TY - JOUR
T1 - Transflection infrared spectroscopy as a rapid screening tool for urinary 8-isoprostane
AU - Kincses, Adele
AU - Sourris, Karly C.
AU - Mohan, Muthukumar
AU - Kantharidis, Phillip
AU - Jandeleit-Dahm, Karin
AU - Wood, Bayden R.
N1 - Funding Information:
The authors would like to thank Mr Finlay Shanks for instrumental support and the animal technicians from the Department of Diabetes, Central Clinical Schools, Monash University Animal House for care of the mice. Funding: This work was supported by Monash University through the provision of a Monash Institute for Medical Engineering Seed Fund 2020/2021 grant. The funding source had no involvement in the conduct of the research or preparation of the article.
Funding Information:
The authors would like to thank Mr Finlay Shanks for instrumental support and the animal technicians from the Department of Diabetes, Central Clinical Schools, Monash University Animal House for care of the mice. Funding: This work was supported by Monash University through the provision of a Monash Institute for Medical Engineering Seed Fund 2020/2021 grant. The funding source had no involvement in the conduct of the research or preparation of the article.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/10
Y1 - 2022/10
N2 - Diabetes mellitus is a chronic disease leading to renal and cardiovascular complications associated with high personal and societal costs. Mouse models are routinely used to assess the mechanisms of disease and disease status, by monitoring biomarkers, including 8-isoprostane, which is a marker of oxidative stress. Here we report that transflection FTIR spectroscopy coupled with partial least squares regression is able to predict urinary 8-isoprostane concentrations in various murine models of type 1 and type 2 diabetes. Regression analysis produced linear correlations, with R2 values of correlation between 0.6 and 0.8, and RMSECV (%) values between 12.3 and 29.4%, between known (measured by enzyme-linked immunosorbent assay) and predicted urinary 8-isoprostane concentration, based on the fourth derivative spectra. This approach has significant advantages over the current methods utilised, namely it has higher throughput and requires minimal sample preparation, compared to enzyme-linked immunosorbent assay and gas- or liquid-chromatography coupled with mass spectrometry. Furthermore, it has the potential to be expanded into analysis of other biomarkers and human samples in a point-of-care context.
AB - Diabetes mellitus is a chronic disease leading to renal and cardiovascular complications associated with high personal and societal costs. Mouse models are routinely used to assess the mechanisms of disease and disease status, by monitoring biomarkers, including 8-isoprostane, which is a marker of oxidative stress. Here we report that transflection FTIR spectroscopy coupled with partial least squares regression is able to predict urinary 8-isoprostane concentrations in various murine models of type 1 and type 2 diabetes. Regression analysis produced linear correlations, with R2 values of correlation between 0.6 and 0.8, and RMSECV (%) values between 12.3 and 29.4%, between known (measured by enzyme-linked immunosorbent assay) and predicted urinary 8-isoprostane concentration, based on the fourth derivative spectra. This approach has significant advantages over the current methods utilised, namely it has higher throughput and requires minimal sample preparation, compared to enzyme-linked immunosorbent assay and gas- or liquid-chromatography coupled with mass spectrometry. Furthermore, it has the potential to be expanded into analysis of other biomarkers and human samples in a point-of-care context.
KW - 8-isoprostane
KW - Fourth derivative spectroscopy
KW - Partial Least Squares Regression
KW - Transflection Fourier Transform IR spectroscopy
KW - Urinalysis
UR - http://www.scopus.com/inward/record.url?scp=85131413668&partnerID=8YFLogxK
U2 - 10.1016/j.microc.2022.107641
DO - 10.1016/j.microc.2022.107641
M3 - Article
AN - SCOPUS:85131413668
SN - 0026-265X
VL - 181
JO - Microchemical Journal
JF - Microchemical Journal
M1 - 107641
ER -