The interplay of sleep characteristics with health factors and gut microbiome

Domain: neuroscience
Published: 2026-02-13
Editorial outcome: major_revision

Abstract
Emerging evidence suggests a bidirectional relationship between sleep and the gut microbiome. In this study, we explore the associations of sleep characteristics with lifestyle factors and gut microbiome composition in 6941 participants from the Lifelines Dutch Microbiome Project. We show that lower alpha diversity is associated with poorer sleep quality, later chronotype, and greater social jet lag, while beta diversity is linked to both sleep quality and social jet lag. Of the 137 bacterial species associated with sleep, 35.6% are validated in an independent cohort. Mediation analyses indicate that, while changes in species abundance are largely a consequence of sleep behavior, certain species may mediate diet’s influence on sleep. For example, we find that Clostridia species UC5_1_1E11 and SGB14844 mediate the effect of coffee intake on social jet lag. These findings highlight the intricate relationship between diet, the gut microbiome, and sleep, suggesting the potential for microbiome-targeted interventions to improve sleep health.

Introduction
Sleep is a vital physiological process that is governed by the circadian system, which synchronizes the internal biological clock with the external light-dark cycle1. Sleep and circadian rhythms regulate various aspects of human physiology and behavior, including memory, attention, hormone balance, immunity, and metabolism.1–3 However, sleep disorders and circadian disruption are highly prevalent in modern society, posing a significant public health challenge4. Compromised sleep quality and quantity, as well as misalignment of sleep and circadian rhythms with the external environment, have been implicated in an increased risk of various chronic diseases, including cognitive impairment5, cardiometabolic disorders6, and mental disorders7. Therefore, elucidating the mechanisms underlying sleep physiology and circadian misalignment is essential for devising effective interventions to enhance sleep health and prevent sleep-associated diseases.

Recent years have witnessed the emergence of the gut microbiome as a key determinant of health8. Many pathological states that are associated with poor sleep and circadian misalignment are also associated with disturbed gut flora9. Moreover, as the role of the gut–brain axis has been increasingly acknowledged, it has become evident that there is a bidirectional communication between sleep, which is regulated by the brain, and the gut microbiome10. In rodent models, manipulation of the gut microbiome has been shown to influence sleep architecture, duration, and quality. For instance, antibiotic-induced depletion of the gut microbiome leads to increased fragmentation and reduced quality of sleep11. Conversely, probiotic or prebiotic supplementation can enhance sleep quality and prevent stress-induced sleep impairments12–14. Furthermore, specific bacterial strains or metabolites have been found to modulate sleep-related neurotransmitters or hormones such as serotonin, melatonin, gamma-aminobutyric acid, and cortisol15. The gut microbiome is also subject to the effects of sleep and circadian rhythms. Mice with aberrant circadian gene expression display altered gut microbiome profiles16,17, and chronic sleep deprivation or jet lag can change the diversity and abundance of gut bacteria and increase intestinal permeability and inflammation18–20. Likewise, circadian disruption can disrupt the diurnal oscillation of the gut microbiome and impair its metabolic functions21,22.

Despite these promising findings in animal models, the evidence for the gut microbiome–sleep relationship in humans remains limited and inconclusive. Most of the existing human studies are constrained by relatively small sample sizes and the use of lower resolution 16S rRNA gene sequencing for microbiome composition analysis, resulting in inconsistent findings23–25. In addition, causal links between sleep and microbiome is not yet established.

In this study, we aimed to address this gap by investigating the links between gut microbiome characteristics and various sleep parameters, including sleep quality, daytime sleepiness, social jet lag, and chronotype, in a large Dutch population cohort of 6941 individuals (Fig. 1). We analyzed microbiome features, including alpha and beta diversity and relative abundance of bacterial taxa, and conducted mediation analyses that incorporated dietary factors to explore the plausible causal relationships between sleep and the microbiome. This study aimed to identify key microbial factors linked to human sleep characteristics and to examine the potential causalities in this relationship, thereby providing insights into the intricate role of the gut–brain axis within the realm of sleep and circadian rhythms.

Results
Sleep characteristics in 6941 Dutch individuals

We analyzed sleep behavior in 6941 Dutch individuals from the Lifelines population cohort26. Key demographic parameters included an average age of 51.02 years (SD = 11.55) and a mean BMI of 26.01 kg/m2 (SD = 4.09), and the cohort was 40.6% males (Supplementary data 1). Sleep characteristics were evaluated through self-reported measures, including the PSQI for sleep quality, the ESS for daytime sleepiness, and the MCTQ for social jet lag and chronotype.

Based on PSQI scores, our analysis indicates that 74.9% of the study population were classified as good sleepers, whereas about a quarter were categorized as poor (22.4%) or very poor (2.7%) sleepers, with a median global score of 3.43 [IQR: 1.71–5.14] (Fig. 1b). Notably, we observed that sleep quality differed between sexes, with female participants reporting inferior sleep quality compared to males (Fig. S2). Daytime sleepiness, as measured by the ESS, showed that 90.8% of participants had normal levels of daytime sleepiness, with a median score of 4 [IQR: 2–7], with the remaining 9.2% experienced excessive sleepiness during the day (Fig. 1b). The MCTQ assessment shows a median social jet lag of one hour [IQR: 0.53–1.46], highlighting a shift toward later sleep times on non-working days. This phenomenon was inversely related to age, suggesting that older individuals experience less of a gap in sleep midpoint between working days and free days (Fig. S2). Furthermore, based on participants’ sleep midpoint on free days, we classified chronotypes into early (24.6%), intermediate (49.9%), and late (25.5%) categories (Fig. 1b), reflecting their natural inclination to sleep at a certain time.

Sleep characteristics are highly associated with diet, disease, socioeconomics, and other factors

We first explored the relationships between sleep characteristics and a broad spectrum of phenotypes, including dietary habits, disease presence, and socioeconomic status, among others, while correcting for age and sex (Fig. 2 and Supplementary data 2). A significant proportion of the phenotypes, spanning various categories, were found to be associated with at least one sleep characteristic at FDR < 0.05 level: 60 out of 85 diseases, 16 out of 24 blood or urine measurements, 28 out of 31 diet factors, 24 out of 62 medication usages, 12 out of 28 exposome factors, and 11 out of 12 socioeconomic factors. These findings underscore the pervasive relevance of sleep across all facets of life, with the strongest associations observed for dietary habits and disease presence.

Our results showed that individuals with optimal health status exhibited more favorable sleep characteristics. Conversely, sub-optimal sleep—defined by lower sleep quality, increased daytime sleepiness, greater social jet lag, and a tendency toward a late chronotype—was linked to a spectrum of health issues, notably psychological and GI disorders. Regarding dietary habits, a healthy diet, as indicated by high HEI scores and increased intake of polysaccharides and proteins, was associated with better sleep metrics. In contrast, diets characterized by adherence to a western pattern and higher intake of maltose were associated with poorer sleep parameters. Notably, alcohol consumption was linked to a later chronotype (P-value = 1.38 × 10−40), whereas higher total caloric intake was related to increased daytime sleepiness (P-value = 4.47 × 10−25).

Sleep characteristics are associated with gut microbiome at the composition level

Next, we explored the relationship between sleep characteristics and various microbial features, including alpha and beta diversity of the microbiota and species-level abundance (Fig. 3).

Alpha diversity, as measured by the Shannon index and PD, was significantly associated with PSQI score (Shannon: Beta = −0.015, FDR = 0.024; PD: Beta = −0.256, FDR = 2.31 × 10⁻⁴), social jet lag (Shannon: Beta = −0.017, FDR = 0.024; PD: Beta = −0.185, FDR = 0.020), and chronotype (Shannon: Beta = −0.023, FDR = 0.024; PD: Beta = −0.239, FDR = 0.019) (Fig. S3, Supplementary data 3), which suggests that variations in sleep patterns are reflected in the diversity of the gut microbiome. Beta diversity, assessed using Bray-Curtis and Weighted UniFrac distances, showed significant associations with PSQI (Bray-Curtis: R² = 5.4 × 10⁻⁴, P = 0.00001; Weighted UniFrac: R² = 5.6 × 10⁻⁴, P = 0.003), social jet lag (Bray-Curtis: R² = 4.2 × 10⁻⁴, P = 0.0004; Weighted UniFrac: R² = 5.5 × 10⁻⁴, P = 0.007), and chronotype (Bray-Curtis: R² = 4.3 × 10⁻⁴, P = 0.00035; Weighted UniFrac: R² = 7.1 × 10⁻⁴, P = 0.002) (Supplementary data 4). These findings indicate that both taxonomic and phylogenetic composition of the gut microbiome are associated with these sleep characteristics.

Further, our species-level analysis identified 137 bacterial species significantly associated with at least one sleep characteristic at FDR < 0.05 (Fig. 3a, Supplementary data 5). Of these, 119 species were associated with sleep quality, 20 with social jet lag, and 24 with chronotype. No species showed a significant association with daytime sleepiness. To further explore specific aspects of sleep, we also examined the associations between species and individual PSQI component scores. These analyses largely reflected the patterns observed with the global PSQI score, with most signals captured by the global score (Supplementary data 6, Figs. S4 and S5). Among the 137 species identified, we observed a consistent directional trend in which a species associated with a positive sleep characteristic (e.g., good sleep quality) tended to also associate positively with other favorable sleep characteristics (e.g., reduced social jet lag). This may suggest a shared biological basis underlying different aspects of sleep, although some of the consistency we observe could also reflect overlap among the sleep measures. Overall, we observed 48 species associated with positive sleep characteristics and 89 associated with negative sleep characteristics. The majority of the species linked to beneficial sleep patterns were found to be prevalent (28 out of 48 found in >50% of participants). This is in contrast to those associated with detrimental sleep patterns, where a significant portion (80 out of 89) were less common, appearing in <50% of the participants.

Among these 137 associated species, the three most prevalent families were Ruminococcaceae (44 species), Lachnospiraceae (27 species), and an unidentified family within the Clostridia class (15 species). All other families were represented by six or fewer species. It is important to note that the direction of effect within the same family was not consistent. For instance, within the Ruminococcaceae family, GGB9602_SGB15031 showed a strong association with good sleep quality (P-value = 1.62 × 10−7), whereas Flavonifractor plautii was linked to poor sleep quality (P-value = 2.94 × 10−5) and a later chronotype (P-value = 1.68 × 10−4).

Most of the sleep–species associations we identified were uniquely linked to a specific sleep aspect: 101 species were exclusively associated with sleep quality, 8 with social jet lag, and 7 with chronotype. The larger number of associations we observed for sleep quality might not only indicate stronger biological connections, but it could also partly result from greater variability in the sleep quality data compared to other sleep measures. Notably, five species demonstrated associations across all three sleep characteristics, suggesting they might have a more integral role in sleep and circadian rhythm regulation. These species were Clostridia unclassified SGB14844, which was associated with negative sleep patterns, and four species associated with positive sleep patterns, Coprococcus eutactus, Oscillibacter sp. ER4, GGB9615_SGB15053 (belonging to the Ruminococcaceae family), and Ruminococcaceae unclassified SGB15236.

In our previous analysis, we had observed strong links between sleep characteristics and mental or GI diseases. To assess whether these or other health and lifestyle factors conditions were confounding our results, we conducted a sensitivity analysis incorporating additional covariates, including mental disease status, GI disease status, metabolic diseases (hypertension, hypercholesterolemia, diabetes), physical activity, alcohol intake, and use of sleep medications in our model (Fig. S6). The consistent distribution of significance and direction of effect, even after accounting for these covariates individually and jointly, indicates that the associations between sleep characteristics and microbial species are robust and not driven by these potential confounders.

We were also able to further validate our findings in an independent cohort, Lifelines-DEEP (Fig. 3c, Supplementary data 7). Although 17% (28 out of 163) of the species identified in our primary analysis were not present in this replication cohort, we successfully replicated 35.6% (48 out of 135) of the species associations at a discovery FDR < 0.05 and a replication P-value < 0.05. It is important to acknowledge here, as noted by Fernández-Pato et al.27, that the DNA isolation protocols differed between the two studies and there was also variation in the time between fecal sample collection and sleep measurement. These methodological differences could have contributed to a lower replication rate. Despite these challenges, the successful replication of 35.6% of the associations, with the effect direction remaining consistent across all replicated associations, strengthens the credibility of our results.

Inferring the putative causal relationship linking sleep, gut microbiome, and diet or disease

Having established a link between sleep and bacterial composition, we sought to determine whether alterations in sleep patterns influence microbial species abundance or if the reverse is true. To investigate this, we incorporated dietary patterns, which we hypothesized to be more likely to be an exposure than a consequence of the microbial features, into our mediation network analysis. This analysis tested three predefined potential directional relationships: (i) diet → microbiome → sleep, where changes in microbial features potentially cause alterations in sleep characteristics, (ii) diet → sleep → microbiome, and (iii) sleep → diet → microbiome, where changes in microbial features are outcomes of lifestyle modifications (Supplementary data 8).

In the first scenario (Fig. S7 and Supplementary data 9), we observed that most species demonstrated a direct link to either sleep or diet. However, two species, Clostridia bacterium UC5_1_1E11 and Clostridia unclassified SGB14844 (both from the unclassified Clostridia genus), acted as putative mediators in the diet-to-sleep pathway, suggesting a potential mediation of the effect of coffee consumption on social jet lag. Increased coffee intake was found to boost the abundance of these two species, consequently intensifying the extent of social jet lag. This interesting dynamic between coffee consumption, the two Clostridia species, and social jet lag was also observed in the second (Fig. S8, and Supplementary data 10) and third scenarios (Fig. S9 and Supplementary data 11), indicating that fluctuations in social jet lag might impact coffee consumption, or vice versa, thereby altering the abundance of these two Clostridia species.

Since these two species appeared in a mediation relationship in all three directions, we could quantify the proportion of mediation based on effect sizes derived from an unpenalized model (Fig. 4). By aggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the abundance of these species mediated 8.3% of the effect of coffee intake on social jet lag. Conversely, social jet lag accounted for only 1.5% of the effect of coffee intake on these species. Notably, coffee intake was found to mediate 65% of the effect of social jet lag on these species. These estimates suggest that variation in the abundance of these two Clostridia species related to social jet lag may largely reflect differences in coffee consumption.

To further investigate the functional and evolutionary basis of these two Clostridia species, we conducted exploratory analyses of their metabolic pathways and phylogenetic relationships. Despite their putative role in mediation, we identified no significant (FDR < 0.05) metabolic pathways or phylogenetic conservation of these species’ associations with social jet lag or coffee intake (Supplementary data 12 and 13). While their functional roles remain unclear, these species could influence circadian rhythms through unmeasured metabolites or indirect mechanisms, warranting deeper functional profiling in targeted studies.

Along the same line, the connections between many microbial features and sleep characteristics were found to be potentially mediated by specific dietary factors (direction III) (Fig. S9 and Supplementary data 11). Similar to the two Clostridia species, many species abundances could also be affected by changes in coffee consumption induced by social jet lag. Lachnospiraceae GGB3570_SGB4777 and Ruminococcaceae GGB9623_SGB15076 showed an increase following coffee intake, while Intestinimonas butyriciproducens and Ruminococcaceae GGB9699_SGB15216 exhibited a decrease. Additionally, a preference for a later chronotype was linked with increased alcohol consumption, which in turn led to a reduced abundance of Clostridium leptum and Coprococcus eutactus. Nevertheless, findings from direction II still revealed that certain species do exhibit a direct connection with sleep patterns, independent of dietary mediation (Fig. S8 and Supplementary data 10). For example, individuals with a late chronotype displayed elevated levels of Lachnospiraceae GGB3570_SGB4777 and a reduction in Clostridium leptum, and individuals with worse sleep quality had higher Ruminococcaceae unclassified GGB9694_SGB15203.

Beyond the species-level findings reported above, we did not observe significant mediation at the composition level. We also evaluated whether the gut microbiome could serve as a mediator in associations between sleep characteristics and disease phenotypes, focusing on GI symptoms and mental health conditions. However, we did not identify any microbial features or diversity measures that met the criteria for significant mediation in these models.

Methods
Study population

This study comprised 6941 participants from the Lifelines Dutch Microbiome Project (DMP) cohort40. The DMP cohort is a sub-cohort of the Lifelines cohort study, which is a large-scale, multi-disciplinary, prospective population-based cohort study that employs a unique three-generation design to examine health and health-related behaviors in 167,729 individuals residing in the northern Netherlands26. The Lifelines study was approved by the medical ethical committee of the University Medical Center Groningen (METc number: 2017/152). Additional written consent was signed by all DMP participants or their parents or legal representatives (for children aged under 18). The Lifelines cohort study utilizes a wide range of investigative procedures to assess the biomedical, socio-demographic, behavioral, physical, and psychological determinants of health and disease, with a particular emphasis on complex genetics and multi-morbidity41. The DMP sub-cohort was created to examine how the gut microbiome is modulated by genetics, exposome, lifestyle, and diet in various health and disease conditions. For the DMP, 8719 distinct fresh-frozen fecal and blood samples were collected from Lifelines participants in 2015 and 2016. For the current study, we included participants from the DMP based on the availability of metagenomic sequencing data of their stool samples and measurements of sleep behavior.

Phenotype data

Metadata. Metadata information from the participants was collected using questionnaires and curated as previously described in the DMP study40. This covered a wide range of phenotypes, including anthropometrics, diet, disease, general health scores, medication usage, exposome, and socioeconomic characteristics. In brief, habitual diet was assessed through a semi-quantitative food frequency questionnaire (FFQ) collected 4 years before DMP fecal sampling41, and the long-term stability of habitual dietary intake has been validated as described in the study40. Daily defecation diaries, Bristol Stool Scale, daily medication usage, and gastrointestinal (GI) symptoms were recorded by DMP participants during the week of stool sample collection40. Self-reported information on the presence of other diseases was collected using Lifelines questionnaires and grouped into 11 disease categories40. Self-reported medications were categorized based on Anatomical Therapeutic Chemical classification (ATC codes)40. Elements of the exposome, neighborhood urbanicity, and income were assessed for the participant’s home address at the time of fecal sampling40.

In addition to the DMP phenotypes, we also incorporated variables related to internalizing disorders, as detailed in Brushett et al.42, and dietary patterns derived from FFQs based on the work by Peters et al.43 In total, 269 phenotypes from a diverse range of categories were included in this study.

Sleep behavior. We collected self-reported sleep behaviors (sleep quality, daytime sleepiness, social jet lag, and chronotype) through questionnaires at the baseline measurement of Lifelines44. These sleep parameters exhibit stability over time (Fig. S1).

Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI)45, a questionnaire that evaluates sleep quality and disturbances over the past month. The PSQI consists of 19 questions that generate seven component scores, each ranging from 0 to 3. The components are subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. The global PSQI score sums up the seven component scores, with a maximum of one missing component score allowed. If a component score is missing, the global PSQI score is adjusted by multiplying by 7/6. A higher global PSQI score indicates worse sleep quality.

Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS)46, a questionnaire that measures a person’s general level of daytime sleepiness or their average sleep propensity in daily life. The ESS consists of eight questions that ask the respondent to rate their usual chances of dozing off or falling asleep while engaged in different activities, such as reading, watching TV, or driving. The ESS score can range from 0 to 24, with higher scores indicating higher daytime sleepiness.

Social jet lag and chronotype were assessed using the Munich Chronotype Questionnaire (MCTQ)47, a questionnaire that assesses sleep and wake behavior on work and free days, as well as other factors that affect sleep quality and quantity. The MCTQ calculates the midpoint of sleep, which is the average time between sleep onset and offset. Social jet lag is the difference between the midpoint of sleep on free days (MSF) and work days (MSW), which reflects the mismatch between the biological and social clocks48. Chronotype is the tendency to sleep at a certain time and represents an individual’s natural biological sleep behavior, which is determined by the midpoint of sleep on free days when not using an alarm clock, adjusted for the sleep debt accumulated over the work week (MSFsc). Based on the MSFsc score, chronotype can be divided into three groups: early, intermediate, and late. Early chronotype corresponds to the lowest quartile of MSFsc, intermediate to the 2nd and 3rd quartiles, and late to the highest quartile.

Microbiome profiling

Fecal sample DNA sequencing. Fecal sample collection, DNA extraction, and sequencing were performed as previously described in Gacesa et al.40 In brief, metagenomic sequencing was used to analyze the gut microbiome of participants who collected their fecal samples at home. The participants were instructed to freeze their samples within 15 min of defecation and to store them until they were retrieved by Lifelines personnel. Samples were then transported on dry ice to the Lifelines biorepository and stored at −80 °C until DNA extraction. The QIAamp Fast DNA Stool Mini Kit and the QIAcube automated system were used to isolate microbial DNA from the samples. Depending on the total DNA yield, either the NEBNext Ultra or the NEBNext Ultra II DNA Library Prep Kit for Illumina was used to prepare the libraries for sequencing. Sequencing was performed by Novogene, China using the Illumina HiSeq 2000 platform, which generated about 8 Gb of 150 bp paired-end reads per sample. Reads were trimmed and cleaned for subsequent analysis.

Taxonomic composition

Metagenomic sequences were processed using KneadData v0.10.0 to remove contaminants, including host DNA, and to trim low-quality reads. The resulting clean reads were then analyzed using MetaPhlAn v4.0.6 with the CHOCOPhlAn Species Genome Bin (SGB) database vOct22 to profile the taxonomic composition. We chose to classify species at the “s__” taxonomic level, aligning closely with the conventional species definition, rather than adopting the SGB approach based on gene similarity. This decision was made to balance the need for high-resolution species identification against practicality of interpretation.

The analysis identified 2583 species distributed across various taxonomic levels: 3 kingdoms, 33 phyla, 218 classes, 269 orders, 350 families, and 1,283 genera. To focus on the most relevant species, we filtered out species that were detected in fewer than 100 participants (approximately 1.4% of the cohort), leaving 813 species for further analysis.

Statistical analysis

All analyses were conducted under the assumption of independent observations. Each participant contributed only one microbiome sample and corresponding phenotype record; no repeated measurements were included in the dataset. Before conducting any statistical analyses, we applied inverse rank normal transformation49 to the sleep indices (PSQI score, ESS score, social jet lag, and MSFsc score) to reduce skewness and improve normality. All statistical analyses were performed using R software (version 4.2.2). A false discovery rate (FDR) was estimated using a permutation-based approach. For each analysis, we shuffled the sleep phenotype labels 1000 times and recalculated association P-values for each permuted dataset, thereby generating a null distribution of association P-values under the assumption of no true association. To estimate the FDR for a given P-value threshold, we calculated the proportion of P-values under the null distribution that were equal to or smaller than the threshold divided by the proportion of observed P-values that were equal to or smaller than the same threshold. Associations were considered statistically significant if their empirical FDR was below 0.05, and all tests were two-sided.

Association between sleep behaviors and phenotypes

Spearman correlation analysis was performed to investigate the interrelationships among sleep behaviors and intrinsic factors (age, sex, and BMI). The association between sleep behaviors and various phenotypes was assessed through linear regression models, with age and sex included as covariates for adjustment. For phenotypes specifically related to sex (such as breast cancer, cervical cancer, and prostate cancer), only age was used as a covariate for adjustment.

Association between microbial features and sleep behaviors

To account for the potential confounding effects of various factors on the gut microbiome, we included the following factors as covariates when conducting any analyses related to the microbial features: technical parameters (metagenomic sequencing batch number, DNA concentration (ng/µl), and number of post-quality control reads), intrinsic factors (age, sex, and BMI), and stool characteristics (frequency and consistency measured by Bristol Stool Scale).

Microbiome diversity was assessed using the Shannon index and phylogenetic diversity (PD) for alpha diversity and the Bray-Curtis dissimilarity and weighted UniFrac distances for beta diversity. The Shannon index and Bray-Curtis dissimilarity were calculated using vegan (version 2.6-4)50 in R. PD was computed using the picante package (version 1.8.2), and weighted UniFrac distances were generated using the calculate_diversity.R script from MetaPhlAn, both based on the MetaPhlAn reference phylogenetic tree (mpa_vOct22_CHOCOPhlAnSGB_202212.nwk). Prior to all abundance-based analyses, microbiome relative abundance data were normalized using the center log-ratio (CLR) transformation to ensure appropriate statistical handling. We employed linear regression models to explore the associations between different microbiome components (including alpha diversity indices and species abundance) and sleep behaviors. For microbiome beta diversity, Permutational Multivariate Analysis of Variance was performed using the adonis2 function from the vegan package, conducted separately for each sleep behavior with univariate adonis and 100,000 permutations.

To assess the robustness of species abundance associations with sleep characteristics, we performed a series of sensitivity analyses, adjusting individually for additional potential confounders, including metabolic diseases (hypertension, hypercholesterolemia, and diabetes), GI diseases, mental health conditions, physical activity, alcohol intake, and sleep medication use. We also examined a fully adjusted model incorporating all covariates simultaneously. For the replication of the association between species abundance and sleep characteristics, we used another subset of the larger Lifelines study, the Lifelines-DEEP cohort, comprising approximately 1500 participants. Fecal sample collection and DNA sequencing were conducted as previously described51. The assessment of sleep behaviors and microbial composition profiling followed the same methodologies outlined above. The same statistical methods were applied, and replication was considered successful if the discovery FDR was < 0.05, the replication P-value was < 0.05, and the effect direction was consistent.

Mediation analysis

A large proportion of the associations we observed between sleep characteristics and other traits were related to dietary patterns, GI discomfort, and mental health conditions. Given that diet is a well-established modulator of the gut microbiome, and GI and mental disorders often co-occur with both sleep disturbances and microbiome alterations, we sought to explore the potential causal relationships between sleep, the microbiome, and either diet or disease. Based on biological plausibility, we pre-specified a set of hypothesized models for mediation analysis. For diet, we tested three plausible pathways: (i) diet → microbiome → sleep, (ii) diet → sleep → microbiome, and (iii) sleep → diet → microbiome. For disease-related traits (GI symptoms and mental health disorders), we tested models where the microbiome may mediate the relationship between disease and sleep: (i) disease → microbiome → sleep and (ii) sleep → microbiome → disease.

For each analytic direction, we fit two separate models: an abundance model using relative abundances of 137 species previously linked to at least one sleep phenotype, and a composition model using measures of overall microbiome structure (diversity). For alpha diversity, we included Shannon index and PD. For beta diversity, we computed the first 20 principal coordinates (PCoAs) based on weighted UniFrac distances. For sleep characteristics, we focused on variables that demonstrated a significant link with species abundance, namely sleep quality, social jet lag, and chronotype. Dietary variables included the Healthy Eating Index (HEI), protein score, alcohol consumption (adjusted for total caloric intake), alternative Mediterranean diet score (aMED), modified Mediterranean diet score (mMED), plant protein score, coffee consumption, total caloric intake, low-carbohydrate score, and free sugar intake. The GI phenotypes included celiac disease, Crohn’s disease, ulcerative colitis, stomach ulcer, and general GI discomfort, including abdominal pain, bloating, flatulence, burping, and nausea. The mental health phenotypes included bipolar disorder, depression, eating disorder, generalized anxiety, and panic disorder. All microbial variables were adjusted for technical factors (sequencing batch, DNA concentration, and post-QC read count), stool characteristics (Bristol Stool Scale, defecation frequency), and intrinsic factors (age and sex). All other variables were also adjusted for age and sex. Residuals from these adjusted models were used as inputs to the mediation framework to reduce confounding by known covariates.

Multivariable mediation was evaluated across five prespecified causal directions spanning diet- and disease-related hypotheses using the regmed package in R for regularized mediation analysis via penalized structural equation modeling52. For species abundance-based analyses, to reduce bias from feature preselection and enhance robustness, lasso-penalized mediation models were embedded within a subsampling-based stability selection procedure. For each direction, 10,000 iterations were executed. At each iteration, 30 candidate microbial species were randomly sampled from the full candidate set, and a lasso-penalized mediation model was fit over a grid of L1 penalty strengths (λ ∈ {0.02, 0.015, 0.01, 0.005})53. The penalty minimizing the Bayesian Information Criterion (BIC) was selected. Linear mediation was parameterized as M = αX + ε and Y = βM + δX + ε where X denotes the exposure, M the putative mediator, and Y the outcome. A species was deemed selected in an iteration if, for the species-as-mediator direction, both α ≠ 0 and β ≠ 0, or if, for the species-as-outcome direction, both β ≠ 0 and δ ≠ 0. For each species, the number of eligible runs (iterations in which the species was included), selected runs (iterations meeting the selection rule), and the selection rate (selected/eligible × 100%) were recorded (Supplementary data 8). Within each direction, species with a selection rate > 10% were retained, and a final model with these species was fit. For composition-based analyses, we did not perform stability selection; instead, we fit one lasso-penalized model per direction to the full set of diversity metrics, using the same λ grid and BIC-based tuning described above.

To reduce shrinkage bias, we refit the multivariable mediation model on the reduced set (variables with nonzero coefficients at the BIC-selected λ) and then obtained effect-size estimates from an unpenalized model restricted to those variables. For each pathway, the proportion of mediation attributable to the mediator was quantified based on the effect sizes from the non-penalized model. This proportion was determined by dividing the mediated effect (the product of the pathways from X to the mediator and from the mediator to Y) by the total effect (the sum of the mediated effect and any direct effect).

Functional and phylogenetic profiling of Clostridia species

To further explore Clostridia bacterium UC5_1_1E11 and Clostridia SGB14844, we performed species-resolved pathway and phylogenetic analyses. For functional profiling, we used HUMAnN 4.0 to quantify MetaCyc pathway abundances within each species. A total of 106 pathways were detected across the two species (48 in SGB14844 and 58 in SGB14895). Associations between CLR-transformed pathway abundances and phenotypes (coffee intake and social jet lag) were tested using linear models adjusted for age, sex, BMI, DNA concentration, sequencing batch, post-QC read count, Bristol Stool Scale, and defecation frequency. Analyses were performed separately per species among individuals with complete data (n = 630 for SGB14844; n = 77 for SGB14895).

To evaluate whether phylogenetic relatedness among strains could help explain phenotype associations, we constructed strain-level phylogenies using StrainPhlAn with minimum marker detection thresholds set to 10 across samples. We then fit phylogenetic generalized linear mixed models using the anpan R package54, modeling phenotypes as a function of covariates and a phylogenetic random effect under a Brownian motion assumption. Model fit was assessed by comparing the expected log predictive density (ELPD) of each phylogenetic model to a null model without the phylogenetic term, using Pareto-smoothed leave-one-out cross-validation. A phylogenetic signal was considered statistically supported if the ELPD difference (ΔELPD) between the null and phylogenetic model was less than –4 and the 95% confidence interval (ΔELPD ± 2 standard errors) did not include zero.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Wu et al. addressed the relationship between self-reported sleep characteristics and gut microbiota through a large-scale\nmicrobiota association analysis, and also conducted a comprehensive mediation analysis between diet, sleep, and\nmicrobial species.\nSo far, the association between sleep and gut microbiome is still understudied, not to mention the potential influence of\nvarious confounding factors (e.g., mental disorders). The present study involved more than 250 phenotypes and ultimately\nidentified that dietary factors may be closely related to both sleep and gut microbiome, thus performed further in-depth\nmediation analyses in a cross-sectional database. Although there is still a long way to go before interventional trials, overall,\nthese findings may help to understand how to combine diet and the gut microbiome to improve sleep health.\nThe paper is in general clear and well-written. I have, however, some relevant comments and there are few questions and\npoints to be clarified.\nMajor comments:\nIndeed, while the authors chose dietary factors (also an intervenable factor) to understand the causal relationship between\nsleep and the gut microbiome, I still think it's important to address other factors that are highly correlated with sleep traits in\nmediation analyses, such as gastrointestinal discomfort and mental - depression. With such a large dataset of sleep-\nmicrobiome, this is a great and unique opportunity to disentangle the complex relationships among them.\nAlso, for the mediation analyses, the authors concentrated on 137 species, how about the overall microbial composition\n(beta-diversity, e.g., using principal components). And for the data presentation, since the authors have involved three\npotential directional relationships, it would be desirable to visualize the main findings more clearly in the figure, such as the\nFigure 4d, which may help the readers to quickly capture several key mediation pathways (what are exposures,\nmediators\nand outcomes).\nThe mediating effect, although significant, remains questionable as to its clinical implications. For example, line 295, “By\naggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the abundance of these species mediated 10%\nof the effect of coffee intake on social jet lag.” Does this represent mainly a direct effect of coffee on social jet lag? How do\nwe interpret the 10% mediation effect in this scenario?\nThe PSQI, as the authors mentioned, is a global sleep quality scale that includes different components, some of which\nactually overlap with other scales (e.g., the ESS and the PSQI-Q7). It would be interesting to look at the associations of gut\nmicrobiome and component scores, e.g., sleep duration & efficiency, which would provide a more comprehensive picture of\nthe sleep-microbiome relationships.\nIn addition, are gut microbial characteristics different across subgroups? E.g., among three chronotypes, as well as those\nwith good and poorer sleep quality\nOther minor comments:\nIn the abstract, line 15-16, “We found that alpha diversity was inversely associated with sleep quality, chronotype, and social\njet lag,..”, either rephrased as “was associated with” or specify: “was inversely associated with poorer sleep quality, earlier\nchronotype..”.\nIn methods, line 135-136, “To focus on the most relevant species, we filtered out species with a mean relative abundance\npresent in fewer than 100 participants, leaving 813 species for further analysis.” – it is unclear, whether the authors filtered\nout species occurring in fewer than 100 participants (? %), or did they filter out species below a certain average relative\nabundance? Or both?\nIn discussion, line 379-380, “When considering diet as an exposure, we identified some connections that suggest a direct\ncausal link between sleep patterns and variations in species abundance”, do the authors mean consider diet as a mediator?", "reviewer_2": "The manuscript presents a comprehensive analysis of the bidirectional relationship between sleep characteristics and gut\nmicrobiome composition in a large-scale Dutch cohort. The integration of various statistical methods, metagenomic\nsequencing that ensures detailed taxonomic profiling at the species level, improving our understanding of the complex\ninterplay between sleep and gut microbiome composition. The identification of specific bacterial species associated with\ndistinct sleep parameters is particularly interesting. However, some areas, such as the analysis methods and interpretation\nof results, require further clarification and additional analysis to strengthen the conclusions. Our specific comments are:\n1. Lines 241-243: The authors report a \"consistent directional trend\" across species associated with positive sleep\ncharacteristics. This interpretation as a biologically meaningful finding is questionable. Instead, the trend could be due to the\nuse of measurement tools that assess overlapping components of sleep, which would result in a technical artifact rather than\na genuine biological phenomenon. This alternative explanation warrants explicit consideration in the manuscript.\n2. Lines 254-257: The observation that not many species are associated with all three sleep characteristics raises concerns\nabout its biological significance. This finding could be influenced by differences in data distributions across the sleep\nmeasurements. For instance, as shown in Figure 1b, most sleep quality measures are concentrated at the good-quality end\nwith decent variability, while social jet lag measurements show limited variability at the good-quality end and are skewed\ntowards poor quality. Furthermore, the analyses for social jet lag and chronotype may lack sufficient statistical power,\nmaking the results potentially driven by random noise. In contrast, the analysis for sleep quality appears better powered.\nThis discrepancy makes the comparability of significant features across the three sleep characteristics questionable and\nrequires further discussion.\n3. In this study, the authors described that alpha diversity was inversely associated with sleep quality. This is somehow an\ninaccurate description, and it needs to be rephrased. According to the results, alpha diversity was inversely associated with\nPSQI score in this study, not sleep quality, as a higher score implies lower sleep quality. Nevertheless, this result is\nconsistent with the notion that a higher microbiome diversity is generally more positively related to health.\n4. Mediation analysis: The authors' approach to mediation analysis is conceptually flawed. Proper causal mediation analysis\nrequires a pre-specified directed acyclic graph (DAG) and adherence to critical assumptions, including correct model\nspecification. The practice of rotating the roles of exposure, mediator, and outcome to fit different causal mediation models\nbased on statistical significance is fundamentally incorrect. A model should be defined a priori and not selected post hoc\nbased on results. The authors are strongly encouraged to read through VanderWeele's Explanation in Causal Inference:\nMethods for Mediation and Interaction (2015) for proper methodology. Additionally, the study may violate two key\nassumptions of causal mediation analysis: (1) temporality, i.e., the exposure, mediator, and outcome are measured\nsequentially; and (2) no unmeasured confounding, as the adjustment for confounders is likely to be insufficient. Finally,\naddressing high dimensionality upfront in the modeling process, rather than adopting a two-step approach, is critical to avoid\noverfitting and false positives.\n5. The study highlights the role of dietary factors in mediating microbiome changes, but direct effects of diet on sleep\ncharacteristics is not discussed in this study. Could you explore more direct relationships between specific dietary patterns\n(e.g., Mediterranean diet) and sleep quality or chronotype independent of microbiome mediation? At least you should\nprovide it in the discussion.\n6. The results presented in Figure 3 lack clarity regarding whether they are multivariable-adjusted. If the models described in\nthe methods were used, additional confounders such as physical activity, sedentary time, dietary quality, total energy intake,\nalcohol consumption, hypertension, hypercholesterolemia, diabetes, and relevant medications (e.g., sleep aids, melatonin)\nshould also be included. Given the observational nature of the study, adjusting for these variables is essential to strengthen\nthe robustness of the conclusions.\n7. How do you reconcile the bidirectional relationship between sleep and the microbiome with the observational nature of\nthe data? Could longitudinal or interventional studies strengthen this aspect? This needs to be at least addressed in the\nlimitation section.\n8. The analysis highlights the role of coffee consumption in modulating social jet lag through specific species. There is a\npaper recently published (doi.org/10.1038/s41564-024-01858-9), showing the strong link between coffee consumption and\nthe species Lawsonibacter asaccharolyticus. How is the role of this species in your data? Did it also show a relationship\nwith coffee intake and sleep quality?\n9. The variability within genera (e.g., Blautia, Dorea) is intriguing but raises questions about the deeper relevance of these\nassociations. Including strain-level profiling and functional pathway analyses for the identified species could be interesting\nfor further studies to provide more detailed mechanistic insights.\n10. Discuss the broader implications on how the identified microbiome-sleep associations could inform public health\nstrategies or medicine approaches for sleep disorders.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "The authors explored the relationship between sleep characteristics, health factors, and the gut microbiome using data from\na large-scale prospective population cohort, the Lifelines Dutch Microbiome Project. They performed alpha diversity,\nbeta\ndiversity, and species-level analyses, identifying numerous significant associations. Notably, they conducted mediation\nanalysis to examine the role of the gut microbiome in mediating the effects of coffee intake and social jet lag, uncovering\nnovel findings at the species level. While the research questions are compelling and the use of large-scale microbiome data\nimpressive, the authors must address the concerns outlined below for me to recommend acceptance of the manuscript.\n1) The data cannot be found. The authors should disclose the raw sequence data and processed data, preferably including\nthe database, access numbers, and/or website. Without this information, it is impossible to verify the discoveries reported in\nthe study. The authors should clearly organize the processed data for the same 6,941 subjects in the following format:\n  Feature Table: This table should include raw counts, with rows representing features (ASVs) and columns representing\nsubjects. Row names should be feature IDs, and column names should be subject IDs.\n  Taxonomic Table: This table should include taxonomic classifications, with rows representing features and columns\ncorresponding to the seven taxonomic ranks: 'Kingdom,' 'Phylum,' 'Class,' 'Order,' 'Family,' 'Genus,' and 'Species.' Row\nnames should be feature IDs.\n  Metadata: This table should contain variables for phenotypes, disease status, or environmental/dietary factors. Rows\nshould represent subjects, and columns should represent variables, with row names as subject IDs and column names as\nvariable names. The authors should also provide a data dictionary to describe the variables in the metadata in detail.\n  Phylogenetic Tree: This should be a rooted tree, with tip labels corresponding to feature IDs.\n  Without this, the discoveries reported in the study cannot be reproduced by others. The phyloseq package\n(https://www.bioconductor.org/packages/release/bioc/html/phyloseq.html) is highly useful for data preparation, though the\nauthors may consider using an alternative comparative software package if preferred. The authors can refer to the following\nwebsite if they wish. https://github.com/angelolimeta/Gut-microbiome-immunotherapy\n2) Alpha- and beta-diversity analysis: The authors conducted alpha-diversity and beta-diversity analyses using only few\nabundance-based indices (Shannon and Bray-Curtis) only. The authors need to survey other widely used phylogenetic\nindices such as phylogenetic diversity (PD) for alpha diversity and UniFrac distances for beta diversity.\n3) Statistical Analysis for Repeated Measurements: Since the data come from the prospective\nLifelines Dutch Microbiome\nProject, there may be repeated measurements for the same subjects. If this is the case, the metadata should include both\nsubject IDs and measurement IDs. Treating repeated measurements as independent subjects can artificially inflate the\nnumber of discoveries, leading to numerous spurious findings. The authors should employ appropriate statistical methods,\nsuch as mixed-effects models, to account for repeated measurements in their analysis.\n  For PERMANOVA, 1,000 permutations is too few; it is recommended to use 100,000 permutations. Additionally,\nPERMANOVA does not account for repeated measurements or covariate adjustments; thus, you can consider mixed-effects\nmodel-based methods.\n4) Mediation analysis: “employing the mediation package in R” is not at all sufficient. The authors need to disclose the\nmethod name and reference. The authors need to disclose how the repeated measurements and covariate adjustments\nwere handled with more specific methodological descriptions.\n  The authors should provide references for the statement: \"Variable selection in the regularized mediation model was\nperformed using a lasso-penalized model, with the optimal lambda for the lasso (L1) penalty determined by minimizing the\nBayesian Information Criterion through a grid search.\"\n  Furthermore, I recommend performing mediation analysis for both alpha- and beta-diversity indices as well.\nOther comments:\n  A reference needs to be provided for the following statement: “Before conducting any statistical analyses, we applied\ninverse rank normal transformation to the sleep indices (PSQI score, ESS score, social jet lag, and MSFsc score) to reduce\nskewness and improve normality.”\n  The statement “To correct for multiple testing, we estimated the FDR by conducting 1,000 rounds of permutation” is unclear\nand requires further clarification.\n  The authors can also conduct functional (gene content) analyses in addition to the alpha-diversity, beta-diversity, and\nspecies analyses."}, "author_response": "Response to Reviewers\nThe Interplay of Sleep Characteristics with Health factors and Gut Microbiome\nReviewer #1:\nWu et al. addressed the relationship between self-reported sleep characteristics and gut microbiota\nthrough a large-scale microbiota association analysis, and also conducted a comprehensive mediation\nanalysis between diet, sleep, and microbial species.\nSo far, the association between sleep and gut microbiome is still understudied, not to mention the\npotential influence of various confounding factors (e.g., mental disorders). The present study involved\nmore than 250 phenotypes and ultimately identified that dietary factors may be closely related to both\nsleep and gut microbiome, thus performed further in-depth mediation analyses in a cross-sectional\ndatabase. Although there is still a long way to go before interventional trials, overall, these findings may\nhelp to understand how to combine diet and the gut microbiome to improve sleep health.\nThe paper is in general clear and well-written. I have, however, some relevant comments and there are few\nquestions and points to be clarified.\nMajor comments:\nPoint 1: Indeed, while the authors chose dietary factors (also an intervenable factor) to understand the\ncausal relationship between sleep and the gut microbiome, I still think it's important to address other\nfactors that are highly correlated with sleep traits in mediation analyses, such as gastrointestinal\ndiscomfort and mental - depression. With such a large dataset of sleep-microbiome, this is a great and\nunique opportunity to disentangle the complex relationships among them. Also, for the mediation\nanalyses, the authors concentrated on 137 species, how about the overall microbial composition (beta-\ndiversity, e.g., using principal components).\nResponse: We thank the reviewer for this thoughtful and helpful suggestion. In addition to dietary factors,\nwe agree that gastrointestinal discomfort and mental health symptoms are important traits closely related\nto sleep, and that considering them in mediation analyses may further clarify their potential connections\nwith the gut microbiome.\nTo address this, we extended our mediation analysis framework to include models involving\ngastrointestinal symptoms (including bloating, abdominal pain, nausea, and diagnosed gastrointestinal\ndisorders such as celiac disease, Crohn’s disease, ulcerative colitis, and stomach ulcer) as well as mental\nhealth conditions (including depression, generalized anxiety, bipolar disorder, panic disorder, and eating\ndisorder). We tested two pre-defined directions: disease → microbiome → sleep and sleep → microbiome →\ndisease. In these models, we focused specifically on testing whether microbial features act as potential\nmediators. Unlike diet, which is more clearly established as an environmental exposure to the microbiome,\ndisease can both influence and result from microbiome changes. This makes the directionality more\ncomplex and introduces a large number of possible models. To keep the analysis focused and\ninterpretable, we therefore limited our analysis to testing the microbiome as a mediator.\nDespite the biological relevance of these pathways and the observed phenotypic associations, we did not\ndetect any statistically significant mediation effects in these models (Supplementary Tables 13–14). This\nmay reflect the limitations of our cross-sectional study design, as well as the relatively low prevalence of\ndiagnosed disease in this population-based cohort, which together reduce the statistical power to detect\nrobust mediation signals in disease-related pathways.\nWe also appreciate the suggestion to consider overall gut microbial composition in our mediation\nframework. In the revised analysis, we included both alpha diversity (Shannon index and Phylogenetic\nalongside species-level features.\nWhile species-level features remained the primary contributors to mediation effects, including these\nbroader compositional metrics allowed us to more comprehensively assess whether community-level\nvariation may contribute to sleep-related pathways. In the penalized model of the sleep → diet →\nmicrobiome direction, we observed that both social jet lag and chronotype exerted indirect effects on\nphylogenetic diversity via coffee and alcohol consumption, respectively. These updates are now reflected\nin the revised Methods and Results sections, and the corresponding results are included in Supplementary\nTables 8-14.\nMethod: “A large proportion of the associations we observed between sleep characteristics and other\ntraits were related to dietary patterns, GI discomfort, and mental health conditions. Given that diet is a\nwell-established modulator of the gut microbiome, and GI and mental disorders often co-occur with both\nsleep disturbances and microbiome alterations, we sought to explore the potential causal relationships\nbetween sleep, the microbiome, and either diet or disease. Based on biological plausibility, we pre-\nspecified a set of hypothesized models for mediation analysis. For diet, we tested three plausible pathways:\n(i) diet → microbiome → sleep, (ii) diet → sleep → microbiome, and (iii) sleep → diet → microbiome. For\ndisease-related traits (GI symptoms and mental health disorders), we tested models where the\nmicrobiome may mediate the relationship between disease and sleep: (i) disease → microbiome → sleep\nand (ii) sleep → microbiome → disease.\nAcross all models, microbial variables included two types of features: (i) relative abundances of 137\nmicrobial species previously found to be associated with at least one sleep phenotype and (ii) measures\nof overall microbiome composition. For alpha diversity, we included Shannon index and PD. For beta\ndiversity, we computed the first 20 principal coordinates (PCoAs) based on weighted UniFrac distances.\nFor sleep characteristics, we focused on variables that demonstrated a significant link with species\nabundance, namely sleep quality, social jet lag, and chronotype. Dietary variables included the Healthy\nEating Index (HEI), protein score, alcohol consumption (adjusted for total caloric intake), alternative\nMediterranean diet score (aMED), modified Mediterranean diet score (mMED), plant protein score, coffee\nconsumption, total caloric intake, low carbohydrate score, and free sugar intake. The GI phenotypes\nincluded celiac disease, Crohn’s disease, ulcerative colitis, stomach ulcer, and general GI discomfort\nincluding abdominal pain, bloating, flatulence, burping, and nausea. The mental health phenotypes\nincluded bipolar disorder, depression, eating disorder, generalized anxiety, and panic disorder. All\nmicrobial variables were adjusted for technical factors (sequencing batch, DNA concentration, and post-\nQC read count), stool characteristics (Bristol Stool Scale, defecation frequency), and intrinsic factors (age\nand sex). All other variables were also adjusted for age and sex. Residuals from these adjusted models\nwere used as inputs to the mediation framework to reduce confounding by known covariates.\nAs an initial screening step, we conducted univariable mediation analyses using the mediate function from\nthe mediation R package39, systematically testing all relevant combinations of exposures, mediators, and\noutcomes across the pre-specified model directions (Table S8–9). This function estimates the average\ncausal mediation effect and average direct effect based on two linear models: a mediator model\n(regressing the mediator on the exposure) and an outcome model (regressing the outcome on both the\nexposure and mediator). For each exposure–mediator–outcome triplet, the mediation effect and its\nsignificance were estimated using quasi-Bayesian Monte Carlo simulation with 1,000 draws. These\nunivariable models served as an exploratory filter to identify candidate variables for multivariable analysis.\nTriplets with a mediation P-value < 0.05 were considered nominally significant, and microbiome variables\nthat appeared in at least one significant triplet were retained for further modeling using a regularized\nmultivariable mediation framework.\nvariables using the regmed package in R for regularized mediation analysis via penalized structural\nequation modeling40. Variable selection in the regularized mediation model was performed using a lasso-\npenalized model, with the optimal lambda for the lasso (L1) penalty determined by minimizing the\nBayesian Information Criterion through a grid search41. A non-penalized model incorporating the identified\nvariables was then employed to compute precise effect sizes. For each pathway, the proportion of\nmediation attributable to the mediator was quantified based on the effect sizes from the non-penalized\nmodel. This proportion was determined by dividing the mediated effect (the product of the pathways from\nX to the mediator and from the mediator to Y) by the total effect (the sum of the mediated effect and any\ndirect effect).”\nResults: “Having established a link between sleep and bacterial composition, we sought to determine\nwhether alterations in sleep patterns influence microbial species abundance and diversity measures, or if\nthe reverse is true. To investigate this, we incorporated dietary patterns, which we hypothesized to be more\nlikely to be an exposure than a consequence of the microbial features, into our mediation network analysis.\nThis analysis tested three predefined potential directional relationships: (i) diet → microbiome → sleep,\nwhere changes in microbial features potentially cause alterations in sleep characteristics, (ii) diet → sleep\n→ microbiome, and (iii) sleep → diet → microbiome, where changes in microbial features are outcomes of\nlifestyle modifications.\nIn the first scenario (Figure 4a, Table S10), we observed that most species demonstrated a direct link to\neither sleep or diet. However, two species, Clostridia bacterium UC5_1_1E11 and Clostridia unclassified\nSGB14844 (both from the unclassified Clostridia genus), acted as putative mediators in the diet-to-sleep\npathway, suggesting a potential mediation of the effect of coffee consumption on social jet lag. Increased\ncoffee intake was found to boost the abundance of these two species, consequently intensifying the extent\nof social jet lag. This interesting dynamic between coffee consumption, the two Clostridia species, and\nsocial jet lag was also observed in the second (Figure 4b, Table S11) and third scenarios (Figure 4c, Table\nS12), indicating that fluctuations in social jet lag might impact coffee consumption, or vice versa, thereby\naltering the abundance of these two Clostridia species.\nSince these two species appeared in a mediation relationship in all three directions, we could quantify the\nproportion of mediation based on effect sizes derived from an unpenalized model (Figure 4d). By\naggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the abundance of these species\nmediated 10% of the effect of coffee intake on social jet lag. Conversely, social jet lag accounted for only\n1.7% of the effect of coffee intake on these species. Notably, coffee intake was found to mediate 59.8% of\nthe effect of social jet lag on these species. These estimates suggest that variation in the abundance of\nthese two Clostridia species related to social jet lag may largely reflect differences in coffee consumption.\nTo further investigate the functional and evolutionary basis of these two Clostridia species, we conducted\nexploratory analyses of their metabolic pathways and phylogenetic relationships. Despite their putative\nrole in mediation, we identified no significant (FDR < 0.05) metabolic pathways or phylogenetic\nconservation of these species’ associations with social jet lag or coffee intake (Table S13–14). While their\nfunctional roles remain unclear, these species could influence circadian rhythms through unmeasured\nmetabolites or indirect mechanisms, warranting deeper functional profiling in targeted studies.\nAlong the same line, the connections between many microbial features and sleep characteristics were\nfound to be potentially mediated by specific dietary factors (direction III) (Figure 4c, Table S12). Similar to\nthe two Clostridia species, many species abundances could also be affected by changes in coffee\nconsumption induced by social jet lag. Lachnospiraceae GGB3570_SGB4777, Ruminococcaceae\nGGB9623_SGB15076, and Clostridium phoceensis showed an increase following coffee intake, while\nRuminococcaceae GGB9699_SGB15216 exhibited a decrease. Additionally, a preference for a later\n3\nchronotype was linked with increased alcohol consumption, which in turn led to a reduced abundance of\nCoprococcus eutactus. Poor sleep quality associated with a lower HEI led to an increased abundance of\nFlavonifractor plautii. Nevertheless, findings from direction II still revealed that certain species do exhibit\na direct connection with sleep patterns, independent of dietary mediation (Figure 4b, Table S11). For\nexample, individuals with a late chronotype displayed elevated levels of Lachnospiraceae\nGGB3570_SGB4777 and a reduction in Clostridium sp. Marseille P3244 and Clostridium leptum.\nTo extend the analysis beyond dietary exposures, we also evaluated whether the gut microbiome could\nserve as a mediator in associations between sleep characteristics and disease phenotypes, focusing on\nGI symptoms and mental health conditions (Table S15-16). However, we did not identify any microbial\nfeatures or diversity measures that met the criteria for significant mediation in these models.”\nPoint 2: And for the data presentation, since the authors have involved three potential directional\nrelationships, it would be desirable to visualize the main findings more clearly in the figure, such as the\nFigure 4d, which may help the readers to quickly capture several key mediation pathways (what are\nexposures, mediators and outcomes).\nResponse: We thank the reviewer for this valuable suggestion. To improve clarity and enhance\nvisualization of the key mediation pathways, we have refined Figure 4 to better illustrate the relationships\namong exposures, mediators, and outcomes across the three potential directional frameworks.\nFor example, line 295, “By aggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the\nabundance of these species mediated 10% of the effect of coffee intake on social jet lag.” Does this\nrepresent mainly a direct effect of coffee on social jet lag? How do we interpret the 10% mediation effect\nin this scenario?\nResponse: We sincerely thank the reviewer for their insightful comment. First, we would like to clarify the\ninterpretation of the effect size in our mediation analysis. Our finding that two Clostridia species mediate\napproximately 10% of the effect of coffee intake on social jet lag indicates that this portion of the total\n5\neffect is mediated indirectly through these microbial species. The remaining 90% of the effect may be\nattributable to either a direct effect or indirect effects mediated by other factors. Second, we fully agree\nwith the reviewer that the clinical implications of this finding require further investigation. Sleep behavior\nis complex and influenced by a wide range of physiological and lifestyle factors. A 10% mediation effect is\nrelatively substantial for a multifactorial trait such as social jet lag. Nevertheless, the extent to which\nsocial jet lag can be modified through coffee consumption or microbiome-targeted strategies requires\nfuture clinical trials. We acknowledge this limitation and have now explicitly discussed it in the revised\nDiscussion section:\n“In particular, two Clostridia species, UC5_1_1E11 and SGB14844, were consistently involved in the\ninterplay between coffee consumption and social jet lag across multiple mediation scenarios. Together,\nthe abundance of these two species mediated approximately 10% of the association between coffee\nintake and social jet lag. Although the majority of the effect is likely direct or mediated through other factors,\nthe observed mediation suggests that these species may play a meaningful biological role and may\nrepresent potential targets for mitigating the impact of coffee intake on social jet lag.”\nPoint 4: The PSQI, as the authors mentioned, is a global sleep quality scale that includes different\ncomponents, some of which actually overlap with other scales (e.g., the ESS and the PSQI-Q7). It would\nbe interesting to look at the associations of gut microbiome and component scores, e.g., sleep duration &\nefficiency, which would provide a more comprehensive picture of the sleep-microbiome relationships.\nResponse: We thank the reviewer for this valuable suggestion. Following the reviewer’s suggestion, we\nperformed additional analyses on each of the seven PSQI components individually (Supplementary Table\n6). We identified a total of 140 unique microbial species that showed significant associations with at least\none PSQI component. The most associations were observed for PSQI-Q1 (Subjective sleep quality; n = 80),\nPSQI-Q7 (Daytime dysfunction; n = 58), and PSQI-Q6 (Use of sleep medication; n = 44. When comparing\nthese results with those from the global score, we found that the component analyses largely reflected\nthe patterns already observed with the global PSQI score, with very few additional signals identified\n(Supplementary Figures 4 and 5). We also did not observe any overlap between associations with daytime\ndysfunction (PSQI-Q7) and the Epworth Sleepiness Scale (ESS), despite both relating to aspects of\ndaytime impairment. We believe this discrepancy arises from both methodological and conceptual\ndifferences. The ESS assesses physiological sleep propensity, the likelihood of dozing off in low-\nstimulation situations, across eight items, yielding a broad and continuous score (0–24). In contrast, PSQI-\nQ7 consists of only two items addressing subjective difficulties staying awake and maintaining\nenthusiasm, rated on a 0–3 frequency scale over the past month. Thus, PSQI-Q7 is more reflective of\nperceived tiredness, motivation, or mood, while the ESS better captures latent hypersomnolence. These\ndifferences in psychometric structure, item framing, and time frame likely contribute to the non-\noverlapping microbiome associations we observed.\nMoreover, due to the ordinal and limited nature of the PSQI component scales (0–3 or 0–4), they offer less\nvariability and sensitivity compared to continuous measures like the ESS or the global PSQI score, possibly\nreducing the power to detect robust associations. We have now added this additional analysis in the\nResults section, which reads:\n“To further explore specific aspects of sleep, we also examined associations between species and\nindividual PSQI component scores. These analyses largely reflected the patterns observed with the global\nPSQI score, with most signals captured by the global score (Table S6, Figure S4 and S5).”\nWe also attach Supplementary Figure S5 here for reference; however, due to its large size, Supplementary\nFigure S4 could not be included—please refer to the supplementary materials for the full figure.\n6\nchronotypes, as well as those with good and poorer sleep quality\nResponse:\nIn our analyses, we treated most sleep indices, including the PSQI global score and midpoint of sleep, as\ncontinuous variables. For chronotype, we coded the variable as 0 (Early), 1 (Intermediate), and 2 (Late)\nwhen associating with species abundance. This approach was chosen to retain as much information and\nstatistical power as possible. The linear regression model generally reflects the microbial differences\nacross different subgroups. To demonstrate it, we compared the estimated effect size (z-scores) from\nlinear regression models treating the sleep index as continuous versus categorical factors (see Figure\nbelow). Overall, we observed a high consistence between two models. It is also not surprising that the\nlinear model using continuous values yielded more significant associations.\n3\n0\n−3\n−6\n−3 0 3\nz−score PSQI Global Score (continuous)\nFigure. Comparison of microbial associations between continuous and categorical models\nEach panel displays results from two modeling approaches assessing microbial associations with sleep-\nrelated traits. Each point represents a microbial species. Blue points indicate species significantly\nassociated with the continuous variable only. Red points represent species differing significantly across\ncategorized subgroups only. Purple points mark species significant in both models.\nBased on these findings, we continued to use continuous variables in the main analyses, as they retain\nmore information across the full range of the sleep traits and offer better sensitivity for detecting\nassociations.\nOther minor comments:\nPoint 6: In the abstract, line 15-16, “We found that alpha diversity was inversely associated with sleep\nquality, chronotype, and social jet lag,..”, either rephrased as “was associated with” or specify: “was\ninversely associated with poorer sleep quality, earlier chronotype..”.\nResponse: We thank the reviewer for the helpful suggestion. We have revised the sentence to clarify the\nnature of the inverse associations. The sentence now reads: “We found that lower alpha diversity was\nassociated with poorer sleep quality, later chronotype, and greater social jet lag.”\n8\nyrogetaC\nIQSP\nerocs−z\na\n4\n2\n0\n−2\n−5.0 −2.5 0.0 2.5\nz−score Midpoint of Sleep (continuous)\nylraE\nsv\netaidemretnI\n:yrogetaC\nepytonorhC\nerocs−z\nb 5.0\n2.5\n0.0\n−2.5\n−5.0 −2.5 0.0 2.5\nz−score Midpoint of Sleep (continuous)\nylraE\nsv\ngninevE\n:yrogetaC\nepytonorhC\nerocs−z\nc\nPoint 7: In methods, line 135-136, “To focus on the most relevant species, we filtered out species with a\nmean relative abundance present in fewer than 100 participants, leaving 813 species for further analysis.”\n– it is unclear, whether the authors filtered out species occurring in fewer than 100 participants (? %),\nor\ndid they filter out species below a certain average relative abundance? Or both?\nResponse: We thank the reviewer for pointing this out. We apologize for the confusion. We filtered out\nspecies that were present in fewer than 100 participants (approximately 1.4% of the cohort), regardless of\ntheir relative abundance. We have now clarified this sentence in the Methods section as: “To focus on the\nmost relevant species, we filtered out species that were detected in fewer than 100 participants\n(approximately 1.4% of the cohort), leaving 813 species for further analysis.”\nPoint 8: In discussion, line 379-380, “When considering diet as an exposure, we identified some\nconnections that suggest a direct causal link between sleep patterns and variations in species\nabundance”, do the authors mean consider diet as a mediator?\nResponse: We thank the reviewer for this important comment. We agree that the original wording was\nunclear. In this analysis, we considered diet as the exposure and sleep characteristics as potential\nmediators between diet and microbiome species abundance. We have revised the sentence in the\nDiscussion to: “When considering diet as the exposure and sleep characteristics as potential mediators,\nwe identified some connections that suggest a direct link between sleep patterns and variations in species\nabundance.”\nReviewer #2:\nThe manuscript presents a comprehensive analysis of the bidirectional relationship between sleep\ncharacteristics and gut microbiome composition in a large-scale Dutch cohort. The integration of various\nstatistical methods, metagenomic sequencing that ensures detailed taxonomic profiling at the species\nlevel, improving our understanding of the complex interplay between sleep and gut microbiome\ncomposition. The identification of specific bacterial species associated with distinct sleep parameters is\nparticularly interesting. However, some areas, such as the analysis methods and interpretation of results,\nrequire further clarification and additional analysis to strengthen the conclusions. Our specific comments\nare:\nPoint 1: Lines 241-243: The authors report a “consistent directional trend” across species associated with\npositive sleep characteristics. This interpretation as a biologically meaningful finding is questionable.\nInstead, the trend could be due to the use of measurement tools that assess overlapping components of\nsleep, which would result in a technical artifact rather than a genuine biological phenomenon. This\nalternative explanation warrants explicit consideration in the manuscript.\nResponse: We thank the reviewer for this valuable point. Indeed, the consistent directional trend we\nobserved across species may be partly due to correlations among the sleep measures. To address this,\nwe have now explicitly pointed it out and revised the corresponding part of the Results section: “Among\nthe 137 species identified, we observed a consistent directional trend, whereby a species associated with\na positive sleep characteristic (e.g., good sleep quality) tended to also associate positively with other\nfavorable sleep characteristics (e.g., reduced social jet lag). This may suggest a shared biological basis\nunderlying different aspects of sleep, although some of the observed consistency could also reflect\noverlap among the sleep measures.”\ncharacteristics raises concerns about its biological significance. This finding could be influenced by\ndifferences in data distributions across the sleep measurements. For instance, as shown in Figure 1b,\nmost sleep quality measures are concentrated at the good-quality end with decent variability, while social\njet lag measurements show limited variability at the good-quality end and are skewed towards poor\nquality. Furthermore, the analyses for social jet lag and chronotype may lack sufficient statistical power,\nmaking the results potentially driven by random noise. In contrast, the analysis for sleep quality appears\nbetter powered. This discrepancy makes the comparability of significant features across the three sleep\ncharacteristics questionable and requires further discussion.\nResponse: We thank the reviewer for this thoughtful comment. While we agree that the original\ndistributions of sleep traits differ (Figure 1b), we note that in the association analyses, sleep quality (PSQI)\nand social jet lag were treated as continuous variables and normalized using inverse rank transformation.\nThis approach minimizes the impact of non-normal distributions and helps maintain comparable\nstatistical power across these traits. Chronotype, on the other hand, was categorized into three ordinal\ngroups (early, intermediate, late), coded as 0, 1, and 2. We acknowledge that the limited number of\ncategories for chronotype could introduce some granularity and potentially reduce power compared to\nfully continuous traits. We have clarified this point in the Results section while keeping the main text\nconcise to maintain readability: “Most of the sleep–species associations we identified were uniquely\nlinked to a specific sleep aspect: 101 species were exclusively associated with sleep quality, 8 with social\njet lag, and 7 with chronotype. The larger number of associations we observed for sleep quality might not\nonly indicate stronger biological connections, it could also partly result from greater variability in the sleep\nquality data compared to other sleep measures.”\nPoint 3: In this study, the authors described that alpha diversity was inversely associated with sleep\nquality. This is somehow an inaccurate description, and it needs to be rephrased. According to the results,\nalpha diversity was inversely associated with PSQI score in this study, not sleep quality, as a higher score\nimplies lower sleep quality. Nevertheless, this result is consistent with the notion that a higher microbiome\ndiversity is generally more positively related to health.\nResponse: We thank the reviewer for this important suggestion. To improve precision, we have revised the\nwording in both the Abstract and the Results section. In the Abstract, we now state: “We found that lower\nalpha diversity was associated with poorer sleep quality, later chronotype, and greater social jet lag.” In\nthe Results section, we specify: “Alpha diversity, as measured by the Shannon index, was significantly\nassociated with PSQI score (Beta = -0.015, P-value = 0.017),” thereby clearly indicating that the\nassociation was with the PSQI score, where higher values reflect poorer sleep quality.\nPoint 4: Mediation analysis: The authors' approach to mediation analysis is conceptually flawed. Proper\ncausal mediation analysis requires a pre-specified directed acyclic graph (DAG) and adherence to critical\nassumptions, including correct model specification. The practice of rotating the roles of exposure,\nmediator, and outcome to fit different causal mediation models based on statistical significance is\nfundamentally incorrect. A model should be defined a priori and not selected post hoc based on results.\nThe authors are strongly encouraged to read through VanderWeele's Explanation in Causal Inference:\nMethods for Mediation and Interaction (2015) for proper methodology. Additionally, the study may violate\ntwo key assumptions of causal mediation analysis: (1) temporality, i.e., the exposure, mediator, and\noutcome are measured sequentially; and (2) no unmeasured confounding, as the adjustment for\n10\nconfounders is likely to be insufficient. Finally, addressing high dimensionality upfront in the modeling\nprocess, rather than adopting a two-step approach, is critical to avoid overfitting and false positives.\nResponse: We appreciate the reviewer’s careful critique and fully agree that causal mediation analysis\nrequires strong assumptions, including a clearly specified causal structure, temporally ordered variables,\nand appropriate adjustment for confounding. We would like to clarify that our study employs mediation\nanalysis in an exploratory, hypothesis-generating context, rather than to make formal causal claims.\nTo avoid post hoc model selection, all directional models were pre-specified based on biological\nplausibility. Specifically, we defined three directional hypotheses involving diet (diet → microbiome →\nsleep, diet → sleep → microbiome, sleep → diet → microbiome) and two involving disease (disease →\nmicrobiome → sleep, sleep → microbiome → disease). The diet models were selected based on the\nrecognition that diet is generally considered upstream of the microbiome due to its well-established role\nas a primary modulator of microbial composition. For the disease-related models, we explored these\ndirections to assess the potential for the gut microbiome to act as a mediator. If such mediation exists, it\ncould point toward microbiome-targeted strategies for disease prevention or intervention. These five\ndirections were tested consistently across all combinations, independent of statistical significance. We\ndid not rotate the roles of exposure, mediator, and outcome based on results. This structure was\nestablished prior to analysis to ensure clarity and interpretability.\nWe acknowledge that our cross-sectional design limits our ability to establish temporality and that\nunmeasured confounding remains a possibility despite extensive covariate adjustment. We have now\nmade these limitations more explicit in the revised manuscript and have taken care to frame our findings\nas exploratory and hypothesis-generating. We hope our results will inform future studies that can more\ndirectly assess causal relationships using longitudinal or interventional designs.\nWith respect to high dimensionality, we recognize the importance of minimizing overfitting and controlling\nfalse positives. In our approach, the univariable mediation analysis served as a screening step to reduce\nthe dimensionality of microbial features prior to multivariable modeling. Including all ~200 microbial\nvariables in a single penalized model can result in excessive shrinkage and unreliable effect estimation,\nmaking pre-selection a necessary compromise. The final effect estimates were obtained solely from the\npenalized multivariable model, which applies an L1 (lasso) penalty to enable robust variable selection and\neffect estimation. We believe this strategy improves model stability and interpretability while helping to\nmitigate the risk of overfitting in a high-dimensional setting.\nWe have revised the manuscript accordingly to clarify the exploratory nature of our mediation analysis, the\nassumptions involved, and the rationale behind our modeling strategy. These revisions are now reflected\nin:\nMethod: “A large proportion of the associations we observed between sleep characteristics and other\ntraits were related to dietary patterns, GI discomfort, and mental health conditions. Given that diet is a\nwell-established modulator of the gut microbiome, and GI and mental disorders often co-occur with both\nsleep disturbances and microbiome alterations, we sought to explore the potential causal relationships\nbetween sleep, the microbiome, and either diet or disease. Based on biological plausibility, we pre-\nspecified a set of hypothesized models for mediation analysis. For diet, we tested three plausible pathways:\n(i) diet → microbiome → sleep, (ii) diet → sleep → microbiome, and (iii) sleep → diet → microbiome. For\ndisease-related traits (GI symptoms and mental health disorders), we tested models where the\nmicrobiome may mediate the relationship between disease and sleep: (i) disease → microbiome → sleep\nand (ii) sleep → microbiome → disease.\nAcross all models, microbial variables included two types of features: (i) relative abundances of 137\nmicrobial species previously found to be associated with at least one sleep phenotype and (ii) measures\n11\nof overall microbiome composition. For alpha diversity, we included Shannon index and PD. For beta\ndiversity, we computed the first 20 principal coordinates (PCoAs) based on weighted UniFrac distances.\nFor sleep characteristics, we focused on variables that demonstrated a significant link with species\nabundance, namely sleep quality, social jet lag, and chronotype. Dietary variables included the Healthy\nEating Index (HEI), protein score, alcohol consumption (adjusted for total caloric intake), alternative\nMediterranean diet score (aMED), modified Mediterranean diet score (mMED), plant protein score, coffee\nconsumption, total caloric intake, low carbohydrate score, and free sugar intake. The GI phenotypes\nincluded celiac disease, Crohn’s disease, ulcerative colitis, stomach ulcer, and general GI discomfort\nincluding abdominal pain, bloating, flatulence, burping, and nausea. The mental health phenotypes\nincluded bipolar disorder, depression, eating disorder, generalized anxiety, and panic disorder. All\nmicrobial variables were adjusted for technical factors (sequencing batch, DNA concentration, and post-\nQC read count), stool characteristics (Bristol Stool Scale, defecation frequency), and intrinsic factors (age\nand sex). All other variables were also adjusted for age and sex. Residuals from these adjusted models\nwere used as inputs to the mediation framework to reduce confounding by known covariates.\nAs an initial screening step, we conducted univariable mediation analyses using the mediate function from\nthe mediation R package39, systematically testing all relevant combinations of exposures, mediators, and\noutcomes across the pre-specified model directions (Table S8–9). This function estimates the average\ncausal mediation effect and average direct effect based on two linear models: a mediator model\n(regressing the mediator on the exposure) and an outcome model (regressing the outcome on both the\nexposure and mediator). For each exposure–mediator–outcome triplet, the mediation effect and its\nsignificance were estimated using quasi-Bayesian Monte Carlo simulation with 1,000 draws. These\nunivariable models served as an exploratory filter to identify candidate variables for multivariable analysis.\nTriplets with a mediation P-value < 0.05 were considered nominally significant, and microbiome variables\nthat appeared in at least one significant triplet were retained for further modeling using a regularized\nmultivariable mediation framework.\nNext, multivariable mediation effects for each direction were estimated using selected microbiome\nvariables using the regmed package in R for regularized mediation analysis via penalized structural\nequation modeling40. Variable selection in the regularized mediation model was performed using a lasso-\npenalized model, with the optimal lambda for the lasso (L1) penalty determined by minimizing the\nBayesian Information Criterion through a grid search41. A non-penalized model incorporating the identified\nvariables was then employed to compute precise effect sizes. For each pathway, the proportion of\nmediation attributable to the mediator was quantified based on the effect sizes from the non-penalized\nmodel. This proportion was determined by dividing the mediated effect (the product of the pathways from\nX to the mediator and from the mediator to Y) by the total effect (the sum of the mediated effect and any\ndirect effect).”\nResults: “Having established a link between sleep and bacterial composition, we sought to determine\nwhether alterations in sleep patterns influence microbial species abundance and diversity measures, or if\nthe reverse is true. To investigate this, we incorporated dietary patterns, which we hypothesized to be more\nlikely to be an exposure than a consequence of the microbial features, into our mediation network analysis.\nThis analysis tested three predefined potential directional relationships: (i) diet → microbiome → sleep,\nwhere changes in microbial features potentially cause alterations in sleep characteristics, (ii) diet → sleep\n→ microbiome, and (iii) sleep → diet → microbiome, where changes in microbial features are outcomes of\nlifestyle modifications.\nIn the first scenario (Figure 4a, Table S10), we observed that most species demonstrated a direct link to\neither sleep or diet. However, two species, Clostridia bacterium UC5_1_1E11 and Clostridia unclassified\nSGB14844 (both from the unclassified Clostridia genus), acted as putative mediators in the diet-to-sleep\n12\npathway, suggesting a potential mediation of the effect of coffee consumption on social jet lag. Increased\ncoffee intake was found to boost the abundance of these two species, consequently intensifying the extent\nof social jet lag. This interesting dynamic between coffee consumption, the two Clostridia species, and\nsocial jet lag was also observed in the second (Figure 4b, Table S11) and third scenarios (Figure 4c, Table\nS12), indicating that fluctuations in social jet lag might impact coffee consumption, or vice versa, thereby\naltering the abundance of these two Clostridia species.\nSince these two species appeared in a mediation relationship in all three directions, we could quantify the\nproportion of mediation based on effect sizes derived from an unpenalized model (Figure 4d). By\naggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the abundance of these species\nmediated 10% of the effect of coffee intake on social jet lag. Conversely, social jet lag accounted for only\n1.7% of the effect of coffee intake on these species. Notably, coffee intake was found to mediate 59.8% of\nthe effect of social jet lag on these species. These estimates suggest that variation in the abundance of\nthese two Clostridia species related to social jet lag may largely reflect differences in coffee consumption.\nTo further investigate the functional and evolutionary basis of these two Clostridia species, we conducted\nexploratory analyses of their metabolic pathways and phylogenetic relationships. Despite their putative\nrole in mediation, we identified no significant (FDR < 0.05) metabolic pathways or phylogenetic\nconservation of these species’ associations with social jet lag or coffee intake (Table S13–14). While their\nfunctional roles remain unclear, these species could influence circadian rhythms through unmeasured\nmetabolites or indirect mechanisms, warranting deeper functional profiling in targeted studies.\nAlong the same line, the connections between many microbial features and sleep characteristics were\nfound to be potentially mediated by specific dietary factors (direction III) (Figure 4c, Table S12). Similar to\nthe two Clostridia species, many species abundances could also be affected by changes in coffee\nconsumption induced by social jet lag. Lachnospiraceae GGB3570_SGB4777, Ruminococcaceae\nGGB9623_SGB15076, and Clostridium phoceensis showed an increase following coffee intake, while\nRuminococcaceae GGB9699_SGB15216 exhibited a decrease. Additionally, a preference for a later\nchronotype was linked with increased alcohol consumption, which in turn led to a reduced abundance of\nCoprococcus eutactus. Poor sleep quality associated with a lower HEI led to an increased abundance of\nFlavonifractor plautii. Nevertheless, findings from direction II still revealed that certain species do exhibit\na direct connection with sleep patterns, independent of dietary mediation (Figure 4b, Table S11). For\nexample, individuals with a late chronotype displayed elevated levels of Lachnospiraceae\nGGB3570_SGB4777 and a reduction in Clostridium sp. Marseille P3244 and Clostridium leptum.\nTo extend the analysis beyond dietary exposures, we also evaluated whether the gut microbiome could\nserve as a mediator in associations between sleep characteristics and disease phenotypes, focusing on\nGI symptoms and mental health conditions (Table S15-16). However, we did not identify any microbial\nfeatures or diversity measures that met the criteria for significant mediation in these models.”\nDiscussion: “Another compelling area for future research involves uncovering the true causal links\nbetween sleep-related disorders, sleep patterns, and the gut microbiome through intervention or\nexperimental studies. However, this topic was beyond the scope of our current study. Given the cross-\nsectional nature of our data, establishing definitive directionality is challenging because any of the factors\ncould be hypothesized as exposures, mediators, or outcomes, resulting in six different hypothetical\nscenarios for analysis. While dealing with such complexity using cross-sectional data is challenging,\nexploring these aspects through future longitudinal studies could help deepen our understanding of these\ncomplex relationships.\nAs mentioned, one of the major limitations of our study is its cross-sectional design, which prevents us\nfrom establishing the temporal order of events. This is particularly relevant for mediation analysis, where\n13\na clear sequence of exposure, mediator, and outcome is required to support causal interpretation. In\naddition, although we adjusted for a broad set of known covariates, we cannot exclude the possibility of\nunmeasured confounding. Therefore, the mediation results should be viewed as exploratory and\ninterpreted with caution. Moreover, the reliance on self-reported data for both sleep patterns and dietary\nhabits introduces potential reporting bias. Although self-reporting is valuable for gathering data in large-\nscale studies, it is prone to inaccuracies and personal biases. Objective measurements, such as\nactigraphy for sleep and detailed diet logs or biomarkers for dietary intake, would enhance the validity of\nresults.”\nPoint 5: The study highlights the role of dietary factors in mediating microbiome changes, but direct effects\nof diet on sleep characteristics is not discussed in this study. Could you explore more direct relationships\nbetween specific dietary patterns (e.g., Mediterranean diet) and sleep quality or chronotype independent\nof microbiome mediation? At least you should provide it in the discussion.\nResponse: We thank the reviewer for the helpful suggestion. Associations of sleep characteristics and\ndietary patterns are presented in Figure 2. In the result section we wrote: “Our results showed that\nindividuals with optimal health status exhibited more favorable sleep characteristics. Conversely,\nsuboptimal sleep—defined by lower sleep quality, increased daytime sleepiness, greater social jet lag,\nand a tendency toward a late chronotype—was linked to a spectrum of health issues, notably psychological\nand GI disorders. Regarding dietary habits, a healthy diet, as indicated by high HEI scores and increased\nintake of polysaccharides and proteins, was associated with better sleep metrics. In contrast, diets\ncharacterized by adherence to a Western pattern and higher intake of maltose were associated with poorer\nsleep parameters. Notably, alcohol consumption was linked to a later chronotype (P-value = 1.38×10-40),\nwhereas higher total caloric intake was related to increased daytime sleepiness (P-value = 4.47×10-25).”\nWe have now also added a paragraph to the Discussion to highlight the strong link between diet and sleep\nobserved in our current analyses. The added text reads: “By incorporating dietary factors, we were able to\nconduct a comprehensive mediation analysis. Diet showed strong direct associations with sleep\ncharacteristics; for example, a higher Healthy Eating Index (HEI) was directly linked to better sleep quality,\nearlier chronotype, and reduced social jet lag; coffee and alcohol consumption were linked to chronotype\nand social jet lag; and higher alternative Mediterranean diet scores (aMED) and low-carbohydrate diet\nscores were linked to chronotype. All these associations were observed in both directions.”\nPoint 6: The results presented in Figure 3 lack clarity regarding whether they are multivariable-adjusted.\nIf the models described in the methods were used, additional confounders such as physical activity,\nsedentary time, dietary quality, total energy intake, alcohol consumption, hypertension,\nhypercholesterolemia, diabetes, and relevant medications (e.g., sleep aids, melatonin)\nshould also be included. Given the observational nature of the study, adjusting for these variables is essential to\nstrengthen the robustness of the conclusions.\nResponse:\nWe thank the reviewer for underscoring the importance of robust confounding control. In the revised\nmanuscript, we have updated Figure 3 caption to explicitly indicate that the results are based on\nmultivariable-adjusted models, accounting for age, sex, BMI, batch, DNA concentration, Bristol Stool\nScale, and defecation frequency.\nWe have also conducted supplementary analyses adjusting for the additional confounders suggested.\nSpecifically, we included physical activity, alcohol consumption, metabolic diseases (hypertension,\nhypercholesterolemia, and diabetes), alcohol consumption, dietary quality (Healthy Eating Index [HEI]),\ntotal energy intake, and use of sleep-aid medications, in addition to GI symptoms and mental diseases.\nThe results of these extended adjustments are presented in the updated Supplementary Figure S6.\nAdjusting for each covariate individually did not substantially alter the observed associations, indicating\nthat our findings are robust to these potential confounders. Adjustment for sleep-aid medication slightly\nchanged the association with PSQI scores. This is likely because the global PSQI score already\nincorporates information on sleep medication use (specifically through component 6), and thus adjusting\nfor medication use separately could introduce overadjustment. In addition, we examined a fully adjusted\nmodel incorporating all covariates simultaneously. Although the strength of some associations was\nslightly attenuated due to a reduction in sample size from missing data, the overall directionality and\nconsistency of the findings were preserved. These results further support the robustness of our\nconclusions. We have updated the Methods and Results sections and Supplementary Figure S6 to\ndocument these analyses.\nMethod: “To account for the potential confounding effects of various factors on the gut microbiome, we\nincluded the following factors as covariates when conducting any analyses related to the microbial\nfeatures: technical parameters (metagenomic sequencing batch number, DNA concentration (ng/µl), and\nnumber of post-quality control reads), intrinsic factors (age, sex, and BMI), and stool characteristics\n(frequency and consistency measured by Bristol Stool Scale).\nTo assess the robustness of species abundance associations with sleep characteristics, we performed a\nseries of sensitivity analyses, adjusting individually for additional potential confounders, including\nmetabolic diseases (hypertension, hypercholesterolemia, and diabetes), GI diseases, mental health\nconditions, physical activity, alcohol intake, and sleep medication use. We also examined a fully adjusted\nmodel incorporating all covariates simultaneously.”\nResult: “In our previous analysis, we had observed strong links between sleep characteristics and mental\nor GI diseases. To assess whether these or other health and lifestyle factors conditions were confounding\nour results, we conducted a sensitivity analysis incorporating additional covariates, including mental\ndisease status, GI disease status, metabolic diseases (hypertension, hypercholesterolemia, diabetes),\nphysical activity, alcohol intake, and use of sleep medications in our model (Figure S6). The consistent\ndistribution of significance and direction of effect, even after accounting for these covariates individually\nand jointly, indicates that the associations between sleep characteristics and microbial species are robust\nand not driven by these potential confounders.”\nPoint 7: How do you reconcile the bidirectional relationship between sleep and the microbiome with the\nobservational nature of the data? Could longitudinal or interventional studies strengthen this aspect?\nThis needs to be at least addressed in the limitation section.\nResponse:\nWe thank the reviewer for the helpful comment. We have emphasized the limitations of our cross-\nsectional design in the Discussion section: “Another compelling area for future research involves\nuncovering the true causal links between sleep-related disorders, sleep patterns, and the gut microbiome\nthrough intervention or experimental studies. However, this topic was beyond the scope of our current\nstudy. Given the cross-sectional nature of our data, establishing definitive directionality is challenging\nbecause any of the factors could be hypothesized as exposures, mediators, or outcomes, resulting in six\ndifferent hypothetical scenarios for analysis. While dealing with such complexity using cross-sectional\ndata is challenging, exploring these aspects through future longitudinal studies could help deepen our\nunderstanding of these complex relationships.\nAs mentioned, one of the major limitations of our study is its cross-sectional design, which prevents us\nfrom establishing the temporal order of events. This is particularly relevant for mediation analysis, where\n15\na clear sequence of exposure, mediator, and outcome is required to support causal interpretation. In\naddition, although we adjusted for a broad set of known covariates, we cannot exclude the possibility of\nunmeasured confounding. Therefore, the mediation results should be viewed as exploratory and\ninterpreted with caution. Moreover, the reliance on self-reported data for both sleep patterns and dietary\nhabits introduces potential reporting bias. Although self-reporting is valuable for gathering data in large-\nscale studies, it is prone to inaccuracies and personal biases. Objective measurements, such as\nactigraphy for sleep and detailed diet logs or biomarkers for dietary intake, would enhance the validity of\nresults.”\nPoint 8: The analysis highlights the role of coffee consumption in modulating social jet lag through specific\nspecies. There is a paper recently published (doi.org/10.1038/s41564-024-01858-9), showing the strong\nlink between coffee consumption and the species Lawsonibacter asaccharolyticus. How is the role of this\nspecies in your data? Did it also show a relationship with coffee intake and sleep quality?\nResponse: Thank you for bringing this interesting recent finding to our attention. Indeed, we observe a\nstrong association between this particular species and both coffee intake and chronotype in our data.\nHowever, in our study, this species is annotated as Clostridium phoceensis rather than Lawsonibacter\nasaccharolyticus. As noted in the original study, “However, due to the majority voting rule, currently the\nSGB ID 15154 when profiled by MetaPhlAn 4 reports the taxonomic label Clostridium phoceensis. This will\nbe corrected in future database releases of bioBakery 4.”\nTo ensure consistency with the version of MetaPhlAn and the ChocoPhlAn database used in our profiling,\nwe will retain the current taxonomic label (Clostridium phoceensis). However, we have clarified in the\nDiscussion that this species corresponds to Lawsonibacter asaccharolyticus as reported in the recent\nstudy, to avoid confusion and to highlight that both studies identify the same species as being associated\nwith coffee intake. The revised text reads : “Our observation of a strong association between coffee intake\nand Clostridium phoceensis aligns with a recent study that reported a link between coffee consumption\nand Lawsonibacter asaccharolyticus. As noted in that work, this species corresponds to SGB15154, which\nis currently annotated as Clostridium phoceensis in the version of the database used for our profiling.\nThese consistent findings further support the robustness of coffee–microbiome relationships across\nindependent cohorts and profiling pipelines.”\nPoint 9: The variability within genera (e.g., Blautia, Dorea) is intriguing but raises questions about the\ndeeper relevance of these associations. Including strain-level profiling and functional pathway analyses\nfor the identified species could be interesting for further studies to provide more detailed mechanistic\ninsights.\nResponse: We thank the reviewer for this thoughtful suggestion. To explore the potential for deeper\nmechanistic insights, we performed additional species-resolved pathway and phylogenetic analyses\nfocused on Clostridia bacterium UC5_1_1E11and Clostridia sp. SGB14844, which emerged as putative\nmediators in the relationship between coffee intake and social jet lag. Using HUMAnN 4.0 and MetaCyc\npathway annotations, we profiled within-species functional variation, but did not identify specific\npathways strongly associated with sleep or dietary traits. We also applied phylogenetic generalized linear\nmixed models (PGLMMs) using the Anpan R package to assess whether phylogenetic relatedness could\nexplain the observed associations, but found no strong phylogenetic signal. These negative results may\n16\nreflect limited statistical power due to the relatively low prevalence and abundance of these species in the\ncohort.\nNonetheless, we agree that incorporating strain-level and functional profiling is a promising direction for\nuncovering mechanistic links in microbiome–host interactions. We have now included these exploratory\nanalyses in the revised manuscript (Results and Methods), with full results reported in Supplementary\nTables 13 and 14.\nMethod: “Functional and phylogenetic profiling of Clostridia species\nTo further explore Clostridia bacterium UC5_1_1E11 and Clostridia sp. SGB14844, we performed species-\nresolved pathway and phylogenetic analyses. For functional profiling, we used HUMAnN 4.0 to quantify\nMetaCyc pathway abundances within each species. A total of 106 pathways were detected across the two\nspecies (48 in SGB14844 and 58 in SGB14895). Associations between CLR-transformed pathway\nabundances and phenotypes (coffee intake and social jet lag) were tested using linear models adjusted for\nage, sex, BMI, DNA concentration, sequencing batch, post-QC read count, Bristol Stool Scale, and\ndefecation frequency. Analyses were performed separately per species among individuals with complete\ndata (n = 630 for SGB14844; n = 77 for SGB14895).\nTo evaluate whether phylogenetic relatedness among strains could help explain phenotype associations,\nwe reconstructed strain-level phylogenies using StrainPhlAn with minimum marker detection thresholds\nset to 10 across samples. We then fit phylogenetic generalized linear mixed models using the anpan R\npackage42, modeling phenotypes as a function of covariates and a phylogenetic random effect under a\nBrownian motion assumption. Model fit was assessed by comparing the expected log predictive density\n(ELPD) of each phylogenetic model to a null model without the phylogenetic term, using Pareto-smoothed\nleave-one-out cross-validation. A phylogenetic signal was considered statistically supported if the ELPD\ndifference (ΔELPD) between the null and phylogenetic model was less than –4 and the 95% confidence\ninterval (ΔELPD ± 2 standard errors) did not include zero.”\nResult: “To further investigate the functional and evolutionary basis of these two Clostridia species, we\nconducted exploratory analyses of their metabolic pathways and phylogenetic relationships. Despite their\nputative role in mediation, we identified no significant (FDR < 0.05) metabolic pathways or phylogenetic\nconservation of these species’ associations with social jet lag or coffee intake (Table S13–14). While their\nfunctional roles remain unclear, these species could influence circadian rhythms through unmeasured\nmetabolites or indirect mechanisms, warranting deeper functional profiling in targeted studies.”\nDiscussion: “In particular, two Clostridia species, UC5_1_1E11 and SGB14844, were consistently involved\nin the interplay between coffee consumption and social jet lag across multiple predefined mediation\nscenarios. Together, the abundance of these two species mediated approximately 10% of the association\nbetween coffee intake and social jet lag. Although the majority of the effect is likely direct, or mediated\nthrough other factors, the observed mediation suggests that these species may play a meaningful\nbiological role and may represent potential targets for mitigating the impact of coffee intake on social jet\nlag. Although these species remain poorly characterized, their membership in class Clostridia, which\nincludes many species known to influence host metabolism53 and neuroactive compound production54,\npoints to plausible mechanisms involving microbial metabolites affecting sleep regulation. To further\nexplore their potential roles, we examined the pathway-level functional profiles and phylogenetic patterns\nof these two species. However, we did not identify any specific metabolic pathways or phylogenetic signals\nthat could explain their associations with coffee intake or sleep-related traits. These null findings may\nreflect the relatively low prevalence and abundance of these species in the population, limiting the\nstatistical power to detect subtle biological effects. Nonetheless, their consistent appearance in\ndirectional models highlights them as intriguing candidates for future mechanistic investigation.”\ninform public health strategies or medicine approaches for sleep disorders.\nResponse: We thank the reviewer for the valuable suggestion. We have added sentences in the\nDiscussion to highlight the broader public health implications of our findings. The added text now reads:\n“Furthermore, we offer insight into the complex interconnection between diet, sleep, and the microbiome,\nhighlighting potential avenues for public health strategies. For instance, dietary interventions could be a\npromising strategy for managing sleep disorders and modulating gut health (e.g., reducing coffee/alcohol\nintake to modulate Clostridia species linked to social jet lag). Similarly, microbiome-targeted\ninterventions, such as probiotics or prebiotics, might mitigate circadian misalignment in shift workers or\nindividuals with late chronotypes. These approaches could complement existing behavioral therapies for\nsleep disorders, though rigorous clinical trials are needed to evaluate efficacy.”\nReviewer #3\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the\nNature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nReviewer #4\nThe authors explored the relationship between sleep characteristics, health factors, and the gut\nmicrobiome using data from a large-scale prospective population cohort, the Lifelines Dutch Microbiome\nProject. They performed alpha diversity, beta diversity, and species-level analyses, identifying numerous\nsignificant associations. Notably, they conducted mediation analysis to examine the role of the gut\nmicrobiome in mediating the effects of coffee intake and social jet lag, uncovering novel findings at the\nspecies level. While the research questions are compelling and the use of large-scale microbiome data\nimpressive, the authors must address the concerns outlined below for me to recommend acceptance of\nthe manuscript.\nMajor Comments:\nPoint 1: The data cannot be found. The authors should disclose the raw sequence data and processed\ndata, preferably including the database, access numbers, and/or website. Without this information, it is\nimpossible to verify the discoveries reported in the study. The authors should clearly organize the\nprocessed data for the same 6,941 subjects in the following format:\n1. Feature Table: This table should include raw counts, with rows representing features (ASVs) and\ncolumns representing subjects. Row names should be feature IDs, and column names should be\nsubject IDs.\n2. Taxonomic Table: This table should include taxonomic classifications, with rows representing\nfeatures and columns corresponding to the seven taxonomic ranks: 'Kingdom,' 'Phylum,' 'Class,'\n'Order,' 'Family,' 'Genus,' and 'Species.' Row names should be feature IDs.\n3. Metadata: This table should contain variables for phenotypes, disease status, or\nenvironmental/dietary factors. Rows should represent subjects, and columns should represent\nvariables, with row names as subject IDs and column names as variable names. The authors should\nalso provide a data dictionary to describe the variables in the metadata in detail.\n4. Phylogenetic Tree: This should be a rooted tree, with tip labels corresponding to feature IDs.\nWithout this, the discoveries reported in the study cannot be reproduced by others. The phyloseq package\n(https://www.bioconductor.org/packages/release/bioc/html/phyloseq.html) is highly useful for data\npreparation, though the authors may consider using an alternative comparative software package if\npreferred. The authors can refer to the following website if they wish:\nhttps://github.com/angelolimeta/Gut-microbiome-immunotherapy\ndeposited the processed microbiome data (Feature Table, Taxonomic Table, and Phylogenetic Tree) in\nZenodo (10.5281/zenodo.15292903) and have updated the Data Availability section. Access to privacy-\nsensitive phenotype data follows GDPR-compliant controlled-access procedures through the Lifelines\nbiobank, as described in the manuscript:\n“The raw metagenomic sequencing data from this study are publicly available in the European Genome-\nPhenome Archive under the Dutch Microbiome Project (EGAS00001005027) and Lifelines-DEEP\n(EGAD00001001991). The processed microbiome data generated during this study, including the Feature\nTable (raw counts), Taxonomic Table, and Phylogenetic Tree, have been deposited in Zenodo [doi:\n10.5281/zenodo.15292903]. Due to GDPR and Lifelines biobank regulations, access to privacy-sensitive\nphenotype data (e.g., clinical and lifestyle information) requires following controlled-access procedures.\nResearchers can apply for access via https://www.lifelines.nl/researcher/how-to-apply/apply-here.\nApplications are reviewed for compliance with informed consent, GDPR, and Lifelines' non-commercial\nuse policies. Upon approval and completion of a Data and Material Transfer Agreement, data access is\nprovided through the secure Lifelines Workspace or High-Performance Cluster (HPC) environments, with\nfees set at €3,500 annually for HPC access and €4,500 annually for Workspace access (prorated options\navailable). Full dataset access for replication studies is available free of charge for three months by\ncontacting research@lifelines.nl. Additional information on data access procedures is available\nat https://www.lifelines.nl/researcher/how-to-apply.”\nPoint 2: Alpha- and beta-diversity analysis: The authors conducted alpha-diversity and beta-diversity\nanalyses using only a few abundance-based indices (Shannon and Bray-Curtis). The authors need to\nsurvey other widely used phylogenetic indices such as phylogenetic diversity (PD) for alpha diversity and\nUniFrac distances for beta diversity.\nResponse: We sincerely thank the reviewer for highlighting the importance of incorporating phylogenetic\ndiversity metrics. As suggested, we conducted additional analyses using Phylogenetic Diversity (PD) for\nalpha diversity and weighted UniFrac distances for beta diversity. Both measures showed significant\nassociations with sleep quality (PSQI), social jet lag, and chronotype. We have updated the Results section\naccordingly, which now reads: \" Alpha diversity, as measured by the Shannon index and Phylogenetic\nDiversity (PD), was significantly associated with PSQI score (Shannon: Beta = -0.015, FDR = 0.024; PD: Beta\n= -0.256, FDR = 2.31 × 10⁻⁴), social jet lag (Shannon: Beta = -0.017, FDR = 0.024; PD: Beta = -0.185, FDR =\n0.020), and chronotype (Shannon: Beta = -0.023, FDR = 0.024; PD: Beta = -0.239, FDR = 0.019) (Figure S3,\nTable S3), which suggests that variations in sleep patterns are reflected in the diversity of the gut\nmicrobiome. Beta diversity, assessed using Bray–Curtis and Weighted UniFrac distances, showed\nsignificant associations with PSQI (Bray–Curtis: R² = 5.4×10⁻⁴, P = 0.00001; Weighted UniFrac: R² =\n5.6×10⁻⁴, P = 0.003), social jet lag (Bray–Curtis: R² = 4.2×10⁻⁴, P = 0.0004; Weighted UniFrac: R² =\n5.5×10⁻⁴, P = 0.007), and chronotype (Bray–Curtis: R² = 4.3×10⁻⁴, P = 0.00035; Weighted UniFrac: R² =\n7.1×10⁻⁴, P = 0.002) (Table S4). These findings indicate that both taxonomic and phylogenetic composition\nof the gut microbiome are associated with these sleep characteristics.\"\nthese new diversity analyses.\nPoint 3: Statistical Analysis for Repeated Measurements: Since the data come from the prospective\nLifelines Dutch Microbiome Project, there may be repeated measurements for the same subjects. If this\nis the case, the metadata should include both subject IDs and measurement IDs. Treating repeated\nmeasurements as independent subjects can artificially inflate the number of discoveries, leading to\nnumerous spurious findings. The authors should employ appropriate statistical methods, such as mixed-\neffects models, to account for repeated measurements in their analysis.\nFor PERMANOVA, 1,000 permutations is too few; it is recommended to use 100,000 permutations.\nAdditionally, PERMANOVA does not account for repeated measurements or covariate adjustments; thus,\nyou can consider mixed-effects model-based methods.\nResponse:\nWe appreciate the reviewer’s thoughtful comment. We would like to clarify that there are no repeated\nmeasurements in our dataset; each participant contributed only one microbiome sample and\ncorresponding phenotype record. To make this explicit, we have added a sentence in the Statistical\nAnalysis section , which now reads:\n“All analyses were conducted under the assumption of independent observations. Each participant\ncontributed only one microbiome sample and corresponding phenotype record; no repeated\nmeasurements were included in the dataset.”\nAdditionally, we have rerun the PERMANOVA analyses with 100,000 permutations, as recommended. The\nupdated results are now reflected in Supplementary Table S4.\nPoint 4: Mediation analysis: “Employing the mediation package in R” is not at all sufficient. The authors\nneed to disclose the method name and reference. The authors need to disclose how the repeated\nmeasurements and covariate adjustments were handled with more specific methodological descriptions.\n1. Provide references for the statement: “Variable selection in the regularized mediation model was\nperformed using a lasso-penalized model, with the optimal lambda for the lasso (L1) penalty\ndetermined by minimizing the Bayesian Information Criterion through a grid search.”\n2. Perform mediation analysis for both alpha- and beta-diversity indices as well.\nResponse:\nWe thank the reviewer for this helpful comment. In response, we have now revised the Methods section to\nprovide a more detailed and transparent description of our mediation analysis procedures. Regarding\nrepeated measurements, we would like to clarify that this study is based on cross-sectional data, and no\nrepeated measurements were available. All phenotypes and microbiome features were measured at a\nsingle time point. In addition to species-level abundance, we now include alpha diversity (Shannon index\nand Phylogenetic Diversity) and beta diversity (the first 20 principal coordinates from weighted UniFrac\ndistances) as microbial traits in both the univariable and multivariable mediation analyses, as\nsuggested. These revisions are now reflected in the Methods section, and the updated mediation results\nincluding diversity metrics can be found in Supplementary Tables 8–14.\n“Inferring the putative causal relationship linking sleep, gut microbiome, and diet or disease\nalterations in sleep patterns influence microbial species abundance and diversity measures, or if\nthe reverse is true. To investigate this, we incorporated dietary patterns, which we hypothesized to be more\nlikely to be an exposure than a consequence to the microbial features, into our mediation network analysis.\nThis analysis tested three pre-defined potential directional relationships: (i) diet → microbiome → sleep,\nwhere changes in microbial features potentially cause alterations in sleep characteristics, (ii) diet → sleep\n→ microbiome, and (iii) sleep → diet → microbiome, where changes in microbial features are outcomes of\nlifestyle modifications.\nIn the first scenario (Figure 4a, Table S10), we observed that most species demonstrated a direct link to\neither sleep or diet. However, two species, Clostridia bacterium UC5_1_1E11 and Clostridia unclassified\nSGB14844 (both from the unclassified Clostridia genus), acted as putative mediators in the diet-to-sleep\npathway, suggesting a potential mediation of the effect of coffee consumption on social jet lag. Increased\ncoffee intake was found to boost the abundance of these two species, consequently intensifying the extent\nof social jet lag. This interesting dynamic between coffee consumption, the two Clostridia species, and\nsocial jet lag was also observed in the second (Figure 4b, Table S11) and third scenarios (Figure 4c, Table\nS12), indicating that fluctuations in social jet lag might impact coffee consumption, or vice versa, thereby\naltering the abundance of these two Clostridia species.\nSince these two species appeared in a mediation relationship in all three directions, we could quantify the\nproportion of mediation based on effect sizes derived from an unpenalized model (Figure 4d). By\naggregating the effects of UC5_1_1E11 and SGB14844, we discovered that the abundance of these species\nmediated 10% of the effect of coffee intake on social jet lag. Conversely, social jet lag accounted for only\n1.7% of the effect of coffee intake on these species. Notably, coffee intake was found to mediate 59.8% of\nthe effect of social jet lag on these species. These estimates suggest that variation in the abundance of\nthese two Clostridia species related to social jet lag may largely reflect differences in coffee consumption.\nAlong the same line, the connection between many microbial features and sleep characteristics was found\nto be potentially mediated by specific dietary factors (direction III) (Figure 4c, Table S12). Similar to the\ntwo Clostridia species, many species abundances could also be affected by changes in coffee\nconsumption induced by social jet lag. Lachnospiraceae GGB3570_SGB4777, Ruminococcaceae\nGGB9623_SGB15076, and Clostridium phoceensis showed an increase following coffee intake, while\nRuminococcaceae GGB9699_SGB15216 exhibited a decrease. Additionally, a preference for a later\nchronotype was linked with increased alcohol consumption, which in turn led to a reduced abundance of\nCoprococcus eutactus. Poor sleep quality associated with a lower HEI led to an increased abundance of\nFlavonifractor plautii. Nevertheless, findings from direction II still revealed that certain species do exhibit\na direct connection with sleep patterns, independent of dietary mediation (Figure 4b, Table S11). For\nexample, individuals with a late chronotype displayed elevated levels of Lachnospiraceae\nGGB3570_SGB4777 and a reduction in Clostridium sp. Marseille P3244 and Clostridium leptum.\nTo extend the analysis beyond dietary exposures, we also evaluated whether the gut microbiome could\nserve as a mediator in associations between sleep characteristics and disease phenotypes, focusing on\ngastrointestinal symptoms and mental health conditions (Table S13-14). However, we did not identify any\nmicrobial features or diversity measures that met the criteria for significant mediation in these models.”\nOther Comments:\nPoint 5: A reference needs to be provided for the following statement: “Before conducting any statistical\nanalyses, we applied inverse rank normal transformation to the sleep indices (PSQI score, ESS score,\nsocial jet lag, and MSFsc score) to reduce skewness and improve normality.”\nal., 2009, Behavior Genetics) to support the use of inverse rank normal transformation in the Methods\nsection.\nPoint 6: The statement “To correct for multiple testing, we estimated the FDR by conducting 1,000 rounds\nof permutation” is unclear and requires further clarification.\nResponse: Thank you for pointing this out. To improve clarity, we have moved the description of FDR\nestimation to the Statistical Analysis section and expanded it to explain the permutation-based approach\nused. The relevant text now reads: “A false discovery rate (FDR) was estimated using a permutation-based\napproach. For each analysis, we shuffled the sleep phenotype labels 1,000 times and recalculated\nassociation p-values for each permuted dataset, thereby generating a null distribution of association p-\nvalues under the assumption of no true association. To estimate the FDR for a given p-value threshold, we\ncalculated the proportion of p-values under the null distribution that were equal to or smaller than the\nthreshold, divided by the proportion of observed p-values that were equal to or smaller than the same\nthreshold. Associations were considered statistically significant if their empirical FDR was below 0.05, and\nall tests were two-sided.”\nPoint 7: The authors can also conduct functional (gene content) analyses in addition to the alpha-diversity,\nbeta-diversity, and species analyses.\nResponse:\nWe thank the reviewer for this valuable suggestion. In addition to our taxonomic and diversity-based\nanalyses, we have now conducted species- level profiling to explore functional variation in two key species\nof interest: Clostridia bacterium UC5_1_1E11 and Clostridia sp. SGB14844. These species were identified\nas potential mediators in the relationship between coffee intake and social jet lag. Using HUMAnN 4.0 and\nMetaCyc pathway annotations, we examined within-species variation in pathway abundance. While this\napproach provided a more detailed view of microbial function, we did not observe strong or consistent\nfunctional signatures related to sleep or dietary traits. These results highlight the current challenges in\ntranslating species-level associations into mechanistic insights. The results of the functional profiling are\nnow included in the revised manuscript and presented in Supplementary Table 15. We have also added a\ndiscussion of the limited functional signals observed, as noted in the Discussion section\nMethod: “Functional and phylogenetic profiling of Clostridia species\nTo further explore Clostridia bacterium UC5_1_1E11 and Clostridia sp. SGB14844, we performed species-\nresolved pathway and phylogenetic analyses. For functional profiling, we used HUMAnN 4.0 to quantify\nMetaCyc pathway abundances within each species. A total of 106 pathways were detected across the two\nspecies (48 in SGB14844 and 58 in SGB14895). Associations between CLR-transformed pathway\nabundances and phenotypes (coffee intake and social jet lag) were tested using linear models adjusted for\nage, sex, BMI, DNA concentration, sequencing batch, post-QC read count, Bristol Stool Scale, and\ndefecation frequency. Analyses were performed separately per species among individuals with complete\ndata (n = 630 for SGB14844; n = 77 for SGB14895).\nTo evaluate whether phylogenetic relatedness among strains could help explain phenotype associations,\nwe reconstructed strain-level phylogenies using StrainPhlAn with minimum marker detection thresholds\nset to 10 across samples. We then fit phylogenetic generalized linear mixed models using the anpan R\npackage42, modeling phenotypes as a function of covariates and a phylogenetic random effect under a\nBrownian motion assumption. Model fit was assessed by comparing the expected log predictive density\n22\n(ELPD) of each phylogenetic model to a null model without the phylogenetic term, using Pareto-smoothed\nleave-one-out cross-validation. A phylogenetic signal was considered statistically supported if the ELPD\ndifference (ΔELPD) between the null and phylogenetic model was less than –4 and the 95% confidence\ninterval (ΔELPD ± 2 standard errors) did not include zero.”\nResult: “To further investigate the functional and evolutionary basis of these two Clostridia species, we\nconducted exploratory analyses of their metabolic pathways and phylogenetic relationships. Despite their\nputative role in mediation, we identified no significant (FDR < 0.05) metabolic pathways or phylogenetic\nconservation of these species’ associations with social jet lag or coffee intake (Table S13–14). While their\nfunctional roles remain unclear, these species could influence circadian rhythms through unmeasured\nmetabolites or indirect mechanisms, warranting deeper functional profiling in targeted studies.”\nDiscussion: “In particular, two Clostridia species, UC5_1_1E11 and SGB14844, were consistently involved\nin the interplay between coffee consumption and social jet lag across multiple predefined mediation\nscenarios. Together, the abundance of these two species mediated approximately 10% of the association\nbetween coffee intake and social jet lag. Although the majority of the effect is likely direct, or mediated\nthrough other factors, the observed mediation suggests that these species may play a meaningful\nbiological role and may represent potential targets for mitigating the impact of coffee intake on social jet\nlag. Although these species remain poorly characterized, their membership in class Clostridia, which\nincludes many species known to influence host metabolism53 and neuroactive compound production54,\npoints to plausible mechanisms involving microbial metabolites affecting sleep regulation. To further\nexplore their potential roles, we examined the pathway-level functional profiles and phylogenetic patterns\nof these two species. However, we did not identify any specific metabolic pathways or phylogenetic signals\nthat could explain their associations with coffee intake or sleep-related traits. These null findings may\nreflect the relatively low prevalence and abundance of these species in the population, limiting the\nstatistical power to detect subtle biological effects. Nonetheless, their consistent appearance in\ndirectional models highlights them as intriguing candidates for future mechanistic investigation.”\nThe Interplay of Sleep Characteristics with Health factors and Gut Microbiome\nReviewer #1:\nThe authors thoroughly explained their revisions and efforts, and all comments were\naddressed satisfactorily.\nReviewer #2:\nThanks to the authors for their efforts in addressing my previous comments. Most\nconcerns have been largely resolved. However, I have two remaining points regarding\nthe authors’ responses:\nPoint 1: This issue relates less to non-normality or outliers, and more to the fundamental\nnature of the variables themselves. The authors’ arguments, such as treating them as\ncontinuous and applying transformations, do not sufficiently address the concern that the\nobserved associations may be artifacts without clear biological relevance. I still believe\nthat these methodological choices alone cannot rule out the possibility that the findings\nlack meaningful interpretation.\nResponse: We appreciate the reviewer’s concern that the observed associations might\nreflect artifacts arising from the fundamental nature of the sleep variables rather than\nbiology. As with any observational study, we cannot completely rule this out, and we see\nit as a general limitation of the field. However, we would like to address the validity and\nbiological significance of these variables from several angles.\nFirst, the sleep phenotypes analyzed in our study (Pittsburgh Sleep Quality Index (PSQI),\nEpworth Sleepiness Scale, social jet lag, and chronotype) are well established and widely\nused. Using PSQI as an example, this self-report questionnaire was developed in 1989\n(1) and has been widely used to assess sleep quality worldwide. It has been translated\ninto 56 languages (2) and is cited in 40,788 studies (3). Furthermore, with the widespread\nadoption of wearable devices, PSQI scores have been validated against smartwatch-\nderived measurements. For instance, Ko et al. assessed sleep quality in patients with\nParkinson’s disease using both PSQI and smartwatch sensors and reported consistent\nresults (4).\nSecondly, in our cohort, we tested the sleep measures against non-microbial phenotypes\nand recapitulated well-described patterns. Social jet lag decreased with age (5). Better\ndiet quality was associated with better PSQI scores (6). Evening chronotype was\nassociated with higher alcohol intake (7). Each of these findings is consistent with prior\nliterature and supports the conclusion that our sleep variables behaved as expected in\nthis dataset. These results are discussed in the Results section (i.e. sleep characteristics\n1\nare highly associated with diet, disease, socioeconomics, and other factors) and\npresented in Table S2. Thus, we believe that these indices do reflect biological facets of\nsleep health.\nThird, to further support robustness, we conducted a targeted replication of previously\nreported sleep–microbiome associations (with results shown in the table below). Most\nprior studies used 16S rRNA sequencing and reported associations at the genus or family\nlevel. Because our dataset is species-resolved by shotgun metagenomics, we evaluated\nreplication at the same genus/family level to match the resolution of the original reports.\nFor each reported genus- or family-phenotype pair, we applied the same modeling\nframework as in our primary analyses and tested whether the corresponding taxon in our\ndata showed a same-direction association with the same sleep trait. We observed\ndirectional concordance for several taxa (see table below). For example, Bacteroides\nshowed positive association (β=0.076, p=1.82×10⁻⁴) and Prevotella negative association\n(β=−0.134, p=0.027) with PSQI, consistent with earlier reports. Coprococcus was also\nnegatively associated with sleep disturbance (β=−0.089, p=0.010). Although the total\nnumber of direct replications was modest, this is unsurprising given platform and\nreference differences (16S vs shotgun), cohort and phenotype variation, and the fact that\ngenus-level tests aggregate species with potentially divergent effects, which can dilute or\ncancel species-specific signals. Consistent with this, we observed within-genus\nheterogeneity in our data (see the Blautia and Dorea examples in the Discussion). Taken\ntogether, the concordance we observe at higher taxonomic ranks supports the biological\nplausibility and validity of the sleep measures and associations reported here.\nStudy Sample Size Microbiome Taxa Estimate Std.Error P-value\nPopulation Trait\nBlautia (Genus) 0.042 0.030 0.163\nGrosicki et al. Young healthy\n[8] subjects\nPrevotella (Genus) -0.134 0.061 0.027\nLachnLachnospiraceae (Family) -0.009 0.012 0.457\nSmith et al.\nHealthy adults 26 Actigraphy 16S seq Corynebacterium (Genus) 0.004 0.005 0.381\n[9]\nBlautia (Genus) 0.042 0.030 0.163\nFirmicutes (Phylum) 0.004 0.007 0.609\nProteobacteria (Phylum) 0.028 0.014 0.051\nElusimicrobia (Phylum) -0.018 0.016 0.274\nZhang et al.\nCollege students 34 PSQI 16S seq Tenericutes (Phylum) -0.115 0.045 0.011\n[10]\nElusimicrobia (Class) -0.035 0.016 0.033\nMollicutes (Class) -0.135 0.048 0.005\nErysipelotrichia (Class) -0.006 0.034 0.861\nBifidobacterium (Genus) -0.012 0.037 0.742\nBacteroides (Genus) 0.076 0.020 1.82E-04\nWang et al. Preschool aged\n[11] children\nBlautia (Genus) 0.042 0.030 0.163\nCoprococcus (Genus) -0.089 0.035 0.010\nDorea (Genus) -0.044 0.036 0.222\nPatients with\nZhang et al. 81 (36 MDD,\nMDD and healthy PSQI 16S seq Intestinibacter (Genus) 0.011 0.020 0.582\n[12] 45 HC)\ncontrols\nCoprococcus (Genus) -0.089 0.035 0.010\nEllagibacter\nisourolithinifaciens Not detected\n(Species)\nPatients with Senegalimassilia faecalis\n0.007 0.020 0.739\nMairinger et psychiatric (Species)\nal. [13] disorders and uncultured Blautia sp.\nNot specified\nhealthy controls (Species)\nSenegalimassilia (Genus) 0.023 0.039 0.544\nuncultured Muribaculaceae\nNot specified\n(Genus)\nTanaka et al.\ndepression and 40 PSQI 16S seq Bacteroides (Genus) 0.076 0.020 1.82E-04\n[14]\nanxiety disorders\nAlistipes (Genus) 0.034 0.026 0.186\nLachnospira (Genus) -0.034 0.026 0.186\nCarasso et al. Medication-free Alistipes putredinis\n[15] individuals (Species)\nLachnospira pectinoschiza\n-0.048 0.041 0.242\n(Species)\nthe reviewer’s concern about the validity of our sleep parameters and the potential\nbiological relevance of our signals. Nevertheless, we acknowledge our study does not\nprovide definitive evidence about causality or underlying biological mechanisms and that\nself-reported questionnaire data have inherent limitations. We have now strengthened\nour discussion of these limitations.\nDiscussion section (L636-642): “As mentioned, one of the major limitations of our study\nis its cross-sectional design, which prevents us from establishing the temporal order of\nevents. This is particularly relevant for mediation analysis, where a clear sequence of\nexposure, mediator, and outcome is required to support causal interpretation. In addition,\nalthough we adjusted for a broad set of known covariates, we cannot exclude the\npossibility of unmeasured confounding. Our mediation results should thus be viewed as\nexploratory and interpreted with caution. Moreover, the reliance on self-reported data for\nboth sleep patterns and dietary habits introduces potential reporting bias. Although self-\nreporting is valuable for gathering data in large-scale studies, it is prone to inaccuracies\nand personal biases. Objective measurements, such as actigraphy for sleep and detailed\ndiet logs or biomarkers for dietary intake, would enhance the validity of results.”\n1. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh\nSleep Quality Index: A new instrument for psychiatric practice and research.\nPsychiatry Res. 1989;28(2):193–213. doi:10.1016/0165-1781(89)90047-4.\n2. Mapi Research Trust. Pittsburgh Sleep Quality Index (PSQI) [Internet].\nePROVIDE; Accessed September 23, 2025. Available from:\nhttps://eprovide.mapi-trust.org/instruments/pittsburgh-sleep-quality-index\n3. Google Scholar. The Pittsburgh Sleep Quality Index: A new instrument for\npsychiatric practice and research[Internet]. Accessed September 23, 2025.\n4. Ko Y-F, Kuo P-H, Wang C-F, Chen Y-J, Chuang P-C, Li S-Z, et al. Quantification\nanalysis of sleep based on smartwatch sensors for Parkinson’s disease.\nBiosensors (Basel). 2022;12(2):74. doi:10.3390/bios12020074.\n5. Gottlieb E, Gupta S, Gahan L, Raymann RJ, Watson NF. Social jetlag decreases\nacross the lifespan: A prospective big data analysis of objective sleep metrics. J\nSleep Res. 2025;e14433. doi:10.1111/jsr.14433.\n6. Behbahani HB, Borazjani F, Sheikhi L, Amiri R, Ahmadi Angali K, Basak Nejad S,\net al. The Association between Diet Quality Scores with Sleep Quality among\nEmployees: A Cross-Sectional Study. Ethiop J Health Sci. 2022;32(1):145-154.\ndoi:10.4314/ejhs.v32i1.16.\n7. Sirtoli R, Gotardo Mata GD, Rodrigues R, Martinez-Vizcaíno V, López-Gil JF,\nGuidoni CM, et al. Is evening chronotype associated with higher alcohol\n1\nconsumption? A systematic review and meta-analysis. Chronobiol\nInt.2023;40(11):1467-1479. doi:10.1080/07420528.2023.2256899.\n8. Grosicki GJ, Riemann BL, Flatt AA, Valentino T, Lustgarten MS. Self-reported\nsleep quality is associated with gut microbiome composition in young, healthy\nindividuals: a pilot study. Sleep Med. 2020;73:76–81.\ndoi:10.1016/j.sleep.2020.04.013.\n9. Smith RP, Easson C, Lyle SM, Kapoor R, Donnelly CP, Davidson EJ, et al. Gut\nmicrobiome diversity is associated with sleep physiology in humans. PLoS One.\n2019;14(10):e0222394. doi:10.1371/journal.pone.0222394.\n10. Zhang J, Zhang X, Zhang K, Lu X, Yuan G, Yang H, et al. The Component and\nFunctional Pathways of Gut Microbiota Are Altered in Populations with Poor\nSleep Quality – A Preliminary Report. Pol J Microbiol. 2022;71(2):241–250.\ndoi:10.33073/pjm-2022-021.\n11. Wang Y, van de Wouw M, Drogos L, Vaghef-Mehrabani E, Reimer RA, Tomfohr-\nMadsen L, Giesbrecht GF. Sleep and the gut microbiota in preschool-aged\nchildren. Sleep. 2022;45(6):zsac020. doi:10.1093/sleep/zsac020.\n12. Zhang Q, Yun Y, An H, Zhao W, Ma T, Wang Z, Yang F. Gut Microbiome\nComposition Associated With Major Depressive Disorder and Sleep Quality.\nFront Psychiatry. 2021;12:645045. doi:10.3389/fpsyt.2021.645045.\n13. Mairinger M, Maget A, Wagner-Skacel J, Mörkl S, Dalkner N, Hellinger T, et al.\nGut Microbiome Composition and Its Association with Sleep in Major Psychiatric\nDisorders. Neuropsychobiology. 2023;82(4):220–233. doi:10.1159/000530386.\n14. Tanaka A, Sanada K, Miyaho K, Tachibana T, Kurokawa S, Ishii C, et al. The\nrelationship between sleep, gut microbiota, and metabolome in patients with\ndepression and anxiety: A secondary analysis of the observational study. PLoS\nOne. 2023;18(12):e0296047. doi:10.1371/journal.pone.0296047.\n15. Carasso S, Levi I, Weissbrod O, Zeevi D, Zmora N, Koren O, et al. Metagenomic\nanalysis reveals the signature of gut microbiota associated with human\nchronotypes. bioRxiv. 2021. doi:10.1101/2021.03.16.435653.\nPoint 2: The authors appear to have rotated the directionality of exposure, mediator, and\noutcome in their mediation analysis, which is not consistent with the assumptions of\nclassical mediation frameworks. This reorientation also compounds concerns about\nmultiple comparison adjustments, particularly in the context of an already high-\ndimensional analysis. Furthermore, the use of univariate models as a screening step for\nmultivariable modeling is methodologically problematic. This approach inflates the risk of\nType I errors (false positives) due to multiple comparisons and reduces power to detect\ntrue associations in a joint model (Type II errors). As a result, the final models may be\n2\nbiased—either overestimating the importance of certain predictors or overlooking key\nrelationships.\nResponse: Thank you for the thoughtful comment. We appreciate the opportunity to\nclarify. We hypothesized that there is a bidirectional effect between the gut microbiome\nand sleep and tested this hypothesis using mediation analysis. This hypothesis is\nevidence-based (16,17). We did not rotate the roles of exposure, mediator, and outcome\npost hoc or based on statistical significance. Roles were fixed a priori, defined by a pre-\nspecified directed acyclic graph consistent with the hypothesized direction of effect, and\neach direction was evaluated as a separate pre-specified model. In human cohort studies,\nit is common to define a set of competing directional models, including bidirectional paths,\nand to evaluate them transparently. In particular, it is common practice to evaluate both\ndirections in scenarios where biological feedback is plausible (18,19), and some studies\nhave even evaluated direction-sensitive serial orders (20,21). Our approach follows this\npractice.\n16. Shiadeh SMJ, Chan WK, Rasmusson S, Hassan N, Joca S, Westberg L, Elfvin\nA, Mallard C, Ardalan M. Bidirectional crosstalk between the gut microbiota and\ncellular compartments of brain: Implications for neurodevelopmental and\nneuropsychiatric disorders. Transl Psychiatry. 2025;15(1):278.\ndoi:10.1038/s41398-025-03504-2\n17. Jansen, Erica C., et al. “Sleep, Diet, and Cardiometabolic Health Investigations:\nA Systematic Review of Analytic Strategies.” Current Nutrition Reports 7, no. 4\n(2018): 235-58.\n18. Thomas, Nathaniel S., et al. “Prospective Associations between Major\nDepressive Disorder, Generalized Anxiety Disorder, Fibromyalgia, and Myalgic\nEncephalomyelitis/Chronic Fatigue Syndrome.” Psychological Medicine 55\n(2025): e232.\n19. Yue, Kaile, et al. “Bidirectional Mediation Effects between Intratumoral\nMicrobiome and Host DNA Methylation Changes Contribute to Stomach\nAdenocarcinoma.” Microbiology Spectrum 11, no. 4 (2023): e0090423.\n20. Gao, Yun, and Lei Wang. “The Association between Diet and Sleep with\nInternalising Symptoms in Young Athletes: A Serial Multiple Mediation Models.”\nFrontiers in Nutrition 11 (2024): 1382068.\n21. Jahn, Danielle R., et al. “The Influence of Experiences of Stigma on Recovery:\nMediating Roles of Internalized Stigma, Self-Esteem, and Self-Efficacy.”\nPsychiatric Rehabilitation Journal 43, no. 2 (2020): 97-105.\nmultivariable selection. In this revision, we have now removed the univariate screen and\ninstead embedded the LASSO-based penalized multivariate mediation in a feature\nsubsampling stability selection framework. This replaces hypothesis test p-values with\nstability metrics that prioritize replicability across many subsampled fits, reducing the\nchance that a single noisy or collinear configuration drives selection. For each direction,\n10,000 iterations were executed. Overall, our pipeline now relies on joint penalization with\nstability selection: the penalty shrinks spurious effects and subsampling mitigates\nswitching among highly correlated features. We believe this yields more robust and\nreproducible results.\nWe have now described this in the Methods Section (L264-290):\n“Multivariable mediation was evaluated across five pre-specified causal directions\nspanning diet- and disease-related hypotheses using the regmed package in R for\nregularized mediation analysis via penalized structural equation modeling40. For\nabundance-based analyses, to reduce bias from feature preselection and enhance\nrobustness, lasso-penalized mediation models were embedded within a subsampling-\nbased stability selection procedure. For each direction, 10,000 iterations were executed.\nIn each iteration, 30 candidate microbial species were randomly sampled from the full\ncandidate set and a lasso-penalized mediation model was fit over a grid of L1 penalty\nstrengths (λ ∈ {0.02, 0.015, 0.01, 0.005})41. The penalty minimizing the Bayesian\nInformation Criterion (BIC) was selected. Linear mediation was parameterized as\nM=αX+…M=αX+… and Y=βM+δX+…Y=βM+δX+…, where X denotes the exposure, M\nthe putative mediator, and Y the outcome. A species was deemed selected in an iteration\nif, for the species-as-mediator direction, both α ≠ 0 and β ≠ 0, or if, for the species-as-\noutcome direction, both β ≠ 0 and δ ≠ 0. For each species, the number of eligible runs\n(iterations in which the species was included), selected runs (iterations meeting the\nselection rule), and the selection rate (selected/eligible × 100%) were recorded (Table\nS8). Within each direction, species with a selection rate >10% were retained, and a final\nmodel with these species was fit. For composition-based analyses, we did not perform\nstability selection; instead, we fit one lasso-penalized model per direction to the full set of\ndiversity metrics, using the same λ grid and BIC-based tuning described above. To reduce\nshrinkage bias, we refit the multivariable mediation model on the reduced set (variables\nwith nonzero coefficients at the BIC-selected λ) and then obtained effect-size estimates\nfrom an unpenalized model restricted to those variables. For each pathway, the proportion\nof mediation attributable to the mediator was quantified based on the effect sizes from the\nnon-penalized model. This proportion was determined by dividing the mediated effect (the\nproduct of the pathways from X to the mediator and from the mediator to Y) by the total\neffect (the sum of the mediated effect and any direct effect).”\nClostridia bacterium UC5_1_1E11) were repeatedly selected (≥50% of 10,000 iterations)\nin each of the three mediation directions. These features were retained in the refitted\nmodel and are highlighted in Fig. 4 with the estimated proportions mediated. In addition,\nwe report a broader set of potential candidates (selection frequency ≥10%) as network\nvisualizations in Supplementary Fig. S7 to aid hypothesis generation and future\nvalidation. Together, these results indicate that the strongest microbial features are stable\nunder resampling, while the extended set provides testable targets for follow-up. That\nsaid, stability alone does not imply causality; some features may reflect correlated or\nunderlying processes rather than direct effects. We therefore present them as replicable\nsignals to guide future studies, rather than as definitive causal claims.\nFigure 4. Mediation among coffee\nintake, social jet lag, and Clostridia\ntaxa.\nThe inferred causal relationship\nbetween Clostridia bacterium\nUC5_1_1E11, Clostridia unclassified\nSGB14844, coffee intake, and social\njet lag. The percentage shown\nquantifies the proportion of mediation\nin this relationship.\nReviewer #3\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and\nto provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #4\nThere are several critical issues with the provided datasets, including the feature table,\ntaxonomic table, and phylogenetic tree. Most notably, the feature table does not contain\nraw count data, contrary to the authors' claim, and the feature IDs are inconsistent across\nthe three datasets. The manuscript states that there are 6,941 subjects, yet the feature\ntable includes 8,355, with no explanation or method provided to extract the 6,941 subjects\nreferenced. Furthermore, without metadata, it is impossible to verify the authors' claims.\nAs a result, the findings presented in the manuscript amount to unsubstantiated\n5\nassertions. The absence of transparent data reporting is detrimental to scientific integrity.\nWithout proper data verification, I cannot recommend this manuscript for publication.\nResponse: We appreciate the reviewer’s close attention to data transparency.\nRegarding identifiers, FeatureID is consistent across the taxonomy table and feature\ntable. In the phylogenetic tree, we use the shorter SGB_ID as tip labels for readability. A\none-to-one mapping between FeatureID and SGB_ID was provided in\nSGB_tree_annotation.csv.\nWe apologize for the confusion around “raw counts.” In the feature table, we provide\nMetaPhlAn-derived relative abundances, not raw counts. MetaPhlAn estimates taxon\nabundance from clade-specific markers, yielding compositional data that are directly\ncomparable across heterogeneous sequencing depths and appropriate for our centered\nlog-ratio (CLR)–based and diversity analyses. To avoid confusion, we have revised the\ntext to state “MetaPhlAn relative abundances” rather than “raw counts”.\nThe feature table includes all post-QC samples (n = 8,355), whereas the analysis cohort\n(n = 6,941) is the subset with complete sleep variables and required covariates. To ensure\nfull reproducibility, we now provide an explicit ID list (analysis_cohort_ids.txt) so\nreaders can filter the matrices to exactly 6,941 samples.\nFinally, the individual-level metadata are owned by the Lifelines biobank. Like any other\nresearcher, we access these data under GDPR and participant-consent restrictions and\ntherefore cannot redistribute them. Within these constraints, we maximize transparency\nand reproducibility by sharing all allowable feature matrices and derived outputs, and by\nproviding step-by-step instructions that enable qualified researchers to obtain the\ncorresponding data directly from Lifelines. For replication of published findings, the\nbiobank offers a 3 month, no-cost access window, and we are happy to point applicants\nto the exact variables and project fields needed to reproduce our results.\nThe Data Availability section now reads as follows (L697-714):\n“The raw metagenomic sequencing data from this study are publicly available in the\nEuropean Genome-Phenome Archive under the Dutch Microbiome Project\n(EGAS00001005027) and Lifelines-DEEP (EGAD00001001991). The processed\nmicrobiome data generated during this study, including the Feature Table (MetaPhlAn-\nderived relative abundances), Taxonomic Table, and Phylogenetic Tree, have been\ndeposited in Zenodo [doi: 10.5281/zenodo.15292903]. Due to GDPR and Lifelines\nbiobank regulations, access to privacy-sensitive phenotype data (e.g., clinical and lifestyle\ninformation) requires following controlled-access procedures. Researchers can apply for\naccess via https://www.lifelines.nl/researcher/how-to-apply/apply-here. Applications are\nreviewed for compliance with informed consent, GDPR, and Lifelines' non-commercial\nuse policies. Upon approval and completion of a Data and Material Transfer Agreement,\ndata access is provided through the secure Lifelines Workspace or High-Performance\nannually for Workspace access (prorated options available). Full dataset access for\nreplication studies is available free of charge for three months by contacting\nresearch@lifelines.nl. Additional information on data access procedures is available at\nhttps://www.lifelines.nl/researcher/how-to-apply. ”\n7"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors thoroughly explained their revisions and efforts, and all comments were addressed satisfactorily.", "reviewer_2": "Thanks to the authors for their efforts in addressing my previous comments. Most concerns have been largely resolved.\nHowever, I have two remaining points regarding the authors’ responses:\nPoint 2:\nThis issue relates less to non-normality or outliers, and more to the fundamental nature of the variables themselves. The\nauthors’ arguments, such as treating them as continuous and applying transformations, do not sufficiently address the\nconcern that the observed associations may be artifacts without clear biological relevance. I still believe that these\nmethodological choices alone cannot rule out the possibility that the findings lack meaningful interpretation.\nPoint 4:\nThe authors appear to have rotated the directionality of exposure, mediator, and outcome in their mediation analysis, which\nis not consistent with the assumptions of classical mediation frameworks. This reorientation also compounds concerns about\nmultiple comparison adjustments, particularly in the context of an already high-dimensional analysis. Furthermore, the use of\nunivariate models as a screening step for multivariable modeling is methodologically problematic. This approach inflates the\nrisk of Type I errors (false positives) due to multiple comparisons and reduces power to detect true associations in a joint\nmodel (Type II errors). As a result, the final models may be biased—either overestimating the importance of certain\npredictors or overlooking key relationships.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "There are several critical issues with the provided datasets, including the feature table, taxonomic table, and phylogenetic\ntree. Most notably, the feature table does not contain raw count data, contrary to the authors' claim, and the feature IDs are\ninconsistent across the three datasets. The manuscript states that there are 6,941 subjects, yet the feature table includes\n8,355, with no explanation or method provided to extract the 6,941 subjects referenced. Furthermore, without metadata, it is\nimpossible to verify the authors' claims. As a result, the findings presented in the manuscript amount to unsubstantiated\nassertions. The absence of transparent data reporting is detrimental to scientific integrity. Without proper data verification, I\ncannot recommend this manuscript for publication."}, "author_response": "Response to Reviewers\nThe Interplay of Sleep Characteristics with Health factors and Gut Microbiome\nReviewer #1:\nThe authors thoroughly explained their revisions and efforts, and all comments were\naddressed satisfactorily.\nReviewer #2:\nThanks to the authors for their efforts in addressing my previous comments. Most\nconcerns have been largely resolved. However, I have two remaining points regarding\nthe authors’ responses:\nPoint 1: This issue relates less to non-normality or outliers, and more to the fundamental\nnature of the variables themselves. The authors’ arguments, such as treating them as\ncontinuous and applying transformations, do not sufficiently address the concern that the\nobserved associations may be artifacts without clear biological relevance. I still believe\nthat these methodological choices alone cannot rule out the possibility that the findings\nlack meaningful interpretation.\nResponse: We appreciate the reviewer’s concern that the observed associations might\nreflect artifacts arising from the fundamental nature of the sleep variables rather than\nbiology. As with any observational study, we cannot completely rule this out, and we see\nit as a general limitation of the field. However, we would like to address the validity and\nbiological significance of these variables from several angles.\nFirst, the sleep phenotypes analyzed in our study (Pittsburgh Sleep Quality Index (PSQI),\nEpworth Sleepiness Scale, social jet lag, and chronotype) are well established and widely\nused. Using PSQI as an example, this self-report questionnaire was developed in 1989\n(1) and has been widely used to assess sleep quality worldwide. It has been translated\ninto 56 languages (2) and is cited in 40,788 studies (3). Furthermore, with the widespread\nadoption of wearable devices, PSQI scores have been validated against smartwatch-\nderived measurements. For instance, Ko et al. assessed sleep quality in patients with\nParkinson’s disease using both PSQI and smartwatch sensors and reported consistent\nresults (4).\nSecondly, in our cohort, we tested the sleep measures against non-microbial phenotypes\nand recapitulated well-described patterns. Social jet lag decreased with age (5). Better\ndiet quality was associated with better PSQI scores (6). Evening chronotype was\nassociated with higher alcohol intake (7). Each of these findings is consistent with prior\nliterature and supports the conclusion that our sleep variables behaved as expected in\nthis dataset. These results are discussed in the Results section (i.e. sleep characteristics\n1\nare highly associated with diet, disease, socioeconomics, and other factors) and\npresented in Table S2. Thus, we believe that these indices do reflect biological facets of\nsleep health.\nThird, to further support robustness, we conducted a targeted replication of previously\nreported sleep–microbiome associations (with results shown in the table below). Most\nprior studies used 16S rRNA sequencing and reported associations at the genus or family\nlevel. Because our dataset is species-resolved by shotgun metagenomics, we evaluated\nreplication at the same genus/family level to match the resolution of the original reports.\nFor each reported genus- or family-phenotype pair, we applied the same modeling\nframework as in our primary analyses and tested whether the corresponding taxon in our\ndata showed a same-direction association with the same sleep trait. We observed\ndirectional concordance for several taxa (see table below). For example, Bacteroides\nshowed positive association (β=0.076, p=1.82×10⁻⁴) and Prevotella negative association\n(β=−0.134, p=0.027) with PSQI, consistent with earlier reports. Coprococcus was also\nnegatively associated with sleep disturbance (β=−0.089, p=0.010). Although the total\nnumber of direct replications was modest, this is unsurprising given platform and\nreference differences (16S vs shotgun), cohort and phenotype variation, and the fact that\ngenus-level tests aggregate species with potentially divergent effects, which can dilute or\ncancel species-specific signals. Consistent with this, we observed within-genus\nheterogeneity in our data (see the Blautia and Dorea examples in the Discussion). Taken\ntogether, the concordance we observe at higher taxonomic ranks supports the biological\nplausibility and validity of the sleep measures and associations reported here.\nStudy Sample Size Microbiome Taxa Estimate Std.Error P-value\nPopulation Trait\nBlautia (Genus) 0.042 0.030 0.163\nGrosicki et al. Young healthy\n[8] subjects\nPrevotella (Genus) -0.134 0.061 0.027\nLachnchnospiraceae (Family) -0.009 0.012 0.457\nSmith et al.\nHealthy adults 26 Actigraphy 16S seq Corynebacterium (Genus) 0.004 0.005 0.381\n[9]\nBlautia (Genus) 0.042 0.030 0.163\nFirmicutes (Phylum) 0.004 0.007 0.609\nProteobacteria (Phylum) 0.028 0.014 0.051\nElusimicrobia (Phylum) -0.018 0.016 0.274\nZhang et al.\nCollege students 34 PSQI 16S seq Tenericutes (Phylum) -0.115 0.045 0.011\n[10]\nElusimicrobia (Class) -0.035 0.016 0.033\nMollicutes (Class) -0.135 0.048 0.005\nErysipelotrichia (Class) -0.006 0.034 0.861\nBifidobacterium (Genus) -0.012 0.037 0.742\nBacteroides (Genus) 0.076 0.020 1.82E-04\nWang et al. Preschool aged\n[11] children\nBlautia (Genus) 0.042 0.030 0.163\nCoprococcus (Genus) -0.089 0.035 0.010\nDorea (Genus) -0.044 0.036 0.222\nPatients with\nZhang et al. 81 (36 MDD,\nMDD and healthy PSQI 16S seq Intestinibacter (Genus) 0.011 0.020 0.582\n[12] 45 HC)\ncontrols\nCoprococcus (Genus) -0.089 0.035 0.010\nEllagibacter\nisourolithinifaciens Not detected\n(Species)\nPatients with Senegalimassilia faecalis\n0.007 0.020 0.739\nMairinger et psychiatric (Species)\nal. [13] disorders and uncultured Blautia sp.\nNot specified\nhealthy controls (Species)\nSenegalimassilia (Genus) 0.023 0.039 0.544\nuncultured Muribaculaceae\nNot specified\n(Genus)\nTanaka et al.\ndepression and 40 PSQI 16S seq Bacteroides (Genus) 0.076 0.020 1.82E-04\n[14]\nanxiety disorders\nAlistipes (Genus) 0.034 0.026 0.186\nLachnospira (Genus) -0.034 0.026 0.186\nCarasso et al. Medication-free Alistipes putredinis\n[15] individuals (Species)\nLachnospira pectinoschiza\n-0.048 0.041 0.242\n(Species)\nthe reviewer’s concern about the validity of our sleep parameters and the potential\nbiological relevance of our signals. Nevertheless, we acknowledge our study does not\nprovide definitive evidence about causality or underlying biological mechanisms and that\nself-reported questionnaire data have inherent limitations. We have now strengthened\nour discussion of these limitations.\nDiscussion section (L636-642): “As mentioned, one of the major limitations of our study\nis its cross-sectional design, which prevents us from establishing the temporal order of\nevents. This is particularly relevant for mediation analysis, where a clear sequence of\nexposure, mediator, and outcome is required to support causal interpretation. In addition,\nalthough we adjusted for a broad set of known covariates, we cannot exclude the\npossibility of unmeasured confounding. Our mediation results should thus be viewed as\nexploratory and interpreted with caution. Moreover, the reliance on self-reported data for\nboth sleep patterns and dietary habits introduces potential reporting bias. Although self-\nreporting is valuable for gathering data in large-scale studies, it is prone to inaccuracies\nand personal biases. Objective measurements, such as actigraphy for sleep and detailed\ndiet logs or biomarkers for dietary intake, would enhance the validity of results.”\n1. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh\nSleep Quality Index: A new instrument for psychiatric practice and research.\nPsychiatry Res. 1989;28(2):193–213. doi:10.1016/0165-1781(89)90047-4.\n2. Mapi Research Trust. Pittsburgh Sleep Quality Index (PSQI) [Internet].\nePROVIDE; Accessed September 23, 2025. Available from:\nhttps://eprovide.mapi-trust.org/instruments/pittsburgh-sleep-quality-index\n3. Google Scholar. The Pittsburgh Sleep Quality Index: A new instrument for\npsychiatric practice and research[Internet]. Accessed September 23, 2025.\n4. Ko Y-F, Kuo P-H, Wang C-F, Chen Y-J, Chuang P-C, Li S-Z, et al. Quantification\nanalysis of sleep based on smartwatch sensors for Parkinson’s disease.\nBiosensors (Basel). 2022;12(2):74. doi:10.3390/bios12020074.\n5. Gottlieb E, Gupta S, Gahan L, Raymann RJ, Watson NF. Social jetlag decreases\nacross the lifespan: A prospective big data analysis of objective sleep metrics. J\nSleep Res. 2025;e14433. doi:10.1111/jsr.14433.\n6. Behbahani HB, Borazjani F, Sheikhi L, Amiri R, Ahmadi Angali K, Basak Nejad S,\net al. The Association between Diet Quality Scores with Sleep Quality among\nEmployees: A Cross-Sectional Study. Ethiop J Health Sci. 2022;32(1):145-154.\ndoi:10.4314/ejhs.v32i1.16.\n7. Sirtoli R, Gotardo Mata GD, Rodrigues R, Martinez-Vizcaíno V, López-Gil JF,\nGuidoni CM, et al. Is evening chronotype associated with higher alcohol\n1\nconsumption? A systematic review and meta-analysis. Chronobiol\nInt.2023;40(11):1467-1479. doi:10.1080/07420528.2023.2256899.\n8. Grosicki GJ, Riemann BL, Flatt AA, Valentino T, Lustgarten MS. Self-reported\nsleep quality is associated with gut microbiome composition in young, healthy\nindividuals: a pilot study. Sleep Med. 2020;73:76–81.\ndoi:10.1016/j.sleep.2020.04.013.\n9. Smith RP, Easson C, Lyle SM, Kapoor R, Donnelly CP, Davidson EJ, et al. Gut\nmicrobiome diversity is associated with sleep physiology in humans. PLoS One.\n2019;14(10):e0222394. doi:10.1371/journal.pone.0222394.\n10. Zhang J, Zhang X, Zhang K, Lu X, Yuan G, Yang H, et al. The Component and\nFunctional Pathways of Gut Microbiota Are Altered in Populations with Poor\nSleep Quality – A Preliminary Report. Pol J Microbiol. 2022;71(2):241–250.\ndoi:10.33073/pjm-2022-021.\n11. Wang Y, van de Wouw M, Drogos L, Vaghef-Mehrabani E, Reimer RA, Tomfohr-\nMadsen Madsen L, Giesbrecht GF. Sleep and the gut microbiota in preschool-aged\nchildren. Sleep. 2022;45(6):zsac020. doi:10.1093/sleep/zsac020.\n12. Zhang Q, Yun Y, An H, Zhao W, Ma T, Wang Z, Yang F. Gut Microbiome\nComposition Associated With Major Depressive Disorder and Sleep Quality.\nFront Psychiatry. 2021;12:645045. doi:10.3389/fpsyt.2021.645045.\n13. Mairinger M, Maget A, Wagner-Skacel J, Mörkl S, Dalkner N, Hellinger T, et al.\nGut Microbiome Composition and Its Association with Sleep in Major Psychiatric\nDisorders. Neuropsychobiology. 2023;82(4):220–233. doi:10.1159/000530386.\n14. Tanaka A, Sanada K, Miyaho K, Tachibana T, Kurokawa S, Ishii C, et al. The\nrelationship between sleep, gut microbiota, and metabolome in patients with\ndepression and anxiety: A secondary analysis of the observational study. PLoS\nOne. 2023;18(12):e0296047. doi:10.1371/journal.pone.0296047.\n15. Carasso S, Levi I, Weissbrod O, Zeevi D, Zmora N, Koren O, et al. Metagenomic\nanalysis reveals the signature of gut microbiota associated with human\nchronotypes. bioRxiv. 2021. doi:10.1101/2021.03.16.435653.\nPoint 2: The authors appear to have rotated the directionality of exposure, mediator, and\noutcome in their mediation analysis, which is not consistent with the assumptions of\nclassical mediation frameworks. This reorientation also compounds concerns about\nmultiple comparison adjustments, particularly in the context of an already high-\ndimensional analysis. Furthermore, the use of univariate models as a screening step for\nmultivariable modeling is methodologically problematic. This approach inflates the risk of\nType I errors (false positives) due to multiple comparisons and reduces power to detect\ntrue associations in a joint model (Type II errors). As a result, the final models may be\n2\nbiased—either overestimating the importance of certain predictors or overlooking key\nrelationships.\nResponse: Thank you for the thoughtful comment. We appreciate the opportunity to\nclarify. We hypothesized that there is a bidirectional effect between the gut microbiome\nand sleep and tested this hypothesis using mediation analysis. This hypothesis is\nevidence-based (16,17). We did not rotate the roles of exposure, mediator, and outcome\npost hoc or based on statistical significance. Roles were fixed a priori, defined by a pre-\nspecified directed acyclic graph consistent with the hypothesized direction of effect, and\neach direction was evaluated as a separate pre-specified model. In human cohort studies,\nit is common to define a set of competing directional models, including bidirectional paths,\nand to evaluate them transparently. In particular, it is common practice to evaluate both\ndirections in scenarios where biological feedback is plausible (18,19), and some studies\nhave even evaluated direction-sensitive serial orders (20,21). Our approach follows this\npractice.\n16. Shiadeh SMJ, Chan WK, Rasmusson S, Hassan N, Joca S, Westberg L, Elfvin\nA, Mallard C, Ardalan M. Bidirectional crosstalk between the gut microbiota and\ncellular compartments of brain: Implications for neurodevelopmental and\nneuropsychiatric disorders. Transl Psychiatry. 2025;15(1):278.\ndoi:10.1038/s41398-025-03504-2\n17. Jansen, Erica C., et al. “Sleep, Diet, and Cardiometabolic Health Investigations:\nA Systematic Review of Analytic Strategies.” Current Nutrition Reports 7, no. 4\n(2018): 235-58.\n18. Thomas, Nathaniel S., et al. “Prospective Associations between Major\nDepressive Disorder, Generalized Anxiety Disorder, Fibromyalgia, and Myalgic\nEncephalomyelitis/Chronic Fatigue Syndrome.” Psychological Medicine 55\n(2025): e232.\n19. Yue, Kaile, et al. “Bidirectional Mediation Effects between Intratumoral\nMicrobiome and Host DNA Methylation Changes Contribute to Stomach\nAdenocarcinoma.” Microbiology Spectrum 11, no. 4 (2023): e0090423.\n20. Gao, Yun, and Lei Wang. “The Association between Diet and Sleep with\nInternalising Symptoms in Young Athletes: A Serial Multiple Mediation Models.”\nFrontiers in Nutrition 11 (2024): 1382068.\n21. Jahn, Danielle R., et al. “The Influence of Experiences of Stigma on Recovery:\nMediating Roles of Internalized Stigma, Self-Esteem, and Self-Efficacy.”\nPsychiatric Rehabilitation Journal 43, no. 2 (2020): 97-105.\nmultivariable selection. In this revision, we have now removed the univariate screen and\ninstead embedded the LASSO-based penalized multivariate mediation in a feature\nsubsampling stability selection framework. This replaces hypothesis test p-values with\nstability metrics that prioritize replicability across many subsampled fits, reducing the\nchance that a single noisy or collinear configuration drives selection. For each direction,\n10,000 iterations were executed. Overall, our pipeline now relies on joint penalization with\nstability selection: the penalty shrinks spurious effects and subsampling mitigates\nswitching among highly correlated features. We believe this yields more robust and\nreproducible results.\nWe have now described this in the Methods Section (L264-290):\n“Multivariable mediation was evaluated across five pre-specified causal directions\nspanning diet- and disease-related hypotheses using the regmed package in R for\nregularized mediation analysis via penalized structural equation modeling40. For\nabundance-based analyses, to reduce bias from feature preselection and enhance\nrobustness, lasso-penalized mediation models were embedded within a subsampling-\nbased stability selection procedure. For each direction, 10,000 iterations were executed.\nIn each iteration, 30 candidate microbial species were randomly sampled from the full\ncandidate set and a lasso-penalized mediation model was fit over a grid of L1 penalty\nstrengths (λ ∈ {0.02, 0.015, 0.01, 0.005})41. The penalty minimizing the Bayesian\nInformation Criterion (BIC) was selected. Linear mediation was parameterized as\nM=αX+…M=αX+… and Y=βM+δX+…Y=βM+δX+…, where X denotes the exposure, M\nthe putative mediator, and Y the outcome. A species was deemed selected in an iteration\nif, for the species-as-mediator direction, both α ≠ 0 and β ≠ 0, or if, for the species-as-\noutcome direction, both β ≠ 0 and δ ≠ 0. For each species, the number of eligible runs\n(iterations in which the species was included), selected runs (iterations meeting the\nselection rule), and the selection rate (selected/eligible × 100%) were recorded (Table\nS8). Within each direction, species with a selection rate >10% were retained, and a final\nmodel with these species was fit. For composition-based analyses, we did not perform\nstability selection; instead, we fit one lasso-penalized model per direction to the full set of\ndiversity metrics, using the same λ grid and BIC-based tuning described above. To reduce\nshrinkage bias, we refit the multivariable mediation model on the reduced set (variables\nwith nonzero coefficients at the BIC-selected λ) and then obtained effect-size estimates\nfrom an unpenalized model restricted to those variables. For each pathway, the proportion\nof mediation attributable to the mediator was quantified based on the effect sizes from the\nnon-penalized model. This proportion was determined by dividing the mediated effect (the\nproduct of the pathways from X to the mediator and from the mediator to Y) by the total\neffect (the sum of the mediated effect and any direct effect).”\nClostridia bacterium UC5_1_1E11) were repeatedly selected (≥50% of 10,000 iterations)\nin each of the three mediation directions. These features were retained in the refitted\nmodel and are highlighted in Fig. 4 with the estimated proportions mediated. In addition,\nwe report a broader set of potential candidates (selection frequency ≥10%) as network\nvisualizations in Supplementary Fig. S7 to aid hypothesis generation and future\nvalidation. Together, these results indicate that the strongest microbial features are stable\nunder resampling, while the extended set provides testable targets for follow-up. That\nsaid, stability alone does not imply causality; some features may reflect correlated or\nunderlying processes rather than direct effects. We therefore present them as replicable\nsignals to guide future studies, rather than as definitive causal claims.\nFigure 4. Mediation among coffee\nintake, social jet lag, and Clostridia\ntaxa.\nThe inferred causal relationship\nbetween Clostridia bacterium\nUC5_1_1E11, Clostridia unclassified\nSGB14844, coffee intake, and social\njet lag. The percentage shown\nquantifies the proportion of mediation\nin this relationship.\nReviewer #3\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review and\nto provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #4\nThere are several critical issues with the provided datasets, including the feature table,\ntaxonomic table, and phylogenetic tree. Most notably, the feature table does not contain\nraw count data, contrary to the authors' claim, and the feature IDs are inconsistent across\nthe three datasets. The manuscript states that there are 6,941 subjects, yet the feature\ntable includes 8,355, with no explanation or method provided to extract the 6,941 subjects\nreferenced. Furthermore, without metadata, it is impossible to verify the authors' claims.\nAs a result, the findings presented in the manuscript amount to unsubstantiated\n5\nassertions. The absence of transparent data reporting is detrimental to scientific integrity.\nWithout proper data verification, I cannot recommend this manuscript for publication.\nResponse: We appreciate the reviewer’s close attention to data transparency.\nRegarding identifiers, FeatureID is consistent across the taxonomy table and feature\ntable. In the phylogenetic tree, we use the shorter SGB_ID as tip labels for readability. A\none-to-one mapping between FeatureID and SGB_ID was provided in\nSGB_tree_annotation.csv.\nWe apologize for the confusion around “raw counts.” In the feature table, we provide\nMetaPhlAn-derived relative abundances, not raw counts. MetaPhlAn estimates taxon\nabundance from clade-specific markers, yielding compositional data that are directly\ncomparable across heterogeneous sequencing depths and appropriate for our centered\nlog-ratio (CLR)–based and diversity analyses. To avoid confusion, we have revised the\ntext to state “MetaPhlAn relative abundances” rather than “raw counts”.\nThe feature table includes all post-QC samples (n = 8,355), whereas the analysis cohort\n(n = 6,941) is the subset with complete sleep variables and required covariates. To ensure\nfull reproducibility, we now provide an explicit ID list (analysis_cohort_ids.txt) so\nreaders can filter the matrices to exactly 6,941 samples.\nFinally, the individual-level metadata are owned by the Lifelines biobank. Like any other\nresearcher, we access these data under GDPR and participant-consent restrictions and\ntherefore cannot redistribute them. Within these constraints, we maximize transparency\nand reproducibility by sharing all allowable feature matrices and derived outputs, and by\nproviding step-by-step instructions that enable qualified researchers to obtain the\ncorresponding data directly from Lifelines. For replication of published findings, the\nbiobank offers a 3 month, no-cost access window, and we are happy to point applicants\nto the exact variables and project fields needed to reproduce our results.\nThe Data Availability section now reads as follows (L697-714):\n“The raw metagenomic sequencing data from this study are publicly available in the\nEuropean Genome-Phenome Archive under the Dutch Microbiome Project\n(EGAS00001005027) and Lifelines-DEEP (EGAD00001001991). The processed\nmicrobiome data generated during this study, including the Feature Table (MetaPhlAn-\nderived relative abundances), Taxonomic Table, and Phylogenetic Tree, have been\ndeposited in Zenodo [doi: 10.5281/zenodo.15292903]. Due to GDPR and Lifelines\nbiobank regulations, access to privacy-sensitive phenotype data (e.g., clinical and lifestyle\ninformation) requires following controlled-access procedures. Researchers can apply for\naccess via https://www.lifelines.nl/researcher/how-to-apply/apply-here. Applications are\nreviewed for compliance with informed consent, GDPR, and Lifelines' non-commercial\nuse policies. Upon approval and completion of a Data and Material Transfer Agreement,\ndata access is provided through the secure Lifelines Workspace or High-Performance\nannually for Workspace access (prorated options available). Full dataset access for\nreplication studies is available free of charge for three months by contacting\nresearch@lifelines.nl. Additional information on data access procedures is available at\nhttps://www.lifelines.nl/researcher/how-to-apply. ”\n7"}, {"round": 3, "reviewer_comments": {"reviewer_2": "Thanks for the authors' efforts. I do not have further comments.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_4": "I thank the authors for their clarifications and additional information regarding data access and cohort description. However,\nthe key concerns remain unresolved. The metadata and underlying data needed to independently verify the analyses are\nstill inaccessible, and the criteria for deriving the analysis subset cannot be confirmed.\nAs reproducibility and data transparency are essential for scientific validation, I am unable to verify the reported results.\nTherefore, I cannot recommend this manuscript for publication at this stage."}, "author_response": ""}]

Decision letter