Rapid, Precise, and Clinically Relevant Quantification of Urinary Albumin and Creatinine Using a NanoDrop UV/Vis Spectrophotometer
Albuminuria is a sensitive biomarker of kidney dysfunction, and the albumin/creatinine ratio (ACR) is an essential measure for monitoring diabetic kidney disease (DKD). Abnormal levels can indicate a propensity for progressive renal failure and other complications such as cardiovascular diseases. This study employed UV/Visible spectroscopy to analyze aqueous urine samples spiked with bovine serum albumin (BSA) and creatinine at clinically relevant concentrations (0-30 mg/L for albumin and 600-1800 mg/L for creatinine) using a multivariate method. UV/Visible spectra of co-spiked samples recorded in triplicate revealed distinct bands at 229 nm and 249 nm, corresponding to BSA and creatinine, respectively, alongside other amino acid bands. Partial Least Squares Regression (PLS-R) analysis for BSA yielded a Root Mean Square Error of Calibration (RMSEC) and Cross-Validation (RMSECV) values of 66.93 and 73.92 mg/L, respectively. For creatinine, RMSEC and RMSECV values were 244.32 and 275.65 mg/L, respectively. Prediction models for both BSA and creatinine compared to ELISA demonstrated a robust performance with R<sup>2</sup><sub>PRED</sub> values of 0.96 and 0.95, respectively, indicating strong model reliability. The Limit of Detection (LOD) for co-spiked samples was 19.82 mg/L for BSA and 58.43 mg/L for creatinine. The significance of the achieved Limit of Detection (LOD) lies in its ability to measure concentrations well below the normal physiological ranges of 0-30 mg/L for albumin and 600-1800 mg/L for creatinine. These results demonstrate the proof of concept of applying an UV/Visible-spectroscopy-based method as a rapid, cost-effective point-of-care (PoC) tool for ACR measurements, offering promising applications in the early diagnosis, monitoring, and prognosis of diabetic kidney disease and associated cardiovascular complications. The next stage will involve a pilot trial to evaluate the technology's potential using clinical patients.
Authors
- Jay C. Jha (ORCID: https://orcid.org/0000-0002-2764-2930)
- Karin Jandeleit‐Dahm (ORCID: https://orcid.org/0000-0003-2192-5822)
- Bayden R. Wood (ORCID: https://orcid.org/0000-0003-3581-447X)
- Karly C. Sourris
- Keith Dias
Institutions
- Deutsches Diabetes-Zentrum e.V. (DE)
- Heinrich Heine University Düsseldorf (DE)
- Monash University (AU)
Publication Details
- Journal
- Sensors
- Published
- 2025-05-24
- DOI
- https://doi.org/10.3390/s25113307
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Monash University