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Metabolomics analysis and cytokine profiling in ARDS: rationale and methodology of standardized laboratory procedures for biological sample analysis
1Department of Health Sciences, University of Basilicata, 85100 Potenza, Italy
2Anesthesia and Intensive Care, San Carlo Hospital, 85100 Potenza, Italy
3Cardiovascular Anesthesia and ICU, San Carlo Hospital, 85100 Potenza, Italy
4Department of Anesthesia and Intensive Care, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
5School of Medicine, Vita-Salute San Raffaele University, 20132 Milan, Italy
6Department of Radiology, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
DOI: 10.22514/sv.2025.141 Vol.21,Issue 10,October 2025 pp.17-27
Submitted: 15 May 2025 Accepted: 30 July 2025
Published: 08 October 2025
*Corresponding Author(s): Alessandro Belletti E-mail: belletti.alessandro@hsr.it
Background: Acute respiratory distress syndrome (ARDS) is a life-threatening condition associated with high short- and long-term morbidity and mortality. One major limitation in the management of ARDS is its biological and clinical heterogeneity, which may explain the lack of consistent benefit observed for most therapeutic interventions in unselected patient populations. Recent studies have suggested the existence of distinct ARDS subphenotypes, potentially characterized by unique inflammatory or metabolic signatures, which may respond differently to treatment. This supports the need for standardized tools to identify these subgroups and develop personalized therapeutic strategies. Methods: This manuscript describes a standardized protocol for metabolomic and cytokine profiling of biological samples from ARDS patients. Specifically, we outline detailed procedures for the collection and processing of serum and bronchoalveolar lavage fluid, and for the subsequent multi-omic analysis. Metabolomic profiling is performed using gas chromatography–mass spectrometry (GC–MS), following a validated sample preparation and derivatization workflow, allowing both targeted and untargeted metabolic analysis. Cytokine profiling is conducted using a Luminex® multiplex immunoassay platform, enabling the simultaneous quantification of multiple inflammatory mediators from low-volume samples. The manuscript also provides recommendations on sample quality control, data integration with clinical and imaging parameters, and multivariate statistical approaches for data interpretation. Conclusions: The described approach enables high-throughput, standardized, and reproducible molecular profiling of ARDS patients across different clinical studies. It is intended to support the identification of ARDS subphenotypes based on inflammatory and metabolic signatures, and to foster the integration of biological data into personalized clinical decision-making. This may serve as a methodological foundation for future prospective investigations aimed at improving outcome prediction and tailoring therapy in patients with ARDS.
Acute respiratory distress syndrome; Cytokine profiling; Disease phenotype; Respiratory failure; Inflammation; Metabolomic; Mechanical ventilation
Gianluca Paternoster,Monica Carmosino,Luigi Milella,Serena Milano,Maria Ponticelli,Mariasilvia Sannicandro,Maria Carmela Izzi,Vittorio Carlucci,Alessandro Belletti,Edoardo Mongardini,Giacomo Monti,Giuseppe Giardina,Diego Palumbo,Erica Ronca,Brian Ferrara,Benedetta Chiodi,Federico Mattia Oliva,Noemi De Piccoli,Nora Di Tomasso. Metabolomics analysis and cytokine profiling in ARDS: rationale and methodology of standardized laboratory procedures for biological sample analysis. Signa Vitae. 2025. 21(10);17-27.
[1] Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016; 315: 788–800.
[2] Grasselli G, Calfee CS, Camporota L, Poole D, Amato MBP, Antonelli M, et al. ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies. Intensive Care Medicine. 2023; 49: 727–759.
[3] Qadir N, Sahetya S, Munshi L, Summers C, Abrams D, Beitler J, et al. An update on management of adult patients with acute respiratory distress syndrome: an official American Thoracic Society Clinical Practice Guideline. American Journal of Respiratory and Critical Care Medicine. 2024; 209: 24–36.
[4] Combes A, Peek GJ, Hajage D, Hardy P, Abrams D, Schmidt M, et al. ECMO for severe ARDS: systematic review and individual patient data meta-analysis. Intensive Care Medicine. 2020; 46: 2048–2057.
[5] Tonna JE, Abrams D, Brodie D, Greenwood JC, Rubio Mateo-Sidron JA, Usman A, et al. Management of adult patients supported with Venovenous Extracorporeal Membrane Oxygenation (VV ECMO): guideline from the Extracorporeal Life Support Organization (ELSO). ASAIO Journal. 2021; 67: 601–610.
[6] Munroe ES, Spicer A, Castellvi-Font A, Zalucky A, Dianti J, Graham Linck E, et al. Evidence-based personalised medicine in critical care: a framework for quantifying and applying individualised treatment effects in patients who are critically ill. The Lancet Respiratory Medicine. 2025; 13: 556–568.
[7] Belletti A, Palumbo D, De Bonis M, Landoni G, Zangrillo A. The role of Macklin effect in management of ARDS: beyond spontaneous pneumomediastinum. Signa Vitae. 2024; 20: 10–14.
[8] Angelini M, Belletti A, Landoni G, Zangrillo A, De Cobelli F, Palumbo D. Macklin effect: from pathophysiology to clinical implication. Journal of Cardiothoracic and Vascular Anesthesia. 2024; 38: 881–883.
[9] Nasa P, Bos LD, Estenssoro E, van Haren FM, Serpa Neto A, Rocco PR, et al. Consensus statements on the utility of defining ARDS and the utility of past and current definitions of ARDS—protocol for a Delphi study. BMJ Open. 2024; 14: e082986.
[10] Nasa P, Bos LD, Estenssoro E, van Haren FMP, Neto AS, Rocco PRM, et al. Defining and subphenotyping ARDS: insights from an international Delphi expert panel. The Lancet Respiratory Medicine. 2025; 13: 638–650.
[11] Reddy K, Sinha P, O’Kane CM, Gordon AC, Calfee CS, McAuley DF. Subphenotypes in critical care: translation into clinical practice. The Lancet Respiratory Medicine. 2020; 8: 631–643.
[12] Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. The Lancet Respiratory Medicine. 2014; 2: 611–620.
[13] Maddali MV, Churpek M, Pham T, Rezoagli E, Zhuo H, Zhao W, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. The Lancet Respiratory Medicine. 2022; 10: 367–377.
[14] Famous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. American Journal of Respiratory and Critical Care Medicine. 2017; 195: 331–338.
[15] Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. The Lancet Respiratory Medicine. 2018; 6: 691–698.
[16] Al-Husinat L, Azzam S, Al Sharie S, Araydah M, Battaglini D, Abushehab S, et al. A narrative review on the future of ARDS: evolving definitions, pathophysiology, and tailored management. Critical Care. 2025; 29: 88.
[17] Battaglini D, Al-Husinat L, Normando AG, Leme AP, Franchini K, Morales M, et al. Personalized medicine using omics approaches in acute respiratory distress syndrome to identify biological phenotypes. Respiratory Research. 2022; 23: 318.
[18] Thompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. The New England Journal of Medicine. 2017; 377: 562–572.
[19] Fan S, Zeng S. Plasma proteomics in pediatric patients with sepsis—hopes and challenges. Clinical Proteomics. 2025; 22: 10.
[20] Monti G, Marzaroli M, Tucciariello MT, Ferrara B, Meroi F, Nakhnoukh C, et al. Pirfenidone to prevent fibrosis in acute respiratory distress syndrome: the PIONEER study protocol. Contemporary Clinical Trials. 2025; 153: 107883.
[21] Zhou K, Lu J. Progress in cytokine research for ARDS: a comprehensive review. Open Medicine. 2024; 19: 20241076.
[22] Chang Y, Yoo HJ, Kim SJ, Lee K, Lim CM, Hong SB, et al. A targeted metabolomics approach for sepsis-induced ARDS and its subphenotypes. Critical Care. 2023; 27: 263.
[23] Klech H, Hutter C. Clinical guidelines and indications for bronchoalveolar lavage (BAL): report of the European Society of Pneumology Task Group on BAL. European Respiratory Journal. 1990; 3: 937–976.
[24] Marcy TW, Merrill WW, Rankin JA, Reynolds HY. Limitations of using urea to quantify epithelial lining fluid recovered by bronchoalveolar lavage. American Review of Respiratory Disease. 1987; 135: 1276–1280.
[25] van de Graaf EA, Jansen HM, Weber JA, Koolen MG, Out TA. Influx of urea during bronchoalveolar lavage depends on the permeability of the respiratory membrane. Clinica Chimica Acta. 1991; 196: 27–39.
[26] Van Vyve T, Chanez P, Bernard A, Bousquet J, Godard P, Lauwerijs R, et al. Protein content in bronchoalveolar lavage fluid of patients with asthma and control subjects. The Journal of Allergy and Clinical Immunology. 1995; 95: 60–68.
[27] Patel PH, Antoine MH, Sankari A, Ullah S. Bronchoalveolar lavage. StatPearls: Tampa. 2024.
[28] Surowiec I, Karimpour M, Gouveia-Figueira S, Wu J, Unosson J, Bosson JA, et al. Multi-platform metabolomics assays for human lung lavage fluids in an air pollution exposure study. Analytical and Bioanalytical Chemistry. 2016; 408: 4751–4764.
[29] Walmsley S, Cruickshank-Quinn C, Quinn K, Zhang X, Petrache I, Bowler RP, et al. A prototypic small molecule database for bronchoalveolar lavage-based metabolomics. Scientific Data. 2018; 5: 180060.
[30] Desbrosses G, Steinhauser D, Kopka J, Udvardi M. Metabolome analysis using GC–MS. In Márquez AJ (ed.) Lotus Japonicus Handbook (pp. 165–174). 1st edn. Springer-Verlag: Dordrecht. 2005.
[31] Chace DH. Mass spectrometry in the clinical laboratory. Chemical Reviews. 2001; 101: 445–477.
[32] Kopka J, Fernie A, Weckwerth W, Gibon Y, Stitt M. Metabolite profiling in plant biology: platforms and destinations. Genome Biology. 2004; 5: 109.
[33] Yan Z, Yang F, Wen S, Ding W, Si Y, Li R, et al. Longitudinal metabolomics profiling of serum amino acids in rotenone-induced Parkinson’s mouse model. Amino Acids. 2022; 54: 111–121.
[34] Wagner C, Sefkow M, Kopka J. Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry. 2003; 62: 887–900.
[35] Strehmel N, Hummel J, Erban A, Strassburg K, Kopka J. Retention index thresholds for compound matching in GC–MS metabolite profiling. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences. 2008; 871: 182–190.
[36] Matyushin DD, Karnaeva AE, Sholokhova AY. Critical evaluation of the NIST retention index database reliability with specific examples. Analytical and Bioanalytical Chemistry. 2024; 416: 6181–6186.
[37] Long FH. Multivariate analysis for metabolomics and proteomics data. In Veenstra TD, Issaq HJ (eds.) Proteomic and metabolomic approaches to biomarker discovery (pp. 299–311). 1st edn. Academic Press: New York. 2013.
[38] Alosaimi ME, Alotaibi BS, Abduljabbar MH, Alnemari RM, Almalki AH, Serag A. Therapeutic implications of dapagliflozin on the metabolomics profile of diabetic rats: a GC–MS investigation coupled with multivariate analysis. Journal of Pharmaceutical and Biomedical Analysis. 2024; 242: 116018.
[39] Zhang Z, Chen L, Sun B, Ruan Z, Pan P, Zhang W, et al. Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration. Nature Communications. 2024; 15: 9028.
[40] Antcliffe DB, Harte E, Hussain H, Jiménez B, Browning C, Gordon AC. Metabolic septic shock sub-phenotypes, stability over time and association with clinical outcome. Intensive Care Medicine. 2025; 51: 529–541.
[41] Huang P, Liu Y, Li Y, Xin Y, Nan C, Luo Y, et al. Metabolomics- and proteomics-based multi-omics integration reveals early metabolite alterations in sepsis-associated acute kidney injury. BMC Medicine. 2025; 23: 79.
[42] Gou Y, Liu JJ, Zhang JF, Yang WP, Yang JZ, Feng K. Identifying biomarkers distinguishing sepsis after trauma from trauma-induced SIRS based on metabolomics data: a retrospective study. Scientific Reports. 2025; 15: 13748.
[43] Miao X, Song C, Zhen P. The role of metabolomics in myocardial infarction: a recent mini-review. Signa Vitae. 2023; 19: 34–42.
[44] Li X, Wang J. Recent application of metabolomics in the diagnosis, pathogenesis, treatment, and prognosis of sepsis. Signa Vitae. 2023; 19: 15–22.
[45] Yu F, Zhu J, Lei M, Wang CJ, Xie K, Xu F, et al. Exploring the metabolic phenotypes associated with different host inflammation of acute respiratory distress syndrome (ARDS) from lung metabolomics in mice. Rapid Communications in Mass Spectrometry. 2021; 35: e8971.
[46] Evans CR, Karnovsky A, Kovach MA, Standiford TJ, Burant CF, Stringer KA. Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health. Journal of Proteome Research. 2014; 13: 640–649.
[47] Stringer KA, McKay RT, Karnovsky A, Quémerais B, Lacy P. Metabolomics and its application to acute lung diseases. Frontiers in Immunology. 2016; 7: 44.
[48] Fabiano A, Gazzolo D, Zimmermann LJ, Gavilanes AW, Paolillo P, Fanos V, et al. Metabolomic analysis of bronchoalveolar lavage fluid in preterm infants complicated by respiratory distress syndrome: preliminary results. The Journal of Maternal-Fetal & Neonatal Medicine. 2011; 24: 55–58.
[49] Chang DW, Hayashi S, Gharib SA, Vaisar T, King ST, Tsuchiya M, et al. Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine. 2008; 178: 701–709.
[50] Bhargava M, Viken K, Wang Q, Jagtap P, Bitterman P, Ingbar D, et al. Bronchoalveolar lavage fluid protein expression in acute respiratory distress syndrome provides insights into pathways activated in subjects with different outcomes. Scientific Reports. 2017; 7: 7464.
[51] Pimentel E, Banoei MM, Kaur J, Lee CH, Winston BW. Metabolomic insights into COVID-19 severity: a scoping review. Metabolites. 2024; 14: 617.
[52] Suber TL, Wendell SG, Mullett SJ, Zuchelkowski B, Bain W, Kitsios GD, et al. Serum metabolomic signatures of fatty acid oxidation defects differentiate host-response subphenotypes of acute respiratory distress syndrome. Respiratory Research. 2023; 24: 136.
[53] Singh A, Siddiqui MA, Pandey S, Azim A, Sinha N. Unveiling pathophysiological insights: serum metabolic dysregulation in acute respiratory distress syndrome patients with acute kidney injury. Journal of Proteome Research. 2024; 23: 4216–4228.
[54] Lin M, Xu F, Sun J, Song J, Shen Y, Lu S, et al. Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS. Critical Care. 2024; 28: 213.
[55] Zhang S, Hagens LA, Heijnen NFL, Smit MR, Brinkman P, Fenn D, et al. Breath metabolomics for diagnosis of acute respiratory distress syndrome. Critical Care. 2024; 28: 96.
[56] Valaperti A, Bezel P, Vonow-Eisenring M, Franzen D, Steiner UC. Variability of cytokine concentration in whole blood serum and bronchoalveolar lavage over time. Cytokine. 2019; 123: 154768.
[57] Kowalski B, Valaperti A, Bezel P, Steiner UC, Scholtze D, Wieser S, et al. Analysis of cytokines in serum and bronchoalveolar lavage fluid in patients with immune-checkpoint inhibitor-associated pneumonitis: a cross-sectional case–control study. Journal of Cancer Research and Clinical Oncology. 2022; 148: 1711–1720.
[58] Hosoki K, Ying S, Corrigan C, Qi H, Kurosky A, Jennings K, et al. Analysis of a panel of 48 cytokines in BAL fluids specifically identifies IL-8 levels as the only cytokine that distinguishes controlled asthma from uncontrolled asthma, and correlates inversely with FEV1. PLOS ONE. 2015; 10: e0126035.
[59] Shanthikumar S, Gubbels L, Davies K, Walker H, Wong ATC, Levi E, et al. Highly multiplexed cytokine analysis of bronchoalveolar lavage and plasma reveals age-related dynamics and correlates of inflammation in children. Mucosal Immunology. 2024; 18: 380–389.
[60] Belletti A, Palumbo D, Landoni G, Zangrillo A, De Bonis M. Air leak, barotrauma susceptibility, and imaging in acute respiratory distress syndrome: novel application of an old tool. Intensive Care Medicine. 2022; 48: 1837–1838.
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