Uric acid and risk of diabetic kidney disease

Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is a common and serious complication of diabetic mellitus (DM). More sensitive methods for early DKD prediction are urgently needed. This study aimed to set up DKD risk prediction models based on machine learning algorithms (MLAs) in patients with type 2 DM (T2DM).

METHODS

The electronic health records of 12,190 T2DM patients with 3-year follow-ups were extracted, and the dataset was divided into a training and testing dataset in a 4:1 ratio. The risk variables for DKD development were ranked and selected to establish forecasting models. The performance of models was further evaluated by the indexes of sensitivity, specificity, positive predictive value, negative predictive value, accuracy, as well as F1 score, using the testing dataset. The value of accuracy was used to select the optimal model.

RESULTS

Using the importance ranking in the random forest package, the variables of age, urinary albumin-to-creatinine ratio, serum cystatin C, estimated glomerular filtration rate, and neutrophil percentage were selected as the predictors for DKD onset. Among the seven forecasting models constructed by MLAs, the accuracy of the Light Gradient Boosting Machine (LightGBM) model was the highest, indicated that the LightGBM algorithms might perform the best for predicting 3-year risk of DKD onset.

CONCLUSIONS

Our study could provide powerful tools for early DKD risk prediction, which might help optimize intervention strategies and improve the renal prognosis in T2DM patients.

The impact of uric acid on musculoskeletal diseases: clinical associations and underlying mechanisms

Serum urate (SU) levels are significantly elevated in conditions such as gout, type 2 diabetes (T2D), obesity, and other metabolic syndromes. Recently, due to the high prevalence of hyperuricemia (HUA), numerous clinical connections between SU and musculoskeletal disorders like sarcopenia, osteoarthritis (OA), rheumatoid arthritis (RA), intervertebral disc degeneration (IDD), and osteoporosis (OP) have been identified. This review discusses the mechanisms linking SU to musculoskeletal disorders, as well as the clinical associations of SU with conditions such as sarcopenia, T2D with sarcopenia, McArdle disease, heart failure, gout, OA, IDD, OP and exercise-induced acute kidney injury (EIAKI), offering valuable insights for improved prevention and treatment strategies. Mechanisms linking SU to musculoskeletal disorders include oxidative stress, MSU (monosodium urate) crystal deposition, inflammation, and other factors. In adults, both age and SU levels should be considered for preventing sarcopenia, while gender and SU may directly impact muscle mass in children and adolescents. HUA and gout may be risk factors for OA progression, although some reports suggest otherwise. A U-shaped relationship between SU and IDD has been reported, particularly in Chinese men, indicating lower or higher SU level may be risk factors for IDD. Maintaining SU levels within a certain range may help prevent OP and fractures. Future research, including epidemiological studies and new pathogenesis findings, will further clarify the relationship between musculoskeletal diseases and SU.

Visual detection of uric acid in serum through catalytic oxidation by a novel cellulose membrane biosensor with schiff base immobilized uricase

Uric acid (UA) serves as an important biochemical marker of various diseases, making the development of a novel method for its rapid and straightforward visual detection highly valuable. In this study, a uricase-based cellulose membrane biosensor (UCMB) was constructed by immobilizing uricase via a Schiff base reaction and nitroblue tetrazolium chloride (NBT) through adsorption. The UCMB detects UA through a mechanism in which uricase catalyzes the oxidation of UA, generation O2-· radicals that subsequently oxidize NBT to formazan, producing a distinctive color change from yellow to purple. The UCMB demonstrated successful visual detection of UA within 15 min, allowing for rapid naked-eye analysis. Additionally, the biosensor quantitatively detected UA over a broad linear range from 0 to 1000 μM, with a low detection limit of 3.88 μM. Most notably, the UCMB has accurately measured UA in human serum samples, comparable to the results from a commercial UA meter. These findings suggest that the UCMB can serve as a simple and reliable tool for early diagnosis of UA-related diseases.

Relationship between metabolomics of T2DM patients and the anti-diabetic effects of Phellodendri Chinensis Cortex-Anemarrhenae Rhizoma herb pair in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Type 2 diabetes mellitus (T2DM) poses significant threats to public health. In Traditional Chinese Medicine (TCM), the Phellodendri Chinensis Cortex-Anemarrhenae Rhizoma (PCC/AR) herb pair has long been used for T2DM treatment, although its specific anti-diabetic mechanisms remain unclear.

AIM OF THE STUDY

This study aimed to elucidate the relationship between metabolomics of T2DM patients and the anti-diabetic effects of PCC/AR herb pair in mice through clinical metabolomics and both in vitro and in vivo experiments.

MATERIALS AND METHODS

In this study, a T2DM mouse model was established via high-fat feeding (HFD) and streptozotocin (STZ) injection. The effects of PCC/AR on blood glucose, lipid metabolism, and inflammatory markers were evaluated. High-performance liquid chromatography-mass spectrometry (HPLC-MS) was performed for metabolomics analysis of T2DM patients.

RESULTS

Serum metabolomics analysis identified significant alterations in metabolites linked to the biosynthesis of unsaturated fatty acids and purine metabolism in T2DM patients, with elevated 2-hydroxyvaleric acid (2HB) levels. In T2DM mice, PCC/AR intervention normalized FBG, GHbA1c, TC, TG, LDL-C, HDL-C, TNF-α and IL-1β levels, while improving insulin sensitivity and pancreatic β-cell function in T2DM mice. Notably, PCC/AR reduced key enzymes in gluconeogenesis and fatty acid synthesis, PEPCK and ACC1.

CONCLUSION

PCC/AR herb pair exerts an anti-diabetes effect in T2DM mice by regulating 2HB through ACC1 inhibition, thereby reducing FFA and TG synthesis. Additionally, PCC/AR may also exert its effects by modulating glucose and lipid metabolism and reducing inflammation. These results support further investigation into the PCC/AR herb pair as a complementary therapy for T2DM.

Metadata information and fundus image fusion neural network for hyperuricemia classification in diabetes

OBJECTIVE

In diabetes mellitus patients, hyperuricemia may lead to the development of diabetic complications, including macrovascular and microvascular dysfunction. However, the level of blood uric acid in diabetic patients is obtained by sampling peripheral blood from the patient, which is an invasive procedure and not conducive to routine monitoring. Therefore, we developed deep learning algorithm to detect noninvasively hyperuricemia from retina photographs and metadata of patients with diabetes and evaluated performance in multiethnic populations and different subgroups.

MATERIALS AND METHODS

To achieve the task of non-invasive detection of hyperuricemia in diabetic patients, given that blood uric acid metabolism is directly related to estimated glomerular filtration rate(eGFR), we first performed a regression task for eGFR value before the classification task for hyperuricemia and reintroduced the eGFR regression values into the baseline information. We trained 3 deep learning models: (1) metadata model adjusted for sex, age, body mass index, duration of diabetes, HbA1c, systolic blood pressure, diastolic blood pressure; (2) image model based on fundus photographs; (3)hybrid model combining image and metadata model. Data from the Shanghai General Hospital Diabetes Management Center (ShDMC) were used to develop (6091 participants with diabetes) and internally validated (using 5-fold cross-validation) the models. External testing was performed on an independent dataset (UK Biobank dataset) consisting of 9327 participants with diabetes.

RESULTS

For the regression task of eGFR, in ShDMC dataset, the coefficient of determination (R2) was 0.684±0.07 (95 % CI) for image model, 0.501±0.04 for metadata model, and 0.727±0.002 for hybrid model. In external UK Biobank dataset, a coefficient of determination (R2) was 0.647±0.06 for image model, 0.627±0.03 for metadata model, and 0.697±0.07 for hybrid model. Our method was demonstrably superior to previous methods. For the classification of hyperuricemia, in ShDMC validation, the area, under the curve (AUC) was 0.86±0.013for image model, 0.86±0.013 for metadata model, and 0.92±0.026 for hybrid model. Estimates with UK biobank were 0.82±0.017 for image model, 0.79±0.024 for metadata model, and 0.89±0.032 for hybrid model.

CONCLUSION

There is a potential deep learning algorithm using fundus photographs as a noninvasively screening adjunct for hyperuricemia among individuals with diabetes. Meanwhile, combining patient's metadata enables higher screening accuracy. After applying the visualization tool, it found that the deep learning network for the identification of hyperuricemia mainly focuses on the fundus optic disc region.

Advancements in diabetic kidney disease management: integrating innovative therapies and targeted drug development

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease and affects approximately 40% of individuals with diabetes . Cases of DKD continue to rise globally as the prevalence of diabetes mellitus increases, with an estimated 415 million people living with diabetes in 2015 and a projected 642 million by 2040. DKD is associated with significant morbidity and mortality, representing 34% and 36% of all chronic kidney disease deaths in men and women, respectively. Common comorbidities including hypertension and ageing-related nephron loss further complicate disease diagnosis and progression. The progression of DKD involves several mechanisms including glomerular endothelial cell dysfunction, inflammation, and fibrosis. Targeting these mechanisms has formed the basis of several therapeutic agents. Renin-angiotensin-aldosterone system (RAAS) blockers, specifically angiotensin receptor blockers (ARBs), demonstrate significant reductions in macroalbuminuria. Sodium-glucose transporter type 2 (SGLT-2) inhibitors demonstrate kidney protection independent of diabetes control while also decreasing the incidence of cardiovascular events. Emerging agents including glucagon-like peptide 1 (GLP-1) agonists, anti-inflammatory agents like bardoxolone, and mineralocorticoid receptor antagonists show promise in mitigating DKD progression. Many novel therapies including monoclonal antibodies CSL346, lixudebart, and tozorakimab; mesenchymal stem/stromal cell infusion; and cannabinoid-1 receptor inverse agonism via INV-202 are currently in clinical trials and present opportunities for further drug development.

Design of Machine Learning Algorithms and Internal Validation of a Kidney Risk Prediction Model for Type 2 Diabetes Mellitus

Objective

This study aimed to explore specific biochemical indicators and construct a risk prediction model for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D).

Methods

This study included 234 T2D patients, of whom 166 had DKD, at the First Hospital of Jilin University from January 2021 to July 2022. Clinical characteristics, such as age, gender, and typical hematological parameters, were collected and used for modeling. Five machine learning algorithms [Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF)] were used to identify critical clinical and pathological features and to build a risk prediction model for DKD. Additionally, clinical data from 70 patients (nT2D = 20, nDKD = 50) were collected for external validation from the Third Hospital of Jilin University.

Results

The RF algorithm demonstrated the best performance in predicting progression to DKD, identifying five major indicators: estimated glomerular filtration rate (eGFR), glycated albumin (GA), Uric acid, HbA1c, and Zinc (Zn). The prediction model showed sufficient predictive accuracy with area under the curve (AUC) values of 0.960 (95% CI: 0.936-0.984) and 0.9326 (95% CI: 0.8747-0.9885) in the internal validation set and external validation set, respectively. The diagnostic efficacy of the RF model (AUC = 0.960) was significantly higher than each of the five features screened with the highest feature importance in the RF model.

Conclusion

The online DKD risk prediction model constructed using the RF algorithm was selected based on its strong performance in the internal validation.

Causality of Diabetic Nephropathy and Age-Related Macular Degeneration: A Mendelian Randomization Study

PURPOSE

Age-related macular degeneration (AMD) currently stands as the leading cause of irreversible vision loss in the present era. The primary objective of this study was to investigate the causal relationships between diabetic nephropathy (DN), its associated risk factors, and AMD among participants of European descent.

METHODS

Genetic variants associated with DN and its risk factors, encompassing glycemic traits, lipidemic traits, systolic/diastolic blood pressure, obesity, and urate, were obtained from previously published genome-wide association studies. Summary-level statistics for AMD were acquired from the FinnGen database. Univariable and multivariable Mendelian randomization (MR) were employed to conduct this investigation.

RESULTS

Our MR analyses indicated that per 1-standard deviation (SD) increase of DN heightened the risk of overall AMD (p = 1.03 × 10-8, OR = 1.24). And these findings remained consistent when examining both dry AMD (p = 2.27 × 10-4, OR = 1.17) and wet AMD (p = 5.15 × 10-6, OR = 1.33). Additionally, there was a causal association between high-density lipoprotein-cholesterol (HDL-C) levels and an increased risk of AMD (p = 2.69 × 10-3, OR = 1.23), while triglycerides were found to mitigate the risk (p = 0.02, OR = 0.83). Notably, no significant associations were observed between other risk factors of DN and AMD.

CONCLUSIONS

These findings suggest that the impact of DN on the development of AMD may be more substantial than previously believed. Furthermore, elevated HDL-C levels appear to heighten the risk of AMD, whereas triglycerides may provide a protective effect.

Effect of Intestinal Flora on Hyperuricemia-Induced Chronic Kidney Injury in Type 2 Diabetic Patients and the Therapeutic Mechanism of New Anti-Diabetic Prescription Medications

This article examined the current research on hyperuricemia (HUA) exacerbating diabetic kidney damage and novel anti-diabetic medications for treating these people. Hyperuricemia and type 2 diabetes (T2D), both of which are frequent metabolic disorders, are closely connected. Recent studies have shown that hyperuricemia can increase kidney injury in T2D patients by aggravating insulin resistance, by activating the renin-angiotensin-aldosterone system (RAAS), and by stimulating inflammatory factors, and the diversity, distribution, and metabolites of intestinal flora. Considering this, there are just a few of the research examining the effect of hyperuricemia on diabetic kidney injury via intestinal flora. Through the gut-kidney axis, intestinal flora primarily influences renal function. The primary mechanism is that variations in diversity, distribution, and metabolites of intestinal flora led to alterations in metabolites (such as short-chain fatty acids, Indoxyl sulfate and p-cresol sulfate, Trimethylamine N-oxide TMAO). This article reviewed the research and investigates the association between hyperuricemia and T2D, as well as the influence of hyperuricemia on diabetic kidney injury via intestinal flora. In addition, the current novel antidiabetic drugs are discussed, and their characteristics and mechanisms of action are reviewed. These novel antidiabetic drugs include SGLT2 inhibitors, GLP-1 receptor agonists, DDP-4 inhibitors, glucokinase (GK) enzyme activators (GK agonists), and mineralocorticoid receptor antagonists (MRA). Recent studies suggest that these new anti-diabetic medications may have a therapeutic effect on hyperuricemia-induced kidney impairment in diabetes patients via various mechanisms. Some of these medications may reduce blood uric acid levels, while others may improve kidney function by attenuating the overstimulation of RAAS or by decreasing insulin resistance and inflammation in the kidneys. These novel antidiabetic medicines may have a multifaceted approach to treating hyperuricemia-induced kidney impairment in diabetic patients; nevertheless, additional study is required to establish their efficacy and comprehend their specific mechanisms.

Establishment of a potent weighted risk model for determining the progression of diabetic kidney disease

BACKGROUND

Diabetic kidney disease (DKD) is a severe complication of diabetes. Currently, no effective measures are available to reduce the risk of DKD progression. This study aimed to establish a weighted risk model to determine DKD progression and provide effective treatment strategies.

METHODS

This was a hospital-based, cross-sectional study. A total of 1104 patients with DKD were included in this study. The random forest method was used to develop weighted risk models to assess DKD progression. Receiver operating characteristic curves were used to validate the models and calculate the optimal cutoff values for important risk factors.

RESULTS

We developed potent weighted risk models to evaluate DKD progression. The top six risk factors for DKD progression to chronic kidney disease were hemoglobin, hemoglobin A1c (HbA1c), serum uric acid (SUA), plasma fibrinogen, serum albumin, and neutrophil percentage. The top six risk factors for determining DKD progression to dialysis were hemoglobin, HbA1c, neutrophil percentage, serum albumin, duration of diabetes, and plasma fibrinogen level. Furthermore, the optimal cutoff values of hemoglobin and HbA1c for determining DKD progression were 112 g/L and 7.2%, respectively.

CONCLUSION

We developed potent weighted risk models for DKD progression that can be employed to formulate precise therapeutic strategies. Monitoring and controlling combined risk factors and prioritizing interventions for key risk factors may help reduce the risk of DKD progression.

The Qi-Bang-Yi-Shen formula ameliorates renal dysfunction and fibrosis in rats with diabetic kidney disease <em>via</em> regulating PI3K/AKT, ERK and PPARγ signaling pathways

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease (CKD) and a growing public health problem worldwide. Losartan potassium (Los), an angiotensin II receptor blocker, has been used to treat DKD clinically. Recently, multi-herbal formula has been shown to exhibit therapeutic activities in DKD in China. Thus, we aimed to explore the protective effects of combination of Los and Qi-Bang-Yi-Shen formula (QBF) on DKD rats. Streptozotocin (STZ) injection was used to establish a rat model of DKD. Next, the bloodurea nitrogen (BUN), creatinine (CRE) and uric acid (UA) levels were detected in serum samples from DKD rats. Hematoxylin and eosin (H&E), periodic Acid Schiff (PAS) and Masson staining were performed to observe glomerular injury and glomerular fibrosis in DKD rats. In this study, we found that QBF or Los treatment could decrease serum BUN, CRE, UA levels and reduce urine albumin-to-creatinine ratio (ACR) in DKD rats. Additionally, QBF or Los treatment obviously inhibited glomerular mesangial expansion and glomerular fibrosis, attenuated glomerular injury in kidney tissues of DKD rats. Moreover, QBF or Los treatment significantly reduced PI3K, AKT and ERK1/2 protein expressions, but increased PPARγ level in kidney tissues of DKD rats. As expected, combined treatment of QBF and Los could exert enhanced reno-protective effects compared with the single treatment. Collectively, combination of QBF and Los could ameliorate renal injury and fibrosis in DKD rats via regulating PI3K/AKT, ERK and PPARγ signaling pathways. These findings highlight the therapeutic potential of QBF to prevent DKD progression.

Hyperuricemia Predicts the Progression of Type 2 Diabetic Kidney Disease in Chinese Patients

INTRODUCTION

Diabetic kidney disease (DKD) has a high global disease burden and substantially increases the risk of end-stage renal disease and cardiovascular events. High levels of serum uric acid (SUA), or hyperuricemia, may indicate patients with type 2 diabetes (T2D) at risk for kidney disease.

METHODS

This study explored the association between SUA levels and progression of kidney disease among patients with T2D. A cross-sectional study of 993 Chinese patients aged 20-75 years with T2D and DKD was conducted. Patients were stratified by progression risk of kidney disease based on estimated glomerular filtration rate and ratio of urinary albumin to creatinine, according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Ordinal logistic regression was used to assess associations between SUA and different KDIGO risk categories.

RESULTS

Among 768 patients in the final analysis, those with hyperuricemia and higher SUA were more likely to be assigned to higher KDIGO risk categories. Patients with SUA > 420 μmol/L were ninefold more likely to be in a higher KDIGO risk category than those with SUA < 300 μmol/L (odds risk 9.74, 95% confidence interval 5.47-17.33, P < 0.001).

CONCLUSIONS

Hyperuricemia may be associated with higher risk of DKD progression in individuals with T2D.

Prediction models for risk of diabetic kidney disease in Chinese patients with type 2 diabetes mellitus

BACKGROUND

Diabetic kidney disease (DKD) is a common and serious complication in patients with diabetic mellitus (DM), the risk of cardiovascular events and all-cause mortality also increases in DKD patients. This study aimed to detect the influencing factors of DKD in type 2 DM (T2DM) patients, and construct DKD prediction models and nomogram for clinical decision-making.

METHODS

A total of 14,628 patients with T2DM were included. These patients were divided into pre-DKD and non-DKD groups, depending on the occurrence of DKD during a 3-year follow-up from first clinic attendance. The influencing indicators of DKD were analyzed, the prediction models were established by multivariable logistic regression, and a nomogram was drawn for DKD risk assessment.

RESULTS

Two prediction models for DKD were built by multivariate logistic regression analysis. Model 1 was created based on 17 variables using the forward selection method, Model 2 was established by 19 variables using the backward elimination method. The Somers' D values of both models were 0.789. Four independent predictors were selected to build the nomogram, including age, UACR, eGFR, and neutrophil percentages. The C-index of the nomogram reached 0.864, suggesting a good predictive accuracy for DKD development.

CONCLUSIONS

Our prediction models had strong predictive powers, and our nomogram provided visual aids to DKD risk calculation, which was simple and fast. These algorithms can provide early DKD risk prediction, which might help to improve the medical care for early detection and intervention in T2DM patients, and then consequently improve the prognosis of DM patients.

Metabolomic Signature of Diabetic Kidney Disease in Cerebrospinal Fluid and Plasma of Patients with Type 2 Diabetes Using Liquid Chromatography-Mass Spectrometry

Diabetic kidney disease (DKD) is the major cause of end stage renal disease in patients with type 2 diabetes mellitus (T2DM). The subtle metabolic changes in plasma and cerebrospinal fluid (CSF) might precede the development of DKD by years. In this longitudinal study, CSF and plasma samples were collected from 28 patients with T2DM and 25 controls, during spinal anesthesia for elective surgery in 2017. These samples were analyzed using liquid chromatography-mass spectrometry (LC-MS) in 2017, and the results were correlated with current DKD in 2017, and the development of new-onset DKD, in 2021. Comparing patients with T2DM having new-onset DKD with those without DKD, revealed significantly increased CSF tryptophan and plasma uric acid levels, whereas phosphatidylcholine 36:4 was lower. The altered metabolites in the current DKD cases were uric acid and paraxanthine in the CSF and uric acid, L-acetylcarnitine, bilirubin, and phosphatidylethanolamine 38:4 in the plasma. These metabolic alterations suggest the defective mitochondrial fatty acid oxidation and purine and phospholipid metabolism in patients with DKD. A correlation analysis found CSF uric acid had an independent positive association with the urine albumin-to-creatinine ratio. In conclusion, these identified CSF and plasma biomarkers of DKD in diabetic patients, might be valuable for monitoring the DKD progression.

Correlation between serum C-peptide-releasing effects and the risk of elevated uric acid in type 2 diabetes mellitus

Our study aimed to investigate the C-peptide-releasing effect associated with the risk of elevated serum uric acid (SUA) levels in patients with type 2 diabetes mellitus (T2DM). In the cross-sectional study, 345 patients with T2DM hospitalized at the First Affiliated Hospital of Harbin Medical University were consecutively enrolled, and their baseline data were collected. The study design used two parameters for C-peptide releasing effects: the multiplication effect of 1 h postprandial C-peptide to fasting C-peptide ratio (1hCp/FCp) and 2hCp/FCp; the incremental effect of 1hCp minus FCp (1hΔCp) and 2hΔCp. The patients with T2DM in the upper quartiles of SUA had higher FCp, 1hCp, 1hΔCp, 2hCp, and 2hΔCp. Multiple linear regression analysis revealed that after adjusting all the confounding factors, the serum C-peptide including 1hCp (β = 5.14, p = 0.036), 1hΔCp (β = 7.80, p = 0.010), 2hCp (β = 4.27, p = 0.009) and 2hΔCp (β = 5.20, p = 0.005) were still positively correlated with SUA levels in patients with T2DM. In female patients, only the 2hCp (β = 4.78, p = 0.017) and 2hΔCp (β = 5.28, p = 0.019) were associated with SUA level; however, in male patients, no C-peptide parameter was associated with SUA levels in T2DM (all p > 0.05). Within a certain range, the elevated SUA levels might be associated with the better C-peptide incremental effect of islet β cell function in T2DM, especially in female patients.

Noninvasive and Individual-Centered Monitoring of Uric Acid for Precaution of Hyperuricemia via Optical Supramolecular Sensing

Characterized by an excessively increased uric acid (UA) level in serum, hyperuricemia induces gout and also poses a great threat to renal and cardiovascular systems. It is urgent and meaningful to perform early warning by noninvasive diagnosis, thus conducing to blockage of disease aggravation. Here, guanidinocalix[5]arene (GC5A) is successfully identified from the self-built macrocyclic library to specifically monitor UA from urine by the indicator displacement assay. UA is strongly bound to GC5A at micromolar-level, while simultaneously excluding fluorescein (Fl) from the GC5A·Fl complex in the "switch-on" mode. This method successfully differentiates patients with hyperuricemia from volunteers except for those with kidney dysfunction and targets a volunteer at high risk of hyperuricemia. In order to meet the trend from hospital-centered to individual-centered testing, visual detection of UA is studied through a smartphone equipped with a color-scanning feature, whose adaptability and feasibility are demonstrated in sensing UA from authentic urine, leading to a promising method in family-centered healthcare style. A high-throughput and visual detection method is provided here for alarming hyperuricemic by noninvasive diagnosis.

Hyperuricemia Is Associated With the Risk of Atrial Fibrillation Independent of Sex: A Dose-Response Meta-Analysis

Background:

Conflicting findings of the association between serum uric acid (SUA) and atrial fibrillation (AF) have been reported in both men and women. The sex-specific associations between SUA and the risk of AF are unclear, although hyperuricemia is independently associated with the risk of AF. We performed this meta-analysis to assess the sex-specific effect of SUA on the risk of AF.

Methods

The PubMed, EMBASE, and Cochrane Library databases were searched up to October 3, 2021, for studies that reported sex-specific associations of SUA levels with AF. Linear relationships were assessed by the generalized least squares trend estimation. This study was registered with PROSPERO (42020193013).

Results

Ten eligible studies with 814,804 participants (415,779 men and 399,025 women) were identified. In the category analysis, high SUA was associated with an increased risk of AF in both men (OR: 1.42; 95% CI, 1.18–1.71, I² = 34%) and women (OR: 2.02; 95% CI, 1.29–3.16, I² = 70%). In the dose-response analysis, for each 60 μmol/L (1 mg/dL) increase in the SUA level, the risk of AF increased by 15% (OR: 1.15; 95% CI, 1.07–1.25, I² = 74%) in men and 35% (OR: 1.35; 95% CI, 1.18–1.53, I² = 73%) in women. There was a borderline difference in the impact of SUA on the risk of AF between men and women (P for interaction = 0.05). A significant linear relationship between SUA and the risk of AF was observed in men (P for non-linearity = 0.91) and women (P for non-linearity = 0.92).

Conclusions

This study suggested that there was a significant linear relationship between SUA and the risk of AF among men and women, with a higher risk estimate for women. Additional trials are required to assess the effect of reduced SUA therapy on AF incidence.

Systematic Review Registration

https:www.crd.york.ac.uk/PROSPERO/, identifier: CRD 42020193013.

Uric Acid and Oxidative Stress-Relationship with Cardiovascular, Metabolic, and Renal Impairment

Background: The connection between uric acid (UA) and renal impairment is well known due to the urate capacity to precipitate within the tubules or extra-renal system. Emerging studies allege a new hypothesis concerning UA and renal impairment involving a pro-inflammatory status, endothelial dysfunction, and excessive activation of renin–angiotensin–aldosterone system (RAAS). Additionally, hyperuricemia associated with oxidative stress is incriminated in DNA damage, oxidations, inflammatory cytokine production, and even cell apoptosis. There is also increasing evidence regarding the association of hyperuricemia with chronic kidney disease (CKD), cardiovascular disease, and metabolic syndrome or diabetes mellitus. Conclusions: Important aspects need to be clarified regarding hyperuricemia predisposition to oxidative stress and its effects in order to initiate the proper treatment to determine the optimal maintenance of UA level, improving patients’ long-term prognosis and their quality of life.

Association between serum uric acid and renal outcome in patients with biopsy-confirmed diabetic nephropathy

OBJECTIVE

To investigate the relationship between serum uric acid (SUA) level and renal outcome in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN).

METHODS

A total of 393 Chinese patients with T2DM and biopsy-proven DN and followed at least 1 year were enrolled in this study. Patients were stratified by the quartiles of baseline level of SUA: Q1 group: 286.02 ± 46.66 μmol/L (n = 98); Q2 group: 358.23 ± 14.03 μmol/L (n = 99); Q3 group: 405.50 ± 14.59 μmol/L (n = 98) and Q4 group: 499.14 ± 56.97μmol/L (n = 98). Renal outcome was defined by progression to end-stage renal disease (ESRD). Kaplan-Meier survival analysis and Cox proportional hazards model were used to analyze the association between SUA quartiles and the renal outcomes.

RESULTS

During the median 3-year follow-up period, there were 173 ESRD outcome events (44.02%). No significant difference between SUA level and the risk of progression of DN (P = 0.747) was shown in the Kaplan-Meier survival analysis. In multivariable-adjusted model, hazard ratios for developing ESRD were 1.364 (0.621-2.992; P = 0.439), 1.518 (0.768-3.002; P = 0.230) and 1.411 (0.706-2.821; P = 0.330) for the Q2, Q3 and Q4, respectively, in comparison with the Q1 (P = 0.652).

CONCLUSIONS

No significant association between SUA level and renal outcome of ESRD in Chinese patients with T2DM and DN was found in our study. Besides, the role of uric acid-lowering therapy in delaying DN progression and improving ESRD outcome had not yet been proven. Further study was needed to clarify the renal benefit of the uric acid-lowering therapy in the treatment of DN.

Pleiotropic Effects of Sodium-Glucose Cotransporter-2 Inhibitors: Renoprotective Mechanisms beyond Glycemic Control

Diabetes mellitus is a major cause of chronic kidney disease and end-stage renal disease. However, the management of chronic kidney disease, particularly diabetes, requires vast improvements. Recently, sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally developed for the treatment of diabetes, have been shown to protect against kidney injury via glycemic control, as well as various other mechanisms, including blood pressure and hemodynamic regulation, protection from lipotoxicity, and uric acid control. As such, regulation of these mechanisms is recommended as an effective multidisciplinary approach for the treatment of diabetic patients with kidney disease. Thus, SGLT2 inhibitors are expected to become key drugs for treating diabetic kidney disease. This review summarizes the recent clinical evidence pertaining to SGLT2 inhibitors as well as the mechanisms underlying their renoprotective effects. Hence, the information contained herein will advance the current understanding regarding the pleiotropic effects of SGLT2 inhibitors, while promoting future research in the field.