SERS-based quantification of albuminuria in the normal-to-mildly increased range

Abstract

The lack of an accurate point-of-care detection system for microalbuminuria represents an important unmet medical need that contributes to the morbidity and mortality of patients with kidney diseases. In this proof-of-concept study, we used SERS spectroscopy to detect urinary albumin concentrations in the normal-to-mildly increased albuminuria range, a strategy that could be useful for the early diagnosis of renal impairment due to uncontrolled hypertension, cardiovascular disease or diabetes. We analyzed 27 urine samples by SERS, using iodide-modified silver nanoparticles and we could discriminate between groups with high and low albumin concentrations with an overall accuracy of 89%, 93% and 89%, using principal component analysis–linear discriminant analysis and cut-off values of 3, 6 and 10 μg mL⁻¹ for urinary albumin concentrations, respectively. We achieved a detection limit of 3 μg mL⁻¹ for human serum albumin based on the 1002 cm⁻¹ SERS band, attributed to the ring breathing vibration of phenylalanine. Our detection limit is similar to that of the immunoturbidimetric assays and around one order of magnitude below the detection limit of urinary dipsticks used to detect microalbuminuria. We used principal least squares regression for building a spectral model for quantifying albumin. Using an independent prediction set, the R² and root mean squared error of prediction between predicted and reference values of human serum albumin concentrations were 0.982 and 2.82, respectively. Here, we show that direct SERS spectroscopy has the sensitivity required for detecting clinically relevant concentrations of urinary albumin, a strategy that could be used in the future for the point-of-care screening of microalbuminuria.