Clinical impact of altered gut microbiota and metabolite profiles on mortality in patients with candidemia: A prospective observation pilot study

Clinical impact of altered gut microbiota and metabolite profiles on mortality in patients with candidemia: A prospective observation pilot 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 Clinical impact of altered gut microbiota and metabolite profiles on mortality in patients with candidemia: A prospective observation pilot study Soo Hyun Park, Seung Min Park, Jin Woong Suh, Jeong Yeon Kim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6551378/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Annals of Clinical Microbiology and Antimicrobials → Version 1 posted 8 You are reading this latest preprint version Abstract Background The gut microbiota plays an important role in defense against infectious diseases. However, data on clinical effects of microbiome profiles in patients with candidemia are limited. This study investigated the intestinal microbiome and the effects on mortality in patients with candidemia. Methods In this prospective observational pilot study, fecal samples from adult patients with candidemia were subjected to 16S rRNA gene sequencing for microbiota analysis and gas chromatography–mass spectrometry metabolomics. Multivariate logistic regression was conducted to identify predictors of in-hospital mortality. Results Fifty-nine patients with candidemia were analyzed, with an in-hospital mortality rate of 40.7%. The median Shannon’s diversity index of the gut microbiota was significantly lower in survivors than in non-survivors ( P = 0.009). Linear discriminant analysis Effect Size revealed 11 bacterial species that significantly differed between the two groups. Among 111 fecal metabolites, only 3-isopropoxy-hexamethyl-tetrasiloxane was differentially expressed between survivors and non-survivors ( P = 0.007). Septic shock (adjusted odds ratio: 10.59; 95% confidence interval, 1.70–65.97), underlying malignancy (7.79 [1.41–43.10]), and Shannon’s diversity index (0.40 [0.19–0.84]) were significant predictors of in-hospital mortality. Conclusion Low gut bacterial diversity was independently associated with increased mortality in patients with candidemia. The intestinal microbiome could offer new perspectives for the prevention and the treatment of candidemia. gastrointestinal microbiome metabolome candidemia mortality Figures Figure 1 Figure 2 Figure 3 Introduction Candidemia is an important fungal disease caused by Candida species and, increasingly, non- albicans Candida pathogens, which are currently the fourth leading cause of nosocomial bloodstream infections. [ 1 ] Despite advancements in antifungal treatment, candidemia treatment is challenging, with mortality rates of up to 30%. [ 2 ] In immunity-impaired individuals, candidemia incidence is particularly elevated, primarily due to immunosuppressive agents, chemotherapy, and disruptions of the gut microbiome. [ 3 ] Patients with bacteremia often exhibit bacterial pathobiont expansion in the intestines, resulting in bloodstream translocation and significant changes in the composition and diversity of the gut microbiota. [ 4 ] Candida species, a commensal fungus, exists as an endogenous human reservoir in several distinct anatomical sites, such as the gastrointestinal tract. [ 5 ] In cases of intra-abdominal bacterial dysbiosis, particularly loss of anaerobic bacteria, overgrown Candida species can translocate into the bloodstream and become opportunistic, causing lethal systemic infections. [ 6 ] The composition and diversity of the gut microbiome in patients with invasive candidiasis has been highlighted using 16s rRNA gene sequencing. Gastrointestinal colonization by Candida species shapes immune responses, and antagonistic and synergistic Candida -bacteria interactions can influence microbial pathogenesis. [ 7 , 8 ] Notably, commensal anaerobic bacteria, such as gut commensal Clostridia in the Firmicutes phylum, have also been suggested as factors that contribute to resistance to intestinal colonization by C. albicans . [ 9 ] Furthermore, invasive candidiasis is strongly associated with intestinal bacterial dysbiosis and extensive alteration in fecal metabolic profiles. [ 6 , 10 ] Although gut microbiota and metabolic profiling have been researched extensively, their clinical implications in patients with candidemia remain poorly understood, particularly from the perspective of the relationship between gut microbiota and metabolites. The present study aimed to characterize intestinal microbiota and metabolites in relation to mortality in patients with candidemia, providing insights into the role of the gut microbiota in invasive fungal infections. Understanding these interactions may reveal novel therapeutic and diagnostic approaches for candidemia management. Materials and Methods Study design This was a prospective observational case-control pilot study conducted at a 1,048-bed university-affiliated hospital in Korea between January 2022 and February 2024. The study included adult patients (≥ 19 years) with blood culture-confirmed candidemia, defined as at least one positive peripheral blood culture for Candida species with compatible clinical features. Non-survivors were defined as patients who died during hospitalization, whereas survivors were those that remained alive to be discharged. Blood culture collection within 2 h after fever onset (≥ 38°C) and before antifungal therapy was prioritized to enhance diagnostic accuracy. Fecal specimens were collected within 5 days after candidemia diagnosis for the evaluation of gut microbiota and metabolite profiles. The fecal samples were aliquoted and stored frozen within 24 h of collection. Patients were excluded from this study if they refused to complete the consent form or if their stool sample was collected > 5 days after the date of candidemia diagnosis. Only the first episode of candidemia was analyzed for each patient. The study protocol was approved by the Institutional Review Board of Korea University Anam Hospital (approval number: 2022AN0232), and written informed consent was provided by all participants or their surrogates prior to participation. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational cohort studies. Data collection The following demographic and clinical data were prospectively obtained from the patients’ electronic medical records: age, sex, underlying diseases, Charlson’s Comorbidity Index [ 11 ], laboratory results, and risk factors identified within 1 month prior to candidemia diagnosis. The evaluated risk factors included neutropenia (absolute neutrophil count < 500 cells/µL), recent surgical history, and the use of immunosuppressive agents. The source of candidemia was determined based on clinical evidence of infection, regardless of whether causative organisms were isolated from the origin. [ 12 – 14 ] Clinical severity at the most severe stage of the disease was assessed using the following factors: presence of septic shock [ 15 ], the Pitt bacteremia scoring system [ 16 ], use of a central venous catheter, need for mechanical ventilation, admission to the intensive care unit, and in-hospital mortality. Clinical microbiologic analysis Identification of Candida spp. in blood culture and assessment of antifungal susceptibility were conducted using the BacT/ALERT 3D Microbial Detection System (bioMe´rieux, Inc., Durham, NC, USA) and the automated Vitek 2 Yeast Biochemical Card (bioMe´rieux, Inc., Durham, NC, USA), following a routine laboratory diagnostic procedure. The Candida strains were confirmed using matrix-assisted laser desorption/ionization-time of fight mass spectrometry (Bruker Daltonics, Bremen, Germany). Stool specimen collection and sequencing The V3–V4 region of the 16S rRNA gene was targeted for amplification to analyze the composition of the intestinal microbiota. The amplified products were purified using a magnetic bead-based purification process, and the appropriate concentration of the purified product was pooled together. Short fragments were removed using the ProNex® Size-Selective Purification System (Promega, Southampton, UK). The quality of the purified products was assessed using the PicoGreen assay (Molecular Probes, Invitrogen, USA). The pooled amplicons were sequenced using an Illumina MiSeq Sequencing System (Illumina, USA). Low-quality sequence reads (Q < 25) were filtered out using Trimmomatic v0.32. Paired-end reads were merged using VSEARCH v2.13.4 with default parameters and trimmed at a similarity cutoff of 0.8 based on the Myers–Miller alignment algorithm. [ 17 ] Non-specific amplicons that did not encode the V3–V4 region of 16S rRNA were identified using HMMER v3.2.1. [ 18 ] Unique sequence reads were extracted, and duplicate reads were clustered using VSEARCH. [ 17 ] Taxonomic assignments were performed using the EzBioCloud 16S rRNA database, and chimeric sequences were removed using the UCHIME algorithm. [ 19 , 20 ] Metabolomic profiling Fecal samples were homogenized using bead-beating and extracted with methanol for metabolomic analysis. After extraction, the samples were centrifuged, and the supernatant was filtered through a 0.45-µm syringe filter. All metabolites, including short chain fatty acids (SCFAs) such as acetic acid, butyric acid, valeric acid, and propionic acid, were analyzed using a gas chromatograph-mass spectrometer (gas chromatograph: Agilent 7890, mass spectrometer: LECO Pegasus HT TOFMS). All metabolites were analyzed using only significant peaks with a signal-to-noise ratio > 9. All procedures were performed in triplicate to ensure accuracy. Statistical Analyses Categorical variables were compared using Fisher’s exact test or Pearson’s chi-square test as appropriate and are expressed as numbers (proportions). Continuous variables were compared using either a two-sample Student’s t -test for normally distributed data or the Mann–Whitney U test for non-normally distributed data, and are summarized as median values (interquartile range [IQR]). Potential prognostic factors associated with mortality were identified using univariate analyses. Variables with a P value < 0.2 in univariate analysis were included in the multivariate analysis. Backward stepwise selection was applied to refine the model and select the most relevant predictors. The goodness-of-fit of the final model was assessed using the Hosmer–Lemeshow test. Model discrimination was assessed by generating receiver operating characteristic curves, and predictive performance was validated using stratified 10-fold cross-validation. Alpha diversity was evaluated using the Abundance-based Coverage Estimator and Shannon’s and Simpson’s diversity indices. Beta diversity was evaluated using the Bray–Curtis distance and visualized through principal-coordinate analysis. Taxonomic biomarkers were identified using linear discriminant analysis effect size (LEfSe) and the Kruskal–Wallis H Test. These analyses were conducted using the EzBioCloud 16s-based Microbiome Taxonomic Profiling platform. Metabolomic data were normalized using the log10 fold-change for each metabolite. Differences between survivors and non-survivors were assessed using two-sample Student’s t -test. Correlations between stool metabolites and bacterial genera were analyzed using Spearman correlation. Partial least-squares discriminant analysis (PLS-DA) was performed to identify the stool metabolite signature of mortality, with a variable importance in projection (VIP) score threshold of 1.3. Functional profiles were predicted from normalized taxonomic data using PICRUSt and MinPath algorithms. Differentially abundant functional pathways were identified using the Kruskal–Wallis H test and LEfSe, with statistical significance set at P < 0.05. IBM SPSS Statistics version 20.0 (IBM Corporation, Armonk, NY, USA), SAS 9.4 (SAS Institute Inc., Cary, NC, USA), and R 4.4.1 with RStudio (v2024.04.2 + 764) (Te R Foundation for Statistical Computing, Vienna, Austria) were used for all statistical analyses. Two-sided P values < 0.05 were considered significant. Results Patient characteristics Fifty-nine patients were diagnosed with clinically significant candidemia during the study period. All the patients were hospitalized in the general ward (n = 20, 33.9%) or intensive care unit (n = 39, 66.1%). The median patient age was 70 years (IQR, 60–80), and 52.5% of them were male. The most common sources of candidemia were central venous catheter (n = 28, 47.5%) and the gastrointestinal tract (n = 20, 33.9%). Candida albicans (36.4%) was the most frequently isolated species, followed by Candida tropicalis (34.5%), Candida parapsilosis (18.2%), and Candida glabrata (7.3%). No case of infection by multiple Candida species was recorded. First-line antifungal therapy included echinocandins (n = 56, 94.9%), fluconazole (n = 2, 3.4%), and amphotericin B (n = 1, 1.7%). Of the 59 patients, 57 (96.6%) received antibiotics before stool specimen collection, and 21 (35.6%) had concomitant bacteremia. The overall in-hospital mortality rate was 40.7% (24/59), and the median time to death was 29.5 days (IQR, 16.8–38.3 days) after candidemia onset. Candidemia and gut microbiota Alpha-diversity indices of gut bacterial communities differed significantly between the survivors and non-survivors (Table 1 ), with the non-survivors showing lower diversity metrics ( P- value < 0.05; Fig. 1 A). However, β-diversity analysis revealed no significant difference between the two groups (Fig. 1 B). Table 1 Comparison of demographic and clinical characteristics between the survivors and the non-survivors of candidemia Characteristics Total (N = 59) Survivors (N = 35) Non-survivors (N = 24) P -value Alpha-diversity index, median (IQR) ACE a 56.4 (28.0–97.8) 73.0 (38.2–103.2) 35.9 (16.4–60.6) 0.044 Shannon’s diversity index 1.7 (1.2–2.7) 2.3 (1.4–2.8) 1.6 (1.0–2.0) 0.009 Simpson’s diversity index 0.3 (0.1–0.4) 0.2 (0.1–0.4) 0.3 (0.2–0.6) 0.007 Demographic variable Age (yrs), median (IQR) 70 (60.0–80.0) 72 (64.0–81.0) 68.5 (59.0–77.8) 0.261 Sex (male), N (%) 31 (62.9) 20 (57.1) 11 (52.5) 0.393 Identified isolates from culture results, N (%) Candida albicans 21 (35.6) 11 (31.4) 10 (41.7) 0.596 Candida tropicalis 25 (42.4) 15 (42.9) 10 (41.7) 1.000 Candida glabrata 13 (22.0) 9 (25.7) 4 (16.7) 0.614 Source of candidemia, N (%) Central venous catheter 28 (47.5) 16 (45.7) 12 (50.0) 0.746 Gastrointestinal tract 20 (33.9) 12 (34.3) 8 (33.3) 0.939 Unknown 11 (18.6) 7 (20.0) 4 (16.7) 1.000 Comorbidities, N (%) Cardiovascular diseases 27 (45.8) 14 (40.0) 13 (54.2) 0.303 Cerebrovascular diseases 13 (37.1) 3 (12.5) 16 (27.1) 0.036 Diabetes mellitus 16 (27.1) 10 (28.6) 6 (25.0) 0.762 Chronic kidney diseases 16 (27.1) 7 (20.0) 9 (37.5) 0.137 Chronic liver diseases 5 (8.5) 3 (8.6) 2 (8.3) 1.000 Chronic pulmonary diseases 5 (8.5) 3 (8.6) 2 (8.3) 1.000 Malignancy 18 (30.5) 8 (22.9) 10 (41.7) 0.123 CCI ≥ 3 34 (57.6) 19 (54.3) 15 (62.5) 0.531 Risk factor within 1 month, N (%) Corticosteroids 18 (30.5) 9 (25.7) 9 (37.5) 0.334 Neutropenia b 4 (6.8) 0 (0.0) 4 (16.7) 0.023 Prior surgery 17 (28.8) 10 (28.6) 7 (29.2) 0.960 Prior antibiotic exposure 57 (96.6) 33 (94.3) 24 (100.0) 0.509 Use of immunosuppressive agents other than corticosteroids 4 (6.8) 2 (5.7) 2 (8.3) 1.000 Clinical severity at worst, N (%) Concomitant bacteremia 21 (35.6) 11 (31.4) 10 (41.7) 0.420 Renal replacement therapy 10 (16.9) 5 (14.3) 5 (20.8) 0.376 ICU admission 39 (66.1) 24 (68.6) 15 (62.5) 0.628 Mechanical ventilation 25 (42.4) 13 (37.1) 12 (50.0) 0.326 Pitt bacteremia score ≥ 2 40 (67.8) 25 (71.4) 15 (62.5) 0.471 Septic shock 40 (67.8) 20 (57.1) 20 (83.3) 0.034 Laboratory findings at time of Candidemia diagnosis, N (%) CRP ≥ 100 mg/L 46 (78.0) 25 (71.4) 21 (87.5) 0.143 Procalcitonin ≥ 1.00 ng/mL 35 (59.3) 17 (48.6) 18 (75.0) 0.042 First-line antifungal agent, N (%) Amphotericin B 1 (1.7) 0 (0.0) 1 (4.2) 0.848 Echinocandins 56 (94.9) 34 (97.1) 22 (91.7) 0.736 Fluconazole 2 (3.4) 1 (2.9) 1 (4.2) 1.000 Footnotes. a Abundance-based Coverage Estimator, b Neutropenia is defined as an absolute neutrophil count (ANC) of < 500 cells/µL. CCI, Charlson’s Comorbidity index; CRP, C-reactive protein; ICU, intensive care unit; IQR, interquartile range; yrs, years. A total of 1,636 unique operational taxonomic units were identified from sequenced specimens, highlighting gut microbial diversity in patients with candidemia (Fig. 1 C). No significant difference in 16s rRNA quantification was observed between survivors and non-survivors (read counts: 2,478,962 vs. 1,730,093; P = 0.077). Firmicutes was the most abundant phylum in both groups; however, no significant differences were observed in the abundances of Firmicutes ( P = 0.253) and Actinobacteria between the two groups ( P = 0.666) (Table 2 ). Table 2 Characteristics of the fecal microbiome stratified according to mortality status in patients with candidemia Total (N = 59) Survivors (N = 35) Non-survivors (N = 24) P -value Relative abundance (%) Phylum level Firmicutes 51.51 55.60 45.53 0.254 Proteobacteria 24.52 23.96 25.34 0.793 Bacteroidetes 16.02 16.85 1.81 0.459 Actinobacteria 4.46 2.23 7.70 0.666 Verrucomicrobia 3.25 1.12 6.34 0.522 Others 0.25 0.23 0.27 0.260 Species level Ruminococcus torques 0.79 1.29 0.05 0.04 Clostridium innocuum group 0.62 0.82 0.34 0.02 Streptococcus salivarius group 0.59 0.67 0.47 0.03 Clostridium symbiosum 0.24 0.34 0.10 0.04 Clostridium scindens 0.13 0.21 0.02 0.01 GL520168_s 0.09 0.15 0.00 0.00 Bacteroides cellulosilyticus 0.09 0.14 0.01 0.02 AJ315979_s 0.07 0.11 0.00 0.02 Clostridium leptum 0.05 0.08 0.01 0.04 PAC001643_s 0.02 0.03 0.00 0.04 PAC001597_s 0.04 0.06 0.01 0.01 Proteus mirabilis 0.04 0.00 0.09 0.04 Acinetobacter pittii group 0.18 0.00 0.44 0.04 Bacteroides caccae 0.05 0.44 0.60 0.05 Metabolites 3-isopropoxy-hexamethyl-tetrasiloxane a 346556.12 441107.44 187000.75 0.047 KKLGZUJIFDXBCT-UHFFFAOYSA-N b 64361.19 98935.56 6016.938 0.235 Phenylacetic acid 20706.44 32976.93 0 0.181 Functional metabolic pathway, median (IQR) Endoglucanase 0.035 (0.020–0.047) 0.036 (0.026–0.052) 0.032 (0.016–0.041) 0.040 Footnotes. a 3-Isopropoxy-1,1,1,7,7,7-hexamethyl-3,5,5-tris(trimethylsiloxy)tetrasiloxane, b Pentacarbonyl(phosphasila-boracyclohexene) tungsten; IQR, interquartile range LEfSe analysis identified 14 bacterial species that were significantly different (LDA score > 2.0) between the two groups (Fig. 1 D). Non-survivors showed increased abundance of Bacteroides caccae (0.60%), Acinetobacter pittii group (0.44%), and Proteus mirabilis (0.09%). The remaining 11 species were more enriched in survivors than in non-survivors: Clostridium genera ( Firmicutes ), Bacteroides cellulosilyticus ( Bacteroidetes ), and Ruminococcus torques ( Bacillota ) (Table 2 ). Candidemia and host stool metabolomic profiles A total of 111 compounds were included in the analysis. Metabolites within each group, including SCFAs, amino acids, cholic acids, and vitamins, were identified (Fig. 2 A). The heatmaps of fecal metabolites derived from the survivors and non-survivors are depicted in Fig. 2 A. PLS-DA plots of all metabolites did not completely distinguish the two groups (Supplementary Fig. 1). However, three metabolites with VIP scores > 1.3 were highlighted for their significant role in distinguishing survivors from non-survivors (Fig. 2 B). Particularly, only one metabolite of 3-isopropoxy-hexamethyl-tetrasiloxane was significantly different between the two groups ( P < 0.05) (Table 2 ). Based on the taxonomic biomarkers, increased endoglucanase activity in the functional metabolic pathway was identified as a key metabolic pathway in survivors compared with non-survivors (Fig. 2 C). Notably, Clostridium genera, one of the significant taxonomic biomarkers, exhibited significant positive correlations with the metabolic pathway of endoglucanase. However, none of the three metabolites with VIP scores > 1.3 were significantly associated with the significant taxonomic biomarkers (Fig. 3 ). Predictors of in-hospital mortality in patients with candidemia The demographic and clinical characteristics of the survivors and non-survivors are presented in Table 1 . Cerebrovascular disease was more prevalent in non-survivors, whereas concomitant bacteremia and septic shock occurred at similar rates in the two groups. Although C-reactive protein level (≥ 100 mg/L) did not differ significantly between the two groups, procalcitonin level ≥ 1.00 ng/mL was more common in non-survivors than in survivors (Table 1 ). Univariate analysis identified potential risk factors for in-hospital mortality (Supplementary Table 1). Despite its significance in the univariate analysis, Simpson’s diversity index was excluded from the multivariate analysis due to its high skewness and collinearity with Shannon’s diversity index (variance inflation factor = 9.831) (Supplementary Fig. 2). Backward stepwise selection was applied to refine the model and select the most relevant predictors. Variables with P values < 0.2 in the univariate analysis were included in the multivariate model, which identified three significant predictors of mortality: lower gut microbiota diversity index (Shannon’s diversity index; odds ratio [OR], 0.401; 95% confidence interval [CI], 0.191–0.844), underlying malignancy (OR, 7.794; 95% CI, 1.410–43.10), and septic shock (OR, 10.59; 95% CI, 1.701–65.97) (Table 3 ). Table 3 Multivariate logistic regression analysis of prognostic factors associated with in-hospital mortality in patients with candidemia Multivariable logistic regression model Multivariable logistic regression model refined using backward stepwise selection based on the Wald statistic Variable OR 95% CI P -value OR 95% CI P -value ACE a 0.998 0.983–1.013 0.815 Shannon’s diversity index 0.469 0.181–1.218 0.120 0.401 0.191–0.844 0.016 Cerebrovascular diseases 0.329 0.062–1.755 0.193 Malignancy 4.769 0.729–31.21 0.103 7.794 1.410–43.10 0.019 Chronic kidney diseases 1.697 0.369–7.806 0.497 Septic shock 9.934 1.351–73.03 0.024 10.59 1.701–65.97 0.011 CRP ≥ 100 mg/L 2.161 0.369–12.66 0.393 Procalcitonin ≥ 1.000 ng/mL 2.234 0.528–9.451 0.275 Footnotes. The abovementioned covariates had P values < 0.2 in univariate logistic regression model and were included in the multivariable logistic regression model. a Abundance-based Coverage Estimator; CRP, C-reactive protein The final model demonstrated a good fit (Hosmer–Lemeshow test: χ 2 = 7.013, P = 0.535) and strong discrimination (area under curve [AUC] = 0.805; 95% CI, 0.67–0.90) (Supplementary Fig. 3). Cross-validation supported model accuracy (10-fold log-loss = 0.606; AUC = 0.773), indicating robust calibration and discrimination. Discussion In this study, the gut microbiota in non-survivors of candidemia was associated with lower α-diversity as indicated by Shannon’s diversity index. Notably, no significant differences in ß-diversity was observed between survivors and non-survivors. Furthermore, analysis of alterations in gut microbiota revealed significant differences in the composition of 11 species between the two groups. Specifically, Clostridium genera involved in endoglucanase activity in the functional metabolic pathway and the metabolite of 3-isopropoxy-hexamethyl-tetrasiloxane were significantly enriched in the survivors compared with the non-survivors. Clinical applications of gut microbiota have shown promise in predicting the prognoses of patients with candidemia. Our results demonstrated that lower microbial α-diversity in patients with candidemia is associated with a higher risk of mortality, which was consistent with the results of previous in vivo studies. [ 10 , 21 ] Decreased gut microbiota diversity and changes in metabolites are well documented as risk factors for several diseases, such as type 2 diabetes, inflammatory bowel disease, and metabolic syndrome. [ 22 – 24 ] Notably, gut microbiome disruption appears to be a known risk factor for sepsis and subsequent organ dysfunction, leading to worse outcomes. [ 25 ] Considering the critical roles of the microbiome in the training and development of the host immune system, these changes may compromise the mucosal barrier and immune function, worsening the prognoses of patients with candidemia. [ 21 , 26 ] Consistent with previous findings, our study showed that underlying malignancy and septic shock are independent predictive factors associated with mortality in patients with candidemia. [ 27 , 28 ] Our analysis identified 14 causal bacterial taxa associated with candidemia, with increased abundance of three taxa that showed positive causal relationships with mortality. A previous study indicated that Acinetobacter pittii can transform deoxynivalenol, which is associated with inflammation and apoptosis, into less toxic or even non-toxic metabolites. [ 29 ] Over-abundance of Bacteroides caccae can lead to lysis of mucus, resulting in thinning of the mucosal barrier and enabling pathogen translocation. [ 30 ] Furthermore, lipopolysaccharides, a virulence factor of Proteus mirabilis , may be associated with pathological changes via gut leakage and inflammatory actions. [ 31 ] These results suggest the roles of these species in the pathogenesis of candidemia or worsening prognosis. The remaining 11 taxa were significantly abundant in survivors compared with non-survivors. This finding may be because Ruminococcus torques degrades intestinal mucin, helping to regulate pH and maintain gut microbial diversity and stability. [ 32 ] In addition, Clostridium innocuum contributes to intestinal nutrient metabolism by breaking down glucose ureide and is detected in approximately 80% of healthy adults. [ 33 ] Furthermore, Streptococcus salivarius suppresses intestinal inflammation and regulates immune response. [ 34 ] In addition to the diverse roles of gut flora in patients with candidemia, these findings suggest the possibility of improving patient outcomes using microbiota-modulating therapy. Interestingly, several Clostridium species and the metabolic pathway of endoglucanase, which showed relatively high abundance and activity, respectively, in survivors compared with non-survivors, showed a significant positive correlation in this study. The endoglucanases, which functionally hydrolyze glycosidic bonds, are enzymes found in microbiomes that break down cellulose. In addition to maintaining the balance of intestinal microbiota, the gut flora associated with the endoglucanase of the functional metabolic pathway contribute to the nutrition and health of the host through the production of SCFAs. [ 35 ] Considering this interplay between the gut flora and endoglucanase, strengthening important functional metabolic pathways using beneficial taxa of the gut microbiota may be useful in the prevention and treatment of candidemia. Our current gut microbiome data provides further evidence that supports a potential contribution of gut microbiota to host fecal metabolic profiles. Although none of the three metabolites with VIP scores > 1.3 was significantly associated with gut taxonomic biomarkers, the levels of 3-isopropoxy-hexamethyl-tetrasiloxane detected in the feces of the survivors were significantly higher than those in the fecal samples of non-survivors. Previous studies have shown that 3-isopropoxy-hexamethyl-tetrasiloxane, a siloxane derivative, exhibits antifungal activity against Candida albicans, Candida krusei , and Candida glabrata . [ 36 , 37 ] These findings suggest that 3-isopropoxy-hexamethyl-tetrasiloxane may play a role in suppressing pathogenic microorganisms and restoring the gut ecosystem. Phenyl acetic acid and KKLGZUJIFDXBCT-UHFFFAOYSA-N (tungsten) with VIP scores > 1.3 were observed in higher levels in the stool of the survivors than in those of non-survivors; however, this finding was not statistically significant. Phenyl acetic acid exhibits antifungal activity against Candida albicans , and the metabolism of Clostridium species is involved in this process. [ 38 , 39 ] Based on a recent study, the calcium tungstate microgel of a heavy metal may act as a microbe-based therapy against inflammatory bowel diseases by restoring gut microbiota homeostasis and improving the intestinal mucosal immune barrier. [ 40 ] This study has some limitations. First, bias in data collection is possible. In addition, the statistical power of the study was possibly limited by its single-center design and small sample size. Moreover, the small sample size may have also affected the detection of significant differences in β-diversity. Larger multicenter cohort studies are needed to confirm these findings and provide more generalizable conclusions. Second, the cause–effect relationship of the changes observed in this study could not be determined. Third, as combined antimicrobial therapy plays a role in gut dysbiosis, co-infections with other pathogens or other treatments may have interfered with our findings. Future large randomized controlled trials are needed to comprehensively investigate the effect of gut microbiome profiling on the prevention and treatment of candidemia. In addition, in vivo models are warranted to decipher its pathogenesis and validate the findings of this study. Conclusion This study investigates the potential dysregulation function of the gut microbiota in patients with candidemia. In addition, it highlights the link between reduced gut microbiota diversity and higher mortality in patients with candidemia. Our findings suggest the alteration of gut microbiota in candidemia and its potential effects on host fecal metabolic profiles. Our study supports the potential therapeutic role of addressing disruptions in the gut microbiota in patients with candidemia. Declarations Acknowledgments. None Author Contributions. Conceptualization and design: Y.K.Y. Acquisition, analysis, and interpretation of data: Y.K.Y., S.H.P., and S.M.P. Statistical analysis: Y.K.Y., S.H.P., and S.M.P. Drafting of the manuscript: Y.K.Y., S.H.P., and S.M.P. Critical review and revision of the manuscript: All authors. Data Availability Statement. Data supporting the findings of this study are available from the corresponding author upon reasonable request. Funding. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2022R1A2C1010808). The Funding sources had no role in the study design, data collection, data analysis, decision to publish, or manuscript preparation. Institutional Review Board Statement. This study was approved by the Institutional Review Board of Korea University Anam Hospital (IRB No. 2024AN0385). Informed Consent Statement. The need for patient consent was waived owing to the nature of the study. 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Eur J Clin Microbiol Infect Dis. 2020;39:1797–819. Zhai B, Ola M, Rolling T, et al. High-resolution mycobiota analysis reveals dynamic intestinal translocation preceding invasive candidiasis. Nat Med. 2020;26:59–64. Eichelberger KR, Paul S, Peters BM, Cassat JE. Candida-bacterial cross-kingdom interactions. Trends Microbiol. 2023;31:1287–99. Eckstein MT, Moreno-Velásquez SD, Pérez JC. Gut Bacteria Shape Intestinal Microhabitats Occupied by the Fungus Candida albicans. Curr Biol 2020; 30:4799 – 807.e4. Fan D, Coughlin LA, Neubauer MM, et al. Activation of HIF-1α and LL-37 by commensal bacteria inhibits Candida albicans colonization. Nat Med. 2015;21:808–14. Hu W, Xu D, Zhou Z, Zhu J, Wang D, Tang J. Alterations in the gut microbiota and metabolic profiles coincide with intestinal damage in mice with a bloodborne Candida albicans infection. Microb Pathog. 2021;154:104826. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. 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Geng J, Sui Z, Dou W, et al. 16S rRNA Gene Sequencing Reveals Specific Gut Microbes Common to Medicinal Insects. Front Microbiol. 2022;13:892767. Wheeler TJ, Eddy SR. nhmmer: DNA homology search with profile HMMs. Bioinformatics. 2013;29:2487–9. Yoon SH, Ha SM, Kwon S, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol. 2017;67:1613–7. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200. Bertolini M, Ranjan A, Thompson A, et al. Candida albicans induces mucosal bacterial dysbiosis that promotes invasive infection. PLoS Pathog. 2019;15:e1007717. Ning L, Zhou YL, Sun H, et al. Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts. Nat Commun. 2023;14:7135. Menni C, Zhu J, Le Roy CI, et al. Serum metabolites reflecting gut microbiome alpha diversity predict type 2 diabetes. Gut Microbes. 2020;11:1632–42. Pallister T, Jackson MA, Martin TC, et al. Hippurate as a metabolomic marker of gut microbiome diversity: Modulation by diet and relationship to metabolic syndrome. Sci Rep. 2017;7:13670. Adelman MW, Woodworth MH, Langelier C, et al. The gut microbiome's role in the development, maintenance, and outcomes of sepsis. Crit Care. 2020;24:278. Iliev ID, Leonardi I. Fungal dysbiosis: immunity and interactions at mucosal barriers. Nat Rev Immunol. 2017;17:635–46. Haroun E, Kumar PA, Saba L, et al. Intestinal barrier functions in hematologic and oncologic diseases. J Transl Med. 2023;21:233. Guzman JA, Tchokonte R, Sobel JD. Septic shock due to candidemia: outcomes and predictors of shock development. J Clin Med Res. 2011;3:65–71. Liu Y, Xu L, Shi Z, et al. Identification of an Acinetobacter pittii acyltransferase involved in transformation of deoxynivalenol to 3-acetyl-deoxynivalenol by transcriptomic analysis. Ecotoxicol Environ Saf. 2023;263:115395. Zafar H, Saier MH. Jr. Gut Bacteroides species in health and disease. Gut Microbes. 2021;13:1–20. Choi JG, Kim N, Ju IG, et al. Oral administration of Proteus mirabilis damages dopaminergic neurons and motor functions in mice. Sci Rep. 2018;8:1275. Tran NTD, Chaidee A, Surapinit A, et al. Chronic Strongyloides stercoralis infection increases presence of the Ruminococcus torques group in the gut and alters the microbial proteome. Sci Rep. 2023;13:4216. Bhattacharjee D, Flores C, Woelfel-Monsivais C, Seekatz AM. Diversity and Prevalence of Clostridium innocuum in the Human Gut Microbiota. mSphere. 2023;8:e0056922. Kaci G, Goudercourt D, Dennin V, et al. Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract. Appl Environ Microbiol. 2014;80:928–34. Froidurot A, Julliand V. Cellulolytic bacteria in the large intestine of mammals. Gut Microbes. 2022;14:2031694. Omoruyi BE, Afolayan AJ, Bradley G. The inhibitory effect of Mesembryanthemum edule (L.) bolus essential oil on some pathogenic fungal isolates. BMC Complement Altern Med. 2014;14:168. Barani K, Manipal S, Prabu D, Ahmed A, Adusumilli P, Jeevika C. Anti-fungal activity of Morinda citrifolia (noni) extracts against Candida albicans: an in vitro study. Indian J Dent Res. 2014;25:188–90. Jiao M, He W, Ouyang Z, Shi Q, Wen Y. Progress in structural and functional study of the bacterial phenylacetic acid catabolic pathway, its role in pathogenicity and antibiotic resistance. Front Microbiol. 2022;13:964019. Kim Y, Cho JY, Kuk JH, et al. Identification and antimicrobial activity of phenylacetic acid produced by Bacillus licheniformis isolated from fermented soybean, Chungkook-Jang. Curr Microbiol. 2004;48:312–7. Yang J, Peng M, Tan S, et al. Calcium Tungstate Microgel Enhances the Delivery and Colonization of Probiotics during Colitis via Intestinal Ecological Niche Occupancy. ACS Cent Sci. 2023;9:1327–41. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryFigure1.docx Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in Annals of Clinical Microbiology and Antimicrobials → Version 1 posted Editorial decision: Revision requested 26 Sep, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 24 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers invited by journal 21 Aug, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 30 Apr, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6551378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506870789,"identity":"4516a782-cc54-447f-a8b6-657b5cad803c","order_by":0,"name":"Soo Hyun Park","email":"","orcid":"","institution":"Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Hyun","lastName":"Park","suffix":""},{"id":506870790,"identity":"847d3080-eb02-4371-a467-4bdcc81a730c","order_by":1,"name":"Seung Min Park","email":"","orcid":"","institution":"Institute of Emerging Infectious Diseases, Korea University","correspondingAuthor":false,"prefix":"","firstName":"Seung","middleName":"Min","lastName":"Park","suffix":""},{"id":506870791,"identity":"de050f69-ff80-42be-8329-a7e8fd45f4d2","order_by":2,"name":"Jin Woong Suh","email":"","orcid":"","institution":"Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Woong","lastName":"Suh","suffix":""},{"id":506870792,"identity":"e4023fad-9e1b-4b56-a04a-9f922c47e96c","order_by":3,"name":"Jeong Yeon Kim","email":"","orcid":"","institution":"Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jeong","middleName":"Yeon","lastName":"Kim","suffix":""},{"id":506870793,"identity":"3fefcd96-114c-4a22-b02b-495385a54ab1","order_by":4,"name":"Jang Wook Sohn","email":"","orcid":"","institution":"Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jang","middleName":"Wook","lastName":"Sohn","suffix":""},{"id":506870794,"identity":"170e6a47-91aa-424d-91dc-5edea8152c79","order_by":5,"name":"Young Kyung Yoon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYLACxn82PPzsbXA+MxF62NJkJHuOkablsI3BjTQitRgc7zF7+IXnMA/DzWeJjysq7Bj42w8wG1fg03LmjLmxjEQ6D+PstMOGZ84kM0icSWBOPINPy40cM2kJA2seZun0NsnGNqCbbjAwH2wgqCWBmYdN8jhQy796BnlitEh+OODMwyPBdkyyseEwUISBORGfFskzx8qkGRvSeCR40pING44d5zE8k9hsiE8L3/HmbZI/G2zs7Y8fM3zYUFMtJ3f88GFJfFoUDgCjgQdJAMhmxKeBgUEeKM34A6+SUTAKRsEoGPEAAGxZSPDxk1QcAAAAAElFTkSuQmCC","orcid":"","institution":"Division of Infectious Diseases, Department of Internal Medicine, Korea University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Young","middleName":"Kyung","lastName":"Yoon","suffix":""}],"badges":[],"createdAt":"2025-04-29 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6551378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6551378/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12941-026-00850-x","type":"published","date":"2026-01-17T16:29:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90315537,"identity":"410dd55f-80c3-45ac-a8b8-66ac4022dadc","added_by":"auto","created_at":"2025-09-01 10:12:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":299512,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of reduced α-diversity with mortality in patients with candidemia: A 16S rRNA Gene-Based Analysis. (A) Boxplots indicate α-diversity in survivors and non-survivors using different metrics (ACE and Shannon’s and Simpson’s diversity index). (B) Principal coordinate analysis was performed using phylogenetic Bray–Curtis distance matrices. (C) 16S rRNA gene sequencing-based taxonomy plots stratified according to survival status at relevant phyla levels of the gut microbiome. (D) Linear discriminant analysis effect size showing the significant (Wilcoxon rank-sum, two-tailed, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) effect sizes of taxa between survivors and non-survivors (blue bars: survivors, red bars: non-survivors). Linear discriminant analysis (LDA) scores greater than 2.0 at the specificity level of the gut microbiota in survivors and non-survivors.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/364f071cb059b848c32d646f.jpeg"},{"id":90315542,"identity":"848649f1-c3da-4cb1-bd4b-83f4416831a6","added_by":"auto","created_at":"2025-09-01 10:12:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":735187,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic profiling of the stool samples of patients with candidemia. (A) Heatmap of stool metabolites in survivors and non-survivors analyzed using a gas chromatograph–mass spectrometer. (B) Variable importance in projection (VIP) score graph derived from partial least-squares discriminant analysis (PLS-DA) showing metabolites with VIP scores greater than 1.3. (C) Comparison of endoglucanase activity in the functional metabolic pathway between the survivors and non-survivors, based on the taxonomic biomarkers predicted using PICRUSt., *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/b11363ae4b28e5462712a493.jpeg"},{"id":90315543,"identity":"047f8bef-b9c1-4a95-a1aa-d8e5aac2a01d","added_by":"auto","created_at":"2025-09-01 10:12:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":932967,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap of gut microbiota, metabolites, and PICRUSt-predicted features. The heatmap shows the Spearman correlation coefficients calculated to assess the associations between microbial genera, metabolic products, and the functional gene endoglucanase inferred using PICRUSt. Each cell represents the strength and direction of the correlation between variables, with a color gradient ranging from dark blue (negative correlation) to dark red (positive correlation). Statistically significant correlations identified using a two-sample Student’s \u003cem\u003et\u003c/em\u003e-test are marked with asterisks, with significance levels as follows: no asterisk (not significant), *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003e P\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003e P\u003c/em\u003e\u0026lt;0.001, and **\u003cem\u003e P\u003c/em\u003e\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/67e2df7fada018eb7ef7574e.jpeg"},{"id":100614569,"identity":"7082ac98-56b8-4a39-a205-cd084e7c005d","added_by":"auto","created_at":"2026-01-19 17:22:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3253255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/55b683ba-0e49-4edc-aa2e-84fb73b17577.pdf"},{"id":90316332,"identity":"a7979bdf-eb7f-4760-83db-4e779df3c8a2","added_by":"auto","created_at":"2025-09-01 10:20:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27434,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/4c60826e877a99e1b0622da5.docx"},{"id":90316333,"identity":"6bb07905-24d1-4087-9e1a-d27032b707e0","added_by":"auto","created_at":"2025-09-01 10:20:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1174799,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6551378/v1/c46491234e610088b2918d1a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical impact of altered gut microbiota and metabolite profiles on mortality in patients with candidemia: A prospective observation pilot study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCandidemia is an important fungal disease caused by \u003cem\u003eCandida\u003c/em\u003e species and, increasingly, non-\u003cem\u003ealbicans Candida\u003c/em\u003e pathogens, which are currently the fourth leading cause of nosocomial bloodstream infections. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Despite advancements in antifungal treatment, candidemia treatment is challenging, with mortality rates of up to 30%. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In immunity-impaired individuals, candidemia incidence is particularly elevated, primarily due to immunosuppressive agents, chemotherapy, and disruptions of the gut microbiome. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\u003cp\u003ePatients with bacteremia often exhibit bacterial pathobiont expansion in the intestines, resulting in bloodstream translocation and significant changes in the composition and diversity of the gut microbiota. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] \u003cem\u003eCandida\u003c/em\u003e species, a commensal fungus, exists as an endogenous human reservoir in several distinct anatomical sites, such as the gastrointestinal tract. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In cases of intra-abdominal bacterial dysbiosis, particularly loss of anaerobic bacteria, overgrown \u003cem\u003eCandida\u003c/em\u003e species can translocate into the bloodstream and become opportunistic, causing lethal systemic infections. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe composition and diversity of the gut microbiome in patients with invasive candidiasis has been highlighted using 16s rRNA gene sequencing. Gastrointestinal colonization by \u003cem\u003eCandida\u003c/em\u003e species shapes immune responses, and antagonistic and synergistic \u003cem\u003eCandida\u003c/em\u003e-bacteria interactions can influence microbial pathogenesis. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Notably, commensal anaerobic bacteria, such as gut commensal Clostridia in the \u003cem\u003eFirmicutes\u003c/em\u003e phylum, have also been suggested as factors that contribute to resistance to intestinal colonization by \u003cem\u003eC. albicans\u003c/em\u003e. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Furthermore, invasive candidiasis is strongly associated with intestinal bacterial dysbiosis and extensive alteration in fecal metabolic profiles. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAlthough gut microbiota and metabolic profiling have been researched extensively, their clinical implications in patients with candidemia remain poorly understood, particularly from the perspective of the relationship between gut microbiota and metabolites.\u003c/p\u003e\u003cp\u003eThe present study aimed to characterize intestinal microbiota and metabolites in relation to mortality in patients with candidemia, providing insights into the role of the gut microbiota in invasive fungal infections. Understanding these interactions may reveal novel therapeutic and diagnostic approaches for candidemia management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis was a prospective observational case-control pilot study conducted at a 1,048-bed university-affiliated hospital in Korea between January 2022 and February 2024. The study included adult patients (\u0026ge;\u0026thinsp;19 years) with blood culture-confirmed candidemia, defined as at least one positive peripheral blood culture for \u003cem\u003eCandida\u003c/em\u003e species with compatible clinical features. Non-survivors were defined as patients who died during hospitalization, whereas survivors were those that remained alive to be discharged.\u003c/p\u003e\u003cp\u003eBlood culture collection within 2 h after fever onset (\u0026ge;\u0026thinsp;38\u0026deg;C) and before antifungal therapy was prioritized to enhance diagnostic accuracy. Fecal specimens were collected within 5 days after candidemia diagnosis for the evaluation of gut microbiota and metabolite profiles. The fecal samples were aliquoted and stored frozen within 24 h of collection. Patients were excluded from this study if they refused to complete the consent form or if their stool sample was collected\u0026thinsp;\u0026gt;\u0026thinsp;5 days after the date of candidemia diagnosis. Only the first episode of candidemia was analyzed for each patient.\u003c/p\u003e\u003cp\u003e The study protocol was approved by the Institutional Review Board of Korea University Anam Hospital (approval number: 2022AN0232), and written informed consent was provided by all participants or their surrogates prior to participation. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational cohort studies.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe following demographic and clinical data were prospectively obtained from the patients\u0026rsquo; electronic medical records: age, sex, underlying diseases, Charlson\u0026rsquo;s Comorbidity Index [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], laboratory results, and risk factors identified within 1 month prior to candidemia diagnosis. The evaluated risk factors included neutropenia (absolute neutrophil count\u0026thinsp;\u0026lt;\u0026thinsp;500 cells/\u0026micro;L), recent surgical history, and the use of immunosuppressive agents. The source of candidemia was determined based on clinical evidence of infection, regardless of whether causative organisms were isolated from the origin. [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Clinical severity at the most severe stage of the disease was assessed using the following factors: presence of septic shock [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the Pitt bacteremia scoring system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], use of a central venous catheter, need for mechanical ventilation, admission to the intensive care unit, and in-hospital mortality.\u003c/p\u003e\n\u003ch3\u003eClinical microbiologic analysis\u003c/h3\u003e\n\u003cp\u003eIdentification of \u003cem\u003eCandida\u003c/em\u003e spp. in blood culture and assessment of antifungal susceptibility were conducted using the BacT/ALERT 3D Microbial Detection System (bioMe\u0026acute;rieux, Inc., Durham, NC, USA) and the automated Vitek 2 Yeast Biochemical Card (bioMe\u0026acute;rieux, Inc., Durham, NC, USA), following a routine laboratory diagnostic procedure. The \u003cem\u003eCandida\u003c/em\u003e strains were confirmed using matrix-assisted laser desorption/ionization-time of fight mass spectrometry (Bruker Daltonics, Bremen, Germany).\u003c/p\u003e\n\u003ch3\u003eStool specimen collection and sequencing\u003c/h3\u003e\n\u003cp\u003eThe V3\u0026ndash;V4 region of the 16S rRNA gene was targeted for amplification to analyze the composition of the intestinal microbiota. The amplified products were purified using a magnetic bead-based purification process, and the appropriate concentration of the purified product was pooled together. Short fragments were removed using the ProNex\u0026reg; Size-Selective Purification System (Promega, Southampton, UK). The quality of the purified products was assessed using the PicoGreen assay (Molecular Probes, Invitrogen, USA). The pooled amplicons were sequenced using an Illumina MiSeq Sequencing System (Illumina, USA).\u003c/p\u003e\u003cp\u003eLow-quality sequence reads (Q\u0026thinsp;\u0026lt;\u0026thinsp;25) were filtered out using Trimmomatic v0.32. Paired-end reads were merged using VSEARCH v2.13.4 with default parameters and trimmed at a similarity cutoff of 0.8 based on the Myers\u0026ndash;Miller alignment algorithm. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Non-specific amplicons that did not encode the V3\u0026ndash;V4 region of 16S rRNA were identified using HMMER v3.2.1. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Unique sequence reads were extracted, and duplicate reads were clustered using VSEARCH. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Taxonomic assignments were performed using the EzBioCloud 16S rRNA database, and chimeric sequences were removed using the UCHIME algorithm. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eMetabolomic profiling\u003c/h3\u003e\n\u003cp\u003eFecal samples were homogenized using bead-beating and extracted with methanol for metabolomic analysis. After extraction, the samples were centrifuged, and the supernatant was filtered through a 0.45-\u0026micro;m syringe filter. All metabolites, including short chain fatty acids (SCFAs) such as acetic acid, butyric acid, valeric acid, and propionic acid, were analyzed using a gas chromatograph-mass spectrometer (gas chromatograph: Agilent 7890, mass spectrometer: LECO Pegasus HT TOFMS). All metabolites were analyzed using only significant peaks with a signal-to-noise ratio\u0026thinsp;\u0026gt;\u0026thinsp;9. All procedures were performed in triplicate to ensure accuracy.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eCategorical variables were compared using Fisher\u0026rsquo;s exact test or Pearson\u0026rsquo;s chi-square test as appropriate and are expressed as numbers (proportions). Continuous variables were compared using either a two-sample Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for normally distributed data or the Mann\u0026ndash;Whitney U test for non-normally distributed data, and are summarized as median values (interquartile range [IQR]).\u003c/p\u003e\u003cp\u003ePotential prognostic factors associated with mortality were identified using univariate analyses. Variables with a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in univariate analysis were included in the multivariate analysis. Backward stepwise selection was applied to refine the model and select the most relevant predictors. The goodness-of-fit of the final model was assessed using the Hosmer\u0026ndash;Lemeshow test. Model discrimination was assessed by generating receiver operating characteristic curves, and predictive performance was validated using stratified 10-fold cross-validation.\u003c/p\u003e\u003cp\u003eAlpha diversity was evaluated using the Abundance-based Coverage Estimator and Shannon\u0026rsquo;s and Simpson\u0026rsquo;s diversity indices. Beta diversity was evaluated using the Bray\u0026ndash;Curtis distance and visualized through principal-coordinate analysis. Taxonomic biomarkers were identified using linear discriminant analysis effect size (LEfSe) and the Kruskal\u0026ndash;Wallis H Test. These analyses were conducted using the EzBioCloud 16s-based Microbiome Taxonomic Profiling platform.\u003c/p\u003e\u003cp\u003eMetabolomic data were normalized using the log10 fold-change for each metabolite. Differences between survivors and non-survivors were assessed using two-sample Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. Correlations between stool metabolites and bacterial genera were analyzed using Spearman correlation. Partial least-squares discriminant analysis (PLS-DA) was performed to identify the stool metabolite signature of mortality, with a variable importance in projection (VIP) score threshold of 1.3.\u003c/p\u003e\u003cp\u003eFunctional profiles were predicted from normalized taxonomic data using PICRUSt and MinPath algorithms. Differentially abundant functional pathways were identified using the Kruskal\u0026ndash;Wallis H test and LEfSe, with statistical significance set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eIBM SPSS Statistics version 20.0 (IBM Corporation, Armonk, NY, USA), SAS 9.4 (SAS Institute Inc., Cary, NC, USA), and R 4.4.1 with RStudio (v2024.04.2\u0026thinsp;+\u0026thinsp;764) (Te R Foundation for Statistical Computing, Vienna, Austria) were used for all statistical analyses. Two-sided \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eFifty-nine patients were diagnosed with clinically significant candidemia during the study period. All the patients were hospitalized in the general ward (n\u0026thinsp;=\u0026thinsp;20, 33.9%) or intensive care unit (n\u0026thinsp;=\u0026thinsp;39, 66.1%). The median patient age was 70 years (IQR, 60\u0026ndash;80), and 52.5% of them were male. The most common sources of candidemia were central venous catheter (n\u0026thinsp;=\u0026thinsp;28, 47.5%) and the gastrointestinal tract (n\u0026thinsp;=\u0026thinsp;20, 33.9%).\u003c/p\u003e\u003cp\u003e\u003cem\u003eCandida albicans\u003c/em\u003e (36.4%) was the most frequently isolated species, followed by \u003cem\u003eCandida tropicalis\u003c/em\u003e (34.5%), \u003cem\u003eCandida parapsilosis\u003c/em\u003e (18.2%), and \u003cem\u003eCandida glabrata\u003c/em\u003e (7.3%). No case of infection by multiple \u003cem\u003eCandida\u003c/em\u003e species was recorded. First-line antifungal therapy included echinocandins (n\u0026thinsp;=\u0026thinsp;56, 94.9%), fluconazole (n\u0026thinsp;=\u0026thinsp;2, 3.4%), and amphotericin B (n\u0026thinsp;=\u0026thinsp;1, 1.7%).\u003c/p\u003e\u003cp\u003eOf the 59 patients, 57 (96.6%) received antibiotics before stool specimen collection, and 21 (35.6%) had concomitant bacteremia. The overall in-hospital mortality rate was 40.7% (24/59), and the median time to death was 29.5 days (IQR, 16.8\u0026ndash;38.3 days) after candidemia onset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCandidemia and gut microbiota\u003c/h2\u003e\u003cp\u003eAlpha-diversity indices of gut bacterial communities differed significantly between the survivors and non-survivors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with the non-survivors showing lower diversity metrics (\u003cem\u003eP-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, β-diversity analysis revealed no significant difference between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\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\u003eComparison of demographic and clinical characteristics between the survivors and the non-survivors of candidemia\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-survivors\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;24)\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlpha-diversity index, median (IQR)\u003c/b\u003e\u003c/p\u003e\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\u003eACE\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.4\u003c/p\u003e\u003cp\u003e(28.0\u0026ndash;97.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.0\u003c/p\u003e\u003cp\u003e(38.2\u0026ndash;103.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.9\u003c/p\u003e\u003cp\u003e(16.4\u0026ndash;60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShannon\u0026rsquo;s diversity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003cp\u003e(1.2\u0026ndash;2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003cp\u003e(1.4\u0026ndash;2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003cp\u003e(1.0\u0026ndash;2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimpson\u0026rsquo;s diversity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003cp\u003e(0.1\u0026ndash;0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003cp\u003e(0.1\u0026ndash;0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003cp\u003e(0.2\u0026ndash;0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemographic variable\u003c/b\u003e\u003c/p\u003e\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 (yrs), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003cp\u003e(60.0\u0026ndash;80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003cp\u003e(64.0\u0026ndash;81.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.5\u003c/p\u003e\u003cp\u003e(59.0\u0026ndash;77.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male), N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (62.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (52.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIdentified isolates from culture results, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003cem\u003eCandida albicans\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCandida tropicalis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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\u003cem\u003eCandida glabrata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSource of candidemia, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eCentral venous catheter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (47.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (45.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal tract\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (18.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eCardiovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (40.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.762\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (27.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic liver diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic pulmonary diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCI\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (57.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (54.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRisk factor within 1 month, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eCorticosteroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (30.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (37.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutropenia\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (16.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (28.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior antibiotic exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57 (96.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (94.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (100.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of immunosuppressive agents other than corticosteroids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical severity at worst, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eConcomitant bacteremia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (35.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (31.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (41.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRenal replacement therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (16.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (20.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (68.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMechanical ventilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (42.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (37.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (50.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePitt bacteremia score\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (67.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (62.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (67.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (57.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (83.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory findings at time of Candidemia diagnosis, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (71.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (87.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcalcitonin\u0026thinsp;\u0026ge;\u0026thinsp;1.00 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (48.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18 (75.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFirst-line antifungal agent, N (%)\u003c/b\u003e\u003c/p\u003e\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\u003eAmphotericin B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEchinocandins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (94.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (97.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (91.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluconazole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnotes. \u003csup\u003ea\u003c/sup\u003eAbundance-based Coverage Estimator, \u003csup\u003eb\u003c/sup\u003e Neutropenia is defined as an absolute neutrophil count (ANC) of \u0026lt;\u0026thinsp;500 cells/\u0026micro;L.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCCI, Charlson\u0026rsquo;s Comorbidity index; CRP, C-reactive protein; ICU, intensive care unit; IQR, interquartile range; yrs, years.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 1,636 unique operational taxonomic units were identified from sequenced specimens, highlighting gut microbial diversity in patients with candidemia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). No significant difference in 16s rRNA quantification was observed between survivors and non-survivors (read counts: 2,478,962 vs. 1,730,093; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.077). \u003cem\u003eFirmicutes\u003c/em\u003e was the most abundant phylum in both groups; however, no significant differences were observed in the abundances of \u003cem\u003eFirmicutes\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.253) and \u003cem\u003eActinobacteria\u003c/em\u003e between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.666) (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\u003eCharacteristics of the fecal microbiome stratified according to mortality status in patients with candidemia\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNon-survivors\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;24)\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRelative abundance (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhylum level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFirmicutes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBacteroidetes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eActinobacteria\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eVerrucomicrobia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpecies level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRuminococcus torques\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClostridium innocuum\u003c/em\u003e group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eStreptococcus salivarius\u003c/em\u003e group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClostridium symbiosum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClostridium scindens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGL520168_s\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAJ315979_s\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eClostridium leptum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePAC001643_s\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePAC001597_s\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAcinetobacter pittii\u003c/em\u003e group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBacteroides caccae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMetabolites\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3-isopropoxy-hexamethyl-tetrasiloxane\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e346556.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441107.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187000.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKKLGZUJIFDXBCT-UHFFFAOYSA-N\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64361.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98935.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6016.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhenylacetic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20706.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32976.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFunctional metabolic pathway, median (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEndoglucanase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003cp\u003e(0.020\u0026ndash;0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.036 (0.026\u0026ndash;0.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032 (0.016\u0026ndash;0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnotes. \u003csup\u003ea\u003c/sup\u003e3-Isopropoxy-1,1,1,7,7,7-hexamethyl-3,5,5-tris(trimethylsiloxy)tetrasiloxane, \u003csup\u003eb\u003c/sup\u003ePentacarbonyl(phosphasila-boracyclohexene) tungsten; IQR, interquartile range\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLEfSe analysis identified 14 bacterial species that were significantly different (LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0) between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Non-survivors showed increased abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e (0.60%), \u003cem\u003eAcinetobacter pittii\u003c/em\u003e group (0.44%), and \u003cem\u003eProteus mirabilis\u003c/em\u003e (0.09%). The remaining 11 species were more enriched in survivors than in non-survivors: \u003cem\u003eClostridium\u003c/em\u003e genera (\u003cem\u003eFirmicutes\u003c/em\u003e), \u003cem\u003eBacteroides cellulosilyticus\u003c/em\u003e (\u003cem\u003eBacteroidetes\u003c/em\u003e), and \u003cem\u003eRuminococcus torques\u003c/em\u003e (\u003cem\u003eBacillota\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eCandidemia and host stool metabolomic profiles\u003c/h2\u003e\u003cp\u003eA total of 111 compounds were included in the analysis. Metabolites within each group, including SCFAs, amino acids, cholic acids, and vitamins, were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The heatmaps of fecal metabolites derived from the survivors and non-survivors are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. PLS-DA plots of all metabolites did not completely distinguish the two groups (Supplementary Fig.\u0026nbsp;1). However, three metabolites with VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.3 were highlighted for their significant role in distinguishing survivors from non-survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Particularly, only one metabolite of 3-isopropoxy-hexamethyl-tetrasiloxane was significantly different between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBased on the taxonomic biomarkers, increased endoglucanase activity in the functional metabolic pathway was identified as a key metabolic pathway in survivors compared with non-survivors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Notably, \u003cem\u003eClostridium\u003c/em\u003e genera, one of the significant taxonomic biomarkers, exhibited significant positive correlations with the metabolic pathway of endoglucanase. However, none of the three metabolites with VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.3 were significantly associated with the significant taxonomic biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePredictors of in-hospital mortality in patients with candidemia\u003c/h2\u003e\u003cp\u003eThe demographic and clinical characteristics of the survivors and non-survivors are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Cerebrovascular disease was more prevalent in non-survivors, whereas concomitant bacteremia and septic shock occurred at similar rates in the two groups. Although C-reactive protein level (\u0026ge;\u0026thinsp;100 mg/L) did not differ significantly between the two groups, procalcitonin level\u0026thinsp;\u0026ge;\u0026thinsp;1.00 ng/mL was more common in non-survivors than in survivors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnivariate analysis identified potential risk factors for in-hospital mortality (Supplementary Table\u0026nbsp;1). Despite its significance in the univariate analysis, Simpson\u0026rsquo;s diversity index was excluded from the multivariate analysis due to its high skewness and collinearity with Shannon\u0026rsquo;s diversity index (variance inflation factor\u0026thinsp;=\u0026thinsp;9.831) (Supplementary Fig.\u0026nbsp;2). Backward stepwise selection was applied to refine the model and select the most relevant predictors. Variables with \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in the univariate analysis were included in the multivariate model, which identified three significant predictors of mortality: lower gut microbiota diversity index (Shannon\u0026rsquo;s diversity index; odds ratio [OR], 0.401; 95% confidence interval [CI], 0.191\u0026ndash;0.844), underlying malignancy (OR, 7.794; 95% CI, 1.410\u0026ndash;43.10), and septic shock (OR, 10.59; 95% CI, 1.701\u0026ndash;65.97) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of prognostic factors associated with in-hospital mortality in patients with candidemia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMultivariable logistic regression model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariable logistic regression model refined using backward stepwise selection based on the Wald statistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACE\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983\u0026ndash;1.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShannon\u0026rsquo;s diversity index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.181\u0026ndash;1.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.191\u0026ndash;0.844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrovascular diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.062\u0026ndash;1.755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalignancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.729\u0026ndash;31.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.410\u0026ndash;43.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.369\u0026ndash;7.806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.351\u0026ndash;73.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.701\u0026ndash;65.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.369\u0026ndash;12.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcalcitonin\u0026thinsp;\u0026ge;\u0026thinsp;1.000 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.528\u0026ndash;9.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eFootnotes. The abovementioned covariates had \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in univariate logistic regression model and were included in the multivariable logistic regression model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003eAbundance-based Coverage Estimator; CRP, C-reactive protein\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe final model demonstrated a good fit (Hosmer\u0026ndash;Lemeshow test: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.013, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.535) and strong discrimination (area under curve [AUC]\u0026thinsp;=\u0026thinsp;0.805; 95% CI, 0.67\u0026ndash;0.90) (Supplementary Fig.\u0026nbsp;3). Cross-validation supported model accuracy (10-fold log-loss\u0026thinsp;=\u0026thinsp;0.606; AUC\u0026thinsp;=\u0026thinsp;0.773), indicating robust calibration and discrimination.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this study, the gut microbiota in non-survivors of candidemia was associated with lower α-diversity as indicated by Shannon\u0026rsquo;s diversity index. Notably, no significant differences in \u0026szlig;-diversity was observed between survivors and non-survivors. Furthermore, analysis of alterations in gut microbiota revealed significant differences in the composition of 11 species between the two groups. Specifically, \u003cem\u003eClostridium\u003c/em\u003e genera involved in endoglucanase activity in the functional metabolic pathway and the metabolite of 3-isopropoxy-hexamethyl-tetrasiloxane were significantly enriched in the survivors compared with the non-survivors. Clinical applications of gut microbiota have shown promise in predicting the prognoses of patients with candidemia.\u003c/p\u003e\u003cp\u003eOur results demonstrated that lower microbial α-diversity in patients with candidemia is associated with a higher risk of mortality, which was consistent with the results of previous \u003cem\u003ein vivo\u003c/em\u003e studies. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Decreased gut microbiota diversity and changes in metabolites are well documented as risk factors for several diseases, such as type 2 diabetes, inflammatory bowel disease, and metabolic syndrome. [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Notably, gut microbiome disruption appears to be a known risk factor for sepsis and subsequent organ dysfunction, leading to worse outcomes. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Considering the critical roles of the microbiome in the training and development of the host immune system, these changes may compromise the mucosal barrier and immune function, worsening the prognoses of patients with candidemia. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Consistent with previous findings, our study showed that underlying malignancy and septic shock are independent predictive factors associated with mortality in patients with candidemia. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOur analysis identified 14 causal bacterial taxa associated with candidemia, with increased abundance of three taxa that showed positive causal relationships with mortality. A previous study indicated that \u003cem\u003eAcinetobacter pittii\u003c/em\u003e can transform deoxynivalenol, which is associated with inflammation and apoptosis, into less toxic or even non-toxic metabolites. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Over-abundance of \u003cem\u003eBacteroides caccae\u003c/em\u003e can lead to lysis of mucus, resulting in thinning of the mucosal barrier and enabling pathogen translocation. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] Furthermore, lipopolysaccharides, a virulence factor of \u003cem\u003eProteus mirabilis\u003c/em\u003e, may be associated with pathological changes via gut leakage and inflammatory actions. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] These results suggest the roles of these species in the pathogenesis of candidemia or worsening prognosis.\u003c/p\u003e\u003cp\u003eThe remaining 11 taxa were significantly abundant in survivors compared with non-survivors. This finding may be because \u003cem\u003eRuminococcus torques\u003c/em\u003e degrades intestinal mucin, helping to regulate pH and maintain gut microbial diversity and stability. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] In addition, \u003cem\u003eClostridium innocuum\u003c/em\u003e contributes to intestinal nutrient metabolism by breaking down glucose ureide and is detected in approximately 80% of healthy adults. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Furthermore, \u003cem\u003eStreptococcus salivarius\u003c/em\u003e suppresses intestinal inflammation and regulates immune response. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] In addition to the diverse roles of gut flora in patients with candidemia, these findings suggest the possibility of improving patient outcomes using microbiota-modulating therapy.\u003c/p\u003e\u003cp\u003eInterestingly, several \u003cem\u003eClostridium\u003c/em\u003e species and the metabolic pathway of endoglucanase, which showed relatively high abundance and activity, respectively, in survivors compared with non-survivors, showed a significant positive correlation in this study. The endoglucanases, which functionally hydrolyze glycosidic bonds, are enzymes found in microbiomes that break down cellulose. In addition to maintaining the balance of intestinal microbiota, the gut flora associated with the endoglucanase of the functional metabolic pathway contribute to the nutrition and health of the host through the production of SCFAs. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Considering this interplay between the gut flora and endoglucanase, strengthening important functional metabolic pathways using beneficial taxa of the gut microbiota may be useful in the prevention and treatment of candidemia.\u003c/p\u003e\u003cp\u003eOur current gut microbiome data provides further evidence that supports a potential contribution of gut microbiota to host fecal metabolic profiles. Although none of the three metabolites with VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.3 was significantly associated with gut taxonomic biomarkers, the levels of 3-isopropoxy-hexamethyl-tetrasiloxane detected in the feces of the survivors were significantly higher than those in the fecal samples of non-survivors. Previous studies have shown that 3-isopropoxy-hexamethyl-tetrasiloxane, a siloxane derivative, exhibits antifungal activity against \u003cem\u003eCandida albicans, Candida krusei\u003c/em\u003e, and \u003cem\u003eCandida glabrata\u003c/em\u003e. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] These findings suggest that 3-isopropoxy-hexamethyl-tetrasiloxane may play a role in suppressing pathogenic microorganisms and restoring the gut ecosystem.\u003c/p\u003e\u003cp\u003ePhenyl acetic acid and KKLGZUJIFDXBCT-UHFFFAOYSA-N (tungsten) with VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.3 were observed in higher levels in the stool of the survivors than in those of non-survivors; however, this finding was not statistically significant. Phenyl acetic acid exhibits antifungal activity against \u003cem\u003eCandida albicans\u003c/em\u003e, and the metabolism of \u003cem\u003eClostridium\u003c/em\u003e species is involved in this process. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Based on a recent study, the calcium tungstate microgel of a heavy metal may act as a microbe-based therapy against inflammatory bowel diseases by restoring gut microbiota homeostasis and improving the intestinal mucosal immune barrier. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study has some limitations. First, bias in data collection is possible. In addition, the statistical power of the study was possibly limited by its single-center design and small sample size. Moreover, the small sample size may have also affected the detection of significant differences in β-diversity. Larger multicenter cohort studies are needed to confirm these findings and provide more generalizable conclusions. Second, the cause\u0026ndash;effect relationship of the changes observed in this study could not be determined. Third, as combined antimicrobial therapy plays a role in gut dysbiosis, co-infections with other pathogens or other treatments may have interfered with our findings. Future large randomized controlled trials are needed to comprehensively investigate the effect of gut microbiome profiling on the prevention and treatment of candidemia. In addition, \u003cem\u003ein vivo\u003c/em\u003e models are warranted to decipher its pathogenesis and validate the findings of this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigates the potential dysregulation function of the gut microbiota in patients with candidemia. In addition, it highlights the link between reduced gut microbiota diversity and higher mortality in patients with candidemia. Our findings suggest the alteration of gut microbiota in candidemia and its potential effects on host fecal metabolic profiles. Our study supports the potential therapeutic role of addressing disruptions in the gut microbiota in patients with candidemia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments.\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u003c/strong\u003e Conceptualization and design: Y.K.Y. Acquisition, analysis, and interpretation of data: Y.K.Y., S.H.P., and S.M.P. Statistical analysis: Y.K.Y., S.H.P., and S.M.P. Drafting of the manuscript: Y.K.Y., S.H.P., and S.M.P. Critical review and revision of the manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement.\u0026nbsp;\u003c/strong\u003eData supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2022R1A2C1010808). The Funding sources had no role in the study design, data collection, data analysis, decision to publish, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement.\u0026nbsp;\u003c/strong\u003eThis study was approved by the Institutional Review Board of Korea University Anam Hospital (IRB No. 2024AN0385).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement.\u0026nbsp;\u003c/strong\u003eThe need for patient consent was waived owing to the nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interests.\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWisplinghoff H, Bischoff T, Tallent SM, Seifert H, Wenzel RP, Edmond MB. Nosocomial bloodstream infections in US hospitals: analysis of 24,179 cases from a prospective nationwide surveillance study. 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ACS Cent Sci. 2023;9:1327\u0026ndash;41.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-clinical-microbiology-and-antimicrobials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmam","sideBox":"Learn more about [Annals of Clinical Microbiology and Antimicrobials](http://ann-clinmicrob.biomedcentral.com/)","snPcode":"12941","submissionUrl":"https://submission.nature.com/new-submission/12941/3","title":"Annals of Clinical Microbiology and Antimicrobials","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gastrointestinal microbiome, metabolome, candidemia, mortality","lastPublishedDoi":"10.21203/rs.3.rs-6551378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6551378/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe gut microbiota plays an important role in defense against infectious diseases. However, data on clinical effects of microbiome profiles in patients with candidemia are limited. This study investigated the intestinal microbiome and the effects on mortality in patients with candidemia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this prospective observational pilot study, fecal samples from adult patients with candidemia were subjected to 16S rRNA gene sequencing for microbiota analysis and gas chromatography\u0026ndash;mass spectrometry metabolomics. Multivariate logistic regression was conducted to identify predictors of in-hospital mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFifty-nine patients with candidemia were analyzed, with an in-hospital mortality rate of 40.7%. The median Shannon\u0026rsquo;s diversity index of the gut microbiota was significantly lower in survivors than in non-survivors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Linear discriminant analysis Effect Size revealed 11 bacterial species that significantly differed between the two groups. Among 111 fecal metabolites, only 3-isopropoxy-hexamethyl-tetrasiloxane was differentially expressed between survivors and non-survivors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Septic shock (adjusted odds ratio: 10.59; 95% confidence interval, 1.70\u0026ndash;65.97), underlying malignancy (7.79 [1.41\u0026ndash;43.10]), and Shannon\u0026rsquo;s diversity index (0.40 [0.19\u0026ndash;0.84]) were significant predictors of in-hospital mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLow gut bacterial diversity was independently associated with increased mortality in patients with candidemia. The intestinal microbiome could offer new perspectives for the prevention and the treatment of candidemia.\u003c/p\u003e","manuscriptTitle":"Clinical impact of altered gut microbiota and metabolite profiles on mortality in patients with candidemia: A prospective observation pilot study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:12:53","doi":"10.21203/rs.3.rs-6551378/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-26T09:32:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-28T10:59:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250507299790993696738991305765842233217","date":"2025-08-25T02:18:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119772552821270300458389006141966236665","date":"2025-08-21T16:41:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T16:22:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T22:52:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T06:41:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Clinical Microbiology and Antimicrobials","date":"2025-04-29T01:20:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-clinical-microbiology-and-antimicrobials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmam","sideBox":"Learn more about [Annals of Clinical Microbiology and Antimicrobials](http://ann-clinmicrob.biomedcentral.com/)","snPcode":"12941","submissionUrl":"https://submission.nature.com/new-submission/12941/3","title":"Annals of Clinical Microbiology and Antimicrobials","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d71709d-2d8a-4e6b-a321-b206e6c2040f","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:47:21+00:00","versionOfRecord":{"articleIdentity":"rs-6551378","link":"https://doi.org/10.1186/s12941-026-00850-x","journal":{"identity":"annals-of-clinical-microbiology-and-antimicrobials","isVorOnly":false,"title":"Annals of Clinical Microbiology and Antimicrobials"},"publishedOn":"2026-01-17 16:29:52","publishedOnDateReadable":"January 17th, 2026"},"versionCreatedAt":"2025-09-01 10:12:53","video":"","vorDoi":"10.1186/s12941-026-00850-x","vorDoiUrl":"https://doi.org/10.1186/s12941-026-00850-x","workflowStages":[]},"version":"v1","identity":"rs-6551378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6551378","identity":"rs-6551378","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}