European Journal of Cardiovascular Medicine

Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia that occurs as the result of deficits in insulin secretion, action, or both. Better knowledge on the complex relationship between different metabolic markers in T2DM can contribute to improve diagnosis, management and prevention of complications. ¹

Serum uric acid, the final product of purine metabolism has been increasingly recognized as an important biomarker for metabolic disease. Hyperuricemia is approximately prevalent in about 10 - 25% of the general population, caused due to excess uric acid production or decreased excretion.² A number of studies have indicated that hyperuricemia may be an independent risk factor for development T2DM.^3-5

However, the relationship between serum uric acid and glycemic status in T2DM has been debated extensively due to the increasing number of literatures published.^4-6 Although some studies have found a positive association, others show no significant correlation or a negative correlation at all. These differences emphasize a requirement of additional research to elucidate how serum uric acid and diabetes interacts and the mechanisms involved. Present study was aimed to study correlation of fasting serum uric acid level with fasting blood glucose level and its association with glycemic status in type 2 diabetes mellitus patients attending a tertiary care hospital.

Present study was Cross sectional observational study, conducted in department of General Medicine, Institute of Diabetology, Madras Medical College and Hospital, Chennai, India. Study duration was of 4-5 months (March 2025- July 2025). Study was approved by institutional ethical committee.

Inclusion criteria

• Patients of age ≥18 years, diagnosed case of Type 2 Diabetes Mellitus according to ADA (American Diabetes Association) criteria 2023, willing to participate in present study

Exclusion criteria

• Age <18 years
• Type 1 Diabetes Mellitus
• Chronic kidney disease
• Pregnancy
• Patients on diuretics
• Sepsis
• Chronic liver disease
• Chronic kidney disease
• Arthritis
• Type 2 Diabetes patients (T2DM) who are on treatment with SGLT2 inhibitors
• Diabetic ketoacidosis

Study was explained to participants in local language & written informed consent was taken. Explanatory variables including weight, blood pressure, BMI, waist circumference, and duration of diabetes will be recorded as well as whether the participants were smokers or alcoholic drinkers and any drug history. Venous blood samples will be obtained under aseptic conditions following an overnight fasting period of 8 to 10 h for the assessment of:

• Fasting serum uric acid
• Fasting blood glucose
• Fasting lipid profile
• Renal function test
• Liver function test

The study population will be grouped based on glycemic status according to fasting blood glucose levels per ADA 2023 and also correlation with fasting serum uric acid level. To analyze the data, SPSS software will be used for statistical analysis and correlation between serum uric acid, glucose and other parameters with Pearson’s correlation coefficient; while multivariate regression analysis method will be used to identify independent factors correlated with serum uric acid and glucose levels.

The majority of the study participants are aged between 50-59 years (35.3%), followed by those aged 40-49 years (29.4%). Participants aged 30-39 years and ≥70 years make up the smallest age groups. The study population comprises 110 males (64.7%) and 60 females (35.3%). The p-value of 0.02 indicates significant difference in gender distribution.

The majority of participants do not smoke (75.9%) and do not consume alcohol (81.8%). The p-values for smoking (0.20) and alcohol use (0.25) indicate no significant difference in these habits among the participants.

Table 1: General characteristics

Most participants have had diabetes for 5-10 years (36.5%), followed by those with a duration of 11-15 years (28.8%). The p-value of 0.15 indicates no significant difference in the duration of diabetes among participants. The majority of participants are overweight (46.5%), with a significant portion also classified as obese (36.5%). The p-value of 0.05 indicates a borderline significant difference in BMI distribution.

A significant proportion of participants (56.4%) have a waist circumference above the recommended threshold, indicating central obesity. The p-value of 0.01 indicates a significant difference in waist circumference distribution, which is a key factor in metabolic syndrome and insulin resistance.

Most participants have elevated blood pressure, with 35.9% in Stage 1 hypertension and 17.1% in Stage 2. Only 28.2% of participants have normal blood pressure. The p-value of 0.03 indicates a significant difference in blood pressure distribution, highlighting the prevalence of hypertension in this population.

Table 2: Other characteristics

A significant majority of participants (71.2%) have fasting blood glucose levels above the target range. The p-value of 0.04 indicates a significant difference in the distribution of fasting blood glucose levels, suggesting poor glycemic control in the majority of participants.

Table 3: Fasting Blood Glucose Levels

A considerable proportion of participants (39.54%) have elevated serum uric acid levels, indicating hyperuricemia. The p-value of 0.02 shows a significant difference in the distribution of uric acid levels, suggesting hyperuricemia as a common issue in the study population.

Table 4: Fasting Serum Uric Acid Levels

There is a moderate positive correlation between fasting blood glucose and serum uric acid levels (r = 0.35, p<0.001). Normouricemic individuals show a weak negative correlation (r = -0.25, p<0.05) with blood glucose, while hyperuricemic individuals have a weak positive correlation (r = 0.28, p<0.05), highlighting the impact of glycemic control on uric acid levels.

Table 5: Correlation Between Fasting Serum Uric Acid Levels & Fasting Blood Glucose Levels

BMI, waist circumference, and blood pressure show moderate positive correlations with serum uric acid levels, indicating that higher values of these parameters are associated with higher uric acid levels. Smoking and alcohol use show weak positive correlations, but they are not statistically significant.

Table 6: Correlation of Serum Uric Acid Levels with Other Variables

A moderate positive correlation (r = 0.42, p < 0.001) exists between serum uric acid and serum creatinine levels, suggesting that hyperuricemia is associated with impaired renal function in this population.

A weak positive correlation (r = 0.30, p = 0.002) between serum uric acid and total cholesterol suggests that higher uric acid levels are modestly associated with increased total cholesterol levels, a key marker in cardiovascular risk.

There is a weak positive correlation (r = 0.18, p = 0.08) between serum uric acid and total bilirubin levels, but it is not statistically significant, indicating that this relationship is minimal in this population.

Table 7: Correlation of serum uric acid with serum creatinine, total cholesterol & total bilirubin

The majority of the study participants are aged between 50-59 years (35.3%), followed by those aged 40-49 years (29.4%). Participants aged 30-39 years and ≥70 years make up the smallest age groups. The study population comprises 110 males (64.7%) and 60 females (35.3%). The p-value of 0.02 indicates significant difference in gender distribution.

The majority of participants do not smoke (75.9%) and do not consume alcohol (81.8%). The p-values for smoking (0.20) and alcohol use (0.25) indicate no significant difference in these habits among the participants.

Table 1: General characteristics

Most participants have had diabetes for 5-10 years (36.5%), followed by those with a duration of 11-15 years (28.8%). The p-value of 0.15 indicates no significant difference in the duration of diabetes among participants. The majority of participants are overweight (46.5%), with a significant portion also classified as obese (36.5%). The p-value of 0.05 indicates a borderline significant difference in BMI distribution.

A significant proportion of participants (56.4%) have a waist circumference above the recommended threshold, indicating central obesity. The p-value of 0.01 indicates a significant difference in waist circumference distribution, which is a key factor in metabolic syndrome and insulin resistance.

Most participants have elevated blood pressure, with 35.9% in Stage 1 hypertension and 17.1% in Stage 2. Only 28.2% of participants have normal blood pressure. The p-value of 0.03 indicates a significant difference in blood pressure distribution, highlighting the prevalence of hypertension in this population.

Table 2: Other characteristics

A significant majority of participants (71.2%) have fasting blood glucose levels above the target range. The p-value of 0.04 indicates a significant difference in the distribution of fasting blood glucose levels, suggesting poor glycemic control in the majority of participants.

Table 3: Fasting Blood Glucose Levels

A considerable proportion of participants (39.54%) have elevated serum uric acid levels, indicating hyperuricemia. The p-value of 0.02 shows a significant difference in the distribution of uric acid levels, suggesting hyperuricemia as a common issue in the study population.

Table 4: Fasting Serum Uric Acid Levels

There is a moderate positive correlation between fasting blood glucose and serum uric acid levels (r = 0.35, p<0.001). Normouricemic individuals show a weak negative correlation (r = -0.25, p<0.05) with blood glucose, while hyperuricemic individuals have a weak positive correlation (r = 0.28, p<0.05), highlighting the impact of glycemic control on uric acid levels.

Table 5: Correlation Between Fasting Serum Uric Acid Levels & Fasting Blood Glucose Levels

BMI, waist circumference, and blood pressure show moderate positive correlations with serum uric acid levels, indicating that higher values of these parameters are associated with higher uric acid levels. Smoking and alcohol use show weak positive correlations, but they are not statistically significant.

Table 6: Correlation of Serum Uric Acid Levels with Other Variables

A moderate positive correlation (r = 0.42, p < 0.001) exists between serum uric acid and serum creatinine levels, suggesting that hyperuricemia is associated with impaired renal function in this population.

A weak positive correlation (r = 0.30, p = 0.002) between serum uric acid and total cholesterol suggests that higher uric acid levels are modestly associated with increased total cholesterol levels, a key marker in cardiovascular risk.

There is a weak positive correlation (r = 0.18, p = 0.08) between serum uric acid and total bilirubin levels, but it is not statistically significant, indicating that this relationship is minimal in this population.

Table 7: Correlation of serum uric acid with serum creatinine, total cholesterol & total bilirubin

The study observed a clear distinction between participants with adequate glycemic control and those with poor control. Patients with fasting blood glucose levels above the target range demonstrated significantly higher serum uric acid levels, reinforcing the association between poor glycemic control and hyperuricemia. Additionally, this study found significant correlations between serum uric acid levels and several key metabolic indicators in T2DM patients. Among the most notable findings was the strong positive correlation between obesity and elevated serum uric acid levels. This finding suggests that elevated serum uric acid may serve as a marker of poor metabolic control in T2DM, potentially offering clinicians an additional tool for identifying patients at higher risk of complications.

Conflict of Interest: None to declare

Source of funding: Nil

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