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

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.