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The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2008 Nov 11;94(2):632–638. doi: 10.1210/jc.2008-0682

Genetics of Variation in Serum Uric Acid and Cardiovascular Risk Factors in Mexican Americans

V Saroja Voruganti 1, Subrata D Nath 1, Shelley A Cole 1, Farook Thameem 1, Jeremy B Jowett 1, Richard Bauer 1, Jean W MacCluer 1, John Blangero 1, Anthony G Comuzzie 1, Hanna E Abboud 1, Nedal H Arar 1
PMCID: PMC2646516  PMID: 19001525

Abstract

Background: Elevated serum uric acid is associated with several cardiovascular disease (CVD) risk factors such as hypertension, inflammation, endothelial dysfunction, insulin resistance, dyslipidemia, and obesity. However, the role of uric acid as an independent risk factor for CVD is not yet clear.

Objective: The aim of the study was to localize quantitative trait loci regulating variation in serum uric acid and also establish the relationship between serum uric acid and other CVD risk factors in Mexican Americans (n = 848; men = 310, women = 538) participating in the San Antonio Family Heart Study.

Methods: Quantitative genetic analysis was conducted using variance components decomposition method, implemented in the software program SOLAR.

Results: Mean ± sd of serum uric acid was 5.35 ± 1.38 mg/dl. Univariate genetic analysis showed serum uric acid and other CVD risk markers to be significantly heritable (P < 0.005). Bivariate analysis showed significant correlation of serum uric acid with body mass index, waist circumference, waist/hip ratio, total body fat, plasma insulin, serum triglycerides, high-density lipoprotein cholesterol, C-reactive protein, and granulocyte macrophage-colony stimulating factor (P < 0.05). A genome-wide scan for detecting quantitative trait loci regulating serum uric acid variation showed a significant logarithm of odds (LOD) score of 4.72 (empirical LOD score = 4.62; P < 0.00001) on chromosome 3p26. One LOD support interval contains 25 genes, of which an interesting candidate gene is chemokine receptor 2.

Summary: There is a significant genetic component in the variation in serum uric acid and evidence of pleiotropy between serum uric acid and other cardiovascular risk factors.

There is a significant genetic component in the variation in serum uric acid and evidence of pleiotropy between serum uric acid and other cardiovascular risk factors.

Uric acid is the final product of purine metabolism in humans, and its circulating concentrations are regulated by the balance in its production and excretion (1). Uric acid is primarily excreted through the kidneys. After filtration, uric acid is reabsorbed, secreted, and then reabsorbed in the proximal tubules. Only 10% of the filtered uric acid is excreted in the urine (2). Increased circulating uric acid levels (hyperuricemia) result from increased consumption of alcohol, purine-rich foods, and fructose-containing sugars and defects in purine metabolism, impaired renal function, hyperinsulinemia leading to increased net tubular absorption, certain types of drugs such as diuretics (3), and genetic predisposition (4).

Hyperuricemia is a risk factor for cardiovascular disease (CVD) (5), which is the leading cause of mortality in Mexican Americans. Two of its major risk factors, obesity and type 2 diabetes, have the highest incidence among this population (6). Increased serum uric acid is associated with other CVD risk factors such as hypertension, inflammation, endothelial dysfunction, and dyslipidemia (7). Potential mechanisms by which hyperuricemia confers CVD risk effects are: 1) impaired endothelial function by interfering with the bioavailability of nitric oxide; 2) renal ischemia and hypertension; 3) inflammation; and 4) production of oxygen radicals during the synthesis of uric acid by xanthine oxidase, especially during ischemia (3).

Aggregation of serum uric acid levels in families (4,8) indicates that variation in serum uric acid levels has a genetic component. Family-based studies have reported significant heritabilties for serum uric acid levels (9,10). Few genome-wide scans for localizing quantitative trait loci (QTLs) influencing the variation in serum uric acid have been undertaken. Moreover, no studies have yet reported the genetic influence on the relationship between serum uric acid and CVD risk factors. Both genetic and environmental factors likely contribute to the variation in serum uric acid. The purpose of this study was 2-fold: to determine 1) whether genetics has significant influence on the variation in serum uric acid levels, and 2) whether serum uric acid is genetically associated with CVD risk factors in this population.

Subjects and Methods

Population characteristics

The San Antonio Family Heart Study (SAFHS) is a family-based genetic study in the Hispanic community in San Antonio, Texas. Begun in 1991, this study focuses on the identification of genes impacting risk for the development of CVD. Probands were recruited without regard to any specific disease status and were at least 90% Mexican-American and between 40–60 yr of age. Approximately 1400 members of more than 40 families have been recruited from low-income neighborhoods in San Antonio (11). Informed consent was obtained from all participants of this study, which was approved by the Institutional Review Board at the University of Texas Health Science Center at San Antonio. Genome-wide scans using 414 polymorphic microsatellite markers (average distance, ∼10 cM) have been successfully conducted for several CVD risk factors (12).

Phenotyping

Blood was collected after an overnight fast, and plasma or serum samples were stored at −80 C until analyzed. Phenotypes that were measured using serum or plasma included uric acid, glucose, insulin, and a lipid panel. Fasting serum uric acid was oxidized in the presence of uricase to form hydrogen peroxide, which was measured photometrically (13). Glucose was analyzed by the glucose oxidase method with an Abbott V/P Analyzer (Abbott Laboratories, Abbott Park, IL). Plasma insulin was measured by a commercially available RIA kit (LINCO Research, Inc., St. Charles, MO).

Anthropometric measurements included height, weight, waist circumference, waist to hip ratio, and body mass index (BMI). Weight was measured to the nearest 0.1 lb, and then to the nearest 0.1 kg, using an ISO-9001 certified Scale-Tronix electronic scale with a capacity of 880 lb (400 kg) (Scale-Tronix, White Plains, NY). Standing height was measured twice, to the nearest 0.1 cm, using a SECA wall-mounted stadiometer (Seca Corp., Hanover, MD). Waist circumference was measured twice, in millimeters, at the level of the umbilicus. The hip circumference was also measured twice, in millimeters, at the widest circumference. These measurements were taken with the participant standing with feet together, arms at their sides, and abdomen relaxed. The average of the two measurements for each variable was then used in the analyses.

BMI was calculated by dividing weight (kilograms) by height (meters) squared. Body fat composition was estimated by bioelectrical impedance technology (Valhalla Scientific, San Diego, CA). Blood pressure was measured three times, with an appropriate arm cuff, using a Random-Zero sphygmomanometer (Gelman-Hawksley Ltd., Sussex, UK). The first measurement of the systolic and diastolic pressures was discarded, and the mean of the second and third readings was used in the analyses.

Genotyping

Genotyping was conducted with DNA isolated from lymphocytes. All members were genotyped for short tandem repeat markers that were spaced at an average interval of 10 cM. All microsatellite markers in the SAFHS were generated using PCR primers from the Research Genetics MapPairs Human screening set versions 6 and 8 (Research Genetics, Inc., Huntsville, AL), and alleles were called using the ABI Prism 377 DNA sequencer and Genescan and Genotyper software (Applied Biosystems, Foster City, CA).

Statistical analyses

Quantitative genetic analyses

Univariate.

A multipoint linkage analysis was employed to detect the presence of a putative QTL or QTLs that might affect serum uric acid. This technique is implemented in the software program SOLAR (14). It is an extension of the variance components approach in which variance due to a specific QTL is added to the basic model. It is based on estimating the effect of a specific QTL on the variation in phenotype and can be modeled as a function of the identity by descent relationship at the marker locus between family members. The phenotypic correlations between family members can be described as the cumulative effect of a specific QTL associated with a marker and residual genetic and environmental effects (15). A model under the null hypothesis in which the additive genetic variance for a specific QTL equals zero was tested against a model under an alternate hypothesis in which the additive variance was estimated. This is known as a likelihood ratio test, and the resultant likelihood ratio test statistic is in this particular case distributed asymptotically as a ½:½ mixture of a χ2 variable with one degree of freedom and a point mass at zero (16). Traditionally, a logarithm of odds (LOD) score, which is computed directly from the likelihood ratio test, is reported in linkage analyses (14).

Bivariate.

Phenotypic, genetic, and environmental correlations were calculated between serum uric acid and other cardiovascular risk factors as summarized by the following model:

graphic file with name M1.gif

where h12 and h22 are heritabilities of the two phenotypes being studied, and rhoG and rhoE are the additive genetic and environmental correlations between the traits, respectively (17).

A model in which all parameters are estimated is compared with a model in which the genetic correlation is constrained to zero. To test for complete pleiotropy between the two traits, a model in which the genetic correlation is constrained to one is compared with a model in which all parameters are estimated. Twice the difference of logarithm likelihood of the two models asymptotically yields a distribution of χ2 with 1 df (16). Evidence of pleiotropy (a common set of genes influencing more than one trait) is indicated by a genetic correlation significantly different from zero.

Prioritization of positional candidate genes

Genesniffer (www.genesniffer.org) was specifically developed to assist in the prioritization of positional candidate genes based on previously detected linkages and one-LOD support chromosomal regions (18). This program provides a quantitative measure of the relevance of the gene to the phenotype of interest. It downloads appropriate web pages (OMIM, PubMed databases, etc.) to locate genes within the given genomic region. This search is based on keywords provided by the investigator related to the biological significance of the particular trait. A cumulative hit score is calculated for each gene. Observed hit scores are compared with the distribution of hit scores generated by random sampling of genes under the null hypothesis of no correlation. Thus, a statistically meaningful measure is given to each gene within the interval.

Results

Quantitative genetic analysis was conducted to localize a QTL regulating the variation in serum uric acid in 848 individuals participating in the SAFHS. Mean ± sd age and serum uric acid levels of these individuals were 47.87 ± 14.8 yr and 5.35 ± 1.38 mg/dl, respectively, with men (n = 310) having higher levels of serum uric acid than women (n = 538) (6.1 ± 1.3 mg/dl vs. 4.94 ± 1.3 mg/dl) (Table 1). Because compromised renal function might cause elevated serum uric acid, we tested traits related to renal function and diabetes that might influence serum uric acid and included significant covariates in our final model. Also, we tested various environmental factors as possible covariates for serum uric acid and found smoking, alcohol intake, and blood pressure medication to be significant. Significant heritability was detected for serum uric acid (h2 = 0.39; P = 2.3 × 10−8) with age, sex, interaction between age and sex, waist to hip ratio, systolic blood pressure, plasma triglyceride, plasma high-density lipoprotein (HDL), serum creatinine, blood pressure medications, alcohol intake, smoking status, and diabetes status as covariates. We also found significant heritabilities for other CVD risk factors (Table 2).

Table 1.

Characteristics of study participants

Variables Women Men
Age (yr) 48.37 ± 14.6 47.02 ± 15.1
BMI (kg/m2) 32.47 ± 7.6 30.52 ± 6.2
Waist circumference (cm) 106.43 ± 17.4 103.84 ± 16.3
Waist to hip ratio 0.94 ± 0.07 0.98 ± 0.09
Systolic blood pressure (mm Hg) 123.36 ± 19.9 126.19 ± 17.3
Diastolic blood pressure (mm Hg) 68.55 ± 9.5 71.94 ± 11.2
Serum (mg/dl)
 Creatinine 0.76 ± 0.5 1.01 ± 0.9
 Glucose 102.57 ± 39.2 110.36 ± 49.7
 Triglycerides 122.33 ± 67 141.11 ± 112
 HDL 50.32 ± 13.1 44.61 ± 13.7
 Uric acid 4.94 ± 1.3 6.10 ± 1.3
Individuals who (%)
 Smoke 10 10
 Take alcohol 22 21
 Are diabetic 13 9
 Take hypertension medication 17 8
 Have hyperuricemia 13 9
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Data are presented as mean ± sd. 

Table 2.

Phenotypic, genetic, and environmental correlation of serum uric acid with circulating levels of CVD risk factors in the SAFHS

Phenotype h2 rhoG rhoE rhoP
BMI 0.547 (0.07)a 0.204 (0.14) 0.341 (0.09)a 0.27a
Waist circumference (cm) 0.541 (0.08)a 0.280 (0.13)b 0.286 (0.09)a 0.28a
Total body fat (g) 0.618 (0.08)a 0.294 (0.13)b 0.035 (0.10) 0.17b
Waist-hip ratio 0.348 (0.09)a 0.289 (0.17) 0.157 (0.08)c 0.20b
Glucose (mg/dl) 0.320 (0.07)a 0.103 (0.18) −0.10 (0.08) NS
Insulin (μIU/ml) 0.458 (0.09)a 0.148 (0.16) 0.235 (0.08)a 0.20b
Triglycerides (mg/dl) 0.535 (0.08)a 0.184 (0.14) 0.234 (0.09)b 0.21a
HDL cholesterol (mg/dl) 0.315 (0.07)a −0.158 (0.17) −0.13 (0.07)c −0.18b
Total cholesterol (mg/dl) 0.445 (0.08)a 0.007 (0.15) 0.064 (0.09) NS
Systolic blood pressure (mm Hg) 0.280 (0.08)a 0.332 (0.21) −0.05 (0.08) 0.08c
Diastolic blood pressure (mm Hg) 0.218 (0.07)a 0.233 (0.21) 0.01 (0.07) NS
CRP (ng/ml) 0.222 (0.15)a −0.09 (0.44) 0.495 (0.15)a 0.31a
GM-CSF (pg/ml) 0.327 (0.17)a −0.18 (0.39) 0.404 (0.23)c 0.19b
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Data in parentheses represent se. h2, Heritability; rhoG, genetic correlation; rhoE, environmental correlation; rhoP, phenotypic correlation; NS, not significant. 

a

P < 0.005. 

b

P < 0.05. 

c

P < 0.07. 

To understand the relationship between serum uric acid and CVD risk factors, we conducted bivariate analyses. Table 2 depicts genetic as well as phenotypic correlations between serum uric acid and CVD risk factors. Genetic correlation of serum uric acid was significant only with waist circumference and total body fat. Significant and positive phenotypic correlations were obtained for BMI, waist circumference, total body fat, waist-hip ratio, plasma insulin, serum triglycerides, C-reactive protein (CRP), and granulocyte macrophage-colony stimulating factor (GM-CSF). Serum uric acid levels were negatively associated with HDL cholesterol.

Multipoint linkage analysis revealed a QTL influencing serum uric acid levels on chromosome 3p with a significant LOD score of 4.72 (empirical LOD score = 4.62; P < 0.00001; Fig. 1). The peak LOD score for linkage occurred between markers D3S2387 and D3S3050 on chromosome 3p26 with the one-LOD support interval spanning about 7 Mb and containing 25 genes. In our previous study (19), we had evidence of suggestive linkage (LOD = 2.2) on chromosome 3p that overlapped with our present linkage signal (Fig. 2). For this analysis, 4092 relative pairs were used (Table 3). A majority of them were first and second cousins, followed by avuncular, parent-offspring, and sibling relationships. We used the software program Genesniffer to narrow the number of potential positional candidate genes based on their potential relevance to the focal phenotype. Results from this analysis are shown on Table 4 with their respective scores. In our one-LOD support interval (∼7 Mb), there are about 25 known genes. Of these, 12 were given scores by the Genesniffer program, and two [chemokine receptor 2 (CCR2) and CCR5] were selected by the available positional data.

Figure 1.

Figure 1

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Summary of the univariate linkage analysis for serum uric acid by each chromosome. Chromosomal location (cM) is represented on the x-axis, and the LOD score shown on the y-axis.

Figure 2.

Figure 2

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Plot of chromosome 3 depicting the overlap between the present study and our previous study. Chromosomal location (cM) is represented on the x-axis, and the LOD score shown on the y-axis.

Table 3.

Relative pairs used in this study

Relationship No. of pairs
Parent-offspring 302
Siblings 386
Grandparent-grandchild 79
Avuncular 692
Half-siblings 74
Great grandparent-grandchild 2
Grand avuncular 107
Half avuncular 57
First cousins 933
Half grand avuncular 8
First cousins, once removed 820
Half first cousins 51
First cousins, twice removed 22
Half first cousins, once removed 21
Second cousins 392
Second cousins, once removed 140
Half second cousins 4
Half first cousins and second cousins 1
Identical sib-pair 1
Total 4092
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Table 4.

Serum uric acid candidate genes in the chromosome 3p one-LOD support interval identified by Genesniffer

Location (Kb)a Gene symbol Size (Kb) Grand total hit score
35.202 CCR2 17.805 NA
54.222 CCR5 27.202 NA
159.458 CHL1 607.749 9680
767.998 CNTN6 1000.424 2621
1768.951 CNTN4 1311.000 939
3080.551 IL5RA 54.108 27
3507.805 LRRN1 580.014 115
4094.770 SETMAR 260.975 137
4356.154 SUMF1 140.633 56
4498.408 ITPR1 430.597 967
4930.735 BHLHB2 139.473 65
5071.208 ARL8B 129.575 968
5201.799 EDEM1 855.476 60
6057.552 GRM7 941.575 2035
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NA, Not available. 

a

Position based on chromosome coordinate. 

Discussion

In this study, we have shown a strong genetic component for the variation of serum uric acid. Serum uric acid was heritable, and a QTL regulating the variation in its levels was localized. The strong signal was in the region of 3p26 and its surrounding one −LOD support interval spans about 7 Mb and contains 25 genes. Heritability of serum uric acid found in our study was similar to those reported by several family-based studies, with estimates ranging from 25 to 73% (9,10). However, few genome-wide scans for localizing QTLs influencing variation in serum uric acid have been undertaken.

Yang et al. (20) reported a genome-wide scan for serum uric acid in a Caucasian population from the Framingham Heart Study with a LOD score of 3.3 on chromosome 15q. In another study where subjects were recruited partly from the Framingham Heart study, serum uric acid was studied as a part of the metabolic syndrome where the variation in serum uric acid was localized to a QTL on chromosome 2q (21). Early studies on the genetics of serum uric acid proposed a major gene for serum uric acid (8), although later studies (segregation analyses) could not support that theory and instead proposed that serum uric acid is affected by several genes (9,10). Although genome-wide linkage studies with serum uric acid have found a number of QTLs on various chromosomes, none of these studies have been replicated. In our previous study (19), we found strong evidence of linkage to a QTL on chromosome 6. However, we also had a suggestive linkage on chromosome 3p, which is about 25 cM distance from our present signal (Fig. 2). The signal that we are reporting in this manuscript represents a significant refinement of our previous marginal signal as a result of the addition of 200 individuals. It is true that there is overlap between the two studies; however, addition of individuals can significantly change family pedigree structures. Increasing family members will affect a network of relationships and their contribution toward the linkage signal. Variation in position for linkages can be substantial (tens of centimorgans) in case of complex disorders, when trying to replicate previous studies (22). Increase in the number of individuals is just one of many factors that can affect genetic information, thereby affecting both location and strength of a linkage signal. In a recently conducted genome-wide association study, variants in solute carrier family 2, member 9 protein (SLC2A9/GLUT9) were strongly associated with serum uric acid (23). This gene, located at 4p15, seems to be involved in fructose as well as uric acid transport and excretion (24).

The region of p26 on chromosome 3 has also been reported to harbor QTLs related to CVD risk factors. In a genome-wide linkage scan in a population comprising African-Americans and Whites, evidence for suggestive linkage was found for resting blood pressure in the same region near the marker D3S2387 (25). The same marker was also associated with BMI (LOD = 3.7) in a cross-sectional family study of African-Americans and Hispanic-Americans (26). In another study, the Hypertension Genetic Epidemiology Network study, genome scan was conducted on factors derived from factor analysis for metabolic syndrome. Significant linkage was found for obesity-insulin factor on chromosome 3p26 (27). In the present study, evidence for suggestive linkage was found on chromosomes 1, 4, and 15 in addition to the strong signal on chromosome 3. In genetic studies exploring cardiovascular risk factors, suggestive linkages were found in the same region of chromosome 1 for metabolic syndrome components (28) and significant linkage for resting heart rate on chromosome 4 (29).

In the one-LOD support interval surrounding our QTL on chromosome 3p26, 14 positional candidate genes have been identified. There are two important clusters of genes in this region. One cluster encodes chemokine receptors (CCR2, CCR3, CCR5, CCR8, CCR9, and CCRL2) and the other, contactins (CNTN4 and CNTN6). Genes CCR2 and CCR5 were not identified by the Genesniffer, possibly because of the confusion about the physical location of these genes. According to NCBI Mapviewer and OMIM web sites, the physical location of these two genes is 3p21-22. However, base pair positions of these genes show that these should be near the telomere and within our one-LOD-support interval. Because these genes are relevant to cardiovascular risk, they are included in the potential candidate genes. CCR2 and CCR5 are receptors for monocyte chemoattractant protein 1 (MCP1), which plays a major role in the recruitment of monocytes from the blood in the early stages of atherosclerosis (30) and “regulated upon activation, normal T cell expressed, and secreted” (RANTES) or CCL5, protein, respectively. Both MCP1 and RANTES have been implicated in the development of atherosclerotic lesions, leukocyte adhesion (31), and host defense (32). Given the role of serum uric acid in inflammation as well as studies showing that uric acid stimulates MCP1 production in smooth muscle cells (33), this location of chromosome 3p where the gene for MCP1 receptor resides combines to support our argument. Other relevant candidate genes in this region are contactins, part of the Ig superfamily (34), and inositol 1, 4, 5 triphosphate receptor 1 (ITPR1), which is known to play a role in cardiac contraction and is known to be affected by diabetes because its mRNA expression was significantly reduced in diabetic compared with nondiabetic subjects (35).

Epidemiological studies have shown that increased serum uric acid concentration is associated with several CVD risk factors such as hypertension, inflammation, endothelial dysfunction, dyslipidemia, and obesity. In this study, we found significant positive correlations between serum uric acid and several phenotypes associated with obesity, adiposity, inflammation, and hypertension. In the National Health and Nutrition Examination Survey follow-up study, 1971–1992, increased serum uric acid levels were found to be positively and significantly associated with CVD mortality (7). It is not yet clear whether serum uric acid is an independent risk factor for CVD or whether it is merely associated with other CVD risk factors. However, a recently published study by Strasak et al. (36) shows that serum uric acid is an independent predictor for all major forms of CVD death. This was a 21-yr prospective follow-up study. Longitudinal studies provided a platform for such studies. In the SAFHS, we have data spanning 15 yr. We are planning to measure uric acid in blood samples collected during earlier phases and investigate whether serum uric acid can indeed predict CVD events and/or mortality. Also, serum uric acid was found to be an independent predictor of hypertension incidence and longitudinal blood pressure progression in a study spanning 4 yr in individuals who had no hypertension, gout, heart, or renal problems at baseline (37). In our present study, we found a positive correlation of serum uric acid with systolic blood pressure, but not with diastolic blood pressure. In another study over 9 yr of follow-up, serum uric acid was positively associated with incident hypertension (38). In addition, childhood serum uric acid concentration is known to predict adult blood pressure (39). Hyperuricemia is known to predict CVD events, particularly in hypertensive individuals (3). It is present in 25% of untreated hypertensive subjects and in more than 75% of those with malignant hypertension (40). Whether hyperuricemia is causally related to hypertension remains to be determined. Uric acid, however, causes endothelial dysfunction, and oxygen radicals generated by xanthine oxidase contribute to the pathogenesis of hypertension (41).

Obesity is characterized by excess body fat, and this excess body fat induces a variety of CVD risk factors such as insulin resistance, hypertension, dyslipidemia, and endothelial dysfunction (42). In this study, we found significant genetic correlation of serum uric acid with waist circumference (visceral adiposity) and total body fat. A significant genetic correlation indicates a shared genetic effect between serum uric acid and body fat (total and visceral). It means that common sets of genes are regulating both serum uric acid and body fat. Studies have shown a positive correlation between visceral fat accumulation and serum uric acid (43). Waist circumference is considered a surrogate measure for visceral fat accumulation. Increased free fatty acid production due to an increase in visceral fat might indirectly affect uric acid production through common pathways involving nicotinamide adenine dinucleotide phosphate. It is also believed that development of insulin resistance due to increased visceral fat might contribute to decreased uric acid excretion and increased serum uric acid concentrations (44). This is significant because body fat (particularly visceral) is a major CVD risk factor, and obese individuals are more likely to have hyperuricemia than their lean counterparts. Because higher uric acid levels have been associated with most of the components of the metabolic syndrome (45,46), it has been suggested that hyperuricemia should be added to the cluster of abnormalities associated with the metabolic syndrome (47). Moreover, the prevalence of the metabolic syndrome is higher in individuals with hyperuricemia (45). We also found a positive association of serum uric acid with several components of the metabolic syndrome in this study and a negative association with HDL cholesterol, implying that an increase in serum uric acid is associated with an increase in all metabolic syndrome components with the exception HDL cholesterol. We found a positive correlation between proinflammatory cytokines CRP and GM-CSF and serum uric acid. Coutinho et al. (46) found a similar association between uric acid and CRP. Uric acid has also been reported to regulate inflammatory pathways in vascular smooth muscle cells (32) and in endothelial cells (41). One limitation of this study is that we did not have individual medication data, such as losartan, allopurinol, etc.; instead we had medication classes, and we could include only those in our analysis. However, not accounting for such covariates could lead to a significant attenuation of the linkage signal; it would not lead to a fake genetic signal. Similarly, we did not have data on urinary pH, which is an important feature in the formation of uric acid stones (48).

To summarize, we found a strong genetic component in the variation in serum uric acid levels in Mexican Americans. We also found significant association of serum uric acid with other CVD risk factors, particularly adiposity phenotypes, and identified a QTL that influences serum uric acid levels. The next logical step is to explore the association between variants in positional candidate genes and serum uric acid and other CVD risk factors.

Acknowledgments

We thank all participants of the San Antonio Family Heart Study for their cooperation and generous participation. We also acknowledge the Fredric C. Bartter General Clinical Research Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas (supported by M01-RR01346), for providing clinical support for this project.

Footnotes

This study was supported by Grant PO1 HL4522 from the National Heart, Lung and Blood Institute, Grant MH59490 from the National Institute of Mental Health, the George O'Brien Kidney Research Center Grant P50-DK-61597 from National Institutes of Health (NIH)/National Institute of Diabetes, Digestive and Kidney Diseases, and the Veterans Administration Merit Review (to H.E.A. and N.H.A.). S.D.N. is supported by an American Heart Association Postdoctoral Fellowship (0525206Y). F.T. is supported by the American Heart Association Scientist Development Grant Award (0530059N). This investigation was conducted in facilities constructed with support for the Research Facilities Improvement Program Grants C06 RR017515 and RR013556 from the National Center for Research Resources/NIH.

Disclosure Statement: The authors have nothing to disclose.

First Published Online November 11, 2008

Abbreviations: BMI, Body mass index; CCR, chemokine receptor; CRP, C-reactive protein; CVD, cardiovascular disease; GM-CSF, granulocyte macrophage-colony stimulating factor; HDL, high-density lipoprotein; LOD, logarithm of odds; MCP1, monocyte chemoattractant protein 1; QTL, quantitative trait locus.

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