Untargeted Lipidomics Profiling Provides Novel Insights into Pediatric Patients with Sepsis: An Exploratory Study

Untargeted Lipidomics Profiling Provides Novel Insights into Pediatric Patients with Sepsis: An Exploratory 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 Untargeted Lipidomics Profiling Provides Novel Insights into Pediatric Patients with Sepsis: An Exploratory Study Reyihangu Awuti, Jiayi Bai, Ye Cheng, Weili Yang, Zimei Cheng, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6040682/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Metabolomics → Version 1 posted 9 You are reading this latest preprint version Abstract Introduction The plasma lipidome has emerged as an important indicator for assessing host metabolic and immune status in sepsis. While previous studies have largely examined specific lipid class changes in adults sepsis, comprehensive investigations into plasma lipidomic alterations in pediatric sepsis are limited. This study aimed to characterize the plasma lipidome in pediatric sepsis using a metabolomics-based exploratory approach, providing insights into pathophysiological mechanisms and potential biomarkers. Methods A retrospective study was conducted on pediatric patients with sepsis admitted to the pediatric intensive care unit (PICU). Untargeted lipidomics analysis using ultra-performance liquid chromatography coupled with Orbitrap mass spectrometry (UPLC-Orbitrap) was performed to compare metabolomic profiles between non-infected control patients and sepsis patients. Results Compared to controls, plasma lipid levels in sepsis patients decreased by 33.3%, increased by 20.2%, and remained unchanged in 46.5% of cases. Several lipid molecules were identified to be associated with disease severity and inflammatory markers. In the recovery and deterioration subgroups, 186 differential lipid molecules were identified, with triglycerides (TG) being the most abundant class. Notably, 15 lipid molecules overlapped between those associated with disease severity and those linked to clinical outcomes. Fatty acid (FA) levels were significantly elevated in the sepsis group compared to controls, with arachidonic acid (FA(20:4)) showing the most significant increase (P < 0.001). Conclusion Alterations in plasma lipid profiles among children with sepsis reflect disease severity, systemic inflammatory responses, and clinical outcomes. These findings underscore the prognostic potential of lipidomics and its value in understanding sepsis pathophysiology. Pediatric sepsis Plasma lipidome Untargeted lipidomics Fatty acid Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Sepsis remains a major global health challenge, contributing to approximately 3.3 million childhood deaths annually(Sanchez-Pinto et al., 2024 ). Despite significant advancements in healthcare that have led to improved clinical outcomes and a steady decline in mortality rates (Bauer et al., 2020 ), severe sepsis continues to be the leading cause of death in Pediatric Intensive Care Units (PICU) (Barber et al., 2023 ; Weiss andFitzgerald, 2024). Alarmingly, one-third of these fatalities are attributed to refractory shock (Zhang andNing, 2021). While advancements in antibiotic therapies and infection control strategies have provided some progress, early recognition and timely effective treatment of sepsis remain critical clinical challenges. Furthermore, among those who survive, many children experience long-term physical, cognitive, and emotional impairments that substantially impact their quality of life and impose considerable burdens on their families. Emerging evidence from metabolomics studies highlights the critical role of lipid metabolism in the pathophysiology of sepsis. Sepsis triggers profound lipid metabolic dysregulation, characterized by elevated levels of certain lipids such as triglycerides (TG) and phosphatidylserine (PS), which in turn contribute to dysregulated inflammatory responses (Yoon et al., 2021 ). These lipid alterations are not merely markers of metabolic imbalance but also function as key signaling molecules that regulate cell recognition, immune responses, and overall disease progression, significantly influencing patient prognosis (Zheng et al., 2018 ). Most studies have concentrated on the changes in lipoprotein composition and content, cholesterol distribution, and the diagnostic potential of lipidomic biomarkers. During sepsis, the interaction between lipopolysaccharide (LPS) and lipoproteins enhances lipoprotein-mediated clearance, simultaneously activating scavenger receptor activity. This process results in a rapid decline in lipoprotein levels (Levels et al., 2003 ; Walley et al., 2019 ). This results in reduced cholesterol levels (Levels et al., 2007 ), hypertriglyceridemia, and increased fatty acid content (Ilias et al., 2014 ; Lee et al., 2015 ). Understanding the intricate interplay between lipid metabolism and inflammatory pathways in sepsis may provide novel insights into disease mechanisms, prognosis, and potential therapeutic targets. Pediatric Sequential Organ Failure Assessment (pSOFA) score has demonstrated a strong correlation with in-hospital mortality, establishing it as a valuable tool for evaluating disease severity in pediatric sepsis patients (Balamuth et al., 2022 ). In addition to pSOFA, the 2015 Chinese pediatric Sepsis Guidelines recommend using white blood cell (WBC), C-reactive protein (CRP), procalcitonin (PCT) and lactate levels as evaluation indicators ("[Expert consensus for the diagnosis and management of septic shock (infectious shock) in children (2015)]," 2015). However, the prognostic utility of some of these markers is limited. For example, WBC count and CRP levels exhibit low specificity for predicting sepsis outcomes, as no significant differences have been observed between the survival and non-survival groups (Schlapbach et al., 2018 ). The pathophysiology of sepsis involves an overwhelming and dysregulated inflammatory response, characterized by excessive release of inflammatory factors that damage endothelial cells and activate the coagulation system. This cascade results in substantial platelet (PLT) consumption, placing the body in a hypercoagulable state and increasing the risk of disseminated intravascular coagulation (DIC). Consequently, monitoring plasma inflammatory factors and PLT levels is critical for assessing sepsis severity and prognosis (Giustozzi et al., 2021 ). Arachidonic acid (AA), an important precursor of inflammatory mediators, plays a central role in the systemic inflammatory response (SIRS) associated with sepsis. During the acute phase of sepsis, accelerated AA metabolism leads to the production of pro-inflammatory mediators such as prostaglandins and leukotrienes, contributing to an amplified immune response (Wang et al., 2019 ). Elevated levels of AA have been observed in cases of strong inflammatory responses, highlighting its potential as a marker of disease activity. Despite significant advancements in understanding lipid metabolism in adult sepsis, studies on pediatric sepsis remain scarce (Chouchane et al., 2024 ; Li et al., 2021 ; Tian et al., 2022 ). Most prior research has focused on specific lipid types, often neglecting a comprehensive analysis of the entire lipidome. Moreover, the relationship between lipid metabolism abnormalities and their potential clinical applications in pediatric sepsis remains underexplored. In this study, we analyzed the plasma lipidom of pediatric sepsis patients to characterize lipid alterations and their association with disease severity, immune response, and prognosis. By identifying specific lipidomic changes, we aimed to provide a basis for improved diagnosis and therapeutic strategies for pediatric sepsis. MATERIALS AND METHODS Study population A total of 51 pediatric patients diagnosed with sepsis were recruited from the PICU at the Children's Hospital of Fudan University between June 2022 and May 2024. Eligible participants were aged 28 days to 18 years and met the diagnostic criteria for sepsis. Exclusion criteria included known lipid metabolism disorders, hyperlipidemia, parenteral nutrition, and severe hepatic dysfunction. Based on outcomes within 28 days of sepsis diagnosis, patients were stratified into the recovery group and the deterioration group. Outpatient subjects from the same period, free of infections or acute illnesses and matched by gender and age, served as controls. The following demographic and clinical variables were recorded for all participants: age, sex, body mass index (BMI), pSOFA score, pediatric critical illness score (PCIS), pediatric multiple organ dysfunction score (pMODS), presence of septic shock, mechanical ventilation, severe pneumonia, DIC, respiratory failure, and comorbidities such as renal disease, cardiac disease, hypertension, and neurological disorders. Pathogen identification was also performed for all septic patients. The study was approved by Ethics Committee of Children's Hospital of Fudan University ([2024] No. 116), and informed consent was obtained from all participants' parents or guardians. Biological sample collection and processing Blood samples (2 mL) were collected from all participants in EDTA anticoagulant tubes to obtain plasma. The samples were subjected to a two-step centrifugation process: an initial centrifugation at 400 ×g for 10 min at 4°C to remove cells, followed by a second centrifugation at 3, 000 ×g for 10 min at 4°C to obtain platelet-poor plasma. Supernatants were aliquoted into 200 µL portions, placed on dry ice, and stored at -80°C until further analysis. Lipidomics Analysis Untargeted lipidomics analysis was performed using ultrahighperformance liquid chromatography-tandem mass spectroscopy (UHPLC-MS/MS). Sample analysis and relative quantification of lipids were conducted by Shanghai Zhongke New Life Biotechnology Co., Ltd (Relative Quantitative Lipidomics). Analyze the differential lipid molecules To identify differential lipid molecules in pediatric sepsis patients, we employed screening criteria based on the Partial Least Squares-Discriminant Analysis (PLS-DA) results. Lipids were considered significant if they met the following thresholds: Variable Importance in Projection (VIP) > 1, P 1.5 or FC < 0.67. Statistical analysis All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables with normal distributions were compared using the Studen's t-test, while non-normally distributed data were analyzed using the nonparametric test (Mann-Whitney U test). Correlations between variables were tested using Spearman's rank correlation test, and scatter plots were created for visualization using GraphPad Prism version 10.1 (GraphPad Software, San Diego, CA, USA). To explore patterns within the lipidomic data, unsupervised hierarchical clustering and additional data visualization were performed using TBtools software (vLa-Π) (Chen et al., 2023 ). The standard Benjamini-Hochberg method was used to control the false discovery rate (FDR) for multiple hypothesis testing corrections. All statistical tests were two-tailed, with P < 0.05 considered statistically significant. Results Clinical characteristics A total of 48 pediatric sepsis patients and 48 control patients were included in the final analysis (Fig. S1 ). Among the sepsis cohort, 37 patients achieved recovery, while 11 experienced clinical deterioration, which included 8 deaths and 3 voluntary discharges. Within the sepsis group: Septic shock was observed in 24 patients, with a significantly higher occurrence in the deterioration group (n = 9, 81.8%). Mechanical ventilation was required for 26 patients, with 90.9% (n = 10) belonging to the deterioration group. Disseminated intravascular coagulation (DIC) developed in 18 patients, of whom 81.8% (n = 9) were in the deterioration group. Respiratory failure occurred in 29 patients, and notably, all patients in the deterioration group (n = 11, 100%) experienced this complication. Moreover, cardiac dysfunction was identified in 17 patients, with 90.9% (n = 10) from the deterioration group. pSOFA score was notably higher in the deterioration group [15.0 (13.0, 16.0)] compared to the recovery group [6.5 (4.2, 11.0)]. Additionally, Gram-positive bacteria were the most commonly identified pathogens in the sepsis group (Table 1 ). Table 1 Baseline Demographic and Clinical Characteristics Control (n = 48) Sepsis_Recovery (n = 37) Sepsis_Deterioration (n = 11) P BASELINE Sex (male) 29 (60.4) 20 (54.1) 6 (54.6) 0.536 Age (years) 5.5 (3.1) 6.3 (4.0) 6.2 (4.2) 0.270 BMI 16.2 [14.3, 18.1] 15.2 [13.2, 17.6] 16.3 [13.8, 17.7] 0.166 PICU stay - 18.1 (19.4) 9.50 (10.2) 0.165 CLINICAL FEATURES pSOFA score - 6.5 [4.2, 11.0] 15.0 [13.0, 16.0] < 0.001 PCIS score - 78.5 (8.5) 71.3 (9.5) 0.020 pMODS score - 5.0 [4.0, 6.0] 6.0 [4.0, 8.0] 0.099 PRISMIII - 7.0 [5.0, 10.5] 19.0 [13.0, 21.0] < 0.001 Septic shock - 15 (40.5) 9 (81.8) 0.016 OMV - 16 (43.2) 10 (90.9) 0.005 Severe pneumonia - 12 (32.4) 7 (63.6) 0.085 DIC - 9 (24.3) 9 (81.8) < 0.001 Respiratory failure - 18 (48.6) 11 (100) 0.002 Positive bacteria - 20 (54.1) 6 (54.5) 0.977 Pathogens 14 (37.8) 5 (45.5) 0.732 COMORBIDITIES Cardiac insufficiency - 7 (18.9) 10 (90.9) < 0.001 Renal insufficiency - 9 (24.3) 6 (54.5) 0.074 Hypertension - 2 (5.4) 0 (0) 1.000 NSD - 20 (54.1) 6( 54.5) 0.977 Data are presented as median [interquartile range], mean (SD) or n (%). The Pathogens refer to the proportion of bacteria. Controls, non-infected, outpatient control. Abbreviations: pSOFA, pediatric sequential organ failure assessment; PCIS, pediatric critical illness score; pMODS, pediatric multiple organ dysfunction score; PRISMIII, pediatric risk of mortality III; OMV, on mechanical ventilation; DIC, disseminated intravascular coagulation; NSD, nervous system disease. Alterations in the Plasma Lipidome of Sepsis Patients A comprehensive analysis of 4,143 lipid molecules spanning 44 lipid classes was performed (Table S1 ). Significant differences in plasma lipid levels were observed between the sepsis and control groups (Fig. 1 a). The PLS-DA model revealed a clear separation of plasma lipid profiles between the two groups, as demonstrated in the score plot (Fig. S2a). The model demonstrated strong performance with an R²Y of 0.975 and a Q²Y of 0.913, indicating excellent explanatory power and satisfactory predictive ability (R²Y ≥ 0.88) (Fig. S2b). Univariate analysis showed that 33.3% of lipids were decreased, 20.2% were increased, and 46.5% remained unchanged in the sepsis group compared to controls (Fig. 1 b, c). Specifically, lipid classes such as lysophosphatidic acid (LPA), trihexosyl di-N-acetylhexosyl ceramide (CerG3GNAc2) and trisialo trihexosyl (GT3) were significantly reduced, whereas monosialo tetrahexosyl ceramide (GM1) and free fatty acids (FA) were elevated in the sepsis group (Fig. S3). Unsupervised hierarchical clustering analysis further underscored these differences, showing distinct patterns of lipid abundance between the sepsis and control groups (Fig. 1 d). Correlation Analysis of Lipid Molecules Correlation analysis revealed a high degree of positive correlation among lipid classes such as phosphatidylinositol (4) phosphate (PIP), phosphatidylserine (PS), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylcholine (PC), phosphatidylethanolamine (PE), diglyceride (DG), cholesteryl ester (ChE), coenzyme Q (Co), zymosteryl ester (ZyE), ceramides (Cer), dihexosyl N-acetylhexosyl ceramide (CerG3GNAc1), ceramide phosphate (CerP), cardiolipin (CL), and TG in both groups. Interestingly, in the control group, LPA and lysophosphatidylcholine (LPC) exhibited weaker correlations with other lipids compared to the sepsis group. Conversely, certain lipids, including GM1, FA, LPA, GT3, CerG3GNAc2, phosphatidylinositol (4, 5) bisphosphate (PIP2), and sialylated ester (SiE), demonstrated weak or negligible correlations in both groups (Fig. 1 e). Changes in Plasma Lipidome Closely Associated with Disease Severity A total of 1,257 differential lipid molecules were identified, encompassing 231 PC (18.4%), 199 PE (15.8%), 163 TG (12.9%), 99 Cer (7.8%), and 91 hexosyl ceramide (Hex1Cer) (7.3%) (Fig. 2 a). Of these, 24 lipid molecules demonstrated significant associations with pSOFA score (Fig. 2 b, Table S2). Notably, lipid molecules positively correlated with pSOFA score included acyl Carnitine (AcCa) (8:0) (R = 0.49, P = 0.003), (O-acyl)-1-hydroxy fatty acid (OAHFA) (47:7) (R = 0.49, P = 0.003), and Cer (18:2_24:2) (R = 0.42, P = 0.016). Conversely, lipid molecules negatively correlated with pSOFA score were LPC (16:1e) (R = -0.49, P = 0.003), LPC (19:1) (R = -0.48, P = 0.004) and LPC (15:0) (R = -0.46, P = 0.007) (Fig. 2 c). Further correlation analysis among these 24 differential lipid molecules showed that Cer was positively correlated with OAHFA and negatively correlated with TG, LPC, CL, and GM3 (Fig. 2 d). Host Response to Infection Linked to Changes in the Plasma Lipidome No significant differences were observed in levels of WBC, CRP, IL-6, PCT and lactate between the recovery and deterioration groups among sepsis patients. However, the recovery group exhibited a significant increase in the absolute counts of CD3 + , CD4 + and CD8 + T cells (Table 2 ). A heat map analysis demonstrated distinct lipid correlations with immune and inflammatory parameters. Specifically, 11 lipid molecules were positively correlated with PLT, WBC, lymphocytes, neutrophils, CD4 + T, and CD8 + T cells, and negatively correlated with CRP, IL-6, and PCT. Conversely, Four lipid molecules showed positive correlations with CRP, IL-6, PCT, and neutrophils, and negative associations with WBC, PLT, lymphocytes, CD4 + T, and CD8 + T cells (Fig. 3 a). Strong correlations were found between the following lipid molecules and inflammatory markers: Hex1Cer and CRP (d42:1 + O) (R = -0.53, P = 0.001), DG and IL-6 (20:1e_10:3) (R = -0.62, P < 0.001), Cer and PCT (d37:6) (R = -0.54, P < 0.001), OAHFA and PLT (47:7) (R= -0.49, P = 0.003), PC and WBC (42:8e) (R = 0.46, P = 0.007), PE and neutrophils (38:6e) (R = 0.40, P = 0.024), sphingomyelin (SM) and lymphocytes (d40:3) (R = 0.40, P = 0.021), PC and CD4 + T cells, CD8 + T cells (21:1_10:4) (R = -0.54, P < 0.001) (R = -0.53, P = 0.001) (Fig. 3 b). Table 2 Laboratory Test Results Routine Lab Recovery (n = 37) Deterioration (n = 11) P WBC, ×10 9 /L 15.5 (12.0) 11.9 (7.3) 0.354 Platelet, ×10 9 /L 188.0 [111.5, 322.5] 96.0 [42.0, 169.0] 0.012 Neutrophil, ×10 9 /L 80.4 [70.0, 88.2] 79.1 [69.1, 91.5] 0.844 Lymphocyte, ×10 9 /L 10.9 [6.5, 20.2] 12.4 [4.4, 21.7] 0.713 CD3 + T cell, cells/µL 1078.0 [398.8, 1195.4] 371.8 [147.7, 544.4] 0.022 CD4 + T cell, cells/µL 562.9 [216.3, 681.9] 166.8 [71.9, 289.9] 0.004 CD8 + T cell, cells/µL 355.1 [162.0, 469.5] 179.5 [90.3, 252.4] 0.020 CD19 + T cell, cells/µL 486.9 [173.9, 660.0] 394.2 [98.5, 497.3] 0.144 CRP, mg/L 86.3 (45.6) 88.5 (55.9) 0.894 PCT, mg/L 7.9 [0.9, 42.1] 10.0 [4.2, 28.3] 0.677 Lactate, mmol/L 1.1 [0.9, 1.8] 2.6 [0.7, 7.8] 0.102 IL-6, pg/mL 286.7 [53.7, 605.2] 357.6 [69.8, 887.8] 0.548 Total bilirubin, µmol/L 6.6 [4.9, 12.0] 17.2 [6.8, 93.0] 0.019 ALT, U/L 17.6 [10.9, 29.7] 54.0 [20.0, 421.5] 0.008 AST, U/L 39.3 [23.4, 61.8] 328.0 [44.7, 827.3] 0.002 D-dimers, µg/mL 2.6 [1.4, 7.1] 5.7 [2.7, 17.7] 0.079 Blood glucose, mmol/L 6.7 [5.4, 7.7] 8.4 [6.1, 11.3] 0.062 Vasoactive drugs 13 (35.1) 10 (90.9) 0.001 Data are presented as median [interquartile range], mean (SD) or n (%). Abbreviations: WBC, white blood cell; CRP, c-reactive protein; PCT, procaicitonin; IL-6, interleukin-6; ALT, alanine aminotransferase; AST, aspartate aminotransferase Alterations in the Plasma Lipidome of Sepsis Patients Associated with a Poorer Prognosis Lipid class analysis revealed significant differences in FA and Lysophosphatidylinositol (LPI) levels ( P < 0.01) between the recovery and deterioration groups, with both lipid classes elevated in the deterioration group (Fig. 4 a). In the lipid molecule analysis, 186 differential lipid molecules were identified between the two groups, with TG was the most discrepant lipid class, accounting for 16.4% of the total differential molecules (Fig. 4 b). Unsupervised hierarchical clustering analysis revealed that PI (29:0_10:0), Hex2Cer (t37:4) and Hex2Cer (t35:5) were elevated in the deterioration group, while LPC (15:0), LPC (20:1), LPC (19:1), n-acetylhexosyl ceramide (CerG2GNAc1) (d32:1) and CerG3GNAc1 (d36:7) were elevated in the recovery group (Fig. 4 c). When comparing the 24 lipid molecules associated with pSOFA score and the 186 differential lipid molecules between the two groups, 15 lipid molecules were found to overlap. The top five most significant lipids were LPC (15:0), LPC (19:1), LPC (20:1), CerG2GNAc1 (d32:1), and PC (18:3e) (Fig. 4 d, e). In addition, clinical parameters demonstrated that patients in the deterioration group exhibited significantly lower platelet counts and higher levels of total bilirubin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) compared to the recovery group (Fig. 4 f). Changes in the Fatty Acid Profile of Sepsis Patients Analysis of the fatty acid profile revealed elevated levels of FA (16:0), FA (20:4), and FA (22:6) in the sepsis group compared to controls, with the most pronounced increase observed in FA (20:4) ( P < 0.001) (Fig. 5 a, b). Further subgroup analysis revealed distinct patterns of these fatty acids in relation to disease prognosis. Specifically, levels of FA (20:4), FA (16:0), and FA (22:6) were significantly higher in the deterioration group compared to the recovery group. FA (16:0) showed the most substantial elevation among the three fatty acids in patients with a poor prognosis ( P < 0.01) (Fig. 5 c). In addition, correlation analysis between FA and cholesterol esters (ChE) demonstrated differing relationships. FA (16:0) and ChE (18:3), as well as FA (20:4) and ChE (19:1), were negatively correlated, whereas FA (22:6) and ChE (18:0) exhibited a positive correlation (Fig. 5 d, e). Discussion We conducted untargeted metabolomics analysis to explore lipid alterations in pediatric sepsis and identified significant differences in plasma lipid profiles between sepsis patients and non-infected controls. Importantly, correlation analyses revealed that these lipid changes were associated with higher pSOFA score and poorer clinical outcomes. Additionally, specific lipid molecules showed significant correlations with inflammatory markers, emphasizing the critical interplay between lipid metabolism and the inflammatory response in pediatric sepsis. Our findings align with those reported by Chouchane et al. (Chouchane et al., 2024 ), who demonstrated that 58% of lipid levels were significantly decreased in community-acquired pneumonia (CAP)-sepsis patients compared to outpatient controls. Their study also observed a significant negative correlation between most lipid levels and SOFA score supporting the concept of lipid dysregulation as a marker of disease severity. However, several key differences exist between our study and that of Chouchane et al., particularly in study populations and methodologies. Chouchane et al. focused on middle-aged and elderly adults (> 60 years old), whereas our study specifically investigated pediatric patients under 18 years of age, a population that may exhibit distinct metabolic responses to sepsis. Additionally, Chouchane et al. evaluated lipidome changes over time, particularly their relationship with lipid recovery and 30-day mortality in ICU patients hospitalized for at least 4 days. In contrast, our study focused on plasma lipid alterations within 24 hours of sepsis diagnosis, emphasizing early lipid changes and their immediate correlation with clinical outcomes. The observed lipid alterations also parallel findings from previous studies on Cer and LPC levels. For instance, elevated ceramide levels have been positively correlated with SOFA score, reflecting disease severity and poor outcomes (Wu et al., 2019 ). Similarly, our findings revealed a significant negative correlation between LPC levels and pSOFA score, suggesting that a decline in LPC concentrations is indicative of disease progression and organ dysfunction in pediatric sepsis. These findings highlight the potential of LPC as a prognostic biomarker for disease deterioration. Furthermore, Chang et al. reported significantly reduced LPC concentrations in sepsis-induced ARDS patients compared to non-ARDS controls, with higher LPC levels observed in patients with direct ARDS compared to those with indirect ARDS (Chang et al., 2023 ). Consistent with these findings, our analysis identified that the top three lipid molecules associated with both pSOFA score and clinical outcomes in pediatric sepsis were LPC species. LPC, a product of PC degradation mediated by phospholipase A2 (PLA2), is strongly linked to inflammation. As a bioactive lipid, LPC not only acts as a ligand for lymphocytes but can also be enzymatically converted into lysophosphatidic acid, an anti-inflammatory mediator. Persistent low LPC levels in sepsis patients are thought to reflect disrupted metabolic homeostasis (Lee et al., 2020 ). These lower LPC levels have been associated with excessive inflammatory responses and worse clinical outcomes (Montague et al., 2022 ), underscoring the critical role of LPC in the pathophysiology of sepsis. Our findings further support the potential of LPC as a key biomarker for sepsis, with utility in diagnosis, prognosis prediction, and the identification of therapeutic targets. Our study revealed a negative correlation between PC and T cells, alongside a positive correlation between PE and neutrophils. PE serves as a precursor for various bioactive molecules, with its metabolites—such as diglycerides, fatty acids, and phosphatidic acid—playing critical roles as second messengers (Vance, 2018 ). Alterations in the PC/PE ratio have been shown to profoundly affect organelle energy metabolism, contributing to the progression of diseases (van der Veen et al., 2017 ). For instance, a decreased PC/PE ratio in the liver compromises membrane integrity, which promotes the development of non-alcoholic fatty liver disease (NAFLD), and in severe cases, may lead to liver failure. In the intestine, similar reductions impair fatty acid uptake and chylomicron secretion, while also increasing cellular leakage, decreasing gluconeogenesis, and enhancing ATP synthesis (Duncan, 2023 ). These findings underscore the importance of lipid homeostasis in maintaining cellular integrity and metabolic function. Notably, PC may exacerbate inflammatory responses by destabilizing cell membranes, positioning it as a potential mediator of inflammation in critical illness. In this study, we observed elevated FA levels in sepsis patients compared to controls, with further increases seen in patients experiencing sepsis deterioration. Consistently, sepsis patients exhibit lower cholesterol levels and FA (Cappi et al., 2012 ). Notably, omega-3 polyunsaturated fatty acids, such as docosahexaenoic acid (DHA, FA 22:6), have been shown to modulate the gut microbiome composition, enhance the production of catabolic mediators and anti-inflammatory factors, inhibit NF-κB activation, and influence the function of membrane lipid rafts (Lu Zhongqiu et al.,2022). Through these mechanisms, omega-3 fatty acids may provide protective effects in sepsis by reducing systemic inflammation and promoting gut microbiome homeostasis, both of which are critical in mitigating organ dysfunction and improving outcomes. In contrast, omega-6 polyunsaturated fatty acids, such as arachidonic acid (AA, FA 20:4), are generally associated with promoting inflammation. AA serves as a key precursor for pro-inflammatory eicosanoids, including prostaglandins, thromboxanes, and leukotrienes. These eicosanoids play pivotal roles in immune and glial cell activation, driving inflammation and tissue injury during sepsis (Vance, 2018 ). Thus, FAs appear to exert a dual role in sepsis pathophysiology. Our findings underscore the potential of targeting fatty acid metabolism as a novel therapeutic strategy in sepsis. Modulating the balance between omega-3 and omega-6 fatty acids could reduce excessive inflammation, restore lipid homeostasis, and improve clinical outcomes in critically ill children. Despite the significance of these findings, our study has several limitations. First, blood samples were collected at a single time point, specifically upon PICU admission. Although efforts were made to obtain samples as early as possible on the first day of diagnosis, variability in the timing of disease onset and clinical presentation may have introduced confounding factors. Second, the relatively small sample size and single-center design limit the generalizability of our findings. The absence of an independent validation cohort further constrains the robustness of our conclusions. Third, we were unable to perform dynamic monitoring of lipid levels over time, which restricts a comprehensive understanding of lipidomic alterations during the progression and resolution of sepsis. To address these limitations, future studies should employ larger, multi-center cohorts with longitudinal sampling to validate and expand upon our findings. A more detailed temporal analysis of lipidomic profiles may provide greater insights into the dynamic role of lipid metabolism in immune responses and sepsis outcomes. Such studies could also elucidate the mechanistic links between lipid dysregulation, inflammation, and organ dysfunction, ultimately identifying novel biomarkers and therapeutic targets in pediatric sepsis. In summary, our study provides new insights into the plasma lipidome during pediatric sepsis, revealing both global and specific lipid alterations associated with disease progression and severity. These findings pave the way for future research aimed at validating lipidomic biomarkers and exploring therapeutic interventions targeting lipid metabolism. Longitudinal studies with larger, multi-center cohorts are needed to further elucidate the dynamic changes in lipid profiles and their mechanistic roles in sepsis pathophysiology. Declarations Acknowledgements The authors would like to thank the participants and their guardians, the collaborating clinicians, and other clinical staff. We would also like to acknowledge the Shanghai Zhongke New Life Biotechnology Co., Ltd for the support of sample analysis and relative quantification of lipids. Author contribution RA, JB, YC, and ZC collected and analyzed the patient data; WY, HZ, GS, RA, and YC performed the statistical analysis; KW, YW, and TL collected and processed clinical samples; YL, WC, RA, and JB wrote the manuscript; CZ, GL, and HC contributed to conceptualization, manuscript writing and editing, statistical analysis, and visualization. All authors have read and approved the final manuscript. Funding The study was supported by grants from the National Key R&D Program of China (2021YFC2701800 to G Lu), National Natural Science Foundation of China (82202374 to C Zhang), Natural Science Foundation of Shanghai (22ZR1408500 to C Zhang), Shanghai Municipal Science and Technology Major Project (ZD2021CY001 to G Lu), Municipal Health System Key Supporting Discipline Project (2023ZDFC0103 to W Chen), Natural Science Foundation of Anhui (2308085MH267 to G Lu), Ningbo Medical and Health Brand Discipline (PPXK2024-06 to H Chen). Availability of data and materials The datasets used for the analysis in the current study are available from the corresponding author on reasonable request. The online version has been uploaded to MetaboLights, and DOIs will be provided after acceptance. Ethics approval and consent to participate Ethical approval for the study was provided by Ethics Committee of Children's Hospital of Fudan University ([2024] No. 116). Written informed consent was obtained from all parents or their surrogates of studied children. Competing interests The authors declare no competing interests. Consent for publication All authors consent to the publication of this manuscript. References Balamuth, F., Scott, H. F., Weiss, S. L., Webb, M., Chamberlain, J. M., Bajaj, L., Depinet, H., Grundmeier, R. W., Campos, D., Deakyne Davies, S. J., Simon, N. J., Cook, L. J., & Alpern, E. R. (2022). Validation of the Pediatric Sequential Organ Failure Assessment Score and Evaluation of Third International Consensus Definitions for Sepsis and Septic Shock Definitions in the Pediatric Emergency Department. JAMA Pediatr , 176 (7), 672-678. https://doi.org/10.1001/jamapediatrics.2022.1301 Barber, G., Tanic, J., & Leligdowicz, A. (2023). Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review. 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(2007). Alterations in lipoprotein homeostasis during human experimental endotoxemia and clinical sepsis. Biochim Biophys Acta , 1771 (12), 1429-1438. https://doi.org/10.1016/j.bbalip.2007.10.001 Li, Q., Cui, Y., Xu, B., Wang, Y., Lv, F., Li, Z., Li, H., Chen, X., Peng, X., Chen, Y., Wu, E., Qu, D., Jian, Y., & Si, H. (2021). Main active components of Jiawei Gegen Qinlian decoction protects against ulcerative colitis under different dietary environments in a gut microbiota-dependent manner. Pharmacol Res , 170 , 105694. https://doi.org/10.1016/j.phrs.2021.105694 Lu Zhongqiu, Ni Jingjing (2022) Pay attention to nutritional treatment of sepsis. Zhejiang Medicine , 44(04),339-344+354. https://doi.org/10.12056/j.issn.1006- Montague, B., Summers, A., Bhawal, R., Anderson, E. T., Kraus-Malett, S., Zhang, S., & Goggs, R. (2022). Identifying potential biomarkers and therapeutic targets for dogs with sepsis using metabolomics and lipidomics analyses. PLoS One , 17 (7), e0271137. https://doi.org/10.1371/journal.pone.0271137 Sanchez-Pinto, L. N., Bennett, T. D., DeWitt, P. E., Russell, S., Rebull, M. N., Martin, B., Akech, S., Albers, D. J., Alpern, E. R., Balamuth, F., Bembea, M., Chisti, M. J., Evans, I., Horvat, C. M., Jaramillo-Bustamante, J. C., Kissoon, N., Menon, K., Scott, H. F., Weiss, S. L., Wiens, M. O., Zimmerman, J. J., Argent, A. C., Sorce, L. R., Schlapbach, L. J., Watson, R. S., Biban, P., Carrol, E., Chiotos, K., Flauzino De Oliveira, C., Hall, M. W., Inwald, D., Ishimine, P., Levin, M., Lodha, R., Nadel, S., Nakagawa, S., Peters, M. J., Randolph, A. G., Ranjit, S., Souza, D. C., Tissieres, P., & Wynn, J. L. (2024). Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock. Jama , 331 (8), 675-686. https://doi.org/10.1001/jama.2024.0196 Schlapbach, L. J., Straney, L., Bellomo, R., MacLaren, G., & Pilcher, D. (2018). Prognostic accuracy of age-adapted SOFA, SIRS, PELOD-2, and qSOFA for in-hospital mortality among children with suspected infection admitted to the intensive care unit. Intensive Care Med , 44 (2), 179-188. https://doi.org/10.1007/s00134-017-5021-8 Tian, T., Mao, Q., Xie, J., Wang, Y., Shao, W. H., Zhong, Q., & Chen, J. J. (2022). Multi-omics data reveals the disturbance of glycerophospholipid metabolism caused by disordered gut microbiota in depressed mice. J Adv Res , 39 , 135-145. https://doi.org/10.1016/j.jare.2021.10.002 van der Veen, J. N., Kennelly, J. P., Wan, S., Vance, J. E., Vance, D. E., & Jacobs, R. L. (2017). The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. Biochim Biophys Acta Biomembr , 1859 (9 Pt B), 1558-1572. https://doi.org/10.1016/j.bbamem.2017.04.006 Vance, J. E. (2018). Historical perspective: phosphatidylserine and phosphatidylethanolamine from the 1800s to the present. J Lipid Res , 59 (6), 923-944. https://doi.org/10.1194/jlr.R084004 Walley, K. R., Boyd, J. H., Kong, H. J., & Russell, J. A. (2019). Low Low-Density Lipoprotein Levels Are Associated With, But Do Not Causally Contribute to, Increased Mortality in Sepsis. Crit Care Med , 47 (3), 463-466. https://doi.org/10.1097/ccm.0000000000003551 Wang, T., Fu, X., Chen, Q., Patra, J. K., Wang, D., Wang, Z., & Gai, Z. (2019). Arachidonic Acid Metabolism and Kidney Inflammation. Int J Mol Sci , 20 (15). https://doi.org/10.3390/ijms20153683 Weiss, S. L., & Fitzgerald, J. C. (2024). Pediatric Sepsis Diagnosis, Management, and Sub-phenotypes. Pediatrics , 153 (1). https://doi.org/10.1542/peds.2023-062967 Wu, X., Hou, J., Li, H., Xie, G., Zhang, X., Zheng, J., Wang, J., Gao, F., Yao, Y., Liu, H., & Fang, X. (2019). Inverse Correlation Between Plasma Sphingosine-1-Phosphate and Ceramide Concentrations in Septic Patients and Their Utility in Predicting Mortality. Shock , 51 (6), 718-724. https://doi.org/10.1097/shk.0000000000001229 Yoon, H., Shaw, J. L., Haigis, M. C., & Greka, A. (2021). Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity. Mol Cell , 81 (18), 3708-3730. https://doi.org/10.1016/j.molcel.2021.08.027 Zhang, Y. Y., & Ning, B. T. (2021). Signaling pathways and intervention therapies in sepsis. Signal Transduct Target Ther , 6 (1), 407. https://doi.org/10.1038/s41392-021-00816-9 Zheng, X., Smith, R. D., & Baker, E. S. (2018). Recent advances in lipid separations and structural elucidation using mass spectrometry combined with ion mobility spectrometry, ion-molecule reactions and fragmentation approaches. Curr Opin Chem Biol , 42 , 111-118. https://doi.org/10.1016/j.cbpa.2017.11.009 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Metabolomics → Version 1 posted Editorial decision: Revision requested 17 Mar, 2025 Reviews received at journal 17 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviews received at journal 16 Mar, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviewers invited by journal 17 Feb, 2025 Editor assigned by journal 17 Feb, 2025 Submission checks completed at journal 17 Feb, 2025 First submitted to journal 16 Feb, 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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10:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6040682/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6040682/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11306-025-02255-x","type":"published","date":"2025-04-26T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76688177,"identity":"9884954b-ccf8-41dd-9678-ac403a0a6f1e","added_by":"auto","created_at":"2025-02-19 16:32:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10427074,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in the Plasma Lipidome of Sepsis Patients.\u003cstrong\u003e a\u003c/strong\u003e PCA score plot of the control and sepsis groups. \u003cstrong\u003eb\u003c/strong\u003e Volcano plot of differential lipids based on univariate analysis. \u003cstrong\u003ec \u003c/strong\u003ePie chart of differential lipids based on univariate analysis. \u003cstrong\u003ed\u003c/strong\u003eUnsupervised hierarchical clustering heatmap of lipid classes in the control and sepsis groups (Only the lipids with differences were analyzed). \u003cstrong\u003ee\u003c/strong\u003e Correlation heatmap of lipid classes in the control and sepsis groups (Only the lipids with differences were analyzed).\u003c/p\u003e","description":"","filename":"fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/887d1eff1725fa8d4aea1510.png"},{"id":76688173,"identity":"19737858-733f-457b-93e8-829f2f6b4457","added_by":"auto","created_at":"2025-02-19 16:32:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2572527,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in plasma lipidome closely associated with disease severity. \u003cstrong\u003ea \u003c/strong\u003ePie chart of differential lipids based on PLS-DA results. \u003cstrong\u003eb \u003c/strong\u003eBar plot of lipids correlated with pSOFA score. \u003cstrong\u003ec \u003c/strong\u003eScatter plot of lipids correlated with pSOFA score. \u003cstrong\u003ed\u003c/strong\u003eCorrelation heatmap of lipids correlated with pSOFA score.\u003c/p\u003e","description":"","filename":"fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/16ef4c2e2a6e6f1d0685605b.png"},{"id":76688169,"identity":"deb57335-fc5a-4fa8-8e75-73a37f0a5317","added_by":"auto","created_at":"2025-02-19 16:32:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1661419,"visible":true,"origin":"","legend":"\u003cp\u003eHost response to infection linked to changes in the plasma lipidome. \u003cstrong\u003ea\u003c/strong\u003e Correlation heatmap of differential lipids and laboratory indicators.\u003cstrong\u003e b\u003c/strong\u003e Scatter plot of lipid molecules correlated with laboratory indicators.\u003c/p\u003e","description":"","filename":"fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/d6c9b34978f16e37c8f3ef18.png"},{"id":76688171,"identity":"48b78409-37bb-4a4a-a747-6cfef00ab7b4","added_by":"auto","created_at":"2025-02-19 16:32:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1749386,"visible":true,"origin":"","legend":"\u003cp\u003eAlterations in the plasma lipidome of sepsis patients associated with a poor prognosis. \u003cstrong\u003ea \u003c/strong\u003eBoxplot of lipid classes in the sepsis recovery and deterioration groups. \u003cstrong\u003eb\u003c/strong\u003e Pie chart of differential lipids between the sepsis recovery and deterioration groups. \u003cstrong\u003ec\u003c/strong\u003e Unsupervised hierarchical clustering heatmap of differential lipids between the sepsis recovery and deterioration groups. \u003cstrong\u003ed, e\u003c/strong\u003e Overlapping lipid molecules between differential lipids and pSOFA-associated lipids in the sepsis recovery and deterioration groups. \u003cstrong\u003ef \u003c/strong\u003eBar chart of laboratory indicators in the sepsis recovery and deterioration groups. Statistically significant differences are indicated, with *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/4f108b799821cf479897086a.png"},{"id":76689198,"identity":"7bbd10e7-728b-4ad5-bf45-16943e0b9fbf","added_by":"auto","created_at":"2025-02-19 16:40:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1237285,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the fatty acid profile of sepsis patients. \u003cstrong\u003ea\u003c/strong\u003e Stacked bar chart of fatty acids comparing the control and sepsis groups.\u003cstrong\u003e b \u003c/strong\u003eBoxplots of fatty acids in the control and sepsis groups. \u003cstrong\u003ec\u003c/strong\u003e Boxplots of fatty acids in the recovery and deterioration groups. \u003cstrong\u003ed \u003c/strong\u003eCorrelation heatmap of fatty acids and cholesterol esters in sepsis patients. \u003cstrong\u003ee\u003c/strong\u003e Scatter plot of fatty acids and cholesterol esters correlation. Statistically significant differences are indicated, with *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/80deb25c77bedfbd18b8e4a9.png"},{"id":81570168,"identity":"a9ba8d88-1d05-46c5-bbed-4b7a18c2d525","added_by":"auto","created_at":"2025-04-28 16:12:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":16902100,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/0b7625e8-ac96-4a39-bed1-611f1ab5c7b9.pdf"},{"id":76688168,"identity":"548b4a41-6451-4a27-909d-e25821a3180b","added_by":"auto","created_at":"2025-02-19 16:32:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1584624,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6040682/v1/d0922c40d11c67cfa2f36f6d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Untargeted Lipidomics Profiling Provides Novel Insights into Pediatric Patients with Sepsis: An Exploratory Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis remains a major global health challenge, contributing to approximately 3.3\u0026nbsp;million childhood deaths annually(Sanchez-Pinto et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite significant advancements in healthcare that have led to improved clinical outcomes and a steady decline in mortality rates (Bauer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), severe sepsis continues to be the leading cause of death in Pediatric Intensive Care Units (PICU) (Barber et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weiss andFitzgerald, 2024). Alarmingly, one-third of these fatalities are attributed to refractory shock (Zhang andNing, 2021). While advancements in antibiotic therapies and infection control strategies have provided some progress, early recognition and timely effective treatment of sepsis remain critical clinical challenges. Furthermore, among those who survive, many children experience long-term physical, cognitive, and emotional impairments that substantially impact their quality of life and impose considerable burdens on their families.\u003c/p\u003e \u003cp\u003eEmerging evidence from metabolomics studies highlights the critical role of lipid metabolism in the pathophysiology of sepsis. Sepsis triggers profound lipid metabolic dysregulation, characterized by elevated levels of certain lipids such as triglycerides (TG) and phosphatidylserine (PS), which in turn contribute to dysregulated inflammatory responses (Yoon et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These lipid alterations are not merely markers of metabolic imbalance but also function as key signaling molecules that regulate cell recognition, immune responses, and overall disease progression, significantly influencing patient prognosis (Zheng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most studies have concentrated on the changes in lipoprotein composition and content, cholesterol distribution, and the diagnostic potential of lipidomic biomarkers. During sepsis, the interaction between lipopolysaccharide (LPS) and lipoproteins enhances lipoprotein-mediated clearance, simultaneously activating scavenger receptor activity. This process results in a rapid decline in lipoprotein levels (Levels et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Walley et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This results in reduced cholesterol levels (Levels et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), hypertriglyceridemia, and increased fatty acid content (Ilias et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Understanding the intricate interplay between lipid metabolism and inflammatory pathways in sepsis may provide novel insights into disease mechanisms, prognosis, and potential therapeutic targets.\u003c/p\u003e \u003cp\u003ePediatric Sequential Organ Failure Assessment (pSOFA) score has demonstrated a strong correlation with in-hospital mortality, establishing it as a valuable tool for evaluating disease severity in pediatric sepsis patients (Balamuth et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition to pSOFA, the 2015 Chinese pediatric Sepsis Guidelines recommend using white blood cell (WBC), C-reactive protein (CRP), procalcitonin (PCT) and lactate levels as evaluation indicators (\"[Expert consensus for the diagnosis and management of septic shock (infectious shock) in children (2015)],\" 2015). However, the prognostic utility of some of these markers is limited. For example, WBC count and CRP levels exhibit low specificity for predicting sepsis outcomes, as no significant differences have been observed between the survival and non-survival groups (Schlapbach et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The pathophysiology of sepsis involves an overwhelming and dysregulated inflammatory response, characterized by excessive release of inflammatory factors that damage endothelial cells and activate the coagulation system. This cascade results in substantial platelet (PLT) consumption, placing the body in a hypercoagulable state and increasing the risk of disseminated intravascular coagulation (DIC). Consequently, monitoring plasma inflammatory factors and PLT levels is critical for assessing sepsis severity and prognosis (Giustozzi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Arachidonic acid (AA), an important precursor of inflammatory mediators, plays a central role in the systemic inflammatory response (SIRS) associated with sepsis. During the acute phase of sepsis, accelerated AA metabolism leads to the production of pro-inflammatory mediators such as prostaglandins and leukotrienes, contributing to an amplified immune response (Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Elevated levels of AA have been observed in cases of strong inflammatory responses, highlighting its potential as a marker of disease activity.\u003c/p\u003e \u003cp\u003eDespite significant advancements in understanding lipid metabolism in adult sepsis, studies on pediatric sepsis remain scarce (Chouchane et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Most prior research has focused on specific lipid types, often neglecting a comprehensive analysis of the entire lipidome. Moreover, the relationship between lipid metabolism abnormalities and their potential clinical applications in pediatric sepsis remains underexplored. In this study, we analyzed the plasma lipidom of pediatric sepsis patients to characterize lipid alterations and their association with disease severity, immune response, and prognosis. By identifying specific lipidomic changes, we aimed to provide a basis for improved diagnosis and therapeutic strategies for pediatric sepsis.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA total of 51 pediatric patients diagnosed with sepsis were recruited from the PICU at the Children's Hospital of Fudan University between June 2022 and May 2024. Eligible participants were aged 28 days to 18 years and met the diagnostic criteria for sepsis. Exclusion criteria included known lipid metabolism disorders, hyperlipidemia, parenteral nutrition, and severe hepatic dysfunction. Based on outcomes within 28 days of sepsis diagnosis, patients were stratified into the recovery group and the deterioration group. Outpatient subjects from the same period, free of infections or acute illnesses and matched by gender and age, served as controls. The following demographic and clinical variables were recorded for all participants: age, sex, body mass index (BMI), pSOFA score, pediatric critical illness score (PCIS), pediatric multiple organ dysfunction score (pMODS), presence of septic shock, mechanical ventilation, severe pneumonia, DIC, respiratory failure, and comorbidities such as renal disease, cardiac disease, hypertension, and neurological disorders. Pathogen identification was also performed for all septic patients. The study was approved by Ethics Committee of Children's Hospital of Fudan University ([2024] No. 116), and informed consent was obtained from all participants' parents or guardians.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiological sample collection and processing\u003c/h3\u003e\n\u003cp\u003eBlood samples (2 mL) were collected from all participants in EDTA anticoagulant tubes to obtain plasma. The samples were subjected to a two-step centrifugation process: an initial centrifugation at 400 \u0026times;g for 10 min at 4\u0026deg;C to remove cells, followed by a second centrifugation at 3, 000 \u0026times;g for 10 min at 4\u0026deg;C to obtain platelet-poor plasma. Supernatants were aliquoted into 200 \u0026micro;L portions, placed on dry ice, and stored at -80\u0026deg;C until further analysis.\u003c/p\u003e\n\u003ch3\u003eLipidomics Analysis\u003c/h3\u003e\n\u003cp\u003eUntargeted lipidomics analysis was performed using ultrahighperformance liquid chromatography-tandem mass spectroscopy (UHPLC-MS/MS). Sample analysis and relative quantification of lipids were conducted by Shanghai Zhongke New Life Biotechnology Co., Ltd (Relative Quantitative Lipidomics).\u003c/p\u003e\n\u003ch3\u003eAnalyze the differential lipid molecules\u003c/h3\u003e\n\u003cp\u003eTo identify differential lipid molecules in pediatric sepsis patients, we employed screening criteria based on the Partial Least Squares-Discriminant Analysis (PLS-DA) results. Lipids were considered significant if they met the following thresholds: Variable Importance in Projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 or FC\u0026thinsp;\u0026lt;\u0026thinsp;0.67.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables with normal distributions were compared using the Studen's t-test, while non-normally distributed data were analyzed using the nonparametric test (Mann-Whitney U test). Correlations between variables were tested using Spearman's rank correlation test, and scatter plots were created for visualization using GraphPad Prism version 10.1 (GraphPad Software, San Diego, CA, USA). To explore patterns within the lipidomic data, unsupervised hierarchical clustering and additional data visualization were performed using TBtools software (vLa-Π) (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The standard Benjamini-Hochberg method was used to control the false discovery rate (FDR) for multiple hypothesis testing corrections. All statistical tests were two-tailed, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics\u003c/h2\u003e \u003cp\u003eA total of 48 pediatric sepsis patients and 48 control patients were included in the final analysis (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among the sepsis cohort, 37 patients achieved recovery, while 11 experienced clinical deterioration, which included 8 deaths and 3 voluntary discharges. Within the sepsis group: Septic shock was observed in 24 patients, with a significantly higher occurrence in the deterioration group (n\u0026thinsp;=\u0026thinsp;9, 81.8%). Mechanical ventilation was required for 26 patients, with 90.9% (n\u0026thinsp;=\u0026thinsp;10) belonging to the deterioration group. Disseminated intravascular coagulation (DIC) developed in 18 patients, of whom 81.8% (n\u0026thinsp;=\u0026thinsp;9) were in the deterioration group. Respiratory failure occurred in 29 patients, and notably, all patients in the deterioration group (n\u0026thinsp;=\u0026thinsp;11, 100%) experienced this complication. Moreover, cardiac dysfunction was identified in 17 patients, with 90.9% (n\u0026thinsp;=\u0026thinsp;10) from the deterioration group. pSOFA score was notably higher in the deterioration group [15.0 (13.0, 16.0)] compared to the recovery group [6.5 (4.2, 11.0)]. Additionally, Gram-positive bacteria were the most commonly identified pathogens in the sepsis group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eBaseline Demographic and Clinical Characteristics\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\u003eControl (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSepsis_Recovery (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSepsis_Deterioration (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBASELINE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.5 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.2 [14.3, 18.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2 [13.2, 17.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.3 [13.8, 17.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePICU stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.1 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.50 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCLINICAL FEATURES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 [4.2, 11.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 [13.0, 16.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCIS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.3 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epMODS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 [4.0, 6.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 [4.0, 8.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRISMIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 [5.0, 10.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.0 [13.0, 21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\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\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOMORBIDITIES\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\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\u003eNSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6( 54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as median [interquartile range], mean (SD) or n (%). The Pathogens refer to the proportion of bacteria. Controls, non-infected, outpatient control. Abbreviations: pSOFA, pediatric sequential organ failure assessment; PCIS, pediatric critical illness score; pMODS, pediatric multiple organ dysfunction score; PRISMIII, pediatric risk of mortality III; OMV, on mechanical ventilation; DIC, disseminated intravascular coagulation; NSD, nervous system disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAlterations in the Plasma Lipidome of Sepsis Patients\u003c/h3\u003e\n\u003cp\u003eA comprehensive analysis of 4,143 lipid molecules spanning 44 lipid classes was performed (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Significant differences in plasma lipid levels were observed between the sepsis and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The PLS-DA model revealed a clear separation of plasma lipid profiles between the two groups, as demonstrated in the score plot (Fig. S2a). The model demonstrated strong performance with an R\u0026sup2;Y of 0.975 and a Q\u0026sup2;Y of 0.913, indicating excellent explanatory power and satisfactory predictive ability (R\u0026sup2;Y\u0026thinsp;\u0026ge;\u0026thinsp;0.88) (Fig. S2b). Univariate analysis showed that 33.3% of lipids were decreased, 20.2% were increased, and 46.5% remained unchanged in the sepsis group compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, c). Specifically, lipid classes such as lysophosphatidic acid (LPA), trihexosyl di-N-acetylhexosyl ceramide (CerG3GNAc2) and trisialo trihexosyl (GT3) were significantly reduced, whereas monosialo tetrahexosyl ceramide (GM1) and free fatty acids (FA) were elevated in the sepsis group (Fig. S3). Unsupervised hierarchical clustering analysis further underscored these differences, showing distinct patterns of lipid abundance between the sepsis and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis of Lipid Molecules\u003c/h2\u003e \u003cp\u003eCorrelation analysis revealed a high degree of positive correlation among lipid classes such as phosphatidylinositol (4) phosphate (PIP), phosphatidylserine (PS), phosphatidylglycerol (PG), phosphatidylinositol (PI), phosphatidylcholine (PC), phosphatidylethanolamine (PE), diglyceride (DG), cholesteryl ester (ChE), coenzyme Q (Co), zymosteryl ester (ZyE), ceramides (Cer), dihexosyl N-acetylhexosyl ceramide (CerG3GNAc1), ceramide phosphate (CerP), cardiolipin (CL), and TG in both groups. Interestingly, in the control group, LPA and lysophosphatidylcholine (LPC) exhibited weaker correlations with other lipids compared to the sepsis group. Conversely, certain lipids, including GM1, FA, LPA, GT3, CerG3GNAc2, phosphatidylinositol (4, 5) bisphosphate (PIP2), and sialylated ester (SiE), demonstrated weak or negligible correlations in both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChanges in Plasma Lipidome Closely Associated with Disease Severity\u003c/h2\u003e \u003cp\u003eA total of 1,257 differential lipid molecules were identified, encompassing 231 PC (18.4%), 199 PE (15.8%), 163 TG (12.9%), 99 Cer (7.8%), and 91 hexosyl ceramide (Hex1Cer) (7.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Of these, 24 lipid molecules demonstrated significant associations with pSOFA score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Table S2). Notably, lipid molecules positively correlated with pSOFA score included acyl Carnitine (AcCa) (8:0) (R\u0026thinsp;=\u0026thinsp;0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), (O-acyl)-1-hydroxy fatty acid (OAHFA) (47:7) (R\u0026thinsp;=\u0026thinsp;0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), and Cer (18:2_24:2) (R\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016). Conversely, lipid molecules negatively correlated with pSOFA score were LPC (16:1e) (R = -0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), LPC (19:1) (R = -0.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and LPC (15:0) (R = -0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Further correlation analysis among these 24 differential lipid molecules showed that Cer was positively correlated with OAHFA and negatively correlated with TG, LPC, CL, and GM3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHost Response to Infection Linked to Changes in the Plasma Lipidome\u003c/h2\u003e \u003cp\u003eNo significant differences were observed in levels of WBC, CRP, IL-6, PCT and lactate between the recovery and deterioration groups among sepsis patients. However, the recovery group exhibited a significant increase in the absolute counts of CD3\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A heat map analysis demonstrated distinct lipid correlations with immune and inflammatory parameters. Specifically, 11 lipid molecules were positively correlated with PLT, WBC, lymphocytes, neutrophils, CD4\u003csup\u003e+\u003c/sup\u003e T, and CD8\u003csup\u003e+\u003c/sup\u003e T cells, and negatively correlated with CRP, IL-6, and PCT. Conversely, Four lipid molecules showed positive correlations with CRP, IL-6, PCT, and neutrophils, and negative associations with WBC, PLT, lymphocytes, CD4\u003csup\u003e+\u003c/sup\u003e T, and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Strong correlations were found between the following lipid molecules and inflammatory markers: Hex1Cer and CRP (d42:1\u0026thinsp;+\u0026thinsp;O) (R = -0.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), DG and IL-6 (20:1e_10:3) (R = -0.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cer and PCT (d37:6) (R = -0.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), OAHFA and PLT (47:7) (R= -0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), PC and WBC (42:8e) (R\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), PE and neutrophils (38:6e) (R\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024), sphingomyelin (SM) and lymphocytes (d40:3) (R\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), PC and CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells (21:1_10:4) (R = -0.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (R = -0.53, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\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\u003eLaboratory Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoutine Lab\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecovery (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeterioration (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.5 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.9 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.0 [111.5, 322.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.0 [42.0, 169.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.4 [70.0, 88.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.1 [69.1, 91.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.9 [6.5, 20.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.4 [4.4, 21.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD3\u003csup\u003e+\u003c/sup\u003e T cell, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1078.0 [398.8, 1195.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e371.8 [147.7, 544.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cell, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e562.9 [216.3, 681.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e166.8 [71.9, 289.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cell, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e355.1 [162.0, 469.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e179.5 [90.3, 252.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD19\u003csup\u003e+\u003c/sup\u003e T cell, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e486.9 [173.9, 660.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e394.2 [98.5, 497.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.3 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.5 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.9 [0.9, 42.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.0 [4.2, 28.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1 [0.9, 1.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.6 [0.7, 7.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6, pg/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286.7 [53.7, 605.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357.6 [69.8, 887.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.6 [4.9, 12.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.2 [6.8, 93.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.6 [10.9, 29.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.0 [20.0, 421.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.3 [23.4, 61.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328.0 [44.7, 827.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimers, \u0026micro;g/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6 [1.4, 7.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7 [2.7, 17.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7 [5.4, 7.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.4 [6.1, 11.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasoactive drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as median [interquartile range], mean (SD) or n (%). Abbreviations: WBC, white blood cell; CRP, c-reactive protein; PCT, procaicitonin; IL-6, interleukin-6; ALT, alanine aminotransferase; AST, aspartate aminotransferase\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAlterations in the Plasma Lipidome of Sepsis Patients Associated with a Poorer Prognosis\u003c/h2\u003e \u003cp\u003eLipid class analysis revealed significant differences in FA and Lysophosphatidylinositol (LPI) levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between the recovery and deterioration groups, with both lipid classes elevated in the deterioration group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In the lipid molecule analysis, 186 differential lipid molecules were identified between the two groups, with TG was the most discrepant lipid class, accounting for 16.4% of the total differential molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Unsupervised hierarchical clustering analysis revealed that PI (29:0_10:0), Hex2Cer (t37:4) and Hex2Cer (t35:5) were elevated in the deterioration group, while LPC (15:0), LPC (20:1), LPC (19:1), n-acetylhexosyl ceramide (CerG2GNAc1) (d32:1) and CerG3GNAc1 (d36:7) were elevated in the recovery group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). When comparing the 24 lipid molecules associated with pSOFA score and the 186 differential lipid molecules between the two groups, 15 lipid molecules were found to overlap. The top five most significant lipids were LPC (15:0), LPC (19:1), LPC (20:1), CerG2GNAc1 (d32:1), and PC (18:3e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, e). In addition, clinical parameters demonstrated that patients in the deterioration group exhibited significantly lower platelet counts and higher levels of total bilirubin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) compared to the recovery group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eChanges in the Fatty Acid Profile of Sepsis Patients\u003c/h2\u003e \u003cp\u003eAnalysis of the fatty acid profile revealed elevated levels of FA (16:0), FA (20:4), and FA (22:6) in the sepsis group compared to controls, with the most pronounced increase observed in FA (20:4) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Further subgroup analysis revealed distinct patterns of these fatty acids in relation to disease prognosis. Specifically, levels of FA (20:4), FA (16:0), and FA (22:6) were significantly higher in the deterioration group compared to the recovery group. FA (16:0) showed the most substantial elevation among the three fatty acids in patients with a poor prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). In addition, correlation analysis between FA and cholesterol esters (ChE) demonstrated differing relationships. FA (16:0) and ChE (18:3), as well as FA (20:4) and ChE (19:1), were negatively correlated, whereas FA (22:6) and ChE (18:0) exhibited a positive correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted untargeted metabolomics analysis to explore lipid alterations in pediatric sepsis and identified significant differences in plasma lipid profiles between sepsis patients and non-infected controls. Importantly, correlation analyses revealed that these lipid changes were associated with higher pSOFA score and poorer clinical outcomes. Additionally, specific lipid molecules showed significant correlations with inflammatory markers, emphasizing the critical interplay between lipid metabolism and the inflammatory response in pediatric sepsis.\u003c/p\u003e \u003cp\u003eOur findings align with those reported by Chouchane et al. (Chouchane et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who demonstrated that 58% of lipid levels were significantly decreased in community-acquired pneumonia (CAP)-sepsis patients compared to outpatient controls. Their study also observed a significant negative correlation between most lipid levels and SOFA score supporting the concept of lipid dysregulation as a marker of disease severity. However, several key differences exist between our study and that of Chouchane et al., particularly in study populations and methodologies. Chouchane et al. focused on middle-aged and elderly adults (\u0026gt;\u0026thinsp;60 years old), whereas our study specifically investigated pediatric patients under 18 years of age, a population that may exhibit distinct metabolic responses to sepsis. Additionally, Chouchane et al. evaluated lipidome changes over time, particularly their relationship with lipid recovery and 30-day mortality in ICU patients hospitalized for at least 4 days. In contrast, our study focused on plasma lipid alterations within 24 hours of sepsis diagnosis, emphasizing early lipid changes and their immediate correlation with clinical outcomes.\u003c/p\u003e \u003cp\u003eThe observed lipid alterations also parallel findings from previous studies on Cer and LPC levels. For instance, elevated ceramide levels have been positively correlated with SOFA score, reflecting disease severity and poor outcomes (Wu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, our findings revealed a significant negative correlation between LPC levels and pSOFA score, suggesting that a decline in LPC concentrations is indicative of disease progression and organ dysfunction in pediatric sepsis. These findings highlight the potential of LPC as a prognostic biomarker for disease deterioration. Furthermore, Chang et al. reported significantly reduced LPC concentrations in sepsis-induced ARDS patients compared to non-ARDS controls, with higher LPC levels observed in patients with direct ARDS compared to those with indirect ARDS (Chang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consistent with these findings, our analysis identified that the top three lipid molecules associated with both pSOFA score and clinical outcomes in pediatric sepsis were LPC species. LPC, a product of PC degradation mediated by phospholipase A2 (PLA2), is strongly linked to inflammation. As a bioactive lipid, LPC not only acts as a ligand for lymphocytes but can also be enzymatically converted into lysophosphatidic acid, an anti-inflammatory mediator. Persistent low LPC levels in sepsis patients are thought to reflect disrupted metabolic homeostasis (Lee et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These lower LPC levels have been associated with excessive inflammatory responses and worse clinical outcomes (Montague et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), underscoring the critical role of LPC in the pathophysiology of sepsis. Our findings further support the potential of LPC as a key biomarker for sepsis, with utility in diagnosis, prognosis prediction, and the identification of therapeutic targets.\u003c/p\u003e \u003cp\u003eOur study revealed a negative correlation between PC and T cells, alongside a positive correlation between PE and neutrophils. PE serves as a precursor for various bioactive molecules, with its metabolites\u0026mdash;such as diglycerides, fatty acids, and phosphatidic acid\u0026mdash;playing critical roles as second messengers (Vance, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alterations in the PC/PE ratio have been shown to profoundly affect organelle energy metabolism, contributing to the progression of diseases (van der Veen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, a decreased PC/PE ratio in the liver compromises membrane integrity, which promotes the development of non-alcoholic fatty liver disease (NAFLD), and in severe cases, may lead to liver failure. In the intestine, similar reductions impair fatty acid uptake and chylomicron secretion, while also increasing cellular leakage, decreasing gluconeogenesis, and enhancing ATP synthesis (Duncan, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings underscore the importance of lipid homeostasis in maintaining cellular integrity and metabolic function. Notably, PC may exacerbate inflammatory responses by destabilizing cell membranes, positioning it as a potential mediator of inflammation in critical illness.\u003c/p\u003e \u003cp\u003eIn this study, we observed elevated FA levels in sepsis patients compared to controls, with further increases seen in patients experiencing sepsis deterioration. Consistently, sepsis patients exhibit lower cholesterol levels and FA (Cappi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Notably, omega-3 polyunsaturated fatty acids, such as docosahexaenoic acid (DHA, FA 22:6), have been shown to modulate the gut microbiome composition, enhance the production of catabolic mediators and anti-inflammatory factors, inhibit NF-κB activation, and influence the function of membrane lipid rafts (Lu Zhongqiu et al.,2022). Through these mechanisms, omega-3 fatty acids may provide protective effects in sepsis by reducing systemic inflammation and promoting gut microbiome homeostasis, both of which are critical in mitigating organ dysfunction and improving outcomes. In contrast, omega-6 polyunsaturated fatty acids, such as arachidonic acid (AA, FA 20:4), are generally associated with promoting inflammation. AA serves as a key precursor for pro-inflammatory eicosanoids, including prostaglandins, thromboxanes, and leukotrienes. These eicosanoids play pivotal roles in immune and glial cell activation, driving inflammation and tissue injury during sepsis (Vance, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, FAs appear to exert a dual role in sepsis pathophysiology. Our findings underscore the potential of targeting fatty acid metabolism as a novel therapeutic strategy in sepsis. Modulating the balance between omega-3 and omega-6 fatty acids could reduce excessive inflammation, restore lipid homeostasis, and improve clinical outcomes in critically ill children.\u003c/p\u003e \u003cp\u003eDespite the significance of these findings, our study has several limitations. First, blood samples were collected at a single time point, specifically upon PICU admission. Although efforts were made to obtain samples as early as possible on the first day of diagnosis, variability in the timing of disease onset and clinical presentation may have introduced confounding factors. Second, the relatively small sample size and single-center design limit the generalizability of our findings. The absence of an independent validation cohort further constrains the robustness of our conclusions. Third, we were unable to perform dynamic monitoring of lipid levels over time, which restricts a comprehensive understanding of lipidomic alterations during the progression and resolution of sepsis. To address these limitations, future studies should employ larger, multi-center cohorts with longitudinal sampling to validate and expand upon our findings. A more detailed temporal analysis of lipidomic profiles may provide greater insights into the dynamic role of lipid metabolism in immune responses and sepsis outcomes. Such studies could also elucidate the mechanistic links between lipid dysregulation, inflammation, and organ dysfunction, ultimately identifying novel biomarkers and therapeutic targets in pediatric sepsis.\u003c/p\u003e \u003cp\u003eIn summary, our study provides new insights into the plasma lipidome during pediatric sepsis, revealing both global and specific lipid alterations associated with disease progression and severity. These findings pave the way for future research aimed at validating lipidomic biomarkers and exploring therapeutic interventions targeting lipid metabolism. Longitudinal studies with larger, multi-center cohorts are needed to further elucidate the dynamic changes in lipid profiles and their mechanistic roles in sepsis pathophysiology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants and their guardians, the collaborating clinicians, and other clinical staff. We would also like to acknowledge the Shanghai Zhongke New Life Biotechnology Co., Ltd for the support of sample analysis and relative quantification of lipids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRA, JB, YC, and ZC collected and analyzed the patient data; WY, HZ, GS, RA, and YC performed the statistical analysis; KW, YW, and TL collected and processed clinical samples; YL, WC, RA, and JB wrote the manuscript; CZ, GL, and HC contributed to conceptualization, manuscript writing and editing, statistical analysis, and visualization. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by grants from the National Key R\u0026amp;D Program of China (2021YFC2701800 to G Lu), National Natural Science Foundation of China (82202374 to C Zhang), Natural Science Foundation of Shanghai (22ZR1408500 to C Zhang), Shanghai Municipal Science and Technology Major Project (ZD2021CY001 to G Lu), Municipal Health System Key Supporting Discipline Project (2023ZDFC0103 to W Chen), Natural Science Foundation of Anhui (2308085MH267 to G Lu), Ningbo Medical and Health Brand Discipline (PPXK2024-06 to H Chen).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used for the analysis in the current study are available from the corresponding author on reasonable request. The online version has been uploaded to MetaboLights, and DOIs will be provided after acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was provided by Ethics Committee of Children\u0026apos;s Hospital of Fudan University ([2024] No. 116). Written informed consent was obtained from all parents or their surrogates of studied children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to the publication of this manuscript.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBalamuth, F., Scott, H. F., Weiss, S. L., Webb, M., Chamberlain, J. M., Bajaj, L., Depinet, H., Grundmeier, R. 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Recent advances in lipid separations and structural elucidation using mass spectrometry combined with ion mobility spectrometry, ion-molecule reactions and fragmentation approaches. \u003cem\u003eCurr Opin Chem Biol\u003c/em\u003e,\u003cem\u003e 42\u003c/em\u003e, 111-118. https://doi.org/10.1016/j.cbpa.2017.11.009 \u003c/li\u003e\n\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":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pediatric sepsis, Plasma lipidome, Untargeted lipidomics, Fatty acid","lastPublishedDoi":"10.21203/rs.3.rs-6040682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6040682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003eThe plasma lipidome has emerged as an important indicator for assessing host metabolic and immune status in sepsis. While previous studies have largely examined specific lipid class changes in adults sepsis, comprehensive investigations into plasma lipidomic alterations in pediatric sepsis are limited. This study aimed to characterize the plasma lipidome in pediatric sepsis using a metabolomics-based exploratory approach, providing insights into pathophysiological mechanisms and potential biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective study was conducted on pediatric patients with sepsis admitted to the pediatric intensive care unit (PICU). Untargeted lipidomics analysis using ultra-performance liquid chromatography coupled with Orbitrap mass spectrometry (UPLC-Orbitrap) was performed to compare metabolomic profiles between non-infected control patients and sepsis patients.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared to controls, plasma lipid levels in sepsis patients decreased by 33.3%, increased by 20.2%, and remained unchanged in 46.5% of cases. Several lipid molecules were identified to be associated with disease severity and inflammatory markers. In the recovery and deterioration subgroups, 186 differential lipid molecules were identified, with triglycerides (TG) being the most abundant class. Notably, 15 lipid molecules overlapped between those associated with disease severity and those linked to clinical outcomes. Fatty acid (FA) levels were significantly elevated in the sepsis group compared to controls, with arachidonic acid (FA(20:4)) showing the most significant increase (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlterations in plasma lipid profiles among children with sepsis reflect disease severity, systemic inflammatory responses, and clinical outcomes. These findings underscore the prognostic potential of lipidomics and its value in understanding sepsis pathophysiology.\u003c/p\u003e","manuscriptTitle":"Untargeted Lipidomics Profiling Provides Novel Insights into Pediatric Patients with Sepsis: An Exploratory Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 16:31:56","doi":"10.21203/rs.3.rs-6040682/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-17T14:44:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-17T14:38:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65112381703914499798667517022493165659","date":"2025-03-17T10:10:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-16T07:04:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129729504121984625705146913949461368507","date":"2025-02-18T04:57:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-17T13:20:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-17T13:14:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-17T13:13:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Metabolomics","date":"2025-02-16T10:43:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"metabolomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mebo","sideBox":"Learn more about [Metabolomics](http://link.springer.com/journal/11306)","snPcode":"11306","submissionUrl":"https://submission.nature.com/new-submission/11306/3","title":"Metabolomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"052d1cd4-bd8a-4704-b42a-7e73217a0dec","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T16:08:00+00:00","versionOfRecord":{"articleIdentity":"rs-6040682","link":"https://doi.org/10.1007/s11306-025-02255-x","journal":{"identity":"metabolomics","isVorOnly":false,"title":"Metabolomics"},"publishedOn":"2025-04-26 15:57:15","publishedOnDateReadable":"April 26th, 2025"},"versionCreatedAt":"2025-02-19 16:31:56","video":"","vorDoi":"10.1007/s11306-025-02255-x","vorDoiUrl":"https://doi.org/10.1007/s11306-025-02255-x","workflowStages":[]},"version":"v1","identity":"rs-6040682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6040682","identity":"rs-6040682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}