Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)
Autoři:
Ryan S. Senger aff001; Meaghan Sullivan aff001; Austin Gouldin aff001; Stephanie Lundgren aff001; Kristen Merrifield aff001; Caitlin Steen aff001; Emily Baker aff001; Tommy Vu aff002; Ben Agnor aff001; Gabrielle Martinez aff001; Hana Coogan aff001; William Carswell aff001; Varun Kavuru aff004; Lampros Karageorge aff004; Devasmita Dev aff004; Pang Du aff005; Allan Sklar aff006; James Pirkle, Jr aff007; Susan Guelich aff008; Giuseppe Orlando aff009; John L. Robertson aff003
Působiště autorů:
Department of Biological Systems Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
aff001; Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia, United States of America
aff002; DialySenors, Inc., Blacksburg, Virginia, United States of America
aff003; Veteran Affairs Medical Center, Salem, Virginia, United States of America
aff004; Department of Statistics, Virginia Tech, Blacksburg, Virginia, United States of America
aff005; Lewis-Gale Medical Center, Salem, Virginia, United States of America
aff006; Department of Internal Medicine–Nephrology, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America
aff007; Valley Nephrology Associates, Roanoke, Virginia, United States of America
aff008; Department of Surgical Sciences–Transplant, Wake Forest University Baptist Medical Center, Winston-Salem, North Carolina, United States of America
aff009; Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, Virginia, United States of America
aff010; Virginia Tech-Carilion School of Medicine and Research Institute, Blacksburg, Virginia, United States of America
aff011
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227281
Souhrn
Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing “unknown” specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify “unknown” urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.
Klíčová slova:
Biomarkers – Chronic kidney disease – Medical dialysis – Metabolomics – principal component analysis – Specimen preparation and treatment – Statistical data – Urine
Zdroje
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