Circulating Lipids Predict Mortality and Drive Immune Gene Regulation in Acute-on-Chronic Liver Failure

Circulating Lipids Predict Mortality and Drive Immune Gene Regulation in Acute-on-Chronic Liver Failure | 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 Circulating Lipids Predict Mortality and Drive Immune Gene Regulation in Acute-on-Chronic Liver Failure Rohini Saha, Sonali Mukherjee, Praveen Singh, Nidhi Gauniyal, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8154019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Acute-on-chronic liver failure (ACLF) has ~ 50% 28-day mortality driven by systemic inflammation, immune dysfunction, and multiorgan failure. The contribution of circulating lipids to ACLF pathogenesis remains poorly understood. We investigated whether plasma lipid signatures predict short-term mortality and participate in immune dysregulation in ACLF. Methods: Targeted plasma lipidomics was performed in ACLF patients (n = 100; discovery n = 60, validation n = 40) and healthy controls (n = 40). Prognostic utility of lipid signatures was compared with MELD, MELD-Na, and CTP scores. Cross-disease lipidome comparisons, ex vivo lipid-leukocyte assays, proteomic profiling of neutrophils and post-mortem liver/kidney tissue, and in silico docking were used to investigate mechanistic relevance. Results: Of 1218 quantified lipids, 316 were altered in ACLF and 30 were validated in an independent cohort. A five-lipid mortality signature [LPE(18:2), LPE(20:4), PE(O-18:0/16:1), TAG(48:1/16:1), PE(P-18:2/18:2)] discriminated survivors from non-survivors, with AUROC values comparable to MELD-Na and CTP. A combined lipid-CTP logit model achieved AUROC of 0.912, outperforming individual clinical scores. ACLF lipid profiles overlapped with those of sepsis, trauma, and COVID-19, indicating conserved critical-illness biology. Ex vivo , ACLF-derived lipids downregulated TLR1/6/7/8/10 and induced CD177 in healthy leukocytes, recapitulating ACLF-like immune signatures. Proteomic analyses across neutrophils, liver, and kidney revealed a shared neutrophil-degranulation program, while in vitro assays showed that ACLF lipids were cytotoxic to HEK293T cells. In silico docking demonstrated potential binding of ACLF-lipids with PPARγ with affinities comparable to known ligands, suggesting a transcriptional mechanism linking lipid alterations to immune gene regulation. Conclusion: Circulating lipids in ACLF serve dual roles as prognostic biomarkers and active mediators of immune dysfunction and tissue injury. A validated five-lipid signature accurately predicts 28-day mortality, and mechanistic analyses implicate lipid-driven PPARγ modulation, TLR suppression, and neutrophil activation in ACLF pathogenesis. These findings offer opportunities for lipid-based risk stratification and therapeutic targeting in ACLF. ACLF lipidomics prognosis neutrophils PPARγ organ failure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Acute-on-Chronic Liver Failure (ACLF) is a complication of cirrhosis, with a very high short-term mortality of ~ 50% 1–4 . ACLF is characterized by profound metabolic derangements and exaggerated but dysfunctional innate immune activation, increased susceptibility to bacterial infections and, multi-organ failure (MOF) 5 – 10 . A heightened neutrophil-to-lymphocyte ratio (NLR) and expansion of neutrophil subsets have been demonstrated in ACLF and linked to patient outcomes 11 – 13 . Several classes of circulating mediators have been implicated in inflammation and MOF in other systems, including cytokines such as TNF-α, IL-1β, and IL-6, reactive oxygen and nitrogen species (ROS/RNS), components of the complement system, platelet-derived factors contributing to disseminated intravascular coagulation; and bacterial products derived from infections 14 – 17 . These mediators, often triggered by acute insults such as sepsis or trauma, drive systemic inflammatory responses that damage organs through mechanisms including hypoxia, oxidative stress, endothelial permeability, and microvascular dysfunction 18 . However, beyond these established inflammatory mediators, other molecular effectors remain poorly understood. Among these potential effectors, lipids are uniquely positioned at the intersection of metabolism and immunity, functioning both as structural components and as signalling molecules 19 , 20 . Yet, despite their recognized roles in regulating inflammation in other critical illnesses, the circulating lipidome has been largely overlooked in ACLF, with only a single prior study reporting broad alterations compared to compensated cirrhosis and healthy controls 21 . We hypothesized that alterations in the plasma lipidome of ACLF patients are linked to clinical outcomes and could complement existing prognostic tools. Understanding the prognostic performance of lipids offers an opportunity to refine risk stratification strategies while also shedding light on their broader significance in the clinical trajectory of ACLF. We further postulated that circulating lipids contribute mechanistically to ACLF pathogenesis, particularly through regulation of immune cell phenotypes. To address this, we performed comprehensive targeted lipidomic profiling in discovery and validation cohorts and analyzed these data in context of proteomic datasets to explore biological relevance. We compared our lipidome to other published lipidomes of ACLF, COVID 19, Sepsis and trauma to identify similarities and differences. We further investigated the biological and mechanistic relevance of ACLF circulating lipids using a combination of in silico and experimental approaches. Together, our investigations aimed to uncover the prognostic and mechanistic relevance of lipids in the pathogenesis of ACLF. METHODS An overall scheme of the study design and work plan are provided in Fig. 1 . The study strictly adhered to the tenets of the Declaration of Helsinki and was approved by the institutional ethics committee (Ethics Committee Ref. No. IEC-630/07.12.2018, RP-36/2018, and IEC-145/11.04.2023). The European Association for the Study of the Liver-Chronic Liver Failure (EASL-CLIF) definition was used for the diagnosis of ACLF 4 . The EASL definition includes patients with acute decompensation of cirrhosis (CLD-AD), and classifies them as ACLF based on the CLIF-Organ Failure (OF) scoring assigned for 6 organ systems (liver, kidney, brain, coagulation, circulation, and respiration). This definition takes into account non-hepatic organ failure, such as kidney and brain for ACLF classification and importance is given to severity (ACLF grades) and association with short-term mortality (28-day). The following parameters are used for classifying each organ failure, (i) Liver- bilirubin ≥ 12 mg/dL; (ii) Kidney-creatinine ≥ 2 mg/dL or requiring renal replacement therapy; (iii) Brain- West Haven grade 22 III/IV for hepatic encephalopathy (HE); (iv) Coagulation-INR ≥ 2.5; (v)Circulatory- requiring vasopressors; and Respiratory-PaO2/FiO2 ≤ 200 or SpO2/FiO2 ≤ 214. Sample Size An expected 28-day mortality rate of ~ 50% in ACLF guided the inclusion of 100 patients recruited across two independent time periods (60 and 40 patients, respectively), together with 40 healthy controls. This sample size provides ~ 80% power to detect moderate-to-large differences between survivors and non-survivors (Cohen’s d ≥ 0.6) at α = 0.05, consistent with the effect sizes commonly observed in lipidomic biomarker studies. For transparency, approximate power calculations were based on the standard two sample effect size formula: $$\:{n}_{per\:group}\approx\:\frac{2{\left({Z}_{1-\alpha\:/2}+{Z}_{1-\beta\:}\right)}^{2}}{{d}^{2}}$$ , where Z₁₋α/2 = 1.96 for α = 0.05, Z₁₋β = 0.84 for 80% power, yielding n per group ≈ 15.7/d². Accordingly, detecting an effect size of d = 0.6 requires ~ 44 subjects per group, closely matching the combined ACLF sample of 100. To further assess the adequacy of precision, Hanley and McNeil’s AUC variance approximation indicates that clinically meaningful prognostic performance (AUC ≈ 0.80) can be estimated with acceptable 95% confidence intervals in a cohort of this size. The use of two temporally independent cohorts, combined with false-discovery rate control and penalized regression, enhances reproducibility and supports the robustness of the identified lipid biomarkers. Patient Recruitment and Sample Collection ACLF patients were recruited on the day of admission (Day 0) or the next day (Day 1), from the Department of Gastroenterology at AIIMS, New Delhi (Nov 2022-Nov 2024). Blood collection was done in the morning for all patients, to minimize diurnal variation in plasma lipid levels. Peripheral blood (~ 6 mL, EDTA) was processed for plasma and other analyses in the Department of Biochemistry. Patients were followed up for 28-day outcomes and classified as survivors (S) or non-survivors (NS). Healthy age- and sex-matched controls were recruited from the Blood Bank. The discovery cohort included 60 ACLF patients (30 S, 30 NS) and 30 healthy controls; the validation cohort included 40 ACLF patients (20 S, 20 NS) and 10 controls (Fig. 1 ). Post-mortem liver and kidney biopsy tissues were obtained from ACLF patients (n = 9) in the Department of Gastroenterology, AIIMS, with consent from next of kin and in accordance with institutional ethical guidelines. Plasma lipidomics, tissue proteomics and PMN proteomics were performed with independent patient cohorts and the baseline characteristics of each cohort have been provided in Supplementary Table_Baseline. Plasma lipidomics A total of 100 ACLF plasma samples were used for comparative plasma lipidomic profiling (Fig. 1 ). The collected blood samples were processed within 2 hours of sample collection. Plasma were separated as per standard protocols and stored at -80℃ until further use 11 . A modified Bligh and Dyer method consisting of a triphasic solution of dichloromethane/methanol/water (2:2:1v/v) was used for total plasma lipid extraction (Supplementary Methods File_M1) 23 , 24 . Targeted LC-MS/MRM was performed using the Waters ACQUITY ™ UPLC BEH HILIC X Bridge Amide column, coupled with the Sciex QTRAP 6500 + LC/MS/MS system as published earlier 25 (Supplementary Methods File_M2). For peak mapping and method development, MultiQuant 3.0.3 (MQ) software was used ( https://sciex.com/products/software/multiquant-software ). A total of 1218 lipid species, belonging to 17 lipid classes were identified. These were (+ ve MRM) sphingomyelin (SM), ceramide (Cer), cholesterol ester (CE), monoacylglycerol (MAG), diacylglycerol (DAG), Triacylglycerol (TAG), (-ve MRM) phosphatidic acid (PA), lysophosphatidylcholine (LPC), phosphatidylcholine (PC), lysophosphatidylethanolamine (LPE), phosphatidylethanolamine (PE), lysophosphatidylinositol (LPI), phosphatidylinositol (PI), lysophosphatidyl glycerol (LPG), phosphatidylglycerol (PG), lysophosphatidylserine (LPS), and phosphatidylserine (PS).Within the PE class of lipids, the 'O-' prefix is used to indicate the presence of an alkyl ether substituent e.g. PE(O-16:0/18:1(9Z)), whereas the 'P-' prefix is used for the 1Z-alkenyl ether (Plasmalogen) substituent e.g. PE(P-16:0/18:1(9Z)). Peak integration and regression were done to incorporate relevant peaks. Relative quantification was performed between the different study groups (ACLF survivors, non-survivors, and healthy controls). The discovery cohort included participant samples which cleared QC: ACLF patients (n = 58; 30 S and 28 NS), and healthy controls (n = 29). Lipid hits were validated in an independent cohort of ACLF patients (n = 40) and healthy controls (n = 10) using the same methodology. These two independent patient cohorts (discovery and validation) were recruited at different time periods under identical inclusion and exclusion criteria at the Department of Gastroenterology, AIIMS, New Delhi. For both cohorts, peripheral blood samples were collected, and plasma lipidomic profiling was performed using the same LC-MS/MS platform, sample preparation workflow, and data processing pipeline to ensure analytical consistency. All statistical analyses were performed using R (v4.3.1) (Supplementary Methods File M3) and MetaboAnalyst 6.0 (Supplementary Methods File M4). After data preprocessing (70% detection filtering, MissForest imputation, log₂ transformation, and ComBat batch correction), initial group-wise comparisons were conducted using Wilcoxon rank-sum tests for pairwise analyses and the Kruskal–Wallis test for three-group comparisons. Lipid species were considered significantly altered at log₂ fold change ≥ 0.58 and p ≤ 0.05. To identify robust discriminatory features across healthy controls (HC), ACLF survivors (ACLF-S), and ACLF non-survivors (ACLF-NS), we applied the Boruta feature selection algorithm (random forest-based), retaining only features classified as “Confirmed.” The Boruta-selected lipids were then subjected to supervised multivariate analysis in MetaboAnalyst. Partial least squares–discriminant analysis (PLS-DA) with 7-fold cross-validation and permutation testing (n = 1000) was used to assess group separation, and variable importance in projection (VIP) scores > 1 were used to prioritize discriminatory lipids. In addition, a one-factor ANOVA (or Kruskal–Wallis where appropriate) with Tukey’s post-hoc correction was performed across the three groups, and volcano plots (log₂FC vs. p-value) were generated to integrate effect size and significance. Using this pipeline, ~ 300 significantly altered lipid species were identified in the discovery cohort for the ACLF vs. healthy comparison. The identical analytical workflow was applied to a temporally independent validation cohort, and 30 lipid species consistently dysregulated in both cohorts were designated as ACLF-associated lipids. To identify mortality-associated lipids, the ~ 300 ACLF-altered species were further analyzed using one-factor analysis between ACLF-S and ACLF-NS groups in MetaboAnalyst, followed by PLS-DA, VIP scoring, and ANOVA, yielding five lipid species that were significantly associated with 28-day mortality. Using Jamovi version 2.7.6, univariate logistic regression was performed to assess associations between plasma lipids, clinical parameters, and 28-day mortality. Variables significant in univariate analysis were entered into multivariable logistic regression, after excluding collinear predictors using variance inflation factor (VIF) scores, to identify independent predictors. Discriminatory performance was evaluated using receiver operating characteristic (ROC) curves with calculation of the area under the curve (AUROC), and optimal cut-offs were determined using the Youden index. Pairwise AUROC comparisons were performed with DeLong’s test, and a p-value ≤ 0.05 was considered statistically significant. Proteomics Analysis of ACLF-derived Neutrophils, Liver, and Kidney Tissue Post-mortem liver and kidney tissues (n = 9 each) were collected from the Dept. of Gastroenterology, snap frozen and stored at -80 0 C until further use. Peripheral blood polymorphonuclear cells (PMNs) (ACLF n = 9; HC n = 9) were isolated using double-density gradient centrifugation protocol (Supplementary Methods File_M5) 11 . Proteomic profiling of liver, kidney, and neutrophil samples was performed at vProteomics (New Delhi, India). PMN samples were pooled to increase protein input per sample, into 3 sets of 3 each. Samples were analyzed using an Easy-nLC 1000 system coupled to an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific). Raw files were processed using Proteome Discoverer v2.5, and searched against the UniProt human reference database using both Sequest and Amanda search engines (Supplementary Methods File_M6). Functional pathway enrichment analyses were conducted using Metascape, and tissue expression data for individual proteins were obtained from the Human Protein Atlas ( proteinatlas.org ). Kinase and transcription factor enrichment analyses Differentially expressed PMN genes and proteins from previously published and the present study, were subjected to kinase and transcription factor enrichment analysis using the KEA3 (version 3) and X2Kweb tool (ChEA and ENCODE databases) ( https://maayanlab.cloud/X2K/ ) respectively. Enriched kinases and TFs were cross-referenced against our original lists of differentially expressed genes (DEGs) and proteins (DEPs) (Saha et. al., 2021 and present study respectively). To evaluate potential binding sites on key genes by the TF PPARγ, transcription factor binding motif analysis was performed using the Search Motif Tool available through the Eukaryotic Promoter Database (EPD; https://epd.epfl.ch/ ). The genomic regions from − 1000 base pairs (bp) upstream to + 100 bp downstream relative to the transcription start site (TSS) were selected for each gene. PPARγ motifs were searched using the JASPAR CORE vertebrates transcription factor motif library, applying a p-value cut-off of 0.001. The location of each predicted PPARγ binding site was recorded relative to the TSS and visualized using the graphical output provided by the EPD motif tool. Genes without any predicted binding sites within the specified region were also noted. Genes assessed included- TLR1, LR10 , SLC25A4 , SLC2A1 , CXCL1 , CD177 , FFAR3 , MAPK1 , MAPK14 , and MT3 , along with neutrophil degranulation protein list from shared signatures of the three proteomes (PMN, liver and kidney). Gene Expression Analysis in Whole Blood Cells and PMN EDTA Whole blood was collected from study participants and processed for total RNA isolation. PMN were isolated using the Bøyum’s method of double gradient centrifugation was used as described (Supplementary Methods File_M7). A panel of 10 genes (TLR1-TLR10) along with neutrophil-specific markers CD177 and ELANE were analyzed to assess neutrophil markers and TLR signaling status in ACLF patients vs HC (MIQE guidelines 1 and MIQE guidelines 2). Amplification results were expressed as ΔC T values normalized to 18S rRNA, and analyzed using GraphPad Prism10.0. Ex Vivo Leukocyte Lipid Treatment Assay Plasma from 15 ACLF NS randomly selected from the lipidomics discovery cohort were pooled for lipid extraction for ex vivo assays and subjected to Bligh and Dyer extraction as described before. The lipid pellet was resuspended in 100% molecular biology-grade ethanol at a concentration of 150 mg/ml. Blood (1 mL) from 15 healthy individuals was processed to isolate leukocytes, which were resuspended in RPMI + 10% autologous plasma and seeded (10 5 cells/well, duplicates) in 12-well plates. Wells were treated with 0.15 mg/mL lipid extract (lipid-treated) or left untreated, with RPMI adjusted to 1 mL total volume. Incubation was performed for 24 h at 37°C, 5% CO₂. Lipid dose was optimized by MTT assays across doses and batches (Supplementary Methods File_M8). qRT-PCR was used to assess TLR1-10, CD177, ELANE, and MPO expression, with calculation of fold change lipid-treated vs untreated cells. To assess lipid incorporation, lipidomes of three independent healthy leukocyte samples were analyzed pre- and post-treatment by targeted MS/MRM as described above. In Vitro Viability Response to ACLF Lipids Plasma from 10 ACLF or 10 HC individuals was pooled and lipids extracted by the Bligh and Dyer method. Serial 10-fold dilutions (100 to 0.001 mg/mL) were prepared in RPMI (no phenol red) and used to treat HEK-293T cells. Untreated cells served as positive controls, while heat-killed cells and lipid-only media served as negatives. After 24 h, MTT (5 mg/mL) was added for 4 h, and formazan solubilized before measuring OD at 570 nm. In Silico Studies for investigation of ACLF lipid-PPARγ interaction Molecular docking was performed using Maestro Version 13.7 (Release 2023-3) from the Schrödinger Suite. The crystal structure of PPARγ in complex with garcinoic acid (GA) (PDB ID: 7AWD) was prepared using the Protein Preparation Wizard with default settings. LigPrep (OPLS4 force field) from the Schrödinger Suite was employed to prepare the ACLF-associated lipids. All possible protonation states were generated using Epik at pH 7.0, and tautomeric forms were enumerated for chemical groups capable of tautomerism. A receptor grid box was generated by defining the ligand-binding site after removing one of the GA molecules from the complex, using the OPLS4 force field. The optimized ligands were subsequently docked into the receptor using Schrödinger’s Glide software (Release 2023-3). Structural similarity between the lipid species was estimated using the RDKit cheminformatics package (rdkit-pypi, v. 2022.9.5) and mapchiral (v.0.0.7) in Python. Molecules were first parsed from SMILES strings and converted into RDKit Mol objects. Structural fingerprints were generated using the Morgan fingerprint (`AllChem.GetMorganFingerprintAsBitVect`, radius = 2, nBits = 2048) and MAP4Chiral fingerprint 26 (maxradius = 2, npermutations = 2048) algorithms.) Tanimoto similarity coefficients were calculated using the `DataStructs.cDataStructs.TanimotoSimilarity` function and Jaccard similarity coefficients were calculated using the mapchiral.jaccard_similarity function. This was exported to R (v 4.4.1) for downstream analysis. Here, the Tanimoto similarity matrix was transformed into a distance matrix (1 - similarity), and hierarchical clustering was performed using the `hclust` function with complete linkage. Statistical Analysis All statistical analyses were performed in GraphPad Prism 10, Jamovi (version 2.7.6), Python, and R (version 4.3.3). Data distribution was assessed using normality tests. Continuous variables were summarized as mean ± SD or median (IQR) and compared using t-test or Mann–Whitney U test, while categorical variables were compared using the χ² test or Fisher’s exact test. For comparisons involving more than two groups, one-way ANOVA or the Kruskal–Wallis test was used. Differential lipidomic analysis between ACLF survivors (ACLF-S) and non-survivors (ACLF-NS) in the discovery cohort was performed using fold-change (FC ≥ 2), p ≤ 0.05, and supervised PLS-DA with VIP scoring to identify mortality-associated lipid markers. This pipeline yielded five lipid species significantly associated with 28-day mortality (LPE 18:2, LPE 20:4, PE(P-18:2/18:2), TAG 48:1/FA16:1, and PE(O-18:0/16:1)). These five mortality-associated lipids, along with associated routine clinical parameters, were entered into univariate logistic regression to evaluate their individual association with 28-day mortality. Variables with p ≤ 0.05 were considered candidate predictors. Collinearity was assessed using variance inflation factors (VIF), and non-collinear variables were included in a multivariable logistic regression model to identify independent predictors. The final multivariable model included two lipid species (TAG 48:1/FA16:1 and PE(O-18:0/16:1)) and the CTP score, all independently associated with 28-day mortality. Regression coefficients derived entirely in the discovery cohort were applied unchanged to the validation cohort to generate predicted mortality probabilities. Model discrimination was evaluated using AUROC, accuracy, sensitivity, specificity, precision, recall, F1-score, and confusion matrices. All statistical tests were two-sided, with p < 0.05 considered statistically significant. RESULTS Altered Plasma Lipidome in ACLF Reveals Enrichment of Pro-inflammatory Lipid Species and a Mortality Associated Lipid Signature ACLF patients (n = 100) were enrolled for targeted plasma lipidomics and randomly assigned to a discovery cohort ( n = 60) and an independent validation cohort ( n = 40) (Fig. 1 , Fig. 2 A, Supplementary Table_Baseline). In the discovery cohort, 58 samples [30 survivors (S), 28 non-survivors(NS)] passed batch correction and quality control (Supplementary Methods File_M3). Baseline clinical parameters, including TLC (p < 0.01), coagulation profile (INR) (p < 0.05), and kidney function parameters (urea and creatinine, p < 0.05 each), were significantly elevated in ACLF NS, whereas liver function parameters (bilirubin, AST, ALT, SAP, and albumin) were comparable between 28-day S and NS (Supplementary Tables S1, Supplementary Table_Baseline). MELD (p < 0.05), MELD-Na (p 70% of samples and retained for downstream analysis. Lipid peak areas were imputed, log-transformed, and median-normalized using MetaboAnalyst. Pre- and post-normalization distributions of lipid features and samples confirmed data quality (Supplementary Figure S1 ). Partial least squares-discriminant analysis (PLS-DA) revealed clear separation of healthy controls, ACLF S, and ACLF NS based on lipid profiles (Fig. 2 B). Component 1 (lipid intensity) explained 13.8%, while Component 2 (intergroup variance) accounted for 6.5% of the variance. VIP plot enlists the top discriminatory lipids based on the PLS-DA clustering (Fig. 2 B). Overall, 316 differential lipid species were identified in ACLF vs. healthy controls (Supplementary Table S2 ). In an independent cohort (ACLF n = 40; healthy controls n = 10), 30 key lipid species were validated (Fig. 2 C, Supplementary Table S3 ). The discovery and validation cohorts represented two completely independent patient groups. Lipidomic profiling of both cohorts, performed using same LC-MS/MS instrumentation and processing pipelines, quantified 1218 lipid species in each set, from which 30 overlapping differential lipids common to both analyses were identified as the final ACLF-associated lipid panel. Monounsaturated and saturated lipid species were consistently elevated, whereas polyunsaturated (PUFA-containing) lipid species were significantly reduced in ACLF. O-linked phosphatidylethanolamines PE(O.18:0/16:1) ( p = 8.15E-32), PE(O.18:0/16:0) ( p = 2.27E-27), phosphatidylinositol PI (18.1.18.1) ( p = 0.004), and several phosphatidylcholines [PC (16.0.16.0), PC (18.0.16.1), PC (18.1.18.1), PC (16.0.16.1) ( p ≤ 0.0001)] were elevated in ACLF. Conversely, lysophosphatidylethanolamines LPE (18:2) and LPE (20:4), and lysophosphatidylcholine LPC (14:0) were significantly decreased ( p ≤ 0.001 for all) in ACLF (Supplementary Tables S3). The box-and-whisker plots show the relative distribution of plasma lipid classes, Lysophospholipids, Glycerolipids, Phosphatidylethanolamine, and phospholipids (Fig. 2 D). A. Overall study design of the lipidomics analysis with two independent cohorts: discovery and a validation cohort of ACLF S, NS and HC. B. PLS-DA (partial least squares-discriminant analysis) plot showing distinct clustering of HC, ACLF S and NS in discovery cohort; VIP (variable importance projection) plot highlighting top discriminatory lipid species. C. Heatmap of 30 validated lipids in an independent ACLF cohort. D. Box-and-whisker plots depicting log-normalized peak area intensity of major lipid classes in ACLF: lysophospholipids, glycerolipids, phosphatidylethanolamines, and phospholipids. *p-value ≤ 0.05, **p-value ≤ 0.01, ***p-value ≤ 0.001, ****p-value ≤ 0.0001 Lipidome comparison between ACLF S and NS revealed overlapping profiles on PLS-DA (Fig. 3 A), with Component 1 explaining 10.4% (lipid species variance) and Component 2 accounting for 3.7% (sample-to-sample variance). Statistical analysis using a fold-change threshold ≥ 2 and p ≤ 0.05 identified 5 lipid species significantly altered between the two groups: LPE (18:2), LPE (20:4) and PE-(P 18:2/18:2) were lowered, whereas TAG (48:1/FA:16:1) and PE-(O 18:0/16:1) were elevated in ACLF NS (Supplementary Figure S2 ). Independent Lipid Predictors of Mortality with Prognostic Performance Are Comparable to Clinical Scores To evaluate the prognostic significance of plasma lipids in ACLF, we conducted logistic regression and ROC analyses 27 – 30 using the lipidomics discovery cohort. Univariate logistic regression showed that lower levels of LPE(18:2) [OR 0.57, 95% CI 0.36–0.89, p = 0.013], LPE(20:4) [OR 0.46, 95% CI 0.26–0.82, p = 0.008], and PE(P-18:2/18:2) [OR 0.57, 95% CI 0.35–0.92, p = 0.02]) were associated with increased mortality risk, whereas higher levels of PE(O-18:0/16:1) [OR 2.69, 95% CI 1.03–7.05, p = 0.044] and TAG48:1-FA16:1 [OR 1.34, 95% CI 1.03–1.74, p = 0.03]) were linked to worse outcomes. Among conventional parameters, INR, urea, creatinine, MELD-Na, and CTP score were significant predictors of mortality (Table 1 ). Table 1 Univariable and Multivariable Logistic Regression Analyses of Plasma Lipids and Clinical Parameters Associated with Mortality in ACLF using the discovery cohort Predictor Univariable Logistic Regression Multivariable Logistic Regression Estimate SE p Odds ratio [95% CI] Estimate SE p Odds ratio [95% CI] LPE(18:2) -0.569 0.23 0.013 0.566 [0.362, 0.887] -0.289 0.54 0.590 0.749 [0.262, 2.140] LPE(20:4) -0.774 0.29 0.008 0.461 [0.260, 0.818] 0.279 0.61 0.650 1.321 [0.397, 4.390] PE(O-18:0/16:1) 0.991 0.49 0.044 2.690 [1.030, 7.054] 2.463 1.04 0.018 11.736 [1.517, 90.780] PE(P-18.2/18.2) -0.569 0.25 0.023 0.566 [0.347, 0.924] -0.349 0.44 0.432 0.706 [0.296, 1.680] TAG48:1-FA16:1 0.291 0.13 0.030 1.338 [1.030, 1.739] 0.631 0.28 0.022 1.879 [1.097, 3.220] TLC 1.08E-04 0.00 0.013 1.000 [1.000, 1.000] INR 1.060 0.39 0.007 2.890 [1.340, 6.230] 1.102 0.74 0.136 3.010 [0.706, 12.840] Urea 0.013 0.01 0.017 1.013 [1.002, 1.024] 0.016 0.02 0.310 1.017 [0.985, 1.050] Creatinine 0.431 0.20 0.035 1.539 [1.032, 2.296] 0.026 0.44 0.954 1.026 [0.434, 2.420] Meld score 0.102 0.05 0.025 1.107 [1.013, 1.211] MELD-NA 0.147 0.05 0.006 1.158 [1.040, 1.286] -0.067 0.13 0.596 0.935 [0.731, 1.200] CTP 0.578 0.20 0.003 1.780 [1.210, 2.623] 0.884 0.39 0.023 2.420 [1.130, 5.180] Accuracy 0.810 Sensitivity 0.833 Specificity 0.786 AUC 0.912 Note. Estimates represent the log odds of "Mortality = 1" vs. "Mortality = 0" AUROC analysis (Supplementary Table S4 ) demonstrated that LPE(20:4) [AUROC 0.729, 95% CI 0.597–0.860, p < 0.001] (Fig. 3 B) and LPE(18:2) [AUROC 0.707, 95% CI 0.568–0.846, p = 0.004] (Fig. 3 C) showed the strongest discriminatory ability among lipids, comparable to MELD-Na [AUROC 0.729, 95% CI 0.596–0.862, p < 0.001] (Fig. 2 H) and MELD [AUROC 0.692, 95% CI 0.551–0.832, p = 0.008] (Supplementary Table S4 ). In addition, PE(O-18:0/16:1) [AUROC 0.674, 95% CI 0.531–0.816, p = 0.017], PE(P-18:2/18:2) [AUC 0.635, 95% CI 0.490–0.779, p = 0.069], and TAG 48:1-FA16:1 [AUC 0.664, 95% CI 0.522–0.807, p = 0.024] also displayed moderate discriminatory performance, though with variable sensitivity and specificity (Fig. 3 B). The CTP score demonstrated the high performance overall [AUC 0.742, 95% CI 0.615–0.870, p < 0.001] (Fig. 3 B). Other parameters such as INR, urea, creatinine, and TLC displayed moderate but significant discriminatory ability (Supplementary Table S4 ). We derived a multivariable logistic regression model for 28-day mortality that included PE(O-18:0/16:1), TAG(48:1/FA16:1) and CTP score. The model is expressed as: $$\:logit\left(p\right)=-66.903+2.463\times\:\left[PE\right(O-18:0/16:1)]+0.631\times\:[TAG48:1-FA16:1]+0.884\times\:[CTP\:score],$$ where $$\:p=\frac{1}{1+\text{e}\text{x}\text{p}\left[-logit\left(p\right)\right]}$$ represents the predicted probability of 28-day mortality. The logit-predicted mortality model (combining PE(O-18:0/16:1), TAG48:1-FA16:1, and CTP score) outperformed all individual markers with an AUROC of 0.912, sensitivity of 83.3% and specificity of 78.6% (Fig. 3 C). Pairwise AUROC comparisons showed no significant differences among lipid predictors or between CTP, MELD, and MELD-Na. However, the combined logit model demonstrated significantly higher discriminatory accuracy compared with LPE(18:2), LPE(20:4), MELD-Na, CTP, and TLC (all p ≤ 0.006), confirming the added predictive value of integrating lipid and clinical parameters (Supplementary Table S5 ). Figure 3 D shows a correlation matrix among the clinical parameters and major plasma metabolites in all subjects, where blue-colored cells indicate a positive correlation and red-colored cells indicate a negative correlation. It was observed that 2 of the lipid species LPE.18:2 and LPE 20:4 correlated negatively with kidney function parameters (urea and creatinine), brain function (hepatic encephalopathy HE grades, OF Brain score), lung function (PaO 2 , FiO 2 ), and clinical scores MELD and MELD-Na (Fig. 3 D). Thus, lowering of these species is associated with multiple organ failure. TAG 48:1/16:1 correlated positively with brain organ failure score (Brain OF) and respiratory organ failure score (Respiration OF). In order to find shared lipid signatures across diseases, we compared our (ACLF1: current study) lipidome with four published cohorts Clària et al. 21 (ACLF2), Mecatti et al. 31 (sepsis), Wu et al. 32 (trauma), and Ciccarelli et al 33 . (COVID-19), using a uniform (carbons:double bonds) nomenclature (SMILES format or Simplified Molecular Input Line Entry System). Fifty-nine species overlapped between ACLF1 and ACLF2, including LPCs (18:1, 18:2), PCs (34:1, 36:3, 36:5), and PIs (38:4), all of which were significantly depleted in both datasets (p < 0.05). Five PCs/PS (34:1, 34:2, 36:1, 36:3; PS 40:1) overlapped with sepsis and showed reduction versus controls (p < 0.05). Fifty-six lipids including LPCs, PCs, PIs, DGs, and PEs overlapped with COVID-19 and showed uniform LPC/PC/PI suppression in severe cases (ANOVA p < 0.001), whereas PEs/DGs rose in COVID-19 but remained depleted in ACLF1. Nineteen PCs, PEs, and PIs overlapped with trauma and were acutely depleted at admission (p < 0.01) with later PE rebound in trauma (p < 0.05) (Supplementary Figure S3 ). A. PLS-DA plot comparing lipidomes of ACLF survivors and non-survivors shows overlapping clusters, with Component 1 (10.4%) and Component 2 (3.7%) explaining lipid and sample variance, respectively. B. AUROC of five mortality-associated lipids (LPE.20:4, LPE.18:2, PE-O 18:0/16:1, PE-P 18:2/18:2, TAG 48:1/FA:16:1 and) comparable to clinical scores (CTP, MELD-Na). C. Combined logit model (PE-O 18:0/16:1, TAG 48:1/16:1, CTP) outperforms clinical scores. D. (Top) Correlation matrix (MetaboAnalyst) showing relationships between 5 mortality-associated lipids and 22 clinical parameters; blue indicates positive, red indicates negative correlations. (Bottom) List of significant correlation coefficients highlighting that LPE.18:2 and LPE.20:4 are negatively associated with markers of kidney, brain, and lung dysfunction, and clinical severity scores (MELD, MELD-Na), while TAG 48:1/16:1 and PE-O 18:0/16:1 correlate positively with organ failure scores and 28-day mortality. Ex vivo lipid stimulation reveals immunomodulatory effects of ACLF-associated lipids on leukocytes To examine whether circulating lipids from ACLF patients exert direct immunomodulatory effects, we performed an ex vivo lipid stimulation assay using leukocytes from healthy donors (n = 15) (Fig. 4 A). Total plasma lipid extracts from ACLF non-survivors were incubated with leukocytes for 24 hours, followed by gene expression analysis by qRT-PCR. The gene expression patterns in pre and post-lipid treated healthy leukocytes were compared with gene expression patterns observed in leukocytes derived from HC vs ACLF (Fig. 4 B, C). Consistent with patient-derived gene expression signatures (Fig. 4 B, C), lipid-treated leukocytes showed an elevated trend in CD177 gene expression (p = 0.08) and marked downregulation of TLR1 (p = 0.02), TLR6 (p < 0.001), TLR7 (p 0.05) (Fig. 4 B). These findings indicate that ACLF-associated lipids impair innate immune receptor expression while selectively driving CD177 induction. To verify lipid uptake and establish concordance with the ACLF mortality lipid signature, we performed LC-MS/MRM on lipid-treated leukocytes and found that the pre- and post- treatment lipid groups were distinct based on their lipidomic- composition (Fig. 4 D). Triacylglycerols (TAGs) were the most discriminatory lipid class enriched in treated cells (Fig. 4 D, Supplementary Table S6 ). Importantly, TAG (48:1/FA16:1) (FC 3.07, p = 0.037), a key component of the validated ACLF mortality-associated lipid signature, was significantly elevated in lipid-treated leukocytes (Fig. 4 D, Supplementary Table S6 ). In addition to TAG(48:1/FA16:1), several other TAG species were markedly elevated in lipid-treated leukocytes, including TAG(48:2/FA18:1) (FC 5.30, p = 0.006), TAG(46:4/FA18:2) (FC 4.95, p = 0.008), and TAG(47:2/FA16:1) (FC 3.99, p = 0.016), confirming incorporation of pathogenic lipid species and supporting their functional role in immune reprogramming. Beyond TAGs, enrichment of ceramides [CER(22:1), FC 4.36, p = 0.012; DCER(24:1), FC 3.06, p = 0.038] and glycerophospholipids such as PG(16:0/22:5) (FC 3.58, p = 0.023) and PA(18:2/16:1) (FC 3.51, p = 0.025) was also observed. Conversely, several ether-linked phosphatidylethanolamines were depleted, including PE(P-16:0/18:0) (FC -3.77, p = 0.020) and PE(O-18:0/18:2) (FC -3.61, p = 0.023), indicating a selective incorporation pattern that mirrors the ACLF mortality-associated lipid signature (Supplementary Table S6 ). Proteomic profiling of ACLF liver, kidney, and PMNs identifies shared degranulation and immune signaling programs To gain mechanistic insights into ACLF related organ failure and immune dysfunction, we first performed descriptive proteomic profiling on post-mortem ACLF liver and kidney biopsies. In total, 2,950 proteins were identified in ACLF liver and 1,232 proteins in ACLF kidney (Supplementary Table_Proteomics). Pathway enrichment analysis showed that the top pathways in liver included purine metabolism, amino acid metabolism, carboxylic acid metabolism, neutrophil degranulation, and small molecule catabolism (Fig. 5 A). In kidney, the most enriched pathways were cellular stress responses, carboxylic acid metabolism, amino acid metabolism, nucleobase metabolism, and neutrophil degranulation (Fig. 5 A). Since neutrophil degranulation (Reactome R-HSA-6798695) was highly enriched in both liver (log10p = 100, 259 proteins; Fig. 5 A) and kidney (log10p = 77.25, 133 proteins; Fig. 5 A), we next profiled circulating PMNs from ACLF patients versus healthy controls. Differential proteomics revealed significantly altered proteins in ACLF PMNs, with neutrophil degranulation emerging as a dominant enriched pathway (log10p = 100; Figs. 5 B,C). (log10p = 100; Fig. 5 C-E). A subset of 711 proteins was shared across ACLF PMN, liver, and kidney proteomes (Fig. 5 D, Supplementary Table_Proteomics). Enrichment analysis of this shared set confirmed neutrophil degranulation as the top pathway as well as cellular responses to stimuli (R-HSA-8953897) and interleukin signaling (R-HSA-449147) (Fig. 5 D). ACLF derived lipids promote cytotoxicity in HEK293T cells Metascape analysis of ACLF tissue proteomes revealed pronounced activation of cellular stress and damage related pathways (Fig. 5 A). In the liver, altered processes included cellular breakdown, purine and carboxylic acid metabolism, carbon metabolism, and vesicle-mediated transport, along with enrichment in neutrophil degranulation, VEGFA-VEGFR2 signaling, prion disease, and cytoskeletal remodeling, which are changes signaling heightened inflammatory and metabolic stress driving tissue injury. The ACLF kidney proteome demonstrated a stress-adaptive profile, marked by the following pathways -enhanced responses to chemical stress, mitochondrial protein degradation, protein folding, detoxification, and branched-chain amino acid catabolism. These signatures pointed towards underlying mitochondrial dysfunction, impaired protein quality control, and sustained inflammatory pressure in ACLF kidneys, converging on a state of cellular stress and cytotoxicity that likely accelerates renal injury and loss of cell viability. We next evaluated the cytotoxic potential of circulating lipids isolated from ACLF patients in comparison with those from healthy controls (HC). Towards this, HEK-293T cells were exposed to a graded series of lipid concentrations (100, 10, 1, 0.1, 0.01, and 0.001 mg/mL) derived from either ACLF or HC plasma, and cell viability was quantified using the MTT assay. Each condition was tested in duplicate across five independent experiments. We observed that HC-derived lipids tended to preserve cell viability and, in some instances, appeared to promote proliferation, whereas ACLF-derived lipids consistently exerted a cytotoxic effect (Fig. 5 E). Specifically, cells treated with ACLF lipids showed significantly reduced viability compared to both untreated controls and HC lipid-treated cells. This effect was most pronounced at 1 mg/mL, indicating a dose-sensitive impact of pathological lipid species on cellular health. In Silico Analysis Suggests ACLF Lipids Modulate Pathways via PPARγ Interaction To identify regulatory drivers, we integrated differential expression data from the published ACLF PMN transcriptome with the current PMN proteome¹¹. MAPK kinases were prominently enriched; transcriptomic DEGs showed regulation of MAP2K6, MAPK14, MAPK1, and MAP3K14 (Supplementary Table S7 ), with BMX most upregulated (p < 0.001, logFC 3.52) and PRKCQ most downregulated (p < 0.0001, logFC − 4.23). Proteomic DEPs included MAP2K1, MAPK3, MAPK14, and MAPK1; among PRKC family members, PRKCB was upregulated (p < 0.005, logFC 2.95) and CSNK2A1 downregulated (p < 0.05, logFC − 2.03) (Supplementary Table S7 ). Among transcription factors, PPARγ (p < 0.01, logFC 2.44), CEBPD (p < 0.01, logFC 1.76), and CEBPB (p < 0.03, logFC 1.35) were upregulated, while GATA1 (p < 0.05, logFC − 1.61) and GATA2 (p < 0.0001, logFC − 3.23) were downregulated (Supplementary Table S7 ). Integrated analysis highlighted two kinase-centered modules: MAPK1-regulated genes (ZMIZ1, E2F6, STAT5A, BHLHE40, USF1, MAX, CEBPD) and MAPK14-regulated genes (SRF, CTCF, ZBTB33, STAT5A, BRCA1, GATA2, SMC3, RAD21, ATF2, FLI1), all upregulated in ACLF PMNs (Supplementary Figure S4 ). To further explore the transcriptional regulation of genes involved in lipid sensing, immune response, and stress signaling in the context of acute-on-chronic liver failure (ACLF), we analyzed the promoter regions of key target genes for potential regulatory elements (Supplementary Tables S8). Given the central role of peroxisome proliferator-activated receptor gamma (PPARγ) in lipid binding, metabolism and inflammation, we specifically searched for PPARγ binding motifs using the JASPAR transcription factor database 34 . Motif scanning was performed for the PPARγ transcription factor binding motif JASPAR CORE vertebrate s transcription factor motif library with a cut-off p-value < 0.001 in Eukaryotic promoter database (EPD). Further, we explored the potential of PPARγ to bind gene targets known to be upregulated in our datasets or in previously reported genes known to be associated with ACLF pathogenesis, by investigating the presence of PPARγ binding sites in these target genes. Genes of interest included MAPK1, MAPK14, FFAR3 (upregulated in ACLF PMN DEPs), CD177 (previously shown to be highly induced in ACLF), multiple TLRs, and metallothioneins (MT1, MT2, MT3), which are strongly expressed in ACLF patients with organ dysfunction 35 . PPARγ binding sites were identified in the promoters of all these genes, suggesting regulatory potential in response to lipid activation (Supplementary Table S8 ). Because neutrophil degranulation emerged as a dominant cross-compartment pathway (see above), we examined the entire shared protein set from liver, kidney, and ACLF PMN proteomes. Of the 129 proteins in the neutrophil degranulation pathway, 96 were encoded by genes containing PPARγ promoter binding sites (Supplementary Table S9 ) We further investigated interactions between ACLF-associated lipids and PPARγ using in silico molecular docking (Fig. 6 A-D). A total of 82 lipids that mapped to all known isomers of the 30 validated differential lipid species discriminating between ACLF S and ACLF NS were selected for analysis (Supplementary Table S10 ). Docking studies were conducted using the X-ray co-crystal structure of PPARγ bound to garcinoic acid (GA) (PDB ID: 7AWD) (Supplementary Figure S5 ). Prior to docking, GA was removed from the co-crystal structure, and non-covalent docking of the ACLF-associated lipids was performed at both the orthosteric and allosteric binding pockets. Overall, the lipids demonstrated better docking scores at the orthosteric site. We selected the ligands with the highest docking scores (Fig. 6 A) and presented their chemical structures (Fig. 6 C). Notably, compounds L9B, L9D, and L25 are glycerol-based phosphoesters with long-chain unsaturated hydrocarbon tails, while L26 is an allo-inositol conjugated glycerol-based phosphoester (Fig. 6 C). For the glycerol derivatives, whether all three hydroxyl groups are fully substituted or the specific substitution pattern appears to have minimal impact on the docking outcomes. Ribbon diagrams of the top docking poses revealed that these compounds occupy both the orthosteric and allosteric sites of PPARγ, which may be attributed to the long hydrocarbon chains of these phospholipids (Fig. 6 B). The docking studies show that L26 (-10.76), L9B (-9.42) and L25 (-9.36) had comparable docking scores to GA (-10.24), which is summarized in Supplementary Figure S5 , Supplementary Table S10 .This suggested that the lipids could effectively bind to and modulate PPARγ function, thus providing a molecular basis for the observed effects in these patients. To gain insights into the structural relationships of these lipids, we conducted cluster analysis with the 82 lipids (Fig. 6 D). Molecular fingerprints (e.g., Morgan fingerprints) were calculated for all compounds based on their SMILES using RDKit, and pairwise Tanimoto similarity coefficients were computed to obtain a similarity matrix. This similarity information was then used to visualize the structural relatedness among the compounds, providing an overview of how structurally similar lipids group together. The compounds with the best docking scores, as shown in Fig. 6 A, were highlighted in the circular dendrogram (Fig. 6 D). Based on the dendrogram, L9B, L9D, L25, and L26 were classified into three distinct clusters, with L9B and L9D showing strong structural similarity (Fig. 6 D). DISCUSSION In this study, we demonstrated that circulating lipids in acute-on-chronic liver failure (ACLF) are not passive metabolic intermediates but active mediators with prognostic and mechanistic significance. The plasma lipidome of ACLF patients was characterized by consistent depletion of polyunsaturated lysophospholipids and accumulation of monounsaturated glycerophospholipids and triacylglycerols (Fig. 2 ). This shift mirrored patterns described in sepsis, trauma, and COVID-19, suggesting a convergent host response to critical illness (Supplementary Figure S3 ). Using a discovery cohort followed by a fully independent validation cohort, we identified a minimal lipid signature [LPE(18:2), LPE(20:4), PE(O-18:0/16:1), TAG(48:1/16:1), and PE(P-18:2/18:2)] that reproducibly discriminated survivors from non-survivors and correlated with multi-organ failure parameters (Fig. 3 ). Multivariable logistic regression revealed that PE(O-18:0/16:1) and TAG(48:1/FA16:1) were independent predictors of 28-day mortality, comparable in discriminatory performance to established clinical scores such as MELD-Na and CTP (Table 1 , Supplementary Table S4 , Supplementary Table S5 ). This positions circulating plasma lipids as potential biomarker candidates that can augment current risk stratification tools. Given these prognostic associations, it is important to consider the biological and analytical features that support the use of lipids as clinical biomarkers; from this perspective, our findings reinforce the strength of lipidomic signatures as clinically relevant indicators of disease severity in ACLF. Lipids exhibit several inherent advantages as biomarkers e.g., they are chemically stable in biofluids, reflect integrated metabolic and inflammatory processes, and can be quantified with high analytical precision using targeted LC-MS workflows 36 , 37 . In conditions like ACLF, where clinical scores may already indicate advanced organ failure at presentation, circulating lipid species may capture upstream immune-metabolic perturbations that precede overt organ failure. The observation that two lipid species (PE(O-18:0/16:1) and TAG(48:1/FA16:1)) remained independent predictors even after adjustment for clinical variables. Moreover, the reproducibility of the five-lipid signature across an independent validation cohort underscores its translational robustness and supports future development of lipid-based prognostic assays in ACLF. The multivariable logistic model integrating PE(O-18:0/16:1), TAG(48:1/FA16:1), and the CTP score demonstrated substantially enhanced discriminatory accuracy for 28-day mortality (AUROC 0.912), outperforming all individual lipid predictors and routinely used clinical indices. This finding has several mechanistic and clinical implications. First, the two lipid species included in the model likely capture complementary aspects of ACLF biology: PE plasmalogens reflect oxidative stress, membrane remodeling, and immune-metabolic imbalance, whereas the TAG(48:1/FA16:1) species may index dysregulated lipid mobilization and altered hepatic energy flux 38 , 39 . Their independent contributions within the model suggest that lipidomic perturbations convey prognostic information that is not covered by MELD-Na, CTP, or other clinical scores, that largely quantify manifest organ dysfunction. By incorporating these mechanistically anchored biomarkers with an established clinical parameter, the model provides a more holistic representation of ACLF pathophysiology. From a translational standpoint, this result supports the potential of lipidomics as a clinically actionable adjunct to existing prognostic tools. The ability to quantify lipid species with high precision using rapid, targeted LC-MS workflows also raises the possibility that lipid-augmented models could be deployed in real-time triage, trial enrichment, or targeted therapeutic stratification. Clinically, such an integrated approach may help identify high-risk patients earlier, guide escalation of organ support, and refine selection for liver transplantation or immuno-metabolic interventions. The role of lipids in ACLF pathophysiology has remained largely underexplored, despite recognition that immune dysregulation and systemic inflammation drive organ failure 22 , 40 , 41 . Our ex vivo experiments directly linked circulating lipids derived from ACLF non-survivors to immunomodulatory effects on healthy leukocytes, reproducing transcriptomic features of ACLF immune responses (Fig. 4 ). Specifically, lipid treatment induced upregulation of CD177, a neutrophil activation marker consistently elevated in ACLF, while suppressing multiple TLRs critical for innate immune sensing, recapitulating an ACLF-like phenotype (Fig. 4 ). These findings provide mechanistic evidence that circulating lipids have the ability to reprogram immune function, potentially contributing to the paradoxical state of hyperinflammation and immune paresis that are the hallmarks of ACLF. Lipidomic uptake experiments further demonstrated enrichment of triacylglycerols (TAGs) within leukocytes, particularly TAG(48:1/FA16:1), a key component of the validated mortality-associated lipid signature (Fig. 4 D). This confirmed not only the incorporation of ACLF-lipid species into immune cells but also their alignment with disease-linked metabolic profiles, reinforcing the notion that circulating lipids act as functional effectors rather than inert biomarkers. Proteomics analyses extended these observations to tissue and cellular compartments, revealing shared signatures of neutrophil degranulation across liver, kidney, and circulating PMNs (Fig. 5 ). The enrichment of this pathway across multiple organs suggests a systemic degranulation program operative in ACLF, with implications for parenchymal injury and multiorgan failure. Integrated transcriptome-proteome analyses identified MAPK1 and MAPK14-centered kinase modules as critical regulatory hubs, linking lipid-induced signaling to transcriptional programs in neutrophils (Supplementary Figure S4 ). Transcription-factor analysis revealed PPARγ as a prominent upstream regulator, with high-confidence binding motifs in MAPKs, TLRs, metallothioneins, and CD177- key genes linked to ACLF pathophysiology (Supplementary Table S8 , Supplementary Table S9 , Supplementary Figure S4 )38. These analyses placed PPARγ at the intersection of lipid metabolism, immune regulation, and stress response, providing a unifying mechanism through which lipid mediators may orchestrate ACLF pathology. Clinically, these results suggest that lipidomic profiling, which is validated here across independent cohorts, could complement MELD, MELD-Na, and CTP scores in refining prognostication, while also highlighting novel therapeutic avenues. Targeting lipid metabolism, blocking pathogenic lipid uptake, or modulating PPARγ centered signaling pathways may represent promising strategies to attenuate immune dysregulation and improve outcomes in ACLF. We acknowledge several limitations. Although two temporally independent ACLF cohorts were included, the overall sample size remains modest, and larger multicenter studies are needed to confirm the stability and generalizability of the lipid-based mortality signature. Our lipidomic analyses were performed on plasma and therefore do not fully capture tissue-specific lipid pools or compartmentalized lipid signaling in ACLF. Ex vivo assays were restricted to healthy donor leukocytes and may not fully recapitulate the complex inflammatory and immunometabolic milieu present in ACLF patients. In addition, the high dimensionality of the lipidomics dataset relative to the number of mortality events introduces a potential risk of overfitting, despite the use of penalized regression and false-discovery rate correction. The limited external performance of lipid-only penalized models also suggests underlying cohort heterogeneity and highlights the need for more robust feature-selection strategies. Finally, although we identify strong associations and mechanistic plausibility, including TLR suppression, neutrophil activation, and PPARγ-lipid interactions, interventional studies will be required to establish definitive causality. Future work should incorporate longitudinal lipidomics, functional assays using ACLF-derived immune cells, and therapeutic modulation in preclinical models to validate these findings. In conclusion, our findings provide a mechanistic framework that bridges metabolic derangements with immune dysfunction and organ failure in ACLF. They extend prior observations of systemic inflammation and immune paralysis by identifying specific lipid classes as potential drivers of some of these processes. The dual role of lipids, as early biomarkers and as biological effectors that shape immune phenotypes, positions them uniquely within ACLF biology and strengthens their relevance for both risk prediction and therapeutic exploration. Abbreviations ACLF Acute-on-Chronic Liver Failure ALT Alanine Aminotransferase ANOVA Analysis of variance AST Aspartate Aminotransferase AUROC Area under the Receiver Operating Characteristic Curve CARS Compensatory anti-inflammatory response syndrome CE Cholesteryl ester CER Ceramide CHB Chronic hepatitis B CLD Chronic Liver disease CLIF-C Chronic liver failure consortium organ failure CTP Child-Turcotte-Pugh DAG Diacylglycerides DAMP Damage-associated molecular patterns EASL European Association for the Study of the Liver EASL-CLIF European Association for the Study of the Liver-Chronic Liver Failure Consortium EDTA Ethylene diamine tetra acetic acid GA Garcinoic acid HC Healthy control HE Hepatic encephalopathy HEK293T Human Embryonic Kidney 293T Cells INR International Normalized Ratio LPC Lysophosphatidic acid LPE Lysophosphatidyl ethanolamine LPG Lysophosphoglycerol LPI Lysophosphatidyl inositol LPS Lipopolysaccharide LPS (lipid) Lysophosphatidyl serine MAG Monoacylglycerides MAPK Mitogen-Activated Protein Kinase MELD Model for end-stage liver disease MELD-NA Model for end-stage liver disease-Sodium MOF Multiple organ failure MRM Multiple reaction monitoring MTT 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide NET Neutrophil extracellular traps NLR Neutrophil to lymphocyte ratio OPLS4 Optimized Potentials for Liquid Simulations, Version 4 PA Phosphatidic acid PAMP Pathogen associated molecular patterns PC Phosphatidyl choline PE Phosphatidyl ethanolaminutee PI Phosphatidyl inositol PLS-DA Partial least squares-discriminant analysis PMN Polymorphonuclear PPARγ Peroxisome Proliferator-Activated Receptor Gamma PS Phsophatidyl serine PUFA Polyunsaturated fatty acid qRT-PCR Quantitative Reverse Transcription Polymerase Chain Reaction QTRAP Quadropule Ion Trap SAP Serum Alkaline Phosphatase SIRS Systemic inflammatory response syndrome SM Sphingomyelin SMILES Simplified Molecular Input Line Entry System SOFA Sequential Organ Failure Assessment TLC Thin Layer Chromatography TLR Toll-like Receptor VEGFA-VEGFR Vascular Endothelial Growth Factor A – Vascular Endothelial Growth Factor Receptor VIP Variable importance projection Declarations Declarations Ethics approval and consent to participate The study has been approved by the All India Institute of Medical Sciences, New Delhi ethics committee [Reference No. IEC/473/9/2016 and, IEC/369/7/2016]. All procedures are as per the declaration of Helsinki. All participants included in the study were > 18 years of age and were recruited in the study after informed consent. Consent for publication This manuscript does not contain any individual person’s data in any form, including images, videos, or identifiable clinical details. All patient information used in this study was fully de-identified prior to analysis. Therefore, written informed consent for publication was not required as per journal guidelines. Competing interests The authors declare that they have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Funding The work carried out in this study was supported by the Anusandhan National Research Foundation (ANRF)-POWER grant, Government of India (Grant no. SPG/2021/002780), ANRF-Core research grant, Government of India (Grant no. CRG/2022/006016) and the All India Institute of Medical Sciences New Delhi Intramural Grant (A-565). We acknowledge support for the Schrodinger license obtained through UNMC Vice Chancellor for Research. Author Contribution R.S., S.M., N.G.: Methodology, Data curation, Investigations, Validation, Formal analysis, Writing- Original draft preparation, Writing- Review and Editing; P.S., R.U., M.R.: Methodology, Investigations, Validation, Formal analysis; W.W., S.S.P., K.M., G.M., S.P.: Formal analysis, Visualization, Writing-Original draft preparation; Y.G. : Data curation, Formal analysis; A.N., V.S.: Conceptualization, Formal Analysis, Visualization, Writing- Original Draft Preparation; S.B., S.: Investigation, Resources, Data Curation, Writing- Original draft preparation; P.A.: Conceptualization, Methodology, Visualization, Data curation, Funding acquisition, Resources, Project administration, Supervision, Writing- Original draft preparation, Writing- Review and Editing. Acknowledgement The authors sincerely thank Dr. Siddhartha Kundu, Associate Professor, Department of Biochemistry, for his timely guidance on the handling of lipid chemical formulae and for valuable inputs on the PPARγ-focused analyses. Data Availability This study generated datasets as part of its analyses. All data supporting the findings of this study are available within the paper and its supplementary files. Any additional information or clarifications regarding the datasets will be made available by the corresponding author upon reasonable request.In addition to our primary dataset, we analysed publicly available lipidomics datasets from four previously published cohorts to identify shared and disease-specific lipid signatures. These datasets were obtained from:1. Claría, J., Curto, A., Moreau, R., Colsch, B., López-Vicario, C., Lozano, J. J., Aguilar, F., Castelli, F. A., Fenaille, F., Junot, C., Zhang, I., Vinaixa, M., Yanes, O., Caraceni, P., Trebicka, J., Fernández, J., Angeli, P., Jalan, R., & Arroyo, V. (2021). Untargeted lipidomics uncovers lipid signatures that distinguish severe from moderate forms of acutely decompensated cirrhosis. Journal of hepatology , 75 (5), 1116–1127. https:/doi.org/10.1016/j.jhep.2021.06.0432. Mecatti, G. C., Messias, M. C. F., & de Oliveira Carvalho, P. (2020). Lipidomic profile and candidate biomarkers in septic patients. Lipids in health and disease , 19 (1), 68. https:/doi.org/10.1186/s12944-020-01246-23. Wu, J., Cyr, A., Gruen, D. S., Lovelace, T. C., Benos, P. V., Das, J., Kar, U. K., Chen, T., Guyette, F. X., Yazer, M. H., Daley, B. J., Miller, R. S., Harbrecht, B. G., Claridge, J. A., Phelan, H. A., Zuckerbraun, B. S., Neal, M. D., Johansson, P. I., Stensballe, J., Namas, R. A., … PAMPer study group (2022). Lipidomic signatures align with inflammatory patterns and outcomes in critical illness. Nature communications , 13 (1), 6789. https:/doi.org/10.1038/s41467-022-34420-44. Ciccarelli, M., Merciai, F., Carrizzo, A., Sommella, E., Di Pietro, P., Caponigro, V., Salviati, E., Musella, S., Sarno, V. D., Rusciano, M., Toni, A. L., Iesu, P., Izzo, C., Schettino, G., Conti, V., Venturini, E., Vitale, C., Scarpati, G., Bonadies, D., Rispoli, A., … Campiglia, P. (2022). Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy). Journal of pharmaceutical and biomedical analysis , 217 , 114827. https:/doi.org/10.1016/j.jpba.2022.114827These publicly available datasets were used strictly for comparative analysis and remain accessible through their respective publications. References Solé C, Solà E. Update on acute-on-chronic liver failure. Gastroenterol Hepatol. 2018;41(1):43–53. Arroyo V, Moreau R, Kamath PS, Jalan R, Ginès P, Nevens F, et al. Acute-on-chronic liver failure in cirrhosis. 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Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy). J Pharm Biomed Anal. 2022;217:114827. Fornes O, Castro-Mondragon JA, Khan A, van der Lee R, Zhang X, Richmond PA, et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2020;48(D1):D87–92. Acharya P, Saha R, Quadri JA, Sarwar S, Khan MA, Sati HC, et al. Quantitative plasma proteomics identifies metallothioneins as a marker of acute-on-chronic liver failure associated acute kidney injury. Front Immunol. 2023;13:1041230. Wenk MR. The emerging field of lipidomics. Nat Rev Drug Discov. 2005;4(7):594–610. Reis GB, Rees JC, Ivanova AA, et al. Stability of lipids in plasma and serum: Effects of temperature-related storage conditions on the human lipidome. J Mass Spectrom Adv Clin Lab. 2021;22:34–42. Bozelli JC Jr, Azher S, Epand RM. Plasmalogens and Chronic Inflammatory Diseases. Front Physiol. 2021;12:730829. Alves-Bezerra M, Cohen DE. Triglyceride Metabolism in the Liver. Compr Physiol. 2017;8(1):1–8. Chiurchiù V, Leuti A, Maccarrone M. Bioactive Lipids and Chronic Inflammation: Managing the Fire Within. Front Immunol. 2018;9:38. Masoodi M, Eiden M, Koulman A, Spaner D, Volmer DA. Comprehensive lipidomics analysis of bioactive lipids in complex regulatory networks. Anal Chem. 2010;82(19):8176–85. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableProteomics.xlsx SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTableS1.docx SupplementaryTableS2.docx SupplementaryTableS3.docx SupplementaryTableS4.docx SupplementaryTableS5.docx SupplementaryTableS6.docx SupplementaryTableS7.docx SupplementaryTableS8.docx SupplementaryTableS9.docx SupplementaryTableS10.docx SupplementaryMethodsFile.docx SupplementaryFigureS1.docx SupplementaryFigureS2.docx SupplementaryFigureS4.docx SupplementaryFigureS3.docx SupplementaryFigureS5.docx MIQEguidelines1.docx MIQEguidelines2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":245438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of the study design investigating the association of circulating lipids with mortality and pathophysiology in acute-on-chronic liver failure (ACLF).\u003c/strong\u003e \u0026nbsp;The figure shows experimental framework employed to investigate lipid-mediated neutrophil dysfunction in Acute-on-Chronic Liver Failure (ACLF). The study utilized plasma samples, polymorphonuclear cells (PMNs), and tissue specimens from ACLF patients and healthy controls across lipidomics, proteomics, and qRT-PCR assays. Plasma lipid profiling was conducted in discovery (ACLF \u003cem\u003en\u003c/em\u003e = 58; Healthy \u003cem\u003en\u003c/em\u003e= 29) and validation cohorts (ACLF \u003cem\u003en\u003c/em\u003e = 40; Healthy \u003cem\u003en\u003c/em\u003e = 10), with subgroup analyses of survivors and non-survivors. Total cell lysates from PMNs (\u003cem\u003en\u003c/em\u003e = 3 per group) and post-mortem liver and kidney tissue samples (\u003cem\u003en\u003c/em\u003e= 9 ACLF) were processed for proteomic analysis. For baseline leukocyte gene expression profiling, RNA was extracted from total leukocytes (\u003cem\u003en\u003c/em\u003e = 38 ACLF, 35 controls) to evaluate immune gene expression. For \u003cem\u003eex vivo\u003c/em\u003e model healthy leukocytes were incubated with lipids derived from ACLF and control plasma, followed by RNA extraction (\u003cem\u003en\u003c/em\u003e = 15 controls). For an \u003cem\u003ein vitro\u003c/em\u003e model of cellular injury, HEK293T cells were treated with ACLF or healthy plasma lipids and subjected to MTT viability assays (\u003cem\u003en\u003c/em\u003e = 5). \u003cstrong\u003eAnalytical methods:\u003c/strong\u003e Targeted lipidomics included peak mapping (MultiQuant MQ4), batch correction (MissForest, SVA, Boruta), and multivariate analysis (PLS-DA, VIP scoring, correlation heatmaps) via MetaboAnalyst. Proteomic data were analyzed using Proteome Discoverer v2.5 with UniProt Human Database and Metascape for pathway enrichment. Neutrophil phenotype was evaluated using qRT-PCR targeting neutrophil degranulation (\u003cem\u003eCD177\u003c/em\u003e, \u003cem\u003eELANE\u003c/em\u003e) and Toll-like receptor (\u003cem\u003eTLR1–10\u003c/em\u003e) genes.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/565fb46a82250b03ded171d5.jpeg"},{"id":97270414,"identity":"721d32e6-b7ba-4fee-a151-0e7295867749","added_by":"auto","created_at":"2025-12-02 14:54:46","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":395441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTargeted plasma lipidomic profiling distinguishes ACLF survivors from non-survivors and healthy controls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Overall study design of the lipidomics analysis with two independent cohorts: discovery and a validation cohort of ACLF S, NS and HC. \u003cstrong\u003eB.\u003c/strong\u003e PLS-DA (partial least squares-discriminant analysis) plot showing distinct clustering of HC, ACLF S and NS in discovery cohort; VIP (variable importance projection) plot highlighting top discriminatory lipid species. \u003cstrong\u003eC.\u003c/strong\u003e Heatmap of 30 validated lipids in an independent ACLF cohort.\u003cstrong\u003e D.\u003c/strong\u003e Box-and-whisker plots depicting log-normalized peak area intensity of major lipid classes in ACLF: lysophospholipids, glycerolipids, phosphatidylethanolamines, and phospholipids. *p-value ≤0.05, **p-value ≤ 0.01, ***p-value ≤ 0.001, ****p-value ≤ 0.0001\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/3a1482679e5318e19b32d5a2.jpeg"},{"id":97270418,"identity":"47822be3-d608-42ff-a0d2-30f6bced47ce","added_by":"auto","created_at":"2025-12-02 14:54:46","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":470715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic value of mortality-associated lipids in ACLF.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e PLS-DA plot comparing lipidomes of ACLF survivors and non-survivors shows overlapping clusters, with Component 1 (10.4%) and Component 2 (3.7%) explaining lipid and sample variance, respectively. \u003cstrong\u003eB.\u003c/strong\u003e AUROC of five mortality-associated lipids (LPE.20:4, LPE.18:2, PE-O 18:0/16:1, PE-P 18:2/18:2, TAG 48:1/FA:16:1 and) comparable to clinical scores (CTP, MELD-Na). \u003cstrong\u003eC.\u003c/strong\u003e Combined logit model (PE-O 18:0/16:1, TAG 48:1/16:1, CTP) outperforms clinical scores. \u003cstrong\u003eD.\u003c/strong\u003e(Top) Correlation matrix (MetaboAnalyst) showing relationships between 5 mortality-associated lipids and 22 clinical parameters; blue indicates positive, red indicates negative correlations. (Bottom) List of significant correlation coefficients highlighting that LPE.18:2 and LPE.20:4 are negatively associated with markers of kidney, brain, and lung dysfunction, and clinical severity scores (MELD, MELD-Na), while TAG 48:1/16:1 and PE-O 18:0/16:1 correlate positively with organ failure scores and 28-day mortality.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/6518d2ecc12dff952ccf6eaa.jpeg"},{"id":97368959,"identity":"ed61b8e6-c8b4-4821-b5e5-b1c928371520","added_by":"auto","created_at":"2025-12-03 16:23:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":788305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eACLF lipids modulate leukocyte gene expression.\u003c/strong\u003e A. Experimental design for lipid-immune assays. B. Expression of neutrophil-associated genes CD177 and ELANE (neutrophil elastase) was elevated in ACLF samples compared with HC (CD177, \u003cem\u003ep\u003c/em\u003e = 0.0002; ELANE, \u003cem\u003ep\u003c/em\u003e = 0.0317) (Top). Post lipid incubation, there was an upregulation of CD177 gene expression in healthy leucocytes (p=0.08) (Bottom). C. Gene expression of Toll-like receptors in ACLF: TLR1, TLR3, TLR6, TLR7, TLR9, and TLR10 were significantly downregulated (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 except TLR7 and TLR9, \u003cem\u003ep\u003c/em\u003e = 0.0003; TLR6, \u003cem\u003ep\u003c/em\u003e= 0.0017), while TLR4 was upregulated (\u003cem\u003ep\u003c/em\u003e = 0.050). TLR2, TLR5, and TLR8 showed no significant differences (Top). ACLF derived lipid treatment of HC leukocytes induced significant downregulation of TLR1 (p=0.02), TLR6 (p\u0026lt;0.001), TLR7 (p\u0026lt;0.01), TLR8 (p=0.04) and TLR 10 (p=0.01), mirroring ACLF-like conditions (Bottom). D. Intracellular lipid remodeling in HC leukocytes after ACLF lipid exposure was validated by LC-MS/MS. Partial least squares-discriminant analysis (PLS-DA) demonstrated clear separation between lipid-treated and untreated cells (Left). Heatmap shows enrichment of triacylglycerol (TAG) species in lipid-treated leukocytes, particularly TAG (48:1/16:1) indicating the incorporation of ACLF-mortality associated lipid as a potential immune-modulator (Right).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/afb8234a33ab985e91daeb07.png"},{"id":97367426,"identity":"5bab0636-2f2a-4b7e-801d-fc5da8a01f65","added_by":"auto","created_at":"2025-12-03 16:18:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":533552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNeutrophil degranulation as a systemic immune signature in ACLF. \u003c/strong\u003eA. Pathway enrichment in ACLF liver and kidney proteomes highlights neutrophil degranulation (R-HSA-6798695) alongside stress and metabolic pathways. Bars represent –log₁₀(\u003cem\u003ep\u003c/em\u003e-value) of enrichment. B. PCA separates HC (red) from ACLF (purple), with volcano plot showing significantly altered proteins (|log₂FC| \u0026gt; 1, adj. p \u0026lt; 0.05). C. Circos plot links altered proteins to enriched processes, with strong enrichment of neutrophil degranulation and additional pathways (tRNA aminoacylation, Rho GTPase signaling, phagocytosis). D. Venn diagram shows 711 core proteins shared across PMN, liver, and kidney, enriched for neutrophil degranulation, indicating a systemic signature. E. ACLF-derived lipids reduce HEK293 cell viability compared to healthy lipids.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/984b89af3a934cc6faadb333.jpeg"},{"id":97368102,"identity":"a88bd620-18df-4adf-8b2f-05ac5b109ef9","added_by":"auto","created_at":"2025-12-03 16:21:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":493875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIn silico\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e docking of ACLF lipids to PPARγ.\u003c/strong\u003e (A,B) Top four compounds with best docking scores shown in ribbon diagram with PPARγ (gray ligands). (C) Chemical structures of the four compounds. (D) Circular dendrogram of 80 lipid species (Jaccard similarity) highlights top-scoring lipids.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/67d6953a63b08832577482d2.jpeg"},{"id":97664594,"identity":"cd786267-d139-4cb8-bf94-e1c3108a0086","added_by":"auto","created_at":"2025-12-08 09:11:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4415422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/626fe1b0-86af-4bfc-b4b3-542e3267bcce.pdf"},{"id":97367459,"identity":"4b4779ad-67f0-4b6f-99a3-2d0b34ad1dc2","added_by":"auto","created_at":"2025-12-03 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16:22:25","extension":"docx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":980753,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/0f02887d48bc94058b9c1266.docx"},{"id":97368441,"identity":"b6e46630-e767-40e9-a2da-c78609042271","added_by":"auto","created_at":"2025-12-03 16:22:17","extension":"docx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":25057,"visible":true,"origin":"","legend":"","description":"","filename":"MIQEguidelines1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/d194514508bc414f68c3cb73.docx"},{"id":97270460,"identity":"cebefb55-c472-4306-a887-f0ace148d25a","added_by":"auto","created_at":"2025-12-02 14:54:47","extension":"docx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":20163,"visible":true,"origin":"","legend":"","description":"","filename":"MIQEguidelines2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8154019/v1/c2985eea07cb416198aec217.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Circulating Lipids Predict Mortality and Drive Immune Gene Regulation in Acute-on-Chronic Liver Failure","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAcute-on-Chronic Liver Failure (ACLF) is a complication of cirrhosis, with a very high short-term mortality of ~\u0026thinsp;50%\u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. ACLF is characterized by profound metabolic derangements and exaggerated but dysfunctional innate immune activation, increased susceptibility to bacterial infections and, multi-organ failure (MOF)\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A heightened neutrophil-to-lymphocyte ratio (NLR) and expansion of neutrophil subsets have been demonstrated in ACLF and linked to patient outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSeveral classes of circulating mediators have been implicated in inflammation and MOF in other systems, including cytokines such as TNF-α, IL-1β, and IL-6, reactive oxygen and nitrogen species (ROS/RNS), components of the complement system, platelet-derived factors contributing to disseminated intravascular coagulation; and bacterial products derived from infections\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These mediators, often triggered by acute insults such as sepsis or trauma, drive systemic inflammatory responses that damage organs through mechanisms including hypoxia, oxidative stress, endothelial permeability, and microvascular dysfunction\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, beyond these established inflammatory mediators, other molecular effectors remain poorly understood. Among these potential effectors, lipids are uniquely positioned at the intersection of metabolism and immunity, functioning both as structural components and as signalling molecules\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Yet, despite their recognized roles in regulating inflammation in other critical illnesses, the circulating lipidome has been largely overlooked in ACLF, with only a single prior study reporting broad alterations compared to compensated cirrhosis and healthy controls\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe hypothesized that alterations in the plasma lipidome of ACLF patients are linked to clinical outcomes and could complement existing prognostic tools. Understanding the prognostic performance of lipids offers an opportunity to refine risk stratification strategies while also shedding light on their broader significance in the clinical trajectory of ACLF. We further postulated that circulating lipids contribute mechanistically to ACLF pathogenesis, particularly through regulation of immune cell phenotypes.\u003c/p\u003e\u003cp\u003eTo address this, we performed comprehensive targeted lipidomic profiling in discovery and validation cohorts and analyzed these data in context of proteomic datasets to explore biological relevance. We compared our lipidome to other published lipidomes of ACLF, COVID 19, Sepsis and trauma to identify similarities and differences. We further investigated the biological and mechanistic relevance of ACLF circulating lipids using a combination of \u003cem\u003ein silico\u003c/em\u003e and experimental approaches. Together, our investigations aimed to uncover the prognostic and mechanistic relevance of lipids in the pathogenesis of ACLF.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eAn overall scheme of the study design and work plan are provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study strictly adhered to the tenets of the Declaration of Helsinki and was approved by the institutional ethics committee (Ethics Committee Ref. No. IEC-630/07.12.2018, RP-36/2018, and IEC-145/11.04.2023). The European Association for the Study of the Liver-Chronic Liver Failure (EASL-CLIF) definition was used for the diagnosis of ACLF\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The EASL definition includes patients with acute decompensation of cirrhosis (CLD-AD), and classifies them as ACLF based on the CLIF-Organ Failure (OF) scoring assigned for 6 organ systems (liver, kidney, brain, coagulation, circulation, and respiration). This definition takes into account non-hepatic organ failure, such as kidney and brain for ACLF classification and importance is given to severity (ACLF grades) and association with short-term mortality (28-day). The following parameters are used for classifying each organ failure, (i) Liver- bilirubin\u0026thinsp;\u0026ge;\u0026thinsp;12 mg/dL; (ii) Kidney-creatinine\u0026thinsp;\u0026ge;\u0026thinsp;2 mg/dL or requiring renal replacement therapy; (iii) Brain- West Haven grade\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e III/IV for hepatic encephalopathy (HE); (iv) Coagulation-INR\u0026thinsp;\u0026ge;\u0026thinsp;2.5; (v)Circulatory- requiring vasopressors; and Respiratory-PaO2/FiO2\u0026thinsp;\u0026le;\u0026thinsp;200 or SpO2/FiO2\u0026thinsp;\u0026le;\u0026thinsp;214.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Size\u003c/h2\u003e\u003cp\u003eAn expected 28-day mortality rate of ~\u0026thinsp;50% in ACLF guided the inclusion of 100 patients recruited across two independent time periods (60 and 40 patients, respectively), together with 40 healthy controls. This sample size provides\u0026thinsp;~\u0026thinsp;80% power to detect moderate-to-large differences between survivors and non-survivors (Cohen\u0026rsquo;s d\u0026thinsp;\u0026ge;\u0026thinsp;0.6) at α\u0026thinsp;=\u0026thinsp;0.05, consistent with the effect sizes commonly observed in lipidomic biomarker studies. For transparency, approximate power calculations were based on the standard two sample effect size formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{n}_{per\\:group}\\approx\\:\\frac{2{\\left({Z}_{1-\\alpha\\:/2}+{Z}_{1-\\beta\\:}\\right)}^{2}}{{d}^{2}}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e\u003cp\u003ewhere Z₁₋α/2\u0026thinsp;=\u0026thinsp;1.96 for α\u0026thinsp;=\u0026thinsp;0.05, Z₁₋β\u0026thinsp;=\u0026thinsp;0.84 for 80% power, yielding n per group\u0026thinsp;\u0026asymp;\u0026thinsp;15.7/d\u0026sup2;. Accordingly, detecting an effect size of d\u0026thinsp;=\u0026thinsp;0.6 requires\u0026thinsp;~\u0026thinsp;44 subjects per group, closely matching the combined ACLF sample of 100. To further assess the adequacy of precision, Hanley and McNeil\u0026rsquo;s AUC variance approximation indicates that clinically meaningful prognostic performance (AUC\u0026thinsp;\u0026asymp;\u0026thinsp;0.80) can be estimated with acceptable 95% confidence intervals in a cohort of this size. The use of two temporally independent cohorts, combined with false-discovery rate control and penalized regression, enhances reproducibility and supports the robustness of the identified lipid biomarkers.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePatient Recruitment and Sample Collection\u003c/h3\u003e\n\u003cp\u003eACLF patients were recruited on the day of admission (Day 0) or the next day (Day 1), from the Department of Gastroenterology at AIIMS, New Delhi (Nov 2022-Nov 2024). Blood collection was done in the morning for all patients, to minimize diurnal variation in plasma lipid levels. Peripheral blood (~\u0026thinsp;6 mL, EDTA) was processed for plasma and other analyses in the Department of Biochemistry. Patients were followed up for 28-day outcomes and classified as survivors (S) or non-survivors (NS). Healthy age- and sex-matched controls were recruited from the Blood Bank. The discovery cohort included 60 ACLF patients (30 S, 30 NS) and 30 healthy controls; the validation cohort included 40 ACLF patients (20 S, 20 NS) and 10 controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Post-mortem liver and kidney biopsy tissues were obtained from ACLF patients (n\u0026thinsp;=\u0026thinsp;9) in the Department of Gastroenterology, AIIMS, with consent from next of kin and in accordance with institutional ethical guidelines. Plasma lipidomics, tissue proteomics and PMN proteomics were performed with independent patient cohorts and the baseline characteristics of each cohort have been provided in Supplementary Table_Baseline.\u003c/p\u003e\n\u003ch3\u003ePlasma lipidomics\u003c/h3\u003e\n\u003cp\u003eA total of 100 ACLF plasma samples were used for comparative plasma lipidomic profiling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The collected blood samples were processed within 2 hours of sample collection. Plasma were separated as per standard protocols and stored at -80℃ until further use\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A modified Bligh and Dyer method consisting of a triphasic solution of dichloromethane/methanol/water (2:2:1v/v) was used for total plasma lipid extraction (Supplementary Methods File_M1) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTargeted LC-MS/MRM was performed using the Waters ACQUITY \u0026trade; UPLC BEH HILIC X Bridge Amide column, coupled with the Sciex QTRAP 6500\u0026thinsp;+\u0026thinsp;LC/MS/MS system as published earlier\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e (Supplementary Methods File_M2). For peak mapping and method development, MultiQuant 3.0.3 (MQ) software was used (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sciex.com/products/software/multiquant-software\u003c/span\u003e\u003cspan address=\"https://sciex.com/products/software/multiquant-software\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e A total of 1218 lipid species, belonging to 17 lipid classes were identified. These were (+\u0026thinsp;ve MRM) sphingomyelin (SM), ceramide (Cer), cholesterol ester (CE), monoacylglycerol (MAG), diacylglycerol (DAG), Triacylglycerol (TAG), (-ve MRM) phosphatidic acid (PA), lysophosphatidylcholine (LPC), phosphatidylcholine (PC), lysophosphatidylethanolamine (LPE), phosphatidylethanolamine (PE), lysophosphatidylinositol (LPI), phosphatidylinositol (PI), lysophosphatidyl glycerol (LPG), phosphatidylglycerol (PG), lysophosphatidylserine (LPS), and phosphatidylserine (PS).Within the PE class of lipids, the 'O-' prefix is used to indicate the presence of an alkyl ether substituent e.g. PE(O-16:0/18:1(9Z)), whereas the 'P-' prefix is used for the 1Z-alkenyl ether (Plasmalogen) substituent e.g. PE(P-16:0/18:1(9Z)). Peak integration and regression were done to incorporate relevant peaks. Relative quantification was performed between the different study groups (ACLF survivors, non-survivors, and healthy controls). The discovery cohort included participant samples which cleared QC: ACLF patients (n\u0026thinsp;=\u0026thinsp;58; 30 S and 28 NS), and healthy controls (n\u0026thinsp;=\u0026thinsp;29). Lipid hits were validated in an independent cohort of ACLF patients (n\u0026thinsp;=\u0026thinsp;40) and healthy controls (n\u0026thinsp;=\u0026thinsp;10) using the same methodology. These two independent patient cohorts (discovery and validation) were recruited at different time periods under identical inclusion and exclusion criteria at the Department of Gastroenterology, AIIMS, New Delhi. For both cohorts, peripheral blood samples were collected, and plasma lipidomic profiling was performed using the same LC-MS/MS platform, sample preparation workflow, and data processing pipeline to ensure analytical consistency.\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed using R (v4.3.1) (Supplementary Methods File M3) and MetaboAnalyst 6.0 (Supplementary Methods File M4). After data preprocessing (70% detection filtering, MissForest imputation, log₂ transformation, and ComBat batch correction), initial group-wise comparisons were conducted using Wilcoxon rank-sum tests for pairwise analyses and the Kruskal\u0026ndash;Wallis test for three-group comparisons. Lipid species were considered significantly altered at log₂ fold change\u0026thinsp;\u0026ge;\u0026thinsp;0.58 and p\u0026thinsp;\u0026le;\u0026thinsp;0.05. To identify robust discriminatory features across healthy controls (HC), ACLF survivors (ACLF-S), and ACLF non-survivors (ACLF-NS), we applied the Boruta feature selection algorithm (random forest-based), retaining only features classified as \u0026ldquo;Confirmed.\u0026rdquo; The Boruta-selected lipids were then subjected to supervised multivariate analysis in MetaboAnalyst. Partial least squares\u0026ndash;discriminant analysis (PLS-DA) with 7-fold cross-validation and permutation testing (n\u0026thinsp;=\u0026thinsp;1000) was used to assess group separation, and variable importance in projection (VIP) scores\u0026thinsp;\u0026gt;\u0026thinsp;1 were used to prioritize discriminatory lipids. In addition, a one-factor ANOVA (or Kruskal\u0026ndash;Wallis where appropriate) with Tukey\u0026rsquo;s post-hoc correction was performed across the three groups, and volcano plots (log₂FC vs. p-value) were generated to integrate effect size and significance. Using this pipeline, ~\u0026thinsp;300 significantly altered lipid species were identified in the discovery cohort for the ACLF vs. healthy comparison. The identical analytical workflow was applied to a temporally independent validation cohort, and 30 lipid species consistently dysregulated in both cohorts were designated as ACLF-associated lipids. To identify mortality-associated lipids, the ~\u0026thinsp;300 ACLF-altered species were further analyzed using one-factor analysis between ACLF-S and ACLF-NS groups in MetaboAnalyst, followed by PLS-DA, VIP scoring, and ANOVA, yielding five lipid species that were significantly associated with 28-day mortality.\u003c/p\u003e\u003cp\u003eUsing Jamovi version 2.7.6, univariate logistic regression was performed to assess associations between plasma lipids, clinical parameters, and 28-day mortality. Variables significant in univariate analysis were entered into multivariable logistic regression, after excluding collinear predictors using variance inflation factor (VIF) scores, to identify independent predictors. Discriminatory performance was evaluated using receiver operating characteristic (ROC) curves with calculation of the area under the curve (AUROC), and optimal cut-offs were determined using the Youden index. Pairwise AUROC comparisons were performed with DeLong\u0026rsquo;s test, and a p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch3\u003eProteomics Analysis of ACLF-derived Neutrophils, Liver, and Kidney Tissue\u003c/h3\u003e\n\u003cp\u003ePost-mortem liver and kidney tissues (n\u0026thinsp;=\u0026thinsp;9 each) were collected from the Dept. of Gastroenterology, snap frozen and stored at -80\u003csup\u003e0\u003c/sup\u003eC until further use. Peripheral blood polymorphonuclear cells (PMNs) (ACLF n\u0026thinsp;=\u0026thinsp;9; HC n\u0026thinsp;=\u0026thinsp;9) were isolated using double-density gradient centrifugation protocol (Supplementary Methods File_M5)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Proteomic profiling of liver, kidney, and neutrophil samples was performed at vProteomics (New Delhi, India). PMN samples were pooled to increase protein input per sample, into 3 sets of 3 each. Samples were analyzed using an Easy-nLC 1000 system coupled to an Orbitrap Exploris 240 mass spectrometer (Thermo Fisher Scientific).\u003c/p\u003e\u003cp\u003eRaw files were processed using Proteome Discoverer v2.5, and searched against the UniProt human reference database using both Sequest and Amanda search engines (Supplementary Methods File_M6). Functional pathway enrichment analyses were conducted using Metascape, and tissue expression data for individual proteins were obtained from the Human Protein Atlas (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eproteinatlas.org\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eKinase and transcription factor enrichment analyses\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed PMN genes and proteins from previously published and the present study, were subjected to kinase and transcription factor enrichment analysis using the KEA3 (version 3) and X2Kweb tool (ChEA and ENCODE databases) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/X2K/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/X2K/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) respectively. Enriched kinases and TFs were cross-referenced against our original lists of differentially expressed genes (DEGs) and proteins (DEPs) (Saha et. al., 2021 and present study respectively).\u003c/p\u003e\u003cp\u003eTo evaluate potential binding sites on key genes by the TF PPARγ, transcription factor binding motif analysis was performed using the Search Motif Tool available through the Eukaryotic Promoter Database (EPD; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://epd.epfl.ch/\u003c/span\u003e\u003cspan address=\"https://epd.epfl.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The genomic regions from \u0026minus;\u0026thinsp;1000 base pairs (bp) upstream to +\u0026thinsp;100 bp downstream relative to the transcription start site (TSS) were selected for each gene. PPARγ motifs were searched using the JASPAR CORE vertebrates transcription factor motif library, applying a p-value cut-off of 0.001. The location of each predicted PPARγ binding site was recorded relative to the TSS and visualized using the graphical output provided by the EPD motif tool. Genes without any predicted binding sites within the specified region were also noted. Genes assessed included-\u003cem\u003eTLR1, LR10\u003c/em\u003e, \u003cem\u003eSLC25A4\u003c/em\u003e, \u003cem\u003eSLC2A1\u003c/em\u003e, \u003cem\u003eCXCL1\u003c/em\u003e, \u003cem\u003eCD177\u003c/em\u003e, \u003cem\u003eFFAR3\u003c/em\u003e, \u003cem\u003eMAPK1\u003c/em\u003e, \u003cem\u003eMAPK14\u003c/em\u003e, and \u003cem\u003eMT3\u003c/em\u003e, along with neutrophil degranulation protein list from shared signatures of the three proteomes (PMN, liver and kidney).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGene Expression Analysis in Whole Blood Cells and PMN\u003c/h2\u003e\u003cp\u003eEDTA Whole blood was collected from study participants and processed for total RNA isolation. PMN were isolated using the B\u0026oslash;yum\u0026rsquo;s method of double gradient centrifugation was used as described (Supplementary Methods File_M7). A panel of 10 genes (TLR1-TLR10) along with neutrophil-specific markers CD177 and ELANE were analyzed to assess neutrophil markers and TLR signaling status in ACLF patients vs HC (MIQE guidelines 1 and MIQE guidelines 2). Amplification results were expressed as ΔC\u003csub\u003eT\u003c/sub\u003e values normalized to 18S rRNA, and analyzed using GraphPad Prism10.0.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEx Vivo\u003c/b\u003e \u003cb\u003eLeukocyte Lipid Treatment Assay\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePlasma from 15 ACLF NS randomly selected from the lipidomics discovery cohort were pooled for lipid extraction for \u003cem\u003eex vivo\u003c/em\u003e assays and subjected to Bligh and Dyer extraction as described before. The lipid pellet was resuspended in 100% molecular biology-grade ethanol at a concentration of 150 mg/ml.\u003c/p\u003e\u003cp\u003eBlood (1 mL) from 15 healthy individuals was processed to isolate leukocytes, which were resuspended in RPMI\u0026thinsp;+\u0026thinsp;10% autologous plasma and seeded (10\u003csup\u003e5\u003c/sup\u003e cells/well, duplicates) in 12-well plates. Wells were treated with 0.15 mg/mL lipid extract (lipid-treated) or left untreated, with RPMI adjusted to 1 mL total volume. Incubation was performed for 24 h at 37\u0026deg;C, 5% CO₂. Lipid dose was optimized by MTT assays across doses and batches (Supplementary Methods File_M8). qRT-PCR was used to assess TLR1-10, CD177, ELANE, and MPO expression, with calculation of fold change lipid-treated vs untreated cells. To assess lipid incorporation, lipidomes of three independent healthy leukocyte samples were analyzed pre- and post-treatment by targeted MS/MRM as described above.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn Vitro\u003c/b\u003e \u003cb\u003eViability Response to ACLF Lipids\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePlasma from 10 ACLF or 10 HC individuals was pooled and lipids extracted by the Bligh and Dyer method. Serial 10-fold dilutions (100 to 0.001 mg/mL) were prepared in RPMI (no phenol red) and used to treat HEK-293T cells. Untreated cells served as positive controls, while heat-killed cells and lipid-only media served as negatives. After 24 h, MTT (5 mg/mL) was added for 4 h, and formazan solubilized before measuring OD at 570 nm.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn Silico\u003c/b\u003e \u003cb\u003eStudies for investigation of ACLF lipid-PPARγ interaction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMolecular docking was performed using Maestro Version 13.7 (Release 2023-3) from the Schr\u0026ouml;dinger Suite. The crystal structure of PPARγ in complex with garcinoic acid (GA) (PDB ID: 7AWD) was prepared using the Protein Preparation Wizard with default settings. LigPrep (OPLS4 force field) from the Schr\u0026ouml;dinger Suite was employed to prepare the ACLF-associated lipids. All possible protonation states were generated using Epik at pH 7.0, and tautomeric forms were enumerated for chemical groups capable of tautomerism.\u003c/p\u003e\u003cp\u003eA receptor grid box was generated by defining the ligand-binding site after removing one of the GA molecules from the complex, using the OPLS4 force field. The optimized ligands were subsequently docked into the receptor using Schr\u0026ouml;dinger\u0026rsquo;s Glide software (Release 2023-3).\u003c/p\u003e\u003cp\u003eStructural similarity between the lipid species was estimated using the RDKit cheminformatics package (rdkit-pypi, v. 2022.9.5) and mapchiral (v.0.0.7) in Python. Molecules were first parsed from SMILES strings and converted into RDKit Mol objects. Structural fingerprints were generated using the Morgan fingerprint (`AllChem.GetMorganFingerprintAsBitVect`, radius\u0026thinsp;=\u0026thinsp;2, nBits\u0026thinsp;=\u0026thinsp;2048) and MAP4Chiral fingerprint\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e (maxradius\u0026thinsp;=\u0026thinsp;2, npermutations\u0026thinsp;=\u0026thinsp;2048) algorithms.) Tanimoto similarity coefficients were calculated using the `DataStructs.cDataStructs.TanimotoSimilarity` function and Jaccard similarity coefficients were calculated using the mapchiral.jaccard_similarity function. This was exported to R (v 4.4.1) for downstream analysis. Here, the Tanimoto similarity matrix was transformed into a distance matrix (1 - similarity), and hierarchical clustering was performed using the `hclust` function with complete linkage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed in GraphPad Prism 10, Jamovi (version 2.7.6), Python, and R (version 4.3.3). Data distribution was assessed using normality tests. Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR) and compared using t-test or Mann\u0026ndash;Whitney U test, while categorical variables were compared using the χ\u0026sup2; test or Fisher\u0026rsquo;s exact test. For comparisons involving more than two groups, one-way ANOVA or the Kruskal\u0026ndash;Wallis test was used.\u003c/p\u003e\u003cp\u003eDifferential lipidomic analysis between ACLF survivors (ACLF-S) and non-survivors (ACLF-NS) in the discovery cohort was performed using fold-change (FC\u0026thinsp;\u0026ge;\u0026thinsp;2), p\u0026thinsp;\u0026le;\u0026thinsp;0.05, and supervised PLS-DA with VIP scoring to identify mortality-associated lipid markers. This pipeline yielded five lipid species significantly associated with 28-day mortality (LPE 18:2, LPE 20:4, PE(P-18:2/18:2), TAG 48:1/FA16:1, and PE(O-18:0/16:1)). These five mortality-associated lipids, along with associated routine clinical parameters, were entered into univariate logistic regression to evaluate their individual association with 28-day mortality. Variables with p\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered candidate predictors. Collinearity was assessed using variance inflation factors (VIF), and non-collinear variables were included in a multivariable logistic regression model to identify independent predictors.\u003c/p\u003e\u003cp\u003eThe final multivariable model included two lipid species (TAG 48:1/FA16:1 and PE(O-18:0/16:1)) and the CTP score, all independently associated with 28-day mortality. Regression coefficients derived entirely in the discovery cohort were applied unchanged to the validation cohort to generate predicted mortality probabilities. Model discrimination was evaluated using AUROC, accuracy, sensitivity, specificity, precision, recall, F1-score, and confusion matrices. All statistical tests were two-sided, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eAltered Plasma Lipidome in ACLF Reveals Enrichment of Pro-inflammatory Lipid Species and a Mortality Associated Lipid Signature\u003c/b\u003e\u003c/p\u003e\u003cp\u003eACLF patients (n\u0026thinsp;=\u0026thinsp;100) were enrolled for targeted plasma lipidomics and randomly assigned to a discovery cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60) and an independent validation cohort (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary Table_Baseline). In the discovery cohort, 58 samples [30 survivors (S), 28 non-survivors(NS)] passed batch correction and quality control (Supplementary Methods File_M3). Baseline clinical parameters, including TLC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), coagulation profile (INR) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and kidney function parameters (urea and creatinine, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 each), were significantly elevated in ACLF NS, whereas liver function parameters (bilirubin, AST, ALT, SAP, and albumin) were comparable between 28-day S and NS (Supplementary Tables S1, Supplementary Table_Baseline). MELD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), MELD-Na (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Child-Turcotte-Pugh (CTP) (p\u0026thinsp;=\u0026thinsp;0.001) were higher in NS (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOf the 1,218 lipid species quantified, 636 were detected in \u0026gt;\u0026thinsp;70% of samples and retained for downstream analysis. Lipid peak areas were imputed, log-transformed, and median-normalized using MetaboAnalyst. Pre- and post-normalization distributions of lipid features and samples confirmed data quality (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePartial least squares-discriminant analysis (PLS-DA) revealed clear separation of healthy controls, ACLF S, and ACLF NS based on lipid profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Component 1 (lipid intensity) explained 13.8%, while Component 2 (intergroup variance) accounted for 6.5% of the variance. VIP plot enlists the top discriminatory lipids based on the PLS-DA clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Overall, 316 differential lipid species were identified in ACLF vs. healthy controls (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn an independent cohort (ACLF \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40; healthy controls \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), 30 key lipid species were validated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The discovery and validation cohorts represented two completely independent patient groups. Lipidomic profiling of both cohorts, performed using same LC-MS/MS instrumentation and processing pipelines, quantified 1218 lipid species in each set, from which 30 overlapping differential lipids common to both analyses were identified as the final ACLF-associated lipid panel. Monounsaturated and saturated lipid species were consistently elevated, whereas polyunsaturated (PUFA-containing) lipid species were significantly reduced in ACLF. O-linked phosphatidylethanolamines PE(O.18:0/16:1) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.15E-32), PE(O.18:0/16:0) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.27E-27), phosphatidylinositol PI (18.1.18.1) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), and several phosphatidylcholines [PC (16.0.16.0), PC (18.0.16.1), PC (18.1.18.1), PC (16.0.16.1) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.0001)] were elevated in ACLF. Conversely, lysophosphatidylethanolamines LPE (18:2) and LPE (20:4), and lysophosphatidylcholine LPC (14:0) were significantly decreased (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.001 for all) in ACLF (Supplementary Tables S3). The box-and-whisker plots show the relative distribution of plasma lipid classes, Lysophospholipids, Glycerolipids, Phosphatidylethanolamine, and phospholipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA.\u003c/b\u003e Overall study design of the lipidomics analysis with two independent cohorts: discovery and a validation cohort of ACLF S, NS and HC. \u003cb\u003eB.\u003c/b\u003e PLS-DA (partial least squares-discriminant analysis) plot showing distinct clustering of HC, ACLF S and NS in discovery cohort; VIP (variable importance projection) plot highlighting top discriminatory lipid species. \u003cb\u003eC.\u003c/b\u003e Heatmap of 30 validated lipids in an independent ACLF cohort. \u003cb\u003eD.\u003c/b\u003e Box-and-whisker plots depicting log-normalized peak area intensity of major lipid classes in ACLF: lysophospholipids, glycerolipids, phosphatidylethanolamines, and phospholipids. *p-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, **p-value\u0026thinsp;\u0026le;\u0026thinsp;0.01, ***p-value\u0026thinsp;\u0026le;\u0026thinsp;0.001, ****p-value\u0026thinsp;\u0026le;\u0026thinsp;0.0001\u003c/p\u003e\u003cp\u003eLipidome comparison between ACLF S and NS revealed overlapping profiles on PLS-DA (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with Component 1 explaining 10.4% (lipid species variance) and Component 2 accounting for 3.7% (sample-to-sample variance). Statistical analysis using a fold-change threshold\u0026thinsp;\u0026ge;\u0026thinsp;2 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 identified 5 lipid species significantly altered between the two groups: LPE (18:2), LPE (20:4) and PE-(P 18:2/18:2) were lowered, whereas TAG (48:1/FA:16:1) and PE-(O 18:0/16:1) were elevated in ACLF NS (Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIndependent Lipid Predictors of Mortality with Prognostic Performance Are Comparable to Clinical Scores\u003c/h2\u003e\u003cp\u003eTo evaluate the prognostic significance of plasma lipids in ACLF, we conducted logistic regression and ROC analyses\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e using the lipidomics discovery cohort. Univariate logistic regression showed that lower levels of LPE(18:2) [OR 0.57, 95% CI 0.36\u0026ndash;0.89, p\u0026thinsp;=\u0026thinsp;0.013], LPE(20:4) [OR 0.46, 95% CI 0.26\u0026ndash;0.82, p\u0026thinsp;=\u0026thinsp;0.008], and PE(P-18:2/18:2) [OR 0.57, 95% CI 0.35\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.02]) were associated with increased mortality risk, whereas higher levels of PE(O-18:0/16:1) [OR 2.69, 95% CI 1.03\u0026ndash;7.05, p\u0026thinsp;=\u0026thinsp;0.044] and TAG48:1-FA16:1 [OR 1.34, 95% CI 1.03\u0026ndash;1.74, p\u0026thinsp;=\u0026thinsp;0.03]) were linked to worse outcomes. Among conventional parameters, INR, urea, creatinine, MELD-Na, and CTP score were significant predictors of mortality (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\u003e\u003cb\u003eUnivariable and Multivariable Logistic Regression Analyses of Plasma Lipids and Clinical Parameters Associated with Mortality in ACLF using the discovery cohort\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eUnivariable Logistic Regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMultivariable Logistic Regression\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003cp\u003e[95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003cp\u003e[95% CI]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPE(18:2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003cp\u003e[0.362, 0.887]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003cp\u003e[0.262, 2.140]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPE(20:4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003cp\u003e[0.260, 0.818]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.321\u003c/p\u003e\u003cp\u003e[0.397, 4.390]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE(O-18:0/16:1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.690\u003c/p\u003e\u003cp\u003e[1.030, 7.054]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.736\u003c/p\u003e\u003cp\u003e[1.517, 90.780]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePE(P-18.2/18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003cp\u003e[0.347, 0.924]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003cp\u003e[0.296, 1.680]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTAG48:1-FA16:1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.338\u003c/p\u003e\u003cp\u003e[1.030, 1.739]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.879\u003c/p\u003e\u003cp\u003e[1.097, 3.220]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08E-04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003cp\u003e[1.000, 1.000]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.890\u003c/p\u003e\u003cp\u003e[1.340, 6.230]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.010\u003c/p\u003e\u003cp\u003e[0.706, 12.840]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.013\u003c/p\u003e\u003cp\u003e[1.002, 1.024]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.017\u003c/p\u003e\u003cp\u003e[0.985, 1.050]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.539\u003c/p\u003e\u003cp\u003e[1.032, 2.296]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.026\u003c/p\u003e\u003cp\u003e[0.434, 2.420]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeld score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.107\u003c/p\u003e\u003cp\u003e[1.013, 1.211]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMELD-NA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.158\u003c/p\u003e\u003cp\u003e[1.040, 1.286]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.935\u003c/p\u003e\u003cp\u003e[0.731, 1.200]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCTP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.780\u003c/p\u003e\u003cp\u003e[1.210, 2.623]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.420\u003c/p\u003e\u003cp\u003e[1.130, 5.180]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAccuracy\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" morerows=\"3\" nameend=\"c5\" namest=\"c2\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSensitivity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e0.833\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSpecificity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAUC\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e Estimates represent the log odds of \"Mortality\u0026thinsp;=\u0026thinsp;1\" vs. \"Mortality\u0026thinsp;=\u0026thinsp;0\"\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAUROC analysis (Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e) demonstrated that LPE(20:4) [AUROC 0.729, 95% CI 0.597\u0026ndash;0.860, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) and LPE(18:2) [AUROC 0.707, 95% CI 0.568\u0026ndash;0.846, p\u0026thinsp;=\u0026thinsp;0.004] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) showed the strongest discriminatory ability among lipids, comparable to MELD-Na [AUROC 0.729, 95% CI 0.596\u0026ndash;0.862, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH) and MELD [AUROC 0.692, 95% CI 0.551\u0026ndash;0.832, p\u0026thinsp;=\u0026thinsp;0.008] (Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). In addition, PE(O-18:0/16:1) [AUROC 0.674, 95% CI 0.531\u0026ndash;0.816, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017], PE(P-18:2/18:2) [AUC 0.635, 95% CI 0.490\u0026ndash;0.779, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.069], and TAG 48:1-FA16:1 [AUC 0.664, 95% CI 0.522\u0026ndash;0.807, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024] also displayed moderate discriminatory performance, though with variable sensitivity and specificity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The CTP score demonstrated the high performance overall [AUC 0.742, 95% CI 0.615\u0026ndash;0.870, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Other parameters such as INR, urea, creatinine, and TLC displayed moderate but significant discriminatory ability (Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe derived a multivariable logistic regression model for 28-day mortality that included PE(O-18:0/16:1), TAG(48:1/FA16:1) and CTP score. The model is expressed as:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:logit\\left(p\\right)=-66.903+2.463\\times\\:\\left[PE\\right(O-18:0/16:1)]+0.631\\times\\:[TAG48:1-FA16:1]+0.884\\times\\:[CTP\\:score],$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:p=\\frac{1}{1+\\text{e}\\text{x}\\text{p}\\left[-logit\\left(p\\right)\\right]}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003erepresents the predicted probability of 28-day mortality. The logit-predicted mortality model (combining PE(O-18:0/16:1), TAG48:1-FA16:1, and CTP score) outperformed all individual markers with an AUROC of 0.912, sensitivity of 83.3% and specificity of 78.6% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Pairwise AUROC comparisons showed no significant differences among lipid predictors or between CTP, MELD, and MELD-Na. However, the combined logit model demonstrated significantly higher discriminatory accuracy compared with LPE(18:2), LPE(20:4), MELD-Na, CTP, and TLC (all p\u0026thinsp;\u0026le;\u0026thinsp;0.006), confirming the added predictive value of integrating lipid and clinical parameters (Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD shows a correlation matrix among the clinical parameters and major plasma metabolites in all subjects, where blue-colored cells indicate a positive correlation and red-colored cells indicate a negative correlation. It was observed that 2 of the lipid species LPE.18:2 and LPE 20:4 correlated negatively with kidney function parameters (urea and creatinine), brain function (hepatic encephalopathy HE grades, OF Brain score), lung function (PaO\u003csub\u003e2\u003c/sub\u003e, FiO\u003csub\u003e2\u003c/sub\u003e), and clinical scores MELD and MELD-Na (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Thus, lowering of these species is associated with multiple organ failure. TAG 48:1/16:1 correlated positively with brain organ failure score (Brain OF) and respiratory organ failure score (Respiration OF).\u003c/p\u003e\u003cp\u003eIn order to find shared lipid signatures across diseases, we compared our (ACLF1: current study) lipidome with four published cohorts Cl\u0026agrave;ria \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e21\u003c/sup\u003e (ACLF2), Mecatti \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e31\u003c/sup\u003e (sepsis), Wu \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e32\u003c/sup\u003e (trauma), and Ciccarelli \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e33\u003c/sup\u003e. (COVID-19), using a uniform (carbons:double bonds) nomenclature (SMILES format or Simplified Molecular Input Line Entry System). Fifty-nine species overlapped between ACLF1 and ACLF2, including LPCs (18:1, 18:2), PCs (34:1, 36:3, 36:5), and PIs (38:4), all of which were significantly depleted in both datasets (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Five PCs/PS (34:1, 34:2, 36:1, 36:3; PS 40:1) overlapped with sepsis and showed reduction versus controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Fifty-six lipids including LPCs, PCs, PIs, DGs, and PEs overlapped with COVID-19 and showed uniform LPC/PC/PI suppression in severe cases (ANOVA p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas PEs/DGs rose in COVID-19 but remained depleted in ACLF1. Nineteen PCs, PEs, and PIs overlapped with trauma and were acutely depleted at admission (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with later PE rebound in trauma (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA.\u003c/b\u003e PLS-DA plot comparing lipidomes of ACLF survivors and non-survivors shows overlapping clusters, with Component 1 (10.4%) and Component 2 (3.7%) explaining lipid and sample variance, respectively. \u003cb\u003eB.\u003c/b\u003e AUROC of five mortality-associated lipids (LPE.20:4, LPE.18:2, PE-O 18:0/16:1, PE-P 18:2/18:2, TAG 48:1/FA:16:1 and) comparable to clinical scores (CTP, MELD-Na). \u003cb\u003eC.\u003c/b\u003e Combined logit model (PE-O 18:0/16:1, TAG 48:1/16:1, CTP) outperforms clinical scores. \u003cb\u003eD.\u003c/b\u003e (Top) Correlation matrix (MetaboAnalyst) showing relationships between 5 mortality-associated lipids and 22 clinical parameters; blue indicates positive, red indicates negative correlations. (Bottom) List of significant correlation coefficients highlighting that LPE.18:2 and LPE.20:4 are negatively associated with markers of kidney, brain, and lung dysfunction, and clinical severity scores (MELD, MELD-Na), while TAG 48:1/16:1 and PE-O 18:0/16:1 correlate positively with organ failure scores and 28-day mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEx vivo lipid stimulation reveals immunomodulatory effects of ACLF-associated lipids on leukocytes\u003c/h2\u003e\u003cp\u003eTo examine whether circulating lipids from ACLF patients exert direct immunomodulatory effects, we performed an \u003cem\u003eex vivo\u003c/em\u003e lipid stimulation assay using leukocytes from healthy donors (n\u0026thinsp;=\u0026thinsp;15) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Total plasma lipid extracts from ACLF non-survivors were incubated with leukocytes for 24 hours, followed by gene expression analysis by qRT-PCR. The gene expression patterns in pre and post-lipid treated healthy leukocytes were compared with gene expression patterns observed in leukocytes derived from HC vs ACLF (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). Consistent with patient-derived gene expression signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C), lipid-treated leukocytes showed an elevated trend in CD177 gene expression (p\u0026thinsp;=\u0026thinsp;0.08) and marked downregulation of TLR1 (p\u0026thinsp;=\u0026thinsp;0.02), TLR6 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TLR7 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), TLR8 (p\u0026thinsp;=\u0026thinsp;0.04) and TLR 10 (p\u0026thinsp;=\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). Expression of ELANE remained unchanged (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings indicate that ACLF-associated lipids impair innate immune receptor expression while selectively driving CD177 induction.\u003c/p\u003e\u003cp\u003eTo verify lipid uptake and establish concordance with the ACLF mortality lipid signature, we performed LC-MS/MRM on lipid-treated leukocytes and found that the pre- and post- treatment lipid groups were distinct based on their lipidomic- composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Triacylglycerols (TAGs) were the most discriminatory lipid class enriched in treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Importantly, TAG (48:1/FA16:1) (FC 3.07, p\u0026thinsp;=\u0026thinsp;0.037), a key component of the validated ACLF mortality-associated lipid signature, was significantly elevated in lipid-treated leukocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). In addition to TAG(48:1/FA16:1), several other TAG species were markedly elevated in lipid-treated leukocytes, including TAG(48:2/FA18:1) (FC 5.30, p\u0026thinsp;=\u0026thinsp;0.006), TAG(46:4/FA18:2) (FC 4.95, p\u0026thinsp;=\u0026thinsp;0.008), and TAG(47:2/FA16:1) (FC 3.99, p\u0026thinsp;=\u0026thinsp;0.016), confirming incorporation of pathogenic lipid species and supporting their functional role in immune reprogramming. Beyond TAGs, enrichment of ceramides [CER(22:1), FC 4.36, p\u0026thinsp;=\u0026thinsp;0.012; DCER(24:1), FC 3.06, p\u0026thinsp;=\u0026thinsp;0.038] and glycerophospholipids such as PG(16:0/22:5) (FC 3.58, p\u0026thinsp;=\u0026thinsp;0.023) and PA(18:2/16:1) (FC 3.51, p\u0026thinsp;=\u0026thinsp;0.025) was also observed. Conversely, several ether-linked phosphatidylethanolamines were depleted, including PE(P-16:0/18:0) (FC -3.77, p\u0026thinsp;=\u0026thinsp;0.020) and PE(O-18:0/18:2) (FC -3.61, p\u0026thinsp;=\u0026thinsp;0.023), indicating a selective incorporation pattern that mirrors the ACLF mortality-associated lipid signature (Supplementary Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eProteomic profiling of ACLF liver, kidney, and PMNs identifies shared degranulation and immune signaling programs\u003c/h2\u003e\u003cp\u003eTo gain mechanistic insights into ACLF related organ failure and immune dysfunction, we first performed descriptive proteomic profiling on post-mortem ACLF liver and kidney biopsies. In total, 2,950 proteins were identified in ACLF liver and 1,232 proteins in ACLF kidney (Supplementary Table_Proteomics). Pathway enrichment analysis showed that the top pathways in liver included purine metabolism, amino acid metabolism, carboxylic acid metabolism, neutrophil degranulation, and small molecule catabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In kidney, the most enriched pathways were cellular stress responses, carboxylic acid metabolism, amino acid metabolism, nucleobase metabolism, and neutrophil degranulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eSince neutrophil degranulation (Reactome R-HSA-6798695) was highly enriched in both liver (log10p\u0026thinsp;=\u0026thinsp;100, 259 proteins; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) and kidney (log10p\u0026thinsp;=\u0026thinsp;77.25, 133 proteins; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), we next profiled circulating PMNs from ACLF patients versus healthy controls. Differential proteomics revealed significantly altered proteins in ACLF PMNs, with neutrophil degranulation emerging as a dominant enriched pathway (log10p\u0026thinsp;=\u0026thinsp;100; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB,C). (log10p\u0026thinsp;=\u0026thinsp;100; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-E). A subset of 711 proteins was shared across ACLF PMN, liver, and kidney proteomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, Supplementary Table_Proteomics). Enrichment analysis of this shared set confirmed neutrophil degranulation as the top pathway as well as cellular responses to stimuli (R-HSA-8953897) and interleukin signaling (R-HSA-449147) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eACLF derived lipids promote cytotoxicity in HEK293T cells\u003c/h2\u003e\u003cp\u003eMetascape analysis of ACLF tissue proteomes revealed pronounced activation of cellular stress and damage related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the liver, altered processes included cellular breakdown, purine and carboxylic acid metabolism, carbon metabolism, and vesicle-mediated transport, along with enrichment in neutrophil degranulation, VEGFA-VEGFR2 signaling, prion disease, and cytoskeletal remodeling, which are changes signaling heightened inflammatory and metabolic stress driving tissue injury. The ACLF kidney proteome demonstrated a stress-adaptive profile, marked by the following pathways -enhanced responses to chemical stress, mitochondrial protein degradation, protein folding, detoxification, and branched-chain amino acid catabolism. These signatures pointed towards underlying mitochondrial dysfunction, impaired protein quality control, and sustained inflammatory pressure in ACLF kidneys, converging on a state of cellular stress and cytotoxicity that likely accelerates renal injury and loss of cell viability. We next evaluated the cytotoxic potential of circulating lipids isolated from ACLF patients in comparison with those from healthy controls (HC). Towards this, HEK-293T cells were exposed to a graded series of lipid concentrations (100, 10, 1, 0.1, 0.01, and 0.001 mg/mL) derived from either ACLF or HC plasma, and cell viability was quantified using the MTT assay. Each condition was tested in duplicate across five independent experiments. We observed that HC-derived lipids tended to preserve cell viability and, in some instances, appeared to promote proliferation, whereas ACLF-derived lipids consistently exerted a cytotoxic effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Specifically, cells treated with ACLF lipids showed significantly reduced viability compared to both untreated controls and HC lipid-treated cells. This effect was most pronounced at 1 mg/mL, indicating a dose-sensitive impact of pathological lipid species on cellular health.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003eIn Silico Analysis Suggests ACLF Lipids Modulate Pathways via PPARγ Interaction\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eTo identify regulatory drivers, we integrated differential expression data from the published ACLF PMN transcriptome with the current PMN proteome\u0026sup1;\u0026sup1;. MAPK kinases were prominently enriched; transcriptomic DEGs showed regulation of MAP2K6, MAPK14, MAPK1, and MAP3K14 (Supplementary Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e), with BMX most upregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, logFC 3.52) and PRKCQ most downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, logFC \u0026minus;\u0026thinsp;4.23). Proteomic DEPs included MAP2K1, MAPK3, MAPK14, and MAPK1; among PRKC family members, PRKCB was upregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, logFC 2.95) and CSNK2A1 downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, logFC \u0026minus;\u0026thinsp;2.03) (Supplementary Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAmong transcription factors, PPARγ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, logFC 2.44), CEBPD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, logFC 1.76), and CEBPB (p\u0026thinsp;\u0026lt;\u0026thinsp;0.03, logFC 1.35) were upregulated, while GATA1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, logFC \u0026minus;\u0026thinsp;1.61) and GATA2 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, logFC \u0026minus;\u0026thinsp;3.23) were downregulated (Supplementary Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). Integrated analysis highlighted two kinase-centered modules: MAPK1-regulated genes (ZMIZ1, E2F6, STAT5A, BHLHE40, USF1, MAX, CEBPD) and MAPK14-regulated genes (SRF, CTCF, ZBTB33, STAT5A, BRCA1, GATA2, SMC3, RAD21, ATF2, FLI1), all upregulated in ACLF PMNs (Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further explore the transcriptional regulation of genes involved in lipid sensing, immune response, and stress signaling in the context of acute-on-chronic liver failure (ACLF), we analyzed the promoter regions of key target genes for potential regulatory elements (Supplementary Tables S8). Given the central role of peroxisome proliferator-activated receptor gamma (PPARγ) in lipid binding, metabolism and inflammation, we specifically searched for PPARγ binding motifs using the JASPAR transcription factor database\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Motif scanning was performed for the PPARγ transcription factor binding motif JASPAR CORE vertebrate\u003cb\u003es\u003c/b\u003e transcription factor motif library with a cut-off p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in Eukaryotic promoter database (EPD). Further, we explored the potential of PPARγ to bind gene targets known to be upregulated in our datasets or in previously reported genes known to be associated with ACLF pathogenesis, by investigating the presence of PPARγ binding sites in these target genes. Genes of interest included MAPK1, MAPK14, FFAR3 (upregulated in ACLF PMN DEPs), CD177 (previously shown to be highly induced in ACLF), multiple TLRs, and metallothioneins (MT1, MT2, MT3), which are strongly expressed in ACLF patients with organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. PPARγ binding sites were identified in the promoters of all these genes, suggesting regulatory potential in response to lipid activation (Supplementary Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause neutrophil degranulation emerged as a dominant cross-compartment pathway (see above), we examined the entire shared protein set from liver, kidney, and ACLF PMN proteomes. Of the 129 proteins in the neutrophil degranulation pathway, 96 were encoded by genes containing PPARγ promoter binding sites (Supplementary Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eWe further investigated interactions between ACLF-associated lipids and PPARγ using \u003cem\u003ein silico\u003c/em\u003e molecular docking (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D). A total of 82 lipids that mapped to all known isomers of the 30 validated differential lipid species discriminating between ACLF S and ACLF NS were selected for analysis (Supplementary Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). Docking studies were conducted using the X-ray co-crystal structure of PPARγ bound to garcinoic acid (GA) (PDB ID: 7AWD) (Supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Prior to docking, GA was removed from the co-crystal structure, and non-covalent docking of the ACLF-associated lipids was performed at both the orthosteric and allosteric binding pockets. Overall, the lipids demonstrated better docking scores at the orthosteric site. We selected the ligands with the highest docking scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and presented their chemical structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Notably, compounds L9B, L9D, and L25 are glycerol-based phosphoesters with long-chain unsaturated hydrocarbon tails, while L26 is an allo-inositol conjugated glycerol-based phosphoester (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). For the glycerol derivatives, whether all three hydroxyl groups are fully substituted or the specific substitution pattern appears to have minimal impact on the docking outcomes. Ribbon diagrams of the top docking poses revealed that these compounds occupy both the orthosteric and allosteric sites of PPARγ, which may be attributed to the long hydrocarbon chains of these phospholipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The docking studies show that L26 (-10.76), L9B (-9.42) and L25 (-9.36) had comparable docking scores to GA (-10.24), which is summarized in Supplementary Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e.This suggested that the lipids could effectively bind to and modulate PPARγ function, thus providing a molecular basis for the observed effects in these patients.\u003c/p\u003e\u003cp\u003eTo gain insights into the structural relationships of these lipids, we conducted cluster analysis with the 82 lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Molecular fingerprints (e.g., Morgan fingerprints) were calculated for all compounds based on their SMILES using RDKit, and pairwise Tanimoto similarity coefficients were computed to obtain a similarity matrix. This similarity information was then used to visualize the structural relatedness among the compounds, providing an overview of how structurally similar lipids group together. The compounds with the best docking scores, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, were highlighted in the circular dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Based on the dendrogram, L9B, L9D, L25, and L26 were classified into three distinct clusters, with L9B and L9D showing strong structural similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we demonstrated that circulating lipids in acute-on-chronic liver failure (ACLF) are not passive metabolic intermediates but active mediators with prognostic and mechanistic significance. The plasma lipidome of ACLF patients was characterized by consistent depletion of polyunsaturated lysophospholipids and accumulation of monounsaturated glycerophospholipids and triacylglycerols (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This shift mirrored patterns described in sepsis, trauma, and COVID-19, suggesting a convergent host response to critical illness (Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Using a discovery cohort followed by a fully independent validation cohort, we identified a minimal lipid signature [LPE(18:2), LPE(20:4), PE(O-18:0/16:1), TAG(48:1/16:1), and PE(P-18:2/18:2)] that reproducibly discriminated survivors from non-survivors and correlated with multi-organ failure parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Multivariable logistic regression revealed that PE(O-18:0/16:1) and TAG(48:1/FA16:1) were independent predictors of 28-day mortality, comparable in discriminatory performance to established clinical scores such as MELD-Na and CTP (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). This positions circulating plasma lipids as potential biomarker candidates that can augment current risk stratification tools.\u003c/p\u003e\u003cp\u003eGiven these prognostic associations, it is important to consider the biological and analytical features that support the use of lipids as clinical biomarkers; from this perspective, our findings reinforce the strength of lipidomic signatures as clinically relevant indicators of disease severity in ACLF. Lipids exhibit several inherent advantages as biomarkers e.g., they are chemically stable in biofluids, reflect integrated metabolic and inflammatory processes, and can be quantified with high analytical precision using targeted LC-MS workflows\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In conditions like ACLF, where clinical scores may already indicate advanced organ failure at presentation, circulating lipid species may capture upstream immune-metabolic perturbations that precede overt organ failure. The observation that two lipid species (PE(O-18:0/16:1) and TAG(48:1/FA16:1)) remained independent predictors even after adjustment for clinical variables. Moreover, the reproducibility of the five-lipid signature across an independent validation cohort underscores its translational robustness and supports future development of lipid-based prognostic assays in ACLF. The multivariable logistic model integrating PE(O-18:0/16:1), TAG(48:1/FA16:1), and the CTP score demonstrated substantially enhanced discriminatory accuracy for 28-day mortality (AUROC 0.912), outperforming all individual lipid predictors and routinely used clinical indices. This finding has several mechanistic and clinical implications. First, the two lipid species included in the model likely capture complementary aspects of ACLF biology: PE plasmalogens reflect oxidative stress, membrane remodeling, and immune-metabolic imbalance, whereas the TAG(48:1/FA16:1) species may index dysregulated lipid mobilization and altered hepatic energy flux\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Their independent contributions within the model suggest that lipidomic perturbations convey prognostic information that is not covered by MELD-Na, CTP, or other clinical scores, that largely quantify manifest organ dysfunction. By incorporating these mechanistically anchored biomarkers with an established clinical parameter, the model provides a more holistic representation of ACLF pathophysiology. From a translational standpoint, this result supports the potential of lipidomics as a clinically actionable adjunct to existing prognostic tools. The ability to quantify lipid species with high precision using rapid, targeted LC-MS workflows also raises the possibility that lipid-augmented models could be deployed in real-time triage, trial enrichment, or targeted therapeutic stratification. Clinically, such an integrated approach may help identify high-risk patients earlier, guide escalation of organ support, and refine selection for liver transplantation or immuno-metabolic interventions.\u003c/p\u003e\u003cp\u003eThe role of lipids in ACLF pathophysiology has remained largely underexplored, despite recognition that immune dysregulation and systemic inflammation drive organ failure \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our \u003cem\u003eex vivo\u003c/em\u003e experiments directly linked circulating lipids derived from ACLF non-survivors to immunomodulatory effects on healthy leukocytes, reproducing transcriptomic features of ACLF immune responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, lipid treatment induced upregulation of CD177, a neutrophil activation marker consistently elevated in ACLF, while suppressing multiple TLRs critical for innate immune sensing, recapitulating an ACLF-like phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings provide mechanistic evidence that circulating lipids have the ability to reprogram immune function, potentially contributing to the paradoxical state of hyperinflammation and immune paresis that are the hallmarks of ACLF. Lipidomic uptake experiments further demonstrated enrichment of triacylglycerols (TAGs) within leukocytes, particularly TAG(48:1/FA16:1), a key component of the validated mortality-associated lipid signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This confirmed not only the incorporation of ACLF-lipid species into immune cells but also their alignment with disease-linked metabolic profiles, reinforcing the notion that circulating lipids act as functional effectors rather than inert biomarkers.\u003c/p\u003e\u003cp\u003eProteomics analyses extended these observations to tissue and cellular compartments, revealing shared signatures of neutrophil degranulation across liver, kidney, and circulating PMNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The enrichment of this pathway across multiple organs suggests a systemic degranulation program operative in ACLF, with implications for parenchymal injury and multiorgan failure. Integrated transcriptome-proteome analyses identified MAPK1 and MAPK14-centered kinase modules as critical regulatory hubs, linking lipid-induced signaling to transcriptional programs in neutrophils (Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Transcription-factor analysis revealed PPARγ as a prominent upstream regulator, with high-confidence binding motifs in MAPKs, TLRs, metallothioneins, and CD177- key genes linked to ACLF pathophysiology (Supplementary Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e, Supplementary Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e, Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e)38. These analyses placed PPARγ at the intersection of lipid metabolism, immune regulation, and stress response, providing a unifying mechanism through which lipid mediators may orchestrate ACLF pathology.\u003c/p\u003e\u003cp\u003eClinically, these results suggest that lipidomic profiling, which is validated here across independent cohorts, could complement MELD, MELD-Na, and CTP scores in refining prognostication, while also highlighting novel therapeutic avenues. Targeting lipid metabolism, blocking pathogenic lipid uptake, or modulating PPARγ centered signaling pathways may represent promising strategies to attenuate immune dysregulation and improve outcomes in ACLF.\u003c/p\u003e\u003cp\u003eWe acknowledge several limitations. Although two temporally independent ACLF cohorts were included, the overall sample size remains modest, and larger multicenter studies are needed to confirm the stability and generalizability of the lipid-based mortality signature. Our lipidomic analyses were performed on plasma and therefore do not fully capture tissue-specific lipid pools or compartmentalized lipid signaling in ACLF. Ex vivo assays were restricted to healthy donor leukocytes and may not fully recapitulate the complex inflammatory and immunometabolic milieu present in ACLF patients. In addition, the high dimensionality of the lipidomics dataset relative to the number of mortality events introduces a potential risk of overfitting, despite the use of penalized regression and false-discovery rate correction. The limited external performance of lipid-only penalized models also suggests underlying cohort heterogeneity and highlights the need for more robust feature-selection strategies. Finally, although we identify strong associations and mechanistic plausibility, including TLR suppression, neutrophil activation, and PPARγ-lipid interactions, interventional studies will be required to establish definitive causality. Future work should incorporate longitudinal lipidomics, functional assays using ACLF-derived immune cells, and therapeutic modulation in preclinical models to validate these findings.\u003c/p\u003e\u003cp\u003eIn conclusion, our findings provide a mechanistic framework that bridges metabolic derangements with immune dysfunction and organ failure in ACLF. They extend prior observations of systemic inflammation and immune paralysis by identifying specific lipid classes as potential drivers of some of these processes. The dual role of lipids, as early biomarkers and as biological effectors that shape immune phenotypes, positions them uniquely within ACLF biology and strengthens their relevance for both risk prediction and therapeutic exploration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACLF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute-on-Chronic Liver Failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlanine Aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnalysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAspartate Aminotransferase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the Receiver Operating Characteristic Curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCARS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCompensatory anti-inflammatory response syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCholesteryl ester\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCeramide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic hepatitis B\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic Liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCLIF-C\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChronic liver failure consortium organ failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChild-Turcotte-Pugh\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiacylglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDAMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDamage-associated molecular patterns\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEASL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEuropean Association for the Study of the Liver\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEASL-CLIF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEuropean Association for the Study of the Liver-Chronic Liver Failure Consortium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEthylene diamine tetra acetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGarcinoic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHealthy control\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHepatic encephalopathy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHEK293T\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Embryonic Kidney 293T Cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eINR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInternational Normalized Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLysophosphatidic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLysophosphatidyl ethanolamine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLysophosphoglycerol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLysophosphatidyl inositol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLipopolysaccharide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPS (lipid)\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLysophosphatidyl serine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMonoacylglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAPK\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMitogen-Activated Protein Kinase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModel for end-stage liver disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMELD-NA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModel for end-stage liver disease-Sodium\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMOF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultiple organ failure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultiple reaction monitoring\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMTT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNET\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil extracellular traps\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeutrophil to lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOPLS4\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOptimized Potentials for Liquid Simulations, Version 4\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphatidic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePAMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePathogen associated molecular patterns\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphatidyl choline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphatidyl ethanolaminutee\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhosphatidyl inositol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLS-DA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePartial least squares-discriminant analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePMN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolymorphonuclear\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPARγ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeroxisome Proliferator-Activated Receptor Gamma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePhsophatidyl serine\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePUFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePolyunsaturated fatty acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantitative Reverse Transcription Polymerase Chain Reaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQTRAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuadropule Ion Trap\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSerum Alkaline Phosphatase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystemic inflammatory response syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSphingomyelin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMILES\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSimplified Molecular Input Line Entry System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThin Layer Chromatography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eToll-like Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVEGFA-VEGFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVascular Endothelial Growth Factor A \u0026ndash; Vascular Endothelial Growth Factor Receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVariable importance projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study has been approved by the All India Institute of Medical Sciences, New Delhi ethics committee [Reference No. IEC/473/9/2016 and, IEC/369/7/2016]. All procedures are as per the declaration of Helsinki. All participants included in the study were \u0026gt;\u0026thinsp;18 years of age and were recruited in the study after informed consent.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eThis manuscript does not contain any individual person\u0026rsquo;s data in any form, including images, videos, or identifiable clinical details. All patient information used in this study was fully de-identified prior to analysis. Therefore, written informed consent for publication was not required as per journal guidelines.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe work carried out in this study was supported by the Anusandhan National Research Foundation (ANRF)-POWER grant, Government of India (Grant no. SPG/2021/002780), ANRF-Core research grant, Government of India (Grant no. CRG/2022/006016) and the All India Institute of Medical Sciences New Delhi Intramural Grant (A-565). We acknowledge support for the Schrodinger license obtained through UNMC Vice Chancellor for Research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.S., S.M., N.G.: Methodology, Data curation, Investigations, Validation, Formal analysis, Writing- Original draft preparation, Writing- Review and Editing; P.S., R.U., M.R.: Methodology, Investigations, Validation, Formal analysis; W.W., S.S.P., K.M., G.M., S.P.: Formal analysis, Visualization, Writing-Original draft preparation; Y.G. : Data curation, Formal analysis; A.N., V.S.: Conceptualization, Formal Analysis, Visualization, Writing- Original Draft Preparation; S.B., S.: Investigation, Resources, Data Curation, Writing- Original draft preparation; P.A.: Conceptualization, Methodology, Visualization, Data curation, Funding acquisition, Resources, Project administration, Supervision, Writing- Original draft preparation, Writing- Review and Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors sincerely thank Dr. Siddhartha Kundu, Associate Professor, Department of Biochemistry, for his timely guidance on the handling of lipid chemical formulae and for valuable inputs on the PPARγ-focused analyses.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study generated datasets as part of its analyses. All data supporting the findings of this study are available within the paper and its supplementary files. Any additional information or clarifications regarding the datasets will be made available by the corresponding author upon reasonable request.In addition to our primary dataset, we analysed publicly available lipidomics datasets from four previously published cohorts to identify shared and disease-specific lipid signatures. These datasets were obtained from:1. Clar\u0026iacute;a, J., Curto, A., Moreau, R., Colsch, B., L\u0026oacute;pez-Vicario, C., Lozano, J. J., Aguilar, F., Castelli, F. A., Fenaille, F., Junot, C., Zhang, I., Vinaixa, M., Yanes, O., Caraceni, P., Trebicka, J., Fern\u0026aacute;ndez, J., Angeli, P., Jalan, R., \u0026amp; Arroyo, V. (2021). Untargeted lipidomics uncovers lipid signatures that distinguish severe from moderate forms of acutely decompensated cirrhosis. Journal of hepatology , 75 (5), 1116\u0026ndash;1127. https:/doi.org/10.1016/j.jhep.2021.06.0432. Mecatti, G. C., Messias, M. C. F., \u0026amp; de Oliveira Carvalho, P. (2020). Lipidomic profile and candidate biomarkers in septic patients. Lipids in health and disease , 19 (1), 68. https:/doi.org/10.1186/s12944-020-01246-23. Wu, J., Cyr, A., Gruen, D. S., Lovelace, T. C., Benos, P. V., Das, J., Kar, U. K., Chen, T., Guyette, F. X., Yazer, M. H., Daley, B. J., Miller, R. S., Harbrecht, B. G., Claridge, J. A., Phelan, H. A., Zuckerbraun, B. S., Neal, M. D., Johansson, P. I., Stensballe, J., Namas, R. A., \u0026hellip; PAMPer study group (2022). Lipidomic signatures align with inflammatory patterns and outcomes in critical illness. Nature communications , 13 (1), 6789. https:/doi.org/10.1038/s41467-022-34420-44. Ciccarelli, M., Merciai, F., Carrizzo, A., Sommella, E., Di Pietro, P., Caponigro, V., Salviati, E., Musella, S., Sarno, V. D., Rusciano, M., Toni, A. L., Iesu, P., Izzo, C., Schettino, G., Conti, V., Venturini, E., Vitale, C., Scarpati, G., Bonadies, D., Rispoli, A., \u0026hellip; Campiglia, P. (2022). Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy). Journal of pharmaceutical and biomedical analysis , 217 , 114827. https:/doi.org/10.1016/j.jpba.2022.114827These publicly available datasets were used strictly for comparative analysis and remain accessible through their respective publications.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSol\u0026eacute; C, Sol\u0026agrave; E. Update on acute-on-chronic liver failure. Gastroenterol Hepatol. 2018;41(1):43\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArroyo V, Moreau R, Kamath PS, Jalan R, Gin\u0026egrave;s P, Nevens F, et al. Acute-on-chronic liver failure in cirrhosis. 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Front Physiol. 2021;12:730829.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlves-Bezerra M, Cohen DE. Triglyceride Metabolism in the Liver. Compr Physiol. 2017;8(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChiurchi\u0026ugrave; V, Leuti A, Maccarrone M. Bioactive Lipids and Chronic Inflammation: Managing the Fire Within. Front Immunol. 2018;9:38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasoodi M, Eiden M, Koulman A, Spaner D, Volmer DA. Comprehensive lipidomics analysis of bioactive lipids in complex regulatory networks. Anal Chem. 2010;82(19):8176\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ACLF, lipidomics, prognosis, neutrophils, PPARγ, organ failure","lastPublishedDoi":"10.21203/rs.3.rs-8154019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8154019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eAcute-on-chronic liver failure (ACLF) has ~\u0026thinsp;50% 28-day mortality driven by systemic inflammation, immune dysfunction, and multiorgan failure. The contribution of circulating lipids to ACLF pathogenesis remains poorly understood. We investigated whether plasma lipid signatures predict short-term mortality and participate in immune dysregulation in ACLF.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eTargeted plasma lipidomics was performed in ACLF patients (n\u0026thinsp;=\u0026thinsp;100; discovery n\u0026thinsp;=\u0026thinsp;60, validation n\u0026thinsp;=\u0026thinsp;40) and healthy controls (n\u0026thinsp;=\u0026thinsp;40). Prognostic utility of lipid signatures was compared with MELD, MELD-Na, and CTP scores. Cross-disease lipidome comparisons, \u003cem\u003eex vivo\u003c/em\u003e lipid-leukocyte assays, proteomic profiling of neutrophils and post-mortem liver/kidney tissue, and \u003cem\u003ein silico\u003c/em\u003e docking were used to investigate mechanistic relevance.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eOf 1218 quantified lipids, 316 were altered in ACLF and 30 were validated in an independent cohort. A five-lipid mortality signature [LPE(18:2), LPE(20:4), PE(O-18:0/16:1), TAG(48:1/16:1), PE(P-18:2/18:2)] discriminated survivors from non-survivors, with AUROC values comparable to MELD-Na and CTP. A combined lipid-CTP logit model achieved AUROC of 0.912, outperforming individual clinical scores. ACLF lipid profiles overlapped with those of sepsis, trauma, and COVID-19, indicating conserved critical-illness biology. \u003cem\u003eEx vivo\u003c/em\u003e, ACLF-derived lipids downregulated TLR1/6/7/8/10 and induced CD177 in healthy leukocytes, recapitulating ACLF-like immune signatures. Proteomic analyses across neutrophils, liver, and kidney revealed a shared neutrophil-degranulation program, while \u003cem\u003ein vitro\u003c/em\u003e assays showed that ACLF lipids were cytotoxic to HEK293T cells. \u003cem\u003eIn silico\u003c/em\u003e docking demonstrated potential binding of ACLF-lipids with PPARγ with affinities comparable to known ligands, suggesting a transcriptional mechanism linking lipid alterations to immune gene regulation.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eCirculating lipids in ACLF serve dual roles as prognostic biomarkers and active mediators of immune dysfunction and tissue injury. A validated five-lipid signature accurately predicts 28-day mortality, and mechanistic analyses implicate lipid-driven PPARγ modulation, TLR suppression, and neutrophil activation in ACLF pathogenesis. These findings offer opportunities for lipid-based risk stratification and therapeutic targeting in ACLF.\u003c/p\u003e","manuscriptTitle":"Circulating Lipids Predict Mortality and Drive Immune Gene Regulation in Acute-on-Chronic Liver Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 14:54:41","doi":"10.21203/rs.3.rs-8154019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc9dad49-af0a-4cf2-a88e-d3cf26134451","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-02T14:54:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 14:54:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8154019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8154019","identity":"rs-8154019","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}