Type 2 diabetes detection based on serum sample Raman spectroscopy

Abstract

In this work, we propose to the Raman spectroscopy as a new technique for the detection of the type 2 diabetes using blood serum samples. The serum samples were obtained from 15 patients who were clinically diagnosed with type 2 diabetes mellitus and 20 healthy volunteers. The average spectra showed equally intense peaks as, 695 cm, the doublet of tyrosine at 828 and 853 cm, phenylalanine at 1002 and 1028 cm, the phospholipid shoulder at 1300-1345 cm, and proteins (amide I) at 1654 cm. The major differences were found at 661 and 1404 cm (glutathione), 714 (polysaccharides), 605 (Phe), 545 cm (tryptophan), and the shoulder of amide III at 1230-1282 cm, where seem to disappear in the diabetes spectrum. On the contrary, the region that is more highlighted due to that diabetes peaks are clearly more intense was 897-955 cm. Principal component analysis and linear discriminate analysis were employed for developing discrimination method. The first three principal components provided a classification of the samples from healthy and diabetes patients with high sensitivity and specificity. In addition, when the first principal component was plotted as a function of the Raman shift, it revealed these shifts accounted for the greatest differences between control and diabetes samples, which coincided with the shifts of spectral differences shown by mean spectra. Our results demonstrated that serum sample Raman spectroscopy promises to become a non-invasive support tool of the currently applied techniques for type 2 diabetes detection, decreasing the false-positive cases.

Superparamagnetic Clustering of Diabetes Patients Raman Spectra

“…It is important to note that when a cross-check is made using another classification method such as principal component analysis and linear discriminant analysis [5], the members 132 and 174 from clusters 1, and 88, 91, and 99 from cluster 2 1 are also misclassified, in perfect agreement with our SPC result, although there is a disagreement with the members 86, 92, 98, 100, 101, and 102 from cluster 2 2. Despite this disagreement in cluster 2 2, the method SPC, based on concepts of statistical physics and stochastic aspects, has high sensitivity and specificity consistent with the number of control patients and the number of patients from the health centers detected with high glucose concentrations.…”

“…Among the main techniques applied in the analysis of spectra, we have multivariate analysis (principal component analysis and linear discriminant analysis) [4,5] and clustering analysis (K-means and spectral norm methods) [6]. Nevertheless, among these clustering methods, the ones that acquire particular interest are those methods that allow exploration of hierarchical structures in data banks, facilitating the study of diseases characterized by being classified into either different types or showing various stages of progress [4].…”

Prospects for Raman spectroscopy in cardiology

“…Для этого сравнивают КР-спектры здоровых лиц и пациентов с СД-2 с последующим выделением характерных особенностей спектров, регистрируемых только у пациентов с СД-2. В частности, в работе [47] (2018) описано исследование сыворотки 15 пациентов, страдающих СД-2 и 20 здоровых добровольцев с помощью метода КРспетроскопии. Основными молекулярными метками, характерными для СД-2 были пики, характерные для глутатиона, триптофана, тирозина, β-каротина и АмидаIII (1230-1282 см -1 ).…”

“…Основными молекулярными метками, характерными для СД-2 были пики, характерные для глутатиона, триптофана, тирозина, β-каротина и АмидаIII (1230-1282 см -1 ). Полученные результаты позволили исследователям предположить, что КР-спектроскопия сыворотки может стать новым методом диагностики СД-2 [47].…”

Silver‐Coated Disordered Silicon Nanowires Provide Highly Sensitive Label‐Free Glycated Albumin Detection through Molecular Trapping and Plasmonic Hotspot Formation

“…For instance, the colorimetric assays are plagued by the lack of specificity [22] and are susceptible to the presence of free glucose, uric acid or lipemia. [23][24][25][26] To address these limitations, we and others have sought to leverage the exquisite molecular specificity of Raman spectroscopy for completely label-free detection of these glycemic markers, [27][28][29][30][31] thereby eliminating the need for expensive reagents and additional touchpoints. Specifically, our laboratory had previously used drop-coating deposition Raman spectroscopy for detection of GA (limit of detection of ≈14 × 10 −6 m).…”