Machine Learning for Bone Biomarker Profiling

Machine Learning for Bone Biomarker Profiling in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, progressive inflammatory disorder which can lead to severe joint damage and disability. In 2019, an estimated 18 million people worldwide were living with this disease (1). Untreated RA can lead to destruction of the joints as well as heart, lung or nervous system problems (2). Skeletal bone loss, referred to as osteopenia or osteoporosis, is a key feature of RA. 

Sclerostin and Dickkopf-1 (DKK-1) are Wnt signaling proteins that are secreted by osteocytes, bone cells embedded in the bone matrix. They are inhibitors of bone formation and play a key role in the pathogeneses of systemic and localized bone loss in RA (3, 4). Serum levels of Sclerostin and DKK-1 have shown to be elevated in patients with RA compared to controls and correlate with bone erosions and inflammation (4, 5, 6). Findings in mice have demonstrated that DKK-1 triggers inflammatory bone degradation and neutralization of DKK-1 protects from systemic bone loss during inflammation (7). Interestingly, blocking the bone destruction molecule Sclerostin with an anti-sclerostin antibody has shown to be effective for the treatment of osteoporosis but may not be safe for patients suffering from inflammatory RA: in a rodent RA model, Weymeyer et al. demonstrated that Sclerostin inhibition did not stop bone loss and worsened clinical RA outcome by promoting TNF-dependent inflammatory joint destruction (8).

Controlling inflammation by biological therapies targeting pro-inflammatory cytokines has shown to have a positive effect in RA patients (5). Interleukin-6 is a key immunomodulatory cytokine that plays an important role in the development of RA. Inhibition of IL-6 has proven to be effective in treating patients with RA (9). A study by Briot et al showed that DKK-1 levels decreased in RA patients treated with an anti-IL-6 inhibitor (6).

Machine Learning for Bone Biomarker Profiling in Rheumatoid Arthritis

A recent cross-sectional study by Adami G et al., with over 1800 enrolled participants diagnosed with RA, Psoriatic Arthritis (PsA), and Systemic Sclerosis (SSc), employed machine learning techniques to assess the capability of biomarker profiles in differentiating RA patients from individuals with PsA and SSc. The Wnt signaling antagonists Sclerostin and Dickkopf-1 (DKK-1) were among the biomarkers measured. The study provided an in-depth understanding into the bone signature of RA that is marked by changes in bone mineral density and by unique biomarker profiles (6). Serum Sclerostin and DKK-1 levels were measured with ELISA assay kits from Biomedica.

SCLEROSTIN ELISA (#BI-20492) and DKK-1 (#BI-20403) ELISA kits

Biomedica´s Sclerostin ELISA Assay

• TRUSTED – cited in more than 290 publications!
• QUALITY – validated according to international guidelines
• EFFICIENT – only 20µl sample / well
• CONVENIENT – ready to use standards and controls included

 

Biomedica´s DKK-1 ELISA assay 

• TRUSTED – cited in more than 180 publications!
• QUALITY – validated according to international guidelines
• EFFICIENT – only 20µl sample / well
• CONVENIENT – direct measurement – no sample pre-dilution. Ready to use standards and controls included

 

Also available from Biomedica : Bioactive Sclerostin ELISA (cat. no. BI-20472), Interleukin-6 ELISA (BI-IL6)

 

Complete ready-to use ELISA kits

 

Literature

1. GBD 2019: Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. https://vizhub.healthdata.org/gbd-results
2. WHO- Rheumatoid arthritis, June 2023
3. Wnt Signaling and Biological Therapy in Rheumatoid Arthritis and Spondyloarthritis. Cici D, Corrado A, Rotondo C, Cantatore FP. Int J Mol Sci. 2019 Nov 7;20(22):5552. doi: 10.3390/ijms20225552. PMID: 31703281; PMCID: PMC6888549.
4. Study of correlation of level of expression of Wnt signaling pathway inhibitors sclerostin and dickkopf-1 with disease activity and severity in rheumatoid arthritis patients. Singh A, Gupta MK, Mishra SP.Drug Discov Ther. 2019;13(1):22-27. doi: 10.5582/ddt.2019.01011. PMID: 30880318.
5. The effect of tocilizumab on bone mineral density, serum levels of Dickkopf-1 and bone remodeling markers in patients with rheumatoid arthritis. Briot K, Rouanet S, Schaeverbeke T, Etchepare F, Gaudin P, Perdriger A, Vray M, Steinberg G, Roux C. Joint Bone Spine. 2015 Mar;82(2):109-15. doi: 10.1016/j.jbspin.2014.10.015. Epub 2014 Dec 31. PMID: 25557658.
6. Machine learning to characterize bone biomarkers profile in rheumatoid arthritis. Adami G, Fassio A, Rossini M, Benini C, Bixio R, Rotta D, Viapiana O, Gatti D. Front Immunol. 2023 Nov 9;14:1291727. doi: 10.3389/fimmu.2023.1291727. PMID: 38022514; PMCID: PMC10665911.
7. Neutralisation of Dkk-1 protects from systemic bone loss during inflammation and reduces sclerostin expression. Heiland GR, Zwerina K, Baum W, Kireva T, Distler JH, Grisanti M, Asuncion F, Li X, Ominsky M, Richards W, Schett G, Zwerina J. Ann Rheum Dis. 2010 Dec;69(12):2152-9. doi: 10.1136/ard.2010.132852. Epub 2010 Sep 21. PMID: 20858621.
8. Sclerostin inhibition promotes TNF-dependent inflammatory joint destruction. Wehmeyer C, Frank S, Beckmann D, Böttcher M, Cromme C, König U, Fennen M, Held A, Paruzel P, Hartmann C, Stratis A, Korb-Pap A, Kamradt T, Kramer I, van den Berg W, Kneissel M, Pap T, Dankbar B. Sci Transl Med. 2016 Mar 16;8(330):330ra35. doi: 10.1126/scitranslmed.aac4351. Epub 2016 Mar 16. PMID: 27089204.
9. Targeting IL-6 or IL-6 Receptor in Rheumatoid Arthritis: What Have We Learned? Avci AB, Feist E, Burmester GR. BioDrugs. 2024 Jan;38(1):61-71. doi: 10.1007/s40259-023-00634-1. Epub 2023 Nov 21. PMID: 37989892; PMCID: PMC10789669.