Uric acid and diabetes mellitus: an update

Prevalence of Hyperuricemia and Associated Factors Among Patients With Type 2 Diabetes Mellitus in Northwestern Tanzania: A Cross-Sectional Study

Background

There has been increasing evidence of the association between hyperuricemia and diabetes mellitus (DM). In the general population, hyperuricemia has been associated with pre-diabetes. In DM patients, hyperuricemia has been associated with poor outcomes.

Objectives

The objective was to determine the proportion of hyperuricemia and associated factors among patients with type 2 DM in Mwanza, Tanzania.

Design

This was a cross-sectional study.

Methods

This study was conducted from January to March 2023 among patients with type 2 DM attending clinic at Bugando Medical Centre, Mwanza. Data was obtained from a structured questionnaire. Serum uric acid, HbA1c, lipid profile, and renal functions were analyzed. Analysis was done via STATA version 17. The primary outcome was the proportion of hyperuricemia among patients with type 2 DM, and logistic regression models were used to analyze associated factors.

Results

Out of 360 patients, 59.7% were female. The median age was 61 years [IQR 57-68], and the median duration of DM was 5 years [IQR 3-9]. The mean HbA1c was 8.2 ± 2.5%, with 60% of patients having poor control. Most patients had hypertension (78.9%) and were overweight or obese (81.9%). The proportion of patients with DM and hyperuricemia was 44.4%, with mean serum uric acid levels among males and females of 410 ± 137 and 385 ± 119 µmol/L, respectively. We found that being female (P = .001), overweight (P = .021), or obese (P = .007), and having chronic kidney disease (P < .001) was associated with hyperuricemia among patients with type 2 DM.

Conclusion

The burden of hyperuricemia among type 2 DM patients is quite high, and it is associated with female gender, high body mass index, lipids, and chronic kidney disease. This calls for regular screening of hyperuricemia in the population, and more studies are needed to establish the outcomes associated with hyperuricemia and create a treatment guideline.

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.

Hyperuricemia - consequences of not initiating therapy. Benefits and drawbacks of treatment

Hyperuricemia, characterized by elevated levels of uric acid in the body, is associated with several health risks, including gout, urolithiasis and cardiovascular disease. Although treatment options are available, they can lead to hypersensitivity reactions, particularly with allopurinol therapy. This paper provides a comprehensive review of the consequences of hyperuricemia, the need for treatment and the potential adverse effects of allopurinol, illustrated by a case study. The study highlights the importance of careful consideration before initiating therapy, particularly in patients with comorbidities and concomitant medication. It emphasizes the need for vigilant monitoring and individualized treatment approaches to reduce adverse effects. In addition, genetic factors, particularly HLA-B*5801, play an important role in determining susceptibility to allopurinol hypersensitivity reactions. This paper highlights the importance of informed decision making in the management of hyperuricemia to optimize patient outcomes while minimizing the risks associated with treatment.

A high level of uric acid is associated with long-term adverse cardiovascular outcomes in patients who received fractional flow reserve with coronary intermediate stenosis

BACKGROUND AND AIMS

The role of fractional flow reserve (FFR) in coronary intermediate lesions is widely recommended by guidelines. The effect of uric acid (UA) on cardiovascular events is also well known. However, the relationship between UA and long-term cardiovascular outcomes in patients who received FFR with intermediate lesions remains unknown.

METHODS AND RESULTS

We retrospectively included 428 patients who underwent both coronary angiography (CAG) and FFR. Participants were stratified into two groups based on the median UA. The primary endpoint was the composite of major adverse cardiovascular and cerebrovascular events (MACCEs), including repeat revascularization, nonfatal stroke, nonfatal myocardial infarction, and all-cause death. A Cox proportional hazards model was utilized to analyze the association between UA and the prevalence of MACCEs. During a median follow-up of 5.8 years, a higher MACCEs rate occurred in the high UA group compared to the low UA group (16.8% vs. 5.1%, p log-rank<0.01). Elevated UA was independently linked to a higher incidence of MACCEs, whether UA was treated as a categorical or continuous variable (hazard ratio [HR] 2.76, 95% confidence interval [CI] 1.27-6.03 or HR 1.01, 95% CI 1.01-1.02). The restricted cubic spline (RCS) analysis illustrated that the HR for MACCEs increased with increasing UA.

CONCLUSION

The present study demonstrates that UA is associated with MACCEs risk and suggests that UA is a reliable predictor of long-term cardiovascular events in coronary intermediate stenosis patients.

Hyperuricemia and associated factors: The case of outpatients at the Bafoussam Regional Hospital- Cameroon, an analytical cross-sectional study

Background and Aims

Hyperuricemia constitutes a major public health issue due to its implication in many chronic diseases and metabolic syndromes. We propose to study the prevalence and associated factors of hyperuricemia to diagnose asymptomatic patients and make prognoses on the state of health of the patients.

Methods

An analytic cross-sectional study has been carried out at the Bafoussam Regional Hospital and the Biochemistry laboratory of the Université des Montagnes over 2 months. Sociodemographic and anthropometric characteristic was obtained; a blood sample was collected from the chosen patients and a biochemical test (uric acid, creatinine, urea, total cholesterol, high density lipoproteins cholesterol, triglyceride) was analyzed by spectrophotometric method. Statistical tests were carried out using SPSS statistical software. Logistic regression analyses identified factors associated with variables of interest. The significance was measured by a p < 0.05 with a confidential level of 95%.

Results

The patient population was made up of 100 patients. The sex ratio was 1.22 in favor of men. The prevalence of hyperuricemia in our study was 28.0% with 31.1% in women and 27.3% in men. The mean average of uric acid in the hyperuricemia population was 7.50 ± 1.24 mg/L and the normal uricemia population was 4.69 ± 1.49 mg/L (p < 0.0001). The mean average triglyceride in the hyperuricemia population was 143 ± 14 and 117.55 ± 55.52 mg/dL in normal uricemia with p = 0.046. Age range [35-45] and hypertriglyceridemia have been associated with hyperuricemia with respectively (odds ratio [OR] = 4.07, p < 0.015) confidence interval, CI: [0.89: 97.0]) and ([OR = 2.50, p < 0.046] CI: [1.01: 6.09]).

Conclusion

The prevalence of hyperuricemia was relatively high and has been associated with metabolic disorders in the population. It is necessary to focus on early diagnoses, treatment, and early intervention in view to prevent chronic diseases associated with hyperuricemia.