Dimethylglycine
Introduction
Section titled “Introduction”Background
Section titled “Background”Dimethylglycine (DMG) is a naturally occurring derivative of the amino acid glycine, found in both plants and animals. Also known as N,N-dimethylglycine, it serves as an intermediate metabolite in the complex biochemical pathway of choline metabolism. In this pathway, DMG is formed from the breakdown of choline and is subsequently metabolized into sarcosine.
Biological Basis
Section titled “Biological Basis”Biologically, DMG functions as a methyl donor, playing a role in various metabolic processes within the body. It is involved in the one-carbon metabolism cycle, where it contributes methyl groups essential for the synthesis of S-adenosylmethionine (SAMe). SAMe is a universal methyl donor crucial for a wide array of biochemical reactions, including the methylation of DNA, RNA, proteins, and neurotransmitters. The metabolism of DMG is closely interconnected with the folate and vitamin B12 cycles, underscoring its importance in maintaining cellular methylation capacity. Some research also suggests its involvement in cellular respiration and oxygen utilization.
Clinical Relevance
Section titled “Clinical Relevance”Due to its engagement in methylation and one-carbon metabolism, DMG has been explored as a nutritional supplement. It has been marketed for a range of potential health benefits, including immune system support, enhanced athletic performance, and as an aid for conditions such as autism. However, scientific evidence supporting many of these claims is often limited or mixed. The rapidly evolving field of metabolomics, which aims at a comprehensive measurement of endogenous metabolites in body fluids, is increasingly identifying genetic variants that influence the homeostasis of key metabolites like DMG.[1] Such studies contribute to a functional understanding of how genetic variations might impact an individual’s metabolic profile, potentially influencing their response to nutritional interventions or susceptibility to complex diseases.[1]
Social Importance
Section titled “Social Importance”Dimethylglycine holds social importance primarily as a widely available dietary supplement. Its perceived health benefits have generated considerable consumer interest, particularly among individuals seeking to support general health, improve athletic performance, or address specific health concerns. This widespread use has fueled ongoing discussions within the scientific and medical communities regarding its efficacy, safety, and the necessity of rigorous scientific validation. As personalized medicine advances, understanding how genetic predispositions influence an individual’s DMG levels and metabolism through studies like genome-wide association studies (GWAS) and metabolomics may become increasingly relevant for guiding dietary and supplemental recommendations.[1]
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genome-wide association studies (GWAS) are limited by moderate sample sizes, which can lead to insufficient statistical power and an increased susceptibility to false negative findings.[2] While some studies achieve replication for associations with larger effect sizes, the effect sizes of genetic associations with complex clinical phenotypes are often small, necessitating very large populations for sufficient statistical power to identify new genetic variants.[1] This challenge is compounded by the observation that only about one-third of reported associations are consistently replicated across studies, with non-replication potentially stemming from false positive initial findings or differences in study cohorts that modify genotype-phenotype relationships.[2] Furthermore, non-replication at the SNP level can occur even if associations exist within the same gene, possibly due to different SNPs being in strong linkage disequilibrium with an unknown causal variant across studies.[3]Further constraints arise from the scope of genetic data analyzed, as current GWAS often utilize a subset of all known single nucleotide polymorphisms (SNPs), potentially missing causal variants due to incomplete genomic coverage.[4] The reliance on imputation to infer missing genotypes, though useful for comparing studies with different marker sets, introduces estimated error rates that can range from 1.46% to 2.14% per allele, influencing the confidence of identified associations.[5] Moreover, considering only SNPs with a high imputation quality score (e.g., RSQR ≥ 0.3) can inadvertently exclude potentially relevant variants.[6]
Generalizability and Phenotype Measurement Challenges
Section titled “Generalizability and Phenotype Measurement Challenges”A significant limitation for many GWAS findings is their generalizability, as discovery and replication cohorts often consist predominantly of individuals of self-reported European ancestry.[2] This demographic homogeneity restricts the applicability of findings to younger populations or individuals of diverse ethnic or racial backgrounds, despite attempts in some replication phases to include multiethnic samples.[2] Additionally, specific cohort characteristics, such as recruitment of middle-aged to elderly participants or DNA collection at later examination points, may introduce survival biases, further limiting the broader relevance of the results.[2] The precise definition and measurement of phenotypes also present challenges, particularly in complex traits like metabolic profiles. For instance, the exclusion of individuals on lipid-lowering therapy or the unavailability of such information in some cohorts can introduce confounding, as these medications directly impact the measured phenotypes.[7] Furthermore, analyses often involve sex-pooled data, potentially overlooking sex-specific genetic associations that might remain undetected.[4] While advanced metabolomics platforms enable the quantification of hundreds of endogenous metabolites, the use of metabolite ratios to reduce variance, though increasing statistical power, adds complexity to the biological interpretation.[1] Population stratification, even if minimal, also requires careful consideration and correction to ensure the validity of association tests.[8]
Unaccounted Factors and Mechanistic Gaps
Section titled “Unaccounted Factors and Mechanistic Gaps”Current GWAS primarily focus on genetic variants, often providing limited insight into the complex interplay between genes and environmental factors that can modify phenotype-genotype associations.[2] The relatively small effect sizes typically observed for genetic associations with clinical outcomes indicate that much of the phenotypic variance, or “missing heritability,” remains unexplained by identified common variants.[1] This gap suggests that a substantial proportion of genetic influence might stem from rarer variants, gene-gene interactions, or complex gene-environment interactions not fully captured by current study designs, even though specific genes like TF and HFE can explain a significant portion of variation for some traits.[8]A fundamental limitation of associating genotypes solely with clinical outcomes is the resulting difficulty in inferring the underlying disease-causing mechanisms.[1] While GWAS effectively identify novel genes or confirm previously less-known genetic influences on a phenotype, they often do not comprehensively elucidate the intricate biochemical pathways and physiological processes through which these genetic variants exert their effects.[1] Bridging this gap requires further functional studies to translate statistical associations into a deeper understanding of biological causality and potential therapeutic targets, emphasizing that gene discovery is only one step in understanding complex traits.[7]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing an individual’s metabolism, including pathways involving dimethylglycine. Several single nucleotide polymorphisms (SNPs) across various genes have been identified that can impact the activity of enzymes or transporters relevant to metabolic health. These genetic differences can lead to subtle or significant alterations in biochemical processes, potentially affecting levels of dimethylglycine and related metabolites.[9], [10] The genes DMGDH and BHMT2are central to one-carbon metabolism and the processing of dimethylglycine.DMGDH(Dimethylglycine Dehydrogenase) is an enzyme responsible for converting dimethylglycine to sarcosine, a critical step in its breakdown. Variants such asrs2947610 , rs248386 , and rs1805073 in DMGDHmay affect the enzyme’s efficiency, influencing the rate at which dimethylglycine is metabolized and potentially leading to altered levels of this compound.BHMT2(Betaine—Homocysteine S-Methyltransferase 2) is involved in the methionine cycle, which is closely interconnected with dimethylglycine metabolism through the generation and utilization of methyl groups. The variantrs10679800 , associated with both DMGDH and BHMT2, suggests a coordinated genetic influence on these interacting pathways. Similarly, rs62377952 and rs670220 , also linked to both genes, could impact the delicate balance of methyl donors and acceptors, thereby affecting overall metabolic flux and dimethylglycine concentrations.[11], [12] The ETFA gene (Electron Transfer Flavoprotein Subunit Alpha) is a vital component of the mitochondrial electron transfer flavoprotein complex, essential for the beta-oxidation of fatty acids and the catabolism of several amino acids. Impaired ETFAfunction can disrupt overall energy metabolism, which may indirectly influence dimethylglycine pathways by altering the availability of cofactors or the metabolic demand for related compounds. Variants likers12592501 and rs77633900 in ETFA could impact the stability or activity of this crucial protein. Additionally, rs78185702 is associated with both ETFA and ISL2, while rs79495512 is linked to TMEM266 and ETFA, highlighting potential complex regulatory interactions that extend beyond ETFAitself, further modulating metabolic health and potentially dimethylglycine levels.[13], [14]Other genetic variations contribute to a broader metabolic landscape that can interact with dimethylglycine. Thers17279437 variant in SLC6A20(Solute Carrier Family 6 Member 20), a proline transporter, may influence amino acid availability, which can affect metabolic pathways including one-carbon metabolism.SFXN2(Sideroflexin 2) is a mitochondrial transporter likely involved in serine transport; its variantrs2902548 could alter mitochondrial serine levels, impacting folate and methionine cycles that are interconnected with dimethylglycine.SACM1L (SAC1 Like Lipid Phosphatase) with its rs73060324 variant is involved in lipid metabolism and membrane function, which can have cascading effects on cellular signaling and overall metabolic health. Furthermore, variants like rs34829124 involving ALDH1A2 (Aldehyde Dehydrogenase 1 Family Member A2), important for aldehyde detoxification and retinoic acid synthesis, and AQP9(Aquaporin 9), a glycerol and urea transporter, can broadly influence cellular environment and nutrient handling, indirectly impacting the intricate balance of dimethylglycine metabolism.[10], [13]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs2947610 rs248386 rs1805073 | DMGDH | dimethylglycine measurement |
| rs10679800 | DMGDH, BHMT2 | dimethylglycine measurement |
| rs62377952 rs670220 | BHMT2, DMGDH | serum metabolite level dimethylglycine measurement |
| rs17279437 | SLC6A20 | metabolite measurement brain connectivity attribute macula attribute macular telangiectasia type 2 brain attribute |
| rs2902548 | SFXN2 | carotid plaque build erythrocyte volume dimethylglycine measurement mean corpuscular hemoglobin concentration |
| rs12592501 rs77633900 | ETFA | dimethylglycine measurement |
| rs73060324 | SACM1L | pneumonia, COVID-19 dimethylglycine measurement cerebrospinal fluid composition attribute, trans-4-hydroxyproline measurement |
| rs78185702 | ETFA - ISL2 | dimethylglycine measurement ethylmalonate measurement isovalerylcarnitine measurement butyrylcarnitine measurement glutarylcarnitine (C5-DC) measurement |
| rs79495512 | TMEM266, ETFA | glutarylcarnitine (C5-DC) measurement protein measurement dimethylglycine measurement |
| rs34829124 | ALDH1A2, AQP9 | dimethylglycine measurement |
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, Nov. 2008, e1000282.
[2] Benjamin EJ et al. “Genome-Wide Association with Select Biomarker Traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, Sep. 2007, p. 59.
[3] Sabatti C et al. “Genome-Wide Association Analysis of Metabolic Traits in a Birth Cohort from a Founder Population.”Nat Genet, vol. 40, no. 12, Dec. 2008, pp. 1396-8.
[4] Yang Q et al. “Genome-Wide Association and Linkage Analyses of Hemostatic Factors and Hematological Phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, Sep. 2007, p. 60.
[5] Willer CJ et al. “Newly Identified Loci That Influence Lipid Concentrations and Risk of Coronary Artery Disease.”Nat Genet, vol. 40, no. 2, Feb. 2008, pp. 161-9.
[6] Yuan X. “Population-Based Genome-Wide Association Studies Reveal Six Loci Influencing Plasma Levels of Liver Enzymes.” Am J Hum Genet, vol. 83, no. 4, Oct. 2008, pp. 520-8.
[7] Kathiresan S et al. “Common Variants at 30 Loci Contribute to Polygenic Dyslipidemia.” Nat Genet, vol. 40, no. 12, Dec. 2008, pp. 1387-95.
[8] Benyamin B et al. “Variants in TF and HFEExplain Approximately 40% of Genetic Variation in Serum-Transferrin Levels.”Am J Hum Genet, vol. 84, no. 1, Jan. 2009, pp. 60-5.
[9] Doring A, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, 2008, pp. 430–436.
[10] Saxena R, et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331–1336.
[11] Kooner JS, et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149–151.
[12] 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. 437–442.
[13] Gieger, Christian, et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 5, no. 11, 2009, e1000282.
[14] 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. 139–149.