Fourier-transform-infrared-spectroscopy based metabolomic spectral biomarker selection towards optimal diagnostic differentiation of diabetes with and without retinopathy

AbstractThe purpose of this study is to identify Fourier transform infrared (FTIR) spectroscopy-based serum metabolomic spectral biomarkers using chemometrics for the diagnosis of Diabetic Retinopathy. FTIR spectroscopy was performed on 85 human serum samples [30 type 2 diabetes patients each without retinopathy and with retinopathy along with 25 normal healthy individuals as control]. Difference between mean spectra (DBMS), forward feature selection (FFS), and Mann–Whitney’s U tests were applied for spectral biomarker selection. Classification of disease conditions was achieved using analysis of different combinations of spectral features with linear, quadratic, and cubic Support Vector Machine at 10-fold cross validation. Twelve spectral signatures extracted by FFS could differentiate diabetes and diabetic retinopathy with 90% sensitivity, 92.7% specificity, and 90.5% overall accuracy. Two peaks (1042, 1114.18 cm−1) were associated with carbohydrate and polysaccharide content and five peaks (1114.18, 11...