Background: Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies.
Methods: Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model.
Results: This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV.
Conclusions: Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.
Keywords: Chemometric analysis; Chronic kidney disease; Diabetes mellitus; Diabetic kidney disease; Discriminant analysis; Immune mediated kidney disease; Raman spectroscopy; Urine.
© 2023 Kavuru et al.