Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies

Abstract

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.

Cancer detection in dogs using rapid Raman molecular urinalysis

“…Where SG preserves the entire Raman spectrum, ISREA can amplify regions of spectra containing disease-related information, while minimizing conserved regions. This has served to improve prediction accuracy for spectra of unknown samples in previous studies ( 32 , 33 , 36 ). Results obtained when using ISREA are presented here, and results obtained when using SG are given in the Supplementary material .…”

“…The locations of the nodes were adjusted to improve baseline fit and predictive capabilities. We have applied similar methods in several of our previous studies with human urine (32)(33)(34)36).…”

“…We have previously reported on this use of a Raman spectroscopy-based technology (Rametrix ® ) ( 29 , 30 ) for detection of chronic kidney disease ( 31 ), diabetic nephropathy ( 32 ), renal effects of COVID19 disease ( 33 ), microhematuria ( 34 ), bladder cancer ( 35 ), and chronic Lyme disease ( 36 ), validated by comparing Raman spectral patterns in more than 3,000 human patient-derived urine samples and more than 200 healthy human individuals with no clinical or laboratory evidence of renal disease.…”

Multifaceted relationship between diabetes and kidney diseases: Beyond diabetes

“…In addition, very recently, it has been demonstrated that the evaluation of urine samples by Raman spectroscopy followed by chemometric analysis may be able to differentiate between DKD and NDKD with high specificity and sensitivity[ 82 ]. Similar results were also found in blood samples, in which the combination of traditional molecular biology and transcriptomic approaches has led to the identification of potential DKD biomarkers[ 83 , 84 ].…”

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

“…This sensitive vibrational spectroscopy excites molecular bonds within a sample, providing a unique biomolecular spectral fingerprint of target biomarkers in a rapid analytical response [21,22]. These spectroscopic barcodes have been shown to identify the various diseases from which the biofluid has been collected, such as blood [23,24], CSF [25,26], urine [27,28], saliva [24,29], and tears [30,31]. In contrast to the in vitro bioassays, the availability of inexpensive, portable Raman devices makes this technique particularly attractive for point-of-care testing, analysis, and screening of biofluids.…”