Dodecanedioate
Dodecanedioate is a dicarboxylic acid, an organic compound containing two carboxyl functional groups. In human biology, it serves as a metabolite, playing a role in the catabolism of fatty acids, particularly through a metabolic pathway known as omega-oxidation. This pathway can become more active when the primary beta-oxidation pathway for fatty acid breakdown is impaired or overwhelmed, leading to the production of dicarboxylic acids like dodecanedioate.
As a detectable compound in human serum, dodecanedioate is a subject of metabolomics, a field dedicated to the comprehensive study of metabolites within an organism. Research, including genome-wide association studies (GWAS), investigates the genetic factors that influence the levels of various metabolites, including dodecanedioate, in the body.[1] Such studies aim to uncover how genetic variations contribute to individual differences in metabolic profiles.
The concentration of dodecanedioate and other metabolites can be clinically relevant, potentially serving as biomarkers for metabolic health. Alterations in fatty acid metabolism, reflected by changes in dicarboxylic acid levels, are often associated with metabolic disorders. Given that many genetic studies focus on lipid levels and cardiovascular disease risk, understanding the genetic determinants of metabolites like dodecanedioate could provide insights into conditions such as dyslipidemia and coronary artery disease.[2] The social importance of this research lies in its potential to advance personalized medicine, enabling earlier risk prediction for metabolic diseases and informing targeted preventative or therapeutic strategies based on an individual’s unique genetic and metabolic makeup.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The studies contributing to the understanding of dodecanedioate levels face several methodological and statistical limitations that impact the robustness and interpretability of findings. Moderate sample sizes in some cohorts, despite being well-characterized, led to insufficient statistical power to detect genetic effects of modest size, increasing the risk of false negative findings, especially after accounting for the extensive multiple testing inherent in genome-wide association studies (GWAS).[3]Furthermore, the reliance on earlier generation SNP arrays with partial coverage of genetic variation, such as 100K gene chips, meant that some genes or causal variants might have been missed due to inadequate SNP density, potentially underestimating the genetic contribution to dodecanedioate levels.[4] Imputation analyses, while expanding SNP coverage, also introduced a small but notable error rate (e.g., 1.46% to 2.14% per allele in some studies), which could affect the accuracy of associations for imputed SNPs. [5]
Replication across studies also presented challenges, with only a fraction of reported associations consistently validated. This can stem from true false positive findings in initial reports, differences in study design, or inadequate statistical power in replication cohorts. [3] Moreover, non-replication at the SNP level does not always imply a lack of true association, as different studies might identify distinct SNPs in strong linkage disequilibrium with an underlying causal variant, or even multiple causal variants within the same gene region, complicating direct comparisons and the identification of the precise genetic drivers. [6] The process of prioritizing SNPs for follow-up remains a significant hurdle in GWAS, highlighting the need for more systematic approaches to confirm and functionally characterize genetic associations. [3]
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”A significant limitation across the studies is the restricted demographic profile of the participant cohorts, predominantly comprising individuals of self-reported European ancestry, often middle-aged to elderly. [3] This narrow demographic base limits the generalizability of the findings to younger populations or individuals of other ethnic and racial backgrounds, where genetic architecture, environmental exposures, and gene-environment interactions might differ substantially. [3] While some efforts were made to extend findings to multiethnic samples, the primary discovery and replication cohorts remained largely homogenous. [7]
Phenotypic characterization also introduced potential biases; for instance, collecting DNA at later examinations in some cohorts may have introduced a survival bias, affecting the representativeness of the sample. [3] The practice of averaging phenotypic traits across multiple examinations, sometimes spanning decades and involving different equipment, could lead to misclassification and mask age-dependent genetic effects. [8]Furthermore, the decision in some studies to perform only sex-pooled analyses, rather than sex-specific investigations, means that genetic associations unique to males or females may have been overlooked, potentially obscuring important biological differences in dodecanedioate regulation.[9]
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”The current research largely focused on identifying genetic associations, but often did not delve into the complex interplay between genes and environmental factors. Genetic variants influencing dodecanedioate levels may act in a context-specific manner, with their effects being modulated by various environmental influences such as diet or lifestyle, which were not comprehensively investigated.[8]The absence of gene-environment interaction analyses represents a significant knowledge gap, as understanding these interactions is crucial for a complete picture of dodecanedioate regulation and for developing personalized health interventions.[8]
Despite identifying a number of associated loci, GWAS inherently provide associations rather than direct causal mechanisms. The studies acknowledge that the identified SNPs may be in linkage disequilibrium with the true causal variants, and further functional studies are essential to elucidate the biological pathways through which these genetic variants influence dodecanedioate levels.[3]The concept of “missing heritability” also remains relevant, as the identified genetic variants typically explain only a fraction of the total phenotypic variance for complex traits, suggesting that many other genetic factors, including rare variants, structural variations, or epigenetic modifications, as well as unmeasured environmental factors, still contribute to dodecanedioate variability and await discovery.
Variants
Section titled “Variants”Genetic variations play a crucial role in shaping an individual’s metabolic profile, including the processing of fatty acids like dodecanedioate. Several variants in genes involved in diverse metabolic and cellular pathways have been identified, with potential implications for how the body handles this dicarboxylic acid. These genes include those directly involved in fatty acid metabolism, as well as those regulating broader cellular functions, transport, and detoxification.
The CYP4A11gene encodes a cytochrome P450 enzyme that is critically involved in the omega-hydroxylation of fatty acids. This biochemical pathway is essential for the metabolism and eventual elimination of various fatty acids, including medium-chain dicarboxylic acids such as dodecanedioate. Variants inCYP4A11, such as rs1126742 , rs28451040 , and rs1126743 , can lead to altered enzyme activity or expression levels, which may influence the rate at which dodecanedioate is metabolized. Consequently, these genetic differences could affect an individual’s capacity to process dodecanedioate, impacting its concentration in the body or its role in energy production. The adjacent pseudogeneCYP4Z2P, linked by the rs12132488 variant, might also exert regulatory effects on CYP4A11 expression, further contributing to inter-individual metabolic variability. [10]
Other notable variants are found in genes responsible for transport, detoxification, and fundamental cellular processes. The SLCO1B1 gene, associated with rs77289848 , produces OATP1B1, an organic anion transporter vital for the liver’s uptake of a wide array of compounds, including drugs and endogenous metabolites. Variations in SLCO1B1can impact the efficiency of hepatic clearance, potentially influencing the disposition of dodecanedioate or related metabolic intermediates. Similarly, theUGT1A8 and UGT1A10 genes, represented by the rs114346341 variant, encode UDP-glucuronosyltransferases, enzymes crucial for glucuronidation—a key detoxification pathway that renders hydrophobic molecules more water-soluble for excretion. Altered glucuronidation capacity due to this variant could affect the elimination of various substances, thereby indirectly influencing overall metabolic homeostasis relevant to dodecanedioate. Furthermore, thers78007058 variant in PGGT1B (Protein Geranylgeranyltransferase Type I Beta Subunit) points to the involvement of protein prenylation in cellular signaling pathways that might broadly impact lipid metabolism. [2]
Further genetic variations highlight the complex regulatory networks that might influence dodecanedioate metabolism. Thers577323349 variant is situated near CREG1 and RCSD1. CREG1 is known for its role in regulating cell growth and differentiation, while RCSD1 participates in the maturation of nicotinic acetylcholine receptors. Variations here could affect broader cellular processes that underpin metabolic health. The HIVEP2 gene, with its rs186230115 variant, encodes a transcription factor involved in neural development and gene regulation, suggesting potential widespread effects on physiological functions. Additionally, the rs13217642 variant within the MIR4462 - MDGA1 locus involves MIR4462, a microRNA that modulates gene expression, and MDGA1, a protein implicated in cell adhesion; both contribute to fundamental biological processes that maintain tissue function and metabolic balance. Lastly, variants such as rs4540297 in LINC02542 and rs3908502 in the OVAAL - XPR1 region underscore the growing recognition of long non-coding RNAs (LINC02542, OVAAL) in gene regulation and the role of XPR1in phosphate transport, all of which are integral to the intricate cellular environment that governs metabolism and potentially influences dodecanedioate levels.[2]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1126742 rs28451040 rs1126743 | CYP4A11 | undecenoylcarnitine (C11:1) measurement X-18899 measurement 10-undecenoate 11:1n1 measurement X-24748 measurement X-24309 measurement |
| rs12132488 | CYP4Z2P - CYP4A11 | metabolite measurement serum metabolite level X-24949 measurement tetradecanedioate measurement dodecanedioate measurement |
| rs77289848 | SLCO1B1 | dodecanedioate measurement urinary metabolite measurement polyunsaturated fatty acids to monounsaturated fatty acids ratio |
| rs577323349 | CREG1 - RCSD1 | metabolite measurement dodecanedioate measurement |
| rs186230115 | HIVEP2 | dodecanedioate measurement |
| rs114346341 | UGT1A10, UGT1A8 | bilirubin measurement dodecanedioate measurement |
| rs78007058 | PGGT1B | lipid measurement dodecanedioate measurement |
| rs13217642 | MIR4462 - MDGA1 | dodecanedioate measurement |
| rs4540297 | LINC02542 | dodecanedioate measurement |
| rs3908502 | OVAAL - XPR1 | dodecanedioate measurement |
Biological Background of Dodecanedioate
Section titled “Biological Background of Dodecanedioate”Metabolic Processing and the Carnitine System
Section titled “Metabolic Processing and the Carnitine System”Dodecanedioate, as a dicarboxylic acid, plays a role in fatty acid metabolism, where it can be transformed into its carnitine conjugate, a dicarboxylacylcarnitine.[1]These carnitine-bound fatty acids are crucial for their transport into the mitochondria, the cellular powerhouses, where they undergo beta-oxidation to generate energy.[1]This complex process, known as the carnitine shuttle, ensures the efficient utilization of fatty acids, including dicarboxylic acids like dodecanedioate, as a vital fuel source for various bodily functions, especially during periods of high energy demand or fasting. The concentrations of these acylcarnitines, including dicarboxylacylcarnitines, serve as important indicators of the metabolic state of fatty acid breakdown within the body.[1]
Genetic Influences on Fatty Acid Oxidation
Section titled “Genetic Influences on Fatty Acid Oxidation”The efficiency of fatty acid oxidation, a fundamental metabolic pathway, is significantly modulated by genetic factors, particularly through polymorphisms in genes encoding key enzymes such as Medium-Chain Acyl-CoA Dehydrogenase (MCAD) and Short-Chain Acyl-CoA Dehydrogenase (SCAD). [1] Genetic variants in these enzymes can lead to altered enzymatic turnover, resulting in reduced dehydrogenase activity and consequently affecting the rate at which fatty acids are broken down. [1] Such genetic variations can manifest as altered concentrations of acylcarnitines; for instance, higher levels of longer-chain acylcarnitine substrates and lower levels of shorter-chain acylcarnitine products may indicate impaired enzyme function. [1]These frequent genetically determined “metabotypes” can interact with environmental factors like nutrition and lifestyle, influencing an individual’s susceptibility to specific metabolic phenotypes.[1]
Physiological Consequences of Impaired Metabolism
Section titled “Physiological Consequences of Impaired Metabolism”Impaired beta-oxidation, often stemming from genetic variants in enzymes like MCAD or SCAD, can lead to a range of physiological disruptions. [1] While severe deficiencies in these enzymes can result in systemic disorders characterized by hypoketotic hypoglycemia, lethargy, encephalopathy, and seizures, more common genetic variants may present with a relatively moderate phenotypic expression. [1] Individuals who are homozygous for at least one of the minor alleles of SCAD or MCAD polymorphisms are likely to show signs of impaired beta-oxidation, particularly under conditions of metabolic stress. [1]This can lead to symptoms such as tiredness, loss of alertness, headache, and memory problems during prolonged starvation or intense physical activity, due to insufficient energy production and glucose regulation.[1]
Interplay with Systemic Energy Homeostasis
Section titled “Interplay with Systemic Energy Homeostasis”Fatty acid oxidation is a critical source of energy, particularly for metabolically active tissues such as muscle and heart, and becomes even more vital when glucose availability is low. Dicarboxylic acids like dodecanedioate, when metabolized, contribute to this energy supply, and their processing is intricately linked to the body’s overall energy homeostasis.[1]Disruptions in the oxidation of these fatty acids and their carnitine conjugates can lead to systemic energy imbalances, impacting the function of various organ systems.[1] The identification of specific acylcarnitine patterns, including those related to dicarboxylacylcarnitines, can serve as valuable biomarkers for detecting these metabolic disturbances, offering insights into the efficiency of mitochondrial energy production and an individual’s metabolic adaptability. [1]
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Processing and Catabolism of Fatty Acids
Section titled “Metabolic Processing and Catabolism of Fatty Acids”Dodecanedioate, as a dicarboxylic acid, is intrinsically linked to fatty acid metabolism, particularly through pathways that regulate the breakdown and synthesis of lipids. The catabolism of fatty acids, primarily through beta-oxidation, is crucial for energy production, where long-chain fatty acids are sequentially shortened to yield acetyl-CoA. Genetic variants in enzymes like short-chain acyl-CoA dehydrogenase (SCAD) and medium-chain acyl-CoA dehydrogenase (MCAD) directly impact this process; individuals homozygous for minor alleles of these polymorphisms exhibit reduced dehydrogenase activity, leading to higher concentrations of longer-chain fatty acid substrates and lower levels of shorter-chain products, indicative of impaired beta-oxidation. [1] This enzymatic inefficiency can result in the accumulation of specific acylcarnitines, which are indirect substrates of these enzymes, highlighting a critical point of metabolic regulation and flux control within the mitochondrial energy metabolism. [1]
Genetic and Transcriptional Regulation of Lipid Metabolism
Section titled “Genetic and Transcriptional Regulation of Lipid Metabolism”The regulation of lipid metabolism involves intricate genetic control, where specific gene clusters and their variants dictate enzyme activity and metabolite profiles. For instance, common genetic variants within theFADS1 gene cluster are associated with the fatty acid composition in phospholipids, directly influencing the delta-5 desaturase reaction that converts eicosatrienoyl-CoA (C20:3) to arachidonyl-CoA (C20:4). [1] This regulation extends to the biosynthesis of complex lipids like glycerol-phosphatidylcholins, where the efficiency of desaturase activity is reflected in the ratio of modified substrates and products. [1] Furthermore, transcriptional factors such as SREBP-2 are known to regulate aspects of lipid metabolism, linking isoprenoid and adenosylcobalamin pathways, demonstrating how gene expression controls the availability and activity of key metabolic enzymes. [11]
Post-Translational Modification and Allosteric Control
Section titled “Post-Translational Modification and Allosteric Control”Beyond transcriptional regulation, the activity of metabolic enzymes is finely tuned through post-translational modifications and allosteric control mechanisms. For example, the alternative splicing of HMGCR, as influenced by common single nucleotide polymorphisms (SNPs), can alter the protein’s structure and function, thereby impacting cholesterol biosynthesis.[12] Such modifications, alongside allosteric regulation by various metabolites, ensure that enzyme activities are dynamically adjusted in response to cellular energy demands and nutrient availability, preventing metabolic imbalances. These regulatory layers provide rapid and reversible control over enzyme function, complementing slower transcriptional changes to maintain metabolic homeostasis.
Systems-Level Integration and Metabolic Crosstalk
Section titled “Systems-Level Integration and Metabolic Crosstalk”Metabolic pathways are not isolated but form complex, interconnected networks where crosstalk and hierarchical regulation are fundamental to emergent physiological properties. The influence of genetic variants on specific metabolite concentrations, termed “metabotypes,” demonstrates how alterations in one pathway can propagate throughout the metabolic network, affecting the homeostasis of key lipids, carbohydrates, or amino acids. [1] For instance, the regulation of lipid metabolism by angiopoietin-like proteins such as ANGPTL3 and ANGPTL4influences triglyceride and HDL levels, illustrating how specific signaling molecules integrate into broader metabolic circuits.[10]This systemic integration underscores that the concentration of a metabolite like dodecanedioate is an emergent property of multiple interacting pathways, influenced by both genetic predisposition and environmental factors.
Clinical Significance and Pathway Dysregulation
Section titled “Clinical Significance and Pathway Dysregulation”Dysregulation within fatty acid metabolic pathways, particularly those involving dicarboxylic acids like dodecanedioate, has significant disease implications. Impaired beta-oxidation, often due to genetic variants in enzymes likeSCAD or MCAD, can manifest as severe systemic disorders including hypoketotic hypoglycemia, lethargy, encephalopathy, and seizures. [1]While extreme deficiencies are systematically identified, more common genetic variants with moderate phenotypic expression can lead to milder symptoms such as tiredness and loss of alertness, especially during periods of prolonged starvation or intense physical activity.[1]Understanding these pathway dysregulations provides insights into the etiology of complex diseases like diabetes and coronary artery disease, identifying potential therapeutic targets for intervention.[1]
References
Section titled “References”[1] 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.
[2] Aulchenko, Y. S. et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet., 2009.
[3] Benjamin, E. J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet., 2007.
[4] O’Donnell, C. J. et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet., 2007.
[5] Willer, C. J. et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet., 2008.
[6] Sabatti, C. et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet., 2008.
[7] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet., 2009.
[8] Vasan, R. 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., 2007.
[9] Yang, Q. et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet., 2007.
[10] Koishi, R., et al. “Angptl3 regulates lipid metabolism in mice.” Nat Genet, vol. 30, no. 2, 2002, pp. 151–157.
[11] Murphy, C., et al. “Regulation by SREBP-2 defines a potential link between isoprenoid and adenosylcobalamin metabolism.” Biochem Biophys Res Commun, vol. 355, no. 2, 2007, pp. 359–364.
[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, 2008.