Metagenomic Profiling of Gut Microbiome in Post-cholecystectomy Patients with Diarrhea: A Nested Case-control Study

Metagenomic Profiling of Gut Microbiome in Post-cholecystectomy Patients with Diarrhea: A Nested Case-control Study | 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 Research Article Metagenomic Profiling of Gut Microbiome in Post-cholecystectomy Patients with Diarrhea: A Nested Case-control Study Jiayi YE, Peiyu MAO, Bo LI, Ying HAO, Yuwen CHEN, Ka LI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8696322/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Cholecystectomy can cause diarrhea, with an incidence as high as 57.2%, seriously impacting patient prognosis. To investigate the gut dysbiosis following cholecystectomy and identify microbial biomarkers and functional genomics associated with post-cholecystectomy diarrhea (PCD), we conducted a nested case-control study within a prospective cohort. Methods We enrolled a cohort of 160 patients. At follow-up completion, 30 patients who developed PCD were matched with 30 non-PCD (NPCD) controls. 16S rRNA sequencing analyzed gut microbiota structure and diversity. Representative fecal samples underwent metagenomic sequencing for species level and genetic differential analysis. Results The potentially pathogenic bacteria Coprococcus _ comes and Blautia _ sp. were found to be significantly enriched in the gut microbiota of PCD patients, with their abundance positively correlated with the degree of intestinal inflammation. In contrast, the potentially beneficial bacterial species Bacteroides intestinalis and Prevotella copri , known to contribute to lipid metabolism and play a role in modulating gut immunity and suppressing inflammatory responses, were found to be significantly depleted in PCD patients. Further functional analysis revealed that the gut microbiota of PCD patients was significantly enriched in gene pathways related to cell motility, membrane transport and sulphur metabolism. Conclusions This work identified potential beneficial and pathogenic bacterial species associated with the onset of PCD, as well as significantly enriched functional pathways within the intestinal microbiota. These findings provide a scientific basis for elucidating the relationship between PCD and gut microbiota, and offer valuable insights for developing microbiota-targeted interventions to alleviate PCD symptoms. Cholecystectomy Diarrhea Symptom relief Gut microbiota Macrogenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Notwithstanding the considerable benefits offered by cholecystectomy in the management of gallstone-related ailments, this surgical procedure entails the removal of the gallbladder. Consequently, it results in alterations to the pattern and rhythm of bile acid excretion into the intestinal lumen, thereby augmenting the rate of enterohepatic recirculation of bile acids and enhancing the exposure of intestinal flora to the bile acid pool ( 1 , 2 ). This series of physiological and biochemical changes may lead to gastrointestinal symptoms such as diarrhea, abdominal pain, nausea, and vomiting, which can severely affect the patient's quality of life ( 3 ). The incidence of gastrointestinal symptoms subsequent to cholecystectomy has reached up to 50%, with reports spanning from the second postoperative day to 32 years following the procedure ( 4 ). Post-cholecystectomy diarrhea (PCD) has been identified as the most common symptom following cholecystectomy. It is characterized by a high incidence, a challenging treatment landscape, recurrent symptoms and a protracted duration of disease ( 5 ). As demonstrated in previous studies, the incidence of PCD has been found to range from 12% to 57.2% ( 6 ), with 23% of patients still experiencing symptoms after six months ( 7 ).The occurrence of PCD has been demonstrated to result in water and electrolyte imbalances ( 8 ), in addition to an elevated risk of proximal colon cancer, which poses a significant threat to the prognosis of patients ( 9 ). Anti-diarrheal agents, including loperamide and diphenoxylate-atropine, have been shown to alleviate diarrheal symptoms by inhibiting intestinal secretion and peristalsis ( 10 ). Nevertheless, it is imperative to acknowledge the potential adverse effects of these agents. Exposure to high doses during use or abuse has been demonstrated to result in cardiotoxicity, which can have a serious impact on patients' recovery and may even be fatal ( 11 ). The enhancement of perioperative symptom management, the reduction of the incidence of post-cholecystectomy diarrhea, and the improvement of the quality of life for patients undergoing cholecystectomy represent critical challenges in symptom relief. The present clinical interventions for PCD includes dietary modifications ( 12 ) and physical therapies such as electroacupuncture ( 13 ). However, the effectiveness and precision of these interventions are often limited ( 14 ). Consequently, there is an urgent need to identify the biomarkers associated with PCD, in order to guide the development of more targeted and efficacious strategies. The gut microbiota refers to the collective microorganisms residing in the human gut, including bacteria, fungi, viruses and protozoa ( 15 ). The complex interplay of diverse gut microbiota is pivotal in preserving their dynamic equilibrium through mutual constraints. This intricate network is crucial for the maturation and development of the intestinal mucosal immune system, as well as the maintenance of intestinal barrier integrity ( 16 ). Changes in the gut microbiota are a major contributing factor to a range of gastrointestinal disorders, including, but not limited to, such as diarrhea ( 17 ), irritable bowel syndrome ( 18 ), and inflammatory bowel disease ( 19 ). However, due to the complexity of the interactions between gut microbiota and their interactions with the human body, the diverse composition of gut microbiota, and the differences in the specific gut microbiota whose abundance has been altered in different studies, the structure and characteristics of the gut microbiota in patients with PCD are still unclear, and the key gut microbiota associated with the development of PCD need to be further clarified. Moreover, the majority of extant studies employed 16S rRNA for gut microbiota sequencing, which was predominantly descriptive and thus unable to elucidate the gene function of the gut microbiota associated with PCD. Metagenomic techniques have been shown to be advantageous in identifying microbial communities down to the species level and in conducting a functional investigation of microbial genes. This, in turn, has the capacity to clarify the metabolic and functional pathways through which microbes may be involved in the development of diseases ( 20 – 22 ). The objective of this study is to investigate the effects of cholecystectomy on the gut microbiota. To this end, changes in the structure and diversity of the microbiota in patients both before and after surgery will be examined. The employment of 16S rRNA and metagenomic techniques is the foundation of our research, which aims to identify the key gut microbiota associated with the occurrence of PCD. Furthermore, we analyze the functions of specific genes within the gut microbiota related to PCD. 2 Methods 2.1 Study design The general information, food frequency and bowel habit questionnaires were collected from the study subjects preoperatively (T0), and the fecal samples were retained at this time point. The patients were followed up one month after surgery (T1), at which time the food frequency and bowel habit questionnaires were completed and further fecal samples were collected. The patients who developed PCD were treated as a case group according to the criteria for determining PCD and were matched in a 1:1 ratio using the time of case entry into the cohort ( 23 ), age (± 3 years), gender, Body Mass Index (BMI) (low weight, BMI < 18.5; normal, 18.5 ≤ BMI ≤ 23.9; overweight, 24.0 ≤ BMI ≤ 27.9; obese, BMI ≥ 28.0), and food frequency score (± 3 points) were used as matching conditions for case-matching non-post-cholecystectomy diarrhea (NPCD) patients as a control group. Once the case and control groups were identified, 16S rRNA sequencing was applied to detect the fecal samples retained at T0 and T1 time points in both groups. Based on the results of 16S rRNA sequencing, representative fecal samples from the PCD and NPCD groups at T1 time point were selected for metagenomic sequencing. This study has been reported in line with the STROCSS guidelines ( 24 ). 2.2 Patients A convenience sampling method was used for enrolling patients with a confirmed diagnosis of gallstones and a proposed laparoscopic cholecystectomy in the department of biliary surgery of a tertiary-level A hospital in Sichuan Province from December 2022 to December 2023. The inclusion criteria, exclusion criteria and withdrawal criteria are provided in Supplementary. Informed consent was obtained from all eligible participants. 2.3 Instruments 2.3.1 Demographic questionnaire The questionnaire was designed to collect demographic information, preoperative disease status and surgical status (details are provided in Supplementary). 2.3.2 Food frequency questionnaire The food frequency questionnaire developed by Zeng Guo et al. was used to assess the frequency of food intake and dietary structure of patients in the last 1 month ( 25 ), a method that has been extensively employed and has demonstrated excellent reliability and validity (details are provided in Supplementary). The questionnaire was grounded in the ‘Balanced Dietary Pagoda for Chinese Residents’, a dietary guideline promulgated by the Chinese Nutrition Society, which categorizes food into nine distinct categories. The frequency of food intake was categorized into five levels: daily (seven times per week), weekly (four to six times), weekly (one to three times), monthly (< 1 time per week) and hardly ever (< 1 time per month). The frequency of intake of each food group, with the exception of fish and shrimp, was evaluated on a scale of 4, 3, 2, 1 and 0, with 4 representing the highest frequency and 0 representing the lowest. The consumption of fish and shrimp is assigned a point value based on the frequency of intake: 4 points for weekly consumption or more, 2 points for monthly consumption, and 0 points for non-consumption. It is important to note that the estimation process did not involve the consideration of fats and oils. The total score of the questionnaire was 32. 2.3.3 Bowel habit questionnaire A self-developed questionnaire on bowel habits was used to evaluate the patients' bowel habits, including frequency of bowel movements, stool form, duration of bowel movements, occurrence of diarrhea and duration of diarrhea (further details are provided in Supplementary). The stool form was assessed using the Bristol Stool Form Scale (details are provided in Supplementary). The classification system employs a scale of 7 grades, with a score of 1–7 assigned to each grade. A score of 5 and above is indicative of probable diarrhea ( 26 ). 2.4 16S rRNA sequencing The 16S rRNA sequencing was performed using the Illumina NovaSeq 6000 sequencing platform. The representative sequences obtained from 16S rRNA sequencing were analyzed for α diversity and β diversity of gut microbiota using QIIME 2 software version 2020.11( 27 )and annotated against the Silva (version 138) database to count the relative abundance of species at different taxonomic levels. Paired Wilcoxon rank-sum test was used to analyze the differences in the structure and diversity of gut microbiota in cholecystectomy patients at T0 and T1 time points, and Wilcoxon rank-sum test was used to analyze the differences in the structure and diversity of gut microbiota between the PCD group and the NPCD group at T1 time points. Linear discriminant analysis Effect Size (LEfSe) was used for screening the landmark species between the two groups( 28 ). 2.5 Metagenomic sequencing In accordance with the findings of the 16S rRNA sequencing, Micro PITA analysis( 29 )was used to screen representative fecal samples from patients in the PCD and NPCD groups at T1 for metagenomic sequencing. Metagenomic sequencing was performed using the Illumina NovaSeq 6000 sequencing platform. The set of non-redundant genes obtained from metagenomic sequencing was annotated against the NR database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using DIAMOND software version 0.9.10.111 to obtain species and gene functional abundance information. The Wilcoxon rank-sum test was employed to analyze the difference of gut microbiota at the species level between the PCD group and the NPCD group at T1. LEfSe was used to analyze the gene functional pathways that exhibited significant differences in KEGG Level 1, Level 2, and Level 3 between the PCD and NPCD groups at T1. 2.6 Statistical methods 2.6.1 Sample size calculation In the absence of a definitive sample size calculation formula has been established for gut microbiota and metagenomic profiling, the sample size was calculated using the formula for the two-sample comparison of means: N = 2[(Z α/2 +Z β )(σ/δ)] 2 , where the level of statistical significance was defined as P < 0.05, Z α/2 =1.96, statistical power(1-β) was taken as 0.90, Z β =1.282 ( 29 ). Pursuant to the findings of the preliminary examination, the standard deviation (σ) was determined to be 1.165, while the mean (δ) was established as 1.03. Consequently, it was ascertained that a minimum of 27 samples were requisite for the case group. The study was conducted in a biliary surgery department where the incidence of PCD was 20% in 2019–2020. This was then adjusted for a 10% loss to follow-up rate, which resulted in a final cohort size of at least 149 samples. 2.6.2 Data analysis The Epidata 3.1 software was utilized to establish the database, and a dual-person data entry approach was implemented, accompanied by rigorous logical error checking of the entered data. Statistical analyses were performed using SPSS 23.0 and R software version 3.5.1. The two-sided α = 0.05 was employed as the test level, and a P < 0.05 was considered to be statistically significant. In the context of the comparison of basic information, the statistical descriptors employed for measurement data that conformed to a normal distribution were means ± standard deviation. For measurement data that did not conform to a normal distribution, the statistical descriptors employed were the median and the interquartile range. Frequencies and percentages were used for enumeration data. The comparison of measurement data was conducted through the utilization of the t-test or the Mann-Whitney U-test, contingent upon the normality of the data. Enumeration data underwent analysis through the implementation of the chi-squared test (χ2) or the Fisher's exact test. In relation to the comparison of differences in gut microbiota, the data were analyzed using a t-test for data with a normal distribution, and a Wilcoxon's rank sum test for data with a non-normal distribution. Boxplot plots were used to visualize species that differed at the phylum, genus and species levels. 3 Results 3.1 Baseline characteristics of the participants The inclusion and grouping of study participants are illustrated in Fig. 1 . 160 patients with gallstones who underwent laparoscopic cholecystectomy were enrolled. Fecal samples were collected preoperatively (T0) from all 160 patients. However, 8 patients declined to be followed up at 1 month postoperatively (T1). Consequently, 152 patients completed the follow-up, and fecal samples were collected from them at the T1 time point. As a consequence of the analysis, it was established that PCD occurred in 30 patients by the conclusion of the follow-up period, with an incidence of 19.74%. 30 patients with NPCD as controls were selected from the cohort and matched 1:1 to cases based on the time of case entry, age, gender, BMI, and food frequency score. Finally, 120 fecal samples from 60 patients at T0 and T1 time points were included for 16S rRNA sequencing. A total of 6 fecal samples were included in the metagenomic sequencing analysis, with 3 samples each from patients in the PCD and NPCD groups at T1. The baseline characteristics of patients in the PCD and NPCD groups are outlined in Table 1 .The Baseline characteristics of the two groups were comparable ( P > 0.05 for all comparisons), including age, gender, BMI, ethnicity, smoking/alcohol history, preoperative (T0) blood markers, intraoperative parameters (blood loss, surgical duration), number of gallstones, food frequency scores at T0 and T1, defecation frequency, Bristol Stool Form Score, and defecation time at T0. However, a number of significant differences were identified between the groups at T1 with regard to defecation frequency ( P < 0.001), Bristol Stool Form Score ( P < 0.001), and defecation time ( P < 0.001). Patients in the PCD group demonstrated an increased frequency of defecation, irregular timing of defecation, and a higher Bristol Stool Form Score, which is consistent with the diagnostic criteria for PCD. Table 1 The baseline characteristics of patients in the post-cholecystectomy diarrhea and non-post-cholecystectomy diarrhea groups Variable PCD group(n = 30) NPCD group(n = 30) t/x 2 /Z P value n(%)/x ± s/ n(%)/x ± s/ M(P25,P75) M(P25,P75) Age(years) 48.33 ± 11.69 48.30 ± 11.37 0.011 1) 0.991 gender 0.000 2) 1.000 male 10(33.33) 10(33.33) female 20(66.67) 20(66.67) BMI(kg/m 2 ) — 1.000 28 2(6.67) 2(6.67) Ethnicity — 1.000 Han Chinese 29(96.67) 30(100.00) Ethnic minority 1(3.33) 0(0.00) Smoking history — 0.671 Yes 4(13.33) 2(6.67) No 26(86.67) 28(93.33) Drinking history 0.111 2) 0.739 Yes 5(16.67) 6(20.00) No 25(83.33) 24(80.00) T0 Total bilirubin(µmol/L ) 11.75(9.08, 16.50) 14.75(11.50, 18.75) -1.797 3) 0.072 T0 Indirect bilirubin(µmol/L ) 8.85(6.90, 12.53) 10.55(8.90, 14.73) -1.494 3) 0.135 T0 Total bile acid(µmol/L ) 2.95(1.83, 4.00) 2.80(1.38, 4.73) -0.192 3) 0.847 T0 Albumin(g/L) 46.66 ± 3.11 45.70 ± 3.36 1.157 1) 0.252 T0 Alanine aminotransferase(IU/L) 18.00(13.75, 27.00) 19.50(12.75, 30.25) -0.244 3) 0.807 T0 Aspartate aminotransferase(IU/L) 19.00(15.75, 23.00) 19.00(15.00, 26.00) -0.267 3) 0.789 T0 Triglycerides(mmol/L) 1.39(1.10, 1.91) 1.37(0.84, 2.38) -0.636 3) 0.525 T0 cholesterol(mmol/L) 4.70 ± 0.82 4.63 ± 0.81 0.291 1) 0.772 T0 HDL-C(mmol/L) 1.24(1.05, 1.52) 1.27(1.05, 1.58) -0.392 3) 0.695 T0 LDL-C(mmol/L) 2.91 ± 0.73 2.82 ± 0.61 0.530 1) 0.598 Intraoperative blood loss(mL) 5.00(5.00, 5.00) 5.00(5.00, 5.00) -0.340 3) 0.734 Operative time(min) 52.00(38.75, 62.0)) 51.00(39.50, 65.00) -0.636 3) 0.525 Number of gallstones 0.300 2) 0.584 ≤ 1 11(36.67) 9(30.00) > 1 19(63.33) 21(70.00) T0 Food Frequency Score 24.70 ± 2.69 25.57 ± 2.46 -1.302 1) 0.198 T1 Food Frequency Score 25.47 ± 2.19 26.37 ± 2.06 -1.639 1) 0.107 T0 Defecation Frequency (times/day) 1.00(1.00, 2.00) 1.00(1.00, 2.00) -0.531 3) 0.595 T1 Defecation Frequency (times/day) 4.00(3.00, 4.00) 1.00(1.00, 2.00) -6.846 3) < 0.001 T0 Bristol Stool Form Scale 3.50(3.00, 4.00) 3.00(3.00, 4.00) -0.796 3) 0.426 T1 Bristol Stool Form Scale 6.00(5.00, 6.00) 3.00(2.75, 4.00) -6.659 3) < 0.001 T0 Defection Timing 0.480 2) 0.488 Fixed schedule 26(86.67) 24(80.00) Irregular timing 4(13.33) 6(20.00) T1 Defection Timing 24.310 2) < 0.001 Fixed schedule 4(13.33) 23(76.67) Irregular timing 26(86.67) 7(23.33) Note: Abbreviations: PCD: post-cholecystectomy diarrhea; NPCD: non-post-cholecystectomy diarrhea; T0: preoperatively; T1: 1 month postoperatively; BMI: Body Mass Index; HDL-C: High-Density Lipoprotein Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; 1): t value; 2): X 2 value; 3): Z value; —: Fisher’s exact test, no test statistic Figure 1 The flow diagram of enrolled patients and study design 3.2 Changes in gut microbiome 3.2.1 α diversity The α diversity of the gut microbiota was evaluated at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups, via the Chao1 index, ACE index, Shannon index, and Simpson index. The Wilcoxon rank-sum test was conducted, revealing that comparisons of all four α diversity indices were statistically non-significant ( P > 0.05). Consequently, these findings suggest no significant differences in community richness and diversity between the T0 and T1 time points for the cholecystectomy patients, or between the PCD and NPCD groups at the T1 time point. Details are shown in Table 2 . Table 2 Comparison of gut microbiota α diversity at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups Group Chao 1 Index ACE Index Shannon Index Simpson Index T0(n = 60) 242.76(182.80, 430.31) 243.30(189.71, 429.87) 5.84(5.19, 6.86) 0.96(0.93, 0.98) T1(n = 60) 242.84(165.97, 352.16) 240.60(164.48, 355.70) 5.68(5.04, 7.08) 0.95(0.92, 0.97) Z -1.038 -1.067 -1.730 -1.656 P 0.299 0.286 0.084 0.098 Group Chao 1 Index ACE Index Shannon Index Simpson Index PCD-T1(n = 60) 230.34(181.96, 376.28) 230.65(179.70, 374.40) 5.77(4.61,6.59) 0.95(0.90, 0.98) NPCD-T1(n = 60) 260.93(162.16, 363.68) 261.12(162.07, 360.64) 5.46(5.15, 6.10) 0.95(0.92,0.97) Z -0.148 -0.163 -0.163 -0.133 P 0.882 0.871 0.871 0.894 Notes: T0: preoperative; T1: 1 month postoperative; PCD-T1: 1 month postoperatively in the post-cholecystectomy diarrhea group; NPCD-T1: 1 month postoperatively in the non post-cholecystectomy diarrhea group; α diversity index does not conform to normal distribution therefore M (P25, P75) was used to indicate 3.2.2 β diversity The β diversity analysis was designed to measure differences in species composition between different environmental groups. The Weighted Unifrac distance revealed differences in the β diversity of the gut microbiota both between T0 and T1 time points in cholecystectomy patients (Fig. 2 A), and between the PCD and NPCD groups at T1(Fig. 2 B). Subsequently, adonis statistical analysis revealed that none of the observed differences were statistically significant ( P > 0.05). Figure 2 Comparison of gut microbiota β diversity at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups 3.2.3 Phylum and Genus Differences The comparison of the differences in the gut microbiota structure at different taxonomic levels revealed significant changes following cholecystectomy. At the phylum level (Fig. 3 A), the relative abundance of Campilobacterota was found to decrease significantly at T1 compared to T0 in cholecystectomy patients ( P = 0.048). At the genus level (Fig. 3 B), the relative abundance of Monoglobus ( P = 0.008) and Adlercreutzia (P = 0.029) significantly increased at T1, while Morganella ( P = 0.025), Enterococcus (P = 0.033), and Clade _III ( P = 0.022) significantly decreased. LEfSe analysis further identified Monoglobus , Monoglobaceae , Adlercreutzia , and Eggerthellaceae as being significantly enriched at T1 (LDA > 2.0, P 2.0, P 0.05). However, at the genus level, the PCD group showed a significant increase in Lachnoclostridium ( P = 0.037), [Ruminococcus]_gnavus_group ( P = 0.003), and Blautia ( P = 0.037), along with a decrease in Paraprevotella (P = 0.031), [Eubacterium]_ruminantium_group ( P = 0.048), and Enhydrobacter ( P = 0.042) compared to the NPCD group (Fig. 3 D). LEfSe analysis confirmed enrichment of Blautia , Erysipelatoclostridium , [Ruminococcus]_gnavus_group , and Lachnoclostridium in the PCD group (LDA > 2.0, P 2.0, P < 0.05) (Fig. 3 E). Figure 3 The comparison of the differences in the gut microbiota structure at phylum and genus level. 3.3 Metagenomic Sequencing Results 3.3.1 Gene number The Wilcoxon's rank sum test revealed that no significant difference in the boxplot of gene number in the T1 gut microbiota of patients in the PCD and NPCD groups (P = 0.513) (Fig. 4 A). 3.3.2 Species differences At the species level, the abundance of 73 species in the intestinal tracts of patients in the PCD and NPCD groups at T1 differed significantly (all P < 0.05). A significantly higher abundance of the following gut microbiota was found in the PCD group compared to the NPCD group. Blautia_wexlerae ( P = 0.013), Coprococcus_comes ( P = 0.006), Blautia _sp. ( P = 0.021) and others was significantly higher in T1 of patients in the PCD group than in the NPCD group. The abundance of Bacteroides intestinalis (P = 0.026), Prevotella copri ( P = 0.030) and Prevotella intermedia ( P = 0.006), was significantly reduced compared to the NPCD group at T1(Fig. 4 B). 3.3.3 KEGG pathway analysis At KEGG Level 1 level, no significant difference in gene function pathways were observed between the gut microbiota of patients in the PCD and NPCD groups at T1(Fig. 4 C). At KEGG Level 2, the abundance of genes in the biosynthesis of other secondary metabolites, cell motility, and membrane transport pathways was significantly higher in PCD group than in NPCD group at T1 (LDA values > 2.0, P 2.0, P < 0.05) (Fig. 4 D, F). At KEGG Level 3, the gene abundance of the functional pathways of sulfur metabolism, flagellar assembly, ATP-binding cassette (ABC) transporters, etc., was significantly higher in PCD group than in NPCD group at T1 (LDA values > 2.0, P 2.0, P < 0.05) (Fig. 4E, G). Figure 4 Metagenomic sequencing results 4 Discussion Genus-level analysis revealed a substantial reduction in the relative abundance of Morganella , Enterococcus , and Clade_III from T0 to T1 following cholecystectomy. Morganella and Enterococcus are known to be potentially harmful bacteria. The decrease in the relative abundance of Morganella , and Enterococcus following cholecystectomy reflects changes in the gut microenvironment that hinder the growth of these bacteria. This alteration may help reduce the incidence of postoperative infections. In addition, we found that the relative abundance of gut microbiota Monoglobus was significantly increased in cholecystectomy patients at T1 compared to T0. It has been reported that higher levels of Monoglobus are associated with increased blood ammonia levels. The intestinal epithelial barrier can be impaired and intestinal permeability can be boosted by this increased ammonia, potentially raising the likelihood of diarrhea in patients after cholecystectomy ( 30 – 32 ). On the other hand, Nitrospiraceae and Bacilli were enriched at T0. Bacilli can produce short-chain fatty acids (SCFAs) from dietary fiber, which regulate host physiology and immunity ( 33 ). SCFAs also acidify the intestinal environment, inhibiting pathogens. Therefore, a reduced abundance of Bacilli after cholecystectomy could alter the gut microenvironment and raise the risk of pathogenic colonization( 34 ). We found that significantly higher relative abundances of [Ruminococcus]_gnavus_group , Lachnoclostridium and Blautia were shown in the gut microbiota of PCD patients, which may contribute to PCD development by increasing secondary bile acids( 35 ). Xu et al. reported that the [Ruminococcus]_gnavus_group and Blautia were significantly enriched in the intestines of PCD patients( 36 ), which is consistent with our own findings. The [Ruminococcus]_gnavus_group utilizes mucin to produce secondary bile acids, which can disrupt the intestinal barrier function( 37 – 39 ). It also influences lipid metabolism via FXR regulation, which may contribute to PCD development( 40 ). We revealed a significant decrease in the relative abundance of Paraprevotella , [Eubacterium]_ruminantium_g , and Enhydrobacter in PCD patients. Notably, all three of these bacteria are involved in the production of short-chain fatty acids( 41 – 43 ). Short-chain fatty acids can improve lipid metabolism and are essential for colonic motility, immunomodulation, and the inhibition of intestinal inflammation( 41 , 44 ). Increasing short-chain fatty acids in the gut can effectively alleviate inflammatory bowel disease( 45 ), antibiotic-associated diarrhea( 46 ), and chronic diarrhea( 47 ). Thus, the decrease in Paraprevotella , [Eubacterium]_ruminantium_group , and Enhydrobacter may contribute to PCD by reducing the production of short-chain fatty acids. At the species level, significant enrichment was found for Blautia_wexlerae , Coprococcus_comes , and Blautia_sp . in PCD patients, while Bacteroides_intestinalis and Prevotella_copri were significantly enriched in NPCD patients. Coprococcus_comes has been identified as a potentially pathogenic organism. As demonstrated in the study by Z. Wang et al., there is a correlation between this bacterium and heightened levels of intestinal inflammation( 48 ). Blautia_sp. can induce the release of large amounts of inflammatory cytokines and enhance intestinal permeability, thereby leading to intestinal barrier dysfunction( 49 ). The presence of these two pro-inflammatory bacteria in the intestines of patients with PCD has been shown to potentially promote inflammation and impair mucosal barrier function, which aligns with findings from other PCD studies( 2 , 50 ). Bacteroides_intestinalis , a significant constituent of the human colonic microbiota, has the capacity to degrade complex dietary fibers, thereby producing beneficial substances such as ferulic acid and short-chain fatty acids( 51 , 52 ). These compounds play a crucial role in regulating the intestinal immune response and suppressing intestinal inflammation. A decline in Bacteroides_intestinalis levels has been observed to result in a reduction in the production of ferulic acid and short-chain fatty acids. This has the potential to weaken gut defenses, increase permeability, and stimulate inflammation, which may lead to an acceleration in the progression of PCD. By comparing the gut microbiota gene sets of PCD and NPCD patients at T1 via the KEGG database, we observed that PCD patients’ microbiota genes were significantly enriched in pathways for cell motility (notably flagellar assembly), membrane transport (especially ABC transporters), and sulfur metabolism. The flagellar assembly functional pathway has found to be significantly enriched in intestinal diseases such as irritable bowel syndrome ( 53 )and ulcerative colitis( 54 ). This enrichment enhances the adhesion capacity of intestinal pathogens to intestinal epithelial cells, thereby promoting pathogen colonization in the intestine( 54 ). Consistent with our study findings, the gut microbiota of PCD patients exhibited significant enrichment of genes associated with the flagellar assembly functional pathway. This enrichment may facilitate the colonization of potentially pathogenic bacteria on the intestinal mucosa, thereby triggering an inflammatory response and impairing mucosal barrier function, ultimately contributing to the development of PCD. ABC transporters are a class of proteins that utilize the energy from ATP hydrolysis to transport diverse molecules across cell membranes, and they are closely associated with lipid metabolism( 53 ). Previous studies have shown that enrichment of functional ABC transporter pathways is associated with an increased risk of pathogen colonization and inflammation( 55 ). Hydrogen sulfide, a key product of gut microbial sulfur metabolism, directly induces DNA damage in colonocytes in animal models( 56 ). Additionally, it has been demonstrated that this process can damage the intestinal mucosa by disrupting the disulfide bonds in the mucus layer, allowing luminal flora and metabolites to penetrate the intestinal wall. This penetration induces epithelial cell apoptosis and triggers intestinal inflammation( 57 , 58 ). Roediger et al. demonstrated that hydrogen sulfide significantly reduces butyrate production in the colon. Butyrate is crucial for maintaining the intestinal barrier, which prevents the invasion of toxins and bacteria into the gut ( 59 ). Furthermore, genes within the gut microbiota of PCD patients showed significant down-regulation in several cofactor and vitamin metabolic pathways, notably folate biosynthesis, vitamin B6 metabolism, and the biosynthesis of ubiquinone and other terpenoid quinones. The down-regulation of genes belonging to the gut microbiota within these functional pathways in PCD patients may lead to reduced levels of short-chain fatty acids in the host intestinal tract, which may in turn influence the development of PCD. This observation is consistent with prior studies on PCD-associated gut microbiota( 2 ), though the precise mechanism underlying its role in PCD pathogenesis requires further confirmation. In summary, our findings indicate that alterations in the gut microbiota structure occur in patients after cholecystectomy. Enrichment of microbial genes involved in pathways such as flagellar assembly and ABC transporters, along with down-regulation of pathways related to folate biosynthesis and vitamin B6 metabolism, may be closely associated with the development of PCD. Early detection of pathogenic bacterial colonization may serve as a critical preventive measure for precisely mitigating PCD. Monitoring dynamic changes in the gut microbiota, early identification of pathogenic colonization, and implementing targeted clinical interventions against specific gut microbiota may serve as crucial strategies for the precise prevention of PCD. Furthermore, dietary guidance should be provided based on clinical symptoms and metabolic profiles, emphasizing the consumption of soluble fiber-rich foods to enhance short-chain fatty acid levels in the gut. Probiotic supplementation may be employed to support the restoration of a healthy microbial community, thereby facilitating postoperative recovery. This study has several limitations. Firstly, the study was constrained by limitations relating to the storage and transportation of specimens, which restricted its applicability to patients with PCD patients from Chengdu City, thereby limiting the broader applicability of the results. A multicenter trial, to be conducted in the future, could offer a more complete picture of the gut microbiota's structure and diversity in this patient group. The trial would be conducted on a randomized basis and would involve patients with PCD from hospitals across the country. Secondly, the study duration and clinical practices influenced its comparison of gut microbiota, which was limited to PCD and NPCD patients only at 1-month post-surgery. However, given the dynamic nature of the gut microbiota, with its capacity to fluctuate over time, it is recommended that future studies should consider extending the observation period. This would facilitate the identification of key microbial taxa associated with PCD at different post-surgical time points, reveal the dynamic patterns of gut microbiota changes in PCD patients. 5 Conclusion Cholecystectomy significantly altered the gut microbiota structure of patients, including changes in the abundance of potentially beneficial and pathogenic bacteria, as well as alterations in α-diversity and β-diversity. We found that post-cholecystectomy diarrhea (PCD) is associated with intestinal enrichment of Lachnoclostridium , [Ruminococcus]_gnavus_group , Blautia , Coprococcus_comes and Blautia_sp . These taxa are therefore promising targets for precise monitoring and intervention in the symptom management of PCD patients. Additionally, the gut microbiota of PCD patients showed significant enrichment of genes involved in functional pathways such as flagellar assembly, ABC transporters, and sulfur metabolism. Specifically, flagellar assembly may enhance bacterial adhesion and intestinal barrier disruption, while ABC transporters and sulfur metabolism may be involved in abnormal bile acid transport and intestinal inflammation—collectively, these pathways may contribute to the pathogenesis of PCD. This study clarifies the taxonomic and functional associations between PCD and gut microbiota, providing a solid foundation for developing precision microbiota-targeted interventions for PCD symptom relief. Declarations Ethics approval and consent to participate This study received ethical approval from Sichuan University West China Hospital Biomedical Ethics Committee (Approval No. 20221824) and registered with the China Clinical Trial Centre (ChiCTR2200066563). Informed consent was given to all patients after introducing the purpose of this study. Names of all the patients and institutions are both anonymous during the whole study, and the anonymized data is only used for research purposes, including publication. Consent for publication Not applicable. Availability of data and materials The datasets of 16S rRNA sequencing and metagenomic sequencing during the current study are available in the NCBI Sequence Read Archive (SRA) with the accession number PRJNA1314443(https://dataview.ncbi.nlm.nih.gov/object/PRJNA1314443?reviewer=m8bkuf5lc6inms64kamuckl0jc) Competing interests The authors declare that they have no competing interests. Funding This work was financially supported by the National Natural Science Foundation of China (U22A20334), Natural Science Foundation of Sichuan Province (2024NSFSC1314, 2025ZNSFSC0243), Sichuan Science and Technology Program (2025HJRC0024, 2024YFFK0034), Chengdu Science and Technology Program (2024-YF05-00206-SN), Seed Fund Project for Cooperation between Sichuan University and Universities in Hong Kong and Macao (2024-19). Authors’ contributions JY and PM contributed equally to this work. JY and PM are co-first authors of this manuscript. Conception and design of the study, drafting the article and revising the article critically for important intellectual content: KL, YC, YH, JY, PM, and BL. Analysis and interpretation of data: YC and YH. Conception and design of the study and final approval of the version to be submitted: KL, YC and YH. All authors read and approved the final manuscript. Acknowledgements Not applicable. 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Hydrogen sulfide induces direct radical-associated DNA damage. Mol cancer research: MCR. 2007;5(5):455–9. Zhang W, An Y, Qin X, Wu X, Wang X, Hou H, et al. Gut Microbiota-Derived Metabolites in Colorectal Cancer: The Bad and the Challenges. Front Oncol. 2021;11:739648. Figliuolo VR, Coutinho-Silva R, Coutinho C. Contribution of sulfate-reducing bacteria to homeostasis disruption during intestinal inflammation. Life Sci. 2018;215:145–51. Roediger WE, Duncan A, Kapaniris O, Millard S. Reducing sulfur compounds of the colon impair colonocyte nutrition: implications for ulcerative colitis. Gastroenterology. 1993;104(3):802–9. Additional Declarations No competing interests reported. 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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-8696322","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597627882,"identity":"6dde82be-d66e-43da-a1a6-946ad482d818","order_by":0,"name":"Jiayi YE","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"YE","suffix":""},{"id":597627884,"identity":"fa04dd60-1d02-43b3-9466-7e6b1d797da6","order_by":1,"name":"Peiyu MAO","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Peiyu","middleName":"","lastName":"MAO","suffix":""},{"id":597627885,"identity":"68134c61-118e-4108-8572-15579ee31567","order_by":2,"name":"Bo LI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACNmbG9h8fftTIsckfPkCcFj525gOSM3uOGfNLsCUQp0WOny1BmoeNOXHmDB4DYh3GY2DAw8PGuOF2z8cbbxjs5HQbiNCSIGEhw2xw5+xmyzkMycZmB4jQcsCAh43N4EDuNmkehgOJ24jQYtiQANaY84xYLWzJDAfYmCUkZ+SwEauF+RhjY88xA36eY8aWcwyI8It8/8E25j8/aurb2Jsf3nhTYSdHUAsKkCA2apC1kKpjFIyCUTAKRgQAAG59OzG6CiisAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"LI","suffix":""},{"id":597627886,"identity":"66611416-f899-4309-a951-893da3dd2d5e","order_by":3,"name":"Ying HAO","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"HAO","suffix":""},{"id":597627890,"identity":"760d90ad-5a49-40b8-a6ce-6243f5f57eb5","order_by":4,"name":"Yuwen CHEN","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yuwen","middleName":"","lastName":"CHEN","suffix":""},{"id":597627891,"identity":"e93f0e95-27af-4170-96de-6056158b9857","order_by":5,"name":"Ka LI","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Ka","middleName":"","lastName":"LI","suffix":""}],"badges":[],"createdAt":"2026-01-26 04:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8696322/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8696322/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104171092,"identity":"edfa4b41-da9d-486a-8f79-42862f2fe25f","added_by":"auto","created_at":"2026-03-08 14:52:12","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62232,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow diagram of enrolled patients and study design\u003c/p\u003e\n\u003cp\u003eNOTE: PCD: post-cholecystectomy diarrhea. NPCD: non-post-cholecystectomy diarrhea\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/01ca94fb73993571b08d7664.jpeg"},{"id":104171090,"identity":"a7bbd795-da4b-470a-a3b8-1bb58c2af20f","added_by":"auto","created_at":"2026-03-08 14:52:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106352,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of gut microbiota B diversity at TO and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups\u003c/p\u003e\n\u003cp\u003eNOTE: (A) Comparison of gut microbiota ß diversity at TO and T1 in cholecystectomy patients. (B) Comparison of gut microbiota B diversity at T1 in both the PCD and NPCD groups. PCD: post-cholecystectomy diarrhea, NPCD: non-post-cholecystectomy diarrhea\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/d92c63c9aa95acc333822c29.jpeg"},{"id":104404511,"identity":"ecdb0bdd-6536-4f47-baaa-19cbe27f50d1","added_by":"auto","created_at":"2026-03-11 12:20:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":321145,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison of the differences in the gut microbiota structure at phylum and genus level\u003c/p\u003e\n\u003cp\u003eNOTE: Differences in species at (A) the phylum level and (B) the genus level in cholecystectomy patients at TO and T1. (C) LEASe analysis of gut microbiota structure in cholecystectomy patients at TO and T1. (D) Top 10 differentially abundant genera in PCD and NPCD groups. (E) LEfSe analysis of gut microbiota structure in PCD and NPCD groups. PCD: post-cholecystectomy diarrhea, NPCD: non-postcholecystectomy diarrhea\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/ac6b30fe01ca35e038d55b3d.jpeg"},{"id":104404651,"identity":"451469cc-0ab0-4041-8917-b77665c37a7b","added_by":"auto","created_at":"2026-03-11 12:20:45","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331904,"visible":true,"origin":"","legend":"\u003cp\u003eMetagenomic sequencing results\u003c/p\u003e\n\u003cp\u003eNOTE: (A) Analysis of gut microbiota gene number in PCD and NPCD groups. (B) Top 10 differentially abundant species in PCD and NPCD groups. KEGG (C) Level 1, (D) Level 2 and (E) Level 3 pathway abundance profiles in PCD and NPCD gut microbiome. LEfSe analysis of KEGG (F) Level 2 and (G) Level 3 pathway abundance profiles in PCD and NPCD gut microbiome. PCD: post-cholecystectomy diarrhea, NPCD: non-post-cholecystectomy diarrhea\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/7f8f61b9f57c6c78a2065d99.jpeg"},{"id":104409345,"identity":"676398d2-9a11-4e46-a100-5607cbb78625","added_by":"auto","created_at":"2026-03-11 12:44:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1916442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/f5dca7ca-98b7-44ce-95c7-aa29f0ca53f6.pdf"},{"id":104403922,"identity":"36b4e197-c9ac-4b5c-9ccc-ca055b11292d","added_by":"auto","created_at":"2026-03-11 12:19:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1272276,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8696322/v1/ceb91a960378d02f844ab02c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metagenomic Profiling of Gut Microbiome in Post-cholecystectomy Patients with Diarrhea: A Nested Case-control Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eNotwithstanding the considerable benefits offered by cholecystectomy in the management of gallstone-related ailments, this surgical procedure entails the removal of the gallbladder. Consequently, it results in alterations to the pattern and rhythm of bile acid excretion into the intestinal lumen, thereby augmenting the rate of enterohepatic recirculation of bile acids and enhancing the exposure of intestinal flora to the bile acid pool (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This series of physiological and biochemical changes may lead to gastrointestinal symptoms such as diarrhea, abdominal pain, nausea, and vomiting, which can severely affect the patient's quality of life (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The incidence of gastrointestinal symptoms subsequent to cholecystectomy has reached up to 50%, with reports spanning from the second postoperative day to 32 years following the procedure (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Post-cholecystectomy diarrhea (PCD) has been identified as the most common symptom following cholecystectomy. It is characterized by a high incidence, a challenging treatment landscape, recurrent symptoms and a protracted duration of disease (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As demonstrated in previous studies, the incidence of PCD has been found to range from 12% to 57.2% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), with 23% of patients still experiencing symptoms after six months (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).The occurrence of PCD has been demonstrated to result in water and electrolyte imbalances (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), in addition to an elevated risk of proximal colon cancer, which poses a significant threat to the prognosis of patients (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Anti-diarrheal agents, including loperamide and diphenoxylate-atropine, have been shown to alleviate diarrheal symptoms by inhibiting intestinal secretion and peristalsis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Nevertheless, it is imperative to acknowledge the potential adverse effects of these agents. Exposure to high doses during use or abuse has been demonstrated to result in cardiotoxicity, which can have a serious impact on patients' recovery and may even be fatal (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The enhancement of perioperative symptom management, the reduction of the incidence of post-cholecystectomy diarrhea, and the improvement of the quality of life for patients undergoing cholecystectomy represent critical challenges in symptom relief. The present clinical interventions for PCD includes dietary modifications (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and physical therapies such as electroacupuncture (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, the effectiveness and precision of these interventions are often limited (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Consequently, there is an urgent need to identify the biomarkers associated with PCD, in order to guide the development of more targeted and efficacious strategies.\u003c/p\u003e \u003cp\u003eThe gut microbiota refers to the collective microorganisms residing in the human gut, including bacteria, fungi, viruses and protozoa (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The complex interplay of diverse gut microbiota is pivotal in preserving their dynamic equilibrium through mutual constraints. This intricate network is crucial for the maturation and development of the intestinal mucosal immune system, as well as the maintenance of intestinal barrier integrity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Changes in the gut microbiota are a major contributing factor to a range of gastrointestinal disorders, including, but not limited to, such as diarrhea (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), irritable bowel syndrome (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and inflammatory bowel disease (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, due to the complexity of the interactions between gut microbiota and their interactions with the human body, the diverse composition of gut microbiota, and the differences in the specific gut microbiota whose abundance has been altered in different studies, the structure and characteristics of the gut microbiota in patients with PCD are still unclear, and the key gut microbiota associated with the development of PCD need to be further clarified. Moreover, the majority of extant studies employed 16S rRNA for gut microbiota sequencing, which was predominantly descriptive and thus unable to elucidate the gene function of the gut microbiota associated with PCD.\u003c/p\u003e \u003cp\u003eMetagenomic techniques have been shown to be advantageous in identifying microbial communities down to the species level and in conducting a functional investigation of microbial genes. This, in turn, has the capacity to clarify the metabolic and functional pathways through which microbes may be involved in the development of diseases (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The objective of this study is to investigate the effects of cholecystectomy on the gut microbiota. To this end, changes in the structure and diversity of the microbiota in patients both before and after surgery will be examined. The employment of 16S rRNA and metagenomic techniques is the foundation of our research, which aims to identify the key gut microbiota associated with the occurrence of PCD. Furthermore, we analyze the functions of specific genes within the gut microbiota related to PCD.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThe general information, food frequency and bowel habit questionnaires were collected from the study subjects preoperatively (T0), and the fecal samples were retained at this time point. The patients were followed up one month after surgery (T1), at which time the food frequency and bowel habit questionnaires were completed and further fecal samples were collected. The patients who developed PCD were treated as a case group according to the criteria for determining PCD and were matched in a 1:1 ratio using the time of case entry into the cohort (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), age (\u0026plusmn;\u0026thinsp;3 years), gender, Body Mass Index (BMI) (low weight, BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5; normal, 18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026le;\u0026thinsp;23.9; overweight, 24.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026le;\u0026thinsp;27.9; obese, BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0), and food frequency score (\u0026plusmn;\u0026thinsp;3 points) were used as matching conditions for case-matching non-post-cholecystectomy diarrhea (NPCD) patients as a control group. Once the case and control groups were identified, 16S rRNA sequencing was applied to detect the fecal samples retained at T0 and T1 time points in both groups. Based on the results of 16S rRNA sequencing, representative fecal samples from the PCD and NPCD groups at T1 time point were selected for metagenomic sequencing. This study has been reported in line with the STROCSS guidelines (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Patients\u003c/h2\u003e \u003cp\u003eA convenience sampling method was used for enrolling patients with a confirmed diagnosis of gallstones and a proposed laparoscopic cholecystectomy in the department of biliary surgery of a tertiary-level A hospital in Sichuan Province from December 2022 to December 2023. The inclusion criteria, exclusion criteria and withdrawal criteria are provided in Supplementary. Informed consent was obtained from all eligible participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Instruments\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Demographic questionnaire\u003c/h2\u003e \u003cp\u003eThe questionnaire was designed to collect demographic information, preoperative disease status and surgical status (details are provided in Supplementary).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Food frequency questionnaire\u003c/h2\u003e \u003cp\u003eThe food frequency questionnaire developed by Zeng Guo et al. was used to assess the frequency of food intake and dietary structure of patients in the last 1 month (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), a method that has been extensively employed and has demonstrated excellent reliability and validity (details are provided in Supplementary). The questionnaire was grounded in the \u0026lsquo;Balanced Dietary Pagoda for Chinese Residents\u0026rsquo;, a dietary guideline promulgated by the Chinese Nutrition Society, which categorizes food into nine distinct categories. The frequency of food intake was categorized into five levels: daily (seven times per week), weekly (four to six times), weekly (one to three times), monthly (\u0026lt;\u0026thinsp;1 time per week) and hardly ever (\u0026lt;\u0026thinsp;1 time per month). The frequency of intake of each food group, with the exception of fish and shrimp, was evaluated on a scale of 4, 3, 2, 1 and 0, with 4 representing the highest frequency and 0 representing the lowest. The consumption of fish and shrimp is assigned a point value based on the frequency of intake: 4 points for weekly consumption or more, 2 points for monthly consumption, and 0 points for non-consumption. It is important to note that the estimation process did not involve the consideration of fats and oils. The total score of the questionnaire was 32.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Bowel habit questionnaire\u003c/h2\u003e \u003cp\u003eA self-developed questionnaire on bowel habits was used to evaluate the patients' bowel habits, including frequency of bowel movements, stool form, duration of bowel movements, occurrence of diarrhea and duration of diarrhea (further details are provided in Supplementary). The stool form was assessed using the Bristol Stool Form Scale (details are provided in Supplementary). The classification system employs a scale of 7 grades, with a score of 1\u0026ndash;7 assigned to each grade. A score of 5 and above is indicative of probable diarrhea (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 16S rRNA sequencing\u003c/h2\u003e \u003cp\u003eThe 16S rRNA sequencing was performed using the Illumina NovaSeq 6000 sequencing platform. The representative sequences obtained from 16S rRNA sequencing were analyzed for α diversity and β diversity of gut microbiota using QIIME 2 software version 2020.11(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)and annotated against the Silva (version 138) database to count the relative abundance of species at different taxonomic levels. Paired Wilcoxon rank-sum test was used to analyze the differences in the structure and diversity of gut microbiota in cholecystectomy patients at T0 and T1 time points, and Wilcoxon rank-sum test was used to analyze the differences in the structure and diversity of gut microbiota between the PCD group and the NPCD group at T1 time points. Linear discriminant analysis Effect Size (LEfSe) was used for screening the landmark species between the two groups(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metagenomic sequencing\u003c/h2\u003e \u003cp\u003eIn accordance with the findings of the 16S rRNA sequencing, Micro PITA analysis(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)was used to screen representative fecal samples from patients in the PCD and NPCD groups at T1 for metagenomic sequencing. Metagenomic sequencing was performed using the Illumina NovaSeq 6000 sequencing platform. The set of non-redundant genes obtained from metagenomic sequencing was annotated against the NR database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using DIAMOND software version 0.9.10.111 to obtain species and gene functional abundance information. The Wilcoxon rank-sum test was employed to analyze the difference of gut microbiota at the species level between the PCD group and the NPCD group at T1. LEfSe was used to analyze the gene functional pathways that exhibited significant differences in KEGG Level 1, Level 2, and Level 3 between the PCD and NPCD groups at T1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical methods\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Sample size calculation\u003c/h2\u003e \u003cp\u003eIn the absence of a definitive sample size calculation formula has been established for gut microbiota and metagenomic profiling, the sample size was calculated using the formula for the two-sample comparison of means: N\u0026thinsp;=\u0026thinsp;2[(Z\u003csub\u003eα/2\u003c/sub\u003e+Z\u003csub\u003eβ\u003c/sub\u003e)(σ/δ)]\u003csup\u003e2\u003c/sup\u003e, where the level of statistical significance was defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Z\u003csub\u003eα/2\u003c/sub\u003e=1.96, statistical power(1-β) was taken as 0.90, Z\u003csub\u003eβ\u003c/sub\u003e=1.282 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Pursuant to the findings of the preliminary examination, the standard deviation (σ) was determined to be 1.165, while the mean (δ) was established as 1.03. Consequently, it was ascertained that a minimum of 27 samples were requisite for the case group. The study was conducted in a biliary surgery department where the incidence of PCD was 20% in 2019\u0026ndash;2020. This was then adjusted for a 10% loss to follow-up rate, which resulted in a final cohort size of at least 149 samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Data analysis\u003c/h2\u003e \u003cp\u003eThe Epidata 3.1 software was utilized to establish the database, and a dual-person data entry approach was implemented, accompanied by rigorous logical error checking of the entered data. Statistical analyses were performed using SPSS 23.0 and R software version 3.5.1. The two-sided α\u0026thinsp;=\u0026thinsp;0.05 was employed as the test level, and a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant. In the context of the comparison of basic information, the statistical descriptors employed for measurement data that conformed to a normal distribution were means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. For measurement data that did not conform to a normal distribution, the statistical descriptors employed were the median and the interquartile range. Frequencies and percentages were used for enumeration data. The comparison of measurement data was conducted through the utilization of the t-test or the Mann-Whitney U-test, contingent upon the normality of the data. Enumeration data underwent analysis through the implementation of the chi-squared test (χ2) or the Fisher's exact test. In relation to the comparison of differences in gut microbiota, the data were analyzed using a t-test for data with a normal distribution, and a Wilcoxon's rank sum test for data with a non-normal distribution. Boxplot plots were used to visualize species that differed at the phylum, genus and species levels.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of the participants\u003c/h2\u003e \u003cp\u003eThe inclusion and grouping of study participants are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 160 patients with gallstones who underwent laparoscopic cholecystectomy were enrolled. Fecal samples were collected preoperatively (T0) from all 160 patients. However, 8 patients declined to be followed up at 1 month postoperatively (T1). Consequently, 152 patients completed the follow-up, and fecal samples were collected from them at the T1 time point. As a consequence of the analysis, it was established that PCD occurred in 30 patients by the conclusion of the follow-up period, with an incidence of 19.74%. 30 patients with NPCD as controls were selected from the cohort and matched 1:1 to cases based on the time of case entry, age, gender, BMI, and food frequency score. Finally, 120 fecal samples from 60 patients at T0 and T1 time points were included for 16S rRNA sequencing. A total of 6 fecal samples were included in the metagenomic sequencing analysis, with 3 samples each from patients in the PCD and NPCD groups at T1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe baseline characteristics of patients in the PCD and NPCD groups are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.The Baseline characteristics of the two groups were comparable (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all comparisons), including age, gender, BMI, ethnicity, smoking/alcohol history, preoperative (T0) blood markers, intraoperative parameters (blood loss, surgical duration), number of gallstones, food frequency scores at T0 and T1, defecation frequency, Bristol Stool Form Score, and defecation time at T0. However, a number of significant differences were identified between the groups at T1 with regard to defecation frequency (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Bristol Stool Form Score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and defecation time (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients in the PCD group demonstrated an increased frequency of defecation, irregular timing of defecation, and a higher Bristol Stool Form Score, which is consistent with the diagnostic criteria for PCD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe baseline characteristics of patients in the post-cholecystectomy diarrhea and non-post-cholecystectomy diarrhea groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCD group(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPCD group(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et/x\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Z\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en(%)/x\u0026thinsp;\u0026plusmn;\u0026thinsp;s/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en(%)/x\u0026thinsp;\u0026plusmn;\u0026thinsp;s/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM(P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM(P25,P75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(33.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(66.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(46.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(46.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026ndash;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHan Chinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(96.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnic minority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(86.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(93.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(83.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Total bilirubin(\u0026micro;mol/L\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.75(9.08, 16.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.75(11.50, 18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.797\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Indirect bilirubin(\u0026micro;mol/L\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.85(6.90, 12.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.55(8.90, 14.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.494\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Total bile acid(\u0026micro;mol/L\u0026nbsp;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.95(1.83, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.80(1.38, 4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.192\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Albumin(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.157\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Alanine aminotransferase(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.00(13.75, 27.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.50(12.75, 30.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.244\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Aspartate aminotransferase(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.00(15.75, 23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.00(15.00, 26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.267\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Triglycerides(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39(1.10, 1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37(0.84, 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.636\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 cholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 HDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24(1.05, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27(1.05, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.392\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 LDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntraoperative blood loss(mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00(5.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00(5.00, 5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.340\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperative time(min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.00(38.75, 62.0))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.00(39.50, 65.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.636\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of gallstones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(36.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(63.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(70.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Food Frequency Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.302\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 Food Frequency Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.639\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Defecation Frequency\u0026nbsp;(times/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.531\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 Defecation Frequency\u0026nbsp;(times/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00(3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.846\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Bristol Stool Form Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.50(3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(3.00, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.796\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 Bristol Stool Form Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.00(5.00, 6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(2.75, 4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.659\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0 Defection Timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.480\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed schedule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(86.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(20.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 Defection Timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.310\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed schedule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(13.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(76.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular timing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(86.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Abbreviations: PCD: post-cholecystectomy diarrhea; NPCD: non-post-cholecystectomy diarrhea; T0: preoperatively; T1: 1 month postoperatively; BMI: Body Mass Index; HDL-C: High-Density Lipoprotein Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; 1): \u003cem\u003et\u003c/em\u003e value; 2): \u003cem\u003eX\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e value; 3): \u003cem\u003eZ\u003c/em\u003e value; \u0026mdash;: Fisher\u0026rsquo;s exact test, no test statistic\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e The flow diagram of enrolled patients and study design\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in gut microbiome\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 α diversity\u003c/h2\u003e \u003cp\u003eThe α diversity of the gut microbiota was evaluated at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups, via the Chao1 index, ACE index, Shannon index, and Simpson index. The Wilcoxon rank-sum test was conducted, revealing that comparisons of all four α diversity indices were statistically non-significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Consequently, these findings suggest no significant differences in community richness and diversity between the T0 and T1 time points for the cholecystectomy patients, or between the PCD and NPCD groups at the T1 time point. Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of gut microbiota α diversity at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao 1 Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACE Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242.76(182.80, 430.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e243.30(189.71, 429.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.84(5.19, 6.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96(0.93, 0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e242.84(165.97, 352.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240.60(164.48, 355.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68(5.04, 7.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.92, 0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChao 1 Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACE Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eShannon Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimpson Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCD-T1(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e230.34(181.96, 376.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.65(179.70, 374.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.77(4.61,6.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.90, 0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPCD-T1(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260.93(162.16, 363.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261.12(162.07, 360.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.46(5.15, 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95(0.92,0.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: T0: preoperative; T1: 1 month postoperative; PCD-T1: 1 month postoperatively in the post-cholecystectomy diarrhea group; NPCD-T1: 1 month postoperatively in the non post-cholecystectomy diarrhea group; α diversity index does not conform to normal distribution therefore M (P25, P75) was used to indicate\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 β diversity\u003c/h2\u003e \u003cp\u003e The β diversity analysis was designed to measure differences in species composition between different environmental groups. The Weighted Unifrac distance revealed differences in the β diversity of the gut microbiota both between T0 and T1 time points in cholecystectomy patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and between the PCD and NPCD groups at T1(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Subsequently, adonis statistical analysis revealed that none of the observed differences were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Comparison of gut microbiota β diversity at T0 and T1 in cholecystectomy patients, and at T1 in both the PCD and NPCD groups\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Phylum and Genus Differences\u003c/h2\u003e \u003cp\u003eThe comparison of the differences in the gut microbiota structure at different taxonomic levels revealed significant changes following cholecystectomy. At the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the relative abundance of \u003cem\u003eCampilobacterota\u003c/em\u003e was found to decrease significantly at T1 compared to T0 in cholecystectomy patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). At the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), the relative abundance of \u003cem\u003eMonoglobus\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and \u003cem\u003eAdlercreutzia\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.029) significantly increased at T1, while \u003cem\u003eMorganella\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), \u003cem\u003eEnterococcus\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.033), and \u003cem\u003eClade\u003c/em\u003e_III (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) significantly decreased. LEfSe analysis further identified \u003cem\u003eMonoglobus\u003c/em\u003e, \u003cem\u003eMonoglobaceae\u003c/em\u003e, \u003cem\u003eAdlercreutzia\u003c/em\u003e, and \u003cem\u003eEggerthellaceae\u003c/em\u003e as being significantly enriched at T1 (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas \u003cem\u003eCampilobacterota\u003c/em\u003e, \u003cem\u003eNitrospiraceae\u003c/em\u003e, \u003cem\u003eClade\u003c/em\u003e_III, and \u003cem\u003eBacilli\u003c/em\u003e were enriched at T0 (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared the PCD with NPCD groups at T1, no significant differences were observed at the phylum level (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, at the genus level, the PCD group showed a significant increase in \u003cem\u003eLachnoclostridium\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and \u003cem\u003eBlautia\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), along with a decrease in \u003cem\u003eParaprevotella\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.031), \u003cem\u003e[Eubacterium]_ruminantium_group\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048), and \u003cem\u003eEnhydrobacter\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) compared to the NPCD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). LEfSe analysis confirmed enrichment of \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eErysipelatoclostridium\u003c/em\u003e, \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e, and \u003cem\u003eLachnoclostridium\u003c/em\u003e in the PCD group (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while \u003cem\u003eBifidobacteriaceae\u003c/em\u003e, \u003cem\u003eParaprevotella\u003c/em\u003e, \u003cem\u003e[Eubacterium]_ruminantium_group\u003c/em\u003e, and \u003cem\u003eEnhydrobacter\u003c/em\u003e were enriched in the NPCD group (LDA\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e The comparison of the differences in the gut microbiota structure at phylum and genus level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Metagenomic Sequencing Results\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Gene number\u003c/h2\u003e \u003cp\u003eThe Wilcoxon's rank sum test revealed that no significant difference in the boxplot of gene number in the T1 gut microbiota of patients in the PCD and NPCD groups (P\u0026thinsp;=\u0026thinsp;0.513) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Species differences\u003c/h2\u003e \u003cp\u003eAt the species level, the abundance of 73 species in the intestinal tracts of patients in the PCD and NPCD groups at T1 differed significantly (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A significantly higher abundance of the following gut microbiota was found in the PCD group compared to the NPCD group. \u003cem\u003eBlautia_wexlerae\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), \u003cem\u003eCoprococcus_comes\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), \u003cem\u003eBlautia _sp.\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) and others was significantly higher in T1 of patients in the PCD group than in the NPCD group. The abundance of \u003cem\u003eBacteroides intestinalis\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.026), \u003cem\u003ePrevotella copri\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030) and \u003cem\u003ePrevotella intermedia\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), was significantly reduced compared to the NPCD group at T1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 KEGG pathway analysis\u003c/h2\u003e \u003cp\u003eAt KEGG Level 1 level, no significant difference in gene function pathways were observed between the gut microbiota of patients in the PCD and NPCD groups at T1(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). At KEGG Level 2, the abundance of genes in the biosynthesis of other secondary metabolites, cell motility, and membrane transport pathways was significantly higher in PCD group than in NPCD group at T1 (LDA values\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the abundance of genes in the glycan biosynthesis and metabolism pathways was significantly lower in the PCD group than in the NPCD group at T1 (LDA values\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, F). At KEGG Level 3, the gene abundance of the functional pathways of sulfur metabolism, flagellar assembly, ATP-binding cassette (ABC) transporters, etc., was significantly higher in PCD group than in NPCD group at T1 (LDA values\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the abundance of genes in functional pathways such as folate biosynthesis, Vitamin B6 metabolism, etc., was significantly lower in PCD group than in NPCD group at T1 (LDA values\u0026thinsp;\u0026gt;\u0026thinsp;2.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4E, G).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Metagenomic sequencing results\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eGenus-level analysis revealed a substantial reduction in the relative abundance of \u003cem\u003eMorganella\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, and \u003cem\u003eClade_III\u003c/em\u003e from T0 to T1 following cholecystectomy. \u003cem\u003eMorganella\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e are known to be potentially harmful bacteria. The decrease in the relative abundance of \u003cem\u003eMorganella\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e following cholecystectomy reflects changes in the gut microenvironment that hinder the growth of these bacteria. This alteration may help reduce the incidence of postoperative infections. In addition, we found that the relative abundance of gut microbiota \u003cem\u003eMonoglobus\u003c/em\u003e was significantly increased in cholecystectomy patients at T1 compared to T0. It has been reported that higher levels of \u003cem\u003eMonoglobus\u003c/em\u003e are associated with increased blood ammonia levels. The intestinal epithelial barrier can be impaired and intestinal permeability can be boosted by this increased ammonia, potentially raising the likelihood of diarrhea in patients after cholecystectomy (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). On the other hand, \u003cem\u003eNitrospiraceae\u003c/em\u003e and \u003cem\u003eBacilli\u003c/em\u003e were enriched at T0. \u003cem\u003eBacilli\u003c/em\u003e can produce short-chain fatty acids (SCFAs) from dietary fiber, which regulate host physiology and immunity (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). SCFAs also acidify the intestinal environment, inhibiting pathogens. Therefore, a reduced abundance of \u003cem\u003eBacilli\u003c/em\u003e after cholecystectomy could alter the gut microenvironment and raise the risk of pathogenic colonization(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found that significantly higher relative abundances of \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e, \u003cem\u003eLachnoclostridium\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e were shown in the gut microbiota of PCD patients, which may contribute to PCD development by increasing secondary bile acids(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Xu et al. reported that the \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e were significantly enriched in the intestines of PCD patients(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), which is consistent with our own findings. The \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e utilizes mucin to produce secondary bile acids, which can disrupt the intestinal barrier function(\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). It also influences lipid metabolism via FXR regulation, which may contribute to PCD development(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe revealed a significant decrease in the relative abundance of \u003cem\u003eParaprevotella\u003c/em\u003e, \u003cem\u003e[Eubacterium]_ruminantium_g\u003c/em\u003e, and \u003cem\u003eEnhydrobacter\u003c/em\u003e in PCD patients. Notably, all three of these bacteria are involved in the production of short-chain fatty acids(\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Short-chain fatty acids can improve lipid metabolism and are essential for colonic motility, immunomodulation, and the inhibition of intestinal inflammation(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Increasing short-chain fatty acids in the gut can effectively alleviate inflammatory bowel disease(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), antibiotic-associated diarrhea(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), and chronic diarrhea(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Thus, the decrease in \u003cem\u003eParaprevotella\u003c/em\u003e, \u003cem\u003e[Eubacterium]_ruminantium_group\u003c/em\u003e, and \u003cem\u003eEnhydrobacter\u003c/em\u003e may contribute to PCD by reducing the production of short-chain fatty acids.\u003c/p\u003e \u003cp\u003eAt the species level, significant enrichment was found for \u003cem\u003eBlautia_wexlerae\u003c/em\u003e, \u003cem\u003eCoprococcus_comes\u003c/em\u003e, and \u003cem\u003eBlautia_sp\u003c/em\u003e. in PCD patients, while \u003cem\u003eBacteroides_intestinalis\u003c/em\u003e and \u003cem\u003ePrevotella_copri\u003c/em\u003e were significantly enriched in NPCD patients. \u003cem\u003eCoprococcus_comes\u003c/em\u003e has been identified as a potentially pathogenic organism. As demonstrated in the study by Z. Wang et al., there is a correlation between this bacterium and heightened levels of intestinal inflammation(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). \u003cem\u003eBlautia_sp.\u003c/em\u003e can induce the release of large amounts of inflammatory cytokines and enhance intestinal permeability, thereby leading to intestinal barrier dysfunction(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The presence of these two pro-inflammatory bacteria in the intestines of patients with PCD has been shown to potentially promote inflammation and impair mucosal barrier function, which aligns with findings from other PCD studies(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). \u003cem\u003eBacteroides_intestinalis\u003c/em\u003e, a significant constituent of the human colonic microbiota, has the capacity to degrade complex dietary fibers, thereby producing beneficial substances such as ferulic acid and short-chain fatty acids(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). These compounds play a crucial role in regulating the intestinal immune response and suppressing intestinal inflammation. A decline in \u003cem\u003eBacteroides_intestinalis\u003c/em\u003e levels has been observed to result in a reduction in the production of ferulic acid and short-chain fatty acids. This has the potential to weaken gut defenses, increase permeability, and stimulate inflammation, which may lead to an acceleration in the progression of PCD.\u003c/p\u003e \u003cp\u003eBy comparing the gut microbiota gene sets of PCD and NPCD patients at T1 via the KEGG database, we observed that PCD patients\u0026rsquo; microbiota genes were significantly enriched in pathways for cell motility (notably flagellar assembly), membrane transport (especially ABC transporters), and sulfur metabolism. The flagellar assembly functional pathway has found to be significantly enriched in intestinal diseases such as irritable bowel syndrome (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)and ulcerative colitis(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This enrichment enhances the adhesion capacity of intestinal pathogens to intestinal epithelial cells, thereby promoting pathogen colonization in the intestine(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Consistent with our study findings, the gut microbiota of PCD patients exhibited significant enrichment of genes associated with the flagellar assembly functional pathway. This enrichment may facilitate the colonization of potentially pathogenic bacteria on the intestinal mucosa, thereby triggering an inflammatory response and impairing mucosal barrier function, ultimately contributing to the development of PCD.\u003c/p\u003e \u003cp\u003eABC transporters are a class of proteins that utilize the energy from ATP hydrolysis to transport diverse molecules across cell membranes, and they are closely associated with lipid metabolism(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Previous studies have shown that enrichment of functional ABC transporter pathways is associated with an increased risk of pathogen colonization and inflammation(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Hydrogen sulfide, a key product of gut microbial sulfur metabolism, directly induces DNA damage in colonocytes in animal models(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Additionally, it has been demonstrated that this process can damage the intestinal mucosa by disrupting the disulfide bonds in the mucus layer, allowing luminal flora and metabolites to penetrate the intestinal wall. This penetration induces epithelial cell apoptosis and triggers intestinal inflammation(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Roediger et al. demonstrated that hydrogen sulfide significantly reduces butyrate production in the colon. Butyrate is crucial for maintaining the intestinal barrier, which prevents the invasion of toxins and bacteria into the gut (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Furthermore, genes within the gut microbiota of PCD patients showed significant down-regulation in several cofactor and vitamin metabolic pathways, notably folate biosynthesis, vitamin B6 metabolism, and the biosynthesis of ubiquinone and other terpenoid quinones. The down-regulation of genes belonging to the gut microbiota within these functional pathways in PCD patients may lead to reduced levels of short-chain fatty acids in the host intestinal tract, which may in turn influence the development of PCD. This observation is consistent with prior studies on PCD-associated gut microbiota(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), though the precise mechanism underlying its role in PCD pathogenesis requires further confirmation.\u003c/p\u003e \u003cp\u003eIn summary, our findings indicate that alterations in the gut microbiota structure occur in patients after cholecystectomy. Enrichment of microbial genes involved in pathways such as flagellar assembly and ABC transporters, along with down-regulation of pathways related to folate biosynthesis and vitamin B6 metabolism, may be closely associated with the development of PCD. Early detection of pathogenic bacterial colonization may serve as a critical preventive measure for precisely mitigating PCD. Monitoring dynamic changes in the gut microbiota, early identification of pathogenic colonization, and implementing targeted clinical interventions against specific gut microbiota may serve as crucial strategies for the precise prevention of PCD. Furthermore, dietary guidance should be provided based on clinical symptoms and metabolic profiles, emphasizing the consumption of soluble fiber-rich foods to enhance short-chain fatty acid levels in the gut. Probiotic supplementation may be employed to support the restoration of a healthy microbial community, thereby facilitating postoperative recovery.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, the study was constrained by limitations relating to the storage and transportation of specimens, which restricted its applicability to patients with PCD patients from Chengdu City, thereby limiting the broader applicability of the results. A multicenter trial, to be conducted in the future, could offer a more complete picture of the gut microbiota's structure and diversity in this patient group. The trial would be conducted on a randomized basis and would involve patients with PCD from hospitals across the country. Secondly, the study duration and clinical practices influenced its comparison of gut microbiota, which was limited to PCD and NPCD patients only at 1-month post-surgery. However, given the dynamic nature of the gut microbiota, with its capacity to fluctuate over time, it is recommended that future studies should consider extending the observation period. This would facilitate the identification of key microbial taxa associated with PCD at different post-surgical time points, reveal the dynamic patterns of gut microbiota changes in PCD patients.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eCholecystectomy significantly altered the gut microbiota structure of patients, including changes in the abundance of potentially beneficial and pathogenic bacteria, as well as alterations in α-diversity and β-diversity. We found that post-cholecystectomy diarrhea (PCD) is associated with intestinal enrichment of \u003cem\u003eLachnoclostridium\u003c/em\u003e, \u003cem\u003e[Ruminococcus]_gnavus_group\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eCoprococcus_comes\u003c/em\u003e and \u003cem\u003eBlautia_sp\u003c/em\u003e. These taxa are therefore promising targets for precise monitoring and intervention in the symptom management of PCD patients. Additionally, the gut microbiota of PCD patients showed significant enrichment of genes involved in functional pathways such as flagellar assembly, ABC transporters, and sulfur metabolism. Specifically, flagellar assembly may enhance bacterial adhesion and intestinal barrier disruption, while ABC transporters and sulfur metabolism may be involved in abnormal bile acid transport and intestinal inflammation\u0026mdash;collectively, these pathways may contribute to the pathogenesis of PCD. This study clarifies the taxonomic and functional associations between PCD and gut microbiota, providing a solid foundation for developing precision microbiota-targeted interventions for PCD symptom relief.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from Sichuan University West China Hospital Biomedical Ethics Committee (Approval No. 20221824) and registered with the China Clinical Trial Centre (ChiCTR2200066563). Informed consent was given to all patients after introducing the purpose of this study. Names of all the patients and institutions are both anonymous during the whole study, and the anonymized data is only used for research purposes, including publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets of 16S rRNA sequencing and metagenomic sequencing during the current study are available in the NCBI Sequence Read Archive (SRA) with the accession number PRJNA1314443(https://dataview.ncbi.nlm.nih.gov/object/PRJNA1314443?reviewer=m8bkuf5lc6inms64kamuckl0jc)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (U22A20334), Natural Science Foundation of Sichuan Province (2024NSFSC1314, 2025ZNSFSC0243), Sichuan Science and Technology Program (2025HJRC0024, 2024YFFK0034), Chengdu Science and Technology Program (2024-YF05-00206-SN), Seed Fund Project for Cooperation between Sichuan University and Universities in Hong Kong and Macao (2024-19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJY and PM contributed equally to this work. JY and PM are co-first authors of this manuscript. Conception and design of the study, drafting the article and revising the article critically for important intellectual content: KL, YC, YH, JY, PM, and BL. Analysis and interpretation of data: YC and YH. Conception and design of the study and final approval of the version to be submitted: KL, YC and YH.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKeren N, Konikoff FM, Paitan Y, Gabay G, Reshef L, Naftali T, et al. Interactions between the intestinal microbiota and bile acids in gallstones patients. Environ Microbiol Rep. 2015;7(6):874\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi YD, Liu BN, Zhao SH, Zhou YL, Bai L, Liu EQ. 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Heliyon. 2023;9(12):e22116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y. Gut Microbiota Profiling and Pathogenesis in Ulcerative Colitis Patients and Their Healthy Spouses. Kunming Medical University; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Zhang H, Assaraf YG, Zhao K, Xu X, Xie J, et al. Overcoming ABC transporter-mediated multidrug resistance: Molecular mechanisms and novel therapeutic drug strategies. Drug Resist updates: reviews commentaries Antimicrob anticancer Chemother. 2016;27:14\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAttene-Ramos MS, Wagner ED, Gaskins HR, Plewa MJ. Hydrogen sulfide induces direct radical-associated DNA damage. Mol cancer research: MCR. 2007;5(5):455\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, An Y, Qin X, Wu X, Wang X, Hou H, et al. Gut Microbiota-Derived Metabolites in Colorectal Cancer: The Bad and the Challenges. Front Oncol. 2021;11:739648.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFigliuolo VR, Coutinho-Silva R, Coutinho C. Contribution of sulfate-reducing bacteria to homeostasis disruption during intestinal inflammation. Life Sci. 2018;215:145\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoediger WE, Duncan A, Kapaniris O, Millard S. Reducing sulfur compounds of the colon impair colonocyte nutrition: implications for ulcerative colitis. Gastroenterology. 1993;104(3):802\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cholecystectomy, Diarrhea, Symptom relief, Gut microbiota, Macrogenomics","lastPublishedDoi":"10.21203/rs.3.rs-8696322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8696322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCholecystectomy can cause diarrhea, with an incidence as high as 57.2%, seriously impacting patient prognosis. To investigate the gut dysbiosis following cholecystectomy and identify microbial biomarkers and functional genomics associated with post-cholecystectomy diarrhea (PCD), we conducted a nested case-control study within a prospective cohort.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe enrolled a cohort of 160 patients. At follow-up completion, 30 patients who developed PCD were matched with 30 non-PCD (NPCD) controls. 16S rRNA sequencing analyzed gut microbiota structure and diversity. Representative fecal samples underwent metagenomic sequencing for species level and genetic differential analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe potentially pathogenic bacteria \u003cem\u003eCoprococcus\u003c/em\u003e_\u003cem\u003ecomes\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e_\u003cem\u003esp.\u003c/em\u003e were found to be significantly enriched in the gut microbiota of PCD patients, with their abundance positively correlated with the degree of intestinal inflammation. In contrast, the potentially beneficial bacterial species \u003cem\u003eBacteroides intestinalis\u003c/em\u003e and \u003cem\u003ePrevotella copri\u003c/em\u003e, known to contribute to lipid metabolism and play a role in modulating gut immunity and suppressing inflammatory responses, were found to be significantly depleted in PCD patients. Further functional analysis revealed that the gut microbiota of PCD patients was significantly enriched in gene pathways related to cell motility, membrane transport and sulphur metabolism.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis work identified potential beneficial and pathogenic bacterial species associated with the onset of PCD, as well as significantly enriched functional pathways within the intestinal microbiota. These findings provide a scientific basis for elucidating the relationship between PCD and gut microbiota, and offer valuable insights for developing microbiota-targeted interventions to alleviate PCD symptoms.\u003c/p\u003e","manuscriptTitle":"Metagenomic Profiling of Gut Microbiome in Post-cholecystectomy Patients with Diarrhea: A Nested Case-control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 14:52:07","doi":"10.21203/rs.3.rs-8696322/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T12:03:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T14:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275604512079832760046359785307832454366","date":"2026-04-23T02:12:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213014055409905202730169640932922933880","date":"2026-04-20T03:27:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8109698579765412044878069539917712042","date":"2026-04-19T18:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92563844456345954197253106160632279065","date":"2026-04-18T12:58:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294314318393359417434686861301327420773","date":"2026-04-17T17:48:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T08:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"239097679344084021732917800594250129172","date":"2026-04-02T07:49:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T14:41:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T06:15:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-28T04:59:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-28T04:56:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-01-26T03:54:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"548fe016-67bf-46b1-b747-8201983cbd1d","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T12:03:17+00:00","index":161,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T14:05:36+00:00","index":160,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-08T14:52:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 14:52:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8696322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8696322","identity":"rs-8696322","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}