Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.
Diabetes Mellitus
Fluorescence spectroscopy
Multivariate examination
Tryptophan
Urinary glucose
Journal
Journal of fluorescence
ISSN: 1573-4994
Titre abrégé: J Fluoresc
Pays: Netherlands
ID NLM: 9201341
Informations de publication
Date de publication:
03 Aug 2023
03 Aug 2023
Historique:
received:
23
06
2023
accepted:
24
07
2023
medline:
3
8
2023
pubmed:
3
8
2023
entrez:
3
8
2023
Statut:
aheadofprint
Résumé
The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an excitation source for recording the fluorescent spectra from the urine samples. A consistent fluorescence emission peak of glucose at 450 nm was found in all samples for an excitation wavelength of 370 nm. For confirmation and comparison, the fluorescence spectra of non-diabetic (healthy controls) were also acquired in the same spectral range. It was found that fluorescence emission intensity at 450 nm increases with increasing glucose concentration in urine. In addition, optimized synchronous fluorescence emission at 357 nm was used for simultaneously determining a potential diabetes biomarker, Tryptophan (Trp) in urine. It was also found that the level of tryptophan decreases with the increase in urinary glucose concentration. The quantitative estimation of urinary glucose can be demonstrated based on the intensity of emission light carried by fluorescence light. Moreover, the dissimilarities were further emphasized using the hierarchical cluster analysis (HCA) algorithm. HCA gives an obvious separation in terms of dendrogram between the two data sets based on characteristic peaks acquired from their fluorescence emission signatures. These results recommend that urinary glucose and tryptophan fluorescence emission can be used as potential biomarkers for the non-invasive analysis of diabetes.
Identifiants
pubmed: 37535232
doi: 10.1007/s10895-023-03366-1
pii: 10.1007/s10895-023-03366-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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