Citrulline
Introduction
Section titled “Introduction”Citrulline is a non-essential amino acid, meaning the human body can synthesize it, though it can also be obtained through diet. It plays a crucial role as an intermediate in the urea cycle, a biochemical pathway that converts ammonia, a toxic byproduct of protein metabolism, into urea for excretion. Beyond its role in detoxification, citrulline is a key precursor to arginine, which in turn is a substrate for nitric oxide synthase, an enzyme that produces nitric oxide (NO).
Biological Basis
Section titled “Biological Basis”The biological significance of citrulline primarily stems from its involvement in nitric oxide synthesis. Once absorbed, citrulline can be converted to arginine in the kidneys, and arginine is then used by endothelial cells to produce nitric oxide. Nitric oxide is a potent vasodilator, meaning it helps relax and widen blood vessels, improving blood flow. This pathway makes citrulline indirectly involved in regulating blood pressure and enhancing circulation. Additionally, its role in the urea cycle highlights its importance in maintaining nitrogen balance and preventing the accumulation of toxic ammonia in the body.
Clinical Relevance
Section titled “Clinical Relevance”Due to its impact on nitric oxide production and vasodilation, citrulline has garnered attention in various clinical contexts. Supplementation with citrulline has been explored for its potential benefits in cardiovascular health, including supporting healthy blood pressure and endothelial function. It is also investigated for improving athletic performance by enhancing oxygen delivery to muscles and aiding in the removal of metabolic byproducts like ammonia and lactate, which can contribute to muscle fatigue. Furthermore, citrulline is used in the management of certain urea cycle disorders, where it helps facilitate the detoxification of ammonia. Research also suggests potential applications in conditions related to impaired blood flow, such as erectile dysfunction.
Social Importance
Section titled “Social Importance”Citrulline has gained considerable social importance, particularly within the fitness and wellness communities. It is widely available as a dietary supplement, often marketed to athletes and individuals seeking to improve exercise performance, reduce muscle soreness, and support cardiovascular health. Its natural presence in foods, most notably watermelon, also contributes to public awareness and interest in its health benefits. As research continues to elucidate its physiological roles, citrulline’s prominence as a natural compound with diverse health implications is likely to grow.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”These studies were susceptible to false negative findings due to moderate cohort sizes, which limited their statistical power to detect modest genetic associations.[1] This challenge is compounded by the extensive multiple statistical testing inherent in genome-wide association studies, which increases the likelihood of false positive findings if not stringently controlled.[1] Consequently, rigorous significance thresholds, while necessary, can make it difficult to identify true, but subtle, genetic effects.
The ultimate validation of genetic findings often requires replication in independent cohorts, yet many reported associations may represent false positives if not successfully replicated.[2]Failure to replicate can stem from various factors, including differences in study design, statistical power, or the possibility that the specific single nucleotide polymorphism (SNP) tested is not the true causal variant but merely in linkage disequilibrium with it.[3] Furthermore, reliance on imputation analyses, particularly those based on older HapMap builds, can introduce inaccuracies and higher error rates, especially for less common variants, potentially affecting the reliability of genotype data.[4]
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”A significant limitation across many studies is the lack of ethnic diversity and national representativeness within the cohorts, which are often predominantly composed of individuals of white European ancestry.[2] This demographic homogeneity restricts the generalizability of findings, making it uncertain how these genetic associations would apply to other ethnic groups or racial descents.[2] Additionally, cohorts largely comprising middle-aged to elderly individuals may introduce survival bias and limit the applicability of results to younger populations.[1]Concerns regarding phenotype characterization include the reliance on surrogate markers, such as using TSH as an indicator of thyroid function without measures of free thyroxine, or the potential for markers likecysCto reflect cardiovascular disease risk beyond kidney function.[2] Such measurement choices can lead to an incomplete or confounded assessment of the true underlying biological trait. Moreover, the exclusion of participants on specific medications, such as lipid-lowering therapies, may introduce selection bias, potentially masking genetic effects in treated populations or altering observed associations.[5]
Environmental Confounders and Unexplored Interactions
Section titled “Environmental Confounders and Unexplored Interactions”Genetic variants may exert their influence on phenotypes in a context-specific manner, with their effects being modulated by various environmental factors.[6]The absence of comprehensive investigations into these gene-environment interactions represents a significant knowledge gap, as such interactions could explain a substantial portion of phenotypic variation that remains unaccounted for. This oversight can lead to an underestimation of the true genetic effects or an incomplete understanding of disease etiology and progression.
The complex genetic architecture of many traits, where multiple causal variants may exist within the same gene or across different genomic regions, adds to the challenge of pinpointing definitive associations.[3]Furthermore, unmeasured environmental or lifestyle confounders could influence observed genetic associations, making it difficult to disentangle direct genetic effects from indirect influences. While studies often employ multivariable models, these approaches may still miss important bivariate associations or subtle interactions that contribute to the overall variability of a trait.[2]
Variants
Section titled “Variants”Genetic variations can profoundly influence an individual’s metabolic pathways, including those involving citrulline, a key amino acid in the urea cycle and nitric oxide synthesis. Variants within genes directly involved in amino acid metabolism can alter enzyme efficiency, while others with more indirect roles can still impact overall physiological balance, affecting citrulline levels and related health traits.
Variants in genes like ASS1(Argininosuccinate Synthase 1) andCPS1(Carbamoyl Phosphate Synthetase 1) are central to the body’s ability to process nitrogenous waste and synthesize amino acids.CPS1is the rate-limiting enzyme in the urea cycle, catalyzing the first committed step, whileASS1is crucial for converting citrulline into argininosuccinate, a precursor to arginine. Genetic variations, such asrs1509820 , rs13411696 , and rs975530777 in CPS1, or rs11243372 and rs10901047 in ASS1, can influence the efficiency of these enzymes. Impaired activity can lead to a buildup of ammonia or affect the availability of arginine and citrulline, impacting metabolic health and cardiovascular function.[7]Such variants may alter the body’s capacity for detoxification and the production of important signaling molecules derived from arginine, thereby influencing systemic citrulline concentrations and related physiological outcomes.[8] Other variants affect genes with broader metabolic or systemic impacts. For instance, ALDH18A1(Aldehyde Dehydrogenase 18 Family Member A1) plays a role in the biosynthesis of proline and ornithine, which are precursors to arginine and thus indirectly linked to citrulline metabolism. Variants likers11188411 and rs56322409 in ALDH18A1could modify the availability of these precursors, affecting the overall flux through the urea cycle and related pathways. TheABO gene, responsible for determining blood groups, has been associated with various health traits, including inflammation; the rs612169 variant is linked to the ABO blood group system, which has been associated with tumor necrosis factor alpha (TNF-alpha) levels.[9]Such inflammatory markers can influence metabolic processes and nutrient utilization, indirectly affecting citrulline levels and overall health.[1] Beyond core metabolic enzymes, variations in genes involved in cellular structure, signaling, and regulation can also have downstream effects. HMCN2 (Hemicentin 2), with variant rs7850549 , contributes to extracellular matrix organization, which can impact cell-cell communication and tissue function. GLP2R(Glucagon-like Peptide 2 Receptor), associated withrs17681684 and rs17810412 , is involved in gut health and nutrient absorption, processes critical for the bioavailability of amino acids like citrulline. Meanwhile, variants such asrs60837490 in the RP1L1 - SOX7 intergenic region, rs6601508 in RP1L1, and rs3732055 in LANCL1 (LanC-like protein 1) represent genetic influences on diverse cellular functions, from photoreceptor development to general signaling. Even complex loci like EXOC7P1 - RPL34-DT, with variant rs551269548 , involving a pseudogene and a long non-coding RNA, can exert regulatory effects on gene expression that ripple through metabolic networks, subtly influencing the intricate balance of amino acids and their derivatives, including citrulline.[5] These wide-ranging genetic influences underscore the complex interplay between genotype and metabolic phenotype.[10]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs11243372 rs10901047 | ASS1 | citrulline measurement |
| rs1509820 rs13411696 rs975530777 | CPS1 | glycine measurement citrulline measurement |
| rs7850549 | HMCN2 | citrulline measurement |
| rs17681684 rs17810412 | GLP2R | glucose-dependent insulinotropic peptide measurement, glucose tolerance test total cholesterol measurement citrulline measurement |
| rs60837490 | RP1L1 - SOX7 | citrulline measurement |
| rs11188411 rs56322409 | ALDH18A1 | citrulline measurement |
| rs612169 | ABO | metabolite measurement cholesteryl ester measurement thrombomodulin measurement FCGR2B/NOS3 protein level ratio in blood CD46/THBD protein level ratio in blood |
| rs6601508 | RP1L1 | citrulline measurement Red cell distribution width |
| rs3732055 | LANCL1 | citrulline measurement gamma-glutamylcitrulline measurement |
| rs551269548 | EXOC7P1 - RPL34-DT | citrulline measurement |
References
Section titled “References”[1] Benjamin EJ et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007.
[2] 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, no. Suppl 1, 2007, p. S3.
[3] Sabatti, Chiara, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 41, no. 1, 2009, pp. 35-42.
[4] Yuan, Xin, 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. 520-28.
[5] Kathiresan S et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008.
[6] 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, vol. 8, no. Suppl 1, 2007, p. S2.
[7] Saxena R et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.” Science, 2007.
[8] McArdle PF et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.” Arthritis Rheum, 2008.
[9] Melzer D et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[10] Willer CJ et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.” Nat Genet, 2008.