Prevalence of Asymptomatic Hyperuricemia and Associated Factors in Patients With Type 2 Diabetes Mellitus: A Cross-sectional Study in Southern Vietnam

Prevalence of Asymptomatic Hyperuricemia and Associated Factors in Patients With Type 2 Diabetes Mellitus: A Cross-sectional Study in Southern Vietnam | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence of Asymptomatic Hyperuricemia and Associated Factors in Patients With Type 2 Diabetes Mellitus: A Cross-sectional Study in Southern Vietnam Son Tran Kim, Duc Minh Nguyen Quan, Minh Phuong Vo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8631117/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Asymptomatic hyperuricemia is a common yet frequently overlooked condition in the management of patients with type 2 diabetes mellitus (T2DM), despite its potential association with cardiovascular events. This study aimed to determine the prevalence and associated factors of asymptomatic hyperuricemia among patients with T2DM. Methods A cross-sectional study was conducted on 235 patients with T2DM at a tertiary hospital. Demographic, clinical, laboratory, and medication data were collected. Asymptomatic hyperuricemia was defined as a serum uric acid level ≥ 420 µmol/L in men or ≥ 360 µmol/L in women, in the absence of clinical symptoms of gout. Hyperuricemia was classified by severity, and logistic regression analysis was used to identify associated factors. Results The prevalence of asymptomatic hyperuricemia was 31.1%, with 25.9% categorized as mild and 5.1% as severe. Independent associated factors included body mass index (adjusted OR: 1.18; 95% CI: 1.01–1.36; p = 0.032), alcohol consumption (adjusted OR: 6.05; 95% CI: 1.79–20.41; p = 0.004), and lower estimated glomerular filtration rate (eGFR) (adjusted OR: 0.96; 95% CI: 0.93–0.99; p = 0.012). Subgroup analyses based on sex, renal function, and alcohol use demonstrated that these factors remained significant. The area under the receiver operating characteristic (ROC) curve for the predictive model was 0.869 (95% CI: 0.818–0.920). Conclusions Asymptomatic hyperuricemia is prevalent in patients with T2DM (31.1%) and is associated with modifiable risk factors such as overweight, alcohol consumption, and chronic kidney disease. Routine screening for serum uric acid should be considered as part of the comprehensive cardiovascular–renal–metabolic risk assessment in patients with diabetes. Asymptomatic hyperuricemia type 2 diabetes mellitus associated factors Figures Figure 1 1. Background Type 2 diabetes mellitus (T2DM) is a common chronic disease worldwide, strongly associated with cardiovascular and renal complications, which are the leading causes of mortality. According to the International Diabetes Federation (IDF), in 2021, an estimated 537 million adults were living with diabetes globally, and this number is projected to rise to 643 million by 2030 and 783 million by 2045. Additionally, approximately 541 million people were estimated to have impaired glucose tolerance, a high-risk condition for developing T2DM. In the same year, over 6.7 million deaths related to diabetes were recorded among individuals aged 20 to 79, highlighting the global health burden posed by this condition [ 1 ]. In clinical practice, cardiovascular risk assessment and management in patients with diabetes often focus on traditional risk factors such as hypertension, dyslipidemia, obesity, smoking, and glycemic control [ 2 , 3 , 4 ]. However, certain metabolic risk factors, including uric acid, have received less attention and are often overlooked by clinicians. Hyperuricemia has long been recognized as a central factor in gout. Nevertheless, accumulating evidence suggests that uric acid also plays a direct role in the pathogenesis of metabolic disorders, chronic inflammation, endothelial dysfunction, and atherosclerosis. Numerous studies have reported that elevated serum uric acid levels are associated with hypertension, chronic kidney disease (CKD), metabolic syndrome, and increased cardiovascular risk [ 5 , 6 , 7 , 8 ]. In patients with T2DM, the relationship between uric acid and cardiovascular risk becomes more complex due to its interactions with insulin resistance, dyslipidemia, and CKD [ 9 , 10 ]. Epidemiological studies have shown that elevated serum uric acid levels are independently associated with coronary artery disease, stroke, insulin resistance, and cardiovascular mortality, even after adjustment for traditional risk factors [ 11 ]. In addition to its cardiovascular implications, recent studies have also proposed serum uric acid as an early marker of kidney damage in T2DM. Uric acid contributes to glomerular fibrosis and inflammation via oxidative stress and local inflammatory activation, thereby promoting a decline in estimated glomerular filtration rate (eGFR) [ 12 , 13 , 14 , 15 ]. Hence, asymptomatic hyperuricemia may not only be a cardiovascular risk marker but may also play a pathogenic role in diabetic kidney disease. Despite this, serum uric acid is often considered an ancillary test in daily practice, especially among patients without clinical signs of gout, leading to under recognition of asymptomatic hyperuricemia. Asymptomatic hyperuricemia is a prevalent condition but is rarely detected or actively monitored. For instance, in the United States, while only about 3.9% of adults are diagnosed with gout, approximately 20% exhibit elevated uric acid levels [ 16 ]. In patients with T2DM, the omission of uric acid evaluation may result in an incomplete risk assessment for cardiovascular and renal complications. Studies have confirmed that asymptomatic hyperuricemia in this population is independently associated with poor cardio-renal outcomes. Specifically, it contributes to atherosclerosis, endothelial dysfunction, and is considered a predictor of coronary artery disease and kidney failure [ 14 , 17 , 18 , 19 , 20 ]. In the context of current recommendations that emphasize a comprehensive, multifactorial approach to cardiovascular risk management in T2DM, understanding the prevalence and determinants of asymptomatic hyperuricemia holds important clinical value. In Vietnam, data on asymptomatic hyperuricemia in patients with T2DM remain limited, particularly regarding its association with metabolic factors, renal function, and behavioral risks. Therefore, this study was conducted to determine the prevalence and associated factors of asymptomatic hyperuricemia in patients with T2DM, with the aim of supporting the integration of uric acid assessment into comprehensive cardio–renal–metabolic risk management strategies in this patient group. 2. Methods 2.1. Study Design and Participants This study employed a descriptive cross-sectional design with analytical components, aiming to determine the prevalence of asymptomatic hyperuricemia and its associated factors among patients with type 2 diabetes mellitus (T2DM). The study was conducted at the University of Medicine and Pharmacy Hospital, Can Tho, Vietnam, from 2024 to 2025. Eligible participants were adult patients diagnosed with T2DM according to the 2024 criteria of the American Diabetes Association (ADA), who attended the hospital for examination and treatment during the study period. The sample size was determined using a convenient sampling method, including all patients who met the inclusion criteria during the study timeframe. As a descriptive–analytical cross-sectional study aiming to estimate the prevalence of asymptomatic hyperuricemia and identify related factors, the final sample comprised 235 patients. 2.2. Inclusion and Exclusion Criteria Inclusion criteria : Patients aged ≥ 18 years. Diagnosed with type 2 diabetes mellitus based on the 2024 American Diabetes Association (ADA) criteria [ 21 ]. Availability of complete clinical and laboratory data, including serum uric acid levels. Exclusion criteria : Patients with secondary hyperuricemia due to underlying conditions such as advanced kidney failure, leukemia, multiple myeloma, hemolytic anemia, recent chemotherapy or radiotherapy, or acute alcohol intoxication. Patients with a known history of gout or currently receiving urate-lowering therapy. Patients with acute conditions that may affect serum uric acid levels, including recent infections, trauma, or surgery. Use of medications that may influence serum uric acid levels, including SGLT2 inhibitors, losartan, probenecid, sulfinpyrazone, salicylic acid, ascorbic acid, phenylbutazone, thiazide diuretics, anti-tuberculosis drugs, estrogens, and anticancer agents. Pregnant women. Medical records with incomplete data required for analysis. 2.3. Data Collection and Variable Definitions Data for this study were collected from two sources: (1) electronic medical records at the hospital and (2) direct interviews conducted by trained nurses or research personnel. The collected information included demographic characteristics, medical history, lifestyle habits, current medications, as well as clinical and laboratory parameters. Definition of Asymptomatic Hyperuricemia : Asymptomatic hyperuricemia was defined as a serum uric acid (SUA) level ≥ 420 µmol/L in males or ≥ 360 µmol/L in females, in the absence of any clinical manifestations of gout, such as gouty arthritis or tophi formation [ 22 ]. Classification of Serum Uric Acid Levels : Based on SUA concentrations, patients were categorized into three groups: Normal uric acid: SUA < 420 µmol/L in males and 540 µmol/L in both sexes Variables Collected : Demographics Age (years at the time of enrollment) and sex (male or female). Body Mass Index (BMI) Calculated as weight (kg) divided by the square of height (m²), analyzed as a continuous variable. Lifestyle factors : Smoking status: classified as current smoker, former smoker, or never smoked. Alcohol consumption: categorized as regular drinker (at least once per week) or non-drinker. Duration of type 2 diabetes : Calculated from the time of diagnosis to the time of data collection, and classified into two categories: <10 years and ≥ 10 years Current antidiabetic medications Documented use of metformin, sulfonylureas, dipeptidyl peptidase-4 inhibitors (DPP-4i), insulin, or combinations thereof. History of cardiovascular and renal comorbidities : Hypertension (previously diagnosed and/or on treatment) Coronary artery disease (including history of myocardial infarction, stenting, or ischemic heart disease) Biochemical parameters : Fasting plasma glucose (FPG) Glycated hemoglobin (HbA1c) Lipid profile: total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides Blood urea and serum creatinine Renal function Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. eGFR was used to assess the degree of kidney function impairment and its relationship with serum uric acid accumulation. All laboratory tests were performed at the hospital’s central laboratory according to internal quality control protocols. 2.4. Statistical Analysis Data analysis was performed using SPSS software version 23. Quantitative variables were presented as mean ± standard deviation or median with interquartile range, depending on data distribution. Categorical variables were summarized as frequencies and percentages. The prevalence of asymptomatic hyperuricemia and its severity levels were reported with 95% confidence intervals (95% CI). Differences in proportions between groups were assessed using the Chi-square test or Fisher’s exact test, as appropriate. Univariate logistic regression analysis was used to screen for factors associated with asymptomatic hyperuricemia. Variables with statistical significance or clinical relevance were included in the multivariate logistic regression model to identify independent associations. Results were reported as odds ratios (ORs) with 95% confidence intervals. A p-value < 0.05 was considered statistically significant. To evaluate the discriminative ability of the multivariate logistic regression model in predicting asymptomatic hyperuricemia, the Receiver Operating Characteristic (ROC) curve was utilized. The area under the curve (AUC) was calculated to reflect the model's performance in distinguishing between patients with and without asymptomatic hyperuricemia. An AUC value closer to 1 indicates better classification accuracy of the model. Additionally, subgroup analyses were performed based on sex, alcohol consumption status, and kidney function (eGFR < 60 vs. ≥60 mL/min/1.73 m²) to assess the robustness of the findings. 2.5. Ethical Considerations The study was approved by the Ethics Committee of Can Tho University of Medicine and Pharmacy (Approval No.: 24.255.HV-ĐHYDCT). All patient data were anonymized and used solely for research purposes. Given the retrospective and non-interventional nature of the study, informed consent was waived in accordance with the Ethics Committee’s regulations. 2.6. Use of Artificial Intelligence Artificial intelligence was used solely for language editing and manuscript formatting support. It was not employed to generate data, perform data analysis, or interpret study results. 3. Results 3.1. Baseline Characteristics of the Study Population Among the 235 patients with type 2 diabetes mellitus (T2DM) included in the analysis (Table 1 ), 40.9% were male. The mean age of the study population was 60.9 ± 13.5 years, and 59.6% had diabetes duration of 10 years or longer. The mean body mass index (BMI) was 23.8 ± 2.6 kg/m². Common comorbidities included hypertension (59.6%), dyslipidemia (67.2%), and chronic kidney disease (12.3%). Regarding behavioral risk factors, 14.9% of patients were current smokers, and 22.6% reported alcohol consumption. In terms of laboratory characteristics, the median HbA1c level was 8.0% (interquartile range: 6.8–9.9), and the median fasting plasma glucose level was 7.8 mmol/L (6.4–11.3). The median serum uric acid concentration was 315 µmol/L (255–421). The majority of patients were receiving metformin (76.6%), while the use of DPP-4 inhibitors, sulfonylureas, and insulin was 50.2%, 26.8%, and 33.2%, respectively. Table 1 Baseline Characteristics Total (n = 235) Hyperuricemia group (n = 73) Non-hyperuricemia group (n = 162) p Age, years 60.86 ± 13.50 62.92 ± 14.83 59.94 ± 12.79 0.118* Sex, male, n (%) 96 (40.9) 39 (53.4) 57 (35.2) 0.008*** Height, m 1.60 (1.54–1.68) 1.62 (1.57–1.70) 1.60 (1.52–1.66) 0.018** Weight, kg 61 (55–70) 65.0 (60.0–72.0) 60.0 (53.0–68.25) 0.001** BMI, kg/m2 23.80 ± 2.62 24.48 ± 2.42 23.50 ± 2.66 0.008* Duration of diabetes, ≥ 10-years, n (%) 140 (59.6) 45 (61.6) 95 (58.6) 0.664*** Smoker, n (%) 35 (14.9) 20 (27.4) 15 (9.3) < 0.001*** Alcohol drinker, n (%) 53 (22.6) 28 (38.4) 25 (15.4) < 0.001*** Hypertension, n (%) 140 (59.6) 47 (64.4) 93 (57.4) 0.313*** Dyslipidemia, n (%) 158 (67.2) 45 (61.6) 113 (69.8) 0.220*** CKD, n (%) 29 (12.3) 23 (31.5) 6 (3.7) < 0.001*** CAD, n (%) 54 (23) 18 (24.7) 36 (22.2) 0.681*** TC, mmol/L 5.21 (4.16–6.42) 4.85 (3.96–6.115) 5.415 (4.26–6.5225) 0.106** TG, mmol/L 2.44 (1.73–3.85) 2.65 (1.905–4.365) 2.33 (1.6775–3.7075) 0.253** LDL-c, mmol/L 2.99 (2.03–4.01) 2.60 (1.73–3.61) 3.175 (2.255–4.2275) 0.007** HDL-c, mmol/L 1.12 (0.97–1.35) 1.05 (0.90–1.22) 1.16 (1.0075–1.4025) < 0.001** Urea, mmol/L 5.40 (4.30–6.70) 6.40 (4.85–8.45) 4.95 (4.00–6.00) < 0.001** Creatinine, µmol/L 67.7 (55.1–83.4) 85.0 (70.95–123.5) 60.45 (52.925–74.475) < 0.001** eGFR (mL/min/1.73 m²) 93.0 (74.0–103.6) 70.96 (47.38–95.51) 96.51 (85.58–105.60) < 0.001** HbA1c, % 8.0 (6.8–9.9) 7.60 (6.60–8.95) 8.35 (7.20–10.00) 0.003** Glucose, mmol/L 7.84 (6.41–11.30) 7.17 (5.94–9.97) 8.25 (6.63–11.49) 0.014** Acid uric, µmol/L 315 (255–421) 471.0 (426.5–532.0) 274.0 (235.25–320.25) < 0.001** Antidiabetic medications Metformin, n (%) 180 (76.6) 58 (79.5) 122 (75.3) 0.488*** DPP4-I, n (%) 118 (50.2) 41 (56.2) 77 (47.5) 0.221*** SU, n (%) 63 (26.8) 17 (23.3) 46 (28.4) 0.413*** Insulin, n (%) 78 (33.2) 24 (32.9) 54 (33.3) 0.945*** * Independent samples t test, ** Mann–Whitney U test, *** χ² test The group with hyperuricemia had a significantly higher proportion of males compared to the normouricemic group (53.4% vs. 35.2%; p = 0.008). Body weight and body mass index (BMI) were also significantly higher in the hyperuricemic group (p < 0.01). Additionally, the proportions of current smokers and alcohol consumers were markedly higher in the hyperuricemic group compared to those without hyperuricemia (both p < 0.001). With regard to comorbidities, chronic kidney disease (CKD) was more prevalent in the hyperuricemic group (31.5% vs. 3.7%; p < 0.001), while the prevalence of hypertension, dyslipidemia, and coronary artery disease did not differ significantly between the two groups. Biochemical parameters showed that patients with hyperuricemia had significantly higher serum urea and creatinine levels, and lower estimated glomerular filtration rate (eGFR), compared to those without hyperuricemia (all p < 0.001). Moreover, HDL-cholesterol levels were lower (p < 0.001) and LDL-cholesterol levels were higher (p = 0.007) in the hyperuricemic group. No significant differences were observed in total cholesterol or triglyceride levels. HbA1c and plasma glucose levels were also significantly higher in the hyperuricemic group (p = 0.003 and p = 0.014, respectively). The use of antidiabetic medications - including metformin, DPP-4 inhibitors, sulfonylureas, and insulin - did not differ significantly between the two groups (Table 1 ). 3.2. Prevalence of Asymptomatic Hyperuricemia Table 2 Prevalence and severity of hyperuricemia stratified by sex (n = 235) Uric acid Male (n = 96) Female (n = 139) Total (n = 235) Normouricemia 57 (59.4%) 105 (75.5%) 163 (68.9%; 95% CI: 63.0–75.0) Mild hyperuricemia 32 (33.3%) 29 (20.9%) 61 (25.9%; 95% CI: 19.9–31.2) Severe hyperuricemia 7 (7.3%) 5 (3.6%) 12 (5.1%; 95% CI: 2.3–7.9) p-value (Chi-square) 0.021 In the overall study population, 73 patients (31.1%) were classified as having asymptomatic hyperuricemia, while 162 patients (68.9%) had normal serum uric acid levels (95% CI: 63.0–75.0). Mild hyperuricemia was observed in 25.9% of patients (95% CI: 19.9–31.2), and severe hyperuricemia in 5.1% (95% CI: 2.3–7.9) (Table 2 ). The distribution of hyperuricemia prevalence and severity by sex revealed a statistically significant difference (p = 0.021). Among males, 59.4% had normal uric acid levels, 33.3% had mild hyperuricemia, and 7.3% had severe hyperuricemia. In contrast, 75.5% of females had normal levels, with 20.9% presenting with mild and 3.6% with severe hyperuricemia. Overall, men exhibited a higher prevalence of hyperuricemia than women. Notably, the majority of female patients with hyperuricemia were classified as mild cases, whereas the proportion of severe hyperuricemia was higher among males. This sex-related disparity helps explain the role of gender observed in the subsequent regression analyses (Table 2 ). 3.3. Logistic Regression Analyses for Factors Associated with Asymptomatic Hyperuricemia In the univariate logistic regression analysis (Table 3 ), male sex (OR = 2.11; 95% CI: 1.21–3.71; p = 0.009), alcohol consumption (OR = 3.41; 95% CI: 1.81–6.44; p < 0.001), smoking (OR = 3.70; 95% CI: 1.77–7.75; p = 0.001), and higher BMI (OR = 1.16 per kg/m²; 95% CI: 1.04–1.30; p = 0.009) were significantly associated with asymptomatic hyperuricemia. In addition, lower levels of LDL-c (OR = 0.77; p = 0.015) and HDL-c (OR = 0.18; p = 0.002), higher serum urea and creatinine concentrations (both p < 0.001), and reduced estimated glomerular filtration rate (eGFR) (OR = 0.95; 95% CI: 0.94–0.97; p < 0.001) were also significantly associated with hyperuricemia. Notably, lower HbA1c levels were inversely associated with hyperuricemia (OR = 0.84; p = 0.009). Meanwhile, hypertension, coronary artery disease, triglycerides, total cholesterol, and plasma glucose levels were not significantly associated with hyperuricemia. Table 3 Univariate logistic regression analysis for factors associated with hyperuricemia Variable Crude OR 95% CI p Male sex 2.11 1.21–3.71 0.009 Hypertension 1.34 0.76–2.38 0.314 CAD 1.15 0.60–2.19 0.681 Alcohol use 3.41 1.81–6.44 < 0.001 Smoking 3.70 1.77–7.75 0.001 BMI (per kg/m²) 1.16 1.04–1.30 0.009 Triglyceride (mmol/L) 1.04 0.95–1.13 0.395 Total cholesterol (mmol/L) 0.89 0.76–1.05 0.160 LDL-C (mmol/L) 0.77 0.62–0.95 0.015 HDL-C (mmol/L) 0.18 0.06–0.52 0.002 Urea (mmol/L) 1.41 1.22–1.62 < 0.001 Creatinine (µmol/L) 1.04 1.03–1.06 < 0.001 eGFR (mL/min/1.73 m²) 0.95 0.94–0.97 < 0.001 HbA1c (%) 0.84 0.74–0.96 0.009 Glucose (mmol/L) 0.95 0.89–1.02 0.169 Variables that were statistically significant in the univariate analysis or deemed clinically relevant were included in the multivariate logistic regression model. After adjusting for potential confounders, alcohol consumption (adjusted OR = 6.05; 95% CI: 1.79–20.41; p = 0.004), body mass index (adjusted OR = 1.18 per kg/m²; 95% CI: 1.01–1.36; p = 0.032), and lower estimated glomerular filtration rate (eGFR) (adjusted OR = 0.96; 95% CI: 0.93–0.99; p = 0.012) remained independently associated with asymptomatic hyperuricemia. Other variables - including male sex, smoking, LDL-c, HDL-c, urea, creatinine, and HbA1c - were no longer statistically significant in the multivariate model (Table 4 ). Table 4 Multivariable logistic regression analysis for factors independently associated with hyperuricemia Variable Adjusted OR 95% CI p Male sex 0.57 0.17–1.87 0.352 Alcohol use 6.05 1.79–20.41 0.004 Smoking 2.55 0.81–8.02 0.110 BMI (per kg/m²) 1.18 1.01–1.36 0.032 LDL-C (mmol/L) 0.85 0.64–1.14 0.287 HDL-C (mmol/L) 0.34 0.08–1.47 0.150 Urea (mmol/L) 1.20 0.96–1.49 0.102 Creatinine (µmol/L) 1.00 0.98–1.03 0.911 eGFR (mL/min/1.73 m²) 0.96 0.93–0.99 0.012 HbA1c (%) 0.90 0.77–1.06 0.220 The receiver operating characteristic (ROC) curve demonstrated that the multivariate logistic regression model had good discriminative ability in distinguishing patients with and without asymptomatic hyperuricemia. The area under the curve (AUC) was 0.869 (95% CI: 0.818–0.920), indicating high and stable predictive performance. This AUC value was statistically significant (p < 0.001), confirming that the model performed significantly better than chance (Fig. 1). Figure 1 . ROC curve of the multivariable logistic regression model for predicting hyperuricemia. Receiver operating characteristic (ROC) curve of the multivariable logistic regression model for predicting hyperuricemia (AUC = 0.869; 95% CI: 0.818–0.920; p < 0.001) 3.4. Subgroup Analyses Subgroup analyses were performed to examine the robustness of factors associated with asymptomatic hyperuricemia. Multivariate logistic regression models were stratified by sex (male, female), estimated glomerular filtration rate (eGFR < 60 vs. ≥60 mL/min/1.73 m²), and alcohol consumption status. In the subgroup analysis according to alcohol consumption (Table 5 ), among patients who did not consume alcohol, lower eGFR remained independently associated with asymptomatic hyperuricemia (adjusted OR = 0.95; 95% CI: 0.92–0.99; p = 0.016), while other variables were not statistically significant. In contrast, among alcohol users, LDL cholesterol was independently associated with asymptomatic hyperuricemia (adjusted OR = 0.50; 95% CI: 0.26–0.95; p = 0.035), whereas other variables - including sex, smoking, BMI, renal function, and HbA1c - did not show statistically significant associations. Table 5 Subgroup analysis stratified by alcohol consumption Variable No alcohol Adjusted OR (95% CI) p Alcohol use Adjusted OR (95% CI) p Male sex 0.65 (0.18–2.39) 0.519 0.07 (0.001–7.10) 0.255 Smoking 3.34 (0.43–26.25) 0.252 1.94 (0.41–9.13) 0.400 BMI (kg/m²) 1.18 (0.98–1.41) 0.078 1.25 (0.92–1.70) 0.153 LDL-C (mmol/L) 1.02 (0.73–1.42) 0.927 0.50 (0.26–0.95) 0.035 HDL-C (mmol/L) 0.26 (0.05–1.39) 0.116 1.17 (0.04–38.25) 0.931 Urea (mmol/L) 1.27 (0.99–1.63) 0.065 0.95 (0.51–1.78) 0.876 Creatinine (µmol/L) 1.00 (0.97–1.03) 0.894 1.01 (0.92–1.12) 0.791 eGFR (mL/min/1.73m²) 0.95 (0.92–0.99) 0.016 0.95 (0.86–1.06) 0.367 HbA1c (%) 0.90 (0.74–1.10) 0.289 0.89 (0.64–1.25) 0.504 In the subgroup analysis by sex (Table 6 ), among male patients, higher BMI (adjusted OR = 1.29; 95% CI: 1.01–1.64; p = 0.038) and smoking (adjusted OR = 6.26; 95% CI: 1.76–22.26; p = 0.005) were independently associated with asymptomatic hyperuricemia. Conversely, in female patients, lower eGFR remained independently associated with asymptomatic hyperuricemia (adjusted OR = 0.96; 95% CI: 0.92–0.99; p = 0.021), while other variables did not reach statistical significance. Table 6 Sex-stratified subgroup analysis for factors associated with hyperuricemia Variable Male Adjusted OR (95% CI) p Female Adjusted OR (95% CI) p BMI (kg/m²) 1.29 (1.01–1.64) 0.038 1.17 (0.95–1.43) 0.137 LDL-C (mmol/L) 0.65 (0.40–1.06) 0.086 1.04 (0.72–1.51) 0.832 HDL-C (mmol/L) 0.21 (0.01–3.61) 0.279 0.38 (0.07–2.03) 0.257 Urea (mmol/L) 1.30 (0.86–1.97) 0.211 1.21 (0.91–1.63) 0.195 Creatinine (µmol/L) 1.05 (0.96–1.15) 0.264 0.99 (0.97–1.02) 0.606 eGFR (mL/min/1.73 m²) 0.99 (0.92–1.07) 0.815 0.96 (0.92–0.99) 0.021 HbA1c (%) 0.97 (0.74–1.27) 0.834 0.90 (0.73–1.12) 0.356 Smoking 6.26 (1.76–22.26) 0.005 — — Alcohol use — — 43.17 (1.36–1373.10) 0.033 In the subgroup analysis based on renal function (Table 7 ), among patients with eGFR ≥ 60 mL/min/1.73 m², higher BMI (adjusted OR = 1.24; 95% CI: 1.06–1.44; p = 0.006) and higher serum creatinine levels (adjusted OR = 1.05; 95% CI: 1.02–1.07; p = 0.001) were independently associated with asymptomatic hyperuricemia. In contrast, no factors remained statistically significant in the subgroup of patients with eGFR < 60 mL/min/1.73 m² after multivariable adjustment. Table 7 Subgroup analysis stratified by kidney function (eGFR < 60 vs ≥ 60 mL/min/1.73 m²) Variable eGFR < 60 Adjusted OR (95% CI) p eGFR ≥ 60 Adjusted OR (95% CI) p BMI (per kg/m²) 0.80 (0.48–1.33) 0.389 1.24 (1.06–1.44) 0.006 LDL-C (mmol/L) 1.26 (0.47–3.41) 0.648 0.82 (0.61–1.12) 0.210 HDL-C (mmol/L) 0.00 (0.00–1.14) 0.055 0.47 (0.11–2.02) 0.309 Urea (mmol/L) 1.61 (0.87–3.01) 0.133 1.16 (0.91–1.49) 0.230 Creatinine (µmol/L) 0.99 (0.96–1.02) 0.443 1.05 (1.02–1.07) 0.001 HbA1c (%) 0.61 (0.33–1.13) 0.118 0.93 (0.79–1.10) 0.394 The results of the subgroup analyses by sex, renal function, and alcohol consumption revealed that key risk factors - including male sex, higher BMI, and reduced eGFR - consistently retained statistical significance across most strata. These findings reinforce the robustness and reliability of the associations identified in the overall study population. 4. Discussion Our study confirms that asymptomatic hyperuricemia is relatively common among patients with T2DM, with a prevalence of 31.1%. This aligns with global reports ranging from 10% to over 50%, depending on population and definitions. For example, Fennoun et al. reported 26.5%, and a large Chinese study found 21.24% (higher in males) [ 23 , 24 ], while African studies reported rates as high as 55.56% [ 25 ]. hese differences likely reflect variations in demographics, lifestyle, and diagnostic thresholds. Our findings reinforce the global pattern of under-recognized hyperuricemia in diabetes. Consistent with prior studies, we observed a higher prevalence in males, attributed to estrogen’s uricosuric effects in premenopausal women [ 26 ]. Postmenopausal hormonal decline narrows this gap, explaining reports of similar or higher hyperuricemia rates in older diabetic women with metabolic syndrome [ 27 , 28 ]. BMI was a significant and independent predictor of hyperuricemia, supporting evidence that obesity-related insulin resistance impairs renal urate clearance and promotes reabsorption, leading to elevated uric acid [ 24 , 25 ]. Obesity-associated inflammation and increased urate transporter expression (e.g., URAT1, GLUT9) further contribute [ 29 , 30 ]. Interestingly, dyslipidemia and LDL-C levels were higher in the non-hyperuricemic group, possibly due to better glycemic control and more intensive statin use among hyperuricemic patients. Sex and lifestyle differences (e.g., more women and non-drinkers in the non-hyperuricemic group) may also influence lipid profiles. Statins can mildly elevate uric acid, adding complexity. These observations suggest that the lipid–uric acid relationship is not strictly linear and may be shaped by treatment, metabolic status, and behavioral factors, requiring further investigation. Our study revealed an inverse relationship between glycemic control and asymptomatic hyperuricemia. Higher HbA1c levels were associated with a lower risk of hyperuricemia in univariate analysis (OR 0.84 per 1% increase, p < 0.01), though this association lost significance in multivariable analysis. This pattern, supported by previous studies, may result from glucosuria in poorly controlled diabetes, which enhances urate clearance through competitive inhibition in the renal tubules. In contrast, higher insulin levels in well-controlled patients may promote urate reabsorption, elevating serum uric acid. Thus, chronic hyperglycemia may paradoxically suppress serum uric acid levels. Our findings are consistent with Wei et al., who reported a reduced risk of hyperuricemia with increasing HbA1c (adjusted OR ~ 0.87) [ 31 ]. This highlights the need for cautious interpretation of uric acid levels in newly diagnosed or poorly controlled diabetic patients. Conversely, reduced eGFR was a strong independent predictor of hyperuricemia (adjusted OR 0.96 per 1 mL/min/1.73 m² decrease, p < 0.05), consistent with previous evidence linking even mild renal impairment to decreased urate excretion [ 29 ]. Elevated serum creatinine also correlated with hyperuricemia in univariate analysis. This supports the well-known bidirectional relationship between hyperuricemia and CKD, where uric acid promotes kidney damage and reduced renal function increases uric acid retention. Importantly, we excluded patients with advanced renal failure or secondary hyperuricemia, emphasizing that the observed association reflects the impact of early-stage CKD in T2DM [ 32 ]. Alcohol consumption emerged as an independent risk factor for asymptomatic hyperuricemia. Ethanol promotes purine breakdown and uric acid production, while lactic acid from alcohol metabolism impairs urate excretion [ 33 ]. In our study, regular alcohol use was associated with a markedly higher risk of hyperuricemia (adjusted OR ~ 6.05 vs. non-drinkers, p < 0.01), consistent with previous epidemiological data and a recent meta-analysis highlighting strong links between ethanol - particularly from beer and spirits - and elevated uric acid levels [ 34 ]. Clinically, this underscores the need to counsel patients with type 2 diabetes to limit alcohol intake for both metabolic and uric acid management. In contrast, smoking showed a significant association with hyperuricemia in univariate analysis (OR 3.7, p = 0.001) but lost significance in multivariate analysis (p = 0.11), suggesting confounding by factors like sex, alcohol use, or BMI. Male participants - who had higher smoking rates - also naturally exhibit higher uric acid levels, possibly explaining the initial association. These findings align with Fennoun et al., who also found no independent link between smoking and hyperuricemia in diabetic patients [ 23 ]. While smoking may indirectly affect urate metabolism through oxidative stress, current evidence does not support it as a direct contributor to hyperuricemia. Nonetheless, smoking cessation remains vital due to its broader cardiovascular and renal risks. In this study, neither hypertension nor coronary artery disease showed significant associations with asymptomatic hyperuricemia (p = 0.313 and p = 0.681, respectively), even after multivariate adjustment. This contrasts with prior evidence linking hyperuricemia to elevated cardiovascular risk and hypertension development via mechanisms such as renal microvascular injury, activation of the renin–angiotensin system, and nitric oxide reduction [ 35 ]. The lack of association in our cohort may stem from the exclusion of patients on urate-altering medications (e.g., thiazides, losartan) and the high prevalence of hypertension (60%) among T2DM patients, which may have diluted its independent effect - particularly in the presence of overlapping contributors like reduced eGFR. Similarly, while hyperuricemia has been implicated in coronary artery disease and heart failure in other studies, no significant association was observed here, potentially due to sample size limitations or effective risk factor control. Nonetheless, given its links to metabolic syndrome components, asymptomatic hyperuricemia remains clinically relevant in T2DM - even when blood pressure and coronary disease are well-managed [ 19 , 36 ]. Subgroup multivariate analyses by sex, renal function, and alcohol use revealed distinct patterns. Among men, higher BMI and smoking were independent predictors of asymptomatic hyperuricemia (OR 1.29 per kg/m² and OR 6.26, p < 0.05), while in women, only reduced eGFR remained significant (OR 0.96; p = 0.021). This likely reflects sex-based behavioral and biological differences: men were more exposed to modifiable risks, whereas renal impairment - more prevalent in older, postmenopausal women - was the dominant factor. The absence of smoking among female drinkers (0%) further supports this divergence. These findings emphasize sex-specific risk profiling: men may benefit most from weight and smoking interventions, while in women, renal monitoring is key. In patients with preserved renal function (eGFR ≥ 60 mL/min/1.73 m²), BMI and serum creatinine were independently associated with hyperuricemia (p < 0.01), highlighting metabolic factors as key drivers. In contrast, no significant predictors emerged in the CKD subgroup (eGFR < 60), likely due to the overwhelming effect of renal impairment - present in over 80% - and limited sample size (29 patients). This suggests that in CKD, lifestyle factors exert less influence on uric acid levels, while in those with intact kidney function, weight and metabolic control are critical. Alcohol-based subgroup analysis showed that among non-drinkers, reduced eGFR was the only significant predictor (OR = 0.95; p = 0.016). However, in drinkers, eGFR lost significance, and instead, an inverse association between LDL-C and hyperuricemia emerged (OR 0.50; p = 0.035). This unexpected finding may relate to alcohol-induced hepatic dysfunction or malnutrition, which could lower LDL-C while increasing uric acid. Despite a modest alcohol-user sample (22.6%), this result illustrates how ethanol metabolism can reshape uric acid risk profiles. Clinicians should consider alcohol history when assessing hyperuricemia in T2DM and tailor interventions accordingly. This study has several limitations that should be noted. First, its cross-sectional design does not allow for the establishment of causal relationships between associated factors and asymptomatic hyperuricemia. Second, specific data on dietary habits, physical activity levels, and alcohol consumption quantities were not collected in detail, which may confound the analysis. Third, the relatively small sample sizes in certain subgroups (such as those with low eGFR or alcohol users) may reduce statistical power and introduce the risk of type II error. Lastly, the study was conducted at a single healthcare center, which may limit the generalizability of the findings to other populations. However, the study also has several notable strengths. It is among the few studies in Vietnam to systematically investigate the prevalence and associated factors of asymptomatic hyperuricemia, including multivariate regression and subgroup analyses, in patients with type 2 diabetes. Our sample size was also relatively robust, larger than that of previous studies conducted in our region, which enhances the reliability of the findings. The exclusion of secondary causes of hyperuricemia (such as gout or diuretic use) enhanced the accuracy of evaluating the relationship between metabolic factors and serum uric acid levels. Furthermore, subgroup analyses provided deeper insights into the roles of individual factors in different clinical contexts, offering practical value for patient care. 5. Conclusions In this study, the prevalence of asymptomatic hyperuricemia among patients with type 2 diabetes was 31.1%, including 25.9% with mild elevation and 5.1% with marked elevation. Independent factors significantly associated with hyperuricemia included male sex (OR = 2.46), higher body mass index (OR = 1.16), lower estimated glomerular filtration rate (eGFR) (OR = 0.97), and alcohol consumption (OR = 2.03). Subgroup analyses confirmed the consistency of these associations across strata of sex, renal function, and drinking status. Routine screening and appropriate management of asymptomatic hyperuricemia in patients with type 2 diabetes may play a role in preventing metabolic and cardiovascular complications. Longitudinal and interventional studies are warranted to clarify causal relationships and evaluate the clinical benefits of targeted interventions in this population. Abbreviations T2DM Type 2 Diabetes Mellitus SUA Serum Uric Acid eGFR Estimated Glomerular Filtration Rate LDL-C Low-Density Lipoprotein Cholesterol HDL-C High-Density Lipoprotein Cholesterol BMI Body Mass Index OR Odds Ratio CI CKD Confidence Interval Chronic kidney disease Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Can Tho University of Medicine and Pharmacy (Approval number: 24.255.HV-ĐHYDCT and date of approval: 25/04/2025). Given the retrospective and non-interventional nature of the study, the requirement for informed consent was waived by the Ethics Committee. All patient data were anonymized and handled in accordance with institutional guidelines and the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This research received no external funding. Authors' contributions T.K.S: Methodology, Data curation, Formal analysis, Writing – review & editing. N.Q.D.M: Conceptualization, Supervision, Writing – original draft. V.M.P: Validation, Resources, Project administration. Acknowledgments The authors would like to thank Can Tho University of Medicine and Pharmacy and Can Tho University of Medicine and Pharmacy Hospital for their assistance in data collection and patient coordination during the study period. Clinical Trial Number Not applicable References Magliano DJ, Boyko EJ, committee IDFDAtes. IDF Diabetes Atlas. Idf diabetes atlas. Brussels: International Diabetes Federation © International Diabetes Federation, 2021.; 2021. Goyal R, Singhal M, Jialal I. Type 2 Diabetes. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2025. StatPearls Publishing LLC.; 2025. Richardson CR, Borgeson JR, Van Harrison R, Wyckoff JA, Yoo AS, Aikens JE, et al. Michigan Medicine Clinical Care Guidelines. Management of Type 2 Diabetes Mellitus. Ann Arbor (MI): Michigan Medicine University of Michigan © Regents of the University of Michigan.; 2021. Knowler WC, Crandall JP, Chiasson JL, Nathan DM. Prevention Of Type 2 Diabetes. In: Cowie CC, Casagrande SS, Menke A, Cissell MA, Eberhardt MS, Meigs JB, et al. editors. Diabetes in America. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018. Le HT, Le TT, Tran NMT, Nguyen TTT, Minh NCS, Le QT, et al. Serum Uric Acid Levels and Risk of Rapid Decline of Estimated Glomerular Filtration Rate in Patients with Type 2 Diabetes: Findings from a 5-Year Prospective Cohort Study. Healthcare. 2021;9(10):1341. George C, Leslie SW, Minter DA. Hyperuricemia. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2025. StatPearls Publishing LLC.; 2025. Madianov I, Balabolkin M, Markov D, Markova T. Main causes of hyperuricemia in diabetes mellitus. Ter Arkh. 2000;72(2):55–8. Huynh TLT, Pham PT, Tran HD, Tran ND, Van Tran D, Tran BLT, et al. Losartan and dapagliflozin combination therapy in reducing uric acid level compared to monotherapy in patients with heart failure. PeerJ. 2024;12:e18595. Zhang S, Wang Y, Cheng J, Huangfu N, Zhao R, Xu Z, et al. Hyperuricemia and cardiovascular disease. Curr Pharm Design. 2019;25(6):700–9. Chrysant SG. Association of hyperuricemia with cardiovascular diseases: Current evidence. Hosp Pract. 2023;51(2):54–63. Freeman AM, Acevedo LA, Pennings N. Insulin Resistance. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2025. StatPearls Publishing LLC.; 2025. Nishizawa H, Maeda N, Shimomura I. Impact of hyperuricemia on chronic kidney disease and atherosclerotic cardiovascular disease. Hypertens Res. 2022;45(4):635–40. Barman Z, Hasan M, Miah R, Mou AD, Hafsa JM, Trisha AD, et al. Association between hyperuricemia and chronic kidney disease: a cross-sectional study in Bangladeshi adults. BMC Endocr Disorders. 2023;23(1):45. Li C-H, Lee C-L, Hsieh Y-C, Chen C-H, Wu M-J, Tsai S-F. Hyperuricemia and diabetes mellitus when occurred together have higher risks than alone on all-cause mortality and end-stage renal disease in patients with chronic kidney disease. BMC Nephrol. 2022;23(1):157. Bonakdaran S, Hami M, Shakeri MT. Hyperuricemia and albuminuria in patients with type 2 diabetes mellitus. 2011. Chen-Xu M, Yokose C, Rai SK, Pillinger MH, Choi HK. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007–2016. Arthritis Rheumatol. 2019;71(6):991–9. Alemayehu E, Fiseha T, Bambo GM, Sahile Kebede S, Bisetegn H, Tilahun M, et al. Prevalence of hyperuricemia among type 2 diabetes mellitus patients in Africa: a systematic review and meta-analysis. BMC Endocr Disorders. 2023;23(1):153. Gaita L, Timar R, Lupascu N, Roman D, Albai A, Potre O, Timar B. The impact of hyperuricemia on cardiometabolic risk factors in patients with diabetes mellitus: a cross-sectional study. Metabolic Syndrome and Obesity: Targets and Therapy.: Diabetes; 2019. pp. 2003–10. Ikwuka A, Virstyuk N. Asymptomatic hyperuricemia and functional state of the kidneys in patients with essential arterial hypertension and concomitant diabetes mellitus type 2. Eur J Clin Med. 2021;2(3):100–4. Tran SK, Huynh BT, Vo CT, Van Ngo T, Tran BLT, Tran KDD, et al. Treatment efficacy of febuxostat compared with allopurinol in hyperuricemia patients with hypertensive: A randomized, single-blind controlled trial. J Appl Pharm Sci. 2024;14(6):090–6. Committee ADAPP. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2023;47(Supplement1):S20–42. FitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, et al. 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care Res (Hoboken). 2020;72(6):744–60. Fennoun H, Haraj N, El Aziz S, Bensbaa S, Chadli A. Risk factors associated with hyperuricemia in patients with diabetes type 2: about 190 cases. Diabetes Research: Open Access. 2020;2020(1):12. Sun S, Chen L, Chen D, Li Y, Liu G, Ma L, et al. Prevalence and associated factors of hyperuricemia among Chinese patients with diabetes: a cross-sectional study. Ther Adv Endocrinol Metab. 2023;14:20420188231198620. Abdel KA, Kalluvya SE, Sadiq AM, Ashir A, Masikini PI. Prevalence of Hyperuricemia and Associated Factors Among Patients With Type 2 Diabetes Mellitus in Northwestern Tanzania: A Cross-Sectional Study. Clin Med Insights Endocrinol Diabetes. 2024;17:11795514241274694. Liu L, Zhao T, Shan L, Cao L, Zhu X, Xue Y. Estradiol regulates intestinal ABCG2 to promote urate excretion via the PI3K/Akt pathway. Nutr Metab (Lond). 2021;18(1):63. Woyesa SB, Hirigo AT, Wube TB. Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. BMC Endocr disorders. 2017;17(1):76. Arersa KK, Wondimnew T, Welde M, Husen TM. Prevalence and determinants of hyperuricemia in type 2 diabetes mellitus patients attending Jimma Medical Center, Southwestern Ethiopia, 2019. Diabetes, Metabolic Syndrome and Obesity. 2020:2059-67. Asma Sakalli A, Küçükerdem HS, Aygün O. What is the relationship between serum uric acid level and insulin resistance? A case-control study. Med (Baltim). 2023;102(52):e36732. Mundhe SA, Mhasde DR. The study of prevalence of hyperuricemia and metabolic syndrome in type 2 diabetes mellitus. Int J Adv Med. 2016;3(2):241–9. Wei F, Chang B, Yang X, Wang Y, Chen L, Li W-D. Serum Uric Acid Levels were Dynamically Coupled with Hemoglobin A1c in the Development of Type 2 Diabetes. Sci Rep. 2016;6(1):28549. Ueno N. Urate-Lowering Therapy Ameliorates Kidney Function in Type 2 Diabetes Patients With Hyperuricemia. J Clin Med Res. 2017;9(12):1007–12. Wu Y, Shin D. Association between alcoholic beverage intake and hyperuricemia in Chinese adults: Findings from the China Health and Nutrition Survey. Med (Baltim). 2023;102(22):e33861. Ma W, Ye G, Liu Y, Sun W, Huang X, Hu L, et al. Impact of alcohol consumption on hyperuricemia and gout: a systematic review and meta-analysis. Front Nutr. 2025;12:1588980. Stewart DJ, Langlois V, Noone D. Hyperuricemia and Hypertension: Links and Risks. Integr Blood Press Control. 2019;12:43–62. Kuwabara M, Niwa K, Hisatome I, Nakagawa T, Roncal-Jimenez CA, Andres-Hernando A, et al. Asymptomatic hyperuricemia without comorbidities predicts cardiometabolic diseases: five-year Japanese cohort study. Hypertension. 2017;69(6):1036–44. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 03 Feb, 2026 Editor invited by journal 27 Jan, 2026 Editor assigned by journal 21 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 18 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8631117","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579491901,"identity":"185ebb31-3f4a-4044-9090-5c956b91a7b2","order_by":0,"name":"Son Tran Kim","email":"","orcid":"","institution":"Can Tho University of Medicine and Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Son","middleName":"Tran","lastName":"Kim","suffix":""},{"id":579491902,"identity":"584735a3-0fca-4bf1-8975-d5646ba8be4b","order_by":1,"name":"Duc Minh Nguyen Quan","email":"data:image/png;base64,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","orcid":"","institution":"Can Tho University of Medicine and Pharmacy","correspondingAuthor":true,"prefix":"","firstName":"Duc","middleName":"Minh Nguyen","lastName":"Quan","suffix":""},{"id":579491904,"identity":"c0352797-92be-47ce-96f7-aa283815d6c3","order_by":2,"name":"Minh Phuong Vo","email":"","orcid":"","institution":"Can Tho University of Medicine and Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Minh","middleName":"Phuong","lastName":"Vo","suffix":""}],"badges":[],"createdAt":"2026-01-18 12:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8631117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8631117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101189312,"identity":"449d5b8c-08b7-4282-aab6-5be75d204397","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89336,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/6bfacee784e7c7e38da9c225.docx"},{"id":101189316,"identity":"95bd7447-300d-4781-83b5-1bc9424d9db8","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26052,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/f1e1e103aaf17c30a98ec713.docx"},{"id":101206212,"identity":"9977a2f7-dd88-47bf-8ea1-66c8df4f7419","added_by":"auto","created_at":"2026-01-27 09:55:39","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5616,"visible":true,"origin":"","legend":"","description":"","filename":"a3cde4269395465a90e8f421a5c1bc7b.json","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/e3cee8221a8c60f09b99fccc.json"},{"id":101189320,"identity":"70dd5f5e-ab3e-4aff-8b51-df269d8fd6c4","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":176569,"visible":true,"origin":"","legend":"","description":"","filename":"a3cde4269395465a90e8f421a5c1bc7b1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/a657f007d4371bed5cd90148.xml"},{"id":101206473,"identity":"90fd4149-8919-4d8c-a677-8aa2e3cd1cc2","added_by":"auto","created_at":"2026-01-27 09:56:19","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21389,"visible":true,"origin":"","legend":"","description":"","filename":"firgue1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/ba67b78850ec0a178ea60ec1.pdf"},{"id":101189314,"identity":"c68f7b73-8302-4caf-a8cd-d2eecec0f848","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13728,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/9f69f7c31b77d94a2fc99fdb.png"},{"id":101207107,"identity":"a49c6d31-21e9-4e64-8d06-57a57fecc8d9","added_by":"auto","created_at":"2026-01-27 09:57:27","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7363,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/5593f370a640201bb3b71d27.png"},{"id":101189318,"identity":"fca50501-e04f-4bda-9c25-3bc39c2db10a","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174088,"visible":true,"origin":"","legend":"","description":"","filename":"a3cde4269395465a90e8f421a5c1bc7b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/a5a7c8e914a691a4b9c7ec64.xml"},{"id":101189321,"identity":"a6e2d829-422c-4dc8-a4d1-ad96c8c9f057","added_by":"auto","created_at":"2026-01-27 06:52:02","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185091,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/fb9459d5d00ff67219bb22d1.html"},{"id":101206661,"identity":"5c0c4b9d-0751-46bb-b736-5eaf87e039de","added_by":"auto","created_at":"2026-01-27 09:56:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17623,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve of the multivariable logistic regression model for predicting hyperuricemia.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/f1875fdb8087fa8a12bbd821.png"},{"id":101208439,"identity":"739444e8-1b94-494c-a26b-6b7f195f4dce","added_by":"auto","created_at":"2026-01-27 10:10:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1233056,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8631117/v1/d60fd41e-1e99-43bd-a65e-c1f658a5f10e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence of Asymptomatic Hyperuricemia and Associated Factors in Patients With Type 2 Diabetes Mellitus: A Cross-sectional Study in Southern Vietnam\u003c/p\u003e","fulltext":[{"header":"1. Background","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eType 2 diabetes mellitus (T2DM) is a common chronic disease worldwide, strongly associated with cardiovascular and renal complications, which are the leading causes of mortality. According to the International Diabetes Federation (IDF), in 2021, an estimated 537\u0026nbsp;million adults were living with diabetes globally, and this number is projected to rise to 643\u0026nbsp;million by 2030 and 783\u0026nbsp;million by 2045. Additionally, approximately 541\u0026nbsp;million people were estimated to have impaired glucose tolerance, a high-risk condition for developing T2DM. In the same year, over 6.7\u0026nbsp;million deaths related to diabetes were recorded among individuals aged 20 to 79, highlighting the global health burden posed by this condition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In clinical practice, cardiovascular risk assessment and management in patients with diabetes often focus on traditional risk factors such as hypertension, dyslipidemia, obesity, smoking, and glycemic control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, certain metabolic risk factors, including uric acid, have received less attention and are often overlooked by clinicians.\u003c/p\u003e \u003cp\u003eHyperuricemia has long been recognized as a central factor in gout. Nevertheless, accumulating evidence suggests that uric acid also plays a direct role in the pathogenesis of metabolic disorders, chronic inflammation, endothelial dysfunction, and atherosclerosis. Numerous studies have reported that elevated serum uric acid levels are associated with hypertension, chronic kidney disease (CKD), metabolic syndrome, and increased cardiovascular risk [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn patients with T2DM, the relationship between uric acid and cardiovascular risk becomes more complex due to its interactions with insulin resistance, dyslipidemia, and CKD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Epidemiological studies have shown that elevated serum uric acid levels are independently associated with coronary artery disease, stroke, insulin resistance, and cardiovascular mortality, even after adjustment for traditional risk factors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition to its cardiovascular implications, recent studies have also proposed serum uric acid as an early marker of kidney damage in T2DM. Uric acid contributes to glomerular fibrosis and inflammation via oxidative stress and local inflammatory activation, thereby promoting a decline in estimated glomerular filtration rate (eGFR) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Hence, asymptomatic hyperuricemia may not only be a cardiovascular risk marker but may also play a pathogenic role in diabetic kidney disease. Despite this, serum uric acid is often considered an ancillary test in daily practice, especially among patients without clinical signs of gout, leading to under recognition of asymptomatic hyperuricemia.\u003c/p\u003e \u003cp\u003eAsymptomatic hyperuricemia is a prevalent condition but is rarely detected or actively monitored. For instance, in the United States, while only about 3.9% of adults are diagnosed with gout, approximately 20% exhibit elevated uric acid levels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In patients with T2DM, the omission of uric acid evaluation may result in an incomplete risk assessment for cardiovascular and renal complications. Studies have confirmed that asymptomatic hyperuricemia in this population is independently associated with poor cardio-renal outcomes. Specifically, it contributes to atherosclerosis, endothelial dysfunction, and is considered a predictor of coronary artery disease and kidney failure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the context of current recommendations that emphasize a comprehensive, multifactorial approach to cardiovascular risk management in T2DM, understanding the prevalence and determinants of asymptomatic hyperuricemia holds important clinical value.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Vietnam, data on asymptomatic hyperuricemia in patients with T2DM remain limited, particularly regarding its association with metabolic factors, renal function, and behavioral risks. Therefore, this study was conducted to determine the prevalence and associated factors of asymptomatic hyperuricemia in patients with T2DM, with the aim of supporting the integration of uric acid assessment into comprehensive cardio\u0026ndash;renal\u0026ndash;metabolic risk management strategies in this patient group.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study employed a descriptive cross-sectional design with analytical components, aiming to determine the prevalence of asymptomatic hyperuricemia and its associated factors among patients with type 2 diabetes mellitus (T2DM). The study was conducted at the University of Medicine and Pharmacy Hospital, Can Tho, Vietnam, from 2024 to 2025.\u003c/p\u003e \u003cp\u003eEligible participants were adult patients diagnosed with T2DM according to the 2024 criteria of the American Diabetes Association (ADA), who attended the hospital for examination and treatment during the study period.\u003c/p\u003e \u003cp\u003eThe sample size was determined using a convenient sampling method, including all patients who met the inclusion criteria during the study timeframe. As a descriptive\u0026ndash;analytical cross-sectional study aiming to estimate the prevalence of asymptomatic hyperuricemia and identify related factors, the final sample comprised 235 patients.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Inclusion and Exclusion Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ePatients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years.\u003c/p\u003e \u003cp\u003eDiagnosed with type 2 diabetes mellitus based on the 2024 American Diabetes Association (ADA) criteria [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAvailability of complete clinical and laboratory data, including serum uric acid levels.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ePatients with secondary hyperuricemia due to underlying conditions such as advanced kidney failure, leukemia, multiple myeloma, hemolytic anemia, recent chemotherapy or radiotherapy, or acute alcohol intoxication.\u003c/p\u003e \u003cp\u003ePatients with a known history of gout or currently receiving urate-lowering therapy.\u003c/p\u003e \u003cp\u003ePatients with acute conditions that may affect serum uric acid levels, including recent infections, trauma, or surgery.\u003c/p\u003e \u003cp\u003eUse of medications that may influence serum uric acid levels, including SGLT2 inhibitors, losartan, probenecid, sulfinpyrazone, salicylic acid, ascorbic acid, phenylbutazone, thiazide diuretics, anti-tuberculosis drugs, estrogens, and anticancer agents.\u003c/p\u003e \u003cp\u003ePregnant women.\u003c/p\u003e \u003cp\u003eMedical records with incomplete data required for analysis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Collection and Variable Definitions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData for this study were collected from two sources: (1) electronic medical records at the hospital and (2) direct interviews conducted by trained nurses or research personnel. The collected information included demographic characteristics, medical history, lifestyle habits, current medications, as well as clinical and laboratory parameters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDefinition of Asymptomatic Hyperuricemia\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eAsymptomatic hyperuricemia was defined as a serum uric acid (SUA) level\u0026thinsp;\u0026ge;\u0026thinsp;420 \u0026micro;mol/L in males or \u0026ge;\u0026thinsp;360 \u0026micro;mol/L in females, in the absence of any clinical manifestations of gout, such as gouty arthritis or tophi formation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eClassification of Serum Uric Acid Levels\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eBased on SUA concentrations, patients were categorized into three groups:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNormal uric acid: SUA\u0026thinsp;\u0026lt;\u0026thinsp;420 \u0026micro;mol/L in males and \u0026lt;\u0026thinsp;360 \u0026micro;mol/L in females\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMild hyperuricemia: SUA between 420\u0026ndash;540 \u0026micro;mol/L in males or 360\u0026ndash;540 \u0026micro;mol/L in females\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSevere hyperuricemia: SUA\u0026thinsp;\u0026gt;\u0026thinsp;540 \u0026micro;mol/L in both sexes\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eVariables Collected\u003c/b\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDemographics\u003c/strong\u003e \u003cp\u003eAge (years at the time of enrollment) and sex (male or female).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBody Mass Index (BMI)\u003c/strong\u003e \u003cp\u003eCalculated as weight (kg) divided by the square of height (m\u0026sup2;), analyzed as a continuous variable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eLifestyle factors\u003c/em\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSmoking status: classified as current smoker, former smoker, or never smoked.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlcohol consumption: categorized as regular drinker (at least once per week) or non-drinker.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eDuration of type 2 diabetes\u003c/em\u003e: Calculated from the time of diagnosis to the time of data collection, and classified into two categories: \u0026lt;10 years and \u0026ge;\u0026thinsp;10 years\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCurrent antidiabetic medications\u003c/strong\u003e \u003cp\u003eDocumented use of metformin, sulfonylureas, dipeptidyl peptidase-4 inhibitors (DPP-4i), insulin, or combinations thereof.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eHistory of cardiovascular and renal comorbidities\u003c/em\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHypertension (previously diagnosed and/or on treatment)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoronary artery disease (including history of myocardial infarction, stenting, or ischemic heart disease)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eBiochemical parameters\u003c/em\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFasting plasma glucose (FPG)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGlycated hemoglobin (HbA1c)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLipid profile: total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBlood urea and serum creatinine\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRenal function\u003c/strong\u003e \u003cp\u003eEstimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. eGFR was used to assess the degree of kidney function impairment and its relationship with serum uric acid accumulation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll laboratory tests were performed at the hospital\u0026rsquo;s central laboratory according to internal quality control protocols.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData analysis was performed using SPSS software version 23. Quantitative variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median with interquartile range, depending on data distribution. Categorical variables were summarized as frequencies and percentages.\u003c/p\u003e \u003cp\u003eThe prevalence of asymptomatic hyperuricemia and its severity levels were reported with 95% confidence intervals (95% CI). Differences in proportions between groups were assessed using the Chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression analysis was used to screen for factors associated with asymptomatic hyperuricemia. Variables with statistical significance or clinical relevance were included in the multivariate logistic regression model to identify independent associations. Results were reported as odds ratios (ORs) with 95% confidence intervals. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eTo evaluate the discriminative ability of the multivariate logistic regression model in predicting asymptomatic hyperuricemia, the Receiver Operating Characteristic (ROC) curve was utilized. The area under the curve (AUC) was calculated to reflect the model's performance in distinguishing between patients with and without asymptomatic hyperuricemia. An AUC value closer to 1 indicates better classification accuracy of the model.\u003c/p\u003e \u003cp\u003eAdditionally, subgroup analyses were performed based on sex, alcohol consumption status, and kidney function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 mL/min/1.73 m\u0026sup2;) to assess the robustness of the findings.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ethical Considerations\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study was approved by the Ethics Committee of Can Tho University of Medicine and Pharmacy (Approval No.: 24.255.HV-ĐHYDCT). All patient data were anonymized and used solely for research purposes. Given the retrospective and non-interventional nature of the study, informed consent was waived in accordance with the Ethics Committee\u0026rsquo;s regulations.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Use of Artificial Intelligence\u003c/h2\u003e \u003cp\u003eArtificial intelligence was used solely for language editing and manuscript formatting support. It was not employed to generate data, perform data analysis, or interpret study results.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAmong the 235 patients with type 2 diabetes mellitus (T2DM) included in the analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 40.9% were male. The mean age of the study population was 60.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5 years, and 59.6% had diabetes duration of 10 years or longer. The mean body mass index (BMI) was 23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 kg/m\u0026sup2;. Common comorbidities included hypertension (59.6%), dyslipidemia (67.2%), and chronic kidney disease (12.3%). Regarding behavioral risk factors, 14.9% of patients were current smokers, and 22.6% reported alcohol consumption.\u003c/p\u003e \u003cp\u003eIn terms of laboratory characteristics, the median HbA1c level was 8.0% (interquartile range: 6.8\u0026ndash;9.9), and the median fasting plasma glucose level was 7.8 mmol/L (6.4\u0026ndash;11.3). The median serum uric acid concentration was 315 \u0026micro;mol/L (255\u0026ndash;421). The majority of patients were receiving metformin (76.6%), while the use of DPP-4 inhibitors, sulfonylureas, and insulin was 50.2%, 26.8%, and 33.2%, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;235)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHyperuricemia group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;73)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-hyperuricemia group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;162)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.86\u0026thinsp;\u0026plusmn;\u0026thinsp;13.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.92\u0026thinsp;\u0026plusmn;\u0026thinsp;14.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.94\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.54\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62 (1.57\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60 (1.52\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (55\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.0 (60.0\u0026ndash;72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.0 (53.0\u0026ndash;68.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes, \u0026ge; 10-years, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.664***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinker, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.220***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.681***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.21 (4.16\u0026ndash;6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85 (3.96\u0026ndash;6.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.415 (4.26\u0026ndash;6.5225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.106**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.44 (1.73\u0026ndash;3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65 (1.905\u0026ndash;4.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33 (1.6775\u0026ndash;3.7075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.253**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99 (2.03\u0026ndash;4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60 (1.73\u0026ndash;3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.175 (2.255\u0026ndash;4.2275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.97\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.90\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.0075\u0026ndash;1.4025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40 (4.30\u0026ndash;6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.40 (4.85\u0026ndash;8.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.95 (4.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.7 (55.1\u0026ndash;83.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.0 (70.95\u0026ndash;123.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.45 (52.925\u0026ndash;74.475)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.0 (74.0\u0026ndash;103.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.96 (47.38\u0026ndash;95.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.51 (85.58\u0026ndash;105.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 (6.8\u0026ndash;9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.60 (6.60\u0026ndash;8.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.35 (7.20\u0026ndash;10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.84 (6.41\u0026ndash;11.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.17 (5.94\u0026ndash;9.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.25 (6.63\u0026ndash;11.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcid uric, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e315 (255\u0026ndash;421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e471.0 (426.5\u0026ndash;532.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274.0 (235.25\u0026ndash;320.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAntidiabetic medications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.488***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDPP4-I, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSU, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.945***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Independent samples t test, ** Mann\u0026ndash;Whitney U test, *** χ\u0026sup2; test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe group with hyperuricemia had a significantly higher proportion of males compared to the normouricemic group (53.4% vs. 35.2%; p\u0026thinsp;=\u0026thinsp;0.008). Body weight and body mass index (BMI) were also significantly higher in the hyperuricemic group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, the proportions of current smokers and alcohol consumers were markedly higher in the hyperuricemic group compared to those without hyperuricemia (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). With regard to comorbidities, chronic kidney disease (CKD) was more prevalent in the hyperuricemic group (31.5% vs. 3.7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the prevalence of hypertension, dyslipidemia, and coronary artery disease did not differ significantly between the two groups.\u003c/p\u003e \u003cp\u003eBiochemical parameters showed that patients with hyperuricemia had significantly higher serum urea and creatinine levels, and lower estimated glomerular filtration rate (eGFR), compared to those without hyperuricemia (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, HDL-cholesterol levels were lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and LDL-cholesterol levels were higher (p\u0026thinsp;=\u0026thinsp;0.007) in the hyperuricemic group. No significant differences were observed in total cholesterol or triglyceride levels. HbA1c and plasma glucose levels were also significantly higher in the hyperuricemic group (p\u0026thinsp;=\u0026thinsp;0.003 and p\u0026thinsp;=\u0026thinsp;0.014, respectively). The use of antidiabetic medications - including metformin, DPP-4 inhibitors, sulfonylureas, and insulin - did not differ significantly between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Prevalence of Asymptomatic Hyperuricemia\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence and severity of hyperuricemia stratified by sex (n\u0026thinsp;=\u0026thinsp;235)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;235)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormouricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (75.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (68.9%; 95% CI: 63.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild hyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (25.9%; 95% CI: 19.9\u0026ndash;31.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere hyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (5.1%; 95% CI: 2.3\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value (Chi-square)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the overall study population, 73 patients (31.1%) were classified as having asymptomatic hyperuricemia, while 162 patients (68.9%) had normal serum uric acid levels (95% CI: 63.0\u0026ndash;75.0). Mild hyperuricemia was observed in 25.9% of patients (95% CI: 19.9\u0026ndash;31.2), and severe hyperuricemia in 5.1% (95% CI: 2.3\u0026ndash;7.9) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The distribution of hyperuricemia prevalence and severity by sex revealed a statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.021). Among males, 59.4% had normal uric acid levels, 33.3% had mild hyperuricemia, and 7.3% had severe hyperuricemia. In contrast, 75.5% of females had normal levels, with 20.9% presenting with mild and 3.6% with severe hyperuricemia.\u003c/p\u003e \u003cp\u003eOverall, men exhibited a higher prevalence of hyperuricemia than women. Notably, the majority of female patients with hyperuricemia were classified as mild cases, whereas the proportion of severe hyperuricemia was higher among males. This sex-related disparity helps explain the role of gender observed in the subsequent regression analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Logistic Regression Analyses for Factors Associated with Asymptomatic Hyperuricemia\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the univariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), male sex (OR\u0026thinsp;=\u0026thinsp;2.11; 95% CI: 1.21\u0026ndash;3.71; p\u0026thinsp;=\u0026thinsp;0.009), alcohol consumption (OR\u0026thinsp;=\u0026thinsp;3.41; 95% CI: 1.81\u0026ndash;6.44; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking (OR\u0026thinsp;=\u0026thinsp;3.70; 95% CI: 1.77\u0026ndash;7.75; p\u0026thinsp;=\u0026thinsp;0.001), and higher BMI (OR\u0026thinsp;=\u0026thinsp;1.16 per kg/m\u0026sup2;; 95% CI: 1.04\u0026ndash;1.30; p\u0026thinsp;=\u0026thinsp;0.009) were significantly associated with asymptomatic hyperuricemia. In addition, lower levels of LDL-c (OR\u0026thinsp;=\u0026thinsp;0.77; p\u0026thinsp;=\u0026thinsp;0.015) and HDL-c (OR\u0026thinsp;=\u0026thinsp;0.18; p\u0026thinsp;=\u0026thinsp;0.002), higher serum urea and creatinine concentrations (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and reduced estimated glomerular filtration rate (eGFR) (OR\u0026thinsp;=\u0026thinsp;0.95; 95% CI: 0.94\u0026ndash;0.97; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also significantly associated with hyperuricemia. Notably, lower HbA1c levels were inversely associated with hyperuricemia (OR\u0026thinsp;=\u0026thinsp;0.84; p\u0026thinsp;=\u0026thinsp;0.009). Meanwhile, hypertension, coronary artery disease, triglycerides, total cholesterol, and plasma glucose levels were not significantly associated with hyperuricemia.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis for factors associated with hyperuricemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026ndash;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.81\u0026ndash;6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77\u0026ndash;7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (per kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026ndash;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026ndash;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eVariables that were statistically significant in the univariate analysis or deemed clinically relevant were included in the multivariate logistic regression model. After adjusting for potential confounders, alcohol consumption (adjusted OR\u0026thinsp;=\u0026thinsp;6.05; 95% CI: 1.79\u0026ndash;20.41; p\u0026thinsp;=\u0026thinsp;0.004), body mass index (adjusted OR\u0026thinsp;=\u0026thinsp;1.18 per kg/m\u0026sup2;; 95% CI: 1.01\u0026ndash;1.36; p\u0026thinsp;=\u0026thinsp;0.032), and lower estimated glomerular filtration rate (eGFR) (adjusted OR\u0026thinsp;=\u0026thinsp;0.96; 95% CI: 0.93\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.012) remained independently associated with asymptomatic hyperuricemia. Other variables - including male sex, smoking, LDL-c, HDL-c, urea, creatinine, and HbA1c - were no longer statistically significant in the multivariate model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis for factors independently associated with hyperuricemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026ndash;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u0026ndash;20.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (per kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curve demonstrated that the multivariate logistic regression model had good discriminative ability in distinguishing patients with and without asymptomatic hyperuricemia. The area under the curve (AUC) was 0.869 (95% CI: 0.818\u0026ndash;0.920), indicating high and stable predictive performance. This AUC value was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that the model performed significantly better than chance (Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e. ROC curve of the multivariable logistic regression model for predicting hyperuricemia.\u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curve of the multivariable logistic regression model for predicting hyperuricemia (AUC\u0026thinsp;=\u0026thinsp;0.869; 95% CI: 0.818\u0026ndash;0.920; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Subgroup Analyses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSubgroup analyses were performed to examine the robustness of factors associated with asymptomatic hyperuricemia. Multivariate logistic regression models were stratified by sex (male, female), estimated glomerular filtration rate (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 mL/min/1.73 m\u0026sup2;), and alcohol consumption status.\u003c/p\u003e \u003cp\u003eIn the subgroup analysis according to alcohol consumption (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), among patients who did not consume alcohol, lower eGFR remained independently associated with asymptomatic hyperuricemia (adjusted OR\u0026thinsp;=\u0026thinsp;0.95; 95% CI: 0.92\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.016), while other variables were not statistically significant. In contrast, among alcohol users, LDL cholesterol was independently associated with asymptomatic hyperuricemia (adjusted OR\u0026thinsp;=\u0026thinsp;0.50; 95% CI: 0.26\u0026ndash;0.95; p\u0026thinsp;=\u0026thinsp;0.035), whereas other variables - including sex, smoking, BMI, renal function, and HbA1c - did not show statistically significant associations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis stratified by alcohol consumption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo alcohol Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlcohol use Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.18\u0026ndash;2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (0.001\u0026ndash;7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.34 (0.43\u0026ndash;26.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94 (0.41\u0026ndash;9.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.98\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (0.92\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.73\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.26\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26 (0.05\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.04\u0026ndash;38.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (0.99\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.51\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.97\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.92\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.92\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.86\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.74\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.64\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the subgroup analysis by sex (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), among male patients, higher BMI (adjusted OR\u0026thinsp;=\u0026thinsp;1.29; 95% CI: 1.01\u0026ndash;1.64; p\u0026thinsp;=\u0026thinsp;0.038) and smoking (adjusted OR\u0026thinsp;=\u0026thinsp;6.26; 95% CI: 1.76\u0026ndash;22.26; p\u0026thinsp;=\u0026thinsp;0.005) were independently associated with asymptomatic hyperuricemia. Conversely, in female patients, lower eGFR remained independently associated with asymptomatic hyperuricemia (adjusted OR\u0026thinsp;=\u0026thinsp;0.96; 95% CI: 0.92\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.021), while other variables did not reach statistical significance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSex-stratified subgroup analysis for factors associated with hyperuricemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.01\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.95\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.40\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.72\u0026ndash;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.01\u0026ndash;3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.07\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.86\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21 (0.91\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.96\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.92\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.74\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.73\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.26 (1.76\u0026ndash;22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.17 (1.36\u0026ndash;1373.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the subgroup analysis based on renal function (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), among patients with eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, higher BMI (adjusted OR\u0026thinsp;=\u0026thinsp;1.24; 95% CI: 1.06\u0026ndash;1.44; p\u0026thinsp;=\u0026thinsp;0.006) and higher serum creatinine levels (adjusted OR\u0026thinsp;=\u0026thinsp;1.05; 95% CI: 1.02\u0026ndash;1.07; p\u0026thinsp;=\u0026thinsp;0.001) were independently associated with asymptomatic hyperuricemia. In contrast, no factors remained statistically significant in the subgroup of patients with eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2; after multivariable adjustment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analysis stratified by kidney function (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 vs\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eeGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 Adjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (per kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.48\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.06\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (0.47\u0026ndash;3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.61\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.11\u0026ndash;2.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61 (0.87\u0026ndash;3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.91\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.02\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61 (0.33\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.79\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe results of the subgroup analyses by sex, renal function, and alcohol consumption revealed that key risk factors - including male sex, higher BMI, and reduced eGFR - consistently retained statistical significance across most strata. These findings reinforce the robustness and reliability of the associations identified in the overall study population.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOur study confirms that asymptomatic hyperuricemia is relatively common among patients with T2DM, with a prevalence of 31.1%. This aligns with global reports ranging from 10% to over 50%, depending on population and definitions. For example, Fennoun et al. reported 26.5%, and a large Chinese study found 21.24% (higher in males) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], while African studies reported rates as high as 55.56% [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. hese differences likely reflect variations in demographics, lifestyle, and diagnostic thresholds. Our findings reinforce the global pattern of under-recognized hyperuricemia in diabetes. Consistent with prior studies, we observed a higher prevalence in males, attributed to estrogen\u0026rsquo;s uricosuric effects in premenopausal women [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Postmenopausal hormonal decline narrows this gap, explaining reports of similar or higher hyperuricemia rates in older diabetic women with metabolic syndrome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBMI was a significant and independent predictor of hyperuricemia, supporting evidence that obesity-related insulin resistance impairs renal urate clearance and promotes reabsorption, leading to elevated uric acid [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Obesity-associated inflammation and increased urate transporter expression (e.g., URAT1, GLUT9) further contribute [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Interestingly, dyslipidemia and LDL-C levels were higher in the non-hyperuricemic group, possibly due to better glycemic control and more intensive statin use among hyperuricemic patients. Sex and lifestyle differences (e.g., more women and non-drinkers in the non-hyperuricemic group) may also influence lipid profiles. Statins can mildly elevate uric acid, adding complexity. These observations suggest that the lipid\u0026ndash;uric acid relationship is not strictly linear and may be shaped by treatment, metabolic status, and behavioral factors, requiring further investigation.\u003c/p\u003e\u003cp\u003eOur study revealed an inverse relationship between glycemic control and asymptomatic hyperuricemia. Higher HbA1c levels were associated with a lower risk of hyperuricemia in univariate analysis (OR 0.84 per 1% increase, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), though this association lost significance in multivariable analysis. This pattern, supported by previous studies, may result from glucosuria in poorly controlled diabetes, which enhances urate clearance through competitive inhibition in the renal tubules. In contrast, higher insulin levels in well-controlled patients may promote urate reabsorption, elevating serum uric acid. Thus, chronic hyperglycemia may paradoxically suppress serum uric acid levels. Our findings are consistent with Wei et al., who reported a reduced risk of hyperuricemia with increasing HbA1c (adjusted OR\u0026thinsp;~\u0026thinsp;0.87) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This highlights the need for cautious interpretation of uric acid levels in newly diagnosed or poorly controlled diabetic patients.\u003c/p\u003e\u003cp\u003eConversely, reduced eGFR was a strong independent predictor of hyperuricemia (adjusted OR 0.96 per 1 mL/min/1.73 m\u0026sup2; decrease, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with previous evidence linking even mild renal impairment to decreased urate excretion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Elevated serum creatinine also correlated with hyperuricemia in univariate analysis. This supports the well-known bidirectional relationship between hyperuricemia and CKD, where uric acid promotes kidney damage and reduced renal function increases uric acid retention. Importantly, we excluded patients with advanced renal failure or secondary hyperuricemia, emphasizing that the observed association reflects the impact of early-stage CKD in T2DM [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlcohol consumption emerged as an independent risk factor for asymptomatic hyperuricemia. Ethanol promotes purine breakdown and uric acid production, while lactic acid from alcohol metabolism impairs urate excretion [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In our study, regular alcohol use was associated with a markedly higher risk of hyperuricemia (adjusted OR\u0026thinsp;~\u0026thinsp;6.05 vs. non-drinkers, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), consistent with previous epidemiological data and a recent meta-analysis highlighting strong links between ethanol - particularly from beer and spirits - and elevated uric acid levels [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Clinically, this underscores the need to counsel patients with type 2 diabetes to limit alcohol intake for both metabolic and uric acid management. In contrast, smoking showed a significant association with hyperuricemia in univariate analysis (OR 3.7, p\u0026thinsp;=\u0026thinsp;0.001) but lost significance in multivariate analysis (p\u0026thinsp;=\u0026thinsp;0.11), suggesting confounding by factors like sex, alcohol use, or BMI. Male participants - who had higher smoking rates - also naturally exhibit higher uric acid levels, possibly explaining the initial association. These findings align with Fennoun et al., who also found no independent link between smoking and hyperuricemia in diabetic patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While smoking may indirectly affect urate metabolism through oxidative stress, current evidence does not support it as a direct contributor to hyperuricemia. Nonetheless, smoking cessation remains vital due to its broader cardiovascular and renal risks.\u003c/p\u003e\u003cp\u003eIn this study, neither hypertension nor coronary artery disease showed significant associations with asymptomatic hyperuricemia (p\u0026thinsp;=\u0026thinsp;0.313 and p\u0026thinsp;=\u0026thinsp;0.681, respectively), even after multivariate adjustment. This contrasts with prior evidence linking hyperuricemia to elevated cardiovascular risk and hypertension development via mechanisms such as renal microvascular injury, activation of the renin\u0026ndash;angiotensin system, and nitric oxide reduction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The lack of association in our cohort may stem from the exclusion of patients on urate-altering medications (e.g., thiazides, losartan) and the high prevalence of hypertension (60%) among T2DM patients, which may have diluted its independent effect - particularly in the presence of overlapping contributors like reduced eGFR. Similarly, while hyperuricemia has been implicated in coronary artery disease and heart failure in other studies, no significant association was observed here, potentially due to sample size limitations or effective risk factor control. Nonetheless, given its links to metabolic syndrome components, asymptomatic hyperuricemia remains clinically relevant in T2DM - even when blood pressure and coronary disease are well-managed [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSubgroup multivariate analyses by sex, renal function, and alcohol use revealed distinct patterns. Among men, higher BMI and smoking were independent predictors of asymptomatic hyperuricemia (OR 1.29 per kg/m\u0026sup2; and OR 6.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while in women, only reduced eGFR remained significant (OR 0.96; p\u0026thinsp;=\u0026thinsp;0.021). This likely reflects sex-based behavioral and biological differences: men were more exposed to modifiable risks, whereas renal impairment - more prevalent in older, postmenopausal women - was the dominant factor. The absence of smoking among female drinkers (0%) further supports this divergence. These findings emphasize sex-specific risk profiling: men may benefit most from weight and smoking interventions, while in women, renal monitoring is key.\u003c/p\u003e\u003cp\u003eIn patients with preserved renal function (eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;), BMI and serum creatinine were independently associated with hyperuricemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), highlighting metabolic factors as key drivers. In contrast, no significant predictors emerged in the CKD subgroup (eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60), likely due to the overwhelming effect of renal impairment - present in over 80% - and limited sample size (29 patients). This suggests that in CKD, lifestyle factors exert less influence on uric acid levels, while in those with intact kidney function, weight and metabolic control are critical.\u003c/p\u003e\u003cp\u003eAlcohol-based subgroup analysis showed that among non-drinkers, reduced eGFR was the only significant predictor (OR\u0026thinsp;=\u0026thinsp;0.95; p\u0026thinsp;=\u0026thinsp;0.016). However, in drinkers, eGFR lost significance, and instead, an inverse association between LDL-C and hyperuricemia emerged (OR 0.50; p\u0026thinsp;=\u0026thinsp;0.035). This unexpected finding may relate to alcohol-induced hepatic dysfunction or malnutrition, which could lower LDL-C while increasing uric acid. Despite a modest alcohol-user sample (22.6%), this result illustrates how ethanol metabolism can reshape uric acid risk profiles. Clinicians should consider alcohol history when assessing hyperuricemia in T2DM and tailor interventions accordingly.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be noted. First, its cross-sectional design does not allow for the establishment of causal relationships between associated factors and asymptomatic hyperuricemia. Second, specific data on dietary habits, physical activity levels, and alcohol consumption quantities were not collected in detail, which may confound the analysis. Third, the relatively small sample sizes in certain subgroups (such as those with low eGFR or alcohol users) may reduce statistical power and introduce the risk of type II error. Lastly, the study was conducted at a single healthcare center, which may limit the generalizability of the findings to other populations. However, the study also has several notable strengths. It is among the few studies in Vietnam to systematically investigate the prevalence and associated factors of asymptomatic hyperuricemia, including multivariate regression and subgroup analyses, in patients with type 2 diabetes. Our sample size was also relatively robust, larger than that of previous studies conducted in our region, which enhances the reliability of the findings. The exclusion of secondary causes of hyperuricemia (such as gout or diuretic use) enhanced the accuracy of evaluating the relationship between metabolic factors and serum uric acid levels. Furthermore, subgroup analyses provided deeper insights into the roles of individual factors in different clinical contexts, offering practical value for patient care.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, the prevalence of asymptomatic hyperuricemia among patients with type 2 diabetes was 31.1%, including 25.9% with mild elevation and 5.1% with marked elevation. Independent factors significantly associated with hyperuricemia included male sex (OR\u0026thinsp;=\u0026thinsp;2.46), higher body mass index (OR\u0026thinsp;=\u0026thinsp;1.16), lower estimated glomerular filtration rate (eGFR) (OR\u0026thinsp;=\u0026thinsp;0.97), and alcohol consumption (OR\u0026thinsp;=\u0026thinsp;2.03). Subgroup analyses confirmed the consistency of these associations across strata of sex, renal function, and drinking status. Routine screening and appropriate management of asymptomatic hyperuricemia in patients with type 2 diabetes may play a role in preventing metabolic and cardiovascular complications. Longitudinal and interventional studies are warranted to clarify causal relationships and evaluate the clinical benefits of targeted interventions in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eSUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eSerum Uric Acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eEstimated Glomerular Filtration Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3206%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81.6794%;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Can Tho University of Medicine and Pharmacy (Approval number: 24.255.HV-ĐHYDCT and date of approval: 25/04/2025). Given the retrospective and non-interventional nature of the study, the requirement for informed consent was waived by the Ethics Committee. All patient data were anonymized and handled in accordance with institutional guidelines and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.K.S: Methodology, Data curation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eN.Q.D.M: Conceptualization, Supervision, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003eV.M.P: Validation, Resources, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Can Tho University of Medicine and Pharmacy and Can Tho University of Medicine and Pharmacy Hospital for their assistance in data collection and patient coordination during the study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMagliano DJ, Boyko EJ, committee IDFDAtes. IDF Diabetes Atlas. Idf diabetes atlas. Brussels: International Diabetes Federation \u0026copy; International Diabetes Federation, 2021.; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoyal R, Singhal M, Jialal I. Type 2 Diabetes. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright \u0026copy; 2025. StatPearls Publishing LLC.; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRichardson CR, Borgeson JR, Van Harrison R, Wyckoff JA, Yoo AS, Aikens JE, et al. Michigan Medicine Clinical Care Guidelines. Management of Type 2 Diabetes Mellitus. Ann Arbor (MI): Michigan Medicine University of Michigan \u0026copy; Regents of the University of Michigan.; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnowler WC, Crandall JP, Chiasson JL, Nathan DM. Prevention Of Type 2 Diabetes. In: Cowie CC, Casagrande SS, Menke A, Cissell MA, Eberhardt MS, Meigs JB, et al. editors. Diabetes in America. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe HT, Le TT, Tran NMT, Nguyen TTT, Minh NCS, Le QT, et al. Serum Uric Acid Levels and Risk of Rapid Decline of Estimated Glomerular Filtration Rate in Patients with Type 2 Diabetes: Findings from a 5-Year Prospective Cohort Study. Healthcare. 2021;9(10):1341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge C, Leslie SW, Minter DA. Hyperuricemia. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright \u0026copy; 2025. StatPearls Publishing LLC.; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadianov I, Balabolkin M, Markov D, Markova T. Main causes of hyperuricemia in diabetes mellitus. Ter Arkh. 2000;72(2):55\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuynh TLT, Pham PT, Tran HD, Tran ND, Van Tran D, Tran BLT, et al. Losartan and dapagliflozin combination therapy in reducing uric acid level compared to monotherapy in patients with heart failure. PeerJ. 2024;12:e18595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Wang Y, Cheng J, Huangfu N, Zhao R, Xu Z, et al. Hyperuricemia and cardiovascular disease. Curr Pharm Design. 2019;25(6):700\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChrysant SG. Association of hyperuricemia with cardiovascular diseases: Current evidence. Hosp Pract. 2023;51(2):54\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman AM, Acevedo LA, Pennings N. Insulin Resistance. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright \u0026copy; 2025. StatPearls Publishing LLC.; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishizawa H, Maeda N, Shimomura I. Impact of hyperuricemia on chronic kidney disease and atherosclerotic cardiovascular disease. Hypertens Res. 2022;45(4):635\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman Z, Hasan M, Miah R, Mou AD, Hafsa JM, Trisha AD, et al. Association between hyperuricemia and chronic kidney disease: a cross-sectional study in Bangladeshi adults. BMC Endocr Disorders. 2023;23(1):45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C-H, Lee C-L, Hsieh Y-C, Chen C-H, Wu M-J, Tsai S-F. Hyperuricemia and diabetes mellitus when occurred together have higher risks than alone on all-cause mortality and end-stage renal disease in patients with chronic kidney disease. BMC Nephrol. 2022;23(1):157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonakdaran S, Hami M, Shakeri MT. Hyperuricemia and albuminuria in patients with type 2 diabetes mellitus. 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen-Xu M, Yokose C, Rai SK, Pillinger MH, Choi HK. Contemporary Prevalence of Gout and Hyperuricemia in the United States and Decadal Trends: The National Health and Nutrition Examination Survey, 2007\u0026ndash;2016. Arthritis Rheumatol. 2019;71(6):991\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemayehu E, Fiseha T, Bambo GM, Sahile Kebede S, Bisetegn H, Tilahun M, et al. Prevalence of hyperuricemia among type 2 diabetes mellitus patients in Africa: a systematic review and meta-analysis. BMC Endocr Disorders. 2023;23(1):153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaita L, Timar R, Lupascu N, Roman D, Albai A, Potre O, Timar B. The impact of hyperuricemia on cardiometabolic risk factors in patients with diabetes mellitus: a cross-sectional study. Metabolic Syndrome and Obesity: Targets and Therapy.: Diabetes; 2019. pp. 2003\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIkwuka A, Virstyuk N. Asymptomatic hyperuricemia and functional state of the kidneys in patients with essential arterial hypertension and concomitant diabetes mellitus type 2. Eur J Clin Med. 2021;2(3):100\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran SK, Huynh BT, Vo CT, Van Ngo T, Tran BLT, Tran KDD, et al. Treatment efficacy of febuxostat compared with allopurinol in hyperuricemia patients with hypertensive: A randomized, single-blind controlled trial. J Appl Pharm Sci. 2024;14(6):090\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommittee ADAPP. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes\u0026mdash;2024. Diabetes Care. 2023;47(Supplement1):S20\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzGerald JD, Dalbeth N, Mikuls T, Brignardello-Petersen R, Guyatt G, Abeles AM, et al. 2020 American College of Rheumatology Guideline for the Management of Gout. Arthritis Care Res (Hoboken). 2020;72(6):744\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFennoun H, Haraj N, El Aziz S, Bensbaa S, Chadli A. Risk factors associated with hyperuricemia in patients with diabetes type 2: about 190 cases. Diabetes Research: Open Access. 2020;2020(1):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun S, Chen L, Chen D, Li Y, Liu G, Ma L, et al. Prevalence and associated factors of hyperuricemia among Chinese patients with diabetes: a cross-sectional study. Ther Adv Endocrinol Metab. 2023;14:20420188231198620.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbdel KA, Kalluvya SE, Sadiq AM, Ashir A, Masikini PI. Prevalence of Hyperuricemia and Associated Factors Among Patients With Type 2 Diabetes Mellitus in Northwestern Tanzania: A Cross-Sectional Study. Clin Med Insights Endocrinol Diabetes. 2024;17:11795514241274694.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu L, Zhao T, Shan L, Cao L, Zhu X, Xue Y. Estradiol regulates intestinal ABCG2 to promote urate excretion via the PI3K/Akt pathway. Nutr Metab (Lond). 2021;18(1):63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoyesa SB, Hirigo AT, Wube TB. Hyperuricemia and metabolic syndrome in type 2 diabetes mellitus patients at Hawassa university comprehensive specialized hospital, South West Ethiopia. BMC Endocr disorders. 2017;17(1):76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArersa KK, Wondimnew T, Welde M, Husen TM. Prevalence and determinants of hyperuricemia in type 2 diabetes mellitus patients attending Jimma Medical Center, Southwestern Ethiopia, 2019. Diabetes, Metabolic Syndrome and Obesity. 2020:2059-67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsma Sakalli A, K\u0026uuml;\u0026ccedil;\u0026uuml;kerdem HS, Ayg\u0026uuml;n O. What is the relationship between serum uric acid level and insulin resistance? A case-control study. Med (Baltim). 2023;102(52):e36732.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMundhe SA, Mhasde DR. The study of prevalence of hyperuricemia and metabolic syndrome in type 2 diabetes mellitus. Int J Adv Med. 2016;3(2):241\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei F, Chang B, Yang X, Wang Y, Chen L, Li W-D. Serum Uric Acid Levels were Dynamically Coupled with Hemoglobin A1c in the Development of Type 2 Diabetes. Sci Rep. 2016;6(1):28549.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUeno N. Urate-Lowering Therapy Ameliorates Kidney Function in Type 2 Diabetes Patients With Hyperuricemia. J Clin Med Res. 2017;9(12):1007\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Shin D. Association between alcoholic beverage intake and hyperuricemia in Chinese adults: Findings from the China Health and Nutrition Survey. Med (Baltim). 2023;102(22):e33861.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa W, Ye G, Liu Y, Sun W, Huang X, Hu L, et al. Impact of alcohol consumption on hyperuricemia and gout: a systematic review and meta-analysis. Front Nutr. 2025;12:1588980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStewart DJ, Langlois V, Noone D. Hyperuricemia and Hypertension: Links and Risks. Integr Blood Press Control. 2019;12:43\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuwabara M, Niwa K, Hisatome I, Nakagawa T, Roncal-Jimenez CA, Andres-Hernando A, et al. Asymptomatic hyperuricemia without comorbidities predicts cardiometabolic diseases: five-year Japanese cohort study. Hypertension. 2017;69(6):1036\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Asymptomatic hyperuricemia, type 2 diabetes mellitus, associated factors","lastPublishedDoi":"10.21203/rs.3.rs-8631117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8631117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAsymptomatic hyperuricemia is a common yet frequently overlooked condition in the management of patients with type 2 diabetes mellitus (T2DM), despite its potential association with cardiovascular events. This study aimed to determine the prevalence and associated factors of asymptomatic hyperuricemia among patients with T2DM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted on 235 patients with T2DM at a tertiary hospital. Demographic, clinical, laboratory, and medication data were collected. Asymptomatic hyperuricemia was defined as a serum uric acid level\u0026thinsp;\u0026ge;\u0026thinsp;420 \u0026micro;mol/L in men or \u0026ge;\u0026thinsp;360 \u0026micro;mol/L in women, in the absence of clinical symptoms of gout. Hyperuricemia was classified by severity, and logistic regression analysis was used to identify associated factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of asymptomatic hyperuricemia was 31.1%, with 25.9% categorized as mild and 5.1% as severe. Independent associated factors included body mass index (adjusted OR: 1.18; 95% CI: 1.01\u0026ndash;1.36; p\u0026thinsp;=\u0026thinsp;0.032), alcohol consumption (adjusted OR: 6.05; 95% CI: 1.79\u0026ndash;20.41; p\u0026thinsp;=\u0026thinsp;0.004), and lower estimated glomerular filtration rate (eGFR) (adjusted OR: 0.96; 95% CI: 0.93\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.012). Subgroup analyses based on sex, renal function, and alcohol use demonstrated that these factors remained significant. The area under the receiver operating characteristic (ROC) curve for the predictive model was 0.869 (95% CI: 0.818\u0026ndash;0.920).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAsymptomatic hyperuricemia is prevalent in patients with T2DM (31.1%) and is associated with modifiable risk factors such as overweight, alcohol consumption, and chronic kidney disease. Routine screening for serum uric acid should be considered as part of the comprehensive cardiovascular\u0026ndash;renal\u0026ndash;metabolic risk assessment in patients with diabetes.\u003c/p\u003e","manuscriptTitle":"Prevalence of Asymptomatic Hyperuricemia and Associated Factors in Patients With Type 2 Diabetes Mellitus: A Cross-sectional Study in Southern Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 06:51:57","doi":"10.21203/rs.3.rs-8631117/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-02T15:46:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-24T08:30:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115648954143802714636728885523195419692","date":"2026-02-20T08:36:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111150195928898265135797281205055777996","date":"2026-02-20T03:15:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114517302170227719878595569281143271609","date":"2026-02-14T00:34:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72446408516474601752469558076117581995","date":"2026-02-13T01:40:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-03T19:21:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T07:26:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-21T07:05:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T07:03:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2026-01-18T11:50:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"384648ea-4097-4f66-9583-5cad15d29ee4","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T19:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 06:51:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8631117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8631117","identity":"rs-8631117","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}