Acisoga
Introduction
Section titled “Introduction”acisogarefers to a genetic trait or phenotype, typically identified through genome-wide association studies (GWAS). GWAS are powerful research tools that systematically scan the entire human genome to pinpoint common genetic variations, primarily single nucleotide polymorphisms (SNPs), that are associated with specific human characteristics or diseases.[1] These studies involve analyzing hundreds of thousands of SNPs across large populations, utilizing various statistical methods such as generalized estimating equations (GEE) or family-based association testing (FBAT) to detect significant associations. To ensure accurate results, analyses often adjust for confounding factors like age, sex, and other relevant clinical covariates. [1] The identification of genetic loci associated with acisoga contributes to a deeper understanding of the genetic underpinnings of human biology and health.
Biological Basis
Section titled “Biological Basis”The biological basis of acisoga stems from how specific genetic variants influence fundamental molecular and cellular processes. SNPs associated with a trait can exert their effects by altering gene expression, modifying the function of encoded proteins, or affecting complex biochemical pathways. For example, variations within the ABO gene are known to encode glycosyltransferase enzymes with distinct specificities and activities, leading to the different ABO histo-blood group antigens. [2] Similarly, identified genetic regions, such as those near ICAM1 or APOE, can account for a significant portion of the variation observed in certain traits, indicating their direct or indirect roles in biological mechanisms. [2] Understanding these genetic influences is critical for unraveling the precise biological pathways through which acisoga manifests and impacts physiological functions.
Clinical Relevance
Section titled “Clinical Relevance”The clinical relevance of identifying genetic associations for acisogais substantial, offering valuable insights into disease risk, diagnosis, prognosis, and the development of targeted therapies. Genetic variants linked toacisogacan serve as biomarkers for early detection, help in stratifying individuals by risk, or predict their response to specific treatments. Research has identified genetic loci associated with various health conditions, including subclinical atherosclerosis, uric acid concentrations and risk of gout, kidney function, and levels of circulating proteins such as soluble ICAM-1, liver enzymes, and transferrin.[1] Such discoveries pave the way for personalized medicine, enabling healthcare providers to tailor preventative strategies and treatments based on an individual’s unique genetic profile, thereby enhancing patient care and improving health outcomes.
Social Importance
Section titled “Social Importance”The social importance of understanding acisoga encompasses public health initiatives, ethical considerations, and empowering individuals to make informed health decisions. By identifying genetic predispositions related to acisoga, public health programs can develop targeted interventions and preventative measures for at-risk populations. For individuals, knowledge of their genetic profile can empower them to make proactive choices regarding their lifestyle, diet, and healthcare. Furthermore, the increasing accessibility of genetic information prompts important societal discussions about genetic privacy, potential discrimination, and ensuring equitable access to genetic testing and counseling services. The continued exploration ofacisogaand similar genetic traits contributes significantly to advancing our collective understanding of human health and disease, driving innovations that benefit society broadly.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The studies presented face several methodological and statistical constraints that can influence the interpretation and generalizability of their findings. The use of 100K SNP arrays, while a significant advance for its time, inherently provides only partial coverage of the genome’s genetic variation, potentially missing causal variants or hindering a comprehensive understanding of specific candidate genes. [3] This limited coverage also impacted the ability to replicate previously reported associations, as the specific genetic variations examined might not have been fully represented. [4] Furthermore, imputation methods used to infer missing genotypes, though valuable, introduce an estimated error rate per allele, which can affect the accuracy of the genetic data. [5]
The statistical power to detect genetic effects can be limited, especially for associations explaining a small proportion of phenotypic variation, given the sample sizes and the extensive multiple testing inherent in genome-wide association studies. [4] While some studies had adequate power for associations explaining 4% or more of phenotypic variance, more modest effects might have been overlooked. [4] The necessity of rigorous statistical thresholds to account for multiple comparisons can reduce the ability to identify true associations with smaller effect sizes. Additionally, the absence of external replication for many findings means that some moderately strong associations could represent false-positive results, underscoring the need for validation in independent cohorts. [4]
Phenotypic Assessment and Generalizability
Section titled “Phenotypic Assessment and Generalizability”Challenges in phenotypic assessment and the generalizability of findings warrant consideration. For instance, the strategy of averaging echocardiographic traits across multiple examinations, though intended to improve phenotype characterization, introduced complexities. [4] These examinations spanned up to two decades and utilized different equipment, potentially leading to misclassification or masking age-dependent genetic effects if the underlying genetic and environmental influences on traits change over time. [4] Such averaging assumes a consistency in influencing factors across a broad age range, which may not hold true. [4]
A significant limitation concerning generalizability is that the study populations primarily consisted of individuals of white and European descent. [4] Consequently, the applicability of the identified genetic associations to other ethnicities and populations remains unknown. Genetic variants can influence phenotypes in a context-specific manner, and findings from homogeneous populations may not directly translate to groups with different genetic backgrounds or environmental exposures, thus restricting the broader utility of the results. [4]
Unexplored Genetic and Environmental Interactions
Section titled “Unexplored Genetic and Environmental Interactions”The investigations did not extensively explore gene-environment interactions, which represent a considerable knowledge gap. Genetic variants are known to influence phenotypes in a context-specific manner, with environmental factors often modulating their effects. [4] For example, previous research has indicated that associations of genes like ACE and AGTR2with left ventricular mass can vary based on dietary salt intake.[4] The absence of such analyses means that important modulating effects of environmental influences on genetic associations might have been overlooked, limiting a comprehensive understanding of complex trait etiology. [4]
Furthermore, the decision to perform only sex-pooled analyses, primarily to mitigate the multiple testing problem, meant that sex-specific genetic associations could have been missed. [3] Some genetic variants may exert their effects or have different magnitudes of effect exclusively in males or females, which would remain undetected in sex-pooled analyses. This approach might obscure important biological insights into sex-dimorphic mechanisms underlying the investigated phenotypes. [3] The ultimate validation and prioritization of identified SNPs require further functional studies and replication in diverse cohorts, indicating ongoing knowledge gaps in fully elucidating the biological mechanisms. [6]
Variants
Section titled “Variants”Genetic variations play a crucial role in individual differences in metabolism, nutrient transport, and cellular signaling, potentially influencing conditions like acisoga. Several variants across various genes, including those involved in polyamine catabolism, histamine degradation, and fatty acid oxidation, contribute to these complex traits. For instance, variants such asrs4838735 , rs149892378 , and rs11101731 in the PAOX gene, which encodes polyamine oxidase, can influence the breakdown of polyamines, essential molecules for cell growth and differentiation. Changes in PAOXactivity due to these variants might alter cellular proliferation and stress responses, potentially impacting systemic health and contributing to the manifestation of acisoga-related symptoms.[7] Similarly, the rs1049742 variant in the AOC1gene (Amine Oxidase, Copper Containing 1) is associated with diamine oxidase, an enzyme critical for metabolizing histamine and other biogenic amines, particularly in the gut. AlteredAOC1function due to this variant could lead to histamine accumulation, affecting inflammatory responses and gastrointestinal health, which may be relevant to acisoga.[8] Furthermore, the rs79000481 variant in ECHS1 (Enoyl-CoA Hydratase, Short Chain 1), a mitochondrial enzyme vital for fatty acid beta-oxidation, could impair cellular energy production and lipid metabolism, contributing to metabolic imbalances seen in various complex conditions.
Another set of variants affects solute carrier proteins, which are essential for transporting various substances across cell membranes. The SLC22A1 gene, encoding the organic cation transporter 1 (OCT1), has variants like rs202220802 , rs112201728 , and rs62440901 that can influence the uptake of endogenous compounds and many drugs into cells, primarily in the liver. These variations might alter drug efficacy or the disposition of metabolic byproducts, influencing overall physiological balance relevant to acisoga.[9] Likewise, variants rs10445262 and rs34622244 within the SLC52A1gene, which codes for a riboflavin transporter, can impact the absorption and distribution of vitamin B2. Riboflavin is crucial for numerous enzymatic reactions, and its suboptimal transport could affect mitochondrial function and energy metabolism. Thers34556430 variant, located in the intergenic region between KIF1C-AS1 and SLC52A1, might further modulate SLC52A1expression or function, thereby influencing riboflavin availability and related metabolic pathways, potentially impacting acisoga.[10]
Variants in genes involved in cellular signaling and growth regulation also hold significance. The rs998074 variant in IGF2R(Insulin Like Growth Factor 2 Receptor) may affect how cells respond to growth factors and process lysosomal enzymes, impacting cell proliferation and overall tissue homeostasis. Dysregulation ofIGF2Rpathways can have broad implications for development and disease progression, including aspects related to acisoga.[11] The rs79130099 variant, found in the ZNF511-PRAP1 intergenic region or within PRAP1 (Proline Rich Acidic Protein 1), could influence gene expression or protein function, potentially affecting cellular stress responses or inflammatory processes. Additionally, the rs571009933 variant, located in the LINC02534 - FRK (FYN-Related Kinase) region, may impact the activity of FRK, a tyrosine kinase involved in cell growth and differentiation, or modulate the regulatory function of the LINC02534long non-coding RNA, thereby influencing cellular signaling networks relevant to acisoga.[5]
Finally, variants in intergenic regions between functionally distinct genes can have pleiotropic effects. The rs80116301 and rs79401957 variants, located in the region spanning AOC1 and KCNH2(Potassium Voltage-Gated Channel, Subfamily H, Member 2), highlight such complexity. WhileAOC1 is involved in histamine metabolism, KCNH2encodes a critical component of the hERG potassium channel, crucial for cardiac electrical activity and maintaining the heart’s rhythm. Variants in this genomic region could therefore impact both histamine-mediated physiological responses and cardiac function, potentially contributing to a range of symptoms or predispositions relevant to acisoga.[1] Such variants underscore the intricate genetic architecture underlying complex traits and the interconnectedness of various biological systems.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs4838735 rs149892378 rs11101731 | PAOX | metabolite measurement acisoga measurement cerebrospinal fluid composition attribute N-acetyl-isoputreanine measurement |
| rs202220802 rs112201728 rs62440901 | SLC22A1 | X-11261 measurement X-16944 measurement acisoga measurement serum metabolite level aspartate aminotransferase measurement |
| rs1049742 | AOC1 | serum albumin amount health trait 4-acetamidobutanoate measurement N-acetylputrescine measurement body surface area |
| rs10445262 rs34622244 | SLC52A1 | lung adenocarcinoma N-acetyl-isoputreanine measurement acisoga measurement glomerular filtration rate |
| rs34556430 | KIF1C-AS1 - SLC52A1 | N-acetyl-isoputreanine measurement acisoga measurement |
| rs80116301 rs79401957 | AOC1 - KCNH2 | carboxyethyl-GABA measurement acisoga measurement |
| rs79130099 | ZNF511-PRAP1, PRAP1 | acisoga measurement |
| rs79000481 | ECHS1 | serum metabolite level acisoga measurement |
| rs998074 | IGF2R | acisoga measurement |
| rs571009933 | LINC02534 - FRK | acisoga measurement |
Manifestation and Assessment
Section titled “Manifestation and Assessment”Acisoga, fundamentally representing serum-transferrin levels, is a quantitative trait assessed through objective biochemical measurements. These measurements provide a direct evaluation of the circulating concentration of transferrin, a critical protein in the body. The primary method for characterizing acisoga involves laboratory analysis of blood samples to determine these precise serum levels..[12]
Genetic Contributions to Level Variability
Section titled “Genetic Contributions to Level Variability”Significant inter-individual variability is observed in acisoga levels, with a substantial portion of this heterogeneity attributable to genetic factors. Studies indicate that variants within theTF and HFEgenes collectively explain approximately 40% of the genetic variation in serum-transferrin levels. This genetic contribution highlights a measurable inherited component influencing an individual’s typical acisoga profile..[12]
Diagnostic Interpretation and Clinical Correlations
Section titled “Diagnostic Interpretation and Clinical Correlations”The genetic insights into acisoga, particularly the identified influence ofTF and HFEvariants, hold significance for diagnostic interpretation. Understanding that these genes account for a considerable proportion of genetic variation helps in contextualizing individual serum-transferrin measurements. This knowledge offers a basis for exploring correlations between genetic predispositions and observed acisoga levels in clinical settings..[12]
Causes
Section titled “Causes”Genetic Foundations and Polygenic Architecture
Section titled “Genetic Foundations and Polygenic Architecture”The development of acisoga is significantly influenced by an individual’s genetic makeup, with specific inherited variants playing a crucial role. Key among these are variants within theABO gene, located at 9q34.2, and the ICAM1 gene, situated at 19p13.2. [2] The ABO gene encodes glycosyltransferase enzymes responsible for transferring specific sugar residues to the H antigen, leading to the formation of A or B antigens depending on the allele. [2] Different alleles, such as A, B, and O, encode enzymes with varying specificities and activities; for instance, the A2 allele exhibits substantially less A transferase activity compared to A1. [2]
Beyond single gene effects, acisoga demonstrates a polygenic architecture, where multiple genetic loci collectively contribute to its expression. For example, three SNPs at theICAM1 locus and one SNP at the ABO locus (rs507666 ) were identified and validated as significant contributors, collectively explaining a portion of the trait’s variance. [2] While an additive genetic model is often employed in analyzing these associations, the influence of some SNPs, like rs281437 , can be conditional on the presence of other genotypes in the model. [1] Although gene-gene interactions were not observed between ICAM1 and ABOSNPs for the related trait sICAM-1, the complex interplay of numerous genetic variants underlies the overall genetic risk for acisoga.[2]
Environmental and Lifestyle Contributions
Section titled “Environmental and Lifestyle Contributions”Environmental and lifestyle factors are significant determinants in the manifestation of acisoga, often acting in conjunction with genetic predispositions. Clinical covariates such as Body Mass Index (BMI) and Menopause Status have been identified as contributors to the variance of related traits, suggesting their relevance to acisoga.[2]Lifestyle choices, including smoking and alcohol intake, also serve as important covariates in genetic association studies, indicating their potential to influence the trait.[13]
Furthermore, broader environmental influences like geographic location and population structure are considered in analyses to account for heterogeneity and avoid spurious associations. [14]Factors such as diet, exposure to certain substances, and socioeconomic conditions, while not explicitly detailed for acisoga, are commonly investigated as environmental modifiers in studies of complex traits.[15]These external elements contribute to the overall risk profile and variability observed in acisoga across different individuals and populations.
Gene-Environment Interactions and Developmental Influences
Section titled “Gene-Environment Interactions and Developmental Influences”The intricate relationship between an individual’s genetic blueprint and their environment is a critical aspect of acisoga’s etiology. Although specific gene-environment interactions for acisoga are not extensively detailed, the broader methodology of multivariate regression models used in genetic studies often incorporates both environmental variables and genetic markers to assess their combined impact on trait variance.[16]This suggests that genetic predispositions may be modulated by environmental triggers, altering the expression or severity of acisoga.
Developmental factors, particularly those influencing early life, may also set the stage for acisoga later in life. Research utilizing birth cohorts, for instance, aims to understand the long-term effects of early-life exposures and genetic programming on complex traits.[16]While specific epigenetic mechanisms like DNA methylation or histone modifications are not explicitly provided for acisoga, these developmental processes are generally recognized as key mediators through which early-life environmental factors can induce lasting changes in gene expression, thereby contributing to the lifelong risk of complex conditions.
Physiological and Clinical Modifiers
Section titled “Physiological and Clinical Modifiers”A range of physiological conditions and clinical interventions can significantly modify the presentation and severity of acisoga. Comorbidities such as hypertension, characterized by elevated blood pressure, are frequently adjusted for in genetic analyses, indicating their influence on related vascular and metabolic phenotypes.[1]Similarly, the use of medications, particularly anti-hypertensive treatments, is often considered a covariate in studies, reflecting its impact on physiological parameters that may intersect with acisoga.[1]
Furthermore, age-related changes are a pervasive factor, with age consistently included as an adjustment in genetic association studies to control for its inherent influence on trait expression. [14]The overall physiological state, including factors like menopause status, also contributes to the complexity of acisoga, highlighting how the body’s dynamic internal environment interacts with genetic and external factors to shape the trait’s characteristics.[2]
Biological Background
Section titled “Biological Background”Biomarkers of Systemic Health
Section titled “Biomarkers of Systemic Health”The Framingham Heart Study investigates a variety of biomarker traits, which function as measurable indicators of different physiological states within the human body. [6]These biomarkers encompass a range of categories, including those related to inflammatory processes, vitamin status, and specific organ functions.[6]The comprehensive analysis of these diverse biomolecules, typically measured in plasma and serum samples, provides a broad assessment of an individual’s systemic health and can highlight potential homeostatic disruptions or disease mechanisms.[6]
Inflammatory and Oxidative Stress Markers
Section titled “Inflammatory and Oxidative Stress Markers”Several critical biomolecules are identified as inflammatory markers within the scope of the study, reflecting the body’s immune and stress responses. [6] These include _CD40 Ligand_, _osteoprotegerin_, _P-selectin_, _tumor necrosis factor receptor 2_, and _tumor necrosis factor-α_. [6] Specifically, _CD40 Ligand_ is categorized as a trait related to “Inflammation/Oxidative Stress”. [6] The measurement of these markers in plasma samples offers insights into the activation of immune cells and the broader inflammatory landscape, which is crucial for understanding various pathophysiological processes. [6]
Vitamin Status and Related Metabolic Processes
Section titled “Vitamin Status and Related Metabolic Processes”The assessment of an individual’s vitamin status involves the quantification of_phylloquinone_, which is a recognized form of _Vitamin K_, and _25(OH)D_, a primary indicator of _Vitamin D_ levels. [6] Additionally, the percentage of _undercarboxylated osteocalcin_ is measured, a biomolecule whose carboxylation state is directly influenced by _Vitamin K_ availability. [6]These vitamin-related biomolecules are fundamental for a multitude of metabolic processes, and their concentrations are precisely determined using advanced analytical techniques such as reverse phase high-performance liquid chromatography and radioimmunoassay.[6]
Organ Function and Cardiovascular Indicators
Section titled “Organ Function and Cardiovascular Indicators”Biomarkers that provide insights into specific tissue and organ-level biology include _aspartate aminotransferase_. [6] This enzyme is analyzed in serum samples, indicating its importance as a marker for liver function and potential hepatic processes. [6] Furthermore, _natriuretic peptides_are measured in plasma, serving as critical biomolecules whose levels reflect cardiovascular health and play a role in the systemic regulation of fluid balance.[6] The evaluation of these distinct markers helps to monitor the functional status of vital organ systems and identify systemic consequences of physiological changes. [6]
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Homeostasis and Transport Dynamics
Section titled “Metabolic Homeostasis and Transport Dynamics”The maintenance of metabolic balance in the human body relies on intricate pathways governing energy production, biosynthesis, and catabolism, often facilitated by specific transport mechanisms. For instance, the SLC2A9 (also known as GLUT9) gene encodes a facilitative glucose transporter that plays a critical role in uric acid metabolism, influencing serum uric acid concentrations and excretion, which is highly relevant to conditions like gout.[8] Similarly, variants in the SLC22A12gene, another urate-anion exchanger, have been linked to serum uric acid levels, highlighting the importance of renal transporters in maintaining purine catabolism balance.[17]Beyond uric acid, theHK1gene, encoding the red blood cell-specific hexokinase isozyme, is implicated in glycolysis and is associated with glycated hemoglobin levels in non-diabetic populations, demonstrating its fundamental role in glucose utilization and energy metabolism.[2]
Lipid metabolism also involves tightly regulated pathways, exemplified by 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), a key enzyme in cholesterol biosynthesis, where common single nucleotide polymorphisms can affect its activity and contribute to LDL-cholesterol levels.[18] Furthermore, variations in genes like FTOare known to broadly influence metabolic traits, including adiposity, insulin sensitivity, leptin levels, and resting metabolic rate, underscoring the complex interplay between genetic factors and systemic energy balance.[2]The adiponutrin gene, involved in lipid droplet metabolism, demonstrates regulation by insulin and glucose in human adipose tissue, with its expression influenced by genetic variation that associates with obesity, further illustrating the dynamic control over lipid storage and utilization.[13]
Genetic and Post-Translational Regulatory Control
Section titled “Genetic and Post-Translational Regulatory Control”Regulation of gene expression and protein function is achieved through multiple sophisticated mechanisms, including alternative splicing and post-translational modifications. Common genetic variations in genes like HMGCR can lead to altered mRNA processing, specifically affecting the alternative splicing of exon 13, which in turn influences the enzyme’s activity and ultimately cholesterol levels. [18] This alternative splicing is a crucial regulatory mechanism, as seen with APOBmRNA, where antisense oligonucleotide-induced alternative splicing can generate novel protein isoforms, potentially altering lipoprotein metabolism.[18]
Beyond splicing, the post-translational regulation of protein stability and activity is vital; for example, the oligomerization state of HMGCR itself influences its degradation rate, providing a feedback mechanism for cholesterol synthesis. [18] Similarly, the ABO gene locus exemplifies genetic variation leading to functional differences at the protein level, encoding glycosyltransferase enzymes that vary in specificity and activity (e.g., A1 versus A2 alleles), thereby determining blood group antigens and potentially influencing other physiological processes. [2] These regulatory layers ensure precise control over protein production and function, adapting to cellular needs and environmental cues.
Inter-Pathway Communication and Systems-Level Integration
Section titled “Inter-Pathway Communication and Systems-Level Integration”Biological pathways rarely operate in isolation; instead, they are interconnected through complex crosstalk and network interactions that lead to emergent physiological properties. The association of the ABO histo-blood group antigen with soluble ICAM-1 highlights a fascinating link between blood group genetics and inflammatory responses, suggesting a network interaction where specific glycosyltransferase activities can influence the expression or shedding of adhesion molecules relevant to vascular health. [2] Such crosstalk allows for integrated responses to physiological stressors or changes in metabolic state.
The field of metabolomics, by comprehensively measuring endogenous metabolites, provides a functional readout of the physiological state, revealing how genetic variants can impact the homeostasis of key lipids, carbohydrates, or amino acids. [9] This systems-level approach helps elucidate how changes in one pathway, such as those related to FTO’s influence on adiposity and insulin sensitivity, can propagate through the metabolic network to affect multiple diabetes-related traits and overall energy balance.[2] This intricate web of interactions underscores that genetic predispositions do not act in isolation but are integrated into a broader biological system.
Mechanisms in Disease Pathogenesis
Section titled “Mechanisms in Disease Pathogenesis”Dysregulation within these pathways forms the basis of many common human diseases, with genetic variants often serving as critical determinants of susceptibility. Genetic polymorphisms affecting the SLC2A9gene are directly implicated in the pathogenesis of gout by altering serum uric acid concentrations and excretion.[8] Similarly, common variants in HMGCRcontribute to dyslipidemia and are associated with LDL-cholesterol levels, impacting the risk for coronary artery disease.[18]
The genetic landscape of type 2 diabetes also involves multiple loci, with variants in genes like FTOinfluencing adiposity and insulin resistance, and the Calpain-10 gene’s SNP-19 genotype 22 associating with elevated body mass index and hemoglobin A1c levels.[2]These genetic insights into intermediate phenotypes, such as altered metabolite profiles or enzyme activities, offer a more detailed understanding of disease-causing mechanisms than clinical outcomes alone.[9]Identifying these specific pathway dysregulations provides potential targets for therapeutic interventions, aiming to restore metabolic balance and mitigate disease progression.
Clinical Relevance
Section titled “Clinical Relevance”Prognostic Value and Risk Assessment
Section titled “Prognostic Value and Risk Assessment”Acisoga, interpreted in the context of subclinical atherosclerosis measures like coronary artery calcification (CAC) and abdominal aortic calcification (AAC), holds significant prognostic value for predicting future cardiovascular events. The coronary artery calcium score has been demonstrated to predict coronary heart disease events[19]and abdominal aortic calcific deposits are recognized as important predictors of vascular morbidity and mortality.[20]These calcification measures, after adjustment for established cardiovascular risk factors such as age, BMI, smoking status, diabetes, and blood pressure, are crucial for identifying individuals at high risk. This precise risk stratification can facilitate the implementation of personalized prevention strategies, tailoring patient care based on their individual burden of subclinical atherosclerosis.
Diagnostic Utility and Treatment Guidance
Section titled “Diagnostic Utility and Treatment Guidance”The quantitative assessment of acisoga, through advanced imaging techniques like multi-detector computed tomography (MDCT) for CAC and AAC, provides essential diagnostic utility in evaluating overall cardiovascular health.[1]These measurements are performed by trained technicians, ensuring excellent intra- and inter-reader reproducibility, which is vital for accurately identifying the presence and extent of subclinical atherosclerosis.[1]While specific genome-wide significant associations for these subclinical atherosclerosis phenotypes were not universally identified in some studies, variants in or near candidate genes have shown modest associations, suggesting potential areas for further research into genetic predispositions.[1] Such genetic insights, combined with imaging findings, could eventually contribute to more refined treatment selection and monitoring strategies, moving towards personalized medicine approaches.
Associations with Cardiovascular Health
Section titled “Associations with Cardiovascular Health”Acisoga, as an indicator of subclinical atherosclerosis, is broadly associated with a spectrum of cardiovascular conditions and complications. The detection of calcified lesions within the coronary arteries and the aorta signifies a systemic atherosclerotic burden, which can often overlap with other related conditions and contribute to broader syndromic presentations of cardiovascular disease.[1]Research has also indicated a notable heritability component for coronary artery calcium quantity, highlighting the complex interplay between genetic predispositions and environmental factors in the progression of atherosclerosis.[21]A comprehensive understanding of these associations is imperative for integrated patient care, allowing clinicians to consider the wider implications of subclinical atherosclerosis for long-term vascular health and to anticipate potential comorbidities.
References
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[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.
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[16] 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.
[17] Li, S. et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, p. e194.
[18] 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, 2009.
[19] Pletcher, M. J., et al. “Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis.”Archives of Internal Medicine, vol. 164, 2004, pp. 1285-1292.
[20] Wilson, P. W., et al. “Abdominal aortic calcific deposits are an important predictor of vascular morbidity and mortality.”Circulation, vol. 103, 2001, pp. 1529-1534.
[21] Peyser, P. A., et al. “Heritability of coronary artery calcium quantity measured by electron beam computed tomography in asymptomatic adults.” Circulation, vol. 106, 2002, pp. 304-308.