An inducible model for unraveling the effects of advanced glycation end-product accumulation in aging connective tissues.

PURPOSE: In connective tissues there is a clear link between increasing age and degeneration. Advanced glycation end-products (AGEs) are believed to play a central role. AGEs are sugar-induced non-enzymatic crosslinks which accumulate in collagen with age and diabetes, altering tissue mechanics and cellular function. Despite ample correlative evidence linking collagen glycation to tissue degeneration, little is known how AGEs impact cell-matrix interactions, limiting therapeutic options. One reason for this limited understanding is that AGEs are typically induced using high concentrations of ribose which decrease cell viability, making it impossible to investigate cell-matrix interactions. The objective of this study was to develop a system to trigger AGE accumulation while maintaining cell viability. MATERIALS AND METHODS: Using cell-seeded high density collagen gels, we investigated the effect of two systems for AGE induction, ribose at low concentrations (30, 100, and 200 mM) over 15 days of culture and riboflavin (0.25 and 0.75 mM) induced with blue light for 40 seconds (riboflavin-465 nm). RESULTS: We found ribose and riboflavin-465 nm treatment produces fluorescent AGE quantities which match and/or exceed human fluorescent AGE levels for various tissues, ages, and diseases, without affecting cell viability or metabolism. Interestingly, a 40 second treatment of riboflavin-465 nm produced similar levels of fluorescent AGEs as 3 days of 100 mM ribose treatment. CONCLUSIONS: Riboflavin-465 nm is a promising method to trigger AGEs on demand in vivo or in vitro without impacting cell viability and offers potential for unraveling the mechanism of AGEs in age and diabetes related tissue damage.

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix).

Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.

Spectral characteristics of urine specimens from healthy human volunteers analyzed using Raman chemometric urinalysis (Rametrix).

Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the "range of normal" for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.