Global Metabolite Profiling of Synovial Fluid for the Specific Diagnosis of Rheumatoid Arthritis from Other Inflammatory Arthritis
Open accessAbstract
Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In a mouse model of multiple sclerosis, transplantation of neural stem cells reduces succinate levels in the cerebrospinal fluid and decreases infiltration of damaging macrophages and microglial cells.
“In a mouse model of multiple sclerosis (MS) transplantation of neural stem cells (NSCs) reduced succinate levels in the cerebrospinal fluid and thereby decreased infiltration of damaging macrophages and microglial cells10.”
From Coupling Krebs cycle metabolites to signalling in immunity and cancer (2018) · cites this paper as [10]
Ligation of SUCNR1 on neural stem cells upregulates the dicarboxylate co-transporter Slc13a3/5, correlating with increased succinate uptake by the NSCs.
“Mechanistically, ligation of SUCNR1 on the surface of NSCs upregulates the expression of the dicarboxylate co-transporter Slc13a3/5, which correlates with increased uptake by the NSCs thereby scavenging succinate away from pro-inflammatory immune cells.”
From Coupling Krebs cycle metabolites to signalling in immunity and cancer (2018) · cites this paper as [10]
A portion of succinate that accumulates during ischemia and inflammation is released from the cell into the extracellular environment.
“a portion of the succinate that accumulates during ischemia and inflammation is released from the cell into the extracellular environment through poorly defined processes. This occurs following IR injury158,159, inflammation10, or cold exposure160.”
From Coupling Krebs cycle metabolites to signalling in immunity and cancer (2018) · cites this paper as [10]
Specific PHD inhibitors such as Vadadustat have shown promise in chronic kidney disease, increasing erythropoiesis and providing a means of attenuating HIF in other pathologies.
“However, specific PHD inhibitors have been developed, such as Vadadustat, and have shown promise in chronic kidney disease, increasing erythropoiesis and providing a means of attenuating HIF in other pathologies [10].”
From Coupling Krebs cycle metabolites to signalling in immunity and cancer (2018) · cites this paper as [10]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Measuring the Accuracy of Diagnostic Systems
Swets, J. A. (1988) · Science · doi:10.1126/science.3287615 · Cited by 10,058 · see citing works
[9] TM4 Microarray Software Suite
Saeed, A. I., BHAGABATI, N. K., Braisted, J. C., Liang, W. P., Sharov, V., Howe, E., Li, J., Thiagarajan, M., White, J., & Quackenbush, J. F. (2006) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/s0076-6879(06)11009-5 · Cited by 1,982 · see citing works
Rantapää‐Dahlqvist, S., Jong, B. A. W. D., Berglin, E. H., Hallmans, G., Wadell, G., Stenlund, H. C., Sundin, U., & VENROOIJ, W. J. V. (2003) · Arthritis & Rheumatism · doi:10.1002/art.11223 · Cited by 1,946 · see citing works
Greiner, M., Pfeiffer, D., & Smith, R. D. (2000) · Preventive Veterinary Medicine · doi:10.1016/s0167-5877(00)00115-x · Cited by 1,923 · see citing works
Clinical tests: sensitivity and specificity
Lalkhen, A. G., & McCluskey, A. (2008) · Continuing Education in Anaesthesia Critical Care & Pain · doi:10.1093/bjaceaccp/mkn041 · Cited by 942 · see citing works
The use of receiver operating characteristic curves in biomedical informatics
Lasko, T. A., Bhagwat, J. G., Zou, K. H., & Lucila Ohno-machado, A. (2005) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2005.02.008 · Cited by 821 · see citing works
Metabolic Phenotyping in Health and Disease
Holmes, E. C., Wilson, I. D., & Nicholson, J. K. (2008) · Cell · doi:10.1016/j.cell.2008.08.026 · Cited by 821 · see citing works
Quality control for plant metabolomics: reporting MSI‐compliant studies
Fiehn, O., Wohlgemuth, G., Scholz, M., Kind, T., Lee, D. Y., Lü, Y., Moon, S. M., & Nikolau, B. J. (2008) · The Plant Journal · doi:10.1111/j.1365-313x.2007.03387.x · Cited by 676 · see citing works
Chemometrics in metabolomics—A review in human disease diagnosis
Madsen, R., Lundstedt, T., & Trygg, J. (2009) · Analytica Chimica Acta · doi:10.1016/j.aca.2009.11.042 · Cited by 559 · see citing works
Kremer, J. M., Lawrence, D. A., Jubiz, W., Digiacomo, R., Rynes, R. I., Bartholomew, L. E., & Sherman, M. J. (1990) · Arthritis & Rheumatism · doi:10.1002/art.1780330607 · Cited by 557 · see citing works
Källberg, H., Padyukov, L., Plenge, R. M., Rönnelid, J., Gregersen, P. K., Mil, A. H. M. V. D. H., Toes, R. E. M., Huizinga, T. W. J., Klareskog, L., & Alfredsson, L. S. (2007) · The American Journal of Human Genetics · doi:10.1086/516736 · Cited by 455 · see citing works
Metabolomic profiling to develop blood biomarkers for Parkinson's disease
Bogdanov, M. B., Matson, W. R., Wang, L., Matson, T., Saunders-Pullman, R. J., Bressman, S., & Beal, M. F. (2008) · Brain · doi:10.1093/brain/awm304 · Cited by 402 · see citing works
The role of metabolites and metabolomics in clinically applicable biomarkers of disease
Mamas, M. A., Dunn, W. B., Neyses, L., & Goodacre, R. (2010) · Archives of Toxicology · doi:10.1007/s00204-010-0609-6 · Cited by 379 · see citing works
Denkert, C., Budczies, J., Kind, T., Weichert, W., Tablack, P., Sehouli, J., Niesporek, S., Könsgen, D., Dietel, M., & Fiehn, O. (2006) · Cancer Research · doi:10.1158/0008-5472.can-06-0755 · Cited by 376 · see citing works
Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma
Chen, T., Xie, G., Wang, X., Fan, J., Qiu, Y., Zheng, X., Qi, X., Cao, Y., Su, M., Wang, X., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.004945 · Cited by 364 · see citing works
Protein Unfolding by Peptidylarginine Deiminase
Tarcsa, E., Marekov, L. N., Mei, G., Melino, G., Lee, S., & Steinert, P. M. (1996) · Journal of Biological Chemistry · doi:10.1074/jbc.271.48.30709 · Cited by 353 · see citing works
Exploring Disease through Metabolomics
Vinayavekhin, N., Homan, E. A., & Saghatelian, A. (2009) · ACS Chemical Biology · doi:10.1021/cb900271r · Cited by 232 · see citing works
Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes
Connor, S. C., Hansen, M. K., Corner, A. S., Smith, R. F., & RYAN, T. E. (2010) · Molecular BioSystems · doi:10.1039/b914182k · Cited by 190 · see citing works
High quality metabolomic data for Chlamydomonas reinhardtii
Lee, D. H., & Fiehn, O. (2008) · Plant Methods · doi:10.1186/1746-4811-4-7 · Cited by 182 · see citing works
Zhai, G., Wang‐Sattler, R., Hart, D. J., Arden, N., Hakim, A. J., Illig, T., & Spector, T. D. (2010) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2009.120857 · Cited by 181 · see citing works
Bas, S. (2002) · British journal of rheumatology · doi:10.1093/rheumatology/41.7.809 · Cited by 171 · see citing works
Metabolome Analysis: An Introduction
Villas-Bôas, S. G., Roessner, U., Hansen, M. A. E., et al. (2007) · Cited by 153 · see citing works
Bladder Cancer Determination Via Two Urinary Metabolites: A Biomarker Pattern Approach
Huang, Z., Lin, L., Gao, Y., Chen, Y., Yan, X., Xing, J., & Hang, W. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.007922 · Cited by 131 · see citing works
Villas-Bôas, S. G., Roessner, U., Hansen, M. A. E., et al. (2006) · Wiley eBooks · doi:10.1002/0470105518 · Cited by 129 · see citing works
Diagnostic properties of metabolic perturbations in rheumatoid arthritis
Madsen, R. K., Lundstedt, T., Gabrielsson, J., et al. (2011) · Arthritis Research & Therapy · doi:10.1186/ar3243 · Cited by 127 · see citing works
Esophageal Cancer Metabolite Biomarkers Detected by LC-MS and NMR Methods
Zhang, J., Bowers, J., Liu, L., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030181 · Cited by 126 · see citing works
Serum Anti-γ-Globulin and Antinuclear Factors in the Aged
Cammarata, R. J. (1967) · JAMA · doi:10.1001/jama.1967.03120070067008 · Cited by 106 · see citing works
System Response of Metabolic Networks in Chlamydomonas reinhardtii to Total Available Ammonium
Lee, D. Y., Park, J.-J., Barupal, D. K., & Fiehn, O. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.016733 · Cited by 103 · see citing works
TEIXEIRA, V. H., Olaso, R., Martin‐Magniette, M., Lasbleiz, S., Jacq, L., Oliveira, C. R., Hilliquin, P., Gut, I. G., Cornélis, F., & Petit, É. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006803 · Cited by 96 · see citing works
Lauridsen, M. B., Bliddal, H., Christensen, R., et al. (2010) · Journal of Proteome Research · doi:10.1021/pr1002774 · Cited by 92 · see citing works
Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma*
Chen, T., Xie, G., Wang, X., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.a110.004945 · Cited by 83 · see citing works
Hutschenreuther, A., Kiontke, A., Birkenmeier, G., & Birkemeyer, C. (2012) · Analytical Methods · doi:10.1039/c2ay25046b · Cited by 68 · see citing works
Hügle, T., Kovacs, H., Heijnen, I., et al. (2012) · PubMed · Cited by 66 · see citing works
Tanino, M., Matoba, R., Nakamura, S., et al. (2009) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2009.06.149 · Cited by 62 · see citing works
Studies of the incidence and significance of anti‐gamma globulin factors in the aging
Litwin, S. D., & Singer, J. M. (1965) · Arthritis & Rheumatism · doi:10.1002/art.1780080408 · Cited by 62 · see citing works
Urine metabolite profiling offers potential early diagnosis of oral cancer
Xie, G. X., Chen, T. L., Qiu, Y. P., et al. (2011) · Metabolomics · doi:10.1007/s11306-011-0302-7 · Cited by 59 · see citing works
Effect of aging and diet on proton NMR spectra of rat urine
Bell, J. D., Sadler, P. J., Morris, V. C., et al. (1991) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910170213 · Cited by 55 · see citing works
Jetten, M. S. M., Pitoc, G. A., Follettie, M. T., et al. (1994) · Applied Microbiology and Biotechnology · doi:10.1007/bf00166080 · Cited by 48 · see citing works
BORENSTEIN, D. G., Gibbs, C. A., & Jacobs, R. P. (1982) · Arthritis & Rheumatism · doi:10.1002/art.1780250806 · Cited by 34 · see citing works
HUMPHREYS, J. H., & Symmons, D. P. M. (2012) · Current Opinion in Rheumatology · doi:10.1097/bor.0b013e32835cfc41 · Cited by 19 · see citing works
Jetten, M. S. M., Pitoc, G. A., Follettie, M. T., et al. (1994) · Applied Microbiology and Biotechnology · doi:10.1007/s002530050105 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The metabolic signature of macrophage responses
Viola, A., Munari, F., Sánchez-Rodríguez, R., Scolaro, T., & Castegna, A. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01462 · Cited by 2,021 · see citing works
Krebs cycle Reborn in macrophage immunometabolism
Ryan, D. G., & O'Neill, L. A. J. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-081619-104850 · Cited by 444 · see citing works
Coupling Krebs cycle metabolites to signalling in immunity and cancer
Ryan, D. G., Murphy, M. P., Frezza, C., Prag, H. A., Chouchani, E. T., O’Neill, L. A., & Mills, E. L. (2018) · Nature Metabolism · doi:10.1038/s42255-018-0014-7 · Cited by 398 · see citing works
The immunobiology of mTOR in autoimmunity
Suto, T., & Karonitsch, T. (2020) · Journal of Autoimmunity · doi:10.1016/j.jaut.2019.102373 · Cited by 149 · see citing works
- Authors
- Kim, S., Hwang, J., Xuan, J., Jung, Y. H., Cha, H.-S., & Kim, K. H.
- Affiliations
- Korea University (KR)
- Samsung Medical Center (KR)
- Corresponding author
- Cha, Hoon-Suk
- Kim, Kyoung Heon
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-06-02
- Volume
- 9
- Issue
- 6
- Pages
- e97501
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4041724
- OpenAlex
- W2041030202
- Cited by
- 169
- References
- 41
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 9 · 2023: 15 · 2022: 17 · 2021: 18 · 2020: 19 · 2019: 14 · 2018: 16 · 2017: 14 · 2016: 14 · 2015: 11 · 2014: 1
- FWCI
- 10.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Korea University
- Topics
- Health Sciences › Medicine › Rheumatology › Rheumatoid Arthritis Research and Therapies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- rheumatoid arthritis, synovial fluid, metabolomics, biomarkers, differential diagnosis, ankylosing spondylitis, Behçet's disease, gout, inflammatory arthritis, citrulline, succinic acid, glutamine, fatty acid metabolism, amino acid metabolism, urea cycle, TCA cycle
- MeSH terms
- Adult, Arthritis, Rheumatoid, Diagnosis, Differential, Female, Humans, Inflammation, Male, Predictive Value of Tests, ROC Curve, Synovial Fluid, Biomarkers, Cluster Analysis, Discriminant Analysis, Least-Squares Analysis, Case-Control Studies, Area Under Curve, Metabolomics, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
