Novel Assessment of Urinary Albumin Excretion in Type 2 Diabetes Patients by Raman Spectroscopy

Abstract

Urinary albumin excretion remains the key biomarker to detect renal complications in type 2 diabetes. As diabetes epidemy increases, particularly in low-income countries, efficient and low-cost methods to measure urinary albumin are needed. In this pilot study, we evaluated the performance of Raman spectroscopy in the assessment of urinary albumin in patients with type 2 diabetes. The spectral Raman analysis of albumin was performed using artificial urine, at five concentrations of albumin and 24 h collection urine samples from ten patients with Type 2 Diabetes. The spectra were obtained after removing the background fluorescence and fitting Gaussian curves to spectral regions containing features of such metabolites. In the samples from patients with type 2 diabetes, we identified the presence of albumin in the peaks of the spectrum located at 663.07, 993.43, 1021.43, 1235.28, 1429.91 and 1633.91 cm−1. In artificial urine, there was an increase in the intensity of the Raman signal at 1450 cm−1, which corresponds to the increment of the concentrations of albumin. The highest concentration of albumin was located at 1630 cm−1. The capability of Raman spectroscopy for detection of small concentrations of urinary albumin suggests the feasibility of this method for the screening of type 2 diabetes renal complications.

Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers

“…This finding corroborated with those found in the literature. [34] Subsequently, they were located in the spectra of the patients' saliva samples to analyze the intensities according to the Raman bands identified.Table 3 shows the six experimental Raman bands described in the current study, those found in the literature, the relative percentage error, the absolute difference between them, and their molecular assignment. [34] The differences in the Raman shifts shown in Table 3 are due to the Raman signal filtering and fitting algorithms used for peak assignment After establishing the bands associated with albumin, the purpose was to locate them both in healthy individuals and periodontitis spectra patients (see Figure 6).…”

“…[34] Subsequently, they were located in the spectra of the patients' saliva samples to analyze the intensities according to the Raman bands identified.Table 3 shows the six experimental Raman bands described in the current study, those found in the literature, the relative percentage error, the absolute difference between them, and their molecular assignment. [34] The differences in the Raman shifts shown in Table 3 are due to the Raman signal filtering and fitting algorithms used for peak assignment After establishing the bands associated with albumin, the purpose was to locate them both in healthy individuals and periodontitis spectra patients (see Figure 6). [12] The high concentration of albumin in the saliva of patients with periodontitis is caused by the leakage of this protein, associated with inflammation and osteological compromise of periodontal damage, as evidenced in clinical case reports.…”

“…The horizontal bars correspond to the FWHM (±15 cm À1 ), and the vertical error bars correspond to the standard deviation of the intensities. Likewise, the first Raman band (cm À1 ) [34] Relative percentage error (%) Absolute difference Raman band (cm À1 ) Raman band assignment [34,35] 839 five bands show that the intensities of patients with periodontitis are higher (>10%) than those of healthy individuals, while band 6 presented a difference of less than 3% for the average healthy individuals.…”

Vibrational Spectroscopy for Detection of Diabetes: A Review

“…Due to the ability of Raman spectroscopy to detect urinary albumin, the authors propose a promising method for detecting T2DM noninvasively in future work. 56 Each of these independent studies are important for their potential to be used for the early identification of diabetes via the minimally invasive monitoring of biomarkers which have previously not been focused on.…”

A comparative analysis of different biofluids using Raman spectroscopy to determine disease activity in ANCA‐associated vasculitis

“…In effect this label‐free technique provides a fast and cost effective surrogate index of a biosamples metabolomic profile and has been successfully used across a wide range of medical applications including immune cell identity [6], cytokine detection [7], malignancy [8,9] and nephrology [10–13]. The complimentary technique of attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) has been assessed in ANCA‐associated vasculitis with promising results demonstrated [14]; however, the application Raman spectroscopy in vasculitis has not previously been reported.…”