Isovalerylcarnitine
Introduction
Section titled “Introduction”Isovalerylcarnitine, also known as C5-carnitine, is a short-chain acylcarnitine, a group of molecules formed from fatty acids and the amino acid carnitine. These compounds are integral to cellular energy production, playing a vital role in transporting fatty acids into mitochondria for beta-oxidation, a process that breaks down fats to generate energy.[1] The comprehensive measurement of such endogenous metabolites is a key aim of metabolomics, providing a functional snapshot of the body’s physiological state. [1]
Biological Basis
Section titled “Biological Basis”Isovalerylcarnitine is specifically involved in the metabolic pathway of leucine, one of the branched-chain amino acids. During the breakdown of leucine, an intermediate called isovaleryl-CoA is produced. This compound can then be converted into isovalerylcarnitine, which facilitates its transport and further metabolism, preventing the accumulation of potentially harmful byproducts. Genetic variations can influence the levels of various acylcarnitines, impacting these metabolic pathways.[1]
Clinical Relevance
Section titled “Clinical Relevance”The levels of isovalerylcarnitine in bodily fluids like blood or urine are a crucial indicator for certain inherited metabolic disorders. High levels of isovalerylcarnitine are a primary biomarker for isovaleric acidemia (IVA), a rare genetic condition. IVA results from a deficiency in the enzyme isovaleryl-CoA dehydrogenase (IVD), which is necessary for the proper breakdown of isovaleryl-CoA. Without sufficient IVDactivity, isovaleryl-CoA and its derivatives, including isovalerylcarnitine, build up to toxic concentrations. If left untreated, isovaleric acidemia can lead to severe neurological damage, developmental delays, and life-threatening metabolic crises.
Social Importance
Section titled “Social Importance”The ability to detect and quantify isovalerylcarnitine holds significant social importance, particularly within the scope of newborn screening programs. The inclusion of acylcarnitine profiling in routine newborn screening panels allows for the early identification of conditions such as isovaleric acidemia. This early diagnosis enables prompt medical intervention and management, which can dramatically improve the prognosis for affected individuals, prevent severe long-term health complications, and enhance their overall quality of life. The advancements in metabolomics have been instrumental in improving the diagnostic capabilities for these conditions.[1]
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”Initial genome-wide association studies (GWAS) screens often face limitations in statistical power to detect modest genetic effects, especially when accounting for the extensive multiple testing inherent in such analyses, which can potentially inflate reported effect sizes. [2] While meta-analyses combine data across studies to increase power, the use of fixed-effects inverse-variance weighting, though common, might not fully account for true heterogeneity among studies, even with assessments like Cochran’s Q test, potentially obscuring population-specific genetic influences. [3]
The quality and coverage of genotype imputation, typically based on reference panels such as HapMap, can vary, with some studies acknowledging limitations due to a lack of high-quality imputation or by only including single nucleotide polymorphisms (SNPs) with high imputation confidence (e.g., RSQR ≥ 0.3).[3] This variability can affect the comprehensiveness of genetic variation captured and potentially lead to false-positive results for associations with moderate statistical support, emphasizing the critical role of independent replication in other cohorts as a gold standard for validating genetic findings. [4]
Generalizability and Phenotype Specificity
Section titled “Generalizability and Phenotype Specificity”A significant limitation in many genetic studies, including those contributing to the understanding of isovalerylcarnitine levels, is the predominant focus on populations of European ancestry.[5] This narrow demographic scope restricts the direct generalizability of findings to other ancestral groups, where genetic architecture, allele frequencies, and patterns of linkage disequilibrium may differ substantially, potentially leading to inconsistent effects or missed associations in diverse populations.
Furthermore, while the targeted quantitative metabolomics platforms, such as electrospray ionization tandem mass spectrometry, provide precise measurements of fasting serum concentrations, these measurements represent a single time point and can be influenced by transient physiological states like recent dietary intake or diurnal rhythms. [1] The practice of excluding individuals on specific medications, such as lipid-lowering therapies, though crucial for isolating clear genetic signals, means that the identified genetic associations might not directly apply to or predict outcomes in individuals undergoing such interventions. [6]
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”The complex interplay between genetic variants and environmental factors remains a largely unaddressed area in many studies, with most investigations not explicitly examining gene-environment interactions, despite recognizing that genetic variants can influence phenotypes in a context-specific manner modulated by environmental influences. [2] Such unmeasured or unmodeled interactions could significantly confound observed genetic associations, contributing to the unexplained portion of heritability for complex traits like metabolite levels.
Despite advancements in identifying genetic loci, a substantial proportion of the heritability for many complex traits, including those related to isovalerylcarnitine, remains unexplained, highlighting ongoing knowledge gaps regarding all contributing genetic factors and their underlying mechanisms. This necessitates further functional validation studies to elucidate the biological roles of associated SNPs and their downstream effects on isovalerylcarnitine metabolism, alongside continued efforts with larger sample sizes and improved statistical power for the discovery of additional sequence variants.[4]
Variants
Section titled “Variants”The Variantssection explores specific genetic variations and their associated genes that may influence isovalerylcarnitine levels, a crucial metabolite in branched-chain amino acid metabolism. Isovalerylcarnitine is an acylcarnitine that, when elevated, can indicate a disruption in the breakdown of leucine, often associated with conditions like Isovaleric Acidemia. Understanding these genetic influences provides insight into individual metabolic differences and potential predispositions to metabolic imbalances.
The IVDgene encodes isovaleryl-CoA dehydrogenase, an enzyme essential for the breakdown of the branched-chain amino acid leucine. This enzyme catalyzes a critical step in mitochondrial beta-oxidation, converting isovaleryl-CoA to 3-methylcrotonyl-CoA. Variants inIVD, such as rs12594728 , rs10518693 , and rs9635324 , can impair this enzymatic activity, leading to an accumulation of isovaleryl-CoA and its derivative, isovalerylcarnitine, which is characteristic of Isovaleric Acidemia.[7] Similarly, the ETFA gene (Electron Transfer Flavoprotein Subunit Alpha) is vital for the overall electron transfer system that supports various acyl-CoA dehydrogenases, including IVD. A variant like rs78185702 in ETFAcan disrupt this electron transfer, affecting the efficient processing of multiple acyl-CoAs, thereby potentially contributing to elevated isovalerylcarnitine levels.[8] The proper functioning of these genes is crucial for maintaining metabolic balance, particularly in the breakdown of fatty acids and amino acids, with their disruption often manifesting as abnormal acylcarnitine profiles.
The solute carrier (SLC) gene family plays a fundamental role in transporting a diverse range of molecules across cell membranes, influencing metabolite levels. SLC22A4 (also known as OCTN1) and SLC22A1 (or OCT1) encode organic cation transporters, which are important for the uptake and excretion of various endogenous compounds and drugs, primarily expressed in tissues like the liver and kidney. [9] A variant such as rs538021413 in SLC22A4 or rs11753995 in SLC22A1could alter the transport efficiency of carnitine or specific acylcarnitines, thereby impacting the cellular concentration or systemic clearance of isovalerylcarnitine. Additionally,SLC16A9 (Monocarboxylate Transporter 9), with variants like rs1171616 , belongs to a family known for transporting monocarboxylates like lactate and pyruvate, and could potentially influence the transport of organic acids related to isovalerylcarnitine metabolism.[10]These transporters are critical for maintaining metabolic homeostasis, and variations can lead to altered metabolite profiles.
Beyond direct metabolic enzymes and transporters, several genes influence isovalerylcarnitine levels through regulatory or indirect mechanisms.MIR3936HG (MIR3936 Host Gene) hosts a microRNA that can regulate the expression of other genes, including those involved in metabolic pathways. Variants like rs272888 and rs11950562 within MIR3936HGcould modulate the abundance or activity of downstream metabolic enzymes, indirectly affecting the production or breakdown of isovalerylcarnitine.[11] Similarly, IRF1 (Interferon Regulatory Factor 1), a transcription factor involved in immune and inflammatory responses, may have indirect metabolic connections, as inflammation can impact mitochondrial function and overall metabolic health; variants such as rs10477741 and rs2706395 could influence these interconnected pathways. [12] The gene CABP1 (Calcium Binding Protein 1) modulates intracellular calcium signaling, a fundamental process impacting numerous cellular functions, including enzyme activity and mitochondrial respiration; therefore, a variant like rs111524607 could subtly alter metabolic efficiency and contribute to variations in isovalerylcarnitine levels. The geneCARINH is also associated with metabolic traits, potentially through its role in broader physiological processes that interact with energy metabolism.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs12594728 | KNSTRN - IVD | isovalerylcarnitine measurement isovalerylcarnitine (C5) measurement |
| rs538021413 | MIR3936HG, SLC22A4 | reticulocyte count oleoylcarnitine measurement linoleoylcarnitine (C18:2) measurement isovalerylcarnitine measurement palmitoylcarnitine measurement |
| rs272888 rs11950562 | SLC22A4, MIR3936HG | isovalerylcarnitine measurement docosahexaenoic acid measurement, fatty acid amount docosahexaenoic acid to total fatty acids percentage omega-3 polyunsaturated fatty acid measurement docosahexaenoic acid measurement |
| rs10518693 rs9635324 | IVD | metabolite measurement isovalerylcarnitine measurement serum metabolite level isovalerylcarnitine (C5) measurement |
| rs1171616 | SLC16A9 | serum metabolite level urate measurement acetylcarnitine measurement N-methylproline measurement propionylcarnitine measurement |
| rs10477741 | CARINH, IRF1 | isovalerylcarnitine measurement coronary artery disease peak expiratory flow hexanoylcarnitine measurement fatty acid amount |
| rs78185702 | ETFA - ISL2 | dimethylglycine measurement ethylmalonate measurement isovalerylcarnitine measurement butyrylcarnitine measurement glutarylcarnitine (C5-DC) measurement |
| rs111524607 | CABP1 | isovalerylcarnitine measurement progranulin measurement |
| rs2706395 | IRF1, CARINH | isovalerylcarnitine measurement |
| rs11753995 | SLC22A1 | total cholesterol measurement low density lipoprotein cholesterol measurement metabolite measurement isovalerylcarnitine measurement adipoylcarnitine (C6-DC) measurement |
Signs and Symptoms
Section titled “Signs and Symptoms”Metabolic Presentation and Measurement
Section titled “Metabolic Presentation and Measurement”Isovalerylcarnitine, a specific short-chain acylcarnitine, plays a role in mitochondrial fatty acid beta-oxidation. Elevated concentrations of short-chain acylcarnitines, particularly when viewed as substrates relative to their metabolic products, can suggest diminished dehydrogenase activity within this critical metabolic pathway. Such distinct metabolic profiles contribute to an individual’s “metabotype,” which may influence susceptibility to various common multi-factorial diseases, potentially modulated by environmental factors such as diet and lifestyle.[1]
The primary method for assessing isovalerylcarnitine levels involves targeted metabolite profiling utilizing electrospray ionization (ESI) tandem mass spectrometry (MS/MS). This highly quantitative platform conducts analyses on serum samples, which are typically collected after an overnight fast to ensure standardized metabolic conditions.[1] This objective measurement approach provides a functional readout of the body’s physiological state, serving as a crucial diagnostic tool for evaluating acylcarnitine homeostasis.
Genetic Determinants and Inter-individual Variability
Section titled “Genetic Determinants and Inter-individual Variability”Significant inter-individual variation in short-chain acylcarnitine levels, including isovalerylcarnitine, is influenced by specific genetic variants. For instance, the intronic single nucleotide polymorphism (SNP)rs2014355 within the SCAD gene, which encodes short-chain acyl-Coenzyme A dehydrogenase, shows a strong association with the ratio of short-chain acylcarnitines C3 and C4, accounting for a substantial portion of the observed variance. [1] This genetic influence indicates that minor allele homozygotes for such polymorphisms may exhibit the lowest enzymatic turnover in these critical beta-oxidation reactions. [1]
These genetically determined “metabotypes” contribute to a wide spectrum of phenotypic diversity in metabolic profiles. While pronounced sex-specific effects have been noted for other metabolites, such as uric acid concentrations influenced bySLC2A9, the specific patterns of age-related changes or sex differences directly impacting isovalerylcarnitine require further detailed investigation to fully characterize its variability and atypical presentations.[1]
Diagnostic Implications
Section titled “Diagnostic Implications”The diagnostic significance of isovalerylcarnitine levels lies in their potential as biomarkers for identifying alterations in fatty acid metabolism. Deviations from normal acylcarnitine profiles, particularly those linked to genetic variants affecting dehydrogenase activity, can act as discriminating cofactors in the etiology of common multi-factorial diseases.[1] Understanding these metabolic correlations is vital for differential diagnosis and for recognizing “red flags” that may indicate underlying metabolic predispositions.
Abnormal isovalerylcarnitine concentrations, especially when interpreted in the context of an individual’s genetic background, can serve as prognostic indicators by reflecting metabolic efficiency. While specific prognostic scales for isovalerylcarnitine are not detailed, the ability to identify individuals with reduced enzymatic turnover through metabolic and genetic assessment provides valuable insights into their susceptibility to certain phenotypes and potential long-term health outcomes.[1]
Biological Background
Section titled “Biological Background”The Role of Acylcarnitines in Cellular Energy Metabolism
Section titled “The Role of Acylcarnitines in Cellular Energy Metabolism”Fatty acids are fundamental energy sources for cells, and their efficient utilization hinges on a specialized transport system involving carnitine. Within cells, fatty acids are converted into fatty acyl-CoAs, which then bind to free carnitine to form acylcarnitines. This crucial molecular transformation enables the transport of fatty acids across the inner mitochondrial membrane, where the process of beta-oxidation occurs.[1]Isovalerylcarnitine, as a type of acylcarnitine, participates in this vital pathway, facilitating the entry of specific fatty acid chains into the mitochondria for subsequent breakdown into acetyl-CoA, which then fuels the citric acid cycle for ATP generation.
Enzymatic Regulation of Fatty Acid Chain Length Metabolism
Section titled “Enzymatic Regulation of Fatty Acid Chain Length Metabolism”The initiation of fatty acid beta-oxidation is precisely regulated by a family of acyl-Coenzyme A dehydrogenase enzymes, each demonstrating specificity for particular fatty acid chain lengths. Key biomolecules in this process include SCAD (short-chain acyl-Coenzyme A dehydrogenase) and MCAD (medium-chain acyl-Coenzyme A dehydrogenase), which catalyze the initial dehydrogenation step for short- and medium-chain fatty acids, respectively. [1]This chain length preference is critical for the orderly processing of diverse fatty acids derived from diet or cellular stores. The coordinated action of these enzymes ensures the efficient breakdown of various fatty acids, maintaining metabolic homeostasis and providing a continuous supply of energy for cellular functions.
Genetic Influences on Acylcarnitine Profiles
Section titled “Genetic Influences on Acylcarnitine Profiles”Genetic mechanisms play a significant role in shaping an individual’s metabolic profile, including the circulating levels of acylcarnitines. Genome-wide association studies have identified specific genetic variants, such as intronic single nucleotide polymorphisms (SNPs), that are strongly linked to variations in acylcarnitine concentrations.[1] For example, the SNP rs2014355 within the SCAD gene is associated with the ratio of short-chain acylcarnitines C3 and C4, while rs11161510 in the MCAD gene correlates with levels of medium-chain acylcarnitines. [1] These genetic associations underscore how inherited differences in gene function and regulatory elements can directly impact the efficiency of fatty acid metabolism and the resulting patterns of acylcarnitine expression.
Acylcarnitines as Biomarkers of Metabolic Health and Disease
Section titled “Acylcarnitines as Biomarkers of Metabolic Health and Disease”The comprehensive analysis of metabolites in bodily fluids, known as metabolomics, offers a functional snapshot of the body’s physiological state, with acylcarnitines serving as important indicators. Alterations in acylcarnitine profiles can signal disruptions in metabolic homeostasis, providing insights into various pathophysiological processes. [1] For instance, deficiencies in enzymes like MCAD can lead to distinct biochemical phenotypes identifiable through newborn screening, demonstrating the critical role these biomolecules play in health. [1]Thus, monitoring acylcarnitine levels, including isovalerylcarnitine, can reveal systemic consequences of metabolic imbalances, offering valuable information for understanding disease mechanisms and developmental processes.
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Role in Fatty Acid Beta-Oxidation
Section titled “Metabolic Role in Fatty Acid Beta-Oxidation”Isovalerylcarnitine, a short-chain acylcarnitine (C5), is an important intermediate in the metabolic pathways of branched-chain amino acid catabolism, particularly leucine, and the subsequent beta-oxidation of fatty acids within the mitochondria. The process of fatty acid transport into the mitochondria relies on carnitine, which binds to fatty acids to form acylcarnitines, making them available as substrates for the beta-oxidation enzymes.[1] This catabolic pathway is fundamental for cellular energy production, converting fatty acids into acetyl-CoA, which then feeds into the citric acid cycle. Specifically, the short-chain acyl-Coenzyme A dehydrogenase (SCAD) enzyme initiates the beta-oxidation of short-chain fatty acids, thereby directly influencing the steady-state levels of compounds like isovalerylcarnitine and other short-chain acylcarnitines.[1]
Genetic Regulation of Acylcarnitine Levels
Section titled “Genetic Regulation of Acylcarnitine Levels”Genetic variations play a crucial role in modulating the efficiency of enzymes involved in fatty acid oxidation, which, in turn, affects acylcarnitine concentrations. A genome-wide association study identified a specific intronic single nucleotide polymorphism,rs2014355 , within the SCAD gene, strongly associated with the ratio of short-chain acylcarnitines C3 and C4. [1] This SCAD variant, possessing a minor allele frequency of 25.1%, accounts for a significant portion (21.8%) of the observed variance in these metabolite ratios, underscoring its pivotal role in regulating short-chain fatty acid metabolism. [1] Similarly, variants in the medium-chain acyl-Coenzyme A dehydrogenase (MCAD) gene, such as rs11161510 , exhibit strong associations with medium-chain acylcarnitine ratios, illustrating how specific genetic loci dictate enzyme function and the resulting metabolomic profiles. [1]
Metabolomic Signatures and Homeostasis
Section titled “Metabolomic Signatures and Homeostasis”The ratios of specific acylcarnitines, such as the C3 to C4 ratio, serve as highly informative intermediate phenotypes that accurately reflect the activity and efficiency of enzymatic reactions within the fatty acid metabolic pathways. [1] Analyzing these metabolite ratios can significantly reduce variation in datasets, particularly when the metabolites are directly linked as substrates and products of a single enzymatic step, providing a precise functional readout of the physiological state. [1] Such detailed metabolomic profiles offer a functional understanding of the human body’s metabolic architecture, facilitating the identification of genetic variants that influence the homeostasis of critical lipids and amino acids. [1]
Broader Metabolic Interactions and Clinical Significance
Section titled “Broader Metabolic Interactions and Clinical Significance”Dysregulation of fatty acid oxidation, as indicated by altered acylcarnitine levels, carries significant implications for overall lipid homeostasis and metabolic health. While the current research highlights specific enzymes like SCAD and MCAD, their pathways are integrated into a complex network of lipid metabolism, involving genes that regulate cholesterol synthesis, such as HMGCR, and those influencing triglyceride and high-density lipoprotein (HDL) levels, includingANGPTL3 and ANGPTL4. [13]Therefore, genetic variants affecting acylcarnitine profiles contribute to a broader genetic architecture that influences plasma lipid concentrations and the risk of conditions like coronary artery disease.[13] Elucidating these specific metabolic pathways and their genetic underpinnings is essential for identifying potential compensatory mechanisms and developing targeted therapeutic strategies 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 Genetics, vol. 4, no. 11, 2008, e1000282.
[2] 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 Medical Genetics, vol. 8, suppl. 1, 2007, S2.
[3] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” American Journal of Human Genetics, vol. 83, no. 5, 2008, pp. 569-82.
[4] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, S11.
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[6] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 40, no. 12, 2008, pp. 1421-26.
[7] Gieger C, et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.” PLoS Genet; PMID: 19043545.
[8] Sabatti C, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.” Nat Genet; PMID: 19060910.
[9] Yuan X, et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet; PMID: 18940312.
[10] Doring A, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.” Nat Genet; PMID: 18327256.
[11] Willer CJ, et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.” Nat Genet; PMID: 18193043.
[12] Benjamin EJ, et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet; PMID: 17903293.
[13] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nature Genetics, vol. 40, no. 1, 2008, pp. 161-69.