Abstract
Systemic sclerosis (SSc) is a heterogeneous autoimmune disease characterized by fibrosis, vascular damage, and immune dysregulation. In this study, we evaluated the potential of Fourier-transform infrared (FTIR) spectroscopy of whole blood samples combined with multivariate and machine learning approaches to differentiate between disease subtypes and the presence of interstitial lung disease (ILD). Subtle but consistent spectral differences were observed in the amide I/II and lipid-associated regions (~ 1500–1700 cm−1 and ~ 2900 cm−1). Principal Component Analysis (PCA) revealed clear clustering along the first principal component (PC1). Subsequently, we developed and evaluated several supervised machine learning models to classify the serum spectra according to SSc subtype . In the classification between diffuse and limited SSc, the Random Forest (RF) model achieved the optimal overall performance. Our results demonstrated the potential of FTIR spectroscopy, particularly when combined with machine learning, as a non-invasive tool for disease stratification and biomarker discovery in SSc. Further improvements in model optimization and spectral feature extraction are needed to enhance clinical applicability.
Data availability
Data are available from the corresponding author upon reasonable request.
References
Di Maggio, G. et al. Biomarkers in systemic sclerosis: An overview. Curr. Issues Mol. Biol. 45, 7775–7802 (2023).
Rosa, I., Romano, E., Fioretto, B. S. & Manetti, M. Autoantibodies as putative biomarkers and triggers of cell dysfunctions in systemic sclerosis. Curr. Opin. Rheumatol. c371, 51–63 (2025).
Colina, M. & Campana, G. Precision medicine in rheumatology: The role of biomarkers in diagnosis and treatment optimization. J. Clin. Med. 14, 1735 (2025).
Manrique-Moreno, M., Howe, J., Suwalsky, M., Garidel, P. & Brandenburg, K. Physicochemical interaction study of non-steroidal anti-inflammatory drugs with dimyristoylphosphatidylethanolamine liposomes. Lett. Drug Des. Discov. 7, 50–56 (2010).
Gieroba, B., Kalisz, G., Krysa, M., Khalavka, M. & Przekora, A. Application of vibrational spectroscopic techniques in the study of the natural polysaccharides and their cross-linking process. Int. J. Mol. Sci. 24, 2630 (2023).
Guleken, Z. et al. Relationship between amide ratio assessed by Fourier-transform infrared spectroscopy: A biomarker candidate for polycythemia vera disease. J. Biophotonics. 17, e202400162 (2024).
Chen, J. et al. Investigating Raman peak enhancement in carboxyl-rich molecules: Insights from Au@Ag core-shell nanoparticles in colloids. Front. Chem. 13, 1522043 (2025).
Vongsvivut, J. et al. FTIR microspectroscopy for rapid screening and monitoring of polyunsaturated fatty acid production in commercially valuable marine yeasts and protists. Analyst 138, 6016–6031 (2013).
Ciumac, D. et al. Influence of acyl chain saturation on the membrane-binding activity of a short antimicrobial peptide. ACS Omega 2, 7482–7492 (2017).
Dai, F., Zhuang, Q., Huang, G., Deng, H. & Zhang, X. Infrared spectrum characteristics and quantification of OH groups in coal. ACS Omega 8, 17064–17076 (2023).
Morales-González, V. et al. Metabolic fingerprinting of systemic sclerosis: A systematic review. Front. Mol. Biosci. 10, 1215039 (2023).
Perelas, A., Silver, R. M., Arrossi, A. V. & Highland, K. B. Systemic sclerosis-associated interstitial lung disease. Lancet. Respir. Med. 8, 304–320 (2020).
Guo, M. et al. Serum metabolomic profiling reveals potential biomarkers in systemic sclerosis. Metabolism 144, 155587 (2023).
Acknowledgements
This research was supported by Medical University of Silesia, statutory funds BNW-1-124/N/5/K.
Author information
Authors and Affiliations
Contributions
Conceptualization: BM, JM, JD; Data Curation: BBC, AF; Funding Acqusition: AF; Investigation: BM, JM, MB, JD; Computed and statistical analysis: WP, MK; Project Administration: BM, JM, JD; Resources: MB, AF; writing—original draft: BM, JM, MB, JD;
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval and consent to participate
Blood samples were obtained subsequent to biobanking during a prior genetic study in SSc patients. The aforementioned study was approved (Approval No. PCN/CBN/0052/KB1/29/22) by the local ethical committee at the Medical University of Silesia in Katowice, Poland, which subsequently granted consent to submit the collected blood samples for spectroscopic analysis.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Miziołek, B., Miszczyk, J., Paja, W. et al. Spectroscopic and machine learning approaches for clinical subtyping in systemic sclerosis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-37690-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-37690-w
