Association of Serum Uric Acid Level with Risk of Abdominal Aortic Calcification: A Large Cross-Sectional Study

Introduction

The abnormal buildup of minerals, such as calcium and phosphate, in blood vessels is called vascular calcification (VC).¹ Calcification of any arterial bed could result in cardiovascular disease (CVD) morbidity and even death; moreover, CT is the standard for assessing VC.² VC is a systemic process, and abdominal aortic calcification (AAC) is linked to calcification and subclinical atherosclerosis in other vascular beds.^3–5 Dual-energy X-ray absorptiometry (DXA), a technique commonly used to assess bone density and body composition, can be used to measure AAC.⁶ Coronary artery calcification (CAC) has been shown to predict CVD morbidity and mortality independently of traditional CVD risk factors.^7–11 Additionally, AAC is a subclinical atherosclerosis marker and a predictor of subsequent vascular-associated morbidity and mortality.¹² Previously, VC primarily focused on CAC, but recently, AAC has received a lot more attention.¹³

Serum uric acid (sUA), the end product of purine metabolism, has been linked to several metabolic abnormalities and coronary artery disease.¹⁴ The glomerular filtration rate excretes the majority of the sUA. The glomerular filtration rate is reduced in patients with renal insufficiency, such as chronic kidney disease, resulting in decreased uric acid excretion and elevated sUA.¹⁵ Recent studies have linked high sUA to gout and CVD.¹⁶ According to studies, high sUA is linked to nondripping blood pressure, myocardial infarction, and mortality due to CVD.¹⁷ Increased sUA levels are linked to increased oxidative stress and inflammation. Several studies have allied elevated sUA levels to hypertension, atherosclerosis, renal disease, obesity, insulin resistance, and dyslipidemia.¹⁸ In addition to aggravated lipid metabolism disorders, elevated sUA can result in high levels of low-density lipoprotein cholesterol.¹⁹ However, research into the relationship between sUA and AAC is currently limited. Therefore, this study analyzed the data from the National Health and Nutrition Examination Survey (NHANES) between 2013 and 2014 to explore the link between sUA and risk of AAC and severe AAC (SAAC) in the US general population.

Materials and Methods

Study Population

The NHANES is a study of the general public’s health and diet undertaken annually by the National Center for Health Statistics (NCHS). The NHANES data from 2013 to 2014 were included in this study. This database comprised cross-sectional socio-demographic, nutritional, and medical information gathered through questionnaires, regular physical exams, and laboratory testing.²⁰ Furthermore, AAC data for 3708 participants were available. After excluding participants with missing sUA data (n = 124), 3016 participants were included in this analysis. The study was approved by the NCHS Ethics Review Board. Each participant provided informed written permission.

Assessment of sUA

Eligible participants were 12 years of age and older. Using a timed endpoint method, the DxC800 measured uric acid concentrations in serum, plasma, and urine. Allantoin and hydrogen peroxide result from the oxidation of sUA by uricase. In a reaction catalyzed by peroxidase, hydrogen peroxide reacts with 4-aminoantipyrine and 3, 5-dichloro-2-hydroxybenzene sulfonate (DCHBS) to produce a colored product. At a fixed time interval, the system monitors the change in absorbance at 520 nm. Absorbance changes in uric acid samples are directly proportional to concentration. Detailed laboratory procedure manuals of the methods are given in the Laboratory Method Files section (https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/BIOPRO_H.htm#LBXSUA).

AAC Measurement

DXA (Densitometer Discovery A, Hologic, Marlborough, MA, USA) was performed on the lumbar spine (vertebrae L1–L4), and the Kauppila score system was utilized to obtain and quantify AAC.^21,22 DXA scans were executed by trained and certified radiology technologists at the NHANES mobile examination center. Higher AAC scores indicated SAAC. In this study, the Kauppila scores ranged from 0 to 24, with a score >6 demonstrating significant calcification and classified as SAAC.^23–25 A detailed description of AAC measurements is available at https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/DXXAAC_H.htm.

Covariates

The following covariates were included in the study: age, sex, race/ethnicity, family poverty income ratio (PIR), marital status, education level, the complication of hypertension, diabetes mellitus (DM), smoking status, alcohol consumption status, physical activity (PA), body mass index (BMI), waist circumference, mean energy intake, dietary phosphorus and calcium intake, hemoglobin (Hb), fast glucose (FBG), glycohemoglobin (HbA1c), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, gamma-glutamyl transferase (GGT), blood urea nitrogen (BUN), serum creatinine (Scr), estimated glomerular filtration rate (eGFR), serum phosphorus and calcium, total cholesterol (TC), triglyceride (TG), and high-density lipoprotein-cholesterol (HDL-C). The following data were self-reported by participants during the home interview: age, sex, race/ethnicity, marital status, education level, smoking status, alcohol consumption status, and dietary intake. Furthermore, data including Hb, FBG, HbA1c, AST, ALT, total bilirubin, GGT, BUN, Scr, eGFR, serum phosphorus and calcium, TC, TG, and HDL-C were obtained from the laboratory tests. A detailed description of the variables used in this research is available at https://www.cdc.gov/nchs/nhanes/.

Statistical Analysis

All analyses were conducted using Stata version 14.0 (Stata Corporation, College Station, TX, USA) and R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). The “mice” package was used for multiple imputations on samples with missing covariate data. The sUA levels were categorized into quartiles, and the lowest quartile served as the reference group (Q1). When calculating all estimates, we accounted for the NHANES sample weights. Results for continuous variables were reported as mean (standard deviation), and categorical variables were expressed as numbers and numbers (%). The significance of differences was determined via weighted linear regression models for the continuous variables and weighted chi-square tests for the categorical variables. A weighted multiple-regression model was used to examine the relationship between sUA levels and the risk of AAC and SAAC. Model 1 was adjusted for age and sex. Model 2 was adjusted for model 1 variables and race/ethnicity, marital status, education level, family PIR, smoking status, alcohol consumption status, the complication of hypertension, and DM. Finally, model 3 was adjusted for model 2 variables plus PA, BMI, waist circumference, mean energy intake, dietary phosphorus and calcium intake, Hb, FBG, HbA1c, AST, ALT, total bilirubin, GGT, BUN, Scr, eGFR, serum phosphorus and calcium, TC, TG, and HDL-C, as the final model. A P-value <0.05 was regarded as statistically significant.

Results

Baseline Characteristics

Weighted socio-demographic and medical characteristics are described in Table 1. The prevalence of AAC and SAAC was 30.1% and 10.8%, respectively. According to the sUA quartiles (Q1: 0.7–4.5 mg/dL, Q2: 4.6–5.4 mg/dL, Q3: 5.5–6.3 mg/dL, and Q4: 6.4–11.3 mg/dL), the individuals were divided into four groups. There were significant differences in sex, the complication of hypertension, alcohol consumption status, BMI, waist circumference, FBG, ALT, GGT, total bilirubin, mean energy intake, calcium and phosphorus intake, Hb, BUN, Scr, eGFR, HDL-C, and TG among the Q1, Q2, Q3, and Q4 groups. Compared with the Q1, Q3, and Q4 groups, the Q2 group had the lowest mean energy, calcium and phosphorus intake levels. However, compared with the Q1, Q2, and Q4 groups, the Q3 group had the lowest levels of TG and phosphorus while the highest levels of DBP and HbA1c. In addition, compared with Q1, Q2, and Q3, participants in Q4 were older, had the highest proportion of hypertension, and DM, highest levels of BMI, waist circumference, FBG, ALT, AST, GGT, total bilirubin, mean energy intake, phosphorus intake, Hb, BUN, Scr, TC, TG, calcium, and degree of calcification, and lower levels of DBP, eGFR, and HDL-C.

Table 1 Characteristics of the Study Population Based on sUA Quartiles

Associations of sUA with AAC and SAAC

The weighted multiple-regression analysis results presented the association of sUA with AAC and SAAC. After adjusting for confounding factors, compared with the Q1 group, the odds ratios (ORs) with 95% confidence intervals (CIs) of sUA association with AAC across Q2, Q3, and Q4 were 0.967 (0.632, 1.480), 1.144 (0.719, 1.821), and 1.275 (0.762, 2.134) (Table 2). Moreover, in the fully adjusted model, the ORs and 95% CIs for SAAC risk across the sUA quartiles were 0.751 (0.378, 1.489), 0.958 (0.433, 2.121), and 1.084 (0.533, 2.205), compared with Q1 (Table 3). The restricted cubic spline (RCS) curves revealed a U-shaped relationship between sUA and the risk of AAC and SAAC in the US population (P for nonlinearity <0.05, Figures 1A and B). An inflection point for sUA was observed in our study. The risk of AAC and SAAC was lowest when the sUA level was 5.42 nmol/L and 6.08 mg/dL, respectively.

Table 2 Adjusted ORs for Associations Between sUA and the Risk of AAC

Table 3 Adjusted ORs for Associations Between sUA and the Incidence of SAAC

Figure 1 RCS curve of the association between sUA and the risk of AAC and SAAC in the NHANES database. (A) the association between sUA and AAC; and (B) the association between sUA and SAAC. Abbreviations: RCS, restricted cubic spline; sUA, serum uric acid; AAC, abdominal aortic calcification; SAAC, severe abdominal aortic calcification.

Subgroup Analyses

Using subgroup analyses stratified by age, sex, hypertension, DM, and BMI, associations between sUA and incident AAC and SAAC were estimated. The results demonstrated a U-shaped linkage between sUA and the risk of AAC in individuals with BMI < 30 kg/m² (Figure 2). In subgroup analysis, a significant interaction was found between age and hypertension with sUA and AAC (P for interaction <0.05, Table 4). In addition, the U-shaped relationship of serum 25(OH)D with SAAC was observed among female participants without hypertension but having DM and BMI ≥ 30 kg/m² (Figure 3). Table 5 illustrates that the association between sUA and SAAC was significantly different in the hypertension population compared to other groups (P for interaction <0.05).

Table 4 Subgroups Analysis for the Associations of sUA with the Risk of AAC

Table 5 Subgroups Analysis for the Associations of sUA with the Risk of SAAC

Figure 2 RCS curve for the association between sUA with the risk of AAC. (A) the association between sUA and AAC stratified by age; (B) the association between sUA and AAC stratified by sex; (C) the association between sUA and AAC stratified by hypertension; (D) the association between sUA and AAC stratified by DM; and (E) the association between SUA and AAC stratified by BMI. Abbreviations: RCS, restricted cubic spline; sUA, serum uric acid; AAC, abdominal aortic calcification; BMI, body mass index.

Figure 3 RCS curve for the association between sUA with the risk of SAAC. (A) the association between sUA and SAAC stratified by age; (B) the association between sUA and SAAC stratified by sex; (C) the association between SUA and SAAC stratified by hypertension; (D) the association between SUA and SAAC stratified by DM; and (E) the association between SUA and SAAC stratified by BMI. Abbreviations: RCS, restricted cubic spline; sUA, serum uric acid; SAAC, severe abdominal aortic calcification; BMI, body mass index.

Associations of sUA with the Degree of AAC

Generalized additive models with smooth functions showed a U-shaped relationship between sUA and the degree of AAC in the US general population. With the increase in the sUA levels, the degree of calcification first decreased and then increased (Figure 4).

Figure 4 The association between sUA and the degree of AAC. Abbreviations: sUA, serum uric acid; AAC, abdominal aortic calcification.

Discussion

Epidemiological data revealed a connection between sUA and calcification. However, the association between sUA levels and VC is controversial due to complex factors such as AAC. According to our knowledge, this is the first study to investigate the association between sUA levels and AAC and SAAC risk in the general US population.

We discovered a U-shaped relationship between the sUA level and the risk of AAC and SAAC in the study. A diet rich in precursors of uric acid results in an increase in sUA. The chief reason is alcohol consumption, especially beer, leading to a sharp increase in sUA levels; at the same time, seafood and soy products can also increase sUA.²⁶ Furthermore, high sUA is associated with hypertension, DM, metabolic syndrome, and renal failure.^27–29 Numerous studies demonstrated the close relationship between sUA levels with CVD.^30,31 A rise in sUA levels is intimately associated with substantial atherosclerosis risk factors, such as hypertension, abdominal obesity, DM, metabolic syndrome, hypertriglyceridemia, endothelial dysfunction, and renal failure.^32–36 A recent meta-analysis indicated that individuals with SUA levels of more than 357 umol/L had a 1.8-fold increased risk of CAC compared with normal sUA levels, and the risk of calcification rose 31% with each 88.4 umol/L increase.³⁷ As the final product of purine metabolism, sUA may promote the proliferation of vascular smooth muscle cells, resulting in premature arterial calcification, and the longer the duration of hyperuricemia exposure, the greater the calcium deposition in the middle layer of blood arteries.³⁸ Santos et al discovered an independent association between high sUA levels and CAC among asymptomatic men at moderate risk with metabolic syndrome.³⁹ In addition, Atar et al found that sUA is an independent risk factor for CAC.⁴⁰ However, Malik et al demonstrated that in healthy octogenarians, higher sUA levels were associated with vascular inflammation (hs-CRP) but not coronary atherosclerosis.⁴¹ Furthermore, Neogi and Coutinho and their collaborators did not find an independent relationship between sUA and CAC but identified a connection between sUA and carotid atherosclerosis, suggesting that sUA might play a role in subclinical atherosclerosis.^42,43

In addition, we also found the U-shaped relationships between sUA and the risk of AAC in individuals with BMI < 30 kg/m². Moreover, the U-shaped associations of sUA with SAAC were observed among female participants without hypertension having DM and BMI ≥ 30 kg/m². The SAAC occurrence was found to be associated with DM. This may be related to the increase of endothelin activity, the activation of the renin-angiotensin system, and the significant impairment of vasomotor function. In addition, hyperglycemia and glycoglycation end products, hyperinsulinism, oxidative stress, lipid metabolism disorder, inflammation, apoptosis, abnormal expression of bone regulatory proteins, and local autocrine and paracrine disorders of blood vessels are important inducing factors for the VC occurrence.⁴⁴ Concludingly, a U-shaped relationship existed between sUA and the degree of AAC in the US general population. Previous research has established that the risk of mild-to-moderate calcification to severe calcification is four times higher in patients with hyperuricemia than those with normal sUA.

Limitations

This study included representative samples of the multiracial population for generalization to the entire US populace. Due to the large sample size, we were able to adjust for numerous potential confounding factors. Despite this, limitations must also be recognized. First, this was a cross-sectional study; hence, determining how sUA, AAC, and SAAC are causally related was impossible. Second, the self-reports of individuals were used to diagnose diseases, including hypertension, and DM, which could have introduced the potential bias. Third, this study did not eliminate the bias introduced by other potentially confounding variables that were not adjusted.

Conclusion

In conclusion, the relationship between sUA levels and the risk of AAC and SACC presented a U-shaped curve in the US general population. Therefore, close monitoring and controlling sUA levels can reduce the risk of AAC and SAAC.