Decanoylcarnitine
Introduction
Section titled “Introduction”Decanoylcarnitine, also known as C10-carnitine, is a medium-chain acylcarnitine, a type of molecule formed when a fatty acid (specifically a 10-carbon decanoic acid) binds to carnitine. These compounds are essential intermediaries in the body’s energy production system.
Biological Basis
Section titled “Biological Basis”The primary biological role of decanoylcarnitine is in the transport and metabolism of medium-chain fatty acids. Fatty acids are a vital source of energy, and for them to be broken down and utilized, they must be transported into the mitochondria, the “powerhouses” of the cell. Carnitine acts as a shuttle, binding to fatty acids to form acylcarnitines like decanoylcarnitine, which can then cross the mitochondrial membrane. Once inside, the fatty acids are released from carnitine and undergo beta-oxidation, a process that generates energy. Decanoylcarnitine specifically serves as an indirect substrate for the enzyme medium-chain acyl-CoA dehydrogenase (MCAD), which is critical for initiating the beta-oxidation of medium-chain fatty acids. [1]Genetic variations, such as the single nucleotide polymorphismrs11161510 in the MCAD gene, have been strongly associated with levels of medium-chain acylcarnitines, suggesting that certain genetic profiles can influence the efficiency of this metabolic pathway. [1] Individuals with minor allele homozygotes for such polymorphisms may exhibit reduced enzymatic turnover for these reactions, leading to higher concentrations of the longer-chain fatty acid substrates relative to their smaller-chain products. [1]
Clinical Relevance
Section titled “Clinical Relevance”The proper functioning of the MCADenzyme and the associated metabolism of medium-chain acylcarnitines, including decanoylcarnitine, has significant clinical implications. A deficiency inMCAD activity, often due to genetic variations in the MCAD gene, can lead to a serious inherited metabolic disorder known as Medium-Chain Acyl-CoA Dehydrogenase Deficiency (MCADD). This condition impairs the body’s ability to break down medium-chain fatty acids for energy, particularly during periods of fasting or illness, which can result in severe hypoglycemia, lethargy, and other life-threatening complications. Newborn screening programs often test for MCADDby measuring acylcarnitine levels, including decanoylcarnitine, to enable early diagnosis and intervention.[2] Beyond rare genetic disorders, variations in metabolic profiles, or “metabotypes,” influenced by genes like MCAD, are increasingly recognized as contributing factors to the susceptibility of individuals to common multi-factorial diseases, especially in interaction with environmental factors such as diet and lifestyle.[1]
Social Importance
Section titled “Social Importance”Understanding decanoylcarnitine and its genetic determinants holds social importance for several reasons. It contributes to the growing field of metabolomics, which aims to provide a comprehensive functional readout of an individual’s physiological state. By linking genetic variants to specific metabolite profiles, researchers can gain deeper insights into the complex interplay between genes, metabolism, and health. This knowledge can inform personalized medicine approaches, allowing for tailored dietary recommendations, lifestyle interventions, and medical treatments based on an individual’s unique metabolic and genetic makeup. For instance, individuals with certainMCAD variants might benefit from specific nutritional guidance to mitigate potential metabolic challenges. Furthermore, the ability to identify genetically determined metabotypes helps in early risk assessment for various diseases, potentially improving preventive healthcare strategies and public health outcomes.
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”The ability to detect subtle genetic influences on decanoylcarnitine is constrained by several methodological and statistical factors. Studies with moderate sample sizes often possess limited power to identify genetic effects that explain a small proportion of phenotypic variation, increasing the likelihood of false negative findings.[3] Furthermore, the use of less dense SNP arrays, such as the Affymetrix 100K chip, provides only partial coverage of genetic variation, which can impede the replication of previously reported associations and limit the discovery of novel variants. [4]Such limitations mean that a comprehensive understanding of the genetic architecture underlying decanoylcarnitine levels may remain incomplete.
A significant challenge in genetic studies is the frequent lack of consistent replication for initial findings, with some meta-analyses indicating that only a fraction of associations are successfully replicated. [3] This non-replication can arise from false positive discoveries in discovery cohorts, fundamental differences in study design, or insufficient statistical power within replication cohorts. [3] Moreover, while genotype imputation is crucial for expanding genomic coverage, it introduces an estimated error rate ranging from 1.46% to 2.14% per allele, which can affect the accuracy of associations, particularly for less confidently imputed variants. [5] The application of fixed-effects meta-analysis in some instances may also fail to fully account for heterogeneity across studies, potentially leading to an overestimation of combined genetic effects. [6]
Generalizability and Phenotype Heterogeneity
Section titled “Generalizability and Phenotype Heterogeneity”A primary limitation affecting the interpretation of genetic findings for decanoylcarnitine is the restricted generalizability of results. Many studies predominantly involve cohorts of European ancestry, primarily white individuals, which limits the applicability of the findings to other diverse ethnic or racial groups.[3] While efforts were made in some instances to extend findings to multiethnic samples, the foundational discovery and replication stages often remained concentrated within European populations. [7] Additionally, cohorts frequently comprise middle-aged to elderly participants, which may introduce a survival bias and constrain the relevance of the findings to younger segments of the population. [3]
Inconsistencies in the definition and measurement of phenotypes across different studies further complicate the synthesis and interpretation of results. Variations include the exclusion of outlier individuals, the handling or omission of information on lipid-lowering therapies, and differential covariate adjustments, such as the inclusion or exclusion of age squared in statistical models. [7]Although advanced targeted metabolomics platforms, like those used to quantify acylcarnitines such as decanoylcarnitine, offer high precision, subtle differences in analytical protocols or sample collection methods across diverse cohorts can introduce biases.[1]These methodological discrepancies hinder the uniform comparison of results and a complete understanding of genetic influences on decanoylcarnitine across varied study settings.
Unaccounted Environmental and Contextual Factors
Section titled “Unaccounted Environmental and Contextual Factors”The genetic influences on decanoylcarnitine may be significantly modulated by environmental factors, yet many studies do not comprehensively investigate these gene-environment interactions.[8] For example, the impact of genetic variants on various phenotypes has been shown to fluctuate based on dietary intake, highlighting the critical need to consider environmental contexts for accurate interpretation. [8] Furthermore, research indicates that genetic risk profiles for metabolic traits can exhibit sex-specific differences, which are frequently overlooked in broader analyses. [9]The failure to account for these crucial interactions and sex-specific effects can lead to an incomplete or potentially misleading understanding of how genetic variants contribute to decanoylcarnitine in different individuals.
Despite the identification of statistical associations, the precise functional roles of many identified genetic loci or neighboring genes in lipid and metabolite metabolism, including those potentially influencing decanoylcarnitine, often remain largely uncharacterized.[9]Current research primarily focuses on establishing statistical links between genetic variants and phenotypes, but further studies are essential to elucidate the underlying biological mechanisms through which these variants exert their effects on metabolic pathways. This gap in functional understanding represents a significant limitation, hindering the translation of genetic discoveries into actionable clinical insights or the development of targeted therapeutic strategies for conditions associated with decanoylcarnitine.
Variants
Section titled “Variants”Variants within genes central to fatty acid metabolism, as well as those involved in cellular transport and regulatory processes, can significantly influence the body’s handling of decanoylcarnitine and related metabolic traits. Decanoylcarnitine (C10-carnitine) is a medium-chain acylcarnitine, a biomarker for the efficiency of mitochondrial beta-oxidation, particularly for medium-chain fatty acids.
Key variants associated with the ACADM gene, including rs145024038 , rs2185152 , rs2788655 , rs12091720 , rs7550949 , and rs4949874 , are critical given ACADM’s role in encoding Medium-chain acyl-CoA dehydrogenase (MCAD). This enzyme is essential for the mitochondrial beta-oxidation of fatty acids with 4 to 12 carbon atoms. Impaired MCAD function, which can be influenced by genetic variations, leads to the accumulation of medium-chain acylcarnitines, including decanoylcarnitine, as these fatty acids cannot be properly broken down for energy. Studies have demonstrated a strong association between variants in genes encoding acyl-CoA dehydrogenases and levels of specific acylcarnitines, highlighting their direct impact on fatty acid metabolism.[1] Similarly, variants in ETFDH (Electron Transfer Flavoprotein Dehydrogenase), such as rs17843966 , rs67481496 , rs6856561 , and rs8396 , can affect the electron transfer chain that supports fatty acid oxidation. Defects in ETFDH can disrupt energy production and lead to an indirect buildup of various acylcarnitines by impeding the overall efficiency of fatty acid breakdown, thereby influencing metabolic profiles and potentially contributing to dyslipidemia. [5]
Other variants, such as rs924135 , rs924138 , and rs7199424 in the ABCC1 gene, point to the broader role of cellular transport in metabolism. ABCC1encodes a multidrug resistance-associated protein (MRP1), an ATP-binding cassette transporter involved in the efflux of various substrates from cells. While not directly linked to carnitine transport, variations inABCC1 could alter the cellular concentrations of other metabolites or drugs, indirectly impacting metabolic pathways that interact with fatty acid oxidation. The variant rs17843929 within PPID (Peptidylprolyl Isomerase D), a gene involved in protein folding and cellular processes, suggests a role in maintaining protein function and stability, which is crucial for the efficient operation of metabolic enzymes. Such genes contribute to the complex polygenic architecture of metabolic traits, influencing overall metabolic homeostasis and potentially impacting conditions like dyslipidemia [7]. [10]
Further genetic loci highlight the diverse biological mechanisms that can influence metabolic health. The variant rs145560419 in COL27A1 (Collagen Type XXVII Alpha 1 Chain), a gene primarily involved in extracellular matrix structure, demonstrates that even genes with seemingly unrelated primary functions can have indirect metabolic implications through their broader effects on tissue integrity and cellular signaling. The intergenic variant rs6810358 , located between SLC6A1(a GABA transporter) andHRH1 (Histamine Receptor H1), involves genes central to neurotransmission and immune responses, which can influence systemic energy balance and inflammation, factors broadly connected to metabolic health. [11] Finally, the variant rs17650138 , found near ASB17 and ST6GALNAC3(a glycosyltransferase), suggests a potential role for protein glycosylation in metabolic regulation. Glycosylation is a vital post-translational modification that can modulate the activity and localization of numerous proteins, including those involved in lipid and lipoprotein metabolism, thus indirectly affecting the processing or transport of fatty acid derivatives like decanoylcarnitine.[7]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs145024038 rs2185152 rs2788655 | ACADM | hexanoylcarnitine measurement octanoylcarnitine measurement Cis-4-decenoyl carnitine measurement decanoylcarnitine measurement acylcarnitine measurement |
| rs12091720 rs7550949 rs4949874 | SLC44A5 - ACADM | decanoylcarnitine measurement hexanoylcarnitine measurement octanoylcarnitine measurement cis-4-decenoate (10:1n6) measurement Cis-4-decenoyl carnitine measurement |
| rs17843966 rs67481496 rs6856561 | ETFDH | blood protein amount protein measurement octanoylcarnitine measurement decanoylcarnitine measurement dodecanoylcarnitine measurement |
| rs8396 | PPID, ETFDH | metabolite measurement serum metabolite level cerebrospinal fluid composition attribute, isovalerylcarnitine (C5) measurement carnitine measurement peptidyl-prolyl cis-trans isomerase D measurement |
| rs17843929 | PPID | nonanoylcarnitine (C9) measurement carnitine measurement peptidyl-prolyl cis-trans isomerase D measurement octanoylcarnitine measurement decanoylcarnitine measurement |
| rs924135 rs924138 rs7199424 | ABCC1 | basophil count interleukin-2 receptor subunit alpha measurement nonanoylcarnitine (C9) measurement coagulation factor X amount serum metabolite level |
| rs145560419 | COL27A1 | octanoylcarnitine measurement decanoylcarnitine measurement |
| rs6810358 | SLC6A1 - HRH1 | decanoylcarnitine measurement |
| rs17650138 | ASB17 - ST6GALNAC3 | decanoylcarnitine measurement |
Biological Background
Section titled “Biological Background”Carnitine Metabolism and Fatty Acid Beta-Oxidation
Section titled “Carnitine Metabolism and Fatty Acid Beta-Oxidation”Fatty acids, vital for cellular energy production, undergo beta-oxidation within the mitochondria. This crucial process begins with the transport of fatty acids across the mitochondrial membrane, a step facilitated by carnitine . This acetyl-CoA then fuels the citric acid cycle, a fundamental pathway for adenosine triphosphate (ATP) production, particularly crucial for energy generation during periods of fasting or increased metabolic demand.
Enzymatic Regulation of Fatty Acid Catabolism
Section titled “Enzymatic Regulation of Fatty Acid Catabolism”The intricate process of fatty acid beta-oxidation is precisely controlled by specific enzymes, notably medium-chain acyl-Coenzyme A dehydrogenase (MCAD) and short-chain acyl-Coenzyme A dehydrogenase (SCAD). MCADis particularly vital for the efficient breakdown of medium-chain fatty acids, which include the precursors and related compounds of decanoylcarnitine. Genetic variations within these enzymes can significantly influence their catalytic activity, thereby directly affecting the metabolic flux through the entire beta-oxidation pathway.[1] A reduction in dehydrogenase activity, often inferred from the accumulation of longer-chain fatty acid substrates relative to their shorter-chain products, indicates impaired catabolic efficiency. [1]
Genetic Modulators of Acylcarnitine Profiles
Section titled “Genetic Modulators of Acylcarnitine Profiles”Genetic polymorphisms serve as key regulatory mechanisms that shape an individual’s circulating acylcarnitine concentrations and, consequently, their metabolic health. Research has identified specific intronic single nucleotide polymorphisms (SNPs), such asrs11161510 within the MCAD gene and rs2014355 in the SCAD gene, as potent modulators of acylcarnitine ratios. [1] These genetic variants can lead to distinct metabolic phenotypes, or “metabotypes,” where individuals homozygous for certain minor alleles may exhibit a reduced enzymatic turnover for the respective fatty acid dehydrogenation reactions. [1] This highlights the profound and intricate connection between an individual’s genetic makeup and their unique metabolic landscape.
Metabolic Biomarkers and Clinical Significance
Section titled “Metabolic Biomarkers and Clinical Significance”The concentrations and specific ratios of various acylcarnitines function as valuable metabolic biomarkers, offering insights into the functional status of fatty acid oxidation pathways. For example, the ratio between certain medium-chain acylcarnitines shows a strong association with polymorphisms in the MCAD gene, providing a direct biochemical indicator of enzyme activity. [1]These genetically determined metabotypes are not merely isolated biochemical observations; they are hypothesized to interact significantly with environmental factors, such as dietary patterns and lifestyle choices, influencing an individual’s susceptibility to complex multifactorial diseases.[1] A comprehensive understanding of these mechanisms is crucial for identifying pathway dysregulation and uncovering potential therapeutic targets for metabolic disorders.
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, 2008.
[2] Maier EM, et al. “Population spectrum of ACADM genotypes correlated to biochemical phenotypes in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency.” Hum Mutat, 2005.
[3] Benjamin, Emelia J., et al. “Genome-Wide Association with Select Biomarker Traits in the Framingham Heart Study.” BMC Medical Genetics, 2007.
[4] O’Donnell, Christopher J., et al. “Genome-Wide Association Study for Subclinical Atherosclerosis in Major Arterial Territories in the NHLBI’s Framingham Heart Study.”BMC Medical Genetics, 2007.
[5] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-169.
[6] Yuan, X., et al. “Population-Based Genome-Wide Association Studies Reveal Six Loci Influencing Plasma Levels of Liver Enzymes.” American Journal of Human Genetics, 2008.
[7] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 11, 2008, pp. 1296-1303.
[8] 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 Medical Genetics, 2007.
[9] Aulchenko, Yuliya S., et al. “Loci Influencing Lipid Levels and Coronary Heart Disease Risk in 16 European Population Cohorts.”Nature Genetics, 2008.
[10] Wallace, Cathryn, et al. “Genome-Wide Association Study Identifies Genes for Biomarkers of Cardiovascular Disease: Serum Urate and Dyslipidemia.”The American Journal of Human Genetics, vol. 82, no. 1, 2008, pp. 139–149.
[11] Saxena, Richa, et al. “Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels.”Science, vol. 316, no. 5829, 2007, pp. 1331–1336.