Acetone
Introduction
Section titled “Introduction”Acetone (propanone) is a colorless, volatile organic compound and the simplest ketone. In the human body, it is a naturally occurring metabolite, primarily produced during the breakdown of fats for energy.
Biological Basis
Section titled “Biological Basis”Acetone is one of three ketone bodies (along with acetoacetate and beta-hydroxybutyrate) synthesized in the liver during periods of low carbohydrate intake, prolonged fasting, or uncontrolled diabetes. When glucose is scarce, the body shifts to burning fatty acids, leading to the production of acetyl-CoA. If acetyl-CoA accumulates faster than it can be processed by the citric acid cycle, it is diverted to ketogenesis, forming ketone bodies. Acetone itself is a byproduct of the spontaneous decarboxylation of acetoacetate. It is largely excreted through the breath and urine.
Clinical Relevance
Section titled “Clinical Relevance”The presence and concentration of acetone in the body, particularly in blood and breath, serve as important biomarkers for various metabolic states. Elevated acetone levels are characteristic of ketosis, which can be a normal physiological adaptation (e.g., during ketogenic diets or prolonged exercise) or a pathological condition. Diabetic ketoacidosis (DKA), a severe complication of diabetes, is marked by very high levels of ketone bodies, including acetone, leading to a distinctive “fruity” breath odor. Research has identified genetic variants associated with metabolite profiles in human serum, including those that might influence the homeostasis of key lipids, carbohydrates, or amino acids, which in turn could impact ketone body levels.[1] Genome-wide association studies (GWAS) have been used to investigate associations between genetic markers and various metabolic traits. [1]
Social Importance
Section titled “Social Importance”The presence of acetone can have noticeable social implications, particularly the characteristic sweet or “fruity” breath odor associated with conditions like uncontrolled diabetes. Awareness of this symptom can prompt individuals or caregivers to seek medical attention, highlighting acetone’s role as an accessible, though non-specific, indicator of metabolic distress.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Studies often face limitations in statistical power, particularly for detecting genetic effects that explain only a small proportion of phenotypic variation. For instance, some research had less than 90% power to detect associations for single nucleotide polymorphisms (SNPs) explaining less than 4% of total phenotypic variation at a stringent significance level.[2]This moderate sample size can lead to false negative findings, where true associations are missed.[3] Furthermore, replicating previously reported associations remains a challenge, with only about one-third of associations examined in some meta-analyses showing replication, which could stem from false positives in initial reports, cohort differences, or insufficient power in replication studies. [3]
The genetic variants analyzed in genome-wide association studies (GWAS) represent only a subset of all possible SNPs available in reference panels like HapMap. [4] This partial coverage means that some causal genes or genetic variants may be missed entirely, limiting the comprehensiveness of the genetic landscape explored. [4] Additionally, while imputation methods are used to infer missing genotypes and increase SNP coverage, these processes introduce a degree of estimation error, with reported error rates ranging from 1.46% to 2.14% per allele, which can affect the accuracy of association results. [5] Stringent filtering criteria for imputed SNPs, such as high posterior probability or information content, also mean that lower-confidence variants are excluded, potentially narrowing the scope of discovery. [6]
Generalizability and Phenotype Definition
Section titled “Generalizability and Phenotype Definition”A significant limitation across several studies is the predominant focus on cohorts of white European descent. [3] This narrow ancestral representation means that findings may not be broadly generalizable to individuals of other ethnic or racial backgrounds. [3] Population stratification was often addressed by excluding individuals who did not cluster with the main Caucasian group, which, while mitigating bias, reinforces the lack of diversity in the analyzed populations. [7]
The precise definition and measurement of phenotypes can introduce limitations. For example, averaging echocardiographic traits over periods as long as twenty years, using different equipment, may lead to misclassification and obscure important temporal variations. [2] This approach also assumes that the same genetic and environmental factors influence traits consistently across a wide age range, an assumption that might mask age-dependent genetic effects. [2] Furthermore, demographic differences and methodological variations in assays across studies can lead to variability in mean phenotype levels, complicating comparisons and meta-analyses. [6]
Unaccounted Genetic and Environmental Interactions
Section titled “Unaccounted Genetic and Environmental Interactions”The current research generally did not undertake comprehensive investigations into gene-environment interactions. [2] Genetic variants are known to influence phenotypes in a context-specific manner, meaning their effects can be modulated by environmental factors, such as dietary salt intake influencing associations of ACE and AGTR2with left ventricular mass.[2] Without exploring these complex interactions, the full biological mechanisms underlying observed genetic associations remain incompletely understood, potentially overlooking crucial modifying effects. [2]
Despite identifying numerous genetic associations, a substantial portion of the heritability for many traits remains unexplained, often referred to as “missing heritability.” This gap may be partly due to the exclusion of rare genetic variants or those with low minor allele frequencies (MAF), as studies often filter out SNPs with MAF below a certain threshold (e.g., 1% or 5%) due to power constraints in moderate-sized cohorts. [8] Consequently, the contribution of these less common variants, or complex interactions not captured by common SNP arrays, to phenotypic variation is largely unexplored in these studies. [4]
Variants
Section titled “Variants”Genetic variations play a crucial role in modulating an individual’s metabolic profile, including the production and utilization of ketone bodies such as acetone. Several single nucleotide polymorphisms (SNPs) in genes central to energy metabolism, glucose transport, and ketone synthesis are associated with metabolic traits that can influence acetone levels. TheOXCT1 gene (3-oxoacid CoA-transferase 1), along with its antisense RNA OXCT1-AS1, is vital for ketone body utilization, as OXCT1catalyzes the rate-limiting step in converting acetoacetate to acetoacetyl-CoA in non-hepatic tissues. A variant likers11745373 in this region could potentially alter the efficiency of ketone metabolism, leading to elevated acetoacetate levels and subsequent increased acetone production, particularly during periods of fasting or metabolic stress.[9] Similarly, HMGCS2 (3-hydroxy-3-methylglutaryl-CoA synthase 2) is the primary enzyme responsible for initiating ketone body synthesis in the liver. Variants such as rs1163547 and rs2582783 in the HMGCS2-REG4 locus could affect the activity or regulation of HMGCS2, directly influencing the rate of ketogenesis and the overall production of acetone.[9]
Variants in genes affecting glucose metabolism and transport can also indirectly impact acetone levels. For instance,SLC2A4 (Solute Carrier Family 2 Member 4), also known as GLUT4, encodes an insulin-responsive glucose transporter critical for glucose uptake in muscle and adipose tissue. Thers117643180 variant in SLC2A4may influence glucose utilization, and impairedGLUT4function can lead to reduced glucose uptake, promoting a shift towards fat oxidation and ketone body formation, thereby increasing acetone concentrations.[9] In a similar vein, the PPP1R3B-DT (Protein Phosphatase 1 Regulatory Subunit 3B, Divergent Transcript) locus, with its variant rs2126264 , may affect glycogen metabolism through its association with PPP1R3B. Dysregulation here could alter glucose availability and push the body into a ketogenic state, elevating acetone levels.[9] Furthermore, MPC1(Mitochondrial Pyruvate Carrier 1), located nearSFT2D1, is essential for transporting pyruvate into mitochondria, linking glycolysis to the TCA cycle. A variant likers117651719 could impair pyruvate entry into mitochondria, favoring fatty acid oxidation and ketogenesis, which would contribute to higher acetone levels.
Other variants in genes involved in broader cellular processes might also have metabolic implications for acetone.MLXIP(MLX-Interacting Protein), a transcription factor involved in glucose and lipid sensing, can regulate genes crucial for glycolysis and lipogenesis. Thers11061153 variant in MLXIPcould alter metabolic fuel partitioning, potentially influencing the balance between glucose and fat metabolism and, consequently, ketone body production.[9] GALNT2 (UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 2), involved in protein glycosylation, has a variant rs11122450 that might affect the function of various metabolic enzymes or signaling pathways, indirectly impacting metabolic states that lead to ketosis. [9] Additionally, FBXO4 (F-box protein 4), through its role in protein ubiquitination and degradation, and ZPR1 (Zinc Finger Protein 1), involved in cell proliferation and stress responses, could, through variants like rs277414 and rs964184 respectively, subtly influence cellular metabolic adaptations or stress responses that are often associated with altered ketone body metabolism. Even less characterized genes like SCART1, with variants rs117810762 , rs7095996 , and rs2265898 , might play indirect roles in metabolic health through as-yet-undiscovered mechanisms or regulatory functions, potentially influencing systemic metabolism and acetone levels.[9]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs11745373 | OXCT1, OXCT1-AS1 | acetoacetate measurement acetone measurement |
| rs277414 | FBXO4 | acetone measurement |
| rs964184 | ZPR1 | very long-chain saturated fatty acid measurement coronary artery calcification vitamin K measurement total cholesterol measurement triglyceride measurement |
| rs2126264 | PPP1R3B-DT | heel bone mineral density acetone measurement metabolic disease hyperlipidemia drug use measurement, Hypercholesterolemia |
| rs117810762 rs7095996 rs2265898 | SCART1 | coffee consumption measurement coffee consumption measurement, tea consumption measurement acetone measurement |
| rs117643180 | SLC2A4 | glucose tolerance test serum alanine aminotransferase amount systolic blood pressure diastolic blood pressure valine measurement |
| rs1163547 rs2582783 | HMGCS2 - REG4 | acetone measurement |
| rs11061153 | MLXIP | acetone measurement |
| rs11122450 | GALNT2 | platelet-to-lymphocyte ratio depressive symptom measurement, non-high density lipoprotein cholesterol measurement body fat percentage high density lipoprotein cholesterol measurement triglyceride measurement |
| rs117651719 | SFT2D1 - MPC1 | acetone measurement |
Biological Background
Section titled “Biological Background”Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Homeostasis and Regulation
Section titled “Metabolic Homeostasis and Regulation”Acetone, as an endogenous metabolite, is an integral component of the comprehensive metabolite profiles that provide a functional readout of an individual’s physiological state.[1] The dynamic balance, or homeostasis, of such metabolites, encompassing key lipids, carbohydrates, and amino acids, is significantly influenced by genetic factors. [1] This intricate regulation is essential for maintaining stable internal conditions, and disruptions can lead to distinct metabolic phenotypes. [9]
The field of metabolomics aims to measure these endogenous compounds comprehensively, thereby offering insights into their steady-state concentrations and the underlying regulatory networks. [1] Such systematic approaches are crucial for understanding how physiological processes are controlled and how genetic variations influence the overall balance of metabolic components within the body. [1]
Genetic Regulation of Metabolic Pathways
Section titled “Genetic Regulation of Metabolic Pathways”Genetic variants exert a substantial influence on the concentrations of various metabolites found in human serum. [1]Genome-wide association studies (GWAS) have successfully identified specific genomic loci that are associated with changes in the homeostasis of critical metabolic compounds, including those involved in lipid and carbohydrate metabolism.[1] For instance, common genetic variants affecting the HMGCR gene, a key enzyme in the mevalonate pathway responsible for cholesterol synthesis, can impact LDL-cholesterol levels through mechanisms such as alternative splicing of exon 13. [10]
Beyond the effects of single genes, complex metabolic phenotypes are often influenced by multiple loci, highlighting their polygenic nature. [11] These genetic associations reveal regulatory mechanisms at the genomic level that dictate the flux through metabolic pathways and determine the overall composition of an individual’s metabolite profile. [1]
Systems-Level Metabolic Integration
Section titled “Systems-Level Metabolic Integration”The intricate network of metabolic pathways operates through extensive crosstalk and hierarchical regulation, where the status of one pathway can significantly influence others. [12] This systems-level integration is vital for maintaining overall metabolic balance and for generating the emergent properties of cellular and organismal function. [13] For example, the regulation of lipid metabolism involves proteins such as ANGPTL3 and ANGPTL4, which are known to influence triglyceride and HDL levels, illustrating complex, interconnected regulatory loops.[5]
Such network interactions are fundamental to comprehending how biological systems adapt to physiological challenges and how dysregulation in one metabolic area can cascade throughout the entire system. [12]Comprehensive analysis of metabolite profiles, including acetone, aids in mapping these complex interdependencies and provides a holistic view of metabolic health.[1]
Disease-Relevant Metabolic Dysregulation
Section titled “Disease-Relevant Metabolic Dysregulation”Dysregulation within metabolic pathways is a significant contributor to various disease states, and elucidating these mechanisms can reveal crucial therapeutic targets.[14]For example, alterations in glucose transporter proteins likeSLC2A9 (GLUT9) are strongly associated with serum uric acid levels, impacting conditions such as gout and the metabolic syndrome.[15] Similarly, variants in the HK1gene have been linked to glycated hemoglobin levels in non-diabetic populations, indicating its relevance in glucose metabolism.[7]
The study of metabolic phenotypes through approaches like metabolomics is instrumental in identifying specific pathway dysregulation and potential compensatory mechanisms that arise in the context of disease.[14] This knowledge is crucial for guiding the development of targeted interventions aimed at restoring metabolic balance and improving health outcomes. [14]
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. PMID: 19043545.
[2] Vasan, R. S. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 65. PMID: 17903301.
[3] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 63. PMID: 17903293.
[4] Yang, Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 64. PMID: 17903294.
[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. PMID: 18193043.
[6] 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. 5, 2008, pp. 520-528. PMID: 18940312.
[7] Pare, G. et al. “Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women’s Genome Health Study.”PLoS Genet, 2008. PMID: 19096518.
[8] Dehghan, A., 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. 1953-1961. PMID: 18834626.
[9] Assfalg, M. et al. “Evidence of different metabolic phenotypes in humans.” Proc Natl Acad Sci U S A, vol. 105, 2008, pp. 1420–1424.
[10] 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. PMID: 18802019.
[11] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008. PMID: 19060906.
[12] Wenk, M.R. “The emerging field of lipidomics.” Nat Rev Drug Discov, vol. 4, 2005.
[13] Griffin, J.L. “The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?” Philos Trans R Soc Lond B Biol Sci, vol. 361, 2006, pp. 147–161.
[14] Nicholson, J.K. et al. “Metabonomics: a platform for studying drug toxicity and gene function.” Nat Rev Drug Discov, vol. 1, 2002.
[15] Li, S. et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, 2007. PMID: 17997608.