Chemometrics-assisted FTIR and machine learning for biochemical profiling of human skeletal muscles in relation to type 2 diabetes

Abdelmonaem, Mohamed Elwy (author), Ugwoke, Chiedozie Kenneth (author), Alibegović, Armin (author), Cvetko, Erika (author), Umek, Nejc (author), Grdadolnik, Jože (author), Zupančič, Barbara (author)

Skeletal muscle is the major site of insulin-stimulated glucose uptake, making it highly susceptible to metabolic disorders. This study investigates Type 2 diabetes mellitus-associated alterations in the macromolecular composition of functionally distinct human skeletal muscles. These modifications are crucial to understanding the molecular basis of diabetic myopathies but remain underexplored. Five skeletal muscles from 80 individuals (40 controls and 40 individuals with Type 2 diabetes) were analysed. To overcome the limitations of traditional histochemical assays, we employed infrared spectroscopy combined with a chemometric decomposition approach to resolve spectra into five spectral components, each dominated by a specific macromolecular group. The corresponding component weights quantified and compared relative macromolecular proportions between the diabetic and control groups for different muscle types. Compared with controls, muscles from individuals with diabetes showed a consistent pattern of higher lipid levels and lower glycogen levels, with a statistically significant increase in lipids within the diaphragm. This biochemical profiling was enriched by building machine learning classifiers, which achieved low-to-modest but statistically significant AUC performance of up to 72% (p < 0.05) in discriminating diabetic from healthy muscle. Our work demonstrates an efficient methodology for the simultaneous screening of various macromolecular constituents using spectral data obtained in a single experiment. It provides medically relevant insights into the biological patterns of diabetes-associated skeletal muscle remodelling to guide future research. However, an independent validation should be performed to overcome this study limitation and draw definitive conclusions.

• Language: English
• Keywords: chemometrics, infrared spectroscopy, machine learning, macromolecular composition, skeletal muscles, type 2 diabetes mellitus
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: MF - Faculty of Medicine; BF - Biotechnical Faculty
• Publication status: Published
• Publication version: Version of Record
• Submitted for review: 02.07.2026
• Article acceptance date: 27.09.2026
• Publication date: 28.09.2026
• Year: 2026
• Number of pages: 11 str.
• Numbering: Vol. 366, art. 128893
• PID: 20.500.12556/RUL-189815
• UDC: 612.74:616.379-008.64:543.422.3-74:004.85
• ISSN on article: 1873-3557
• DOI: 10.1016/j.saa.2026.128893
• COBISS.SI-ID: 301191427
• Publication date in RUL: 16.10.2026

Record is a part of a journal

• Title: Spectrochimica acta : Molecular and biomolecular spectroscopy
• Publisher: Elsevier
• ISSN: 1873-3557
• COBISS.SI-ID: 23335685

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: kemoterapija, infrardeča spektroskopija, strojno učenje, makromolekularna sestava, skeletne mišice, sladkorna bolezen tipa 2