Date Published: September 27, 2019
Publisher: Public Library of Science
Author(s): Ryan S. Senger, Varun Kavuru, Meaghan Sullivan, Austin Gouldin, Stephanie Lundgren, Kristen Merrifield, Caitlin Steen, Emily Baker, Tommy Vu, Ben Agnor, Gabrielle Martinez, Hana Coogan, William Carswell, Lampros Karageorge, Devasmita Dev, Pang Du, Allan Sklar, Giuseppe Orlando, James Pirkle, John L. Robertson, Benedetta Bussolati.
Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.
Metabolomic analysis of normal human urine has identified over 2,000 separate chemical entities . This and other -omics technologies, along with deep sequencing, have been used over the past decade in search of disease biomarkers, particularly for kidney disease [1–3]. In particular, biomarkers have been sought for chronic kidney disease (CKD) and end-stage kidney disease (ESKD), multifactorial diseases affecting up to 10% of the US population, and acute kidney injury (AKI), a significant cause of morbidity/mortality in hospitalized patients [4,5]. While a few candidate biomarker molecules for these diseases have been identified (cystatin C, periostin), these are not commonly measured (or readily available) to caregivers in patient care settings. This is due to expense, requirement for advanced technology (such as mass spectrometry), and lack of validation for the broad spectrum of clinical presentations of CKD and AKI [4–7].
Determining what is normal is critical for identifying what is abnormal. Ultimately, Rametrix™ analysis of urine will be used to screen for the presence of diseases, and our previously-published proof-of-concept with healthy individuals and ESKD patients  supports this notion. However, the variance in Raman spectra of urine specimens from healthy individuals needs to be known for more reliable datasets of Raman spectra of normal urine to be constructed and validated. This study represents a first attempt to obtain this range of normal for use with Rametrix™, a Raman spectroscopy-based technology. Interestingly, in this expanded study (relative to our proof-of-concept) with 235 urine specimens from healthy individuals, different levels of spectral variance were observed at different Raman shifts. PCA with the Rametrix™ LITE Toolbox for MATLAB® helped determine which Raman shifts were most significant in accounting for differences between urine specimens. Many of these correlated with well-characterized urine components, such as urea, creatinine, and glucose, but there are several other bands yet to be identified (Fig 2C). The use of previously published urine metabolomics and Raman spectral libraries are being used to identify these remaining metabolites, but adequate standards and validations are still needed.