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Oldham JM, Huang Y, Bose S, Ma SF, Kim JS, Schwab A, Ting C, Mou K, Lee CT, Adegunsoye A, Ghodrati S, Pugashetti JV, Nazemi N, Strek ME, Linderholm AL, Chen CH, Murray S, Zemans RL, Flaherty KR, Martinez FJ, Noth I. Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2024; 209:1111-1120. [PMID: 37847691 PMCID: PMC11092951 DOI: 10.1164/rccm.202301-0117oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023] Open
Abstract
Rationale: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. Objectives: To identify and validate circulating protein biomarkers of IPF survival. Methods: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California, Davis; Chicago; and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning was used to derive a proteomic signature of TFS. Measurements and Main Results: Of 2,921 proteins tested in the discovery cohort (n = 871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n = 355). After cohorts were combined, the validated proteins with the strongest TFS association were latent-transforming growth factor β-binding protein 2 (hazard ratio [HR], 2.43; 95% confidence interval [CI] = 2.09-2.82), collagen α-1(XXIV) chain (HR, 2.21; 95% CI = 1.86-2.39), and keratin 19 (HR, 1.60; 95% CI = 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. Conclusions: In the largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.
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Affiliation(s)
- Justin M. Oldham
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
- Department of Epidemiology, and
| | - Yong Huang
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
| | - Swaraj Bose
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Shwu-Fan Ma
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
| | - John S. Kim
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
| | - Alexandra Schwab
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
| | - Christopher Ting
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
| | - Kaniz Mou
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
| | - Cathryn T. Lee
- Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois
| | - Ayodeji Adegunsoye
- Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois
| | - Sahand Ghodrati
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, Davis, Davis, California
| | | | - Nazanin Nazemi
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
| | - Mary E. Strek
- Section of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, Illinois
| | - Angela L. Linderholm
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, Davis, Davis, California
| | - Ching-Hsien Chen
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, Davis, Davis, California
| | - Susan Murray
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Rachel L. Zemans
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
| | - Kevin R. Flaherty
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine
- Pulmonary Fibrosis Foundation, Chicago, Illinois; and
| | | | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, University of Virginia, Charlottesville, Virginia
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Summer R, Todd JL, Neely ML, Lobo LJ, Namen A, Newby LK, Shafazand S, Suliman S, Hesslinger C, Keller S, Leonard TB, Palmer SM, Ilkayeva O, Muehlbauer MJ, Newgard CB, Roman J. Circulating metabolic profile in idiopathic pulmonary fibrosis: data from the IPF-PRO Registry. Respir Res 2024; 25:58. [PMID: 38273290 PMCID: PMC10809477 DOI: 10.1186/s12931-023-02644-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND The circulating metabolome, reflecting underlying cellular processes and disease biology, has not been fully characterized in patients with idiopathic pulmonary fibrosis (IPF). We evaluated whether circulating levels of metabolites correlate with the presence of IPF, with the severity of IPF, or with the risk of clinically relevant outcomes among patients with IPF. METHODS We analyzed enrollment plasma samples from 300 patients with IPF in the IPF-PRO Registry and 100 individuals without known lung disease using a set of targeted metabolomics and clinical analyte modules. Linear regression was used to compare metabolite and clinical analyte levels between patients with IPF and controls and to determine associations between metabolite levels and measures of disease severity in patients with IPF. Unadjusted and adjusted univariable Cox regression models were used to evaluate associations between circulating metabolites and the risk of mortality or disease progression among patients with IPF. RESULTS Levels of 64 metabolites and 5 clinical analytes were significantly different between patients with IPF and controls. Among analytes with greatest differences were non-esterified fatty acids, multiple long-chain acylcarnitines, and select ceramides, levels of which were higher among patients with IPF versus controls. Levels of the branched-chain amino acids valine and leucine/isoleucine were inversely correlated with measures of disease severity. After adjusting for clinical factors known to influence outcomes, higher levels of the acylcarnitine C:16-OH/C:14-DC were associated with all-cause mortality, lower levels of the acylcarnitine C16:1-OH/C14:1DC were associated with all-cause mortality, respiratory death, and respiratory death or lung transplant, and higher levels of the sphingomyelin d43:2 were associated with the risk of respiratory death or lung transplantation. CONCLUSIONS IPF has a distinct circulating metabolic profile characterized by increased levels of non-esterified fatty acids, long-chain acylcarnitines, and ceramides, which may suggest a more catabolic environment that enhances lipid mobilization and metabolism. We identified select metabolites that were highly correlated with measures of disease severity or the risk of disease progression and that may be developed further as biomarkers. TRIAL REGISTRATION ClinicalTrials.gov; No: NCT01915511; URL: www. CLINICALTRIALS gov .
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Affiliation(s)
- Ross Summer
- Thomas Jefferson University, Philadelphia, PA, USA.
| | - Jamie L Todd
- Duke Clinical Research Institute, Durham, NC, USA
- Duke University Medical Center, Durham, NC, USA
| | - Megan L Neely
- Duke Clinical Research Institute, Durham, NC, USA
- Duke University Medical Center, Durham, NC, USA
| | - L Jason Lobo
- University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Andrew Namen
- Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - L Kristin Newby
- Duke Clinical Research Institute, Durham, NC, USA
- Duke University Medical Center, Durham, NC, USA
| | | | | | | | - Sascha Keller
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | | | - Scott M Palmer
- Duke Clinical Research Institute, Durham, NC, USA
- Duke University Medical Center, Durham, NC, USA
| | - Olga Ilkayeva
- Duke Molecular Physiology Institute, Durham, NC, USA
- Department of Medicine, Division of Endocrinology, Metabolism, and Nutrition, Duke University School of Medicine, Durham, NC, USA
| | | | | | - Jesse Roman
- Jane and Leonard Korman Institute, Thomas Jefferson University, Philadelphia, PA, USA
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