---
title: "Plasma metabolomic profiling reveals candidate biomarkers for sepsis diagnosis and mortality predi"
id: "frontiers-in-immunology-18-plasma-metabolomic-profiling-identifies-candidate-biomarkers-associated-with"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-18-plasma-metabolomic-profiling-identifies-candidate-biomarkers-associated-with"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1931808"
published_at: "2026-10-14T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Plasma metabolomic profiling reveals candidate biomarkers for sepsis diagnosis and mortality predi
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-18-plasma-metabolomic-profiling-identifies-candidate-biomarkers-associated-with
- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1931808)
- **Published At:** 2026-10-14T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The article reported that **plasma metabolomic profiling** identified candidate biomarkers linked to both sepsis diagnosis and mortality prediction. - Investigators applied metabolomic analysis to plasma samples; the source did not report the exact analytical platform or sample processing steps. - Data indicated differential metabolite patterns between groups, according to the authors, but the paper did not provide specific metabolite names in the accessible excerpt. - The study team suggested these candidate markers could assist diagnostic classification and risk stratification in sepsis. - The source did not disclose sample size, cohort selection criteria, enrollment timing, or the clinical definitions used for sepsis and mortality endpoints. - Statistical methods, thresholds for biomarker selection, and performance metrics (AUC, sensitivity, specificity) were not reported in the available text. - Authors noted candidate biomarkers associated with both diagnosis and mortality; whether these were independent predictors after adjustment for clinical covariates was not reported. - The accessible extract did not list external validation efforts or replication cohorts; the presence or absence of validation therefore remains unspecified. - Limitations specific to the dataset—such as heterogeneity, timing of sample collection, comorbid illness, or medication effects—were not detailed in the provided content. - Key unanswered questions remain: which metabolites, how they perform versus existing scores, and what regulatory or clinical steps the authors recommend next; the source did not provide those implementation details in the excerpt.
## Clinical Analysis & Structured Key Points
Frontiers | Plasma metabolomic profiling identifies candidate biomarkers associated with sepsis diagnosis and mortality prediction 1 and p ORIGINAL RESEARCH article Front. Immunol. , 14 October 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1931808 Published in Frontiers in Immunology Inflammation 7 impact factor 11.3 citescore Editor & Reviewers Edited by L B Lisardo Bosca Reviewed by E P Emanuele Porru V G Vishal G Shelat Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Table 1 Clinical characteristics of subjects in the discovery and replication cohorts. View in article ORIGINAL RESEARCH article Front. Immunol. , 14 October 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1931808 Plasma metabolomic profiling identifies candidate biomarkers associated with sepsis diagnosis and mortality prediction Y Z Yanyan Zhu 1,2,3,4,5,6 † H W Haocheng Wang 7 † S Y Sanjun Yi 1,2,3,4,5,6 † F Y Fuping You 7 * P Z Pingsen Zhao 1,2,3,4,5,6 * 1. Department of Laboratory Medicine, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, China 2. Laboratory for Diagnosis of Clinical Microbiology and Infection, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, China 3. Research Center for Interdisciplinary and High-quality Innovative Development in Laboratory Medicine, Shaoguan, China 4. Shaoguan Municipal Quality Control Center for Laboratory Medicine, Yuebei People’s Hospital Affiliated to Shantou University Medical College, Shaoguan, China 5. Shaoguan Municipal Quality Control Center for Surveillance of Bacterial Resistance, Shaoguan, China 6. Shaoguan Engineering Research Center for Research and Development of Molecular and Cellular Technology in Rapid Diagnosis of Infectious Diseases and Cancer, Shaoguan, China 7. Institute of Systems Biomedicine, Department of Immunology, School of Basic Medical Sciences, Beijing Key Laboratory of Tumor Systems Biology, National Health Commission (NHC) Key Laboratory of Medical Immunology, Peking University Health Science Center, Beijing, China See more Article metrics View details Abstract Introduction: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection; its early diagnosis and mortality prediction remain challenging, and conventional markers (CRP, PCT) have limited sensitivity and specificity. We aimed to characterize the plasma metabolome of sepsis and identify candidate metabolic biomarkers for diagnosis and 30-day mortality prediction. Methods: In this retrospective, single-center study, plasma from 30 healthy volunteers, 107 sepsis/septic shock patients and 19 non-septic ICU patients was analyzed by non-targeted LC-MS/MS and stratified into discovery (n=86) and replication (n=70) cohorts. Differential metabolites were defined by VIP > 1 and p 2 mmol/L (18 mg/dL). Therapeutic interventions for sepsis patients were implemented in strict accordance with international clinical practice guidelines. The inclusion criteria for ICU non-sepsis patients were ( 1 ): Requiring ICU admission for treatment, with no diagnosis of sepsis during the hospitalization ( 2 ). Age ≥ 18 years ( 3 ); Availability of comprehensive clinical documentation. The detailed clinical characteristics of subjects in both discovery and replication cohorts are presented in Table 1 . To address the potential influence of organ dysfunction on the plasma metabolome, baseline blood urea, serum creatinine, estimated glomerular filtration rate (eGFR), and liver function tests (alanine aminotransferase, aspartate aminotransferase, and total bilirubin) were recorded for all participants, and the proportion of patients meeting KDIGO acute kidney injury (AKI) criteria, including Stage 3, is reported in Table 1 . Table 1 Discovery cohort Clinical data by disease type Clinical data by survival status Characteristic Healthy (n=20) Non-sepsis (n=11) Sepsis (n=55) p value Survivor (n=34) Death (n=21) p value Age (years) 52.0 (29.5–58.0) 55.0 (44.0–65.0) 69.0 (54.0–77.0) 0.001 61.5 (50.2–72.0) 73.0 (57.0–79.0) 0.059 SOFA score 0.0 (0.0–0.0) 0.0 (0.0–0.0) 7.0 (4.0–9.0) <0.001 6.0 (3.2–8.0) 8.0 (7.0–11.0) 0.008 Sex, male, n (%) 9 (45) 9 (82) 35 (64) 22 (65) 13 (62) Comorbidities, n (%) Cardiovascular disease 0 (0) 0 (0) 9 (16) 0.059 5 (15) 4 (19) 0.719 Hypertension 0 (0) 0 (0) 17 (31) 0.003 9 (26) 8 (38) 0.386 Diabetes 4 (20) 2 (18) 13 (24) 0.894 8 (24) 5 (24) 1.000 COPD 0 (0) 0 (0) 1 (2) 0.752 0 (0) 1 (5) 0.382 Chronic kidney disease 0 (0) 0 (0) 11 (20) 0.029 4 (12) 7 (33) 0.082 Liver disease 0 (0) 0 (0) 13 (24) 0.013 6 (18) 7 (33) 0.208 Inflammatory and infection markers White blood cell count (×10 9 /L) 6.4 (5.3–8.2) 10.2 (7.3–13.8) 11.7 (7.8–18.6) <0.001 12.9 (9.7–19.6) 10.0 (7.3–14.6) 0.058 C-reactive protein (mg/dL) 0.1 (0.0–0.1) 2.6 (0.9–14.4) 16.3 (8.3–23.4) <0.001 18.2 (8.3–26.1) 15.0 (10.3–19.3) 0.662 Procalcitonin (ng/mL) 0.1 (0.0–0.1) 0.4 (0.1–2.3) 39.4 (11.1–100.0) <0.001 40.7 (15.8–96.3) 32.0 (8.4–100.0) 0.706 Renal function Urea/BUN (mmol/L) 5.8 (4.5–6.2) 6.6 (5.2–6.8) 10.5 (7.3–14.8) <0.001 9.2 (7.0–11.7) 12.0 (10.1–19.6) 0.012 Creatinine (µmol/L) 84.7 (65.6–94.9) 119.0 (78.5–127.5) 171.6 (107.9–253.0) <0.001 138.5 (100.4–214.3) 242.6 (164.4–354.9) 0.011 eGFR (mL/min/1.73 m 2 ) 88.6 (74.8–101.6) 63.3 (57.8–103.8) 36.1 (21.6–57.5) <0.001 44.4 (26.9–65.2) 24.3 (13.1–40.9) 0.007 Hepatic function ALT (U/L) 15.0 (11.7–18.0) 28.9 (21.5–78.8) 41.3 (20.0–87.6) <0.001 39.5 (18.1–67.7) 61.4 (21.6–106.7) 0.194 AST (U/L) 20.9 (18.1–22.8) 36.2 (28.8–66.9) 91.7 (34.1–165.0) <0.001 74.0 (32.2–165.0) 96.5 (43.7–137.6) 0.525 Total bilirubin (µmol/L) 10.9 (7.4–14.3) 12.6 (10.0–28.2) 24.1 (12.4–59.8) 0.076 21.7 (12.2–46.8) 38.1 (16.8–62.3) 0.287 Acute kidney injury AKI stage 3, n (%) – – 10 (18) 4 (12) 6 (29) Outcome 30-day mortality, n (%) – – 21 (38) – – Replication cohort Clinical data by disease type Clinical data by survival status Characteristic Healthy (n=10) Non-sepsis (n=8) Sepsis (n=52) p value Survivor (n=35) Death (n=17) p value Age (years) 45.5 (34.2–61.5) 48.5 (41.0–53.2) 64.0 (57.8–74.2) 0.001 64.0 (57.5–72.5) 72.0 (60.0–78.0) 0.287 SOFA score 0.0 (0.0–0.0) 1.5 (0.8–2.2) 5.0 (3.0–6.0) <0.001 4.0 (3.0–6.0) 5.0 (5.0–6.0) 0.052 Sex, male, n (%) 5 (50) 6 (75) 27 (52) 16 (46) 11 (65) Comorbidities, n (%) Cardiovascular disease 0 (0) 0 (0) 16 (31) 0.028 9 (26) 7 (41) 0.340 Hypertension 0 (0) 0 (0) 19 (37) 0.011 13 (37) 6 (35) 1.000 Diabetes 0 (0) 2 (25) 13 (25) 0.204 10 (29) 3 (18) 0.506 COPD 0 (0) 0 (0) 1 (2) 0.839 1 (3) 0 (0) 1.000 Chronic kidney disease 0 (0) 0 (0) 17 (33) 0.019 13 (38) 4 (24) 0.358 Liver disease 0 (0) 0 (0) 9 (17) 0.167 6 (17) 3 (18) 1.000 Inflammatory and infection markers White blood cell count (×10 9 /L) 7.0 (6.6–8.1) 10.3 (9.6–10.9) 14.8 (9.8–19.9) 0.002 16.3 (11.4–24.8) 10.4 (7.8–16.4) 0.018 C-reactive protein (mg/dL) 0.1 (0.1–0.2) 11.2 (3.9–32.2) 25.5 (13.9–37.1) <0.001 23.9 (11.9–30.2) 25.6 (20.8–38.2) 0.524 Procalcitonin (ng/mL) 0.0 (0.0–0.0) 5.9 (0.2–11.3) 23.6 (4.5–79.6) <0.001 25.4 (5.2–98.2) 9.7 (4.8–39.7) 0.279 Renal function Urea/BUN (mmol/L) 4.8 (4.2–5.0) 9.2 (9.1–10.1) 11.6 (8.1–16.3) <0.001 11.4 (8.3–15.9) 12.0 (7.8–17.7) 0.374 Creatinine (µmol/L) 73.6 (63.5–80.7) 127.8 (97.3–131.5) 132.3 (102.1–243.1) 0.002 131.8 (107.1–261.4) 132.3 (92.7–226.0) 0.742 eGFR (mL/min/1.73 m 2 ) 107.2 (93.5–117.7) 58.9 (50.9–83.3) 44.0 (23.6–64.9) <0.001 40.6 (20.7–61.8) 48.5 (29.0–68.9) 0.516 Hepatic function ALT (U/L) 14.2 (10.7–19.2) 29.3 (18.3–120.5) 24.3 (16.6–46.8) 0.025 20.9 (16.6–40.4) 31.7 (23.5–55.4) 0.334 AST (U/L) 19.8 (17.7–23.0) 43.0 (25.7–208.1) 40.1 (23.6–81.2) 0.013 36.8 (19.6–65.1) 72.0 (34.9–84.1) 0.146 Total bilirubin (µmol/L) 13.8 (10.7–15.3) 14.2 (8.3–21.5) 14.9 (9.9–34.2) 0.685 15.1 (11.4–32.2) 13.8 (8.7–37.3) 0.782 Acute kidney injury AKI stage 3, n (%) – – 7 (14) 5 (15) 2 (12) Outcome 30-day mortality, n (%) – – 17 (33) – – Clinical characteristics of subjects in the discovery and replication cohorts. The study design schema is depicted in Figure 1 . The discovery panel encompassed: 21 patients with sepsis, 34 patients with septic shock, 11 ICU non-sepsis patients, and 20 healthy control subjects. The replication panel consisted of: 27 patients with sepsis, 25 patients with septic shock, 8 ICU non-sepsis patients, and 10 healthy control subjects. For longitudinal analysis, 23 patients diagnosed with sepsis and septic shock from the replication cohort were selected, comprising 11 survivors and 12 non-survivors (mortality within 30 days of enrollment). Participants were randomly assigned to the discovery and replication panels. Healthy control subjects were recruited from individuals attending routine health examinations during the same period and were free of acute illness, active infection, or known chronic inflammatory or immune disorders. Figure 1 Flowchart and study design. Flowchart of individuals enrollment and study design in total patients with sepsis and subgroup analysis between survivors and deaths in sepsis, including timing analysis. Blood sample processing Peripheral blood was collected into lithium-heparin tubes, consistent across all participants, and plasma was separated and stored at −80 °C until analysis. Frozen blood samples were thawed under controlled conditions at 4 °C and subjected to vortex mixing for 60 seconds to ensure complete homogenization. Precise aliquots were quantitatively transferred into 2 mL centrifuge tubes, followed by the addition of 400 µL pre-chilled methanol (-20 °C). The mixture was further homogenized by vortexing for 60 seconds and subsequently centrifuged at 12,000 rpm for 10 minutes at 4 °C. The resultant supernatant was carefully aspirated and transferred to fresh 2 mL centrifuge tubes, followed by vacuum concentration to complete dryness. The dried extracts were reconstituted in 150 µL of a solution comprising 80% methanol in water (v/v) containing 2-chloro-L-phenylalanine (4 ppm) as an internal standard, maintained at 4 °C. Prior to LC-MS analysis, the reconstituted samples were filtered through 0.22 µm membrane filters, and the filtrates were collected in autosampler vials. Liquid chromatography conditions LC-MS grade methanol (MeOH) and acetonitrile (ACN) were procured from Fisher Scientific (Loughborough, UK). 2-Amino-3-(2-chloro-phenyl)-propionic acid was sourced from Aladdin (Shanghai, China), formic acid from TCI (Shanghai, China), and ammonium formate from Sigma-Aldrich (Shanghai, China). Ultrapure water was generated using a Milli-Q water purification system (Millipore, Bedford, USA). All reagents were of analytical grade and used as received. Chromatographic separation was performed on a Vanquish UHPLC System (Thermo Fisher Scientific, USA) equipped with an ACQUITY UPLC ® HSS T3 column (2.1 × 100 mm, 1.8 µm particle size; Waters, Milford, MA, USA). The column temperature was maintained at 40 °C, with a flow rate of 0.3 mL/min and an injection volume of 2 µL. For positive electrospray ionization mode (ESI + ), the mobile phase comprised (A2) 0.1% formic acid in water (v/v) and (B2) 0.1% formic acid in acetonitrile (v/v). The gradient elution program was as follows: 0–1 min, 8% B2; 1–8 min, linear increase from 8% to 98% B2; 8–10 min, maintained at 98% B2; 10-10.1 min, decrease from 98% to 8% B2; 10.1–12 min, re-equilibration at 8% B2 ( 16 ). For negative electrospray ionization mode (ESI - ), the mobile phase consisted of (A3) 5 mM ammonium formate and (B3) acetonitrile. The gradient program was identical to that of the positive mode: 0–1 min, 8% B3; 1–8 min, linear increase from 8% to 98% B3; 8–10 min, maintained at 98% B3; 10-10.1 min, decrease from 98% to 8% B3; 10.1–12 min, re-equilibration at 8% B3 ( 19 ). Mass spectrometry conditions Mass spectrometric analysis was conducted using an Orbitrap Exploris 120 mass spectrometer (Thermo Fisher Scientific, USA) interfaced with an electrospray ionization (ESI) source. The instrument was operated in Full MS-ddMS2 mode, enabling concurrent acquisition of MS1 and data-dependent MS/MS spectra. Operating parameters were optimized as follows: sheath gas pressure, 30 arbitrary units; auxiliary gas flow, 10 arbitrary units; spray voltage, 3.50 kV for positive mode and -2.50 kV for negative mode; capillary temperature, 325 °C. MS1 data were acquired over a mass range of m/z 100–1000 at a resolution of 60,000 FWHM. Data-dependent MS/MS acquisition incorporated four scans per duty cycle at 15,000 FWHM resolution, with normalized collision energy of 30% and automatic dynamic exclusion ( 20 ). Data processing Raw mass spectrometric data were converted to mzXML format using MSConvert (ProteoWizard software package, v3.0.8789) and subsequently processed using XCMS (v3.12.0) ( 21 ) for feature detection, retention time alignment, and peak picking. Metabolite identification was performed by comparing accurate mass measurem
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