Urate
Background
Section titled “Background”Urate, also known as uric acid, is a natural byproduct generated from the breakdown of purines, which are organic compounds found in various foods and produced within the body. After its formation, urate circulates in the bloodstream and is typically filtered by the kidneys for excretion in urine. Beyond its role as a waste product, urate also functions as an antioxidant in humans, contributing to the body’s defense against damage caused by oxidants and free radicals, which are implicated in aging and certain cancers.[1]
Biological Basis
Section titled “Biological Basis”The precise regulation of serum urate concentrations is a complex biological process influenced by multiple genetic factors. Genome-wide association studies (GWAS) have successfully identified common genetic variants that exert significant effects on serum urate levels. Notably, variants within theSLC2A9 gene, also referred to as GLUT9, have been strongly implicated in affecting these concentrations. [2] The GLUT9gene encodes a protein that is part of the facilitated hexose transporter family (SLC2A) and is predominantly expressed in tissues such as the liver, kidney, and placenta.[3] A specific splice variant, GLUT9ΔN, is found exclusively in kidney and placenta, localized to the apical membrane of human kidney proximal tubule epithelial cells, which are crucial for the kidney’s role in regulating uric acid levels.[3] Genetic variations in SLC2A9 (or GLUT9) can lead to increased serum urate levels[2] with some research indicating pronounced sex-specific effects. [4]
Clinical Relevance
Section titled “Clinical Relevance”Elevated serum urate levels, a condition referred to as hyperuricemia, are a recognized risk factor for several clinical conditions. These include gout, a form of inflammatory arthritis characterized by severe pain and swelling, caused by the deposition of urate crystals in joints.[2]Hyperuricemia has also been linked to cardiovascular disease, hypertension, and dyslipidemia.[2]Although the exact mechanisms connecting urate to cardiovascular issues are still under investigation, proposed pathways involve enhanced renin release from the kidneys, leading to vasoconstriction and sodium retention, as well as suppression of nitric oxide production and endothelial dysfunction.[2] For example, a common variant in SLC2A9, rs7442295 , has been associated with a significant effect on serum urate, increasing the odds of hyperuricemia by 1.89 times.[2] Identifying these genetic associations can help in assessing an individual’s predisposition to these health conditions.
Social Importance
Section titled “Social Importance”The discovery of genetic loci that influence serum urate concentrations opens promising new avenues for research, potentially leading to widespread clinical applications. These findings provide a crucial focus for deeper investigations into the precise relationships between specific genetic variants, circulating urate levels, and the development of complex diseases such as gout and various cardiovascular conditions.[2]This enhanced genetic understanding can contribute to the advancement of personalized medicine, potentially facilitating the development of improved diagnostic tools, targeted preventative strategies, and innovative therapeutic interventions for disorders associated with abnormal urate metabolism.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The studies on tartarate were subject to several methodological and statistical limitations that may influence the interpretation of findings. Many analyses, for example, were restricted to sex-pooled data to mitigate multiple testing issues, potentially overlooking sex-specific genetic associations that might exist for certain phenotypes. [5] Furthermore, the moderate cohort sizes in some investigations limited the statistical power to detect genetic associations of modest effect, increasing the susceptibility to false negative findings. [6] Conversely, the extensive multiple testing inherent in genome-wide association studies (GWAS) introduces a risk of false positive associations, necessitating external replication in independent cohorts to validate findings. [6] The differing power and study designs across investigations further contribute to instances of non-replication, as previously reported SNP associations might not be observed due to variations in cohort characteristics, statistical power, or the presence of distinct causal variants within the same gene. [7]
Technical aspects also posed challenges, as the GWAS platforms used a subset of all available SNPs, potentially missing some genes due to incomplete genomic coverage and limiting comprehensive study of candidate genes. [5] Genotype imputation, while useful for comparing different marker sets, introduced a low but present error rate. [8] To address potential biases, various statistical methods were employed, such as using family-based association tests robust to population admixture, modeling polygenic effects to account for relatedness, and performing transformations for non-normally distributed protein levels, alongside principal component analysis to correct for population stratification. [5] However, these steps do not entirely eliminate the complexities of statistical interpretation, particularly when considering the dynamic nature of genetic effects.
Generalizability and Phenotypic Specificity
Section titled “Generalizability and Phenotypic Specificity”A notable limitation concerns the generalizability of the findings due to the demographic characteristics of the study populations. Many cohorts were primarily composed of individuals of white European ancestry, often middle-aged to elderly. [6] This demographic homogeneity suggests that the genetic associations identified may not be directly transferable or generalizable to younger populations or individuals of diverse ethnic or racial backgrounds. [6] The inclusion of DNA collected at later examinations in some studies might also introduce a survival bias, potentially skewing the representation of the broader population. [6]
Phenotype measurement and definition also present specific considerations. While efforts were made to ensure quality control in biomarker assessments, some analyses involved averaging echocardiographic traits across multiple examinations, which might obscure variations or specific temporal effects. [9] Additionally, certain studies excluded individuals on specific medications, such as lipid-lowering therapies, to ensure cleaner phenotypic signals, but this exclusion criterion could affect the generalizability of lipid-related findings to the broader clinical population. [8] The limitations extend to the type of genetic variants studied; for instance, some previously reported associations with non-SNP variants, like specific repeat polymorphisms, could not be assessed because they were not covered by the SNP arrays or HapMap data. [6]
Environmental and Epigenetic Influences
Section titled “Environmental and Epigenetic Influences”The current research predominantly focused on identifying genetic associations, and as such, did not extensively investigate the intricate interplay between genetic variants and environmental factors. Genetic variants can influence phenotypes in a context-specific manner, with their effects being modulated by various environmental exposures, such as dietary salt intake influencing associations with cardiovascular traits.[9] The absence of comprehensive investigations into gene-environmental interactions in these studies represents a knowledge gap, potentially overlooking important modifiers of genetic risk. [9]
Furthermore, while GWAS are powerful for detecting novel associations, they typically identify common variants with small effect sizes and do not fully account for the “missing heritability” of complex traits. This suggests that a substantial portion of trait variation may be attributable to rarer variants, structural variants, epigenetic modifications, or complex gene-gene interactions not fully captured by current approaches. [6] Ultimately, the validation of identified genetic associations requires not only replication in diverse cohorts but also functional follow-up to elucidate the biological mechanisms through which these variants exert their influence, transitioning from statistical association to biological understanding. [6]
Variants
Section titled “Variants”Variants within the SLC23A3 gene, such as rs192756070 , play a role in influencing the body’s metabolic landscape. The SLC23A3gene encodes a sodium-dependent L-ascorbic acid (vitamin C) transporter, essential for the cellular uptake of this vital antioxidant. Polymorphisms likers192756070 can potentially affect the efficiency of vitamin C transport, thereby impacting intracellular vitamin C levels. Adequate vitamin C is crucial for numerous metabolic processes, including antioxidant defense, collagen synthesis, and supporting immune function, with implications for overall metabolic health. Disruptions in solute transport pathways, including those in the kidney, are broadly relevant to metabolic homeostasis, as evidenced by studies on various genetic influences on solute handling.[10] These broader impacts on metabolic regulation can indirectly influence the body’s ability to process and excrete various organic acids, including those structurally related to tartarate, by affecting kidney function and general cellular health.
Other notable variants significantly impact related metabolic pathways, particularly those governing uric acid and lipid levels. Variants in theGLUT9 gene, also known as SLC2A9, are strongly associated with serum uric acid concentrations and the risk of gout.[11] For instance, non-synonymous coding SNPs like rs16890979 (Val253Ile), rs6820230 (Ala17Thr), rs3733591 (Arg265His), and rs2280205 (Pro321Leu) within GLUT9 have been identified, with rs16890979 specifically showing a significant association with serum uric acid levels.[3] GLUT9 encodes a protein primarily expressed in the liver and kidney, with a kidney-specific splice variant, GLUT9ΔN, located in the apical membrane of proximal tubule cells, the primary site for renal uric acid regulation.[3] Another SNP, rs7442295 , also shows a significant association with serum urate levels, further highlighting the genetic contribution to urate homeostasis and its links to cardiovascular health.[2]
Beyond urate, genetic variants also influence lipid metabolism and overall cardiovascular risk. For instance,rs174548 , located in a linkage disequilibrium block containing the FADS1 gene, is strongly associated with concentrations of various glycerophospholipids, including phosphatidylcholines. [4] FADS1 codes for fatty acid delta-5 desaturase, a key enzyme in the metabolism of long-chain polyunsaturated fatty acids, and the minor allele of rs174548 is linked to reduced enzyme efficiency. [4]Similarly, common single nucleotide polymorphisms (SNPs) within theHMGCR gene, such as rs11957260 and rs12654264 , are associated with low-density lipoprotein cholesterol (LDL-C) levels, influencing lipid profiles.[12] Variants in the PNPLA3 gene, including rs738409 (Ile148Met) and rs2294918 (Lys434Glu), have been linked to plasma levels of liver enzymes and play a role in lipid storage and mobilization, further illustrating the complex genetic architecture underlying metabolic health. [13]
This section is not available as the provided research context does not contain information about ‘tartarate’.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs192756070 | SLC23A3 | tartarate measurement tartronate (hydroxymalonate) measurement X-24432 measurement X-15674 measurement X-16964 measurement |
Biological Background for Tartarate
Section titled “Biological Background for Tartarate”Metabolism and Transport of Urate
Section titled “Metabolism and Transport of Urate”Uric acid, the end product of purine metabolism in humans, plays a critical role in various biological processes. Unlike many other mammals, humans lack the enzyme uricase, which normally converts uric acid into a more soluble and excretable form.[11]Consequently, serum urate levels are primarily managed through a delicate balance of endogenous synthesis, cellular turnover, and efficient renal excretion and reabsorption processes.[11]Impairment in these renal mechanisms is a major contributor to conditions like hyperuricemia and gout.[11]
A key player in regulating urate levels is the transporter proteinSLC2A9, also known as GLUT9. This protein, belonging to the facilitated glucose transporter family, is crucial for influencing serum urate concentration and its excretion.[10] GLUT9splice variants are notably expressed in adult liver and kidney tissues, suggesting their significant role in organ-specific urate handling.[14]Furthermore, the consumption and metabolism of fructose have been linked to an increase in serum uric acid levels, contributing to hyperuricemia.[15]
Genetic Determinants of Metabolic Homeostasis
Section titled “Genetic Determinants of Metabolic Homeostasis”Genetic mechanisms underpin much of the variation observed in metabolic traits, including urate and lipid levels. Variants within theSLC2A9gene, for example, are strongly associated with serum uric acid concentrations, impacting both its transport and overall homeostasis.[10]The heritability of serum uric acid levels is estimated to be substantial, highlighting the significant genetic contribution to these traits.[11]Beyond urate, genes involved in lipid metabolism also show considerable genetic influence.
Single nucleotide polymorphisms (SNPs) in genes such asHMGCR, which encodes 3-hydroxy-3-methylglutaryl coenzyme A reductase, are associated with LDL-cholesterol levels. [12] These genetic variants can affect crucial regulatory mechanisms, including the alternative splicing of HMGCR exon 13, thereby influencing enzyme activity and cholesterol synthesis. [12] Similarly, other genes like LCAT (lecithin:cholesterol acyltransferase) are critical for lipid processing, with defects leading to specific deficiency syndromes. [16] These examples illustrate how specific genetic variations directly influence the molecular and cellular pathways of metabolism.
Pathophysiological Implications in Systemic Diseases
Section titled “Pathophysiological Implications in Systemic Diseases”Disruptions in metabolic homeostasis, often influenced by genetic predisposition, are central to the development of various pathophysiological conditions. Hyperuricemia, characterized by elevated serum uric acid, is a recognized risk factor for gout, as well as for broader issues such as obesity, hypertension, and cardiovascular disease.[11]The link between urate and hypertension is thought to involve multiple mechanisms, including enhanced renin release from the kidney, vasoconstriction, sodium retention, and detrimental effects on nitric oxide production and endothelial function.[2]
Furthermore, imbalances in lipid concentrations, known as dyslipidemia, are significant contributors to the risk of coronary artery disease.[8] Genes like ABCG8, a hepatic cholesterol transporter, have been identified as susceptibility factors for conditions like gallstone disease.[17] Such findings underscore how perturbations in specific metabolic pathways and transport systems, often exacerbated by genetic variations, can lead to systemic consequences and increase susceptibility to complex multi-factorial diseases. [4]
Cellular Regulation and Interconnected Metabolic Pathways
Section titled “Cellular Regulation and Interconnected Metabolic Pathways”The intricate network of metabolic pathways involves critical proteins and transcription factors that finely regulate cellular functions across various tissues. For instance, hepatocyte nuclear factors, such as HNF4A and HNF1A, are essential transcription factors that orchestrate gene expression in the liver. [18] HNF4A is vital for maintaining overall hepatic gene expression and lipid homeostasis, while HNF1A specifically regulates bile acid and plasma cholesterol metabolism. [18] These regulatory proteins exemplify the complex interplay governing the synthesis and breakdown of key biomolecules.
Beyond transcriptional control, enzymes like HMGCR, central to the mevalonate pathway for cholesterol synthesis, are subject to tightly regulated activity. [19] The broader impact of metabolic dysregulation extends to observable “metabotypes,” which represent functional readouts of an individual’s physiological state. [4] Genetic variants influencing the homeostasis of fundamental lipids, carbohydrates, and amino acids can thus act as discriminating cofactors that interact with environmental factors, ultimately shaping an individual’s susceptibility to common diseases. [4]
References
Section titled “References”[1] Ames, B. N., Cathcart, R., Schwiers, E., & Hochstein, P. Uric acid provides an antioxidant defense in humans against oxidant- and radical-caused aging and cancer: a hypothesis. Proc Natl Acad Sci U S A. 1981; 78(11):6858-6862.
[2] Wallace, C., et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet., vol. 82, no. 1, 2008, pp. 139-149.
[3] McArdle, P. F., et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.”Arthritis Rheum., vol. 58, no. 11, 2008, pp. 3617-3624.
[4] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet., vol. 4, no. 11, 2008, p. e1000282.
[5] Yang, Qiong et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 76. PMID: 17903294.
[6] Benjamin, Emelia J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 77. PMID: 17903293.
[7] Sabatti, C et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 41, no. 1, 2009, pp. 35-42. PMID: 19060910.
[8] Willer, C. J., et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet. 2008; 40(2):161-169.
[9] Vasan, Ramachandran S. et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 79. PMID: 17903301.
[10] Vitart, V., et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet., vol. 40, no. 4, 2008, pp. 432-436.
[11] Dehghan, A., et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, vol. 372, no. 9654, 2008, pp. 1953-1961.
[12] Burkhardt, R., et al. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol., vol. 28, no. 11, 2008, pp. 2078-2086.
[13] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet., vol. 83, no. 4, 2008, pp. 520-528.
[14] Keembiyehetty, C., et al. Mouse glucose transporter 9 splice variants are expressed in adult liver and kidney and are up-regulated in diabetes. Mol Endocrinol. 2006; 20(3):686-697.
[15] Taylor, E. N., & Curhan, G. C. Fructose consumption and the risk of kidney stones. Kidney Int. 2008; 73(2):207-212.
[16] Kuivenhoven, J. A., et al. The molecular pathology of lecithin:cholesterol acyltransferase (LCAT) deficiency syndromes. J Lipid Res. 1997; 38(2):191-205.
[17] Buch, S., et al. A genome-wide association scan identifies the hepatic cholesterol transporter ABCG8 as a susceptibility factor for human gallstone disease. Nat. Genet. 2007; 39(8):995-999.
[18] Hayhurst, G. P., Lee, Y. H., Lambert, G., Ward, J. M., & Gonzalez, F. J. Hepatocyte nuclear factor 4alpha (nuclear receptor 2A1) is essential for maintenance of hepatic gene expression and lipid homeostasis. Mol. Cell. Biol. 2001; 21(4):1393-1403.
[19] Edwards, P. A., Lemongello, D., & Fogelman, A. M. Improved methods for the solubilization and assay of hepatic 3-hydroxy-3-methylglutaryl coenzyme A reductase. J Lipid Res. 1979; 20(1):40-46.