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Fourier-transform-infrared-spectroscopy based metabolomic spectral biomarker selection towards optimal diagnostic differentiation of diabetes with and without retinopathy
A.G. Mazumder, , F. Zevictovich, S. Ghosh, A. Mukherjee, J. Chatterjee
Published in Taylor and Francis Inc.
Volume: 51
Issue: 7
Pages: 340 - 349
The 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, 1165, 1211.18, 1402.70, 1451.14, 1657 cm−1) represented aberrations in total lipid content. Four peaks (1114.18, 1117, 1147, 1165 cm−1) were associated with protein phosphorylation and three peaks (1527, 1544.71, 1591.10 cm−1) with Amide II group. Again, lipidic signatures were strongly corroborated with glycosylated hemoglobin levels in diabetic retinopathy and diabetic subjects. Spectral signatures also revealed an elevated level of β-sheet containing proteins in serum in diabetic retinopathy condition. The method was validated through spectral biomarker selection by the DBMS technique. Thus, this method has the capability of diagnostic cost minimization for detection of diabetic retinopathy by label-free spectral biomarker identification. © 2018, © 2018 Taylor & Francis Group.
About the journal
JournalData powered by TypesetSpectroscopy Letters
PublisherData powered by TypesetTaylor and Francis Inc.