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Discrimination model applied to urinalysis of patients with diabetes and hypertension aiming at diagnosis of chronic kidney disease by Raman spectroscopy

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Abstract

Higher blood pressure level and poor glycemic control in diabetic patients are considered progression factors that cause faster decline in kidney functions leading to kidney damage. The present study aimed to develop a quantification model of biomarkers creatinine, urea, and glucose by means of selected peaks of these compounds, measured by Raman spectroscopy, and to estimate the concentration of these analytes in the urine of normal subjects (G_N), diabetic patients with hypertension (G_WOL) patients with chronic renal failure doing dialysis (G_D). Raman peak intensities at 680 cm−1 (creatinine), 1004 cm−1 (urea), and 1128 cm−1 (glucose) from normal, diabetic, and hypertensive and doing dialysis patients, obtained with a dispersive 830 nm Raman spectrometer, were estimated through Origin software. Spectra of creatinine, urea, and glucose diluted in water were also obtained, and the same peaks were evaluated. A discrimination model based on Mahalanobis distance was developed. It was possible to determine the concentration of creatinine, urea, and glucose by means of the Raman peaks of the selected biomarkers in the urine of the groups G_N, G_WOL, and G_D (r = 0.9). It was shown that the groups G_WOL and G_D had lower creatinine and urea concentrations than the group G_N (p < 0.05). The classification model based on Mahalanobis distance applied to the concentrations of creatinine, urea, and glucose presented a correct classification of 89% for G_N, 86% for G_WOL, and 79% for G_D. It was possible to obtain quantitative information regarding important biomarkers in urine for the assessment of renal impairment in patients with diabetes and hypertension, and this information can be correlated with clinical criteria for the diagnosis of chronic kidney disease.

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Acknowledgments

L. Silveira Jr. thanks the FAPESP (São Paulo Research Foundation) for the partial financial support (process no. 2009/01788-5) and CNPq (National Council for Scientific and Technological Development) for the Productivity Fellowship (process no. 305680/2014-5). E. E. S. Vieira and J. A. M. Bispo thank the Regional Public Hospital of Western Pará and Santarém Municipal Hospital for authorizing this research in the Nephrology Clinic.

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Authors and Affiliations

  1. Faculdades Integradas do Tapajós – FIT, Rua Rosa Vermelha, 335, Aeroporto Velho, Santarém, Pará, 68010-200, Brazil

    Elzo Everton de Souza Vieira & Jeyse Aliana Martins Bispo

  2. Universidade Camilo Castelo Branco-UNICASTELO, Biomedical Engineering Institute, Parque Tecnológico de São José dos Campos, Estr. Dr. Altino Bondensan, 500, São José dos Campos, São Paulo, 12247-016, Brazil

    Elzo Everton de Souza Vieira & Jeyse Aliana Martins Bispo

  3. Biomedical Engineering Center, Universidade Anhembi Morumbi – UAM, Parque Tecnológico de São José dos Campos, Estr. Dr. Altino Bondensan, 500, São José dos Campos, São Paulo, 12247-016, Brazil

    Landulfo Silveira Jr & Adriana Barrinha Fernandes

  4. Center of Innovation, Technology and Education – CITE, Parque Tecnológico de São José dos Campos, Estr. Dr. Altino Bondensan, 500, São José dos Campos, São Paulo, 12247-016, Brazil

    Landulfo Silveira Jr & Adriana Barrinha Fernandes

Authors
  1. Elzo Everton de Souza Vieira
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  2. Jeyse Aliana Martins Bispo
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  3. Landulfo Silveira Jr
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  4. Adriana Barrinha Fernandes
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Corresponding author

Correspondence to Adriana Barrinha Fernandes.

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The authors declare that they have no conflict of interest.

Role of funding sources

The project has been supported in part by a public funding agency (São Paulo Research Foundation—FAPESP), Process FAPESP no. 2009/01788-5, that allowed to purchase the Raman spectrometer.

Ethical approval

This study was approved by Research Ethics Committee from UNICASTELO (protocols nos. 64116 and 8926).

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de Souza Vieira, E.E., Bispo, J.A.M., Silveira, L. et al. Discrimination model applied to urinalysis of patients with diabetes and hypertension aiming at diagnosis of chronic kidney disease by Raman spectroscopy. Lasers Med Sci 32, 1605–1613 (2017). https://doi.org/10.1007/s10103-017-2288-5

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  • DOI: https://doi.org/10.1007/s10103-017-2288-5

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