Delta Cehc

Introduction

‘delta cehc’ refers to a conceptual area of genetic investigation concerning variations within the human genome that contribute to the diversity of human traits and disease susceptibility. In the context of genome-wide association studies (GWAS), specific single nucleotide polymorphisms (SNPs) and genetic regions are identified for their statistical association with particular phenotypes. Understanding these genetic influences, often represented by concepts like ‘delta cehc’, is fundamental to modern genomics and personalized medicine.

Biological Basis

The biological basis of genetic associations, such as those that might be grouped under ‘delta cehc’, lies in how DNA sequence variations impact biological function. These variations, particularly SNPs, can influence gene expression, alter protein structure or function, or affect regulatory elements that control biological pathways. Such genetic changes can contribute to individual differences in metabolism, immune response, and overall physiological processes. For instance, variations near genes likeHNF1Ahave been linked to C-reactive protein levels, indicating their role in inflammatory pathways.^[1] Similarly, variants in genes like HK1have been associated with glycated hemoglobin, highlighting their involvement in glucose metabolism.^[2]

Clinical Relevance

Genetic variations identified through large-scale studies have significant clinical relevance, informing risk prediction and potential therapeutic strategies for a range of conditions. Research has associated genetic markers with various cardiovascular traits, including echocardiographic dimensions, subclinical atherosclerosis, and brachial artery endothelial function.^[3]Additionally, associations have been found with metabolic traits such as lipid concentrations, diabetes-related measures, serum urate, and kidney function.^[4]Hematological phenotypes and hemostatic factors also show genetic influences.^[5]These findings highlight the potential of genetic information, including insights related to ‘delta cehc’, to enhance diagnostic capabilities and guide clinical decision-making.

Social Importance

The study of genetic variations and their links to health, exemplified by investigations into concepts like ‘delta cehc’, holds considerable social importance. It contributes to the development of precision medicine, allowing for more tailored prevention and treatment strategies based on an individual’s genetic makeup. By identifying populations at higher risk for certain diseases, these genetic insights can inform public health initiatives and screening programs, ultimately aiming to improve overall population health and reduce the burden of chronic diseases.

Limitations

Methodological and Statistical Constraints

The ability to detect subtle genetic effects was limited by the cohort’s moderate sample size, especially when accounting for the extensive multiple testing inherent in genome-wide association studies (GWAS).^[3] While some studies had adequate power to detect associations explaining a significant portion of phenotypic variation, the overall statistical power was often insufficient for more modest effects, leading to a susceptibility for false negative findings. ^[3] Furthermore, some reported associations, despite appearing moderately strong, might represent false positive results, emphasizing the critical need for independent replication. ^[3] Previous research indicates a low replication rate for phenotype-genotype associations, underscoring the challenge of distinguishing true signals from noise without external validation. ^[6]

The genotyping platforms used, such as the Affymetrix 100K gene chip or various Illumina arrays, provided partial coverage of genetic variation, which may have limited the ability to identify all relevant genetic associations or replicate prior findings. ^[3] The choice of genotyping algorithms, such as the Dynamic Modeling algorithm, was noted as being less precise than more recently developed methods, potentially impacting the accuracy of genotype calls. ^[3] Moreover, relying on imputation to infer missing genotypes, while standard, introduces a degree of estimation error, even if generally consistent with expectations. ^[7] These technical aspects of genotyping and analysis can collectively influence the robustness and completeness of the genetic associations identified.

Generalizability and Phenotypic Assessment

A significant limitation of the research is the demographic homogeneity of the study cohorts, which were predominantly middle-aged to elderly individuals of white European descent. ^[6] This demographic profile restricts the generalizability of the findings to younger populations or individuals of other ethnic or racial backgrounds, where genetic architectures and environmental exposures may differ significantly. ^[6] Additionally, the timing of DNA collection, often occurring at later examinations, could introduce a survival bias, as only individuals who remained in the study cohort were included. ^[6]

Phenotypic characterization also presented challenges, particularly when traits were averaged across multiple examinations spanning several years. ^[3] This approach, while aiming to reduce regression dilution bias, risked misclassification due to changes in echocardiographic equipment over time and the assumption that similar genetic and environmental factors influence traits across a wide age range, potentially masking age-dependent gene effects. ^[3] Specific trait measurements, such as kidney function based on a single serum creatinine measure or estimated glomerular filtration rate using equations like MDRD, could lead to misclassification or underestimation. ^[8]The use of spot urine specimens instead of 24-hour collections for urinary albumin excretion, or cystatin C as a kidney function marker without ruling out its cardiovascular disease risk implications, further highlights potential measurement nuances.^[8]Similarly, the use of TSH as a sole indicator for thyroid function, lacking measures of free thyroxine, represents another phenotypic proxy that may not fully capture the underlying biology.^[8] Furthermore, the exclusion of individuals on lipid-lowering therapies, while necessary for genetic studies of natural lipid levels, means the findings may not directly apply to the broader population using such treatments. ^[4]

Unexplored Biological Complexities

The studies generally did not undertake investigations into gene-environment interactions, which are crucial for a comprehensive understanding of complex traits. ^[3] Genetic variants are known to influence phenotypes in a context-specific manner, with environmental factors capable of modulating these associations; for instance, the impact of ACE and AGTR2on left ventricular mass has been shown to vary with dietary salt intake.^[3] The omission of such analyses represents a significant gap, as it limits the ability to identify conditions under which specific genetic effects are more pronounced or attenuated.

Despite identifying numerous genetic associations, significant knowledge gaps remain regarding the precise biological mechanisms and functional implications of these variants. ^[6] The ultimate validation of findings requires not only replication in independent cohorts but also functional studies to elucidate the biological roles of the identified genetic markers. ^[6]The focus on multivariable models, while important for adjusting confounders, might also lead to overlooking important bivariate associations between single nucleotide polymorphisms and trait measures, thus potentially missing simpler, yet significant, genetic relationships.^[8] Continuing research with larger samples, denser SNP arrays, and advanced analytical approaches is necessary to uncover additional sequence variants and fully characterize the polygenic architecture of these traits. ^[9]

Variants

Genetic variations play a significant role in an individual’s predisposition to various health traits, including those that contribute to conditions like delta cehc. These variants can influence a wide range of biological processes, from vascular health and metabolic regulation to inflammation and blood composition. Understanding these genetic associations provides insights into potential mechanisms underlying complex health outcomes.

Several genetic variants are associated with markers of cardiovascular health and subclinical atherosclerosis, which are critical factors in overall well-being. For instance, the single nucleotide polymorphism (SNP)rs1376877 , located within the ABI2gene, has been linked to maximum internal carotid artery intima-media thickness (IMT), a measure of arterial wall thickness often indicative of atherosclerosis.^[9] ABI2 encodes a protein involved in actin cytoskeleton organization, cell migration, and signaling pathways, suggesting that variants might affect vascular remodeling and plaque stability. Similarly, rs4814615 in the PCSK2 gene shows an association with maximum common carotid artery IMT. ^[9] PCSK2 produces proprotein convertase 2, an enzyme that processes precursor proteins into their active forms, potentially influencing factors like blood pressure or lipid metabolism that impact arterial health. Additionally, variants in genes such as NOS3 (nitric oxide synthase 3), which is crucial for producing nitric oxide that relaxes blood vessels, and ESR1(estrogen receptor 1) have been identified as candidates for atherosclerosis.^[9]The chromosome 9p21 region has also shown consistent associations with coronary artery calcification (CAC) phenotypes, further linking genetic factors to cardiovascular disease risk.^[9]

Metabolic and inflammatory pathways are also influenced by specific genetic variants, impacting systemic health. The ABO histo-blood group antigen gene, which determines blood type, has been associated with soluble intercellular adhesion molecule-1 (ICAM-1) levels. ^[2]ICAM-1 is an inflammatory marker involved in immune responses and atherosclerosis development, suggesting a link between blood type genetics and inflammatory processes. Furthermore, loci related to metabolic syndrome pathways, includingLEPR(leptin receptor),HNF1A (hepatocyte nuclear factor 1 alpha), IL6R (interleukin 6 receptor), and GCKR(glucokinase regulator), are associated with plasma C-reactive protein (CRP) levels.^[10] LEPR plays a role in appetite and metabolism, while HNF1Ais a transcription factor important for pancreatic beta-cell function and glucose homeostasis. Variants inIL6R can modulate inflammatory signaling, and GCKRinfluences glucose and lipid metabolism, with their combined effects contributing to systemic inflammation as indicated by CRP levels. Another important gene,SLC2A9(solute carrier family 2 member 9), significantly influences uric acid concentrations, with pronounced sex-specific effects.^[11] SLC2A9is a urate transporter, and its variants can alter uric acid excretion, affecting the risk of conditions like gout and potentially impacting metabolic syndrome.

Genetic factors also dictate hematological traits and the body’s physiological response to stress, such as exercise. The hemoglobin gene cluster, includingHBB, HBD, HBG1, HBG2, and HBE1, contains variants like rs10488676 , rs10488675 , rs10499199 , rs10499200 , and rs10499201 that are associated with hematocrit levels.^[5]These genes are crucial for producing hemoglobin, the protein responsible for oxygen transport in red blood cells, and variants can affect red blood cell production and function. TheBCL11Agene is also notable for its association with persistent fetal hemoglobin production, which can ameliorate the severe phenotype of beta-thalassemia and sickle cell disease.^[12] Additionally, specific variants, such as rs10491167 , rs10491168 , and rs10495298 , have been linked to exercise systolic blood pressure and post-exercise heart rate recovery, indicating genetic influences on cardiovascular fitness and stress response.^[3] Other variants like rs10509999 , rs10510000 , and rs10510001 are associated with brachial artery hyperemic flow velocity, reflecting their role in endothelial function and vascular reactivity during exercise.^[3]

Key Variants

RS ID	Gene	Related Traits
chr19:15899292	N/A	octadecanedioate measurement delta-CEHC measurement

Biological Background

Molecular Mechanisms of Lipid Desaturation

The delta-5 desaturase reaction represents a pivotal step in the metabolism of polyunsaturated fatty acids. This critical enzymatic process, primarily catalyzed by the protein encoded by the FADS1 gene, facilitates the conversion of eicosatrienoyl-CoA (C20:3) into arachidonyl-CoA (C20:4). ^[13] These newly formed fatty acyl-CoAs are then integrated into various complex lipids, including glycerophospholipids, which are essential components of cell membranes. For example, specific phosphatidylcholines such as PC aa C36:3 and PC aa C36:4 are considered modified substrates and products, respectively, of the delta-5 desaturase reaction. ^[13] Consequently, the ratio between the concentrations of these product-substrate pairs, such as [PC aa C36:4]/[PC aa C36:3], serves as a robust indicator for assessing the efficiency of the FADS1 enzyme’s catalytic activity. ^[13]

Genetic Regulation of Fatty Acid Metabolism

Genetic variations significantly modulate the efficiency of fatty acid desaturation, thereby influencing lipid profiles. Polymorphisms located within the FADS1 gene itself or in its associated regulatory elements can lead to a reduction in the catalytic activity or the overall protein abundance of the delta-5 desaturase enzyme. ^[13] Such genetic influences directly alter the availability of specific fatty acids for lipid synthesis, resulting in an increased concentration of glycerophospholipids derived from eicosatrienoyl-CoA (C20:3), such as PC aa C36:3, and a corresponding decrease in glycerophospholipids derived from arachidonyl-CoA (C20:4), like PC aa C36:4. ^[13] Genetic variants that are directly involved in the conversion or modification of metabolites are often associated with substantial effect sizes, providing valuable insights into the fundamental molecular mechanisms that shape an individual’s lipid composition. ^[13]

Cellular and Systemic Impact of Lipid Balance

The precise homeostasis of key lipids, including various glycerophospholipids, is fundamental to maintaining overall physiological function and provides a functional readout of the body’s metabolic state. ^[13] Alterations in delta-5 desaturase activity, and the subsequent imbalance of fatty acids like C20:3 and C20:4, directly affect the cellular synthesis of complex lipids such as phosphatidylcholines. While the specific tissue-level consequences of this particular lipid phenotype are not explicitly detailed, disruptions in systemic lipid profiles can exert widespread effects across different organs and tissues. Understanding these intermediate phenotypes on a continuous scale can offer more detailed insights into potentially affected metabolic pathways and their broader systemic consequences. ^[13]

Metabolic Health and Disease Associations

Dysregulation in fatty acid desaturation and lipid homeostasis is intricately linked to broader metabolic health and plays a role in the etiology of complex diseases. Genetic variants that influence the balance of key lipids, particularly those affecting delta-5 desaturase activity, are expected to be directly involved in the underlying mechanisms of disease development.^[13]Serum lipid levels are well-established determinants of cardiovascular disease and significantly contribute to morbidity.^[14] Furthermore, specific genetic loci associated with metabolic-syndrome pathways, including genes such as LEPR, HNF1A, IL6R, and GCKR, have been shown to associate with plasma C-reactive protein, an inflammatory biomarker implicated in early diabetogenesis and atherogenesis.^[10]These interconnections underscore the critical importance of understanding specific lipid phenotypes in the context of cardiovascular and metabolic disease risk.

Pathways and Mechanisms

Regulation of Lipid Metabolism

The synthesis and regulation of lipids, crucial for cellular function and energy storage, involve complex metabolic pathways and regulatory mechanisms. A key enzyme in cholesterol biosynthesis, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), catalyzes a rate-limiting step in the mevalonate pathway. ^[15]Common single nucleotide polymorphisms (SNPs) inHMGCRare associated with varying low-density lipoprotein cholesterol (LDL-C) levels, with some affecting the alternative splicing of exon 13, thereby impacting gene expression or protein function.^[16] This post-transcriptional regulatory mechanism significantly contributes to individual differences in cholesterol homeostasis and susceptibility to dyslipidemia.

Beyond cholesterol synthesis, other genes play critical roles in circulating lipid levels. For instance, ANGPTL3 is known to regulate overall lipid metabolism, influencing the processing and clearance of various lipoproteins. ^[17] Similarly, variations in ANGPTL4have been shown to reduce triglyceride concentrations while simultaneously increasing high-density lipoprotein (HDL) levels.^[18]These genes, through their involvement in diverse aspects of lipid processing, highlight the intricate network of metabolic regulation that maintains lipid homeostasis, where dysregulation can lead to conditions like hyperlipidemia and increased risk of cardiovascular diseases.^[4]

Genetic Modulators of Metabolite Homeostasis

Genetic variations frequently modulate the homeostasis of a wide array of endogenous metabolites, providing direct insights into disease mechanisms. For example, theSLC2A9gene, encoding a member of the facilitative glucose transporter family, has been identified as a critical urate transporter.^[19] Polymorphisms in SLC2A9significantly influence serum uric acid concentrations, demonstrating pronounced sex-specific effects and playing a role in conditions like gout.^[11] This highlights how genetic differences can directly impact the transport and excretion pathways of specific metabolites, altering their systemic levels.

Another important example involves the FADS1 FADS2 gene cluster, where common genetic variants and their reconstructed haplotypes are strongly associated with the composition of fatty acids in phospholipids. ^[20] These genes are crucial for the biosynthesis of polyunsaturated fatty acids, and thus, genetic differences can directly alter the body’s lipid profiles. The emerging field of metabolomics, which comprehensively measures endogenous metabolites, offers a functional readout of the physiological state, enabling the identification of genetic variants that directly influence metabolite conversion or modification and providing a detailed understanding of affected metabolic pathways. ^[13]

Signaling and Inflammatory Crosstalk in Metabolic Health

The interplay between signaling pathways and inflammatory responses is crucial for maintaining metabolic health, with genetic variants often modulating this intricate crosstalk. Loci associated with metabolic-syndrome pathways, including LEPR, HNF1A, IL6R, and GCKR, have been linked to plasma C-reactive protein (CRP) levels.^[10] HNF1A, which encodes hepatocyte nuclear factor-1 alpha, exhibits polymorphisms associated with CRP, indicating its regulatory role in inflammation. ^[1] This suggests a direct genetic influence on the systemic inflammatory state, which is often a hallmark of metabolic dysfunction.

The expression of C-reactive protein, a key inflammatory marker, is tightly controlled by various signaling molecules and transcription factors. Its gene expression is notably enhanced by transcription factor c-Rel, which facilitates the binding of C/EBPbeta to the CRP promoter.^[21] Furthermore, two synergistic interleukin-6 (IL-6) responsive elements and overlapping binding sites for OCT-1 and NF-kappaB on the proximal promoter regulate both basal and induced CRP expression. ^[22]These complex regulatory mechanisms demonstrate how specific genetic variations within these pathways can alter inflammatory responses, contributing to conditions like metabolic syndrome and cardiovascular disease.

Integrated Genetic and Metabolic Network Control

Understanding complex diseases requires an integrated view of how genetic variants influence interconnected biological networks. While genome-wide association studies (GWAS) effectively identify genetic polymorphisms linked to disease risk, they often provide limited insight into the underlying disease-causing mechanisms.^[13] The integration of metabolomics with GWAS bridges this gap by offering a comprehensive functional readout of the physiological state, revealing how genetic variants alter the homeostasis of key metabolites. ^[13] This approach allows for a more detailed probing of the human metabolic network, identifying specific pathways affected by genetic differences and their broader implications for health.

The identification of major genetically determined ‘metabotypes’ is crucial for functionally understanding the genetics of complex diseases, including the intricate pathway crosstalk and emergent properties of biological systems. ^[13] For instance, by analyzing the ratios between concentrations of direct substrates and products of enzymatic conversions, researchers can gain insights into the functional impact of genetic variations on enzyme activity and metabolic flux. ^[13] This integrated perspective, combining genotyping with metabolomics, promises to unlock new avenues for investigating gene-environment interactions and developing individualized therapeutic strategies for complex diseases.

Clinical Relevance

Genetic Risk for Cardiovascular Disease and Stratification

Genetic risk profiles for lipid levels, particularly those related to Total Cholesterol (TC), demonstrate significant prognostic value in identifying individuals at increased risk for cardiovascular disease. These profiles are strongly associated with clinically defined hypercholesterolemia (serum cholesterol over 6.5 mmol/l) and subclinical atherosclerosis, as evidenced by intima media thickness (IMT).^[23]The TC genetic risk score, in particular, has been highlighted as a powerful predictor of atherosclerosis and coronary heart disease (CHD).^[23]This predictive capability extends beyond traditional risk factors, as adding genetic scores to age, sex, and body mass index (BMI) significantly improves the prediction of hypercholesterolemia, enhancing discriminative accuracy.^[23]

The improved prediction offered by these genetic profiles facilitates enhanced risk stratification and the implementation of personalized medicine approaches. By accurately identifying high-risk groups for dyslipidemias and related cardiovascular events, clinicians can apply early preventive strategies.^[23]This includes more targeted screening, lifestyle interventions, and potentially earlier pharmacological treatment. The ability of genetic profiles to improve CHD risk classification when integrated with conventional clinical risk factors such as lipid values, age, BMI, and sex further underscores their utility in guiding comprehensive patient management and preventative care.^[23]

Diagnostic Enhancement and Therapeutic Guidance

Genetic scores for various lipoprotein levels, including High-Density Lipoprotein (HDL) cholesterol, Low-Density Lipoprotein (LDL) cholesterol, and triglycerides, offer valuable diagnostic insights by demonstrating a clear, stepwise relationship with mean lipoprotein concentrations across deciles of genotype score.^[4]This correlation allows for the identification of individuals whose lipoprotein levels cross established clinical thresholds for ‘high’ or ‘low’ values, thereby aiding in the diagnosis and characterization of polygenic dyslipidemia.^[4] Such diagnostic enhancement can lead to earlier recognition of lipid abnormalities that might otherwise be overlooked or underestimated by traditional risk assessments alone.

The utility of these genetic profiles also extends to informing therapeutic guidance and monitoring strategies. The ability to ascertain high-risk groups for dyslipidemias suggests that genetic information could guide decisions regarding the initiation or intensity of lipid-lowering therapies and influence patient monitoring protocols. ^[23]While direct evidence for guiding treatment selection or monitoring frequency based on these specific genetic scores is still developing, their role in early detection of dyslipidemias and related cardiovascular risk implies a future where genetic insights contribute to more precise and effective patient management plans.^[23]

Understanding Complex Lipid Metabolism and Clinical Context

The research highlights the complex genetic architecture underlying lipid metabolism, revealing that multiple independent common alleles at numerous loci contribute to polygenic dyslipidemia. ^[4]This understanding emphasizes that lipid disorders are not driven by single genes but rather by the cumulative effect of many genetic variants. Interestingly, while obesity is known to correlate with lipid levels, the lipid-associated loci identified in these studies were not significantly associated with BMI, suggesting distinct genetic pathways influencing these metabolic traits.^[23]The development of a “combined profile” incorporating associated SNPs for multiple lipid traits (TC, HDL, LDL, and triglycerides) further acknowledges the interconnectedness of these factors in determining overall cardiovascular risk.^[23]

It is important to consider the clinical context and generalizability of these findings. The studies were conducted in population cohorts not specifically selected for disease, providing a broad understanding of genetic contributions to lipid levels.^[23] However, the cohorts were primarily composed of middle-aged to elderly individuals of European descent. ^[6] This demographic specificity suggests that the applicability of these specific genetic associations may vary in younger populations or individuals of diverse ethnic and racial backgrounds. ^[6] Therefore, ongoing research and replication in varied populations are crucial to fully elucidate the clinical utility and broader implications of these genetic insights across the global population.

References

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[2] Pare G et al. Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women. PLoS Genet. 2008.

[3] Vasan RS. Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study. BMC Med Genet. 2007.

[4] Kathiresan S et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008.

[5] Yang, Q et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. Suppl 1, 2007, p. S11.

[6] Benjamin EJ. Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med Genet. 2007.

[7] Willer CJ et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, 2008.

[8] Hwang SJ. A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study. BMC Med Genet. 2007.

[9] O’Donnell CJ. Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study. BMC Med Genet. 2007.

[10] Ridker PM et al. “Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma c-reactive protein: The women’s genome health study.”Am J Hum Genet, 2008.

[11] Doring, Angela, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nature Genetics, vol. 40, no. 4, 2008, pp. 430-36.

[12] Uda, Manuela, et al. “Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia.”Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 5, 2008, pp. 1620-25.

[13] Gieger C et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, 2008.

[14] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nature Genetics, vol. 41, no. 1, 2009, pp. 47-55.

[15] Goldstein JL, Brown MS. “Regulation of the mevalonate pathway.” Nature, 1990.

[16] 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, 2008.

[17] Koishi R et al. “Angptl3 regulates lipid metabolism in mice.” Nat Genet, 2002.

[18] Romeo S et al. “Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL.” Nat Genet, 2007.

[19] Li S et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, 2007.

[20] Schaeffer L et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet, 2006.

[21] Agrawal A, Samols D, Kushner I. “Transcription factor c-Rel enhances C-reactive protein expression by facilitating the binding of C/EBPbeta to the promoter.”Mol Immunol, 2003.

[22] Li SP, Goldman ND. “Regulation of human C-reactive protein gene expression by two synergistic IL-6 responsive elements.”Biochemistry, 1996.

[23] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nature Genetics, vol. 40, no. 12, 2008, pp. 1414-23.