Abstract: FR-PO970

Comparing Raman Spectroscopy-Derived Metabolomic Signatures of Urine from Patients with CKD to Those with Normal Kidney Function

Session Information

Category: Bioengineering and Informatics

  • 101 Bioengineering and Informatics

Authors

  • Pirkle, James L., Wake Forest School of Medicine, Winston Salem, North Carolina, United States
  • Robertson, John L, Virginia Tech, Blacksburg, Virginia, United States
  • Warren, Mitchell, Wake Forest School of Medicine, Winston Salem, North Carolina, United States
  • Senger, Ryan S, Virginia Tech, Blacksburg, Virginia, United States
Background

The field of metabolomics is being used increasingly in clinical research to improve aspects of clinical care ranging from early diagnosis to monitoring treatment progress and prognosis. It would be useful to have an affordable metabolomic test for urine that could identify patients with chronic kidney disease in the absence of albuminuria. Raman spectroscopy is an analytic tool used chiefly in solid state chemistry that has shown increasing application in analyzing molecular compositions of biological samples. We performed a pilot study to assess the ability of Raman spectroscopy analysis of urine samples to differentiate between patients with CKD and those with normal kidney function.

Methods

Free-catch urine samples were collected from 93 patients with CKD (on peritoneal dialysis) and from 25 generally healthy volunteers with normal kidney function. Raman spectra were analyzed using an Agiltron PeakSeeker Pro spectrometer with 785 nm laser excitation at 5 mW. A 1 s integration time was used, spectra were collected between 200-2000 cm-1, and each sample was scanned 10 times. Spectra were analyzed by a multivariate statistical pipeline involving (i) principal component analysis and (ii) discriminate analysis of principal components following baseline correction and vector normalization of raw spectra.

Results

The individual raw spectra show that only the urea band at 1003 cm-1 is distinguishable. The multivariate statistical pipeline was used to determine whether differing molecular signatures existed elsewhere for urine from patients with CKD and those with normal kidney function. Results are shown in a canonical plot, where every data point represents an entire Raman spectrum. Distinct clusters of data points are observed between the urine of CKD patients (receiving PD) and those with normal kidney function, indicating recognizably different Raman signals and molecular compositions for each group.

Conclusion

Raman spectroscopy provides a rapid, cost-effective way to assess differences in the molecular composition of urine between patients with CKD and those with normal kidney function. Future research will focus on quantifying these differences and testing of unknown populations. Potential applications for this technology would include community screening for CKD using rapid urine testing.