.7 ± 0.4 Milk: similar composition with the addition of lactic acid
Nakakita et al. [83]2016NR/DB/PC/CO NPEEG, AIS, Sleep journalsHealthy males suffering from sleep challenges (AIS ≥ 6)10 days6 53.9 ± 8.8 1 × Capsule/Day Heat-killed L. brevis SBC8803NS8 53.9 ± 8.8Capsules: caramel pigment, finely powdered silica, calcium stearate, starch, cellulose
Sawada et al. [84]2017R/DB/PC/CO Gene expression analysis PSQIMale medical students undertaking the cadaver dissection course 4 weeksNS241 × Bag/DayL. gasseri CP23051 × 1010NS24Lyophilized powder: skim milk (20%), yeast extract (0.50%)
Yamamura et al. [85]2009R/DB/PC/CO NPActigraphy, SHRIHealthy subjects3 weeks 1472.1 ± 21.2100 g Fermented Milk/DayL. helveticusNS1570.7 ± 21.9Artificially acidified milk added with L-lactic acid
Calandre et al. [86]2021R/DB/PC/P NPISIFibromyalgia patients12 weeks2856.0 ± 7.5 4 × Sachets/DayS. thermophilus BT01
B. breve BB02
B. animalis subsp. lactis BL03
B. animalis subsp. lactis BI04
L. acidophilus BA05
L. plantarum BP06
L. paracasei BP07
L. helveticus BD08		4.5 × 101435 55.5 ± 8.6 Sachets: maltose, cornstarch, silicon dioxide
Dhiman et al. [87]2014R/DB/PC/P NPPSQI, ESSCirrhosis patients 24 weeks1648.0 ± 1.4 1 × VSL#3 Sachet/DayL. paracasei DSM 24733
L. plantarum DSM 24730
L acidophilus DSM 24735,
L. delbrueckii bulgaricus DSM 24734)
B. longum DSM 24736
B. infantis DSM 24737
B. breve DSM 24732
Streptococcus thermophilus DSM 24731		9 × 10111150.1 ± 9.8 Sachets: corn flour
Wong et al. [88]2015R/DB/PC/P NPPSQI, ESSIBS6 weeks 2053.3 ± 18.68 Capsules/DayL. acidophilus
L. casei
L. delbrueckii bulgaricus
L. plantarum
B. longum
B. infantis
B. breve
Streptococcus salivarius thermophilus		1.125 × 101122 40.9 ± 16.5Capsules: NS
Majeed et al. [89]2018R/DB/PC/P NPmESSMDD and IBS90 days 2040.4 ± 10.31 × Tablet/DayB. coagulans MTCC 58562 × 10920 43.9 ± 9.8Tablets: microcrystalline cellulose, starch, sodium starch glycolate, magnesium stearate
Shafie et al. [90]2022R/DB/PC/PNPPSQIPost-menopausal women 6 weeks 3351.8 ± 2.3100 g Yogurt/DayB. lactis
L. acidophilus		1 × 108 C CFU/g33 52.4 ± 2.4Yogurt: containing L. bulgaricus and Streptococcus thermophilus
AIS: Athena Insomnia Scale; CO: cross-over design; DB: double blinded; ECG: electrocardiography; EEG: Electroencephalography; ESS: Epworth sleepiness scale; HE: hepatic encephalopathy; HLPCQ: Healthy Lifestyle and Personal Control Questionnaire; IBS: Irritable Bowel Syndrome; ISI: Insomnia Severity Index; MDD: Major Depressive Disorder; mESS: modified Epworth sleepiness scale; MEQ: Morningness-Eveningness Questionnaire; NP: not performed; NR: non-randomized; OSHAS: obstructive sleep apnea; P: parallel design; PC: placebo-controlled; PCR: polymerase chain reaction; PLA: placebo group; PRO: probiotic group; PSG: Polysomnography; PSQI: Pittsburgh Sleep Quality Index; R: randomized; SD: standard deviation; SHRI: Sleep-Health Risk Index; VAS: Visual analog scales. Italics are used for bacterial and viral taxa at the level of family and below.
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Santi, D.; Debbi, V.; Costantino, F.; Spaggiari, G.; Simoni, M.; Greco, C.; Casarini, L. Microbiota Composition and Probiotics Supplementations on Sleep Quality—A Systematic Review and Meta-Analysis. Clocks & Sleep 2023, 5, 770-792. https://doi.org/10.3390/clockssleep5040050

AMA Style

Santi D, Debbi V, Costantino F, Spaggiari G, Simoni M, Greco C, Casarini L. Microbiota Composition and Probiotics Supplementations on Sleep Quality—A Systematic Review and Meta-Analysis. Clocks & Sleep. 2023; 5(4):770-792. https://doi.org/10.3390/clockssleep5040050

Chicago/Turabian Style

Santi, Daniele, Valentina Debbi, Francesco Costantino, Giorgia Spaggiari, Manuela Simoni, Carla Greco, and Livio Casarini. 2023. "Microbiota Composition and Probiotics Supplementations on Sleep Quality—A Systematic Review and Meta-Analysis" Clocks & Sleep 5, no. 4: 770-792. https://doi.org/10.3390/clockssleep5040050

APA Style

Santi, D., Debbi, V., Costantino, F., Spaggiari, G., Simoni, M., Greco, C., & Casarini, L. (2023). Microbiota Composition and Probiotics Supplementations on Sleep Quality—A Systematic Review and Meta-Analysis. Clocks & Sleep, 5(4), 770-792. https://doi.org/10.3390/clockssleep5040050

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