Marker, kit for predicting or diagnosing sepsis in children, and application thereof

Abstract

The application provides a marker, a kit and application thereof for predicting or diagnosing child sepsis, and provides a marker for predicting or diagnosing child sepsis, wherein the marker is deoxycholic acid-3-sulfate. The method for detecting child sepsis by using DCA-3S is non-invasive, avoids the invasiveness and complexity of traditional diagnosis methods, reduces the pain and medical cost of patients, provides a reliable and convenient diagnosis tool for clinicians, and is particularly suitable for predicting or diagnosing child sepsis.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more specifically to biomarkers, reagent kits, and their applications for predicting or diagnosing sepsis in children. Background Technology

[0002] Sepsis is a systemic inflammatory response syndrome triggered by infection and is one of the leading causes of death worldwide. Approximately 20.3 million cases of sepsis occur annually, with 41.5% occurring in children under the age of five, accounting for 20% of global deaths. Childhood sepsis not only poses a direct threat to the patient's life but can also lead to long-term organ dysfunction and neurodevelopmental disorders, placing a heavy burden on families and society. Because children's immune systems are not fully developed, sepsis often progresses more rapidly in children, and early symptoms are often atypical, easily confused with other illnesses such as the common cold or gastroenteritis, leading to delayed diagnosis.

[0003] Currently, the diagnosis of sepsis mainly relies on clinical manifestations, laboratory tests, and imaging examinations. While clinical manifestations such as fever, chills, and hypotension are common, they often lack specificity in the early stages and are easily confused with other infectious or non-infectious diseases. Laboratory tests typically include the detection of inflammatory markers such as white blood cell count, C-reactive protein (CRP), and procalcitonin (PCT). However, while these indicators can reflect the inflammatory state, they cannot accurately distinguish between infectious and non-infectious inflammation, and their assessment of disease severity is not precise enough. Imaging examinations such as X-rays, CT scans, and ultrasound, while helpful in identifying the source of infection, are often difficult to detect abnormalities in the early stages of infection and have a certain lag, failing to reflect the dynamic changes of the disease in a timely manner.

[0004] In recent years, tools such as the SOFA score and qSOFA score have been introduced for the early assessment of sepsis, but their effectiveness in children is limited. Children's physiological characteristics and disease presentations differ significantly from adults, resulting in insufficient sensitivity and specificity of these scoring tools in pediatric patients. Furthermore, existing diagnostic methods often fail to provide timely, specific diagnostic information, leading to delays in treatment decisions and further increasing the risk of patient death.

[0005] In recent years, metabolomics research has provided new insights into the early diagnosis of sepsis. Studies have shown that the occurrence and development of sepsis are accompanied by significant metabolic disorders, among which abnormalities in bile acid metabolism are particularly prominent. Bile acids, as important products of gut microbial metabolism, play a crucial role in the pathophysiology of sepsis by regulating the gut microbiota and host immune response. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention provides a biomarker for predicting or diagnosing sepsis in children, wherein the biomarker is deoxycholic acid-3-sulfate (DCA-3S).

[0007] In one embodiment, the present invention provides a kit for predicting or diagnosing sepsis in children, the kit comprising reagents for detecting the level of a biomarker in a sample to be tested, the biomarker being deoxycholic acid-3-sulfate.

[0008] In one embodiment, the sample to be tested is feces.

[0009] In one embodiment, the present invention provides the use of a reagent for detecting deoxycholic acid-3-sulfate in the preparation of a kit for predicting or diagnosing sepsis in children.

[0010] This invention first proposes deoxycholic acid-3-sulfate (DCA-3S, CAS No.: 67030-48-2, molecular formula: C 24 H 40 O8S (molecular weight: 472.64 g / mol) was used as a biomarker for predicting or diagnosing childhood sepsis. The reliability of DCA-3S as a sepsis biomarker was confirmed, and a high-accuracy and robust diagnostic model for childhood sepsis based on predictive factors and led by DCA-3S was proposed. By detecting the level of DCA-3S in patients, doctors can make an accurate diagnosis in the early stages of the disease, thereby initiating targeted treatment in a timely manner and improving patient prognosis and survival. Furthermore, the DCA-3S detection method is non-invasive, avoiding the invasiveness and complexity of traditional diagnostic methods, reducing patient suffering and medical costs, and providing clinicians with a reliable and convenient diagnostic tool, particularly suitable for predicting or diagnosing childhood sepsis. This invention's biomarker has excellent diagnostic efficacy, convenient sampling, and high safety, making it particularly suitable for children, providing a new tool and solution for the early and accurate diagnosis of childhood sepsis. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1The main coordinate analysis (PCoA) (Brey-Curtis distance) plot of bile acid profiles between children with sepsis and healthy controls was used, and the differences in bile acid profiles were analyzed using adonis (i.e., permutation multivariate analysis of variance, permanova). Figure 2 This is a volcano diagram showing the accumulation of bile acids in children with sepsis and healthy controls; Figure 3 This is a ROC curve plot showing the diagnostic potential of bile acid features in childhood sepsis (feature selection, FS). Figure 4 This is a permutation importance score map of bile acid characteristics in the diagnostic model, showing only bile acids for which there are significant differences in levels between healthy controls and sepsis patients. The color indicates the enrichment of bile acids in healthy controls (blue) or sepsis patients (orange). Figure 5 In the validation cohort, the DCA-3S level was significantly higher in patients with sepsis compared with healthy controls. P values ​​were calculated using a two-sided Wilcoxon rank-sum test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; Figure 6 This is a partial Spearman correlation plot (adjusted for age, sex, and BMI) between bile acid levels and clinical diagnostic indicators of sepsis (including PCT, FIB, hs_CRP, D_dimer, IL6, and IL10). An asterisk (*) indicates a significant association (P.adj < 0.05). The p-values ​​of multiple tests were adjusted using the Benjamini-Hochberg method. Figure 7 This is a graph validating the increase in DCA-3S levels in sepsis patients compared to healthy controls. P values ​​were calculated using a two-sided Wilcoxon rank-sum test. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001; Figure 8 This is a graph comparing DCA levels between healthy controls and children with sepsis in the cohort. The p-value was calculated using a two-sided Wilcoxon rank-sum test. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this application, the present invention will be further described below with reference to embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0014] Unless otherwise specified, the experimental methods used in the following specific embodiments are conventional methods. Unless otherwise specified, the materials and reagents used in the following specific embodiments are commercially available. I. Queue Collection

[0015] All human subjects in this study were from Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology. Two cohorts were included: a discovery cohort and a validation cohort. The discovery cohort, recruited in 2023, included 117 children with sepsis and 48 age-matched healthy controls; the validation cohort, recruited in 2024, included 49 children with sepsis and 47 age-matched healthy controls. The diagnosis of sepsis in children was based on the Sequential Organ Failure Assessment (SOFA) score. Inclusion criteria were: (i) age not exceeding 10 years, (ii) clinically diagnosed sepsis, and (iii) no history of malignant tumors. Children without sepsis symptoms were also recruited as a control group. Metadata (e.g., age, sex, body mass index) and clinical data (e.g., organ function assessment and inflammatory markers) were collected from participants through on-site assessment and clinical examination. All participants were Chinese citizens. All experimental protocols in this study underwent rigorous evaluation and approval by the Medical Ethics Committee of Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology (Approval No.: 2024R050-E01). Each participant (or their guardian) signed an informed consent form before participating in the study. Demographic analysis showed that the sepsis group and the control group were well-matched in terms of age (P=0.28, discovery cohort; P=0.54, validation cohort), sex distribution (P=0.07, discovery cohort; P=0.64, validation cohort), and body mass index (BMI) score (P=0.51, discovery cohort), providing a solid foundation for subsequent comparative analyses.

[0016] II. Extraction of bile acids from fecal samples To extract bile acids from human feces, a 30 mg fecal sample was first weighed and transferred to a 1.5 mL microcentrifuge tube. Next, 1 mL of ice-cold physiological saline was added to the tube, and homogenization was performed using an ULTRA-TURRAX® IKA T10 homogenizer (Wilmington, Delaware, USA) for 3 minutes. Then, 4 mL of ice-cold methanol was added to the tube, and the mixture was vortexed thoroughly. The mixed sample was incubated overnight at 4°C to complete the bile acid extraction process. After incubation, the sample was centrifuged at 6,000 × g at 4°C for 15 minutes, and the supernatant was collected. The supernatant was filtered using a nylon 66 syringe filter (13 mm, 0.22 µm), and the filtrate was dried under nitrogen. Finally, the dried extract was resuspended in an acetonitrile / water (1 / 9, 100 µL) solution containing deuterated cholic acid (cholic acid-2,2,4,4-d4, 100 ng / mL) as an internal standard. 5 µL of this solution was injected into a liquid chromatography-triple quadrupole mass spectrometry (LC-QqQ-MS) system for bile acid analysis.

[0017] III. Targeted Bile Acid Analysis Quantitative analysis of bile acids was performed using a liquid chromatography-tandem mass spectrometry (LC-MS / MS) system coupled with a Shimadzu LC-30AD ultra-high performance liquid chromatography system (Kyoto, Japan) and a Triple Quad™ 4500 mass spectrometer (ABSciex, Framingham, Massachusetts, USA). Chromatographic separation was performed using an ACQUITY UPLC BSH C18 column (50 mm × 2.1 mm, 1.7 μm, Waters, Milford, Massachusetts, USA) at 40°C with a flow rate of 0.4 mL / min. Mobile phase A was 0.1% formic acid aqueous solution, and mobile phase B was acetonitrile. Linear gradient elution was used as follows: 0–1 min, 10% B; 1–11 min, 10–90% B; 11–12 min, 90–100% B; 12–13 min, 100–10% B. Reequilibration was then performed for 5 min at 10% B. All analytes were detected using multiple reaction monitoring (MRM) mode. LC-MS / MS operation control was performed using Analyst® software (version 1.6.3), and quantitative analysis was performed using MultiQuant™ software (version 3.0.2). IV. Statistical Analysis

[0018] All statistical analyses were performed using R (version 4.3.2). The Shapiro-Wilk normality test was used to determine the normality of the data distribution. Principal coordinate analysis based on Bray-Cutis distance (PCoA) was used to analyze the differences in bile acid abundance between healthy controls and children with sepsis. Permuted multivariate ANOVA (also known as ADONIS, R package vegan, version 2.6-4) was used to compare whether there were significant differences in bile acid profiles between the two groups. For statistical comparisons, two-tailed t-tests (for normal probability distributions) or Wilcoxon rank-sum tests (for outlier probability distributions) were used to compare continuous variables. The Benjamini-Hochberg method was used to adjust for p-values ​​in multiple tests. P < 0.05 was considered significant. V. Experimental Results

[0019] This invention employs advanced liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS) to analyze the bile acid metabolomic profile in children's fecal samples. Using this method, we successfully identified 92 bile acids, including 44 free bile acids, 42 amino acid-bound bile acids, 5 glucuronidated bile acids, and 1 acetylated bile acid, and accurately determined their levels. This comprehensive bile acid profiling provides rich data support for subsequent biomarker screening.

[0020] Based on the obtained bile acid (BA) profiles, we analyzed the differences in BA between pediatric sepsis patients and healthy individuals. Principal coordinate analysis (PCoA) provided a panoramic overview of the BA profiles, showing a significant separation in BA between the two groups. Figure 1 Permutational multivariate ANOVA, Permanento test, P=1.00e-03. Difference analysis (Wilcoxon-P.adj<0.05, |log2FC|>1) showed significant differences in 19 types of BA between the two groups, of which 17 types of BA remained significant after adjusting for age, sex, and BMI using a generalized linear model (GLM). Figure 2Integrating these findings with partial least squares discriminant analysis (PLS-DA) results, we obtained a sepsis-specific BA profile comprising 13 unique BAs, as detailed in Table 1, which identifies enriched bile acids in pediatric sepsis patients and healthy controls. Among these 13 BAs, sulfated BAs constituted a significant subgroup, accounting for 23.08% (3 / 13) of sepsis-associated BAs. Specifically, deoxycholic acid-3-sulfate (DCA-3S) (log2FC=1.72, P.adj=2.22e-05), 3-sulfoglycocholic acid (Gly-DCA-3S; log2FC=2.70, P.adj=6.95e-04), and 3-sulfoglycodeoxycholic acid (Gly-DCA-3S; log2FC=2.99, P.adj=8.97e-04) consistently showed elevated levels in sepsis patients. These results suggest a potential link between BA sulfation and sepsis pathology.

[0021] Table 1 bile acids GLM P value Wilcox uncorrected p-value Wilcox correction P-value VIP Points log2FC norCA 0.054899899 0.023035576 0.056474315 1.046000281 0.57410066 CA-3G 0.00239774 3.72287E-05 0.000282938 1.56623685 2.483262948 Gly-CA 0.249089148 0.321846274 0.452968831 0.755480951 1.038878001 UDCA-3S 0.918304048 0.013708762 0.037209496 0.144205564 0.11930319 Ala-CA 0.174053542 0.017943012 0.047023066 0.954308416 -1.51048555 CA-7S 0.296782241 0.878805808 0.908339525 0.302284247 0.209023963 TUDCA / THDCA 0.291083718 0.526617016 0.678354123 0.58846685 1.639545578 T-α-MCA / T-β-MCA 0.816926167 0.688178253 0.804639188 0.292680597 0.306667425 TCA 0.139730621 0.15225965 0.251559422 1.098922949 2.931026965 DHCA 0.533797848 0.488563233 0.640186306 0.301546563 -0.28336216 N6-Lys-CDCA 0.025139801 0.001686493 0.00712075 0.723336442 2.311993485 N6-Lys-DCA 0.28465638 0.83058433 0.876727903 0.536457316 1.025943124 Gly-CDCA-3S 0.042596675 0.000141641 0.000897062 1.488328418 2.989594827 Gly-DCA-3S 0.045960074 0.000100655 0.000695434 1.369204518 2.696888755 Gln-β-MCA 0.555694039 0.068880148 0.137760296 0.079966654 0.116377694 Gln-CA 0.222788918 0.803845491 0.876727903 0.385578947 0.356405863 Glu-DCA 0.333141572 0.124849564 0.220664345 0.473911195 -0.96023151 7,12-KLCA 0.086616256 0.004860533 0.017590501 0.776456841 -0.72040517 isoalloLCA 0.179295599 0.786486667 0.876727903 0.698622257 0.270010994 6-KLCA 0.004233582 7.09391E-07 1.34784E-05 1.277161358 -1.26728163 7-KLCA 0.03034665 0.003576759 0.013591683 1.095050152 -0.69477844 MDCA 0.001389116 0.001656073 0.00712075 1.769956193 -1.36684185 β-UDCA 0.001588223 0.008358954 0.025411219 1.835566557 -1.56572929 β-HDCA 0.019777489 0.120102973 0.220664345 0.912766966 -0.48852548 UDCA / HDCA 0.001203503 0.012584416 0.035422801 1.465970017 -0.76757766 3β-DCA / 12β-DCA 1.30667E-05 4.32251E-06 5.47518E-05 1.947332526 -1.12395154 3-oxoCA 0.411026151 0.179072909 0.283532105 0.623823661 -0.3257498 HCA 0.700421126 0.274625678 0.393802859 0.112645261 -0.05652307 7-oxoCA / 3-oxoACA / 12-oxoCA 0.465994418 0.231494875 0.345729568 0.453470635 -0.21275109 ACA 0.025711167 0.151769565 0.251559422 0.979069329 0.445308106 CA 0.008663364 0.064906579 0.133321622 1.098811076 0.384553806 Gly-CDCA 0.483354501 0.884435853 0.908339525 0.544968139 0.655804713 Gly-DCA 0.960497356 0.337174951 0.465914478 0.107818873 0.262745353 coproCA 0.548184982 0.000858828 0.004079431 0.158239471 -0.20727218 Ala-DCA 0.979209671 0.12471471 0.220664345 0.055187163 0.094270842 CDCA-3S 0.003021338 0.025288527 0.059160882 1.280920578 0.796035308 DCA-3S 0.00111635 1.46051E-06 2.21998E-05 1.61196443 1.719989506 TCDCA 0.085324694 0.05558447 0.120697707 1.282293772 2.95452066 12β-CA / UCA 0.006649375 0.020760637 0.052593614 1.374925159 -0.69984758 TDCA 0.760352857 0.628035693 0.753509927 0.280555099 0.858036155 Val-CA 0.509093079 0.608253437 0.753509927 0.346829365 0.49760128 Gly-LCA-3S 0.563841369 0.006124793 0.020238445 0.402952282 0.523601109 Gln-DCA 0.613823615 0.92405348 0.936374193 0.31125122 1.612875483 Leu-CA 0.236929374 0.176988142 0.283532105 0.688619516 1.243150765 Phe-CA 0.050905631 0.009552137 0.027921631 1.079957469 1.189900515 CDCA-3G / DCA-3G / CDCA-24G 0.001409075 7.02805E-07 1.34784E-05 1.621099344 2.380659712 ω-MCA / 3β-CA 0.050688503 0.007242743 0.022935354 1.031044468 -0.53261299 △5-isoLCA 0.119691187 0.62015925 0.753509927 0.744924829 0.913374976 △11-LCA 0.214424895 0.000572309 0.003106818 0.668510176 -0.74256467 3-dehydroLCA 0.000919974 9.57396E-06 9.09526E-05 1.920584185 -1.9650267 isoLCA 0.092782285 0.000769936 0.00390101 0.677434411 -0.98002029 alloLCA 0.000465273 0.002436175 0.009744702 1.51544242 -1.51640855 LCA 6.02387E-05 8.2778E-06 8.98733E-05 1.790624224 -1.52997295 norDCA 0.524544844 0.349945138 0.474925544 0.074898017 0.048055428 dioxo-HDCA / dioxo-CDCA 0.01983429 6.41786E-07 1.34784E-05 1.131495408 -1.27804468 12-KLCA 0.000387875 0.005670933 0.019590495 1.885252202 -1.48024715 apoCA 0.982152232 0.556266462 0.704604185 0.110525197 -0.11104611 α-MCA 0.772935558 0.789244752 0.876727903 0.049543186 0.02595519 3-oxoCDCA 0.333763903 0.826763279 0.876727903 0.472349095 0.536028225 3-oxoDCA 0.000616405 2.43222E-05 0.000205387 1.638830254 -0.87584092 CDCA 0.011389889 0.092094359 0.17946593 1.095196614 0.420682665 DCA 0.416878852 0.231494875 0.345729568 0.726770683 0.397871797 isoDCA 0.017515384 4.20627E-08 3.19677E-06 1.391683185 -1.37821059 LCA-3S 0.161570882 0.034431414 0.076964337 0.713470684 0.77315298 TLCA 0.000199928 0.000451365 0.002638746 2.195063018 -2.74783632 β-MCA 0.90239617 0.058415458 0.123321522 0.164986506 -0.11500328 Val-CDCA 0.814104637 0.829245234 0.876727903 0.073992912 0.21980773 Val-DCA 0.209622662 0.114614875 0.217768263 0.149754328 -0.20455447 Leu-CDCA 0.552691966 0.826262455 0.876727903 0.347548927 0.989966551 Leu-DCA 0.829824567 0.634534676 0.753509927 0.137029007 0.208495332 Phe-CDCA 0.252672894 0.025688278 0.059160882 0.634859924 1.026182333 Phe-DCA 0.978700973 0.937907946 0.937907946 0.496812348 0.804869746 Gly-HDCA / Gly-UDCA 0.915715583 0.236551809 0.345729568 0.207084013 0.593852913 Tyr-DCA 0.005944127 0.149231092 0.251559422 1.037530877 -1.20483285 Trp-CDCA 0.168448157 0.426782547 0.569043397 0.758489022 1.266092882 Trp-DCA 0.127026402 0.235911643 0.345729568 0.172777169 -0.22850712 .

[0022] Given the significant differences in bile acid (BA) profiles between sepsis patients and healthy individuals, we investigated the diagnostic potential of BA profiles for childhood sepsis. Based on individual BA levels, we constructed a random forest model to distinguish between childhood sepsis patients and age-matched healthy controls. Figure 3 A greedy feature selection method was employed to remove irrelevant features, thereby improving the model's diagnostic performance. Notably, DCA-3S became the most significant predictor in the final diagnostic model, with an area under the curve (AUC) of 0.91 in the discovery cohort. When applied to the independent validation cohort, the model maintained robust diagnostic accuracy, with an AUC of 0.85. Figure 3 Table 2 shows the diagnostic efficacy of the model based on microbial and bile acid biomarkers in the discovery and validation cohorts. Furthermore, BA profile analysis revealed that 8 BAs in the validation cohort exhibited the same significant alterations as the 13 significantly altered BAs in the discovery cohort (Table 3 shows that 8 of the 13 differentially abundant bile acids (including DCA-3S) identified in the discovery cohort were validated in the validation cohort). Importantly, deoxycholic acid-3-sulfate (DCA-3S) showed persistent and significant enrichment in sepsis patients in both cohorts. Figure 5 ).

[0023] Table 2 Model queue accuracy Sensitivity Specificity Accuracy Recall rate F1 value AUC AUPR bile acid model Discovery queue 0.7455 0.7009 0.8542 0.9213 0.7009 0.7961 0.8560 0.9261 Verification queue / / / / / / / / Bile acid model (feature screening) Discovery queue 0.8545 0.8889 0.7708 0.9043 0.8889 0.8966 0.9088 0.9607 Verification queue 0.8125 0.7959 0.8298 0.8298 0.7959 0.8125 0.8506 0.8772 Microbiome model Discovery queue 0.7576 0.7094 0.8750 0.9326 0.7094 0.8058 0.8567 0.9341 Verification queue / / / / / / / / Microbiome model (feature screening) Discovery queue 0.8242 0.7778 0.9375 0.9681 0.7778 0.8626 0.9045 0.9620 Verification queue 0.7917 0.7143 0.8723 0.8537 0.7143 0.7778 0.8545 0.8827 Microbiome + Bile Acid Combined Model Discovery queue 0.7939 0.7607 0.8750 0.9368 0.7607 0.8396 0.8780 0.9406 Validation queue / / / / / / / / Microbiome + Bile Acid Combined Model (Feature Screening) Discovery queue 0.8545 0.8376 0.8958 0.9515 0.8376 0.8909 0.9257 0.9681 Verification queue 0.8438 0.8163 0.8723 0.8696 0.8163 0.8421 0.8934 0.9169 .

[0024] Table 3 .

[0025] Furthermore, we explored the association between plasma protein A (BA) profiles and clinical indicators of sepsis. Random forest regression analysis showed that the BA profile had reliable predictive power for multiple clinical indicators, especially high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and D-dimer levels. Correlation analysis further confirmed that each BA was significantly correlated with clinical diagnostic indicators of sepsis. Figure 6 Notably, DCA-3S was significantly positively correlated with all six clinical endpoints, including procalcitonin (PCT, adjusted P=3.30e-03), fibrinogen (FIB, adjusted P=2.71e-03), hs-CRP (adjusted P=5.68e-03), D-dimer (adjusted P=1.64e-02), IL-6 (adjusted P=9.63e-04), and interleukin-10 (IL-10, adjusted P=2.61e-04). In contrast, the precursor of DCA-3S, DCA, was not significantly correlated with any of these clinical endpoints. Figure 7 ).

[0026] The altered bile acid (BA) profile (primarily DCA-3S) observed in pediatric sepsis patients suggests that BA may be involved in the progression of sepsis. Given the lack of a significant difference in DCA levels between sepsis patients and healthy controls (P = 0.23), the elevated DCA-3S levels may be attributed to the upregulation of sulfation modification. Figure 8 ).

[0027] In summary, principal coordinate analysis (PCoA) based on Bray-Cutis distance revealed significant differences in bile acid abundance between healthy controls and children with sepsis, with a significant difference in PCOA2 between the two groups. This finding suggests that bile acid metabolism may play an important role in the development and progression of sepsis. Further two-sided Wilcoxon rank-sum test analysis revealed significantly higher DCA-3S abundance in children with sepsis compared to healthy controls, thus establishing DCA-3S as a biomarker.

[0028] It should be understood that the disclosed invention is not limited to the specific methods, schemes, and substances described, as these are all subject to variation. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the scope of the invention, which is limited only by the appended claims.

[0029] Those skilled in the art will also recognize, or be able to identify, many equivalents of the specific embodiments of the invention described herein using no more than conventional experiments. These equivalents are also included in the appended claims.

Claims

1. A biomarker for predicting or diagnosing sepsis in children, characterized in that, The marker is deoxycholic acid-3-sulfate.

2. A kit for predicting or diagnosing sepsis in children, characterized in that, The kit contains reagents for detecting the level of a biomarker in the sample to be tested, the biomarker being deoxycholic acid-3-sulfate.

3. The reagent kit according to claim 2, characterized in that, The sample to be tested was feces.

4. The use of a reagent for detecting deoxycholic acid-3-sulfate in the preparation of a kit for predicting or diagnosing sepsis in children.

Images