Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease

Abstract

Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine Raman spectroscopy and biochemical lipid profiling, complemented by machine learning (ML) techniques to evaluate chemical composition changes in kidneys induced by Type 2 Diabetes mellitus (T2DM). Raman spectroscopy identified significant differences in lipid content and specific molecular vibrations, with the 1777 cm⁻¹ band emerging as a potential spectroscopic marker for diabetic kidney damage. The integration of ML algorithms improved the analysis, providing high accuracy, selectivity, and specificity in detecting these changes. Moreover, lipids metabolic profiling revealed distinct variations in the concentration of 11 phosphatydylocholines and 9 acyl-alkylphosphatidylcholines glycerophospholipids. Importantly, the correlation between Raman data and lipids metabolic profiling differed for control and T2DM groups. This study underscores the combined power of Raman spectroscopy and ML in offering a low-cost, fast, precise, and comprehensive approach to diagnosing and monitoring diabetic nephropathy, paving the way for improved clinical interventions. However, taking into account small number of data related to ethical committee approvals, the study should be verified on a larger number of cases.