Integrative multi-omics analysis reveals chrono-modulation by time-restricted eating ameliorates non-alcoholic fatty liver disease

Integrative multi-omics analysis reveals chrono-modulation by time-restricted eating ameliorates non-alcoholic fatty liver disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative multi-omics analysis reveals chrono-modulation by time-restricted eating ameliorates non-alcoholic fatty liver disease Shuang Rong, Zhiming Li, Yan Deng, Yuze Sun, Hefu Zhen, Zhiyuan Shi, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4693158/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To assess the impact of early and late time-restricted eating (eTRE and lTRE) on non-alcoholic fatty liver disease (NAFLD) and its underlying mechanisms involving gut microbiota and serum metabolome changes. Design This 8-week randomized clinical trial involved 40 NAFLD patients divided into eTRE (8:00 am to 4:00 pm) and lTRE (12:00 pm to 8:00 pm) groups, with unrestricted caloric intake within these windows. Comprehensive analyses including nutrient intake, gut microbiota composition, serum metabolome profiling, and phenotypic assessments were conducted pre- and post-intervention. Results Both eTRE and lTRE significantly improved liver fat levels, visceral fat, body fat percentage, and body mass index (BMI). eTRE led to more pronounced changes in dietary patterns and exhibited a stronger influence on gut microbiota composition and the serum metabolome than lTRE. Notably, TRE increased the abundance of Enterocloster asparagiformis , a beneficial microbe linked to positive shifts in NAFLD phenotypes. This bacterium's presence was significantly associated with reduced levels of branched-chain amino acids (BCAAs) in both human subjects and mouse models, correlating with reductions in liver fat content, suggesting a metabolic modulation pathway. Metabolomic analysis further revealed significant alterations in amino acids and bile acids, which correlate with key NAFLD markers. Conclusion Time-restricted eating (eTRE and lTRE) provides a viable non-pharmacological strategy for NAFLD management by altering gut microbiota and serum metabolomics. The differential effects of eTRE versus lTRE highlight the importance of meal timing in dietary interventions, supporting personalized dietary strategies based on individual responses to optimize NAFLD outcomes. Trial registration number ChiCTR2100052876. Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic fatty liver disease Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity time-restricted eating non-alcoholic fatty liver disease gut microbiota branched-chain amino acids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The prevalence of non-alcoholic fatty liver disease (NAFLD), a condition characterized by excessive fat accumulation in the liver 1 , is rising with the global rise in obesity and type 2 diabetes mellitus rates 2 . NAFLD is now recognized as one of the most common forms of chronic liver disease, affecting nearly a quarter of the global population 3 . The disease spectrum ranges from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis and hepatocellular carcinoma 4, 5 , underscoring the urgent need for effective therapeutic strategies. Time-restricted eating (TRE) has emerged as a promising dietary approach that confines food intake to a consistent window of 8–12 hours per day, potentially aligning eating patterns with circadian rhythms to optimize metabolic health 6 . The novel aspect of TRE is its focus on when to eat rather than what to eat, which represents a paradigm shift in dietary interventions for NAFLD. Previous studies have shown that TRE can lead to weight loss and improve metabolic markers 7, 8 , but the differential effects of the timing of the eating window have not been thoroughly investigated. Longitudinal studies reported that an early first meal coupling with longer nighttime fasting period was associated with lower incidence of metabolic disease, indicating that restricting food intake to the earlier part of the day (eTRE) may be more beneficial than shifting the eating window to later in the day (lTRE) 9, 10 . However, our recent study compared the impact of eTRE and lTRE on NAFLD and demonstrated that both eTRE and lTRE induced reductions in liver fat and improvements in liver function with similar magnitude 11 . This similarity may suggest that, despite the potential benefits of shortening eating window for metabolic diseases, eTRE and lTRE may produce similar therapeutic effects through different mechanisms in the specific case of NAFLD. This may involve regulation of the gut microbiome, as the gut microbiome has been shown to play an important role in the pathogenesis of NAFLD, and their activity is influenced by the host's feeding rhythm 12, 13 . Thus, this study seeks to explore how the timing of food intake affects the gut microbiome, serum metabolome, and phenotypes within the context of TRE, providing insights into the broader implications and underlying mechanisms of dietary timing on human health. By conducting an 8-week randomized parallel-arm trial, we aim to provide a comprehensive analysis of the effects of eTRE versus lTRE on gut microbiome, serum metabolome, and phenotypes and their cross-talk with host's metabolism in human with NAFLD. Materials and Methods Clinical Trial To investigate the effects of different fasting times on NAFLD, we conducted a randomized, highly controlled clinical trial for 8 weeks. Forty NAFLD patients (26 males and 14 females) who typically consumed food over a window of 10 hours or more per day were successfully enrolled and randomly assigned to either eTRE or lTRE group. The eTRE group was instructed to consume their daily meals within an 8-hour window, from 8:00 am to 4:00 pm, while the lTRE group was allocated an 8-hour window from 12:00 am to 8:00 pm (Figure 1). Patients were allowed to eat freely without caloric or dietary restrictions during their designated 8-hour window and were only permitted to drink water during the remaining 16 hours of fasting (Figure 1). The study design, method, and primary results have been described elsewhere 11 . Mouse Studies All experiments procedures performed in mice were approved by the Institutional Animal Care and Use Committee at Wuhan University. Two-month-old male C57BL/6N mice were purchased from Hubei Provincial Center for Disease Control and Prevention (SYXK-2020-0018) and fed a standard chow diet until 14 months of age. The mice were housed under controlled conditions with a constant temperature of 24 ± 3°C and a 12-hour light/12-hour dark cycle. Mice were fed a normal chow diet or 60% high-fat diet (HFD) that we previously confirmed to potently induce NAFLD in mice. For the time-restricted feeding intervention, mice had access to food only during the dark phase for 8 hours, starting from Zeitgeber15 (ZT15) to ZT23. The study included four groups: 1. Ad libitum group feeding with normal chow diet (AL+ ND); 2. Ad libitum group feeding with HFD (AL+ HFD) ; 3. Time-restricted feeding with normal chow diet (8h TRE + ND) or ; 4. Time-restricted feeding with HFD (8h TRE + HFD). The intervention period lasted for seven months, during which food intake and body weight were monitored weekly. Serum and liver tissue samples were collected after sacrifice for subsequent testing. Processing and Detection of Blood and Fecal Samples For information on the detection of blood metabolites and the extraction and metagenomic sequencing of fecal samples, please refer to the online supplementary methods material. Statistical Analyses Power and Sample Size: The absolute 4% reduction in liver fat has been reported as the clinically significant change associated with liver histological responses in previous study 14 . After assuming a 20% dropout rate, the sample size of 20 participants per group provided 85% statistical power for the trial to detect a 4% reduction of liver fat with a standard deviation of 4% and two-tailed P <0.05. Multivariate analysis: Multivariate statistical analysis (Principal Component Analysis (PCA) and Principal Co-ordinates Analysis (PCoA)) were used to differentiate and analyze the following variations: differences across various omics between the lTRE and eTRE regimens within the human population, differences within the omics data before and after the intervention of the lTRE and eTRE protocols, and the differences in mice subjected to interventions of Normal Diet (ND), High-Fat Diet (HFD), ND with Time-Restricted Feeding (ND+TRF), and HFD with Time-Restricted Feeding (HFD+TRF). PCA was performed by using the ade4 15 package in the R platform. Based on Bray‒Curtis dissimilarity, PCoA was performed by using pcoa (a function in the ape 16 package in R). Permutational multivariate analysis of variance (PERMANOVA) analysis : Permutational multivariate analysis of variance (PERMANOVA, adonis analysis) was realized with the R vegan 17 package, and the adonis P-value was generated based on 1000 permutations. Co-inertia analysis (CIA) : To assess the associations between different omics data, Coinertia Analysis (CIA) was performed across the various omics data. The CIA was conducted utilizing the coinertia function within the ade4 15 package in R, with parameters set to scannf=FALSE and nf=2. Hypothesis testing and multiple test correction : To detect differences in diet and host phenotypes before and after the lTRE and eTRE interventions, t-tests were conducted. The Wilcoxon rank-sum test 18 was employed to assess differences in gut microbiota and serum metabolomic features before and after the lTRE and eTRE interventions. The Kruskal-Wallis test 19 was utilized to evaluate the differences in microbiota at multiple time points within the gut microbiome. False Discovery Rate (FDR) adjustments were made using the Benjamini-Hochberg method 20 , as implemented by the p.adjust function in R. Bidirectional mediation analysis : For microbial features associated with metabolites and aging, we first checked whether the microbial features were associated with the metabolite using Spearman correlation ( P < 0.05). Next, we carried out bidirectional mediation analysis with interactions (y = x + m + x × m, where y is the outcome, x is the variable and m represents the mediator) between mediator and outcome using the mediate function from mediation (version 4.5.0) to infer the mediation effect of serum metabolites and the gut microbiota on aging 21 . The statistical scripts were available at https://github.com/lizhiming11/TRE-NAFLD. Results TRE changes dietary pattern and improves non-alcoholic fatty liver disease We collected the multidimensional datasets encompassing dietary intake, gut microbiota composition, serum metabolome, and a range of host characteristics (referred to as the ‘phenotype’) were collected in this trial (Fig. 1 ). The age, BMI, gender, and other indicators of participants in the eTRE and lTRE groups showed no significant differences between the eTRE and lTRE groups (Table S1 ). Additionally, initial analysis of baseline the nutrient intakes, gut microbiota, serum metabolome, and phenotype data did not unveil significant differences between the eTRE and lTRE groups (Supplementary Figure S1 , PERMANOVA P > 0.05). Our investigation into the impact of TRE on NAFLD assessed two distinct TRE protocols. Both protocols resulted in significant dietary modifications from baseline to the two-month mark (Fig. 2 a). Notably, dietary changes were more pronounced in the eTRE group than in the lTRE group (Fig. 2 ab). Across both TRE interventions, we observed a marked reduction in the macronutrient intake — energy, carbohydrates, proteins, and total fat (Fig. 2 b). Notably, the eTRE group showed broader impacts on dietary patterns, affecting a wider range of micronutrients and amino acids compared to lTRE. Crucially, both eTRE and lTRE protocols had effectively reduced liver fat levels, visceral fat, body fat percentage, and body mass index (BMI). The lTRE group experienced a more substantial reduction in liver fat (Δ = -3.51%) and visceral fat (Δ = -6.21%) compared to the eTRE group (Fig. 2 c and Table S3 ). Conversely, the eTRE group exhibited a greater reduction in body fat percentage (Δ = -1.54 vs. Δ = -1.39%) and a marginally larger decrease in BMI (Δ = -0.87 vs. Δ = -0.85 kg/m 2 ) compared to the lTRE group (Fig. 2 c and Table S3 ). Meanwhile, markers for assessing liver health 22 , including gamma-glutamyl transferase (GGT), Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), and cardiometabolic risk factors that associated with NAFLD 23, 24, 25 , such as fasting glucose (GLU), insulin, serum uric acid (SUA), were also significantly reduced in both TRE groups by the second month. In addition, a robust correlation was observed between phenotypic data from both TRE interventions. Specifically, a strong association was noted among liver fat content, BMI, AST, ALT, magnetic resonance imaging transverse relaxation rate (R 2 ), prealbumin (PA), SUA, GGT, visceral fat, and triglycerides (TG) (Figure S2 a). We also explored the relationship between dietary intake and phenotypic changes. In eTRE group, dietary patterns significantly influenced phenotypic alterations, while such a relationship was not apparent in lTRE group (Fig. 2 d). Within eTRE diet, amino acids such as leucine, lysine, and valine were found to have a significant negative correlation with serum urea, alkaline phosphatase, hemoglobin A1c, and albumin-globulin ratio, yet a positive correlation with globulin, TP (total protein), PA, and SUA (Figure S2 b). However, these associations were not observed to the same extent in the lTRE group (Figure S2 b). In summary, our findings suggest that the association between dietary patterns and phenotypic changes is more pronounced in eTRE group compared to lTRE group, highlighting the potential efficacy of eTRE in managing NAFLD. Both eTRE and lTRE alters serum metabolome in patients with NAFLD We observed that the time of intervention is the main factor explaining the differences in serum metabolomics among the subjects (eTRE: 20.03%; lTRE: 16.04%) (Fig. 3 a, PERMANOVA, P < 0.001). The fat content, PA, ALT, and AST/ALT in both groups significantly affected the serum metabolome. Particularly, it should be noted that visceral fat in eTRE group is significantly associated with the serum metabolome, while such a connection was not found in lTRE group. These significantly impacted indicators collectively account for the changes in serum metabolomics of eTRE: 52.3% and lTRE: 49.26% (Fig. 3 a). In terms of diet, the change in energy intake in the diet of the lTRE group has the most significant impact on the serum metabolome (15.77%) (Figure S3 a). In addition, carbohydrates, protein, fat, sodium, meat, and VE also have significant effects on the serum metabolome, together explaining 25.01% of the variation in the serum metabolome (Figure S3 b). Compared with lTRE group, the role of diet in the eTRE group is relatively more significant, with vitamins, amino acids, minerals, etc. in the diet all having a significant impact on the serum metabolome, together explaining 88.65% of the variation in the serum metabolome (Figure S3 a). Further analysis revealed that the two interventions showed significant separation at both the baseline period and after 2 months (Fig. 3 b); in the early group, 53 out of 156 metabolites showed significantly different abundance, and in the late group, 46 had significantly different abundance (Fig. 3 c and Table S3 ). The early group and the late group respectively included changes in 16 and 15 types of amino acids. Among them, Norleucine, Tryptophan, Methionine, Valine, and Isoleucine showed a significant decrease in both intervention schemes after two months. Meanwhile, Selenomethionine, Acetylserine, Cetylhistidine, and Acetamidobutanoic acid all showed a significant increase in both intervention schemes after two months (Fig. 3 c and Table S4 ). Excess amino acids can lead to the accumulation of fat in the liver, which may reflect poor dietary habits as well as dysregulation of amino acid metabolism in the liver and peripheral tissues 26, 27 . In regards to bile salts, the early group and the late group respectively exhibited changes in 9 and 5 types of bile salts (Fig. 3 c and Table S4 ). Consistent with previous study findings 28, 29 , we discovered that both intervention protocols led to bile acid dysregulation, as evidenced by significant increases in diketocholic acid, chenodeoxycholic acid, cholic acid, and hyodeoxycholic acid following the interventions. We examined the correlation between serum metabolites from two intervention schemes and clinical variables associated with NAFLD respectively (Figure S4 ). Importantly, in both intervention schemes, liver fat content, BMI, and visceral fat showed significantly positively correlations with Tryptophan, Isoleucine, and Valine. In eTRE group, fat content and BMI exhibited negatively correlations with Isovalerylglycine, N − a−Acetyl − L−arginine, p − Hydroxyphenylacetic acid, Lauroylcarnitine, Decanoylcarnitine, and some bile salts (Alloisolithocholic Acid, Cholic Acid Methyl Ester) (Figure S4 ). These findings emphasize the physiological relevance of these circulating metabolites to the clinical status of patients. The effects on gut microbiota vary with different intervention schemes To investigate whether the gut microbiota mediates the impact of TRE on the serum metabolome and clinical indicators of patients with NAFLD, we analyzed the gut microbiota using shotgun metagenomic sequencing. Utilizing the BGI-seq platform, an average of 20.68 Gb data was generated per fecal sample. After aligning high-quality sequencing reads with the Unified Human Gastrointestinal Genome reference 30 , the relative abundance of microbes in the samples was calculated for subsequent analysis (Table S5). Through metagenomic analysis, we found that the two TRE intervention did not significantly alter the microbial diversity of the microbiota. There was a difference in microbial diversity between the two intervention groups after 8 weeks ( P = 0.077) (Figure S5a). Moreover, principal component analysis indicated that the gut microbiota did not separate before and after the intervention for both intervention methods (Figure S5b). By analyzing the differences in gut microbiota before and after the two interventions, we found that the changes in the early group were greater than those in the late group. This was primarily manifested by the identification of 362 differential species in the early group before and after the intervention, with 358 species significantly enriched before the intervention, spanning 10 phyla. In the late group, 43 differential species were identified before and after the intervention, spanning 3 phyla (Fig. 4ab and Table S6). We conducted cluster analysis on the microbiota changes throughout interventions in two groups. In the late group, the microbiota changes were classified into three clusters (Fig. 3 c and Table S6): Cluster 1 featured an initial spike then returned to baseline levels without further changes, involving species such as Corynebacterium bouchesdurhonense , Alistipes ihumii , and Roseburia hominis . Cluster 2 showed a gradual increase over time with species like Enterocloster asparagiformis and Bifidobacterium pseudolongum and. Cluster 3 experienced an initial drop, then stabilized, including species such as Bacteroides acidifaciens and Coprobacter fastidiosus . In the early group, similar patterns were observed. Cluster 1 had a gradual decrease, including Fusobacterium nucleatum , Corynebacterium propinquum and Clostridium botulinum . Cluster 2 saw an initial rise followed by a drop and stabilization, involving species like Alistipes finegoldii and Akkermansia muciniphila . Cluster 3 dropped initially and then remained stable, including species such as Enterococcus casseliflavus , Clostridioides difficile and Bifidobacterium animalis (Fig. 3 c and Table S6). Importantly, the species that showed significant changes before and after the intervention were significantly correlated with clinical variables associated with NAFLD. In the late intervention group, E. asparagiformis and A. colihominis were correlated with multiple NAFLD-associated clinical variables, such as fat content, ALT, AST, and visceral fat (Figure S6). KEGG functional analysis also showed no significant changes before and after intervention (Figure S7a). The functions that decreased significantly after early TRE intervention include Aromatics degradation, Biosynthesis of secondary metabolites, Drug resistance, and Two-component regulatory system. The functions that increased significantly after late TRE intervention include Two-component regulatory system, Saccharide, polyol, and lipid transport system, and Drug resistance (Figure S7b). Overall, these findings suggest that the two intervention schemes have different effects on the functionality of gut microbiota. Different intervention methods affect different multi-omics relationships To examine the interplay between dietary structure, gut microbiota, and metabolome, we conducted a Co-inertia analysis across these variables. In eTRE group, post-intervention analysis two months later revealed strengthened connections between the gut microbiome and both phenotype and serum metabolome, while the link between diet and gut microbiome weakened (Fig. 5 a). Conversely, in lTRE group, the connections between the gut microbiome and phenotype, as well as between the gut microbiome and serum metabolome, weakened. Meanwhile, the relationships between diet and both phenotype and serum metabolome strengthened (Fig. 5 a). These findings indicate that the two intervention strategies differentially influence the interrelationships among diet, gut microbiota, and metabolic profiles. We carried out inter-group correlation analyses among diet, gut microbiome, serum metabolites, and phenotypic data. The lTRE group exhibited 28,813 significant multi-omics associations, while the eTRE group recorded 46,686 significant associations (Fig. 5 b, p < 0.01). This disparity largely stems from a stronger diet-gut microbiota correlation in the eTRE group compared to the lTRE group. Additionally, in the eTRE, the variables closely linked to the intervention showed far more positive correlations, with eight times as many as in lTRE group (Fig. 5 c). In the correlation networks for both interventions, 72.7% of the negative correlations primarily arose from the serum metabolome-gut microbiota relationship (Fig. 5 c). More importantly, the correlation networks for the two interventions were distinctly different, sharing only about 7.8% of correlations (Fig. 5 d and Figure S8). Consistent with previous studies 31, 32, 33 , among these shared correlations, fat content was closely related to L-Valine and L-Isoleucine. Excessive branched-chain amino acids may interfere glucose and fat metabolism, potentially leading to liver fat accumulation 27 . In individuals with NAFLD, where insulin's lipogenic activity remains high and compensatory insulin release increases due to insulin resistance, potentially driving lipid accumulation in the liver 34 . Diet, Microbiome, and Metabolites in Mediating Non-Alcoholic Fatty Liver Disease Phenotypes Subsequently, we performed mediation analyses to investigate the mediating effects among diet, gut microbiome, serum metabolome, and phenotype. For the variables significantly associated with the intervention, in both eTRE and lTRE groups, we established mediating links for the following sets of factors: diet, gut microbiome, and serum metabolome; diet and gut microbiome phenotype; diet, serum metabolites, and phenotype; and gut microbiome, serum metabolome, and phenotype. In the eTRE group and the lTRE group, we established 276, 335, 113, and 711 mediating links, and 47, 8, 19, and 123 mediating links, respectively (p < 0.1, Figure S9, Figure S10, Table S7 and Table S8). Most of these associations are related to the impact on markers of NAFLD, such as liver fat content, AST, and ALT. In the lTRE group, E. asparagiformis showed a significant impact on branched-chain amino acids (BCAAs) and NAFLD-related indicators. Specifically, BCAAs such as L-Isoleucine and L-Valine were found to mediate the effect of E. asparagiformis on fat content, AST, and SUA (Fig. 6ab). Additionally, metabolites like Kynurenic acid and L-Tryptophan were implicated in mediating the effects of E. asparagiformis on AST (Fig. 6ab). These findings suggest that E. asparagiformis may contribute to the regulation of amino acid metabolism and liver health through these pathways. Interestingly, in the eTRE group, we observed a similar trend. Although the correlation between E. asparagiformis and fat content was not statistically significant (correlation coefficient = -0.12), the negative relationship was consistent with the significant findings in the lTRE group. Furthermore, BCAAs, including L-Valine, L-Isoleucine, and L-Norleucine, significantly decreased following the TRE intervention in both groups (Fig. 3 c). This consistency across both dietary interventions suggests that E. asparagiformis might play a role in modulating fat content and BCAAs levels, albeit to varying extents depending on the timing of the eating window. To confirm E. asparagiformis 's ability to synthesize L-Tryptophan and BCAAs, we examined its complete set of functional genes. E. asparagiformis possesses all necessary genes for metabolizing valine, leucine, isoleucine, and tryptophan (Fig. 6 c). Subsequent analysis revealed a significant correlation between these metabolic pathways' genes and the corresponding plasma metabolites—L-Isoleucine, L-Valine, L-Norleucine, and L-Tryptophan (Fig. 6 d). These metabolites also showed significant associations with NAFLD markers including liver fat content, visceral fat, AST, ALT, and SUA (Fig. 6 d). The consistent decrease in BCAAs following TRE and the observed correlations with metabolic markers highlight the potential of E. asparagiformis as a key microbial mediator. The differences in statistical significance and correlation strength between the eTRE and lTRE groups might be attributed to distinct microbial community dynamics and metabolic interactions influenced by the timing of food intake. The decline in NAFLD related indicators and serum BCAAs are associated with an increase in Enterocloster asparagiformis in mice To explore the microbiota's role in NAFLD progression and its influence on the serum metabolome, we conducted a six-month study with normal and high-fat diet mice, comparing the effects of an 8-hour time-restricted eating (TRE) schedule (22:00–6:00) and ad libitum feeding. We collected feces and blood from the mice to measure the gut microbiome, serum metabolome, and NAFLD-related indicators. After six months, the mice subjected to TRE intervention showed a significant reduction in body weight (Fig. 6 e). More importantly, TRE intervention in the high-fat diet group significantly reduced TG and TC in the blood and liver (Fig. 6 f and Figure S11a). The further analysis of the gut microbiota and serum metabolome indicated that there was a distinct separation between the gut microbiota and serum metabolomic profiles of the high-fat diet group and the normal diet group (Figure S11b). Within the high-fat diet group itself, there was also a clear separation between the microbiota and serum metabolomes of those undergoing TRE intervention compared to those with an ad-libitum (free-feeding) diet (Figure S11b). Consistent with previous reports, a high-fat diet, compared to a normal diet, leads to a significant decrease in serum branched-chain amino acids 35, 36 (Fig. 6 h). Most notably, the TRE intervention led to a significant increase in the abundance of E. asparagiformis in the gut (Fig. 6 g). Concurrently, there was a significant reduction in the levels of BCAAs in the plasma of mice fed a high-fat diet (Fig. 6 h). These results further establish a close connection between E. asparagiformis , BCAAs, and NAFLD, suggesting that TRE may exert beneficial effects in the context of NAFLD by modulating the gut microbiota and serum metabolome. Discussion The present study offers a comprehensive look into the effects of TRE on individuals with NAFLD, providing valuable insights into the synchronization of meal timing with circadian biology. Our findings showed that although eTRE and lTRE both significantly reduce liver fat and improve liver function, eTRE reveals a tight-knit network of biomolecular changes in gut microbiota and metabolites than lTRE. In addition, both TRE interventions can improve NAFLD through the common mechanism involving E. asparagiformis and BCAAs pathways, which supports the efficacy of TRE as a therapeutic intervention for NAFLD. We observed that both eTRE and lTRE were shown to significantly improve NAFLD, which contributes to a growing body of evidence that supports the use of TRE in NAFLD management 37, 38, 39 . Compared to lTRE, the eTRE in alignment with diurnal circadian rhythms revealed a more closely molecular network characterized by more positive correlations between diet and gut microbiome, highlighting the important role of meal timing, dietary patterns, and gut microbiome in NAFLD 40, 41 . Although TRE not require individuals to change dietary intake, participants may spontaneously alter their diet when shorten eating windows 42 , which may simultaneously affect metabolic health and microbiota. Additionally, we further analyzed the shared correlation networks to explore the common mechanism by which eTRE and lTRE action. Among common correlations, fat content was closely related to BCAAs, which are not only essential amino acids but also pivotal regulatory molecules in metabolic health 43 . Elevated levels of BCAAs have been implicated in the pathogenesis of insulin resistance—a key feature of NAFLD—and can exacerbate hepatic lipid accumulation 26, 44, 45 . Notably, an shift in the metabolomic landscape, specifically a reduction in serum BCAAs, correlates with the presence of E. asparagiformis . The bacterium's presence correlates with a reduction in plasma BCAAs, suggesting that it may actively catabolize these molecules. This catabolic activity could potentially attenuate the insulin resistance and lipid accumulation typically observed in NAFLD. In existing studies, Enterocloster asparagiformis has been shown to be significantly reduced in patients with metabolic syndrome 46 and negatively correlated with uric acid, AST, and ALT 47 . These findings confirm the positive role of this bacterial strain in metabolic health. Moreover, the mechanistic link becomes even more compelling when considering the functional gene analysis. The presence of genes related to the metabolism of valine, leucine, and isoleucine in E. asparagiformis supports a direct role in BCAAs catabolism. This catabolic process may not only reduce the hepatic load of BCAAs but also generate metabolites that can influence host signaling pathways, including mTOR and AMPK, which are integral to metabolic regulation and energy homeostasis 48, 49 . The translational potential of our findings is underscored by our murine model, where TRE on a high-fat diet not only impacted weight and lipid profiles but also modulated the gut microbiota in ways that mirror the human data. The observed decrease in plasma BCAAs and increase in E. asparagiformis with TRE suggests that this dietary intervention can fundamentally reshape metabolic pathways, which may be pivotal in preventing or treating NAFLD. However, our study is not without limitations. While we have demonstrated associations, causality cannot be confirmed, and further interventional studies are needed. The study's transferability to the general population may be restricted by the specific dietary patterns and timings examined. Additionally, the gut microbiota is highly individualistic, and its response to dietary interventions like TRE can vary between individuals 50, 51 . Therefore, while our study provides a robust foundation for the beneficial effects of early TRE, further exploration into the individual responses to dietary timing is warranted. In conclusion, our study showcases the therapeutic promise of TRE for NAFLD management and highlights the importance of considering individual circadian preferences when tailoring dietary interventions. The differential impacts of eTRE and lTRE on various health markers underscore the potential for personalized nutrition strategies based on circadian alignment. These findings pave the way for future research into personalized dietary recommendations that could optimize metabolic health outcomes for individuals with NAFLD and potentially other metabolic disorders. Declarations Acknowledgments The authors thank all participants for their valuable contributions and the Geriatric Hospital Affiliated with Wuhan University of Science and Technology for assisting recruitment and biochemical examination. Author contributions S.R. conceived and designed the study. Y.D., Y.S., F.L., and H.Y. recruited participants and collected and analyzed clinical data. H.L. and Z.W. conducted animal experiments. H.Z., Z.S., and W.X. collected serum and fecal samples and extracted DNA from feces. W.Z. and X.Z. generated the targeted metabolomics data. Z.L. and Y.S. performed bioinformatics analyses. Z.L., Y.D., and Y.S. wrote the manuscript. H.Z., Z.S., H. Li, W.X., W.Z., X.Z., C.N., and S.R. reviewed and edited the manuscript. All authors made substantial contributions and approved the final version of the manuscript. Funding This study was supported by the National Natural Science Foundation of China (NSFC-82373575, NSFC-82073552). Declaration of interests The authors declared no competing interests. Ethics approval This study was approved by the Ethics Committees of Wuhan University of Science and Technology (WUST-202193) and registered at the World Health Organization's International Clinical Trial Platform as ChiCTR2100052876. The study was conducted according to the Declaration of Helsinki guidelines. Participants provided informed consent prior to participation in the study. Data availability statement Data have been deposited in the CNSA (https://db.cngb.org/cnsa/) of CNGBdb with accession number CNP0005414. Other data that support the findings of this study are available from the corresponding authors upon request. References Chalasani N , et al. 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The role of bile acids in nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Molecular aspects of medicine 56 , 34-44 (2017). Almeida A , et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature biotechnology 39 , 105-114 (2021). Pietzner M , et al. Hepatic steatosis is associated with adverse molecular signatures in subjects without diabetes. The Journal of Clinical Endocrinology & Metabolism 103 , 3856-3868 (2018). Chashmniam S, Ghafourpour M, Farimani AR, Gholami A, Ghoochani BFNM. Metabolomic biomarkers in the diagnosis of non-alcoholic fatty liver disease. Hepatitis Monthly 19 , (2019). Hasegawa T , et al. Changed amino acids in NAFLD and liver fibrosis: a large cross-sectional study without influence of insulin resistance. Nutrients 12 , 1450 (2020). Haufe S , et al. Branched-chain and aromatic amino acids, insulin resistance and liver specific ectopic fat storage in overweight to obese subjects. 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JAMA Network Open 6 , e233513-e233513 (2023). Marjot T, Tomlinson JW, Hodson L, Ray DW. Timing of energy intake and the therapeutic potential of intermittent fasting and time-restricted eating in NAFLD. Gut 72 , 1607-1619 (2023). Aron-Wisnewsky J , et al. Gut microbiota and human NAFLD: disentangling microbial signatures from metabolic disorders. Nat Rev Gastroenterol Hepatol 17 , 279-297 (2020). Steger FL , et al. Impact of early time-restricted eating on diet quality, meal frequency, appetite, and eating behaviors: A randomized trial. Obesity (Silver Spring) 31 Suppl 1 , 127-138 (2023). Nie C, He T, Zhang W, Zhang G, Ma X. Branched chain amino acids: beyond nutrition metabolism. International journal of molecular sciences 19 , 954 (2018). Kawanaka M , et al. Tyrosine levels are associated with insulin resistance in patients with nonalcoholic fatty liver disease. Hepatic medicine: evidence and research , 29-35 (2015). Lake AD , et al. Branched chain amino acid metabolism profiles in progressive human nonalcoholic fatty liver disease. Amino acids 47 , 603-615 (2015). Qin Q , et al. A Metagenome-Wide Association Study of the Gut Microbiome and Metabolic Syndrome. Front Microbiol 12 , 682721 (2021). Wang T , et al. Divergent age-associated and metabolism-associated gut microbiome signatures modulate cardiovascular disease risk. Nat Med , (2024). Moberg M, Apró W, Ekblom B, Van Hall G, Holmberg H-C, Blomstrand E. Activation of mTORC1 by leucine is potentiated by branched-chain amino acids and even more so by essential amino acids following resistance exercise. American Journal of Physiology-Cell Physiology 310 , C874-C884 (2016). Chotechuang N , et al. mTOR, AMPK, and GCN2 coordinate the adaptation of hepatic energy metabolic pathways in response to protein intake in the rat. American Journal of Physiology-Endocrinology and Metabolism 297 , E1313-E1323 (2009). Leeming ER, Johnson AJ, Spector TD, Le Roy CI. Effect of diet on the gut microbiota: rethinking intervention duration. Nutrients 11 , 2862 (2019). Kolodziejczyk AA, Zheng D, Elinav E. Diet–microbiota interactions and personalized nutrition. Nature Reviews Microbiology 17 , 742-753 (2019). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterialsFigures.docx SupplementaryMaterialsMethodsNC.docx SupplementaryMaterialsTables.xlsx completedCONSORTchecklist.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4693158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361107473,"identity":"c3adb2e4-394c-4ab5-a536-8e7d3803bf6c","order_by":0,"name":"Shuang 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15:15:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4693158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4693158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66067289,"identity":"fe1df375-90df-4a25-8a81-123f9d731921","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall study flow.\u003c/strong\u003e Forty NAFLD patients were successfully enrolled and randomly assigned to either the eTRE or lTRE group. The eTRE group was instructed to consume their daily meals within an 8-hour window, from 8:00 am to 4:00 pm, while the lTRE group was allocated an 8-hour window from 12:00 am to 8:00 pm. Multidimensional datasets encompassing dietary intake, gut microbiota composition, serum metabolome, and a range of host characteristics were collected to assess the changes of microbial composition, metabolite profiles and their cross-talk.\u003c/p\u003e","description":"","filename":"Binder21.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/13dc92bd68ea8bd3984b5f7a.png"},{"id":66069451,"identity":"5bfd00b9-94f8-4327-be50-691ce8601907","added_by":"auto","created_at":"2024-10-07 11:47:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe dietary and clinical indicator characteristics of eTRE and lTRE interventions and their relationships. a.\u003c/strong\u003e Principal Component Analysis showed significant differences in the dietary data of patients with NAFLD before and after the eTRE and lTRE interventions. \u003cstrong\u003eb.\u003c/strong\u003e Significant differences in dietary data variables of patients with NAFLD were observed before and after the eTRE and lTRE interventions (\u003cem\u003eP \u003c/em\u003e\u0026lt;0.05, t. test). A deeper color indicates a higher abundance of the variable. \u003cstrong\u003ec.\u003c/strong\u003e The boxplot shows significant differences in the clinical phenotypes of patients with NAFLD before and after the eTRE and lTRE interventions (*FDR\u0026lt;0.05, **FDR\u0026lt;0.01). \u003cstrong\u003ed.\u003c/strong\u003e Covariance Inertia Analysis displays the association between dietary data and clinical phenotype data for the eTRE and lTRE intervention.\u003c/p\u003e","description":"","filename":"Binder22.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/68ffd97449c02371aab234aa.png"},{"id":66067290,"identity":"ac9c331d-4831-4f16-8184-fced4d75571d","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe serum metabolomic characteristics of eTRE and lTRE interventions. a.\u003c/strong\u003e In the eTRE and lTRE intervention protocols, the effect sizes of phenotype indices that significantly contribute to the variance (R\u003csup\u003e2\u003c/sup\u003e) of the serum metabolome (adonis \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05). \u003cstrong\u003eb. \u003c/strong\u003ePrincipal Component Analysis showed significant differences in the serum metabolome of NAFLD patients before and after eTRE and lTRE interventions. \u003cstrong\u003ec. \u003c/strong\u003eThe bar plot displays serum metabolites that show significant differences before and after eTRE and lTRE interventions (\u003cem\u003eP \u003c/em\u003e\u0026lt;0.05, Wilcoxon rank-sum test).\u003c/p\u003e","description":"","filename":"Binder23.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/f3f3bb4e46ee77f50b0dd1ea.png"},{"id":66067292,"identity":"6eef60a4-a6e9-46a1-bfc7-3c2b3937ceaf","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":89147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe gut microbiome characteristics of eTRE and lTRE interventions. a. \u003c/strong\u003eThe lollipop chart shows the bacterial families with significant differences before and after eTRE (a) and lTRE (b) interventions (\u003cem\u003eP \u003c/em\u003e\u0026lt;0.05, Wilcoxon rank-sum test). For each microbial family, the number of species enriched at baseline (blue) or enriched after 2 months of intervention (orange) is displayed.\u003cstrong\u003e c. \u003c/strong\u003eClustering of significantly different gut microbiota at multiple time points in eTRE and lTRE interventions. Both eTRE and lTRE can be divided into three clusters, with only cluster 2 in the lTRE group showing a gradual upward trend with the intervention.\u003c/p\u003e","description":"","filename":"Binder24.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/1341282fd68383ce03cb8a50.png"},{"id":66067298,"identity":"df738102-6c12-47a6-9b59-d77d10595861","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":411965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effects of eTRE and lTRE intervention protocol differ on the multi-omics relationships. a. \u003c/strong\u003eThe size of the association between different omics before and after the intervention in the eTRE and lTRE groups. The RV coefficient is calculated through co-inertia analysis. \u003cstrong\u003eb. \u003c/strong\u003eThe number of significant associations (Spearman correlation, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) between variables of different omics before and after the intervention in the eTRE and lTRE groups. \u003cstrong\u003ec.\u003c/strong\u003e In the eTRE and lTRE groups, the interomics correlation networks of intervention-related variables for the diet, gut microbiome, serum metabolome, and phenotypes. Vertices indicate omics variables, and lines indicate a significant Spearman correlation coefficient at \u003cem\u003eP\u003c/em\u003e \u0026lt;0.01. \u003cstrong\u003ed. \u003c/strong\u003eIn the eTRE and lTRE groups, the shared correlation network among diet, gut microbiota, serum metabolome, and phenotypes.\u003c/p\u003e","description":"","filename":"Binder25.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/bcc03ead4d5bd0373463bfcd.png"},{"id":66067296,"identity":"55a385cb-8584-474a-914d-41110cfb0b43","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79821,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant associations were observed between gut \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eE. asparagiformis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, serum BCAAs, and clinical indicators related to NAFLD in human and mouse cohorts. a-b. \u003c/strong\u003eAnalysis of the effect of \u003cem\u003eE. asparagiformis\u003c/em\u003e on fat content, AST and SUA as mediated by BCAAs, L- Tryptophan and 2-Hydroxy-3-methylbutyric acid. \u003cstrong\u003ec. \u003c/strong\u003eThe potential KEGG function of the BCAAs and Tryptophan metabolism pathways in \u003cem\u003eE. asparagiformis\u003c/em\u003e. A darker color represents a higher copy number of the corresponding KO (Kegg Orthology). \u003cstrong\u003ed. \u003c/strong\u003eThe heatmap displays the relative abundance of KOs in the BCAA and Tryptophan metabolism pathways in relation to serum levels of BCAA, Tryptophan, and phenotypic measures including fat content, AST, ALT, SUA and visceral fat. +, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05; *, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001. \u003cstrong\u003ee-f. \u003c/strong\u003eThe impact of TRE intervention on the body weight, liver triglyceride and liver total cholesterol of mice on a normal diet and those on a high-fat diet. \u003cstrong\u003eg\u003c/strong\u003e. The effect of TRE intervention on the abundance of \u003cem\u003eE. asparagiformis\u003c/em\u003e in the gut microbiota of mice on a normal diet and those on a high-fat diet. \u003cstrong\u003eh.\u003c/strong\u003e The effect of TRE intervention on the levels of BCAAs in the serum metabolome of mice on a normal diet and those on a high-fat diet.\u003c/p\u003e","description":"","filename":"Binder26.png","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/7d0c91157ef6bddfb969a525.png"},{"id":76694122,"identity":"52ba283f-1b27-42ab-a466-8208c5bdf6a4","added_by":"auto","created_at":"2025-02-19 18:08:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2103443,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/f94f6abf-af16-45d1-94ea-0bffc483d647.pdf"},{"id":66067295,"identity":"2d52a528-5e10-4f33-b1ae-339aa47efcb8","added_by":"auto","created_at":"2024-10-07 11:31:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7165232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryMaterialsFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/80a2b9318c678c4df211c39e.docx"},{"id":66069073,"identity":"a16dc04d-df15-4e25-9db9-b09c27fb46fa","added_by":"auto","created_at":"2024-10-07 11:39:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":25725,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsMethodsNC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/22723658ccf2f6f0ed88e944.docx"},{"id":66069450,"identity":"d3d28263-f2dc-4736-9ba7-e58493fc48df","added_by":"auto","created_at":"2024-10-07 11:47:32","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":209815,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialsTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/104bec50843ece3c7d948ba2.xlsx"},{"id":66070339,"identity":"68a9b998-a2b5-4c36-be59-5e3eda1fa1b8","added_by":"auto","created_at":"2024-10-07 11:55:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19237,"visible":true,"origin":"","legend":"","description":"","filename":"completedCONSORTchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-4693158/v1/bba55117fc09d322694f7c8a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Integrative multi-omics analysis reveals chrono-modulation by time-restricted eating ameliorates non-alcoholic fatty liver disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of non-alcoholic fatty liver disease (NAFLD), a condition characterized by excessive fat accumulation in the liver\u003csup\u003e1\u003c/sup\u003e, is rising with the global rise in obesity and type 2 diabetes mellitus rates\u003csup\u003e2\u003c/sup\u003e. NAFLD is now recognized as one of the most common forms of chronic liver disease, affecting nearly a quarter of the global population\u003csup\u003e3\u003c/sup\u003e. The disease spectrum ranges from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis and hepatocellular carcinoma\u003csup\u003e4, 5\u003c/sup\u003e, underscoring the urgent need for effective therapeutic strategies.\u003c/p\u003e \u003cp\u003eTime-restricted eating (TRE) has emerged as a promising dietary approach that confines food intake to a consistent window of 8\u0026ndash;12 hours per day, potentially aligning eating patterns with circadian rhythms to optimize metabolic health\u003csup\u003e6\u003c/sup\u003e. The novel aspect of TRE is its focus on when to eat rather than what to eat, which represents a paradigm shift in dietary interventions for NAFLD. Previous studies have shown that TRE can lead to weight loss and improve metabolic markers\u003csup\u003e7, 8\u003c/sup\u003e, but the differential effects of the timing of the eating window have not been thoroughly investigated.\u003c/p\u003e \u003cp\u003eLongitudinal studies reported that an early first meal coupling with longer nighttime fasting period was associated with lower incidence of metabolic disease, indicating that restricting food intake to the earlier part of the day (eTRE) may be more beneficial than shifting the eating window to later in the day (lTRE)\u003csup\u003e9, 10\u003c/sup\u003e. However, our recent study compared the impact of eTRE and lTRE on NAFLD and demonstrated that both eTRE and lTRE induced reductions in liver fat and improvements in liver function with similar magnitude\u003csup\u003e11\u003c/sup\u003e. This similarity may suggest that, despite the potential benefits of shortening eating window for metabolic diseases, eTRE and lTRE may produce similar therapeutic effects through different mechanisms in the specific case of NAFLD. This may involve regulation of the gut microbiome, as the gut microbiome has been shown to play an important role in the pathogenesis of NAFLD, and their activity is influenced by the host's feeding rhythm\u003csup\u003e12, 13\u003c/sup\u003e. Thus, this study seeks to explore how the timing of food intake affects the gut microbiome, serum metabolome, and phenotypes within the context of TRE, providing insights into the broader implications and underlying mechanisms of dietary timing on human health. By conducting an 8-week randomized parallel-arm trial, we aim to provide a comprehensive analysis of the effects of eTRE versus lTRE on gut microbiome, serum metabolome, and phenotypes and their cross-talk with host's metabolism in human with NAFLD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eClinical Trial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the effects of different fasting times on NAFLD, we conducted a randomized, highly controlled clinical trial for 8 weeks. Forty NAFLD patients (26 males and 14 females) who typically consumed food over a window of 10 hours or more per day were successfully enrolled and randomly assigned to either eTRE or lTRE group. The eTRE group was instructed to consume their daily meals within an 8-hour window, from 8:00 am to 4:00 pm, while the lTRE group was allocated an 8-hour window from 12:00 am to 8:00 pm (Figure 1). Patients were allowed to eat freely without caloric or dietary restrictions during their designated 8-hour window and were only permitted to drink water during the remaining 16 hours of fasting (Figure 1). The study design, method, and primary results have been described elsewhere\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments procedures performed in mice were approved by the Institutional Animal Care and Use Committee at Wuhan University. Two-month-old male C57BL/6N mice were purchased from Hubei Provincial Center for Disease Control and Prevention (SYXK-2020-0018) and fed a standard chow diet until 14 months of age. The mice were housed under controlled conditions with a constant temperature of 24 \u0026plusmn; 3\u0026deg;C and a 12-hour light/12-hour dark cycle. Mice were fed a normal chow diet or 60% high-fat diet (HFD) that we previously confirmed to potently induce NAFLD in mice. For the time-restricted feeding intervention, mice had access to food only during the dark phase for 8 hours, starting from Zeitgeber15 (ZT15) to ZT23. The study included four groups: 1. Ad libitum\u0026nbsp;group feeding with normal chow diet (AL+ ND); 2. Ad libitum\u0026nbsp;group feeding with HFD (AL+ HFD) ; 3. Time-restricted feeding with normal chow diet (8h TRE + ND) or ; 4. Time-restricted feeding with HFD (8h TRE + HFD). The intervention period lasted for seven months, during which food intake and body weight were monitored weekly. Serum and liver tissue samples were collected after sacrifice for subsequent testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessing and Detection of Blood and Fecal Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor information on the detection of blood metabolites and the extraction and metagenomic sequencing of fecal samples, please refer to the online supplementary methods material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePower and Sample Size:\u0026nbsp;\u003c/em\u003eThe absolute 4% reduction\u0026nbsp;in liver fat\u0026nbsp;has been reported as the clinically significant change associated with liver histological responses in previous study\u003csup\u003e14\u003c/sup\u003e. After\u0026nbsp;assuming a 20% dropout rate, the sample size of\u0026nbsp;20 participants per\u0026nbsp;group\u0026nbsp;provided 85% statistical power for the trial to detect a 4% reduction of liver fat with a standard deviation of 4% and two-tailed \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMultivariate analysis:\u003c/em\u003eMultivariate statistical analysis (Principal Component Analysis (PCA) and\u0026nbsp;Principal Co-ordinates Analysis\u0026nbsp;(PCoA)) were used to differentiate and analyze the following variations:\u0026nbsp;differences across various omics between the lTRE and eTRE regimens within the human population, differences within the omics data before and after the intervention of the lTRE and eTRE protocols, and the differences in mice subjected to interventions of Normal Diet (ND), High-Fat Diet (HFD), ND with Time-Restricted Feeding (ND+TRF), and HFD with Time-Restricted Feeding (HFD+TRF). PCA was performed by using the ade4\u003csup\u003e15\u003c/sup\u003e package in the R platform. Based on Bray‒Curtis dissimilarity, PCoA was performed by using pcoa (a function in the ape\u003csup\u003e16\u003c/sup\u003e package in R).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePermutational multivariate analysis of variance\u0026nbsp;\u003c/em\u003e\u003cem\u003e(PERMANOVA) analysis\u003c/em\u003e: Permutational multivariate analysis of variance (PERMANOVA, adonis analysis) was realized with the R vegan\u003csup\u003e17\u003c/sup\u003e package, and the adonis P-value was generated based on 1000 permutations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCo-inertia analysis (CIA)\u003c/em\u003e:\u0026nbsp;To assess the associations between different omics data, Coinertia Analysis (CIA) was performed across the various omics data. The CIA was conducted utilizing the coinertia function within the ade4\u003csup\u003e15\u003c/sup\u003e package in R, with parameters set to scannf=FALSE and nf=2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHypothesis testing and multiple test correction\u003c/em\u003e:\u0026nbsp;To detect differences in diet and host phenotypes before and after the lTRE and eTRE interventions, t-tests were conducted. The Wilcoxon rank-sum test\u003csup\u003e18\u003c/sup\u003e was employed to assess differences in gut microbiota and serum metabolomic features before and after the lTRE and eTRE interventions. The Kruskal-Wallis test\u003csup\u003e19\u003c/sup\u003e was utilized to evaluate the differences in microbiota at multiple time points within the gut microbiome. False Discovery Rate (FDR) adjustments were made using the Benjamini-Hochberg method\u003csup\u003e20\u003c/sup\u003e, as implemented by the p.adjust function in R.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBidirectional mediation analysis\u003c/em\u003e: For microbial features associated with metabolites and aging, we first checked whether the microbial features were associated with the metabolite using Spearman correlation (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Next, we carried out bidirectional mediation analysis with interactions (y = x + m + x \u0026times; m, where y is the outcome, x is the variable and m represents the mediator) between mediator and outcome using the mediate function from mediation (version 4.5.0) to infer the mediation effect of serum metabolites and the gut microbiota on aging\u003csup\u003e21\u003c/sup\u003e. The statistical scripts were available at https://github.com/lizhiming11/TRE-NAFLD.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eTRE changes dietary pattern and improves non-alcoholic fatty liver disease\u003c/h2\u003e\n \u003cp\u003eWe collected the multidimensional datasets encompassing dietary intake, gut microbiota composition, serum metabolome, and a range of host characteristics (referred to as the \u0026lsquo;phenotype\u0026rsquo;) were collected in this trial (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The age, BMI, gender, and other indicators of participants in the eTRE and lTRE groups showed no significant differences between the eTRE and lTRE groups (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, initial analysis of baseline the nutrient intakes, gut microbiota, serum metabolome, and phenotype data did not unveil significant differences between the eTRE and lTRE groups (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, PERMANOVA P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eOur investigation into the impact of TRE on NAFLD assessed two distinct TRE protocols. Both protocols resulted in significant dietary modifications from baseline to the two-month mark (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). Notably, dietary changes were more pronounced in the eTRE group than in the lTRE group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eab). Across both TRE interventions, we observed a marked reduction in the macronutrient intake \u0026mdash; energy, carbohydrates, proteins, and total fat (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). Notably, the eTRE group showed broader impacts on dietary patterns, affecting a wider range of micronutrients and amino acids compared to lTRE.\u003c/p\u003e\n \u003cp\u003eCrucially, both eTRE and lTRE protocols had effectively reduced liver fat levels, visceral fat, body fat percentage, and body mass index (BMI). The lTRE group experienced a more substantial reduction in liver fat (\u0026Delta; = -3.51%) and visceral fat (\u0026Delta; = -6.21%) compared to the eTRE group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Conversely, the eTRE group exhibited a greater reduction in body fat percentage (\u0026Delta; = -1.54 vs. \u0026Delta; = -1.39%) and a marginally larger decrease in BMI (\u0026Delta; = -0.87 vs. \u0026Delta; = -0.85 kg/m\u003csup\u003e2\u003c/sup\u003e) compared to the lTRE group (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Meanwhile, markers for assessing liver health\u003csup\u003e22\u003c/sup\u003e, including gamma-glutamyl transferase (GGT), Aspartate aminotransferase (AST), Alanine aminotransferase (ALT), and cardiometabolic risk factors that associated with NAFLD \u003csup\u003e23, 24, 25\u003c/sup\u003e, such as fasting glucose (GLU), insulin, serum uric acid (SUA), were also significantly reduced in both TRE groups by the second month. In addition, a robust correlation was observed between phenotypic data from both TRE interventions. Specifically, a strong association was noted among liver fat content, BMI, AST, ALT, magnetic resonance imaging transverse relaxation rate (R\u003csup\u003e2\u003c/sup\u003e), prealbumin (PA), SUA, GGT, visceral fat, and triglycerides (TG) (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eWe also explored the relationship between dietary intake and phenotypic changes. In eTRE group, dietary patterns significantly influenced phenotypic alterations, while such a relationship was not apparent in lTRE group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed). Within eTRE diet, amino acids such as leucine, lysine, and valine were found to have a significant negative correlation with serum urea, alkaline phosphatase, hemoglobin A1c, and albumin-globulin ratio, yet a positive correlation with globulin, TP (total protein), PA, and SUA (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eb). However, these associations were not observed to the same extent in the lTRE group (Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eb).\u003c/p\u003e\n \u003cp\u003eIn summary, our findings suggest that the association between dietary patterns and phenotypic changes is more pronounced in eTRE group compared to lTRE group, highlighting the potential efficacy of eTRE in managing NAFLD.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBoth eTRE and lTRE alters serum metabolome in patients with NAFLD\u003c/h2\u003e\n \u003cp\u003eWe observed that the time of intervention is the main factor explaining the differences in serum metabolomics among the subjects (eTRE: 20.03%; lTRE: 16.04%) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea, PERMANOVA, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The fat content, PA, ALT, and AST/ALT in both groups significantly affected the serum metabolome. Particularly, it should be noted that visceral fat in eTRE group is significantly associated with the serum metabolome, while such a connection was not found in lTRE group. These significantly impacted indicators collectively account for the changes in serum metabolomics of eTRE: 52.3% and lTRE: 49.26% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of diet, the change in energy intake in the diet of the lTRE group has the most significant impact on the serum metabolome (15.77%) (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003ea). In addition, carbohydrates, protein, fat, sodium, meat, and VE also have significant effects on the serum metabolome, together explaining 25.01% of the variation in the serum metabolome (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003eb). Compared with lTRE group, the role of diet in the eTRE group is relatively more significant, with vitamins, amino acids, minerals, etc. in the diet all having a significant impact on the serum metabolome, together explaining 88.65% of the variation in the serum metabolome (Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003eFurther analysis revealed that the two interventions showed significant separation at both the baseline period and after 2 months (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb); in the early group, 53 out of 156 metabolites showed significantly different abundance, and in the late group, 46 had significantly different abundance (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). The early group and the late group respectively included changes in 16 and 15 types of amino acids. Among them, Norleucine, Tryptophan, Methionine, Valine, and Isoleucine showed a significant decrease in both intervention schemes after two months. Meanwhile, Selenomethionine, Acetylserine, Cetylhistidine, and Acetamidobutanoic acid all showed a significant increase in both intervention schemes after two months (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Excess amino acids can lead to the accumulation of fat in the liver, which may reflect poor dietary habits as well as dysregulation of amino acid metabolism in the liver and peripheral tissues\u003csup\u003e26, 27\u003c/sup\u003e. In regards to bile salts, the early group and the late group respectively exhibited changes in 9 and 5 types of bile salts (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Consistent with previous study findings\u003csup\u003e28, 29\u003c/sup\u003e, we discovered that both intervention protocols led to bile acid dysregulation, as evidenced by significant increases in diketocholic acid, chenodeoxycholic acid, cholic acid, and hyodeoxycholic acid following the interventions.\u003c/p\u003e\n \u003cp\u003eWe examined the correlation between serum metabolites from two intervention schemes and clinical variables associated with NAFLD respectively (Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). Importantly, in both intervention schemes, liver fat content, BMI, and visceral fat showed significantly positively correlations with Tryptophan, Isoleucine, and Valine. In eTRE group, fat content and BMI exhibited negatively correlations with Isovalerylglycine, N\u0026thinsp;\u0026minus;\u0026thinsp;a\u0026minus;Acetyl\u0026thinsp;\u0026minus;\u0026thinsp;L\u0026minus;arginine, p\u0026thinsp;\u0026minus;\u0026thinsp;Hydroxyphenylacetic acid, Lauroylcarnitine, Decanoylcarnitine, and some bile salts (Alloisolithocholic Acid, Cholic Acid Methyl Ester) (Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e). These findings emphasize the physiological relevance of these circulating metabolites to the clinical status of patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eThe effects on gut microbiota vary with different intervention schemes\u003c/h2\u003e\n \u003cp\u003eTo investigate whether the gut microbiota mediates the impact of TRE on the serum metabolome and clinical indicators of patients with NAFLD, we analyzed the gut microbiota using shotgun metagenomic sequencing. Utilizing the BGI-seq platform, an average of 20.68 Gb data was generated per fecal sample. After aligning high-quality sequencing reads with the Unified Human Gastrointestinal Genome reference\u003csup\u003e30\u003c/sup\u003e, the relative abundance of microbes in the samples was calculated for subsequent analysis (Table S5).\u003c/p\u003e\n \u003cp\u003eThrough metagenomic analysis, we found that the two TRE intervention did not significantly alter the microbial diversity of the microbiota. There was a difference in microbial diversity between the two intervention groups after 8 weeks (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077) (Figure S5a). Moreover, principal component analysis indicated that the gut microbiota did not separate before and after the intervention for both intervention methods (Figure S5b). By analyzing the differences in gut microbiota before and after the two interventions, we found that the changes in the early group were greater than those in the late group. This was primarily manifested by the identification of 362 differential species in the early group before and after the intervention, with 358 species significantly enriched before the intervention, spanning 10 phyla. In the late group, 43 differential species were identified before and after the intervention, spanning 3 phyla (Fig.\u0026nbsp;4ab and Table S6).\u003c/p\u003e\n \u003cp\u003eWe conducted cluster analysis on the microbiota changes throughout interventions in two groups. In the late group, the microbiota changes were classified into three clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table S6): Cluster 1 featured an initial spike then returned to baseline levels without further changes, involving species such as \u003cem\u003eCorynebacterium bouchesdurhonense\u003c/em\u003e, \u003cem\u003eAlistipes ihumii\u003c/em\u003e, and \u003cem\u003eRoseburia hominis\u003c/em\u003e. Cluster 2 showed a gradual increase over time with species like \u003cem\u003eEnterocloster asparagiformis\u003c/em\u003e and \u003cem\u003eBifidobacterium pseudolongum\u003c/em\u003e and. Cluster 3 experienced an initial drop, then stabilized, including species such as \u003cem\u003eBacteroides acidifaciens\u003c/em\u003e and \u003cem\u003eCoprobacter fastidiosus\u003c/em\u003e. In the early group, similar patterns were observed. Cluster 1 had a gradual decrease, including \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, \u003cem\u003eCorynebacterium propinquum\u003c/em\u003e and \u003cem\u003eClostridium botulinum\u003c/em\u003e. Cluster 2 saw an initial rise followed by a drop and stabilization, involving species like \u003cem\u003eAlistipes finegoldii\u003c/em\u003e and \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e. Cluster 3 dropped initially and then remained stable, including species such as \u003cem\u003eEnterococcus casseliflavus\u003c/em\u003e, \u003cem\u003eClostridioides difficile\u003c/em\u003e and \u003cem\u003eBifidobacterium animalis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Table S6). Importantly, the species that showed significant changes before and after the intervention were significantly correlated with clinical variables associated with NAFLD. In the late intervention group, \u003cem\u003eE. asparagiformis\u003c/em\u003e and \u003cem\u003eA. colihominis\u003c/em\u003e were correlated with multiple NAFLD-associated clinical variables, such as fat content, ALT, AST, and visceral fat (Figure S6).\u003c/p\u003e\n \u003cp\u003eKEGG functional analysis also showed no significant changes before and after intervention (Figure S7a). The functions that decreased significantly after early TRE intervention include Aromatics degradation, Biosynthesis of secondary metabolites, Drug resistance, and Two-component regulatory system. The functions that increased significantly after late TRE intervention include Two-component regulatory system, Saccharide, polyol, and lipid transport system, and Drug resistance (Figure S7b). Overall, these findings suggest that the two intervention schemes have different effects on the functionality of gut microbiota.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferent intervention methods affect different multi-omics relationships\u003c/h2\u003e\n \u003cp\u003eTo examine the interplay between dietary structure, gut microbiota, and metabolome, we conducted a Co-inertia analysis across these variables. In eTRE group, post-intervention analysis two months later revealed strengthened connections between the gut microbiome and both phenotype and serum metabolome, while the link between diet and gut microbiome weakened (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). Conversely, in lTRE group, the connections between the gut microbiome and phenotype, as well as between the gut microbiome and serum metabolome, weakened. Meanwhile, the relationships between diet and both phenotype and serum metabolome strengthened (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). These findings indicate that the two intervention strategies differentially influence the interrelationships among diet, gut microbiota, and metabolic profiles.\u003c/p\u003e\n \u003cp\u003eWe carried out inter-group correlation analyses among diet, gut microbiome, serum metabolites, and phenotypic data. The lTRE group exhibited 28,813 significant multi-omics associations, while the eTRE group recorded 46,686 significant associations (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This disparity largely stems from a stronger diet-gut microbiota correlation in the eTRE group compared to the lTRE group. Additionally, in the eTRE, the variables closely linked to the intervention showed far more positive correlations, with eight times as many as in lTRE group (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). In the correlation networks for both interventions, 72.7% of the negative correlations primarily arose from the serum metabolome-gut microbiota relationship (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). More importantly, the correlation networks for the two interventions were distinctly different, sharing only about 7.8% of correlations (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed and Figure S8). Consistent with previous studies\u003csup\u003e31, 32, 33\u003c/sup\u003e, among these shared correlations, fat content was closely related to L-Valine and L-Isoleucine. Excessive branched-chain amino acids may interfere glucose and fat metabolism, potentially leading to liver fat accumulation\u003csup\u003e27\u003c/sup\u003e. In individuals with NAFLD, where insulin\u0026apos;s lipogenic activity remains high and compensatory insulin release increases due to insulin resistance, potentially driving lipid accumulation in the liver\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDiet, Microbiome, and Metabolites in Mediating Non-Alcoholic Fatty Liver Disease Phenotypes\u003c/h2\u003e\n \u003cp\u003eSubsequently, we performed mediation analyses to investigate the mediating effects among diet, gut microbiome, serum metabolome, and phenotype. For the variables significantly associated with the intervention, in both eTRE and lTRE groups, we established mediating links for the following sets of factors: diet, gut microbiome, and serum metabolome; diet and gut microbiome phenotype; diet, serum metabolites, and phenotype; and gut microbiome, serum metabolome, and phenotype. In the eTRE group and the lTRE group, we established 276, 335, 113, and 711 mediating links, and 47, 8, 19, and 123 mediating links, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, Figure S9, Figure S10, Table S7 and Table S8). Most of these associations are related to the impact on markers of NAFLD, such as liver fat content, AST, and ALT.\u003c/p\u003e\n \u003cp\u003eIn the lTRE group, \u003cem\u003eE. asparagiformis\u003c/em\u003e showed a significant impact on branched-chain amino acids (BCAAs) and NAFLD-related indicators. Specifically, BCAAs such as L-Isoleucine and L-Valine were found to mediate the effect of \u003cem\u003eE. asparagiformis\u003c/em\u003e on fat content, AST, and SUA (Fig. 6ab). Additionally, metabolites like Kynurenic acid and L-Tryptophan were implicated in mediating the effects of \u003cem\u003eE. asparagiformis\u003c/em\u003e on AST (Fig. 6ab). These findings suggest that \u003cem\u003eE. asparagiformis\u003c/em\u003e may contribute to the regulation of amino acid metabolism and liver health through these pathways.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003eInterestingly, in the eTRE group, we observed a similar trend. Although the correlation between \u003cem\u003eE. asparagiformis\u003c/em\u003e and fat content was not statistically significant (correlation coefficient = -0.12), the negative relationship was consistent with the significant findings in the lTRE group. Furthermore, BCAAs, including L-Valine, L-Isoleucine, and L-Norleucine, significantly decreased following the TRE intervention in both groups (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). This consistency across both dietary interventions suggests that \u003cem\u003eE. asparagiformis\u003c/em\u003e might play a role in modulating fat content and BCAAs levels, albeit to varying extents depending on the timing of the eating window.\u003c/p\u003e\n \u003cp\u003eTo confirm \u003cem\u003eE. asparagiformis\u003c/em\u003e\u0026apos;s ability to synthesize L-Tryptophan and BCAAs, we examined its complete set of functional genes. \u003cem\u003eE. asparagiformis\u003c/em\u003e possesses all necessary genes for metabolizing valine, leucine, isoleucine, and tryptophan (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec). Subsequent analysis revealed a significant correlation between these metabolic pathways\u0026apos; genes and the corresponding plasma metabolites\u0026mdash;L-Isoleucine, L-Valine, L-Norleucine, and L-Tryptophan (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed). These metabolites also showed significant associations with NAFLD markers including liver fat content, visceral fat, AST, ALT, and SUA (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ed). The consistent decrease in BCAAs following TRE and the observed correlations with metabolic markers highlight the potential of \u003cem\u003eE. asparagiformis\u003c/em\u003e as a key microbial mediator. The differences in statistical significance and correlation strength between the eTRE and lTRE groups might be attributed to distinct microbial community dynamics and metabolic interactions influenced by the timing of food intake.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe decline in NAFLD related indicators and serum BCAAs are associated with an increase in\u003c/strong\u003e \u003cstrong\u003eEnterocloster asparagiformis\u003c/strong\u003e \u003cstrong\u003ein mice\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo explore the microbiota\u0026apos;s role in NAFLD progression and its influence on the serum metabolome, we conducted a six-month study with normal and high-fat diet mice, comparing the effects of an 8-hour time-restricted eating (TRE) schedule (22:00\u0026ndash;6:00) and ad libitum feeding. We collected feces and blood from the mice to measure the gut microbiome, serum metabolome, and NAFLD-related indicators. After six months, the mice subjected to TRE intervention showed a significant reduction in body weight (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ee). More importantly, TRE intervention in the high-fat diet group significantly reduced TG and TC in the blood and liver (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ef and Figure S11a).\u003c/p\u003e\n \u003cp\u003eThe further analysis of the gut microbiota and serum metabolome indicated that there was a distinct separation between the gut microbiota and serum metabolomic profiles of the high-fat diet group and the normal diet group (Figure S11b). Within the high-fat diet group itself, there was also a clear separation between the microbiota and serum metabolomes of those undergoing TRE intervention compared to those with an ad-libitum (free-feeding) diet (Figure S11b).\u003c/p\u003e\n \u003cp\u003eConsistent with previous reports, a high-fat diet, compared to a normal diet, leads to a significant decrease in serum branched-chain amino acids\u003csup\u003e35, 36\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eh). Most notably, the TRE intervention led to a significant increase in the abundance of \u003cem\u003eE. asparagiformis\u003c/em\u003e in the gut (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eg). Concurrently, there was a significant reduction in the levels of BCAAs in the plasma of mice fed a high-fat diet (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eh). These results further establish a close connection between \u003cem\u003eE. asparagiformis\u003c/em\u003e, BCAAs, and NAFLD, suggesting that TRE may exert beneficial effects in the context of NAFLD by modulating the gut microbiota and serum metabolome.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study offers a comprehensive look into the effects of TRE on individuals with NAFLD, providing valuable insights into the synchronization of meal timing with circadian biology. Our findings showed that although eTRE and lTRE both significantly reduce liver fat and improve liver function, eTRE reveals a tight-knit network of biomolecular changes in gut microbiota and metabolites than lTRE. In addition, both TRE interventions can improve NAFLD through the common mechanism involving \u003cem\u003eE. asparagiformis\u003c/em\u003e and BCAAs pathways, which supports the efficacy of TRE as a therapeutic intervention for NAFLD.\u003c/p\u003e \u003cp\u003eWe observed that both eTRE and lTRE were shown to significantly improve NAFLD, which contributes to a growing body of evidence that supports the use of TRE in NAFLD management \u003csup\u003e37, 38, 39\u003c/sup\u003e. Compared to lTRE, the eTRE in alignment with diurnal circadian rhythms revealed a more closely molecular network characterized by more positive correlations between diet and gut microbiome, highlighting the important role of meal timing, dietary patterns, and gut microbiome in NAFLD\u003csup\u003e40, 41\u003c/sup\u003e. Although TRE not require individuals to change dietary intake, participants may spontaneously alter their diet when shorten eating windows\u003csup\u003e42\u003c/sup\u003e, which may simultaneously affect metabolic health and microbiota.\u003c/p\u003e \u003cp\u003eAdditionally, we further analyzed the shared correlation networks to explore the common mechanism by which eTRE and lTRE action. Among common correlations, fat content was closely related to BCAAs, which are not only essential amino acids but also pivotal regulatory molecules in metabolic health\u003csup\u003e43\u003c/sup\u003e. Elevated levels of BCAAs have been implicated in the pathogenesis of insulin resistance\u0026mdash;a key feature of NAFLD\u0026mdash;and can exacerbate hepatic lipid accumulation\u003csup\u003e26, 44, 45\u003c/sup\u003e. Notably, an shift in the metabolomic landscape, specifically a reduction in serum BCAAs, correlates with the presence of \u003cem\u003eE. asparagiformis\u003c/em\u003e. The bacterium's presence correlates with a reduction in plasma BCAAs, suggesting that it may actively catabolize these molecules. This catabolic activity could potentially attenuate the insulin resistance and lipid accumulation typically observed in NAFLD. In existing studies, \u003cem\u003eEnterocloster asparagiformis\u003c/em\u003e has been shown to be significantly reduced in patients with metabolic syndrome\u003csup\u003e46\u003c/sup\u003e and negatively correlated with uric acid, AST, and ALT\u003csup\u003e47\u003c/sup\u003e. These findings confirm the positive role of this bacterial strain in metabolic health. Moreover, the mechanistic link becomes even more compelling when considering the functional gene analysis. The presence of genes related to the metabolism of valine, leucine, and isoleucine in \u003cem\u003eE. asparagiformis\u003c/em\u003e supports a direct role in BCAAs catabolism. This catabolic process may not only reduce the hepatic load of BCAAs but also generate metabolites that can influence host signaling pathways, including mTOR and AMPK, which are integral to metabolic regulation and energy homeostasis\u003csup\u003e48, 49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe translational potential of our findings is underscored by our murine model, where TRE on a high-fat diet not only impacted weight and lipid profiles but also modulated the gut microbiota in ways that mirror the human data. The observed decrease in plasma BCAAs and increase in \u003cem\u003eE. asparagiformis\u003c/em\u003e with TRE suggests that this dietary intervention can fundamentally reshape metabolic pathways, which may be pivotal in preventing or treating NAFLD.\u003c/p\u003e \u003cp\u003eHowever, our study is not without limitations. While we have demonstrated associations, causality cannot be confirmed, and further interventional studies are needed. The study's transferability to the general population may be restricted by the specific dietary patterns and timings examined. Additionally, the gut microbiota is highly individualistic, and its response to dietary interventions like TRE can vary between individuals\u003csup\u003e50, 51\u003c/sup\u003e. Therefore, while our study provides a robust foundation for the beneficial effects of early TRE, further exploration into the individual responses to dietary timing is warranted.\u003c/p\u003e \u003cp\u003eIn conclusion, our study showcases the therapeutic promise of TRE for NAFLD management and highlights the importance of considering individual circadian preferences when tailoring dietary interventions. The differential impacts of eTRE and lTRE on various health markers underscore the potential for personalized nutrition strategies based on circadian alignment. These findings pave the way for future research into personalized dietary recommendations that could optimize metabolic health outcomes for individuals with NAFLD and potentially other metabolic disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e The authors thank all participants for their valuable contributions and the Geriatric Hospital Affiliated with Wuhan University of Science and Technology for assisting recruitment and biochemical examination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e S.R. conceived and designed the study. Y.D., Y.S.,\u0026nbsp;F.L., and\u0026nbsp;H.Y. recruited participants and collected and analyzed clinical data. H.L. and Z.W. conducted animal experiments. H.Z., Z.S., and W.X. collected serum and fecal samples and extracted DNA from feces. W.Z. and X.Z. generated the targeted metabolomics data. Z.L. and Y.S. performed bioinformatics analyses. Z.L., Y.D., and Y.S. wrote the manuscript. H.Z., Z.S., H. Li, W.X., W.Z., X.Z., C.N., and S.R. reviewed and edited the manuscript. All authors made substantial contributions and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was supported by the National Natural Science Foundation of China (NSFC-82373575, NSFC-82073552).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e The authors declared no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eThis study\u0026nbsp;was approved by the Ethics Committees of Wuhan University of Science and Technology (WUST-202193) and registered at the World Health Organization's International Clinical Trial Platform as ChiCTR2100052876. The study was conducted according to the Declaration of Helsinki guidelines. Participants\u0026nbsp;provided\u0026nbsp;informed consent\u0026nbsp;prior to participation\u0026nbsp;in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003eData have been deposited in the CNSA (https://db.cngb.org/cnsa/) of CNGBdb with accession number CNP0005414. Other data that support the findings of this study are available from the corresponding authors upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChalasani N\u003cem\u003e, et al.\u003c/em\u003e The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Gastroenterological Association, American Association for the Study of Liver Diseases, and American College of Gastroenterology. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cstrong\u003e142\u003c/strong\u003e, 1592-1609 (2012).\u003c/li\u003e\n\u003cli\u003eGolabi P, Paik JM, AlQahtani S, Younossi Y, Tuncer G, Younossi ZM. 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Diet\u0026ndash;microbiota interactions and personalized nutrition. \u003cem\u003eNature Reviews Microbiology\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 742-753 (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"time-restricted eating, non-alcoholic fatty liver disease, gut microbiota, branched-chain amino acids","lastPublishedDoi":"10.21203/rs.3.rs-4693158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4693158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003eTo assess the impact of early and late time-restricted eating (eTRE and lTRE) on non-alcoholic fatty liver disease (NAFLD) and its underlying mechanisms involving gut microbiota and serum metabolome changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003eThis 8-week randomized clinical trial involved 40 NAFLD patients divided into eTRE (8:00 am to 4:00 pm) and lTRE (12:00 pm to 8:00 pm) groups, with unrestricted caloric intake within these windows. Comprehensive analyses including nutrient intake, gut microbiota composition, serum metabolome profiling, and phenotypic assessments were conducted pre- and post-intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003eBoth eTRE and lTRE significantly improved liver fat levels, visceral fat, body fat percentage, and body mass index (BMI). eTRE led to more pronounced changes in dietary patterns and exhibited a stronger influence on gut microbiota composition and the serum metabolome than lTRE. Notably, TRE increased the abundance of \u003cem\u003eEnterocloster asparagiformis\u003c/em\u003e, a beneficial microbe linked to positive shifts in NAFLD phenotypes. This bacterium's presence was significantly associated with reduced levels of branched-chain amino acids (BCAAs) in both human subjects and mouse models, correlating with reductions in liver fat content, suggesting a metabolic modulation pathway. Metabolomic analysis further revealed significant alterations in amino acids and bile acids, which correlate with key NAFLD markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eTime-restricted eating (eTRE and lTRE) provides a viable non-pharmacological strategy for NAFLD management by altering gut microbiota and serum metabolomics. The differential effects of eTRE versus lTRE highlight the importance of meal timing in dietary interventions, supporting personalized dietary strategies based on individual responses to optimize NAFLD outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration number\u003c/strong\u003e ChiCTR2100052876.\u003c/p\u003e","manuscriptTitle":"Integrative multi-omics analysis reveals chrono-modulation by time-restricted eating ameliorates non-alcoholic fatty liver disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-07 11:31:27","doi":"10.21203/rs.3.rs-4693158/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"66e89edd-6bf4-4d7a-958d-b47c9b91e466","owner":[],"postedDate":"October 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":38417753,"name":"Health sciences/Gastroenterology/Hepatology/Liver diseases/Non-alcoholic fatty liver disease"},{"id":38417754,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2025-02-19T18:00:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-07 11:31:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4693158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4693158","identity":"rs-4693158","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}