Study on the correlation between circadian rhythm, intestinal flora and Liver stiffness in patients with Hepatic fibrosis

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However, the interplay between these factors in HF remains poorly understood. This study aimed to investigate the relationship between gut microbiota composition, circadian rhythm disturbances, and HF, providing new insights into potential therapeutic strategies. Methods: A cross-sectional study was conducted, enrolling patients with HF and healthy controls. Liver stiffness measurement (LSM) was assessed using transient elastography. Circadian rhythm status was evaluated with the Morningness-Eveningness Questionnaire-5 (MEQ-5). Gut microbiota composition was analyzed via 16S rRNA sequencing, and differences in microbial diversity and taxa abundance were compared between groups. Correlation analyses were performed to explore the associations between gut microbiota, LSM, and circadian rhythm. Results: Patients with HF exhibited significant alterations in gut microbiota composition at both the phylum and genus levels ( p <0.05, r = − 0.244). The relative abundances of Escherichia-Shigella, Klebsiella, Pseudomonadota, Ruminococcus gnavus group , and Enterocloster were significantly increased, while Dorea, Holdemanella, [Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, CAG-352 , and Marvinbryantia were markedly decreased. These microbial shifts were associated with enhanced intestinal inflammation and hepatic immune activation. Notably, Escherichia-Shigella may contribute to HF progression via LPS-TLR4/inflammasome activation, inflammatory cytokine release, and reduced short-chain fatty acid (SCFA) production. Conversely, SCFA-producing bacteria in the Firmicutes phylum showed a potential protective role by mitigating hepatic inflammation and lipid accumulation. Furthermore, circadian rhythm disruption was negatively correlated with LSM, and an increased abundance of Mediterraneibacter was observed in patients with circadian rhythm disturbances. As Mediterraneibacter is known to produce ethanol, its elevated levels may exacerbate hepatic injury and inflammation, potentially contributing to HF development. Conclusion: This study reveals a significant association between gut microbiota dysbiosis, circadian rhythm disruption, and HF severity. Our findings suggest that circadian rhythm disturbances may influence HF progression by modulating gut microbiota composition and metabolic activity. These insights highlight potential therapeutic strategies, including circadian rhythm modulation (e.g., light therapy, timed medication) and gut microbiota-targeted interventions, to slow or reverse HF progression. Physical Medicine & Rehab Hepatic fibrosis Gut Microbiota Circadian rhythm Intestinal flora Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Hepatic fibrosis (HF) is characterized by the excessive and aberrant deposition of extracellular matrix (ECM) components, including collagen, glycoproteins, and proteoglycans, as a pathological repair response to chronic liver injury [ 1 ] .The activation of hepatic stellate cells (HSCs) is recognized as a key driver in the pathogenesis of HF. Notably, HF represents a critical transition stage in the progression of chronic liver diseases toward cirrhosis, with its severity closely linked to disease prognosis. According to the 2023 Global Burden of Liver Disease Report 22 [ 2 ] ,chronic liver diseases account for approximately 2 million deaths annually, representing 4% of all global deaths, with cirrhosis ranking among the top ten causes of mortality worldwide. Beyond its significant impact on global health, liver fibrosis imposes a substantial economic burden on healthcare systems. For instance, the total expenditure on liver disease management in the United States reached $ 32.5 billion in 2016 [ 3 ] . Given the urgent need to elucidate the underlying mechanisms of HF and develop effective therapeutic strategies, further research in this area is of paramount importance. Pathogenesis of HF: Multifactorial Regulation and Systemic Impact HF is closely associated with chronic liver injury, in which persistent inflammatory stimuli trigger the release of profibrotic factors from hepatocytes, HSCs, and immune cells, ultimately driving the transdifferentiation of HSCs into myofibroblast-like cells and the excessive production of ECM [ 4 ] .This process is regulated by multiple signaling pathways, including the mitogen-activated protein kinase (MAPK), transforming growth factor-beta (TGF-β), Wnt, oxidative stress pathways, as well as mechanisms involving apoptosis and autophagy [ 5 , 6 ] . In recent years, increasing attention has been directed toward the role of the gut-liver axis and gut-brain axis in HF [ 7 ] . Moreover, circadian rhythm dysregulation has been identified as a key contributor to HF progression [ 8 ] . For example, the loss of the core clock gene Per2 has been shown to enhance HSC activation, thereby accelerating HF development [ 9 ] . Additionally, circadian rhythm disturbances have been linked to alterations in gut microbiota composition, a phenomenon observed in both animal models and human studies [ 10 ] . Gut Microbiota and HF The gut microbiota comprises thousands of microbial species that play a crucial role in maintaining host metabolic and immune homeostasis. Under normal physiological conditions, the gut microbiota is dominated by Firmicutes and Bacteroidetes, while aerobic bacteria constitute a minor proportion [ 11 ] . However, environmental or physiological perturbations can disrupt microbial diversity and composition, leading to gut dysbiosis. Such dysbiosis has been implicated in metabolic dysfunction, immune dysregulation, and inflammatory responses [ 12 , 13 ] . Notably, an altered Firmicutes/Bacteroidetes ratio has been associated with increased intestinal permeability and upregulated expression of lipopolysaccharide (LPS) synthesis and transport genes [ 14 ] . LPS translocation via the portal circulation activates Kupffer cells, promoting the release of proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), thereby facilitating HSC activation and fibrosis progression [ 15 ] . Furthermore, studies have demonstrated that modulating gut microbiota composition can activate interferon signaling pathways and inhibit hepatic bile acid synthesis, exerting antifibrotic effects [ 16 ] . Interestingly, gut microbiota composition is also closely linked to circadian clock gene expression. Specific gut microbial taxa have been found to positively correlate with the hepatic expression of Clock, Cry1 , and Per2 , while negatively correlating with Per1 and Cry2 expression [ 17 ] . These findings suggest that circadian rhythms may influence HF progression through the modulation of gut microbiota metabolic functions. Circadian Rhythms and HF Circadian rhythms are governed by an endogenous biological clock system, consisting of a central clock located in the suprachiasmatic nucleus (SCN) and peripheral clocks distributed across various tissues, including the liver and gut [ 18 ] . However, modern lifestyle factors, such as shift work, excessive electronic device usage, and artificial light exposure, have significantly increased the prevalence of circadian disruption, which is associated with an elevated risk of metabolic syndrome, obesity, diabetes, autoimmune disorders, and cancer [ 19 – 21 ] . Notably, the International Agency for Research on Cancer (IARC) has classified circadian rhythm disruption as a probable human carcinogen [ 22 ] . Emerging evidence highlights the critical role of core clock genes in HF pathogenesis. For instance, the deletion of Bmal1 promotes HSC transdifferentiation into a profibrotic phenotype via activation of the TGF-β/Smad pathway [ 23 ] . Additionally, suppression of Clock gene expression, while upregulating Per1, Per2 , and Per3 , has been shown to downregulate ECM-related genes (Col1a1, Col4a1, Col4a2, Col6a1, and Col14a1) , thereby exerting antifibrotic effects [ 24 ] . Despite growing research on HF, gut microbiota, and circadian rhythms, most studies have focused on their binary relationships, whereas the integrated mechanisms involving all three factors remain largely unexplored. Based on this gap, we hypothesize that circadian rhythms may modulate HF development through the regulation of gut microbiota and their metabolites. To test this hypothesis, we will conduct a cross-sectional study incorporating questionnaire-based assessments of circadian rhythm patterns and lifestyle factors, transient elastography-based liver stiffness measurements (LSM) for HF evaluation, and 16S rRNA sequencing to analyze gut microbiota composition and metabolic profiles. By elucidating the interplay between circadian rhythms, gut microbiota, and HF progression, this study aims to provide empirical evidence supporting their mechanistic interactions and offer novel insights into precision interventions for HF. If our hypothesis is validated, future therapeutic strategies targeting circadian regulation and gut microbiota modulation may hold promise for HF prevention and treatment. 2. Materials and Methods 2.1 Subjects This cross-sectional study will recruit participants from the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine. All participants will provide written informed consent, and ethical approval has been obtained from the hospital's ethics review committee (Approval Number: EC2024063). 2.2 Inclusion and Exclusion Criteria 2.2.1 Inclusion Criteria: (1) Diagnosis of HF based on transient elastography (TE), a noninvasive imaging modality recommended by clinical guidelines for HF assessment due to its rapidity, reproducibility, and high patient compliance [1] ; (2) Age ≥ 18 years, with no restrictions on sex; (3) Ability to provide informed consent and complete study procedures. 2.2.2 Exclusion Criteria: (1) Failure to meet inclusion criteria; (2) Presence of severe primary diseases affecting the heart, brain, liver, lungs, kidneys, or hematopoietic system; (3) Cognitive impairment, dementia, or severe psychiatric disorders that hinder questionnaire completion; (4) Pregnancy or lactation; (5) Incomplete clinical data. 2.3 Study Design This study employs a 2 × 2 factorial design based on circadian rhythm status (normal vs. disrupted) and HF status (with vs. without HF), resulting in four groups: 1. Normal circadian rhythm (NCRD) + HF (NCRD-HF) 2. NCRD + non-HF (NCRD-NHF) 3. Circadian rhythm disruption (CRD) + HF (CRD-HF) 4. CRD + non-HF (CRD-NHF) 2.4 Data Collection 2.4.1 General Information : A structured questionnaire will collect demographic data and lifestyle factors, including age, sex, smoking, alcohol consumption, dietary habits, physical activity, and sleep patterns. 2.4.2 Circadian Rhythm Assessment : The Chinese version of the Morningness-Eveningness Questionnaire-5 (MEQ-5) was utilized to evaluate participants' circadian rhythms. The MEQ-5 is a concise and time-efficient instrument that has been validated for its reliability and validity in previous studies [25] . This questionnaire consists of five items (Items 1, 7, 10, 18, and 19) derived from the original MEQ. The total score, ranging from 5 to 23, is obtained by summing the scores of these five items. A lower score indicates greater circadian rhythm disruption, whereas a higher score reflects a more stable circadian rhythm. 2.4.3 Transient Elastography (TE) Measuremen : Liver fibrosis was assessed using a shear wave-based quantitative ultrasound elastography system (Hepatus, Hisky Medical Technologies, China). The appropriate probe was selected for each patient based on real-time skin-to-liver capsule distance measurements. Participants were instructed to fast before the examination and adopt a supine position with their right hand placed behind the head and the right arm fully abducted to expose the intercostal space of the right hepatic lobe. The measurement region was defined as the area enclosed by the xiphoid horizontal line, the right midaxillary line, and the costal margin. The probe was placed perpendicularly to the skin, and measurements were taken through an intercostal space. Each subject underwent at least 10 consecutive measurements at the same location, with the median value recorded as the final result. The interquartile range (IQR) to median ratio was maintained at < 30% to ensure measurement reliability. All measurements were conducted by the same trained and certified clinician. For each patient, at least three different measurement sites were assessed, and the mean value of these measurements was used as the final liver stiffness measurement. 2.4.4 Gut Microbiota Analysis : All participants provided fecal samples at the time of enrollment. Fresh stool samples were immediately frozen and stored at − 80°C within 4–6 hours of collection. (1) DNA Extraction Total microbial DNA was extracted using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer's protocol. The quality of extracted DNA was assessed via 1% agarose gel electrophoresis, and DNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). 2. PCR Amplification and Library Construction The extracted DNA was used as a template for polymerase chain reaction (PCR) amplification of the full-length 16S rRNA gene or internal transcribed spacer (ITS) region. The following primer pairs were used: * 16S rRNA: 27F (5’-AGRGTTYGATYMTGGCTCAG-3’) and 1492R (5’-RGYTACCTTGTTACGACTT-3’) * ITS region: ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS4R (5’-TCCTCCGCTTATTGATATGC-3’) Each PCR reaction was performed in a 20 µL reaction system containing 4 µL of 5× FastPfu buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of 5 µM forward primer, 0.8 µL of 5 µM reverse primer, 0.4 µL of FastPfu polymerase, 0.2 µL of bovine serum albumin (BSA), and 10 ng of template DNA. Each sample was amplified in triplicate. The PCR cycling conditions were as follows: initial denaturation at 95°C for 3 min, followed by 27 cycles of denaturation at 95°C for 30 s, annealing at 60°C for 30 s, extension at 72°C for 30 s, and a final extension at 72°C for 10 min, with storage at 4°C (T100 Thermal Cycler, Bio-Rad, USA). The PCR products were verified using 2% agarose gel electrophoresis, purified with magnetic beads, and quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA). Library preparation was performed using the SMRTbell Prep Kit 3.0, which involved (1) DNA damage repair, (2) end repair, and (3) adapter ligation. Sequencing was conducted on the PacBio Sequel IIe System (Shanghai Majorbio Bio-Pharm Technology Co., Ltd., China). HiFi reads were generated from subreads using the CCS mode in SMRT-Link v11.0 for downstream data analysis. (3) Sequencing Data Analysis Raw sequencing reads were demultiplexed based on barcode sequences, followed by length filtering and orientation correction. Sequences within the length range of 1000–1800 bp (bacteria) or 300–900 bp (fungi) were retained. Chloroplast and mitochondrial sequences were removed from all samples. To minimize sequencing depth-related biases in downstream alpha and beta diversity analyses, all samples were rarefied to 6000 sequences per sample. Post-rarefaction, the average sequencing coverage (Good’s coverage) remained at 99.09%. Taxonomic classification of amplicon sequence variants (ASVs) was performed using the Naïve Bayes classifier in Qiime2 against the Silva 16S rRNA database (v138). Functional prediction analyses were conducted using PICRUSt2 (version 2.2.0). 2.5 Statistical Analysis Statistical analyses were performed using SPSS 26.0 (IBM, USA). Data were expressed as mean ± standard deviation (SD). The Shapiro-Wilk test was applied to assess normality and homogeneity of variance. If data met both normality and homogeneity assumptions, two-way analysis of variance (ANOVA) was used for group comparisons; If data followed a normal distribution but violated the homogeneity of variance assumption, Welch’s ANOVA was applied; If neither assumption was met, the Kruskal-Wallis H test was used. Bivariate correlations between MEQ-5 scores and liver stiffness values were analyzed using Pearson’s correlation for normally distributed data and Spearman’s rank correlation for non-normally distributed data. A p-value of < 0.05 was considered statistically significant. 3. Results 3.1.Circadian Rhythm and Liver Fibrosis 1.1 Descriptive Statistics A total of 108 participants were included in this study, comprising 58 males and 50 females, with an age range of 21–79 years (mean: 41.1 ± 15.09 years). The liver stiffness measurement (LSM) ranged from 3.7 to 21.2 kPa (mean: 6.35 ± 2.21 kPa), while the MEQ-5 score ranged from 8 to 23 (mean: 16.29 ± 3.63), as summarized in Table 1. Table 1 Analysis results of basic information of subjects Category 是/男 否/女 Category Minimum Maximum Mean SD HF 54 54 LSM 3.7 21.2 6.35 2.21 CRD 54 54 MEQ 8 23 16.29 3.63 Gender 58 50 age 21 79 41.1 15.09 1.2 Correlation Analysis Spearman correlation analysis revealed a significant negative correlation between LSM and circadian rhythm scores ( p = 0.011, r = − 0.244). Additionally, LSM was negatively correlated with gender, education level, and certain lifestyle factors, including smoking and high-sugar diets. Conversely, LSM showed positive correlations with height, weight, waist circumference, BMI, and fat attenuation index. Detailed correlation coefficients and p-values are presented in Table 2. Among participants with hepatic fibrosis (HF), the LSM in the circadian rhythm disorder (CRD) group was significantly higher than in the normal circadian rhythm (NCRD) group, with an increase of 1.88 kpa( p = 0.017) A similar trend was observed in participants without fibrosis, where the LSM in the CRD group was 0.47 kPa higher than in the NCRD group ( p = 0.001). 2. Liver Fibrosis and Gut Microbiota 2.1 Alpha Diversity Analysis Alpha diversity analysis indicated no significant differences in the Chao, Shannon, and Simpson indices between the HF and non-HF (NHF) groups ( p > 0.05), suggesting that overall microbial diversity was not significantly altered in HF (Fig. 2). 2.2 Beta Diversity Analysis Beta diversity analysis demonstrated no significant differences in principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) indices between HF and NHF groups ( p > 0.05), indicating no substantial changes in gut microbial community structure associated with HF (Fig. 3). 2.3 Taxonomic Differences At both the phylum and genus levels (Fig. 4), the relative abundances of Pseudomonadota, Escherichia-Shigella, [ Ruminococcus ] _gnavus_group , Klebsiella , and Enterocloster were significantly increased in the HF group compared to the NHF group. Conversely, the relative abundances of Dorea, Holdemanella, [ Ruminococcus ]_gauvreauii_group, CAG-352 , Marvinbryantia , and [ Eubacterium ] _ventriosum_group were significantly reduced, indicating notable alterations in gut microbiota composition in HF patients. 3. Circadian Rhythm Disorder (CRD) and Non-CRD (NCRD) 3.1 Alpha Diversity Analysis Alpha diversity analysis showed no significant differences in the Chao, Shannon, and Simpson indices between the CRD and NCRD groups ( p > 0.05), suggesting no significant alterations in microbial diversity in CRD patients (Fig. 5). 3.2 Beta Diversity Analysis Beta diversity analysis revealed no significant differences in PCA and NMDS indices between CRD and NCRD groups ( p > 0.05), indicating no major shifts in gut microbiota community structure in CRD (Fig. 6). 3.3 Taxonomic Differences At the genus level (Fig. 7 ), the relative abundance of Mediterraneibacter was significantly increased in the CRD group compared to the NCRD group, suggesting a distinct microbial signature associated with CRD. 4 Discussion HF represents a critical pathological stage in the progression of chronic liver disease toward cirrhosis and hepatocellular carcinoma, involving multiple mechanisms such as gut microbiota dysbiosis, circadian rhythm disruption, inflammation, oxidative stress, and metabolic disorders [ 26 – 28 ] . This study explored the relationship between circadian rhythm, gut microbiota, and HF, highlighting the potential role of circadian disruption in shaping gut microbial composition and function, thereby contributing to HF pathogenesis. Our findings provide new insights into the role of the gut-liver axis in liver fibrosis. Firstly, Spearman correlation analysis demonstrated a significant negative correlation between LSM and circadian rhythm scores ( r = -0.244, p = 0.011), suggesting that circadian rhythm disruption may exacerbate HF progression. This finding is supported by the observation that LSM was significantly higher in the CRD group than in the NCRD group (1.88 kpa, p = 0.017), indicating that circadian misalignment may influence hepatic metabolism and immune responses, promoting fibrotic progression. Although alpha diversity analysis did not reveal significant differences between HF and NHF groups ( p > 0.05), this may be attributed to sample size limitations or population heterogeneity. Similarly, beta diversity analysis did not show significant shifts in gut microbial community structure ( p > 0.05). These findings align with some previous studies, suggesting that gut microbiota diversity alone may not be the sole determinant of HF progression [ 27 , 29 , 30 ] . Future research should expand sample sizes and integrate metabolomics and functional analyses to further elucidate the role of gut microbiota in HF. In the species-level differential analysis, we observed a significant increase in the relative abundance of Escherichia-Shigella, Klebsiella, Pseudomonadota, Ruminococcus gnavus group , and Enterocloster, whereas Dorea, Holdemanella, [Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, CAG-352, and Marvinbryantia exhibited a marked reduction. These alterations in microbial composition are closely associated with gut inflammatory responses and the activation of hepatic immune function [ 31 , 32 ] . The enrichment of Ruminococcus has been linked to heightened immune responses, increased inflammation, and fibrosis progression in the liver [ 33 ] . Given the bidirectional communication of the gut-liver axis, microbial dysbiosis may further influence hepatic pathology. Although our sample size is limited, these findings underscore the potential role of gut microbiota in HF. Future research should focus on functional analyses and metabolic profiling to elucidate the precise mechanisms underlying gut microbiota-mediated hepatic pathophysiology. Furthermore, we identified significant alterations in Mediterraneibacter in individuals with CRD, suggesting a potential role in promoting hepatocellular injury and inflammatory responses leading to HF. The liver exhibits intrinsic circadian rhythmicity, and disruptions in biological clocks have been shown to accelerate chronic liver injury and fibrosis progression [ 23 ] . Our findings reveal a negative correlation between circadian rhythmicity and liver stiffness measurements (LSM), further supporting the hypothesis that circadian misalignment may contribute to fibrosis progression via metabolic and immunological modulation [ 33 ] . Our study demonstrates significant microbial shifts at both the phylum and genus levels in HF patients. Pathogenic taxa, including Escherichia-Shigella, Klebsiella, Ruminococcus gnavus group , and Enterocloster , were markedly enriched in HF patients, suggesting their potential contribution to disease progression through mechanisms such as metabolic reprogramming and aberrant inflammatory activation. Specifically, Escherichia-Shigella is known to promote HF and even cirrhosis by activating the LPS-TLR4/inflammasome axis [ 34 ] , inducing pro-inflammatory cytokines such as IL-8 and IL-6, and reducing SCFA production [ 35 ] , thereby exacerbating hepatic injury. Klebsiella has been implicated in the pathogenesis of spontaneous bacterial peritonitis, accelerating cirrhosis progression [ 36 ] . Enterocloster contributes to hepatic inflammation by modulating bile acid metabolism through microbial bile salt hydrolase, which converts conjugated bile acids into free bile acids, subsequently transformed by 7α-dehydroxylating bacteria into secondary bile acids [ 37 ] . These secondary bile acids inhibit farnesoid X receptor (FXR), leading to pro-inflammatory cytokine release and localized hepatic inflammation [ 38 ] .Additionally, Ruminococcus gnavus produces pro-inflammatory polysaccharides that stimulate dendritic cells to release TNFα and other cytokines [ 39 ] , elevating ALT and AST levels [ 40 ] , thus promoting HF and cirrhosis. Conversely, beneficial Firmicutes members (Dorea, Holdemanella, [Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, Marvinbryantia, CAG-352) exhibited significant depletion in HF patients. These bacteria are known to produce SCFAs, which mitigate hepatic inflammation and exert protective effects against HF. Studies have demonstrated that SCFA supplementation reduces hepatic lipid accumulation, inhibits cholesterol biosynthesis, and ameliorates non-alcoholic fatty liver disease (NAFLD) progression [ 41 ] . Consistent with previous findings [ 42 ] , our results suggest that increasing the proportion of Firmicutes at the phylum level and enhancing the abundance of beneficial genera may provide a therapeutic avenue for improving hepatic fibrosis. Additionally, we found a significant increase in Mediterraneibacter in CRD patients. Notably, Mediterraneibacter is an ethanol-producing gut bacterium that can exacerbate hepatocellular injury and inflammatory responses, leading to the development of non-alcoholic steatohepatitis (NASH) [ 43 ] . Circadian rhythm disruption has been shown to accelerate hepatic steatosis, fibrosis, and even hepatocarcinogenesis by perturbing lipid metabolism and exacerbating oxidative stress [ 44 ] . Based on our findings, we propose that circadian misalignment may aggravate HF by upregulating Mediterraneibacter abundance, thereby increasing the hepatic metabolic burden and exacerbating disease progression. 5 Limitations This study has several limitations. First, the use of MEQ-5 as a subjective questionnaire introduces recall bias. Future studies should incorporate objective assessments of circadian rhythm, such as circadian clock gene expression (e.g., Clock, BMAL1 ). Second, our study is a cross-sectional analysis with a relatively small sample size, which precludes causal inferences between gut microbiota, circadian rhythms, and HF. Longitudinal cohort studies are warranted to establish temporal relationships and validate our findings in larger populations. 6 Conclusion In summary, our study provides preliminary evidence that increased liver stiffness in HF patients is associated with circadian rhythm disruption and gut microbiota dysbiosis. These findings suggest that circadian rhythmicity may influence HF progression through gut microbiota-derived metabolites. 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Circulating microbiome in patients with portal hypertension[J]. Gut Microbes, 2022,14(1):2029674. Baltazar-Diaz T A, Gonzalez-Hernandez L A, Aldana-Ledesma J M, et al. Escherichia/Shigella, SCFAs, and Metabolic Pathways-The Triad That Orchestrates Intestinal Dysbiosis in Patients with Decompensated Alcoholic Cirrhosis from Western Mexico[J]. Microorganisms, 2022,10(6). Song D S. [Spontaneous Bacterial Peritonitis][J]. Korean J Gastroenterol, 2018,72(2):56-63. Joyce S A, Shanahan F, Hill C, et al. Bacterial bile salt hydrolase in host metabolism: Potential for influencing gastrointestinal microbe-host crosstalk[J]. Gut Microbes, 2014,5(5):669-674. Wang Q, Hao C, Yao W, et al. Intestinal flora imbalance affects bile acid metabolism and is associated with gallstone formation[J]. BMC Gastroenterol, 2020,20(1):59. Henke M T, Kenny D J, Cassilly C D, et al. Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn's disease, produces an inflammatory polysaccharide[J]. Proc Natl Acad Sci U S A, 2019,116(26):12672-12677. Jiao M, Yan S, Shi Q, et al. Alcohol-Related Elevation of Liver Transaminase Is Associated With Gut Microbiota in Male[J]. Front Med (Lausanne), 2022,9:823898. Yin Y, Sichler A, Ecker J, et al. Gut microbiota promote liver regeneration through hepatic membrane phospholipid biosynthesis[J]. J Hepatol, 2023,78(4):820-835. He X, Liang J, Li X, et al. Dahuang zhechong pill ameliorates hepatic fibrosis by regulating gut microbiota and metabolites[J]. J Ethnopharmacol, 2024,321:117402. Mbaye B, Magdy W R, Borentain P, et al. Increased fecal ethanol and enriched ethanol-producing gut bacteria Limosilactobacillus fermentum, Enterocloster bolteae, Mediterraneibacter gnavus and Streptococcus mutans in nonalcoholic steatohepatitis[J]. Front Cell Infect Microbiol, 2023,13:1279354. Tong X, Yin L. Circadian rhythms in liver physiology and liver diseases[J]. Compr Physiol, 2013,3(2):917-940. Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table2.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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6224838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428750999,"identity":"b1172620-6a89-49bb-af2c-41111c38a8ab","order_by":0,"name":"Zhenghua Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACZiBmbAASEkCcYFAjx8befIAELR8qjhnz8RxLIGwTTAvjjDPMifMkchTwqjY4zvzw4c8dNnny0T2Gn3nb2NLbGHIYGH5UbMOpRbKZzdhA8kxaseGdM8bSvG0yuW0MZw8w9py5jVMLPzODmYRh2+HEjTNyDIBa2HLbGPsSmBnbcGthY2b/JpHY9h+kxfg3bxtzOhszjwFeLfzMPGYSB9sOJM6XyDGTBHo/gY2NgBbJZp5iw8a25MQNEmllFsBANmzjYUs4iM8vBuePb3z4s80ucf6M5M03gFEpLz//8cEHPypwa0HoPYDEOYBDESqQbyBK2SgYBaNgFIxEAADZs1ZCWSh6pQAAAABJRU5ErkJggg==","orcid":"","institution":"Guizhou University Of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Zhenghua","middleName":"","lastName":"Xiao","suffix":""},{"id":428751000,"identity":"1fd3d2db-0e37-41be-90e1-c1625141a08a","order_by":1,"name":"Menglan Guo","email":"","orcid":"","institution":"The Second Clinic School of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Menglan","middleName":"","lastName":"Guo","suffix":""},{"id":428751001,"identity":"1efd3c81-b88a-4f0e-b2b6-9582ef6cb1a5","order_by":2,"name":"Weiwei Tang","email":"","orcid":"","institution":"The Second Clinic School of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Tang","suffix":""},{"id":428751002,"identity":"028eb998-a8f6-4d1e-ab9f-49cc334f0d7f","order_by":3,"name":"Menglu Chen","email":"","orcid":"","institution":"The Second Clinic School of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Menglu","middleName":"","lastName":"Chen","suffix":""},{"id":428751003,"identity":"1df7f880-4307-45b4-8a0b-3ac4ba52ce53","order_by":4,"name":"Qingwan Yang","email":"","orcid":"","institution":"The Second Clinic School of Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qingwan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-03-14 09:20:00","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6224838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224838/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78658122,"identity":"0eb637c7-a604-4e3c-856e-512c9057669e","added_by":"auto","created_at":"2025-03-17 09:44:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55167,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of difference analysis \u0026nbsp;\u0026nbsp;between circadian rhythm and HF *p\u0026lt;0.05,**p\u0026lt;0.01\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/96cb2bf92de9a762b5758914.png"},{"id":78658133,"identity":"7bf91b7b-0190-408b-9412-015a23a5547e","added_by":"auto","created_at":"2025-03-17 09:44:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222087,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of chao, shannon and simpson index analysis results of HF and NHF intestinal flora\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/0396ad048b6b87f56bb94fbb.png"},{"id":78658733,"identity":"ddb03789-9bc6-46cb-a243-326bdcb7ed57","added_by":"auto","created_at":"2025-03-17 09:52:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":252501,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of HF and NHF intestinal flora index results of PCA and NMDS analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/a5f7aa8b98d25aff24529cf5.png"},{"id":78658730,"identity":"9f34472c-91e1-4ad2-a3eb-1991f63e6656","added_by":"auto","created_at":"2025-03-17 09:52:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112425,"visible":true,"origin":"","legend":"\u003cp\u003eIntestinal differential microbiome patterns at the mesophyla and genus levels of HF and NHF intestinal flora\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/fabdd61fdba636adbea0ea10.png"},{"id":78658135,"identity":"e07c6744-cea5-4ba7-b3d0-1976ae2387df","added_by":"auto","created_at":"2025-03-17 09:44:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":222087,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of chao, shannon and simpson analysis results of CRD and NCRD intestinal flora\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/d863ff187a92f0fd05c016ea.png"},{"id":78659821,"identity":"2d9e4eda-4190-4479-bba6-c9c9fe6a3426","added_by":"auto","created_at":"2025-03-17 10:00:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":245930,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of PCA and NMDS analysis results of CRD and NCRD intestinal flora\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/7bee9ca0384a07bf655c4b40.png"},{"id":78658736,"identity":"2ba1b5f3-b387-4c55-ac7c-390ad4d463ac","added_by":"auto","created_at":"2025-03-17 09:52:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":94749,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagram of the level of intestinal microbial differences between CRD \u0026nbsp;\u0026nbsp;and NCRD in intestinal flora\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/0fefe086c53f3f0526421533.png"},{"id":78661405,"identity":"affc43b1-3368-4339-ad37-e471f41ca048","added_by":"auto","created_at":"2025-03-17 10:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025270,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/90e1dd64-57e2-44ab-9539-f598b2a4f711.pdf"},{"id":78658124,"identity":"0ef0d6e6-4bcb-435b-8db0-e1a839ef7e12","added_by":"auto","created_at":"2025-03-17 09:44:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":127660,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6224838/v1/97b049e4b511dc51f169092d.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eStudy on the correlation between circadian rhythm, intestinal flora and Liver stiffness in patients with Hepatic fibrosis\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHepatic fibrosis (HF) is characterized by the excessive and aberrant deposition of extracellular matrix (ECM) components, including collagen, glycoproteins, and proteoglycans, as a pathological repair response to chronic liver injury\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.The activation of hepatic stellate cells (HSCs) is recognized as a key driver in the pathogenesis of HF. Notably, HF represents a critical transition stage in the progression of chronic liver diseases toward cirrhosis, with its severity closely linked to disease prognosis. According to the 2023 Global Burden of Liver Disease Report 22\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e,chronic liver diseases account for approximately 2 million deaths annually, representing 4% of all global deaths, with cirrhosis ranking among the top ten causes of mortality worldwide. Beyond its significant impact on global health, liver fibrosis imposes a substantial economic burden on healthcare systems. For instance, the total expenditure on liver disease management in the United States reached \u003cspan\u003e$\u003c/span\u003e32.5\u0026nbsp;billion in 2016\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Given the urgent need to elucidate the underlying mechanisms of HF and develop effective therapeutic strategies, further research in this area is of paramount importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathogenesis of HF: Multifactorial Regulation and Systemic Impact\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHF is closely associated with chronic liver injury, in which persistent inflammatory stimuli trigger the release of profibrotic factors from hepatocytes, HSCs, and immune cells, ultimately driving the transdifferentiation of HSCs into myofibroblast-like cells and the excessive production of ECM\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.This process is regulated by multiple signaling pathways, including the mitogen-activated protein kinase (MAPK), transforming growth factor-beta (TGF-\u0026beta;), Wnt, oxidative stress pathways, as well as mechanisms involving apoptosis and autophagy\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In recent years, increasing attention has been directed toward the role of the gut-liver axis and gut-brain axis in HF\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Moreover, circadian rhythm dysregulation has been identified as a key contributor to HF progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. For example, the loss of the core clock gene Per2 has been shown to enhance HSC activation, thereby accelerating HF development\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Additionally, circadian rhythm disturbances have been linked to alterations in gut microbiota composition, a phenomenon observed in both animal models and human studies \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut Microbiota and HF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gut microbiota comprises thousands of microbial species that play a crucial role in maintaining host metabolic and immune homeostasis. Under normal physiological conditions, the gut microbiota is dominated by Firmicutes and Bacteroidetes, while aerobic bacteria constitute a minor proportion\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, environmental or physiological perturbations can disrupt microbial diversity and composition, leading to gut dysbiosis. Such dysbiosis has been implicated in metabolic dysfunction, immune dysregulation, and inflammatory responses\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Notably, an altered Firmicutes/Bacteroidetes ratio has been associated with increased intestinal permeability and upregulated expression of lipopolysaccharide (LPS) synthesis and transport genes\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. LPS translocation via the portal circulation activates Kupffer cells, promoting the release of proinflammatory cytokines such as tumor necrosis factor-alpha (TNF-\u0026alpha;) and interleukin-6 (IL-6), thereby facilitating HSC activation and fibrosis progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Furthermore, studies have demonstrated that modulating gut microbiota composition can activate interferon signaling pathways and inhibit hepatic bile acid synthesis, exerting antifibrotic effects\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInterestingly, gut microbiota composition is also closely linked to circadian clock gene expression. Specific gut microbial taxa have been found to positively correlate with the hepatic expression of \u003cem\u003eClock, Cry1\u003c/em\u003e, and \u003cem\u003ePer2\u003c/em\u003e, while negatively correlating with \u003cem\u003ePer1\u003c/em\u003e and \u003cem\u003eCry2\u003c/em\u003e expression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. These findings suggest that circadian rhythms may influence HF progression through the modulation of gut microbiota metabolic functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCircadian Rhythms and HF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCircadian rhythms are governed by an endogenous biological clock system, consisting of a central clock located in the suprachiasmatic nucleus (SCN) and peripheral clocks distributed across various tissues, including the liver and gut\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, modern lifestyle factors, such as shift work, excessive electronic device usage, and artificial light exposure, have significantly increased the prevalence of circadian disruption, which is associated with an elevated risk of metabolic syndrome, obesity, diabetes, autoimmune disorders, and cancer\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Notably, the International Agency for Research on Cancer (IARC) has classified circadian rhythm disruption as a probable human carcinogen\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEmerging evidence highlights the critical role of core clock genes in HF pathogenesis. For instance, the deletion of Bmal1 promotes HSC transdifferentiation into a profibrotic phenotype via activation of the TGF-\u0026beta;/Smad pathway\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Additionally, suppression of Clock gene expression, while upregulating \u003cem\u003ePer1, Per2\u003c/em\u003e, and \u003cem\u003ePer3\u003c/em\u003e, has been shown to downregulate ECM-related genes \u003cem\u003e(Col1a1, Col4a1, Col4a2, Col6a1, and Col14a1)\u003c/em\u003e, thereby exerting antifibrotic effects \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite growing research on HF, gut microbiota, and circadian rhythms, most studies have focused on their binary relationships, whereas the integrated mechanisms involving all three factors remain largely unexplored. Based on this gap, we hypothesize that circadian rhythms may modulate HF development through the regulation of gut microbiota and their metabolites. To test this hypothesis, we will conduct a cross-sectional study incorporating questionnaire-based assessments of circadian rhythm patterns and lifestyle factors, transient elastography-based liver stiffness measurements (LSM) for HF evaluation, and 16S rRNA sequencing to analyze gut microbiota composition and metabolic profiles. By elucidating the interplay between circadian rhythms, gut microbiota, and HF progression, this study aims to provide empirical evidence supporting their mechanistic interactions and offer novel insights into precision interventions for HF. If our hypothesis is validated, future therapeutic strategies targeting circadian regulation and gut microbiota modulation may hold promise for HF prevention and treatment.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Subjects\u003c/h2\u003e\n \u003cp\u003eThis cross-sectional study will recruit participants from the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine. All participants will provide written informed consent, and ethical approval has been obtained from the hospital\u0026apos;s ethics review committee (Approval Number: EC2024063).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\n \u003cp\u003e2.2.1 Inclusion Criteria: (1) Diagnosis of HF based on transient elastography (TE), a noninvasive imaging modality recommended by clinical guidelines for HF assessment due to its rapidity, reproducibility, and high patient compliance\u003csup\u003e[1]\u003c/sup\u003e; (2) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, with no restrictions on sex; (3) Ability to provide informed consent and complete study procedures.\u003c/p\u003e\n \u003cp\u003e2.2.2 Exclusion Criteria: (1) Failure to meet inclusion criteria; (2) Presence of severe primary diseases affecting the heart, brain, liver, lungs, kidneys, or hematopoietic system; (3) Cognitive impairment, dementia, or severe psychiatric disorders that hinder questionnaire completion; (4) Pregnancy or lactation; (5) Incomplete clinical data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Study Design\u003c/h2\u003e\n \u003cp\u003eThis study employs a 2 \u0026times; 2 factorial design based on circadian rhythm status (normal vs. disrupted) and HF status (with vs. without HF), resulting in four groups:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1. Normal circadian rhythm (NCRD)\u0026thinsp;+\u0026thinsp;HF (NCRD-HF)\u003c/p\u003e2. NCRD\u0026thinsp;+\u0026thinsp;non-HF (NCRD-NHF)\u003cbr\u003e3. Circadian rhythm disruption (CRD)\u0026thinsp;+\u0026thinsp;HF (CRD-HF)\u003cbr\u003e4. CRD\u0026thinsp;+\u0026thinsp;non-HF (CRD-NHF)\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Data Collection\u003c/h2\u003e\n \u003cp\u003e2.4.1 \u003cstrong\u003eGeneral Information\u003c/strong\u003e: A structured questionnaire will collect demographic data and lifestyle factors, including age, sex, smoking, alcohol consumption, dietary habits, physical activity, and sleep patterns.\u003c/p\u003e\n \u003cp\u003e2.4.2 \u003cstrong\u003eCircadian Rhythm Assessment\u003c/strong\u003e: The Chinese version of the Morningness-Eveningness Questionnaire-5 (MEQ-5) was utilized to evaluate participants\u0026apos; circadian rhythms. The MEQ-5 is a concise and time-efficient instrument that has been validated for its reliability and validity in previous studies\u003csup\u003e[25]\u003c/sup\u003e. This questionnaire consists of five items (Items 1, 7, 10, 18, and 19) derived from the original MEQ. The total score, ranging from 5 to 23, is obtained by summing the scores of these five items. A lower score indicates greater circadian rhythm disruption, whereas a higher score reflects a more stable circadian rhythm.\u003c/p\u003e\n \u003cp\u003e2.4.3 \u003cstrong\u003eTransient Elastography (TE) Measuremen\u003c/strong\u003e: Liver fibrosis was assessed using a shear wave-based quantitative ultrasound elastography system (Hepatus, Hisky Medical Technologies, China). The appropriate probe was selected for each patient based on real-time skin-to-liver capsule distance measurements. Participants were instructed to fast before the examination and adopt a supine position with their right hand placed behind the head and the right arm fully abducted to expose the intercostal space of the right hepatic lobe. The measurement region was defined as the area enclosed by the xiphoid horizontal line, the right midaxillary line, and the costal margin. The probe was placed perpendicularly to the skin, and measurements were taken through an intercostal space. Each subject underwent at least 10 consecutive measurements at the same location, with the median value recorded as the final result. The interquartile range (IQR) to median ratio was maintained at \u0026lt;\u0026thinsp;30% to ensure measurement reliability. All measurements were conducted by the same trained and certified clinician. For each patient, at least three different measurement sites were assessed, and the mean value of these measurements was used as the final liver stiffness measurement.\u003c/p\u003e2.4.4\u0026nbsp;\u003cstrong\u003eGut Microbiota Analysis\u003c/strong\u003e: All participants provided fecal samples at the time of enrollment. Fresh stool samples were immediately frozen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C within 4\u0026ndash;6 hours of collection.\u003cbr\u003e\n \u003cp\u003e\u003cstrong\u003e(1) DNA Extraction\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTotal microbial DNA was extracted using the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer\u0026apos;s protocol. The quality of extracted DNA was assessed via 1% agarose gel electrophoresis, and DNA concentration and purity were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2. PCR Amplification and Library Construction\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe extracted DNA was used as a template for polymerase chain reaction (PCR) amplification of the full-length 16S rRNA gene or internal transcribed spacer (ITS) region. The following primer pairs were used:\u003c/p\u003e\n \u003cp\u003e* 16S rRNA: 27F (5\u0026rsquo;-AGRGTTYGATYMTGGCTCAG-3\u0026rsquo;) and 1492R (5\u0026rsquo;-RGYTACCTTGTTACGACTT-3\u0026rsquo;)\u003c/p\u003e\n \u003cp\u003e* ITS region: ITS1F (5\u0026rsquo;-CTTGGTCATTTAGAGGAAGTAA-3\u0026rsquo;) and ITS4R (5\u0026rsquo;-TCCTCCGCTTATTGATATGC-3\u0026rsquo;)\u003c/p\u003e\n \u003cp\u003eEach PCR reaction was performed in a 20 \u0026micro;L reaction system containing 4 \u0026micro;L of 5\u0026times; FastPfu buffer, 2 \u0026micro;L of 2.5 mM dNTPs, 0.8 \u0026micro;L of 5 \u0026micro;M forward primer, 0.8 \u0026micro;L of 5 \u0026micro;M reverse primer, 0.4 \u0026micro;L of FastPfu polymerase, 0.2 \u0026micro;L of bovine serum albumin (BSA), and 10 ng of template DNA. Each sample was amplified in triplicate. The PCR cycling conditions were as follows: initial denaturation at 95\u0026deg;C for 3 min, followed by 27 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 60\u0026deg;C for 30 s, extension at 72\u0026deg;C for 30 s, and a final extension at 72\u0026deg;C for 10 min, with storage at 4\u0026deg;C (T100 Thermal Cycler, Bio-Rad, USA). The PCR products were verified using 2% agarose gel electrophoresis, purified with magnetic beads, and quantified using a Qubit 4.0 Fluorometer (Thermo Fisher Scientific, USA).\u003c/p\u003e\n \u003cp\u003eLibrary preparation was performed using the SMRTbell Prep Kit 3.0, which involved (1) DNA damage repair, (2) end repair, and (3) adapter ligation. Sequencing was conducted on the PacBio Sequel IIe System (Shanghai Majorbio Bio-Pharm Technology Co., Ltd., China). HiFi reads were generated from subreads using the CCS mode in SMRT-Link v11.0 for downstream data analysis.\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e(3) \u003cstrong\u003eSequencing Data Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eRaw sequencing reads were demultiplexed based on barcode sequences, followed by length filtering and orientation correction. Sequences within the length range of 1000\u0026ndash;1800 bp (bacteria) or 300\u0026ndash;900 bp (fungi) were retained. Chloroplast and mitochondrial sequences were removed from all samples. To minimize sequencing depth-related biases in downstream alpha and beta diversity analyses, all samples were rarefied to 6000 sequences per sample. Post-rarefaction, the average sequencing coverage (Good\u0026rsquo;s coverage) remained at 99.09%. Taxonomic classification of amplicon sequence variants (ASVs) was performed using the Na\u0026iuml;ve Bayes classifier in Qiime2 against the Silva 16S rRNA database (v138). Functional prediction analyses were conducted using PICRUSt2 (version 2.2.0).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 \u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were performed using SPSS 26.0 (IBM, USA). Data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). The Shapiro-Wilk test was applied to assess normality and homogeneity of variance. If data met both normality and homogeneity assumptions, two-way analysis of variance (ANOVA) was used for group comparisons; If data followed a normal distribution but violated the homogeneity of variance assumption, Welch\u0026rsquo;s ANOVA was applied; If neither assumption was met, the Kruskal-Wallis H test was used. Bivariate correlations between MEQ-5 scores and liver stiffness values were analyzed using Pearson\u0026rsquo;s correlation for normally distributed data and Spearman\u0026rsquo;s rank correlation for non-normally distributed data. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e3.1.Circadian Rhythm and Liver Fibrosis\u003c/h2\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e1.1 Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eA total of 108 participants were included in this study, comprising 58 males and 50 females, with an age range of 21–79 years (mean: 41.1 ± 15.09 years). The liver stiffness measurement (LSM) ranged from 3.7 to 21.2 kPa (mean: 6.35 ± 2.21 kPa), while the MEQ-5 score ranged from 8 to 23 (mean: 16.29 ± 3.63), as summarized in Table\u0026nbsp;1.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis results of basic information of subjects\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e是/男\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e否/女\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e1.2 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003eSpearman correlation analysis revealed a significant negative correlation between LSM and circadian rhythm scores (\u003cem\u003ep\u003c/em\u003e = 0.011, r = − 0.244). Additionally, LSM was negatively correlated with gender, education level, and certain lifestyle factors, including smoking and high-sugar diets. Conversely, LSM showed positive correlations with height, weight, waist circumference, BMI, and fat attenuation index. Detailed correlation coefficients and p-values are presented in Table\u0026nbsp;2.\u003c/p\u003e\n \u003cp\u003eAmong participants with hepatic fibrosis (HF), the LSM in the circadian rhythm disorder (CRD) group was significantly higher than in the normal circadian rhythm (NCRD) group, with an increase of 1.88 kpa(\u003cem\u003ep\u003c/em\u003e = 0.017) A similar trend was observed in participants without fibrosis, where the LSM in the CRD group was 0.47 kPa higher than in the NCRD group (\u003cem\u003ep\u003c/em\u003e = 0.001).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e2. Liver Fibrosis and Gut Microbiota\u003c/h3\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Alpha Diversity Analysis\u003c/h2\u003e\n \u003cp\u003eAlpha diversity analysis indicated no significant differences in the Chao, Shannon, and Simpson indices between the HF and non-HF (NHF) groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting that overall microbial diversity was not significantly altered in HF (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003cdiv\u003e\u003cstrong\u003e2.2 Beta Diversity Analysis\u003c/strong\u003e\u003c/div\u003e\n \u003cp\u003eBeta diversity analysis demonstrated no significant differences in principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) indices between HF and NHF groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no substantial changes in gut microbial community structure associated with HF (Fig. 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Taxonomic Differences\u003c/h2\u003e\n \u003cp\u003eAt both the phylum and genus levels (Fig. 4), the relative abundances of Pseudomonadota, Escherichia-Shigella, [\u003cem\u003eRuminococcus\u003c/em\u003e]\u003cem\u003e_gnavus_group\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, and Enterocloster were significantly increased in the HF group compared to the NHF group. Conversely, the relative abundances of Dorea, Holdemanella, [\u003cem\u003eRuminococcus\u003c/em\u003e]_gauvreauii_group, \u003cem\u003eCAG-352\u003c/em\u003e, \u003cem\u003eMarvinbryantia\u003c/em\u003e, and [\u003cem\u003eEubacterium\u003c/em\u003e]\u003cem\u003e_ventriosum_group\u003c/em\u003e were significantly reduced, indicating notable alterations in gut microbiota composition in HF patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Circadian Rhythm Disorder (CRD) and Non-CRD (NCRD)\u003c/h3\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Alpha Diversity Analysis\u003c/h2\u003e\n \u003cp\u003eAlpha diversity analysis showed no significant differences in the Chao, Shannon, and Simpson indices between the CRD and NCRD groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting no significant alterations in microbial diversity in CRD patients (Fig. 5).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2 Beta Diversity Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBeta diversity analysis revealed no significant differences in PCA and NMDS indices between CRD and NCRD groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no major shifts in gut microbiota community structure in CRD (Fig. 6).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Taxonomic Differences\u003c/h2\u003e\n \u003cp\u003eAt the genus level (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e), the relative abundance of Mediterraneibacter was significantly increased in the CRD group compared to the NCRD group, suggesting a distinct microbial signature associated with CRD.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eHF represents a critical pathological stage in the progression of chronic liver disease toward cirrhosis and hepatocellular carcinoma, involving multiple mechanisms such as gut microbiota dysbiosis, circadian rhythm disruption, inflammation, oxidative stress, and metabolic disorders\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This study explored the relationship between circadian rhythm, gut microbiota, and HF, highlighting the potential role of circadian disruption in shaping gut microbial composition and function, thereby contributing to HF pathogenesis. Our findings provide new insights into the role of the gut-liver axis in liver fibrosis.\u003c/p\u003e\n\u003cp\u003eFirstly, Spearman correlation analysis demonstrated a significant negative correlation between LSM and circadian rhythm scores (\u003cem\u003er\u003c/em\u003e = -0.244, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), suggesting that circadian rhythm disruption may exacerbate HF progression. This finding is supported by the observation that LSM was significantly higher in the CRD group than in the NCRD group (1.88 kpa, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), indicating that circadian misalignment may influence hepatic metabolism and immune responses, promoting fibrotic progression.\u003c/p\u003e\n\u003cp\u003eAlthough alpha diversity analysis did not reveal significant differences between HF and NHF groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), this may be attributed to sample size limitations or population heterogeneity. Similarly, beta diversity analysis did not show significant shifts in gut microbial community structure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings align with some previous studies, suggesting that gut microbiota diversity alone may not be the sole determinant of HF progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Future research should expand sample sizes and integrate metabolomics and functional analyses to further elucidate the role of gut microbiota in HF.\u003c/p\u003e\n\u003cp\u003eIn the species-level differential analysis, we observed a significant increase in the relative abundance of \u003cem\u003eEscherichia-Shigella, Klebsiella, Pseudomonadota, Ruminococcus gnavus group\u003c/em\u003e, and \u003cem\u003eEnterocloster, whereas Dorea, Holdemanella, [Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, CAG-352, and Marvinbryantia\u003c/em\u003e exhibited a marked reduction. These alterations in microbial composition are closely associated with gut inflammatory responses and the activation of hepatic immune function\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The enrichment of \u003cem\u003eRuminococcus\u003c/em\u003e has been linked to heightened immune responses, increased inflammation, and fibrosis progression in the liver \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Given the bidirectional communication of the gut-liver axis, microbial dysbiosis may further influence hepatic pathology. Although our sample size is limited, these findings underscore the potential role of gut microbiota in HF. Future research should focus on functional analyses and metabolic profiling to elucidate the precise mechanisms underlying gut microbiota-mediated hepatic pathophysiology.\u003c/p\u003e\n\u003cp\u003eFurthermore, we identified significant alterations in Mediterraneibacter in individuals with CRD, suggesting a potential role in promoting hepatocellular injury and inflammatory responses leading to HF. The liver exhibits intrinsic circadian rhythmicity, and disruptions in biological clocks have been shown to accelerate chronic liver injury and fibrosis progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our findings reveal a negative correlation between circadian rhythmicity and liver stiffness measurements (LSM), further supporting the hypothesis that circadian misalignment may contribute to fibrosis progression via metabolic and immunological modulation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur study demonstrates significant microbial shifts at both the phylum and genus levels in HF patients. Pathogenic taxa, including \u003cem\u003eEscherichia-Shigella, Klebsiella, Ruminococcus gnavus group\u003c/em\u003e, and \u003cem\u003eEnterocloster\u003c/em\u003e, were markedly enriched in HF patients, suggesting their potential contribution to disease progression through mechanisms such as metabolic reprogramming and aberrant inflammatory activation. Specifically, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e is known to promote HF and even cirrhosis by activating the LPS-TLR4/inflammasome axis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, inducing pro-inflammatory cytokines such as IL-8 and IL-6, and reducing SCFA production\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, thereby exacerbating hepatic injury. \u003cem\u003eKlebsiella\u003c/em\u003e has been implicated in the pathogenesis of spontaneous bacterial peritonitis, accelerating cirrhosis progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Enterocloster contributes to hepatic inflammation by modulating bile acid metabolism through microbial bile salt hydrolase, which converts conjugated bile acids into free bile acids, subsequently transformed by 7\u0026alpha;-dehydroxylating bacteria into secondary bile acids\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. These secondary bile acids inhibit farnesoid X receptor (FXR), leading to pro-inflammatory cytokine release and localized hepatic inflammation\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e.Additionally, \u003cem\u003eRuminococcus gnavus\u003c/em\u003e produces pro-inflammatory polysaccharides that stimulate dendritic cells to release TNF\u0026alpha; and other cytokines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, elevating ALT and AST levels\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, thus promoting HF and cirrhosis.\u003c/p\u003e\n\u003cp\u003eConversely, beneficial Firmicutes members \u003cem\u003e(Dorea, Holdemanella, [Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, Marvinbryantia, CAG-352)\u003c/em\u003e exhibited significant depletion in HF patients. These bacteria are known to produce SCFAs, which mitigate hepatic inflammation and exert protective effects against HF. Studies have demonstrated that SCFA supplementation reduces hepatic lipid accumulation, inhibits cholesterol biosynthesis, and ameliorates non-alcoholic fatty liver disease (NAFLD) progression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Consistent with previous findings\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, our results suggest that increasing the proportion of Firmicutes at the phylum level and enhancing the abundance of beneficial genera may provide a therapeutic avenue for improving hepatic fibrosis.\u003c/p\u003e\n\u003cp\u003eAdditionally, we found a significant increase in Mediterraneibacter in CRD patients. Notably, Mediterraneibacter is an ethanol-producing gut bacterium that can exacerbate hepatocellular injury and inflammatory responses, leading to the development of non-alcoholic steatohepatitis (NASH)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Circadian rhythm disruption has been shown to accelerate hepatic steatosis, fibrosis, and even hepatocarcinogenesis by perturbing lipid metabolism and exacerbating oxidative stress\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. Based on our findings, we propose that circadian misalignment may aggravate HF by upregulating Mediterraneibacter abundance, thereby increasing the hepatic metabolic burden and exacerbating disease progression.\u003c/p\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eThis study has several limitations. First, the use of MEQ-5 as a subjective questionnaire introduces recall bias. Future studies should incorporate objective assessments of circadian rhythm, such as circadian clock gene expression (e.g., \u003cem\u003eClock, BMAL1\u003c/em\u003e). Second, our study is a cross-sectional analysis with a relatively small sample size, which precludes causal inferences between gut microbiota, circadian rhythms, and HF. Longitudinal cohort studies are warranted to establish temporal relationships and validate our findings in larger populations.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn summary, our study provides preliminary evidence that increased liver stiffness in HF patients is associated with circadian rhythm disruption and gut microbiota dysbiosis. These findings suggest that circadian rhythmicity may influence HF progression through gut microbiota-derived metabolites. Clinically, interventions targeting circadian rhythm (e.g., light therapy, timed medication) and gut microbiota modulation may offer novel strategies to delay or even reverse HF progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, this study strictly adheres to the Helsinki Declaration, and all methods are implemented in accordance with relevant guidelines and regulations, and this study were approved by The Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (Approval No. EC2024063). Finally, all participants voluntarily provided informed and written consent, indicating their willingness to participate and support the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e陆伦根, 尤红, 谢渭芬, 等. 肝纤维化诊断及治疗共识(2019年)[J]. 临床肝胆病杂志, 2019,35(10):2163-2172.\u003c/li\u003e\n\u003cli\u003eDevarbhavi H, Asrani S K, Arab J P, et al. Global burden of liver disease: 2023 update[J]. J Hepatol, 2023,79(2):516-537.\u003c/li\u003e\n\u003cli\u003eMa C, Qian A S, Nguyen N H, et al. Trends in the Economic Burden of Chronic Liver Diseases and Cirrhosis in the United States: 1996-2016[J]. Am J Gastroenterol, 2021,116(10):2060-2067.\u003c/li\u003e\n\u003cli\u003e廖昭辉, 谢正元. 肝纤维化发病的分子机制及其相关治疗靶点的研究进展[J]. 吉林大学学报(医学版), 2024,50(05):1450-1456.\u003c/li\u003e\n\u003cli\u003eSultana M, Islam M A, Khairnar R, et al. A guide to pathophysiology, signaling pathways, and preclinical models of liver fibrosis[J]. 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Escherichia/Shigella, SCFAs, and Metabolic Pathways-The Triad That Orchestrates Intestinal Dysbiosis in Patients with Decompensated Alcoholic Cirrhosis from Western Mexico[J]. Microorganisms, 2022,10(6).\u003c/li\u003e\n\u003cli\u003eSong D S. [Spontaneous Bacterial Peritonitis][J]. Korean J Gastroenterol, 2018,72(2):56-63.\u003c/li\u003e\n\u003cli\u003eJoyce S A, Shanahan F, Hill C, et al. Bacterial bile salt hydrolase in host metabolism: Potential for influencing gastrointestinal microbe-host crosstalk[J]. Gut Microbes, 2014,5(5):669-674.\u003c/li\u003e\n\u003cli\u003eWang Q, Hao C, Yao W, et al. Intestinal flora imbalance affects bile acid metabolism and is associated with gallstone formation[J]. BMC Gastroenterol, 2020,20(1):59.\u003c/li\u003e\n\u003cli\u003eHenke M T, Kenny D J, Cassilly C D, et al. Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn\u0026apos;s disease, produces an inflammatory polysaccharide[J]. 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Compr Physiol, 2013,3(2):917-940.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The sceond affiliated hospital of guizhou university of traditional Chinese medicine","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":"Hepatic fibrosis, Gut Microbiota, Circadian rhythm, Intestinal flora","lastPublishedDoi":"10.21203/rs.3.rs-6224838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHepatic fibrosis (HF) is a critical pathological process in chronic liver diseases, and its progression is closely associated with gut microbiota dysbiosis and circadian rhythm disruption. However, the interplay between these factors in HF remains poorly understood. This study aimed to investigate the relationship between gut microbiota composition, circadian rhythm disturbances, and HF, providing new insights into potential therapeutic strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted, enrolling patients with HF and healthy controls. Liver stiffness measurement (LSM) was assessed using transient elastography. Circadian rhythm status was evaluated with the Morningness-Eveningness Questionnaire-5 (MEQ-5). Gut microbiota composition was analyzed via 16S rRNA sequencing, and differences in microbial diversity and taxa abundance were compared between groups. Correlation analyses were performed to explore the associations between gut microbiota, LSM, and circadian rhythm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with HF exhibited significant alterations in gut microbiota composition at both the phylum and genus levels (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, r = − 0.244). The relative abundances of \u003cem\u003eEscherichia-Shigella, Klebsiella, Pseudomonadota, Ruminococcus gnavus group\u003c/em\u003e, and \u003cem\u003eEnterocloster\u003c/em\u003e were significantly increased, while Dorea, Holdemanella, \u003cem\u003e[Ruminococcus] gauvreauii group, [Eubacterium] ventriosum group, CAG-352\u003c/em\u003e, and \u003cem\u003eMarvinbryantia\u003c/em\u003e were markedly decreased. These microbial shifts were associated with enhanced intestinal inflammation and hepatic immune activation. Notably, \u003cem\u003eEscherichia-Shigella\u003c/em\u003e may contribute to HF progression via LPS-TLR4/inflammasome activation, inflammatory cytokine release, and reduced short-chain fatty acid (SCFA) production. Conversely, SCFA-producing bacteria in the Firmicutes phylum showed a potential protective role by mitigating hepatic inflammation and lipid accumulation. Furthermore, circadian rhythm disruption was negatively correlated with LSM, and an increased abundance of Mediterraneibacter was observed in patients with circadian rhythm disturbances. As Mediterraneibacter is known to produce ethanol, its elevated levels may exacerbate hepatic injury and inflammation, potentially contributing to HF development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study reveals a significant association between gut microbiota dysbiosis, circadian rhythm disruption, and HF severity. Our findings suggest that circadian rhythm disturbances may influence HF progression by modulating gut microbiota composition and metabolic activity. These insights highlight potential therapeutic strategies, including circadian rhythm modulation (e.g., light therapy, timed medication) and gut microbiota-targeted interventions, to slow or reverse HF progression.\u003c/p\u003e","manuscriptTitle":"Study on the correlation between circadian rhythm, intestinal flora and Liver stiffness in patients with Hepatic fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 09:44:33","doi":"10.21203/rs.3.rs-6224838/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":"7ebf4c88-875d-4f71-8220-c5b185e415ff","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45676219,"name":"Physical Medicine \u0026 Rehab"}],"tags":[],"updatedAt":"2025-03-17T09:44:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 09:44:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6224838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6224838","identity":"rs-6224838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}