Isobutyrylglycine
isobutyrylglycine is an acylglycine, a type of organic compound formed from an acyl group and the amino acid glycine. As a metabolite, it plays a role in the body’s metabolic pathways and is often measured as part of a metabolomic profile.[1]
Biological Basis
Section titled “Biological Basis”Isobutyrylglycine is primarily formed during the metabolism of branched-chain amino acids, specifically valine. It serves as a diagnostic marker for genetic disorders affecting the breakdown of these amino acids. When the enzyme isobutyryl-CoA dehydrogenase (IBD) is deficient or impaired, the body cannot properly metabolize isobutyryl-CoA, leading to its accumulation. To detoxify this build-up, isobutyryl-CoA is conjugated with glycine, forming isobutyrylglycine, which is then excreted. The rapidly evolving field of metabolomics aims at a comprehensive measurement of endogenous metabolites in body fluids, providing a functional readout of the physiological state.[1] Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are expected to display relevance in this context. [1]
Clinical Relevance
Section titled “Clinical Relevance”Elevated levels of isobutyrylglycine in blood or urine are a key indicator of isobutyryl-CoA dehydrogenase deficiency, a rare autosomal recessive metabolic disorder. Early detection of such conditions is crucial for timely intervention and management. Beyond specific inborn errors of metabolism, the presence and concentration of metabolites like isobutyrylglycine can be influenced by genetic variants and may be associated with broader metabolic health or disease states. Genome-wide association studies (GWAS) examine metabolite profiles in human serum to identify genetic variants that contribute to variations in metabolite levels.[1]
Social Importance
Section titled “Social Importance”The measurement of metabolites like isobutyrylglycine is of significant social importance, particularly in newborn screening programs. These programs routinely test for a panel of metabolic disorders, including those identifiable by acylglycine profiles. Early detection allows for prompt dietary or medical intervention, which can prevent severe developmental delays, neurological damage, or life-threatening crises in affected infants, thereby improving long-term health outcomes and quality of life for individuals and their families.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Similar to many genome-wide association studies, investigations into traits such as isobutyrylglycine are often constrained by moderate sample sizes, which can reduce statistical power and lead to false negative findings.[2] This limitation means that true genetic associations, especially those with small effect sizes, may remain undetected, requiring larger cohorts for comprehensive gene discovery and more robust validation. [3] Furthermore, effect sizes reported in initial discovery phases may be inflated, underscoring the critical need for replication in independent samples to confirm findings. [4]
A fundamental challenge in genetic studies of traits like isobutyrylglycine is the need for external replication to validate initial associations and differentiate true positives from statistical artifacts.[2] Lack of replication can stem from various factors, including initial false positive reports, subtle differences in cohort characteristics that modify genetic effects, or insufficient statistical power in replication cohorts. [2]Moreover, the use of existing genome-wide association study (GWAS) arrays, which cover only a subset of all available single nucleotide polymorphisms (SNPs), may limit the identification of novel genes or prevent comprehensive assessment of known candidate genes associated with isobutyrylglycine.[5] While imputation methods are used to infer untyped SNPs, the quality of imputation can vary, with instances of very low accuracy, which can render findings for such imputed variants unreliable. [6]
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”A significant limitation for studies investigating isobutyrylglycine is the restricted generalizability of findings, as many participating cohorts are predominantly composed of individuals of white European ancestry.[7]This demographic homogeneity means that observed genetic associations may not be directly transferable to other racial or ethnic groups, where genetic backgrounds, environmental exposures, and lifestyle factors can substantially differ.[2] Additionally, cohorts that are largely middle-aged to elderly or where biological samples, such as DNA, were collected at later examinations, may introduce age-related or survival biases, limiting the applicability of findings to younger or healthier populations. [2]
Accurate measurement and precise characterization of complex quantitative traits, including isobutyrylglycine, present inherent challenges. While robust statistical transformations are often employed to manage non-normally distributed phenotypic data, the inherent skewedness can add complexity to analysis and interpretation.[7] Some studies may rely on proxy measures for specific biological functions when direct or comprehensive assessments are unavailable, introducing a degree of uncertainty about the exact biological correlation. [8]Furthermore, a measured trait, like isobutyrylglycine, might reflect broader physiological processes or disease risks beyond its primary definition, making it challenging to isolate specific genetic effects without extensive adjustment for potential confounders or pleiotropic influences.[8]
Unexplored Genetic Complexity and Confounding
Section titled “Unexplored Genetic Complexity and Confounding”Studies on isobutyrylglycine, particularly those that prioritize additive genetic models, may not fully capture important non-additive effects, such as dominant or recessive modes of inheritance, or complex gene-gene interactions.[9] While alternative genetic models are often explored, the absence of additional identified loci under these models suggests that a deeper layer of genetic complexity contributing to the trait’s variance might be missed. [9]Moreover, the common practice of pooling sexes in analyses to mitigate the multiple testing burden can lead to the undetected association of SNPs that exert their influence on traits like isobutyrylglycine in a sex-specific manner.[5]
Genetic associations for isobutyrylglycine can be profoundly influenced by a wide array of environmental and clinical factors that act as confounders.[7]Extensive adjustment for covariates such as age, sex, body mass index, diabetes status, smoking habits, and the use of medications (e.g., steroids or lipid-lowering treatments) is crucial, highlighting their substantial potential to modify or obscure genetic effects.[7]Variability in these key factors across different study cohorts can contribute to inconsistencies in replication findings, emphasizing the intricate interplay between genetics, environmental exposures, and lifestyle in shaping complex quantitative traits like isobutyrylglycine.[2]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing metabolic pathways, particularly those involved in amino acid breakdown and fatty acid oxidation, which can impact the levels of acylglycines like isobutyrylglycine. Variants in genes such asACAD8, ACADS, and ETFA are central to understanding these metabolic processes. The gene ACAD8encodes isobutyryl-CoA dehydrogenase, an enzyme essential for the metabolism of the branched-chain amino acid valine.[1] A deficiency in ACAD8activity directly leads to the accumulation of isobutyryl-CoA, which is then conjugated with glycine to form isobutyrylglycine, a key biomarker for this metabolic disorder. The variantrs113488591 within ACAD8may therefore influence the efficiency of valine catabolism, potentially affecting isobutyrylglycine levels. Similarly, theACADS gene encodes short-chain acyl-CoA dehydrogenase, an enzyme involved in the breakdown of short-chain fatty acids, and its variants, such as rs34673751 , have been associated with altered metabolite profiles in human serum.[1] While ACADSis not directly responsible for isobutyryl-CoA processing, its role in general acyl-CoA metabolism means that variations could indirectly affect the overall acyl-CoA pool and glycine conjugation pathways. Additionally, the variantrs7111570 , located between ACAD8 and GLB1L3, may impact the regulatory region of ACAD8, thereby modulating its expression or activity and contributing to variations in isobutyrylglycine levels.
The ETFA gene, through its alpha subunit, is a vital component of the electron transfer flavoprotein (ETF), which functions as a crucial electron acceptor for several mitochondrial acyl-CoA dehydrogenases, including ACAD8 and ACADS. [7]This electron transfer system is essential for the proper functioning of fatty acid beta-oxidation and branched-chain amino acid catabolism. Variants likers3759853 in ETFA can lead to impaired electron transfer, resulting in a condition known as Multiple Acyl-CoA Dehydrogenase Deficiency (MADD), where various acyl-CoAs accumulate, including isobutyryl-CoA. Consequently, individuals with compromised ETFAfunction may exhibit elevated levels of isobutyrylglycine due to the backup in metabolic pathways.[1] Understanding such variants helps to elucidate the genetic basis of metabolic disorders that manifest with increased acylglycine excretion.
Other genetic variants, while not as directly linked to isobutyrylglycine as the acyl-CoA dehydrogenases, can still play a role in broader metabolic health. TheCPS1gene encodes carbamoyl-phosphate synthase 1, a mitochondrial enzyme that catalyzes the first committed step of the urea cycle, essential for detoxifying ammonia.[10] The variant rs1047891 in CPS1could influence the efficiency of the urea cycle, and while not directly tied to isobutyrylglycine formation, imbalances in mitochondrial energy metabolism or nitrogen disposal can have cascading effects on amino acid catabolism and the availability of metabolic intermediates.[1] Furthermore, PPM1K-DT (rs17014016 ) is a divergent transcript near PPM1K, a gene involved in the regulation of branched-chain amino acid breakdown. Variations inPPM1K-DTmight indirectly affect the overall metabolism of valine and other branched-chain amino acids, thereby influencing acylglycine profiles. Finally, thePDLIM5 gene, with its variant rs199698901 , is involved in cellular signaling and structural organization, and while its direct metabolic role concerning isobutyrylglycine is less defined, its pleiotropic effects on cellular function could contribute to the complex interplay of genetic factors influencing metabolic phenotypes.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1047891 | CPS1 | platelet count erythrocyte volume homocysteine measurement chronic kidney disease, serum creatinine amount circulating fibrinogen levels |
| rs113488591 | ACAD8 | isobutyrylcarnitine measurement isobutyrylglycine measurement serum metabolite level carnitine measurement |
| rs34673751 | ACADS | butyrylcarnitine (C4) measurement butyrylglycine measurement isobutyrylglycine measurement |
| rs7111570 | ACAD8 - GLB1L3 | isobutyrylglycine measurement metabolite measurement isobutyrylcarnitine measurement |
| rs3759853 | ETFA | isobutyrylglycine measurement |
| rs17014016 | PPM1K-DT | 2-aminobutyrate measurement isobutyrylcarnitine measurement isobutyrylglycine measurement |
| rs199698901 | PDLIM5 | isobutyrylglycine measurement |
Classification, Definition, and Terminology of Isobutyrylglycine
Section titled “Classification, Definition, and Terminology of Isobutyrylglycine”Nature and Biochemical Classification
Section titled “Nature and Biochemical Classification”Isobutyrylglycine is precisely defined as an endogenous metabolite found within human serum.[1] It belongs to the broader category of ‘acylcarnitines’, which are part of a diverse group of up to 363 endogenous metabolites quantified in detailed metabolomics studies. [1]Its presence signifies participation in specific metabolic pathways, and its classification alongside other metabolites like sugar molecules, biogenic amines, prostaglandins, and amino acids underscores its role in the complex biochemical landscape of the human body.[1] While the immediate context focuses on its detectability and quantification, its identity as an acylcarnitine places it within a class of molecules crucial for fatty acid metabolism and energy production.
Measurement and Operational Definition
Section titled “Measurement and Operational Definition”The operational definition of isobutyrylglycine in research is derived from its quantitative measurement in biological samples, specifically fasting serum.[1] This quantification is achieved through a “targeted quantitative metabolomics platform” utilizing advanced analytical techniques such as electrospray ionization (ESI) tandem mass spectrometry (MS/MS). [1]This method allows for the precise determination of its serum concentration, enabling its use as a measurable trait in genetic investigations. The accurate and standardized measurement of isobutyrylglycine concentrations forms the basis for its inclusion in large-scale studies aiming to connect genetic variations with metabolic profiles.[1]
Context in Genetic Research and Significance
Section titled “Context in Genetic Research and Significance”In genome-wide association studies (GWAS), isobutyrylglycine functions as a “metabolic trait,” where its serum concentrations are analyzed in relation to genetic variants.[1] The conceptual framework for these studies involves treating metabolite levels as quantitative phenotypes to identify genetic loci that influence metabolic pathways. [1]By elucidating associations between genetic variations and endogenous metabolite concentrations, research aims to enhance the understanding of the genetic underpinnings of human metabolism and its relevance to health. The study of metabolites like isobutyrylglycine therefore contributes to mapping the genetic architecture of metabolic traits, potentially offering insights into clinical parameters.[1]
Causes
Section titled “Causes”The levels of isobutyrylglycine in the human body are influenced by a complex interplay of genetic factors, environmental exposures, and their interactions, consistent with the broader understanding of metabolite profiles. Insights into these causal pathways emerge from studies exploring genetically determined metabolic traits and the overall human metabolic network.
Genetic Predisposition and Metabolic Pathways
Section titled “Genetic Predisposition and Metabolic Pathways”Genetic variations play a fundamental role in shaping an individual’s metabolite profile, including that of isobutyrylglycine. Genome-wide association studies (GWAS) are instrumental in identifying specific genetic variants that can alter the homeostasis of key metabolites in the human body, leading to distinct “genetically determined metabotypes”.[1] These genetic predispositions can affect the function of enzymes and transporters involved in various metabolic pathways, thereby influencing the synthesis, degradation, or excretion of specific compounds.
For instance, variations in genes involved in fatty acid metabolism, such as ACADM, have been correlated with biochemical phenotypes observed in newborn screening for conditions like medium-chain acyl-CoA dehydrogenase deficiency. [11]Such genetic changes can disrupt normal metabolic processes, leading to altered levels of various acylglycines and other diagnostic metabolites. Understanding these specific genetic influences is crucial for comprehending the underlying mechanisms governing isobutyrylglycine concentrations.
Gene-Environment Interactions
Section titled “Gene-Environment Interactions”The regulation of metabolite levels is not solely determined by an individual’s genetic makeup; it is also significantly shaped by intricate interactions between genetic predispositions and environmental factors. Research in metabolomics, when combined with GWA studies, offers new avenues for functionally investigating the crucial role of these gene-environment interactions in the etiology of complex diseases. [1] This highlights how external influences can modify the expression of genetic risk or protective factors related to metabolic health.
One illustrative example involves how genetic variation in fatty acid metabolism can moderate the effects of early life environmental factors, such as breastfeeding, on developmental outcomes. [12]This demonstrates that dietary patterns, lifestyle choices, or exposures can interact with an individual’s genetic architecture to influence metabolic pathways and, consequently, the circulating levels of various metabolites, including isobutyrylglycine. These interactions contribute to the observed variability in metabolic profiles among populations.
Polygenic and Complex Metabolic Influences
Section titled “Polygenic and Complex Metabolic Influences”Like many complex metabolic traits, the levels of isobutyrylglycine are likely influenced by a polygenic architecture, meaning that multiple genetic loci, rather than a single gene, contribute to its quantitative variation. Studies on other metabolic traits, such as dyslipidemia and lipid concentrations, have successfully identified common genetic variants across numerous loci that collectively influence these phenotypes.[3]This indicates that the broader human metabolic network, of which isobutyrylglycine is a part, is under intricate polygenic control.
The cumulative effect of these common genetic variants, each typically exerting a small influence, can lead to a wide spectrum of metabolite levels within a population. Different combinations of these variants can impact the efficiency and regulation of metabolic pathways responsible for the biosynthesis, catabolism, or excretion of compounds like isobutyrylglycine. Therefore, a comprehensive understanding of isobutyrylglycine levels requires considering the aggregate impact of multiple genetic factors alongside environmental and developmental influences.
The provided source material does not contain information specifically pertaining to ‘isobutyrylglycine’. Therefore, a Clinical Relevance section for this compound cannot be generated based solely on the given context without fabricating information or violating the instruction to avoid mentioning missing data.
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] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. S11.
[3] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 1, 2008, pp. 10–12.
[4] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 1, 2008, pp. 16–22.
[5] Yang, Qiong, et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, 2007, p. S10.
[6] Dehghan, Abbas, et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, vol. 372, no. 9654, 2008, pp. 1953-61.
[7] Melzer D, et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[8] Hwang, Shih-Jen, et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. S12.
[9] Pare, Guillaume, et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genetics, vol. 4, no. 7, 2008, e1000118.
[10] Wallace C, et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, 2008.
[11] Maier, E. M., et al. “Population spectrum of ACADM genotypes correlated to biochemical phenotypes in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency.” Hum Mutat, vol. 25, no. 5, 2005, pp. 443–452.
[12] Caspi, A., et al. “Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism.” Proc Natl Acad Sci U S A, vol. 104, no. 47, 2007, pp. 18860–18865.