Imidazole Lactate
Introduction
Section titled “Introduction”Imidazole lactate, also known as imidazolelactic acid, is an organic acid derived from the essential amino acid histidine. It is characterized by an imidazole ring structure and a lactate side chain, playing a role as an intermediate in several biochemical pathways within the human body.
Biological Basis
Section titled “Biological Basis”Imidazole lactate is a product of histidine metabolism. Specifically, it can be formed from the deamination of histidine to urocanic acid, followed by the hydration of urocanic acid. Alternatively, it can be generated from imidazolepyruvate, a transamination product of histidine, through the action of lactate dehydrogenase. These processes are part of the broader histidine degradation pathway, which is crucial for maintaining amino acid homeostasis.
Clinical Relevance
Section titled “Clinical Relevance”Abnormal levels of imidazole lactate in biological fluids are clinically relevant, particularly in the context of inherited metabolic disorders. Elevated concentrations are a biochemical hallmark of histidinemia, an autosomal recessive condition resulting from a deficiency in the enzyme histidase (HAL). While histidinemia is often considered benign, some studies have investigated potential associations with neurodevelopmental outcomes such as speech and learning difficulties. The study of genetic variants influencing the levels of various metabolites, including those involved in amino acid metabolism, is an active area of research. Genome-wide association studies (GWAS) have emerged as a powerful tool to identify genetic loci influencing circulating metabolite profiles in human serum, providing insights into the genetic underpinnings of metabolic variations and their potential impact on health.[1]Such research can help elucidate the genetic factors contributing to individual differences in histidine catabolism and imidazole lactate levels.
Social Importance
Section titled “Social Importance”Understanding the metabolism and clinical significance of compounds like imidazole lactate holds social importance, particularly in the realm of public health and personalized medicine. Early detection of metabolic disorders through newborn screening programs, which may identify elevated imidazole lactate, allows for timely intervention if necessary, although the long-term clinical impact of histidinemia remains a subject of ongoing debate. Furthermore, research into genetic determinants of metabolite levels contributes to a broader understanding of human metabolic health, offering potential avenues for identifying individuals at risk for certain conditions or for developing targeted nutritional and therapeutic strategies.
Limitations
Section titled “Limitations”Research into complex biological traits, such as imidazole lactate, often encounters a range of methodological, statistical, and interpretational challenges. Understanding these limitations is crucial for a balanced interpretation of findings and for guiding future investigations.
Methodological and Statistical Design Constraints
Section titled “Methodological and Statistical Design Constraints”Early genome-wide association studies (GWAS) frequently adopted sex-pooled analyses to manage the extensive burden of multiple hypothesis testing, a strategy that risks overlooking significant genetic associations specific to either males or females. [2]Furthermore, these foundational GWAS often relied on a subset of available single nucleotide polymorphisms (SNPs) from resources like HapMap, potentially leading to incomplete genomic coverage. This incomplete coverage may result in missed genetic variants, including non-SNP polymorphisms such as tandem repeats, that contribute to trait variability but are not represented on standard genotyping arrays.[2] The necessity for imputing missing genotypes to enable comparisons across studies utilizing different SNP platforms, despite meticulous quality control, introduces inherent error rates in genotype inference, typically ranging from 1.46% to 2.14% per allele. [3]
Statistical power issues are also common; moderate cohort sizes can limit the ability to detect genetic associations with modest effect sizes, thereby increasing the potential for false negative findings. [4] Conversely, the vast number of statistical tests performed in GWAS heightens the risk of identifying spurious associations if rigorous multiple testing corrections are not applied. [4] Replication studies often reveal inconsistencies, with some initially reported associations failing to replicate due to differing effect directions across cohorts, inadequate statistical significance in validation sets, or variations in linkage disequilibrium (LD) patterns between distinct ancestral populations. [3]Such heterogeneity and replication gaps underscore the complexity of identifying robust genetic signals and can lead to overestimation or “effect-size inflation” in initial discovery phases.[5]
Generalizability and Phenotypic Heterogeneity
Section titled “Generalizability and Phenotypic Heterogeneity”A significant constraint in understanding traits like imidazole lactate is the limited generalizability of findings, largely stemming from discovery cohorts predominantly composed of individuals of specific ancestries, such as European descent, and often skewed towards middle-aged to elderly populations.[4] This demographic uniformity means that genetic associations observed may not translate directly, or with similar effect magnitudes, to younger populations or individuals from other racial or ethnic backgrounds, thereby underscoring the critical need for validation in diverse multi-ethnic cohorts. [4] Furthermore, the timing of biological sample collection, such as DNA extraction at later examination points in longitudinal studies, can introduce survival bias by only including individuals who have survived to these later stages, potentially skewing the genetic landscape under investigation. [4]
Phenotypic measurements themselves introduce further challenges. Minor demographic variations among study populations, coupled with methodological differences in laboratory assays or kits used for trait quantification, can lead to discrepancies in reported mean trait levels across different studies. [3] Moreover, specific study design choices, such as the exclusion of participants based on medication use (e.g., lipid-lowering therapies) or outlier status, influence the composition of the study population and can impact the generalizability of findings. [5] The selection of covariates for statistical adjustment—including age, sex, and clinical conditions—can substantially alter association results, highlighting the potential for confounding by unmeasured or inadequately controlled environmental or physiological factors that interact with genetic predispositions. [6]
Unaccounted Confounding and Remaining Knowledge Gaps
Section titled “Unaccounted Confounding and Remaining Knowledge Gaps”Even with sophisticated analytical methods, population stratification remains a persistent confounder in genetic association studies of traits like imidazole lactate. Subtle differences in ancestral substructure within a cohort can generate spurious associations if not meticulously addressed, even if corrected for using tools like principal component analysis or genomic control.[7] Residual stratification or population-specific differences in linkage disequilibrium patterns can still impact the accurate detection and subsequent replication of genetic signals across diverse populations. [7]The intricate interplay between an individual’s genetic makeup and their environmental exposures—such as diet, lifestyle, and therapeutic interventions—introduces complex gene-environment confounders that are often difficult to comprehensively capture and adjust for within current study designs, potentially influencing observed genetic effect sizes.[6]
Significant knowledge gaps persist in fully elucidating the genetic architecture of complex traits. Current GWAS, particularly those relying on common SNPs, may not entirely capture the contribution of rare variants, structural variations, or other non-SNP polymorphisms that collectively account for a portion of a trait’s heritability. [2]The inability to thoroughly investigate gene-environment interactions, or to discern genetic associations that are specifically sex-dependent or contingent on unique population-specific genetic backgrounds, means that a considerable fraction of the genetic variance for traits like imidazole lactate may remain unexplained.[2] These ongoing challenges emphasize the need for advanced genomic sequencing technologies, the inclusion of more ethnically diverse cohorts, and innovative analytical strategies to comprehensively uncover the genetic foundations of human traits.
Variants
Section titled “Variants”Genetic variations play a crucial role in shaping an individual’s metabolic profile, including pathways related to amino acid breakdown and the formation of metabolites like imidazole lactate. Imidazole lactate is an intermediate in histidine metabolism, and its levels can be influenced by the activity of enzymes and regulatory elements throughout the metabolic network. Understanding variants in genes associated with these processes can provide insights into individual differences in metabolic health.
Several variants in genes directly involved in amino acid processing and those with broader metabolic implications are relevant.KYAT3(Kynurenine-aminotransferase 3) encodes an enzyme critical for amino acid transamination, a fundamental process in both synthesizing and breaking down amino acids.[1]Specifically, it can influence the handling of histidine and its derivatives, thereby affecting the availability of precursors for imidazole lactate.[8] Variants such as rs10801696 , rs1206228892 , rs7530513 , and rs74100109 (also affecting RBMXL1) within KYAT3could alter enzyme efficiency or stability, thus impacting the rate at which histidine metabolites are processed. Similarly,GOT2(Glutamic-oxaloacetic transaminase 2) is a mitochondrial aminotransferase essential for linking amino acid metabolism with the Krebs cycle and gluconeogenesis.[3]Its role in nitrogen balance and amino acid interconversion can indirectly affect the metabolic flux leading to imidazole lactate, with variants likers11076256 and rs1058192 potentially modifying this intricate balance. [9]
HAL(Histidine Ammonia-Lyase) is a key enzyme that catalyzes the initial step of histidine catabolism, converting histidine into urocanic acid.[10] The activity of HALis critical for regulating systemic histidine levels; therefore, variations such asrs61937878 could lead to altered histidine concentrations, directly impacting the availability of histidine for alternative metabolic pathways that produce imidazole lactate.[11] Changes in HALfunction represent a significant factor in the overall histidine metabolic landscape and its downstream products. Beyond direct metabolic enzymes, genes involved in transport and general gene regulation also play a role.SLC6A13(Solute Carrier Family 6 Member 13) encodes a GABA transporter, primarily affecting neurotransmission.[1]While its direct link to imidazole lactate is not immediate, broader metabolic states, which imidazole lactate levels can reflect, often impact neurotransmitter systems. Variants likers10774021 , rs10774020 , and rs11613331 might influence transporter efficiency, leading to systemic effects that could interact with metabolic pathways. [6]
Furthermore, variants in non-coding and regulatory regions can have widespread metabolic consequences. PKN2-AS1 (Protein Kinase N2 Antisense RNA 1) is a long non-coding RNA (lncRNA) known to regulate gene expression through various mechanisms, including transcriptional control. [12] The variant rs17433375 in PKN2-AS1could influence the expression of genes involved in metabolic pathways, indirectly impacting imidazole lactate production. Similarly,GTF2H1 (General Transcription Factor IIH Subunit 1) is part of a complex essential for initiating transcription of numerous genes. [2] Variants such as rs34554228 , rs4150678 , and rs4596 could affect transcription efficiency, thereby altering the expression of metabolic enzymes and potentially influencing imidazole lactate levels. Non-coding intergenic variants, such asrs66609725 between RNU6-1155P and RN7SL143P, rs11643460 between GEMIN8P2 and RPL12P36, and rs11646417 near RPL12P36 and Metazoa_SRP, are located in regions that can modulate the expression or function of adjacent genes or non-coding RNAs. [13]These genetic changes can have subtle yet significant effects on overall metabolic profiles by influencing regulatory pathways that converge on metabolites like imidazole lactate.[1]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10801696 rs1206228892 rs7530513 | KYAT3 | imidazole lactate measurement |
| rs11076256 rs1058192 | GOT2 | X-13684 measurement Phenyllactate (PLA) measurement imidazole lactate measurement serum metabolite level aspartate aminotransferase measurement |
| rs66609725 | RNU6-1155P - RN7SL143P | X-11334 measurement imidazole lactate measurement indolelactate measurement Phenyllactate (PLA) measurement |
| rs10774021 rs10774020 rs11613331 | SLC6A13 | chronic kidney disease, serum creatinine amount serum creatinine amount, glomerular filtration rate BMI-adjusted waist circumference 1-methylimidazoleacetate measurement deoxycarnitine measurement |
| rs11643460 | GEMIN8P2 - RPL12P36 | indolelactate measurement imidazole lactate measurement |
| rs11646417 | RPL12P36 - Metazoa_SRP | imidazole lactate measurement urinary metabolite measurement |
| rs17433375 | PKN2-AS1 | imidazole lactate measurement body height |
| rs61937878 | HAL | vitamin D amount gamma-glutamylhistidine measurement histidine measurement imidazole lactate measurement N-acetylhistidine measurement |
| rs74100109 | KYAT3, RBMXL1 | imidazole lactate measurement |
| rs34554228 rs4150678 rs4596 | GTF2H1 | imidazole lactate measurement serum metabolite level 2-hydroxy-3-methylvalerate measurement |
Biological Background
Section titled “Biological Background”Lipid Metabolism and Systemic Homeostasis
Section titled “Lipid Metabolism and Systemic Homeostasis”Lipid concentrations are tightly regulated within the body, playing a crucial role in energy storage, cellular structure, and signaling pathways. Disturbances in these metabolic processes can lead to systemic consequences, impacting overall homeostatic balance. For instance, dysregulation of lipid levels, such as elevated cholesterol, is a significant factor in the development of coronary artery disease, reflecting a broader disruption in metabolic health.[5]The intricate network of enzymes, transport proteins, and regulatory factors ensures that lipids are synthesized, transported, and catabolized appropriately across various tissues. These molecular and cellular pathways are essential for maintaining cardiovascular health and preventing pathological accumulation of lipids in arterial walls.
Key Biomolecules in Cholesterol Esterification
Section titled “Key Biomolecules in Cholesterol Esterification”A critical enzyme in lipid metabolism, particularly in cholesterol transport, is lecithin:cholesterol acyltransferase (LCAT). This enzyme catalyzes the esterification of free cholesterol with a fatty acid derived from lecithin, forming cholesterol esters.LCATis primarily synthesized in the liver and circulates in the plasma, where it is instrumental in the maturation of high-density lipoprotein (HDL) particles and the reverse cholesterol transport pathway.[14] Functional LCAT is essential for maintaining appropriate plasma lipid profiles, facilitating the removal of excess cholesterol from peripheral tissues and its transport back to the liver for excretion.
Pathophysiological Consequences of LCAT Deficiency
Section titled “Pathophysiological Consequences of LCAT Deficiency”Deficiencies in LCAT activity lead to distinct pathophysiological processes, collectively known as LCATdeficiency syndromes. These conditions manifest with characteristic disruptions in lipid homeostasis, including very low HDL cholesterol levels, accumulation of unesterified cholesterol, and the presence of abnormal lipoprotein particles.[14]Such severe dyslipidemia can result in various clinical symptoms, affecting kidney function (renal impairment), red blood cell integrity (hemolytic anemia), and ocular clarity (corneal opacities). The impact ofLCAT deficiency underscores the enzyme’s critical role in systemic lipid metabolism and its intricate connection to organ-level biology and overall health.
Genetic Influences on Lipid Concentrations and Disease Risk
Section titled “Genetic Influences on Lipid Concentrations and Disease Risk”Genetic mechanisms significantly influence an individual’s lipid concentrations and, consequently, their susceptibility to conditions like coronary artery disease. Genetic loci have been identified that contribute to the variability in lipid profiles, suggesting that specific gene functions or regulatory elements play a role in metabolic control.[5] Variations in genes encoding enzymes like LCATcan alter protein function or expression patterns, leading to altered lipid metabolism and increased disease risk. Understanding these genetic underpinnings provides insights into the complex interplay between inherited factors and the development of common diseases.
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Urate Homeostasis and Anion Transport
Section titled “Urate Homeostasis and Anion Transport”The regulation of urate levels, a crucial aspect of metabolic homeostasis, involves specialized transport mechanisms heavily influenced by specific genetic components. The proteinSLC2A9, also known as GLUT9, plays a pivotal role in this process, acting as a facilitative glucose transporter that also functions as a renal urate anion exchanger.[10] This dual function enables SLC2A9to significantly influence serum urate concentrations and modulate urate excretion.[10]Dysregulation within this pathway, potentially impacted by fructose metabolism, can lead to elevated uric acid levels, which is a key mechanism underlying conditions like gout.[10]
Interplay of Carbohydrate and Energy Metabolism
Section titled “Interplay of Carbohydrate and Energy Metabolism”Carbohydrate metabolism pathways are fundamental to cellular energy production and overall glycemic control, with several genetic factors influencing their efficiency and regulation. For instance, theHK1gene, encoding Hexokinase 1, has been associated with glycated hemoglobin levels in non-diabetic populations, highlighting its role in glucose phosphorylation and entry into glycolysis.[7] Variations in genes like CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11are linked to susceptibility to type 2 diabetes, often through their impact on insulin secretion, insulin sensitivity, and beta-cell function.[7] These intricate networks ensure energy balance, and their perturbations can lead to significant metabolic disorders.
Regulation of Lipid and Sterol Biosynthesis
Section titled “Regulation of Lipid and Sterol Biosynthesis”Lipid and sterol biosynthesis pathways are critically controlled to maintain membrane integrity, hormone synthesis, and energy storage, involving complex regulatory feedback loops. The enzyme HMG-CoA reductase (HMGCR), a rate-limiting enzyme in the mevalonate pathway, is central to cholesterol synthesis, with genetic variants inHMGCR influencing LDL-cholesterol levels. [12] Furthermore, common genetic variants within the FADS1 FADS2 gene cluster are associated with the fatty acid composition in phospholipids, affecting the biosynthesis of polyunsaturated fatty acids. [1]This precise regulation, including allosteric control and post-translational modifications, ensures proper lipid homeostasis, with dysregulation contributing to dyslipidemia and cardiovascular risk.[5]
Genetic and Epigenetic Metabolic Regulation
Section titled “Genetic and Epigenetic Metabolic Regulation”Metabolic pathways are under tight genetic and epigenetic control, with single nucleotide polymorphisms (SNPs) and alternative splicing mechanisms profoundly influencing enzyme activity and transporter function. For example, common SNPs inHMGCR affect alternative splicing of exon 13, altering protein expression and potentially enzyme activity. [12]Similarly, intronic SNPs in the urate-anion exchanger geneSLC22A12are associated with serum uric acid levels, illustrating how genetic variations can modulate regulatory elements.[15] Such regulatory mechanisms, including the action of ubiquitin ligases like PJA1, define the expression and stability of key metabolic proteins, ensuring adaptive responses to physiological changes.
Systems-Level Metabolic Crosstalk and Disease Relevance
Section titled “Systems-Level Metabolic Crosstalk and Disease Relevance”The human body’s metabolic landscape is characterized by extensive pathway crosstalk and network interactions, where alterations in one pathway can have cascading effects across multiple systems, influencing disease susceptibility. Genetic variants that affect intermediate phenotypes, such as specific metabolite profiles like imidazole lactate, provide insight into these interconnected pathways.[1] For instance, variations in SLC2A9that influence urate homeostasis are directly linked to gout, while genetic factors affecting lipid and carbohydrate metabolism pathways contribute to complex diseases like type 2 diabetes and coronary artery disease.[10] Understanding these emergent properties and hierarchical regulation is crucial for identifying therapeutic targets and developing personalized medicine strategies.
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, e1000282.
[2] Yang, Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, 2007, p. 64. PubMed, PMID: 17903294.
[3] 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. 4, 2008, pp. 520-28.
[4] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. 62. PubMed, PMID: 17903293.
[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] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000076.
[7] Pare, G., 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. PubMed, PMID: 18604267.
[8] Saxena, R. et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331-36.
[9] Hwang, S. J. et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, no. Suppl 1, 2007, S10.
[10] Vitart, V. et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, vol. 40, no. 4, 2008, pp. 437-42.
[11] Wallace, C. et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 139-49.
[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, vol. 28, no. 11, 2008, pp. 2071-77.
[13] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.
[14] Kuivenhoven, Jan A., et al. “The molecular pathology of lecithin:cholesterol acyltransferase (LCAT) deficiency syndromes.” J Lipid Res, vol. 38, 1997, pp. 191–205.
[15] Li, S., et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, e194.