Serum uric acid levels and diabetic kidney disease in patients with type 2 diabetes mellitus: A dose-response meta-analysis

Uric acid in diabetic microvascular complications: Mechanisms and therapy

Uric acid (UA) is mainly synthesized in the liver, intestine, and vascular endothelium and excreted by the kidney (70 %) and intestine (30 %). Hyperuricemia (HUA) occurs when UA production exceeds excretion. Many studies have found that elevated UA is associated with diabetic microvascular complications (DMC), including diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic peripheral neuropathy (DPN). In addition, too high or too low UA levels will promote the occurrence and development of chronic diseases, but the relationship between UA and diabetic microvascular complications (DMC) is not clear. Therefore, the rational treatment of UA in patients with diabetes is essential. In this review, we summarize and discuss the mechanism and treatment of UA and DMC and may provide potential advice for rational drug selection.

Visceral Adiposity as an Independent Risk Factor for Diabetic Peripheral Neuropathy in Type 2 Diabetes Mellitus: A Retrospective Study

Background: Diabetic peripheral neuropathy (DPN) impacts approximately 50% of individuals with Type 2 diabetes mellitus (T2DM), leading to severe complications such as foot ulcers and amputations. Notably, visceral adiposity is increasingly recognized as a pivotal factor in augmenting the risk of DPN. We aim to evaluate the correlation between obesity-related body composition, particularly visceral fat, and DPN to facilitate early identification of high-risk patients with T2DM. Methods: This cross-sectional analysis encompassed 113 T2DM patients from the Department of Endocrinology and Metabolism at the Second Affiliated Hospital of Fujian Medical University, conducted between September 2020 and January 2021. Patients were categorized into two cohorts: those with DPN (DPN group) and those without (NDPN group). We utilized bioelectrical impedance analysis (BIA) to determine body measurements, such as weight and visceral fat area, in addition to collecting clinical and biochemical data. Logistic regression was employed to analyze the data. Results: The study uncovered a statistically significant difference in the visceral fat area between the DPN and NDPN groups (p = 0.048). Through multivariate logistic regression analysis, the visceral fat area was identified as an independent risk factor for DPN among T2DM patients (OR 1.027; 95% CI 1.004-1.051, p = 0.022). Other significant risk factors included the duration of diabetes and the presence of diabetic retinopathy. Conclusion: The visceral fat area serves as an independent risk factor for DPN in individuals with T2DM. Implementing measures to assess and manage visceral obesity could be vital in the prevention and management of DPN. This underscores the value of technologies such as BIA in clinical and community settings for early intervention.

A Multi-Channel Urine Sensing Detection System Based on Creatinine, Uric Acid, and pH

Urine analysis represents a crucial diagnostic technique employed in clinical laboratories. Creatinine and uric acid in urine are essential biomarkers in the human body and are widely utilized in clinical analysis. Research has demonstrated a correlation between the normal physiological concentrations of creatinine and uric acid in urine and an increased risk of hypertension, cardiovascular diseases, and kidney disease. Furthermore, the pH of urine indicates the body's metabolic processes and homeostatic balance. In this study, an integrated multi-channel electrochemical sensing system was developed, combining electrochemical analysis techniques, microelectronic design, and nanomaterials. The architecture of an intelligent medical detection system and the production of an interactive interface for smartphones were accomplished. Initially, multi-channel selective electrodes were designed for creatinine, uric acid, and pH detection. The detection range was 10 nM to 100 μM for creatinine, 100 μM to 500 μM for uric acid, and 4 to 9 for pH. Furthermore, interference experiments were also conducted to verify the specificity of the sensors. Subsequently, multi-channel double-sided sensing electrodes and function-integrated hardware were designed, with the standard equations of target analytes stored in the system's read-only memory. Moreover, a WeChat mini-program platform was developed for smartphone interaction, enabling off-body detection and real-time display of target analytes through smartphones. Finally, the aforementioned electrochemical detection electrodes were integrated with the smart sensing system and wirelessly interfaced with smartphones, allowing for intelligent real-time detection in primary healthcare and individual household settings.

Development of a machine learning-based model for the prediction and progression of diabetic kidney disease: A single centred retrospective study

BACKGROUND

Diabetic kidney disease (DKD) is a diabetic microvascular complication often characterized by an unpredictable progression. Hence, early detection and recognition of patients vulnerable to progression is crucial.

OBJECTIVE

To develop a prediction model to identify the stages of DKD and the factors contributing to progression to each stage using machine learning.

METHODOLOGY

A retrospective study was conducted in a South Indian tertiary care hospital and collected the details of patients diagnosed with DKD from January 2017 to January 2022. Bayesian optimization-based machine learning techniques such as classification and regression were employed. The model was developed with the help of an optimization framework that effectively balances classification, prediction accuracy, and explainability.

RESULTS

Of the 311 patients diagnosed with DKD, 227 were selected for the study. A system for predicting DKD has been created for a patient dataset utilizing a variety of machine-learning approaches. The eXtreme gradient (XG) Boost method excelled, achieving 88.75% accuracy, 88.57% precision, 91.4% sensitivity,100% specificity, and 89.49% F1-score. An interpretable data-driven method highlights significant features for early DKD diagnosis. The best explainable prediction model uses the XG Boost classifier, revealing serum uric acid, urea, phosphorous, red blood cells, calcium, and absolute eosinophil count as the major predictors influencing the progression of DKD. In the case of regression models, the gradient boost regressor performed the best, with an R2 score of 0.97.

CONCLUSION

Machine learning algorithms can effectively predict the stages of DKD and thus help physicians in providing patients with personalized care at the right time.

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.

Association Between Arsenic Toxicity, AS3MT Gene Polymorphism and Onset of Type 2 Diabetes

Arsenic (As) exposure in drinking water has become a serious public health issue. AS3MT gene is involved in the metabolism of arsenic, so a single nucleotide polymorphism in this gene may lead to the development of type 2 diabetes in arsenic-exposed areas. This study aimed to evaluate the association of the AS3MT gene with the development of type 2 diabetes in highly arsenic-exposed areas of Punjab, Pakistan. Total 200 samples equal in number from high arsenic exposed-areas of Lahore (Nishtar) and Kasur (Mustafa Abad) were collected. rs11191439 was utilized as an influential variable to evaluate the association between arsenic metabolism and diabetes status to find a single nucleotide polymorphism in the AS3MT gene. We observed the arsenic level in drinking water of the arsenic-exposed selected areas 115.54 ± 1.23 µg/L and 96.88 ± 0.48 µg/L, respectively. The As level in the urine of diabetics (98.54 ± 2.63 µg/L and 56.38 ± 12.66 µg/L) was higher as compared to non-diabetics (77.58 ± 1.8 µg/L and 46.9 ± 8.95 µg/L) of both affected areas, respectively. Correspondingly, the As level in the blood of diabetics (6.48 ± 0.08 µg/L and 5.49 ± 1.43 µg/L) and non-diabetics (6.22 ± 0.12 µg/L and 5.26 ± 0.24 µg/L) in the affected areas. Genotyping showed significant differences in the frequencies of alleles among cases and controls. Nevertheless, notable disparities in genotype distribution were observed in SNPs rs11191439 (T/C) (P < 0.05) and when comparing T2D patients and non-diabetic control subjects. The AS3MT gene and clinical parameters show a significant association with the affected people with diabetes living in arsenic-exposed areas.

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Purpose

To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients.

Patients and methods

A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA).

Results

T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively.

Conclusion

The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

Anti-Hyperuricemic, Anti-Arthritic, Hemolytic Activity and Therapeutic Safety of Glycoconjugated Triazole-Phthalimides

Hyperuricemia, the metabolic alteration that leads to gout or gouty arthritis, is increasing worldwide. Glycoconjugated triazole-phthalimides show potent anti-inflammatory activity. The aim of this study was to evaluate the anti-hyperuricemia effect of glycoconjugated triazole-phthalimides. To develop hyperuricemia, groups of mice received orally potassium oxonate (250 mg/kg) for 7 days, and F2, F3 and F4 glycoconjugated triazole-phthalimides (20 mg/kg), allopurinol (300 mg/kg), and 1% carboxymethylcellulose; indomethacin (2 and 4 mg/kg) was the positive control for anti-arthritic effect. Genotoxic and mutagenic effects were evaluated by the comet and micronucleus assays, respectively. The hemolytic action of the compounds was evaluated. Phthalimides F2, F3 and F4 significantly reduced the levels of serum uric acid, creatinine and urea in hyperuricemic animals. In addition, the compounds were efficient in reducing protein denaturation in a dose-dependent manner. In an interesting way, the histopathological analysis of kidneys from groups treated with F2, F3 and F4 showed a glomerular architecture, with the Bowman's capsule and renal tubules having a normal appearance and without inflammatory changes. Also, F2 and F4 showed a small increase in micronuclei, indicating a low mutagenic effect, whilst by comet assay only, we could infer that F4 affected the frequency and damage index, thus indicating a very small genotoxic action. Similarly, the phthalimides showed a low degree of erythrocyte hemolysis (<3%). Our data demonstrate that the new glycoconjugate triazole-phthalimides have potential to treat hyperuricemia and its secondary complications, such as gouty arthritis, with a low to non-significant rate of erythrocytes hemolysis, genotoxicity and mutagenicity making these molecules strong candidates as pharmaceutical agents for treatment requiring uric-acid-lowering therapy.

Genotypic Analysis of COL4A1 Gene in Diabetic Nephropathy and Type 2 Diabetes Mellitus Patients: A Comparative Genetic Study

Diabetic nephropathy (DN) is specified by microalbuminuria, glomerular lesions, and renal fibrosis leading to end-stage renal disease. The pathophysiology of DN is multifactorial as a result of gene-environment interaction. Clinical studies suggested that gene mutations affect various pathways involved in DN, including extracellular matrix (ECM). During chronic hyperglycemia, collagen type-4-mediated ECM overproduction occurs, leading to renal fibrosis and DN development. In this study, COL4A1 gene variant rs605143 (G/A) was analyzed in diabetes and DN patients from the study population. We genotyped 386 study subjects, comprising 120 type 2 diabetes mellitus (T2DM) patients, 120 DN, and 146 healthy controls. All study subjects were analyzed for biochemical assays by commercially available kits and genotypic analysis by polymerase chain reaction-restriction fragment length polymorphism and confirmed by Sanger sequencing. Statistical analyses were done using SPSS and GraphPad. Anthroclinicopathological parameters showed a significant association between T2DM and DN. Genotype AA of COL4A1 gene variant rs605143 (G/A) showed a significant association with T2DM and DN compared with controls with 5.87- and 8.01-folds risk, respectively. Mutant allele A also significantly associated with T2DM and DN independently compared with healthy controls with 2.29- and 2.81-time risk in the study population. This study's findings suggested that COL4A1 gene variant rs605143 (G/A) can be used as predictive biomarkers for T2DM and DN independently. However, this gene variant needs to be analyzed in a large sample to explore the shared genetic association between T2DM and DN.

Serum uric acid and the risk of gestational diabetes mellitus: a systematic review and meta-analysis

AIMS

Serum uric acid (SUA) is considered as a risk factor for gestational diabetes mellitus (GDM). However, current studies showed inconsistent results. This study aimed to explore the relationship between SUA levels and GDM risk.

METHODS

Eligible studies were retrieved from PubMed, Web of Science, Embase, China National Knowledge Infrastructure, and Wanfang databases up to November 1, 2022. The pooled standardized mean difference (SMD) and 95% confidence interval (CI) were used to represent the difference in SUA levels between GDM women and controls. The combined odds ratios (OR) and 95% CI were applied to assess association between SUA levels and GDM risk. Subgroup analyses were conducted on study continents, design, and quality, detection time of SUA, and GDM diagnostic criteria.

RESULTS

Totally 11 studies including five case-control and six cohort studies, in which 80,387 pregnant women with 9815 GDM were included. The overall meta-analysis showed that the mean SUA level in GDM group was significantly higher than in controls (SMD = 0.423, 95%CI = 0.019-0.826, p = .040, I² = 93%). Notably, pregnant women with elevated levels of SUA had a significantly increased risk of GDM (OR = 1.670, 95%CI = 1.184-2.356, p = .0035, I² = 95%). Furthermore, subgroup analysis performed on the detection time of SUA showed a significant difference in the association between SUA and GDM risk within different trimesters (1st trimester: OR = 3.978, 95%CI = 2.177-7.268; 1st to 2nd trimester: OR = 1.340, 95%CI = 1.078-1.667; p between subgroups <.01).

CONCLUSIONS

Elevated SUA was positively associated with GDM risk, particularly in the 1st trimester of pregnancy. Further studies with high quality are required to validate the findings of this study.

Serum Uric Acid Levels Are Related to Diabetic Peripheral Neuropathy, Especially for Motor Conduction Velocity of Tibial Nerve in Type 2 Diabetes Mellitus Patients

Background

Oxidative stress is one of the most critical factors that contribute to the pathogenesis of neuronal damage, including diabetic peripheral neuropathy (DPN). Uric acid is a kind of natural antioxidant that plays a major role in the antioxidant capacity against oxidative stress. Here, we aim to determine the role of serum uric acid (SUA) in the DPN of patients with type 2 diabetes mellitus (T2DM). Patients and Methods. 106 patients with T2DM were recruited and divided into the DPN group and the control group. Clinical parameters, especially for motor nerve fiber conduction velocity and sensory nerve fiber conduction velocity, were collected. Differences between T2DM patients with and without DPN were compared. Correlation and regression analyses were performed to explore the association between SUA and DPN.

Results

Compare with 57 patients with DPN, 49 patients without DPN showed lower HbA1c and elevated SUA levels. Additionally, SUA levels are negatively associated with the motor conduction velocity of tibial nerve with or without adjusting for HbA1c. Besides, it is suggested that decreased SUA levels may influence the motor conduction speed of the tibial nerve by multiple linear regression analysis. Moreover, we demonstrated that decreased SUA level is a risk factor for DPN in patients with T2DM by binary logistic regression analysis.

Conclusion

Lower SUA is a risk factor for DPN in patients with T2DM. Additionally, decreased SUA may influence the damage of peripheral neuropathy, especially for motor conduction velocity of the tibial nerve.

The potential relationship of coronary artery disease and hyperuricemia: A cardiometabolic risk factor

Coronary arterial disease (CAD) is the leading cause of mortality in the world. Hyperuricemia has recently emerged as a novel independent risk factor of CAD, in addition to the traditional risk factors such as hyperlipidemia, smoking, and obesity. Several clinical studies have shown that hyperuricemia is strongly associated with the risk, progression and poor prognosis of CAD, as well as verifying an association with traditional CAD risk factors. Uric acid or enzymes in the uric acid production pathway are associated with inflammation, oxidative stress, regulation of multiple signaling pathways and the renin-angiotensin-aldosterone system (RAAS), and these pathophysiological alterations are currently the main mechanisms of coronary atherosclerosis formation. The risk of death from CAD can be effectively reduced by the uric acid-lowering therapy, but the interventional treatment of uric acid levels in patients with CAD remains controversial due to the diversity of co-morbidities and the complexity of causative factors. In this review, we analyze the association between hyperuricemia and CAD, elucidate the possible mechanisms by which uric acid induces or exacerbates CAD, and discuss the benefits and drawbacks of uric acid-lowering therapy. This review could provide theoretical references for the prevention and management of hyperuricemia-associated CAD.

Effects of sodium-glucose cotransporter 2 inhibitors on renal risk factors in patients with abnormal glucose metabolism: a meta-analysis of randomized controlled trials

AIMS

Several trials have assessed the antihyperglycemic effects of sodium-glucose cotransporter 2 inhibitors (SGLT2Is) in patients with type 2 diabetes mellitus (T2DM). We conducted a quantitative analysis to assess the effects of SGLT2Is on renal risk factors in patients with abnormal glucose metabolism.

MATERIALS AND METHODS

Randomized controlled trials (RCTs) were identified by searching the PubMed, Embase, Scopus, and Web of Science databases published before September 30, 2022. The intervention group received SGLT2Is as monotherapy or add-on treatment, and the control group received placebos, standard care, or active control. Risk of bias assessment was performed using the Cochrane risk of bias assessment tool. Meta-analysis was performed on studies with abnormal glucose metabolism populations and studies using the weighted mean differences (WMDs) as the measure of the effect size. Clinical trials providing changes in serum uric acid (SUA) were included. The mean change of SUA, glycated hemoglobin (HbA1c), body mass index (BMI), and estimated glomerular filtration rate (eGFR) were calculated.

RESULTS

After a literature search and detailed evaluation, a total of 11 RCTs were included for quantitative analysis to analyze the differences between the SGLT2I group and the control group. The results showed that SGLT2I significantly reduced SUA (MD =  -0.56, 95% CI =  -0.66 ~ -0.46, I² = 0%, P < 0.00001), HbA1c (MD =  -0.20, 95% CI =  -0.26 ~ -0.13, I² = 0%, P < 0.00001), and BMI (MD =  -1.19, 95% CI =  -1.84 ~ -0.55, I² = 0%, P = 0.0003). There was no significant difference in the reduction of eGFR observed in the SGLT2I group (MD =  -1.60, 95% CI =  -3.82 ~ 0.63, I² = 13%, P = 0.16).

CONCLUSIONS

These results showed that the SGLT2I group caused greater reductions in SUA, HbA1c, and BMI but had no effect on eGFR. These data suggested that SGLT2Is may have numerous potentially beneficial clinical effects in patients with abnormal glucose metabolism. However, these results need to be consolidated by further studies.

Altered Serum Uric Acid Levels in Kidney Disorders

Serum uric acid levels are altered by kidney disorders because the kidneys play a dominant role in uric acid excretion. Here, major kidney disorders which accompany hyperuricemia or hypouricemia, including their pathophysiology, are discussed. Chronic kidney disease (CKD) and hyperuricemia are frequently associated, but recent clinical trials have not supported the pathogenic roles of hyperuricemia in CKD incidence and progression. Diabetes mellitus (DM) is often associated with hyperuricemia, and hyperuricemia may be associated with an increased risk of diabetic kidney disease in patients with type 2 DM. Sodium-glucose cotransporter 2 inhibitors have a uricosuric effect and can relieve hyperuricemia in DM. Autosomal dominant tubulointerstitial kidney disease (ADTKD) is an important hereditary kidney disease, mainly caused by mutations of uromodulin (UMOD) or mucin-1 (MUC-1). Hyperuricemia and gout are the major clinical manifestations of ADTKD-UMOD and ADTKD-MUC1. Renal hypouricemia is caused by URAT1 or GLUT9 loss-of-function mutations and renders patients susceptible to exercise-induced acute kidney injury, probably because of excessive urinary uric acid excretion. Hypouricemia derived from renal uric acid wasting is a component of Fanconi syndrome, which can be hereditary or acquired. During treatment for human immunodeficiency virus, hepatitis B or cytomegalovirus, tenofovir, adefovir, and cidofovir may cause drug-induced renal Fanconi syndrome. In coronavirus disease 2019, hypouricemia due to proximal tubular injury is related to disease severity, including respiratory failure. Finally, serum uric acid and the fractional excretion of uric acid are indicative of plasma volume status; hyperuricemia caused by the enhanced uric acid reabsorption can be induced by volume depletion, and hypouricemia caused by an increased fractional excretion of uric acid is the characteristic finding in syndromes of inappropriate anti-diuresis, cerebral/renal salt wasting, and thiazide-induced hyponatremia. Molecular mechanisms by which uric acid transport is dysregulated in volume or water balance disorders need to be investigated.

Mendelian randomization study supports the causal association between serum cystatin C and risk of diabetic nephropathy

AIMS

Cystatin C, an inhibitor of cysteine protease, has been used as a biomarker for estimating glomerular filtration rate. However, the causal relation between cystatin C and diabetic nephropathy remains uncertain.

METHODS

We assessed the causal effect of cystatin C together with other five serum biomarkers including KIM-1, GDF-15, TBIL, uric acid, and Scr on diabetic nephropathy by Mendelian randomization (MR) analysis. 234 genetic variants were selected as instrumental variables to evaluate the causal effect of cystatin C (N_GWAS=361194) on diabetic nephropathy (Ncase/Ncontrol up to 3283/210463). Multivariable MR (MVMR) was performed to assess the stability of cystatin C's causal relationship. Two-step MR was used to assess the mediation effect of BMI and SBP.

RESULTS

Among the six serum biomarkers, only cystatin C causally associated with diabetic nephropathy (IVW OR: 1.36, 95%CI [1.15, 1.61]). After adjusting for the potential confounders BMI and SBP, cystatin C maintained its causal effect on the DN (OR: 1.17, 95%CI [1.02, 1.33]), which means that the risk of DN increased by 17% with an approximate 1 standard deviation (SD) increment of serum cystatin C level. Two-step MR results indicated that BMI might mediate the causal effect of cystatin C on diabetic nephropathy.

INTERPRETATION

Our findings discovered that cystatin C was a risk factor for diabetic nephropathy independent of BMI and SBP in diabetes mellitus patients. Future research is required to illustrate the underlying mechanism and prove targeting circulating cystatin C could be a potential therapy method.