한빛사논문
- 조회65
Kevin Y Cunningham1, Benjamin Hur2, Vinod K Gupta2, Matthew J Koster3, Cornelia M Weyand3,4, David Cuthbertson5, Nader A Khalidi6, Curry L Koening7, Carol A Langford8, Carol A McAlear9, Paul A Monach10, Larry W Moreland11, Christian Pagnoux12, Rennie L Rhee9, Philip Seo13, Peter A Merkel9, Kenneth J Warrington3, Jaeyun Sung2,3,14
1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, Minnesota, USA
2Microbiomics Program, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
3Division of Rheumatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
4Department of Immunology, Mayo Clinic, Rochester, Minnesota, USA
5Department of Biostatistics and Informatics, Department of Pediatrics, University of South Florida, Tampa, Florida, USA
6Division of Rheumatology, St. Joseph’s Healthcare Hamilton, McMaster University, Hamilton, Ontario, Canada
7Division of Rheumatology, University of Utah, Salt Lake City, Utah, USA
8Division of Rheumatology, Cleveland Clinic, Cleveland, Ohio, USA
9Division of Rheumatology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
10Rheumatology Section, VA Boston Healthcare System, Boston, Massachusetts, USA
11Division of Rheumatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
12Division of Rheumatology, Mount Sinai Hospital, Toronto, Ontario, Canada
13Division of Rheumatology, Johns Hopkins University, Baltimore, Maryland, USA
14Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
Correspondence to Dr Jaeyun Sung
KYC and BH are joint first authors.
KJW and JS are joint senior authors.
Abstract
Objectives: This study aimed to identify plasma proteomic signatures that differentiate active and inactive giant cell arteritis (GCA) from non-disease controls. By comprehensively profiling the plasma proteome of both patients with GCA and controls, we aimed to identify plasma proteins that (1) distinguish patients from controls and (2) associate with disease activity in GCA.
Methods: Plasma samples were obtained from 30 patients with GCA in a multi-institutional, prospective longitudinal study: one captured during active disease and another while in clinical remission. Samples from 30 age-matched/sex-matched/race-matched non-disease controls were also collected. A high-throughput, aptamer-based proteomics assay, which examines over 7000 protein features, was used to generate plasma proteome profiles from study participants.
Results: After adjusting for potential confounders, we identified 537 proteins differentially abundant between active GCA and controls, and 781 between inactive GCA and controls. These proteins suggest distinct immune responses, metabolic pathways and potentially novel physiological processes involved in each disease state. Additionally, we found 16 proteins associated with disease activity in patients with active GCA. Random forest models trained on the plasma proteome profiles accurately differentiated active and inactive GCA groups from controls (95.0% and 98.3% in 10-fold cross-validation, respectively). However, plasma proteins alone provided limited ability to distinguish between active and inactive disease states within the same patients.
Conclusions: This comprehensive analysis of the plasma proteome in GCA suggests that blood protein signatures integrated with machine learning hold promise for discovering multiplex biomarkers for GCA.
논문정보
- Research article
- 2024년 08월 (BRIC 등록일 2024-09-05)
- 국외 연구진
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