Hypertension is a principal driver of coronary artery disease, stroke, heart failure, chronic kidney disease, and premature mortality. Even among patients receiving antihypertensive therapy, cardiovascular risk often remains high because blood-pressure elevation coexists with obesity, impaired glucose regulation, dyslipidaemia, smoking, and renal dysfunction. Contemporary hypertension guidelines therefore emphasise total cardiovascular risk rather than an isolated blood-pressure value [18,19]. In routine clinical practice, inexpensive biochemical indicators that reflect this clustered risk could help identify patients who require closer assessment and more intensive risk-factor control.

Serum uric acid is the final product of purine metabolism in humans. It is generated through xanthine oxidoreductase activity and is cleared predominantly by the kidneys. At physiological concentrations, uric acid contributes to plasma antioxidant capacity. At higher concentrations, however, intracellular urate and xanthine oxidase activity may promote oxidative stress, endothelial dysfunction, reduced nitric oxide availability, inflammation, vascular smooth-muscle proliferation, and activation of the renin-angiotensin system [1,2]. These pathways provide biological plausibility for an association between hyperuricaemia and hypertension, insulin resistance, renal injury, and atherosclerotic disease.

Observational studies have repeatedly reported that higher serum uric acid predicts incident hypertension. Meta-analyses have found a graded increase in hypertension risk across uric acid concentrations, although heterogeneity exists across age, sex, ethnicity, renal function, and study definitions [4-6]. Large cohorts such as the ARIC study and community studies in Asia have also shown that uric acid precedes the development or progression of hypertension [7-10]. Cross-sectional evidence indicates that the relationship persists after adjustment for several metabolic covariates, but residual confounding remains difficult to exclude [11,12].

The relation between uric acid and established cardiovascular disease is more complex. Prospective studies and pooled analyses have linked higher uric acid with cardiovascular mortality, coronary events, and major adverse cardiovascular outcomes [3,13-17]. In contrast, the Framingham Heart Study suggested that much of the apparent association was explained by coexisting risk factors and diuretic exposure [16]. This disagreement has led to the view that serum uric acid may be a useful risk marker even when its independent causal role remains unsettled.

Patients with hypertension and established cardiovascular disease represent a clinically important group because they commonly have multiple interacting risk factors and receive drugs that influence urate handling. Diuretics can increase serum uric acid by reducing renal urate excretion, while reduced kidney function may both raise uric acid and amplify cardiovascular risk. Consequently, examining uric acid alongside blood-pressure control, adiposity, glycaemia, lipids, and renal function may provide a more clinically meaningful picture than studying uric acid in isolation.

The present study was designed to assess the association of serum uric acid with major cardiovascular risk factors among hypertensive adults with documented cardiovascular disease. The primary objective was to compare clinical and biochemical risk profiles between normouricaemic and hyperuricaemic participants. Secondary objectives were to examine risk-factor prevalence across uric acid tertiles, quantify correlations between uric acid and continuous risk variables, and identify independent predictors of hyperuricaemia after adjustment for relevant covariates.

2.1 Study design and setting This analytical cross-sectional study was planned in the Department of General Medicine at SR Patil Medical College and Hospital, Bagalkot, Karnataka, India. The study period was April 2025 to March 2026. Patients were evaluated during outpatient visits or clinically stable inpatient admissions. The institutional setting allowed coordinated clinical assessment, cardiovascular history review, blood-pressure measurement, and biochemical testing on the same study visit. 2.2 Study population Adults aged 30 years or older with essential hypertension and established cardiovascular disease were eligible. Cardiovascular disease was defined by documented coronary artery disease, previous myocardial infarction or revascularisation, chronic heart failure, previous ischaemic stroke or transient ischaemic attack, or peripheral arterial disease. Hypertension was considered present when it had been previously diagnosed by a physician, when the patient was receiving antihypertensive medication, or when repeated clinic blood-pressure readings met accepted diagnostic thresholds. Consecutive eligible patients were approached to reduce selection based on disease severity or laboratory profile. 2.3 Eligibility criteria Participants were included when they had essential hypertension, documented cardiovascular disease, and complete clinical and biochemical measurements. Exclusion criteria were acute myocardial infarction, acute stroke, decompensated heart failure, or major surgery within the preceding four weeks; secondary hypertension; known gout; current urate-lowering therapy; chronic kidney disease stage 4 or 5; active malignancy; severe hepatic disease; pregnancy; prolonged systemic corticosteroid therapy; and inability to provide informed consent. Diuretic use was not an exclusion criterion because its association with serum uric acid was one of the prespecified analyses. 2.4 Sample-size estimation and sampling The minimum sample size was estimated for detection of a correlation coefficient of 0.25 between serum uric acid and a major cardiovascular risk variable, with a two-sided alpha level of 0.05 and 80% power. This yielded approximately 123 participants. Allowing for incomplete records, subgroup comparisons, and multivariable analysis, the target was increased to at least 160. The illustrative dataset contained 180 participants. Consecutive sampling was proposed until the required sample was reached. 2.5 Clinical and anthropometric assessment Age, sex, duration of hypertension, smoking status, diabetes, cardiovascular diagnosis, medication history, and diuretic exposure were recorded using a structured case-record form. Body weight was measured to the nearest 0.1 kg with light clothing and height to the nearest 0.1 cm. Body mass index was calculated as weight in kilograms divided by height in metres squared. Waist circumference was measured midway between the lowest rib and the iliac crest at the end of normal expiration. Obesity was defined as body mass index of at least 30 kg/m² for the principal analysis. Current smoking referred to active cigarette or bidi use at the time of assessment. 2.6 Blood-pressure measurement Blood pressure was measured with a validated automated device after at least five minutes of seated rest. An appropriately sized cuff was placed on the supported upper arm, and two readings were obtained one to two minutes apart. A third reading was taken when the first two differed substantially, and the average of the closest two readings was used. Uncontrolled blood pressure was defined as systolic blood pressure of at least 140 mmHg or diastolic blood pressure of at least 90 mmHg at the study visit. Medication adherence was discussed but was not used as an exclusion criterion. 2.7 Laboratory measurements After an overnight fast of eight to twelve hours, venous blood was collected under standard aseptic conditions. Serum uric acid was measured by an enzymatic uricase method. Fasting plasma glucose was measured by the glucose oxidase-peroxidase method, glycated haemoglobin by a standardised assay, and total cholesterol, triglycerides, and high-density lipoprotein cholesterol by enzymatic methods. Low-density lipoprotein cholesterol was measured directly or estimated when triglycerides were within the valid range. Serum creatinine was measured by a method traceable to isotope-dilution mass spectrometry. Estimated glomerular filtration rate was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation [20]. Internal quality-control samples were analysed with each laboratory batch. 2.8 Operational definitions and outcomes Hyperuricaemia was defined as serum uric acid greater than 7.0 mg/dL in men and greater than 6.0 mg/dL in women. Diabetes mellitus was defined by a documented diagnosis, use of glucose-lowering medication, fasting plasma glucose of at least 126 mg/dL, or glycated haemoglobin of at least 6.5%. Dyslipidaemia was defined by triglycerides of at least 150 mg/dL, low-density lipoprotein cholesterol of at least 130 mg/dL, or low high-density lipoprotein cholesterol below 40 mg/dL in men and below 50 mg/dL in women. Renal impairment was defined as estimated glomerular filtration rate below 60 mL/min/1.73 m². A cardiovascular risk-factor count was derived from uncontrolled blood pressure, obesity, diabetes, dyslipidaemia, current smoking, and renal impairment. The primary outcome was the association between serum uric acid status and these cardiovascular risk factors. 2.9 Data quality and bias control Standard operating procedures were used for anthropometry, blood-pressure measurement, sample collection, and laboratory analysis. Consecutive recruitment was intended to reduce selective enrolment. Clinical variables were recorded before review of the serum uric acid result whenever feasible. Prespecified definitions were applied uniformly, and continuous variables were retained in correlation and regression analyses to minimise information loss. Because diuretic treatment, renal function, age, sex, adiposity, and diabetes can confound the uric acid-risk relationship, these variables were included in adjusted models. 2.10 Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, version 28.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarised as mean ± standard deviation, and categorical variables as frequency and percentage. Normality was assessed using histograms, Q-Q plots, and the Shapiro-Wilk test. Independent-samples t tests compared continuous variables between uric acid groups, while the chi-square test compared categorical variables. Serum uric acid was also divided into tertiles to examine graded differences in risk-factor prevalence. Pearson correlation coefficients were calculated for serum uric acid and continuous clinical or biochemical variables. Multivariable logistic regression identified factors independently associated with hyperuricaemia, with adjusted odds ratios and 95% confidence intervals. Multiple linear regression was used with serum uric acid as a continuous dependent variable. Model assumptions, multicollinearity, and influential observations were assessed. All tests were two-sided, and p<0.05 was considered statistically significant. 2.11 Ethical considerations Ethical clearance was obtained from the Institutional Ethics Committee of SR Patil Medical College and Hospital before commencement of the study. The verified approval number and approval date should be inserted in the final manuscript. Written informed consent was obtained from each participant, and study procedures followed the principles of the Declaration of Helsinki. Identifiers were removed before analysis, and access to the study database was restricted to authorised investigators.

3.1 Participant profile

The analysis included 180 hypertensive patients with established cardiovascular disease. Their mean age was 59.0 ± 10.8 years, and 108 (60.0%) were men. Coronary artery disease was documented in 154 participants (85.6%), chronic heart failure in 32 (17.8%), and previous ischaemic stroke or transient ischaemic attack in 31 (17.2%); diagnoses could overlap. Uncontrolled blood pressure was present in 96 participants (53.3%), diabetes in 50 (27.8%), obesity in 53 (29.4%), dyslipidaemia in 141 (78.3%), and renal impairment in 24 (13.3%).

3.2 Serum uric acid distribution and hyperuricaemia

The mean serum uric acid concentration was 6.36 ± 1.34 mg/dL. Hyperuricaemia was identified in 76 patients (42.2%), while 104 (57.8%) were normouricaemic. The hyperuricaemic group was older and had greater body mass index and waist circumference. Mean systolic and diastolic pressures were also higher in hyperuricaemic participants (Table 1).

Table 1: Clinical and biochemical characteristics according to serum uric acid status

Values are mean ± standard deviation or number (percentage). Hyperuricaemia: serum uric acid >7.0 mg/dL in men and >6.0 mg/dL in women. eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein. Independent-samples t test or chi-square test was used, as appropriate

3.3 Cardiometabolic and renal profile

Hyperuricaemic patients had higher fasting plasma glucose, glycated haemoglobin, total cholesterol, triglycerides, low-density lipoprotein cholesterol, and serum creatinine than normouricaemic patients. Estimated glomerular filtration rate was lower in the hyperuricaemic group. High-density lipoprotein cholesterol did not differ significantly. The categorical comparisons showed higher frequencies of uncontrolled blood pressure, obesity, diabetes, dyslipidaemia, renal impairment, and diuretic use among patients with hyperuricaemia (Table 1).

3.4 Risk factors across serum uric acid tertiles

A graded pattern was evident when serum uric acid was divided into equal tertiles. The prevalence of uncontrolled blood pressure increased from 30.0% in the lowest tertile to 66.7% in the highest tertile. Obesity increased from 10.0% to 58.3%, diabetes from 10.0% to 51.7%, renal impairment from 5.0% to 26.7%, and diuretic use from 20.0% to 48.3%. These differences were statistically significant, whereas the tertile differences for dyslipidaemia and current smoking were not significant (Table 2 and Figure 1).

Table 2: Prevalence of cardiovascular risk factors across serum uric acid tertiles

Values are number (percentage within each tertile). Tertiles were based on the sample distribution of serum uric acid. Chi-square tests compared proportions across the three groups

Figure 1: Prevalence of selected cardiovascular risk factors across serum uric acid tertiles. The highest tertile showed a marked concentration of uncontrolled blood pressure, obesity, diabetes, renal impairment, and diuretic use

3.5 Risk-factor burden and serum uric acid

Mean serum uric acid rose progressively with the number of coexisting risk factors. It was 5.54 ± 1.08 mg/dL among patients with zero or one risk factor, 6.08 ± 0.93 mg/dL with two factors, 6.51 ± 0.94 mg/dL with three factors, and 8.00 ± 1.23 mg/dL with four or more factors (F=40.10, p<0.001). This dose-like pattern is illustrated in Figure 2.

Figure 2: Mean serum uric acid according to the number of coexisting cardiovascular risk factors. Bars show mean values and error bars show standard error of the mean

3.6 Correlation analysis

Serum uric acid showed moderate positive correlations with body mass index, waist circumference, systolic blood pressure, fasting plasma glucose, glycated haemoglobin, triglycerides, and serum creatinine. It was inversely correlated with high-density lipoprotein cholesterol and estimated glomerular filtration rate. The correlation with low-density lipoprotein cholesterol did not reach statistical significance (Table 3). The relationship with systolic blood pressure remained visible across renal-function strata (Figure 3).

Table 3: Correlation of serum uric acid with clinical and biochemical variables

Positive coefficients indicate higher serum uric acid with increasing values of the corresponding variable. eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein

Figure 3: Scatter plot showing the positive relationship between serum uric acid and systolic blood pressure. Participants are distinguished by estimated glomerular filtration rate category. The solid line represents the fitted linear relationship

3.7 Multivariable analysis

After adjustment for age, sex, smoking, dyslipidaemia, renal impairment, and diuretic use, obesity was associated with more than eightfold higher odds of hyperuricaemia. Diabetes and uncontrolled blood pressure were also independent predictors. Dyslipidaemia, renal impairment, and diuretic use showed positive but statistically non-significant adjusted associations in the logistic model, reflecting overlap among metabolic and renal covariates (Table 4 and Figure 4).

Table 4:  Multivariable logistic regression for factors associated with hyperuricaemia

Outcome: hyperuricaemia. Model likelihood-ratio p<0.001; Nagelkerke-style pseudo-R² is approximated by the model fit (McFadden R²=0.310). OR, odds ratio; CI, confidence interval

Figure 4: Forest plot of adjusted odds ratios for hyperuricaemia. Points show adjusted odds ratios and horizontal lines show 95% confidence intervals. The vertical reference line represents an odds ratio of 1

When serum uric acid was treated as a continuous outcome, male sex, body mass index, systolic blood pressure, triglycerides, glycated haemoglobin, diuretic use, and lower estimated glomerular filtration rate were independently associated with higher uric acid. Age was not significant after adjustment. The final linear model explained 73.1% of the variability in serum uric acid (adjusted R²=0.719; Table 5).

Table 5: Multiple linear regression with serum uric acid as the dependent variable

Model R²=0.731; adjusted R²=0.719; overall F=58.12, p<0.001. B represents the expected change in serum uric acid in mg/dL for the stated unit increase. eGFR, estimated glomerular filtration rate; HbA1c, glycated haemoglobin

This cross-sectional analysis showed that serum uric acid was closely associated with a broad cluster of cardiovascular risk factors in hypertensive patients who already had cardiovascular disease. Hyperuricaemia affected approximately two in five participants and was accompanied by higher adiposity, poorer blood-pressure control, adverse glycaemic indices, higher triglycerides, higher creatinine, and lower estimated glomerular filtration rate. The rise in mean uric acid with increasing risk-factor count supports the interpretation that serum uric acid reflects cumulative cardiometabolic and cardiorenal burden rather than a single isolated abnormality. The association with blood pressure was consistent across categorical, tertile, correlation, and adjusted analyses. Serum uric acid correlated positively with systolic pressure, and uncontrolled blood pressure remained independently associated with hyperuricaemia. These findings accord with systematic reviews that have shown a dose-response relation between uric acid and incident hypertension [4-6]. The ARIC study, the Qingdao community cohort, and Japanese cohort studies similarly reported that higher uric acid predicted the onset or progression of hypertension [7-10]. The present study differs because all participants already had hypertension and cardiovascular disease. In this setting, the observed relationship may represent a combination of vascular dysfunction, treatment effects, renal handling of urate, and greater disease severity. Several mechanisms could link uric acid with elevated blood pressure. Xanthine oxidase activity can generate reactive oxygen species, while intracellular urate can reduce endothelial nitric oxide and promote oxidative stress. Experimental and clinical evidence also implicates renin-angiotensin activation, renal microvascular injury, sodium sensitivity, and vascular smooth-muscle proliferation [1,2]. These mechanisms are biologically plausible, but cross-sectional data cannot establish whether uric acid drives poor blood-pressure control or rises as a consequence of renal and metabolic dysfunction. Obesity was the strongest adjusted predictor of hyperuricaemia. Higher body mass index and waist circumference were also strongly correlated with uric acid. Adiposity may increase urate production through greater purine turnover and may reduce renal urate excretion through insulin resistance. The pronounced gradient in obesity across uric acid tertiles supports a metabolic link. Mediation studies have suggested that obesity, lipids, and glucose regulation account for part of the association between uric acid and hypertension, which is consistent with the clustering observed in this analysis [5,11]. The glycaemic findings were similarly coherent. Diabetes was more common in the hyperuricaemic group, and both fasting glucose and glycated haemoglobin were higher. Glycated haemoglobin remained independently associated with serum uric acid in the linear model. Hyperinsulinaemia can reduce renal urate excretion, while oxidative stress and chronic inflammation may connect hyperuricaemia with insulin resistance. The direction of association can change in advanced glycosuria because urinary glucose may promote uricosuria, but the present pattern is compatible with the earlier insulin-resistant stage commonly seen in hypertensive cardiovascular populations. Triglycerides showed one of the strongest biochemical correlations with uric acid, whereas low-density lipoprotein cholesterol had only a weak association and high-density lipoprotein cholesterol was inversely related. This pattern resembles atherogenic dyslipidaemia associated with insulin resistance. Although dyslipidaemia was not independently significant in the fully adjusted logistic model, it was highly prevalent and overlapped with obesity and diabetes. The attenuation after adjustment should therefore not be interpreted as absence of a clinically relevant relationship. Renal function and diuretic treatment require particular attention. Serum uric acid increased as estimated glomerular filtration rate declined, and renal impairment was concentrated in the highest uric acid tertile. The kidney is the principal route of urate elimination, so reduced filtration and altered tubular handling can raise serum uric acid. Diuretic use was also more common with higher uric acid and remained independently associated with the continuous uric acid level. These factors can make serum uric acid an indicator of cardiorenal treatment complexity rather than an independent therapeutic target. The finding is consistent with longstanding concerns that confounding by renal function and diuretic use explains part of the uric acid-cardiovascular association [16,17]. The clinical literature on prognosis remains mixed. Meta-analyses and cohort studies have linked higher uric acid with cardiovascular mortality and adverse outcomes among hypertensive and general populations [3,13-15]. Nevertheless, the Framingham analysis concluded that uric acid did not independently cause cardiovascular disease after accounting for other risk factors [16]. These positions are not mutually exclusive. A biomarker can improve recognition of high-risk physiology without being a direct causal factor. The present findings support serum uric acid as a risk marker, but they do not demonstrate that lowering uric acid will reduce cardiovascular events. From a practical perspective, serum uric acid is inexpensive and widely available. In a hypertensive patient with cardiovascular disease, an elevated result should prompt review of blood-pressure control, body weight, glycaemic status, triglycerides, kidney function, dietary factors, alcohol intake, and medication exposure. It should not automatically lead to urate-lowering treatment in the absence of gout or another accepted indication. Prospective trials are needed to determine whether selected patients with hyperuricaemia and uncontrolled hypertension benefit from targeted urate reduction. The study has several strengths. It evaluated uric acid together with clinical, metabolic, renal, and treatment variables; used sex-specific hyperuricaemia thresholds; examined both categorical and continuous relationships; and adjusted for major confounders. The graded analyses across tertiles and risk-factor counts provided internal consistency. The hospital-based design also focused on a high-risk population in whom the findings may have direct clinical relevance. 5. Limitations The cross-sectional design prevents assessment of temporal sequence and causality. Serum uric acid and blood pressure were measured at a single study visit, so biological variation and treatment-related fluctuation could not be fully captured. The hospital-based sample may contain more severe disease and may not represent all hypertensive patients in the community. Dietary purine intake, alcohol consumption, physical activity, detailed medication doses, and adherence were not quantified. Residual confounding is possible despite multivariable adjustment. Cardiovascular diagnoses were heterogeneous and could overlap. Most importantly, the numerical dataset used in this manuscript is synthetic and intended only to demonstrate a complete analysis and reporting structure. It must be replaced by validated study data before scientific submission.

Higher serum uric acid was associated with uncontrolled blood pressure, obesity, diabetes, hypertriglyceridaemia, diuretic exposure, and reduced renal function in hypertensive patients with established cardiovascular disease. Uric acid increased progressively with the number of coexisting cardiovascular risk factors and remained independently related to several metabolic and cardiorenal variables. These findings support serum uric acid as a readily available marker of cumulative risk burden. Longitudinal studies and intervention trials are required to clarify causality and determine whether urate-lowering strategies improve cardiovascular outcomes in selected hypertensive populations.

1. Borghi C, Agabiti-Rosei E, Johnson RJ, Kielstein JT, Lurbe E, Mancia G, et al. Uric acid and hypertension: a review of evidence and future perspectives for the management of cardiovascular risk. Hypertension. 2022;79(9):1927-1936. doi:10.1161/HYPERTENSIONAHA.122.17956.
2. Feig DI, Kang DH, Johnson RJ. Uric acid and cardiovascular risk. N Engl J Med. 2008;359(17):1811-1821. doi:10.1056/NEJMra0800885.
3. Yang Y, Zhang X, Jin Z, Zhao Q. Association of serum uric acid with mortality and cardiovascular outcomes in patients with hypertension: a meta-analysis. J Thromb Thrombolysis. 2021;52(4):1084-1093. doi:10.1007/s11239-021-02453-z.
4. Wang J, Qin T, Chen J, Li Y, Wang L, Huang H, et al. Hyperuricemia and risk of incident hypertension: a systematic review and meta-analysis of observational studies. PLoS One. 2014;9(12):e114259. doi:10.1371/journal.pone.0114259.
5. Grayson PC, Kim SY, LaValley M, Choi HK. Hyperuricemia and incident hypertension: a systematic review and meta-analysis. Arthritis Care Res (Hoboken). 2011;63(1):102-110. doi:10.1002/acr.20344.
6. Liu L, et al. Dose-response association between serum uric acid levels and incident hypertension: a systematic review and meta-analysis of 17 prospective cohort studies of 32 thousand participants. Acta Cardiol. 2021;76(7):748-753. doi:10.1080/00015385.2020.1779476.
7. Zhang W, Sun K, Yang Y, Zhang H, Hu FB, Hui R. Plasma uric acid and hypertension in a Chinese community: prospective study and meta-analysis. Clin Chem. 2009;55(11):2026-2034. doi:10.1373/clinchem.2009.124891.
8. Mellen PB, Bleyer AJ, Erlinger TP, Evans GW, Nieto FJ, Wagenknecht LE, et al. Serum uric acid predicts incident hypertension in a biethnic cohort: the Atherosclerosis Risk in Communities study. Hypertension. 2006;48(6):1037-1042. doi:10.1161/01.HYP.0000249768.26560.66.
9. Nakanishi N, Okamoto M, Yoshida H, Matsuo Y, Suzuki K, Tatara K. Serum uric acid and risk for development of hypertension and impaired fasting glucose or type 2 diabetes in Japanese male office workers. Eur J Epidemiol. 2003;18(6):523-530. doi:10.1023/A:1024600905574.
10. Kuwabara M, Hisatome I, Niwa K, Hara S, Roncal-Jimenez CA, Bjornstad P, et al. Uric acid is a strong risk marker for developing hypertension from prehypertension: a 5-year Japanese cohort study. Hypertension. 2018;71(1):78-86. doi:10.1161/HYPERTENSIONAHA.117.10370.
11. Ali N, Mahmood S, Islam F, Rahman S, Haque T, Islam S, et al. Relationship between serum uric acid and hypertension: a cross-sectional study in Bangladeshi adults. Sci Rep. 2019;9:9061. doi:10.1038/s41598-019-45680-4.
12. Buzas R, Tautu OF, Dorobantu M, Ivan V, Lighezan D. Serum uric acid and arterial hypertension: data from the SEPHAR III survey. PLoS One. 2018;13(7):e0199865. doi:10.1371/journal.pone.0199865.
13. Fang J, Alderman MH. Serum uric acid and cardiovascular mortality: the NHANES I Epidemiologic Follow-up Study, 1971-1992. JAMA. 2000;283(18):2404-2410. doi:10.1001/jama.283.18.2404.
14. Zhao G, Huang L, Song M, Song Y. Baseline serum uric acid level as a predictor of cardiovascular disease-related mortality and all-cause mortality: a meta-analysis of prospective studies. Atherosclerosis. 2013;231(1):61-68. doi:10.1016/j.atherosclerosis.2013.08.023.
15. Wheeler JG, Juzwishin KD, Eiriksdottir G, Gudnason V, Danesh J. Serum uric acid and coronary heart disease in 9,458 incident cases and 155,084 controls: prospective study and meta-analysis. PLoS Med. 2005;2(3):e76. doi:10.1371/journal.pmed.0020076.
16. Culleton BF, Larson MG, Kannel WB, Levy D. Serum uric acid and risk for cardiovascular disease and death: the Framingham Heart Study. Ann Intern Med. 1999;131(1):7-13. doi:10.7326/0003-4819-131-1-199907060-00003.
17. Dawson J, Jeemon P, Hetherington L, Judd C, Hastie C, Schulz C, et al. Serum uric acid level, longitudinal blood pressure, renal function, and long-term mortality in treated hypertensive patients. Hypertension. 2013;62(1):105-111. doi:10.1161/HYPERTENSIONAHA.113.00859.
18. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J. 2018;39(33):3021-3104. doi:10.1093/eurheartj/ehy339.
19. Whelton PK, Carey RM, Aronow WS, Casey DE Jr, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults. Hypertension. 2018;71(6):e13-e115. doi:10.1161/HYP.0000000000000065.
20. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006.