Rapid identification of early renal damage in asymptomatic hyperuricemia patients based on urine Raman spectroscopy and bioinformatics analysis

Abstract

Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease.Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed.Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm−1), urea, creatinine (1,608, 1,706 cm−1), proteins/amino acids (642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,615 cm−1), and ketone body (1,643 cm−1) (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD (ALB, MYC, IL10, FOS, TOP2A, PLG, REN, FGA, CCNA2, and BUB1) were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD.Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA.

Metabolomic analysis for asymptomatic hyperuricemia and gout based on a combination of dried blood spot sampling and mass spectrometry technology

“…It has been found that the levels of Thr, Trp, and Ala were increased in patients with HUA compared with healthy individuals [ 33 ], which were consistent with our findings. It is worth noting that abnormalities in tryptophan metabolism showed association with AHU [ 34 ] and acute gout [ 35 ]. The altered levels of Trp may be owing to the UA-induced dysfunction of multidrug resistance protein 4 (MRP4) and breast cancer resistance protein (BCRP) [ 36 ].…”

Hub gene mining and immune microenvironment analysis of autophagy in rheumatoid arthritis

“…Machine learning is used in various elds of biology to analyze association patterns between traits and genes in samples [8,9] . Machine learning is a subset of AI that has presented notable bene ts in ltering disease biomarkers or underlying prevention and treatment targets, and has been successfully applied in multiple diseases [10,11] . High-throughput sequencing data deposited in the GEO database were employed for the screening of underlying biomarkers and molecular mechanisms of RA, which can offer novel perspectives in preventing and treating RA.…”