Uric acid in the pathogenesis of metabolic, renal, and cardiovascular diseases: A review

Din, Usama A.A. Sharaf El; Salem, Mona M; Abdulazim, Dina O.

doi:10.1016/j.jare.2016.11.004

Cited by 289 publications

(187 citation statements)

References 192 publications

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“…The prevalence of hyperuricaemia was found to be 32.6% in T2DM patients with central obesity, which was far above that of the general population. HUA has proven to be an independent predictor of the development and progression of diabetic nephropathy . Additionally, elevated serum uric acid (SUA) levels have been associated with increased risk of hypertension and cardiovascular disease in individuals with T2DM .…”

Section: Introductionmentioning

confidence: 99%

Effects of sodium‐glucose co‐transporter 2 (SGLT2) inhibitors on serum uric acid level: A meta‐analysis of randomized controlled trials

Zhao

Tian

et al. 2017

Diabetes Obesity Metabolism

277

218

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The aim of this study was to describe the effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors on serum uric acid (SUA) in patients with type 2 diabetes mellitus (T2DM). PubMed, CENTRAL, EMBASE and ClinicalTrials.gov were searched for randomized controlled trials of SGLT2 inhibitors in patients with T2DM up to May 20, 2017. A total of 62 studies, comprising 34 941 patients, were included. Any of the SGLT2 inhibitors (empagliflozin, canagliflozin, dapagliflozin, tofogliflozin, luseogliflozin or ipragliflozin) significantly decreased SUA levels compared with control (total weighted mean difference [WMD] -37.73 μmol/L, 95% CI [-40.51, -34.95]). Treatment with empagliflozin resulted in a superior reduction in SUA (WMD -45.83 μmol/L, 95% CI [-53.03, -38.63]). The effect persisted during long-term treatment. Dapagliflozin decreased SUA in a dose-dependent manner (from 5 to 50 mg, P = .014). In subgroup analyses, greater reductions could be observed during the course of early diabetes and the SUA-lowering effect was abolished in patients with chronic kidney disease (estimated glomerular filtration rate <60 mL/min per 1.73 m ). The effect of SGLT2 inhibitors on SUA reduction suggests that this class of drugs might be beneficial for diabetic patients with hyperuricaemia.

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Section: Introductionmentioning

confidence: 99%

Effects of sodium‐glucose co‐transporter 2 (SGLT2) inhibitors on serum uric acid level: A meta‐analysis of randomized controlled trials

Zhao

Tian

et al. 2017

Diabetes Obesity Metabolism

277

218

View full text Add to dashboard Cite

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“…UA is considered as a biomarker of oxidative stress. UA acts as a pro‐oxidant inside the cell to stimulate nicotinamide adenine dinucleotide phosphate oxidase enzyme causing increased intracellular oxidative stress and mitochondrial injury (Becker, ; Sharaf El Din, Salem, & Abdulazim, ). In the present study, the intensity of the final oxidized product of purine metabolism (UA) was significantly increased (Supporting information Table S4) in the renal tissues of rats in the high‐dose groups, which may be related to Cd‐induced oxidative stress in kidneys.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have shown that glomerular lesions lead to reduced UA filtration, and renal tubular lesions lead to reduced UA secretion, which cause elevated serum UA. The elevated UA in the body can act directly on the kidneys and is an independent risk factor for the development of chronic kidney disease (Barkas, Elisaf, Liberopoulos, Kalaitzidis, & Liamis, ; Sharaf El Din et al, ). In the present study, the renal tubule injury markers, such as the levels of β2‐MG, RBP and (NAG)/CR, were significantly increased, and the level of UA was significantly decreased (Table ) in urine in the high‐dose group, all of which further supported the fact that the renal tubules were damaged.…”

Section: Discussionmentioning

confidence: 99%

Metabonomics analysis of kidneys in rats administered with chronic low‐dose cadmium by ultra‐performance liquid chromatography‐mass spectrometry

Zhang

Jia

Liu

et al. 2018

J of Applied Toxicology

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This study aimed to investigate the nephrotoxicity in rats administered with chronic low‐dose cadmium (Cd) by ultra‐performance liquid chromatography‐mass spectrometry. A total of 40 male Sprague‐Dawley rats were randomly assigned to four groups, namely: control; low‐dose (0.13 mg/kg·body weight [bw]); middle‐dose (0.80 mg/kg·bw); and high‐dose (4.89 mg/kg·bw). The rats received CdCl2 daily via drinking water for 24 weeks. Rat kidneys were collected for metabonomics analysis. Principal components analysis and partial least‐squares discriminant analysis were used to investigate the metabonomics profile changes in the kidney samples and to screen the potential biomarkers. Ten metabolites were identified in the positive and negative ion modes. Compared with the control group, the intensities of tetranor 12‐HETE, uric acid, hypoxanthine, phenylacetylglycine, guanidinosuccinic acid and xanthosine significantly increased (P < 0.01), and those of imidazolelactic acid, lactose 6‐phosphate, l‐urobilinogen and arachidonic acid significantly decreased (P < 0.01) in the high‐dose group. Results showed that exposure to Cd in rats induced oxidative stress to the kidneys and disrupted amino acid metabolism, fatty acid metabolism and energy metabolism.

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“…The kernel parameter 1/2σ 2 and c 2 search ranges are [2 −8 , 2 8 ] in steps of 2 0 5 for Gaussian, semi-local, exponential, inverse multiquadric, rational, and Kmod kernels. The kernel parameter d search ranges are [1,5] in steps of 1 for polynomial kernel. For kernel PLS, the search ranges are [1,30] in steps of 1 for the number of latent variants N lv .…”

Section: Methodsmentioning

confidence: 99%

“…Fast and accurate determination is very essential to the early diagnosis of the diseases. For instance, the serum uric acid (UA) level can be used as an indicator for the detection of diseases related to purine metabolism [1][2][3] and leukemia pneumonia [4][5][6]. Various analytical methods are developed for the determination of UA.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

Cao

Zhang

Zhou

2018

Journal of Spectroscopy

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Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.

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Uric acid in the pathogenesis of metabolic, renal, and cardiovascular diseases: A review

Abstract: Graphical abstract

Cited by 289 publications

References 192 publications

Effects of sodium‐glucose co‐transporter 2 (SGLT2) inhibitors on serum uric acid level: A meta‐analysis of randomized controlled trials

Effects of sodium‐glucose co‐transporter 2 (SGLT2) inhibitors on serum uric acid level: A meta‐analysis of randomized controlled trials

Metabonomics analysis of kidneys in rats administered with chronic low‐dose cadmium by ultra‐performance liquid chromatography‐mass spectrometry

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis

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