A new and fast method for diabetes and dyslipidemia diagnosis using FTIR-MIR, spectroscopy and multivariate data analysis: a proof of concept

Abstract

Diabetes and dyslipidemia are well-established risk factors for cardiovascular disease, which is the primary cause of death both in Brazil and globally. Fourier-transform mid-infrared spectroscopy (FTIR-MIR) generates spectral fingerprints of biomolecules, allowing for correlation with metabolic changes, while remaining a rapid, non-invasive, and non-destructive method. The study provided a proof of concept for the effectiveness of FTIR-MIR in screening diabetes, pre-diabetes, hypercholesterolemia, hypertriglyceridemia, and mixed dyslipidemia in blood serum. After acquiring mid-infrared spectra of 60 human serum samples, both unsupervised and supervised analysis models were developed. Principal component analysis (PCA) was used for pattern recognition and to determine how closely related the samples were based on their spectral profiles. The results obtained by the supervised models showed a clear discriminative ability to distinguish both diabetic and dyslipidemic samples from healthy subjects by multivariate analysis performed on FTIR-MIR spectra. High accuracy rates of more than 90% were achieved for diabetes and dyslipidemia diagnosis with PLS-DA. Dyslipidemia type discrimination could be attributed mainly to the amide I region [1720-1600 cm-1, (ν(C=O)] and altered lipid concentration in the 3000-2800 cm-1 region, whereas the discrimination of diabetes and prediabetes was primarily due to the altered conformational protein in the Amides I [1720-1600 cm-1, ν(C=O)] and Amide II [1570-1480 cm-1, δ(N=H) + ν(CH)] range.