Vitamin C
Background
Section titled “Background”Vitamin C, also known as ascorbic acid, is an essential water-soluble vitamin that plays a crucial role in numerous physiological processes in the human body. Unlike most animals, humans cannot synthesize vitamin C endogenously and must obtain it through dietary intake. It is found abundantly in various fruits and vegetables.
Biological Basis
Section titled “Biological Basis”As a potent antioxidant, vitamin C helps protect cells from damage caused by free radicals.[1]It is also a vital cofactor for several enzyme reactions, notably those involved in the synthesis of collagen, a primary structural protein in connective tissues, skin, bones, and blood vessels. Additionally, vitamin C is essential for immune system function and aids in the absorption of non-heme iron.
Clinical Relevance
Section titled “Clinical Relevance”Deficiency in vitamin C leads to scurvy, a historical disease characterized by weakness, anemia, gum disease, and skin hemorrhages, primarily due to impaired collagen synthesis. Adequate intake of vitamin C is important for maintaining overall health. Research has explored the potential health effects of antioxidant vitamins and minerals, including vitamin C, in areas such as cardiovascular diseases and cancers.[1] However, the exact mechanisms and extent of these benefits, particularly through supplementation, remain areas of ongoing study.
Social Importance
Section titled “Social Importance”Vitamin C holds significant social importance as a widely recognized health supplement and a key component of a balanced diet. Public health recommendations often highlight the importance of consuming vitamin C-rich foods. The widespread availability of vitamin C supplements reflects a common public interest in its perceived immune-boosting and general health-promoting properties.
Limitations
Section titled “Limitations”Research into genetic associations with biomarker traits, such as vitamin C levels, inherently faces several methodological and interpretative limitations that warrant consideration when evaluating findings. These constraints can influence the robustness, generalizability, and ultimate utility of identified genetic variants.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genetic association studies are susceptible to false negative findings due to moderate cohort sizes, which can limit statistical power to detect associations of modest effect. [2] Conversely, a significant challenge in genome-wide association studies (GWAS) is the potential for false positive findings arising from the extensive number of statistical tests performed. [2] This necessitates stringent statistical thresholds and robust replication to ensure the reliability of reported associations. The ultimate validation of genetic discoveries often hinges on successful replication in independent cohorts [2] with non-replication potentially stemming from differences in study design, population characteristics, or the complex genetic architecture where distinct but functionally related variants might influence the trait across different studies. [3] Furthermore, some identified associations, even when statistically significant, may not hold true under different analytical models, indicating a potential weakness in the individual effect of a single genetic variant. [4]
Technical aspects of genotyping and data analysis also introduce limitations. The coverage of single nucleotide polymorphisms (SNPs) in earlier arrays, such as 100K platforms, may be insufficient to capture all true genetic associations within a given gene region, potentially missing causal variants.[5] When harmonizing data from studies using different marker sets, imputation methods are often employed to infer missing genotypes. While advanced, these methods introduce a degree of estimation error, with reported error rates ranging from 1.46% to 2.14% per allele, which can impact the accuracy of imputed genotypes. [6] Moreover, genetic variants that are not SNPs, such as certain repeat polymorphisms, may not be captured by standard SNP arrays or imputation panels, precluding their assessment in GWAS and limiting a complete understanding of genetic influences on the trait. [2]
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”The generalizability of genetic findings is frequently constrained by the ethnic and geographic composition of the study populations. Many studies are conducted in samples that are neither ethnically diverse nor nationally representative, with participants often self-reporting European ancestry. [7] This lack of diversity means that the applicability of the results to other ethnic groups or broader, more heterogeneous populations remains uncertain [7] highlighting the need for more inclusive research to understand population-specific genetic effects.
Limitations in the precise characterization of the biomarker trait also exist. Researchers may rely on proxy measures when direct, comprehensive assessments are unavailable, such as using TSH as an indicator of thyroid function without measures of free thyroxine or a thorough assessment of thyroid disease.[7] Additionally, the methods used for quantifying biomarker levels may rely on equations or assays developed in smaller, specific samples, which might not be appropriate or fully accurate when applied to large, population-based cohorts. [7] It is also possible that a biomarker may reflect broader physiological risks beyond its primary intended function, introducing potential confounding effects that could obscure direct genetic associations or lead to misinterpretation of a variant’s biological role. [7]
Interpretation Challenges and Remaining Knowledge Gaps
Section titled “Interpretation Challenges and Remaining Knowledge Gaps”A fundamental challenge in GWAS is the interpretation and prioritization of numerous associated genetic variants for functional follow-up. [2] Many identified SNPs may not be the direct causal variants but rather markers in linkage disequilibrium with an unknown functional locus. The complex genetic architecture of traits means that multiple causal variants might exist within the same gene, further complicating the elucidation of specific genetic mechanisms. [3] Furthermore, research designs focusing on multivariable models, while important for controlling confounders, might inadvertently overlook important bivariate associations between SNPs and the trait, thus missing simpler, yet significant, genetic relationships. [7]
The reporting of genetic associations can also introduce a degree of bias, particularly when studies emphasize only the strongest signals in a locus, potentially presenting an incomplete picture of the genetic landscape. [6]Despite comprehensive statistical adjustments for known covariates like age, smoking status, and body-mass index, the potential influence of unmeasured environmental factors, gene-environment interactions, or epigenetic modifications remains. These unaddressed confounders and complexities contribute to the ‘missing heritability’ phenomenon and underscore the ongoing knowledge gaps in fully understanding the complete genetic and environmental etiology of complex traits.
Variants
Section titled “Variants”Genetic variations across the human genome play a significant role in individual differences in health, disease susceptibility, and nutrient metabolism, including that of vitamin C. Among these, variants within genes directly involved in vitamin C transport or indirectly affecting cellular processes where vitamin C plays a critical role are particularly noteworthy. Understanding these genetic influences can shed light on personal nutritional requirements and disease risk profiles.
Variations in genes encoding vitamin C transporters directly impact its availability and utilization within the body. TheSLC23A1gene, for instance, codes for the sodium-dependent vitamin C transporter 1 (SVCT1), which is crucial for absorbing vitamin C from the diet in the intestines and reabsorbing it in the kidneys to maintain systemic levels. A variant likers33972313 within or near SLC23A1can influence the transporter’s efficiency, potentially leading to individual differences in plasma vitamin C concentrations. Similarly, theSLC23A3gene encodes SVCT3, a transporter found in various tissues, including the brain, heart, and skeletal muscle, contributing to tissue-specific vitamin C uptake and protection against oxidative stress. The variantrs13028225 in SLC23A3may alter vitamin C accumulation in these vital organs, affecting local antioxidant defense mechanisms. These genetic differences can influence how individuals respond to dietary vitamin C intake.[8]Proper vitamin C levels are essential for numerous physiological functions, including immune response and collagen synthesis.
Other variants influence fundamental cellular processes that are indirectly linked to vitamin C’s broader roles in health. For example,CHPT1 (rs2559850 ) is involved in phosphatidylcholine biosynthesis, a vital component of cell membranes, and its activity can influence lipid metabolism and cellular integrity. RER1 (rs6693447 ) plays a role in protein quality control by retrieving specific proteins to the endoplasmic reticulum, ensuring proper protein folding and function, a process critical for overall cell health. Meanwhile, variants near or within SNRPF-DT (rs117885456 ), an uncharacterized divergent transcript of SNRPF(a gene critical for mRNA splicing), could potentially impact fundamental gene expression and protein synthesis pathways. Disruptions in these core cellular functions can increase oxidative stress, highlighting the importance of antioxidants like vitamin C in maintaining cellular balance and protecting against damage.[8]
Non-coding RNAs and pseudogenes also harbor variants with potential implications. The variant rs9895661 is located near TBX2-AS1, a long non-coding RNA, and BCAS3, a gene linked to cell growth and survival; variations here might modulate cellular proliferation and differentiation, processes where vitamin C can act as an epigenetic regulator or antioxidant. Similarly,rs56738967 lies near LINC01229 and MAFTRR, both non-coding RNAs that regulate gene expression, potentially affecting diverse cellular pathways. The pseudogene GSTA11P contains the variant rs7740812 , and while pseudogenes are generally non-functional, variations within them can sometimes influence the expression of active functional genes in the GSTfamily, which are crucial for detoxification and antioxidant defense, pathways significantly supported by vitamin C. Genetic differences can impact the efficiency of these regulatory elements, potentially influencing disease susceptibility.
Finally, variants in genes governing cell signaling and nutrient transport can also have downstream effects relevant to vitamin C. Thers10051765 variant is located in a region between RGS14, which modulates G protein signaling important for cell communication, and SLC34A1, a kidney-specific sodium-phosphate cotransporter. WhileSLC34A1does not transport vitamin C, healthy kidney function is critical for maintaining overall nutrient balance, and cellular signaling pathways can interact with vitamin C’s roles as a cofactor or antioxidant. Perhaps most notably,AKT1 (rs10136000 ) is a central kinase in the PI3K/AKT pathway, which governs cell growth, survival, and metabolism. Variants in AKT1can alter the sensitivity of cells to growth factors and stress, and vitamin C has been observed to modulateAKTsignaling, particularly in cancer research, by influencing cell proliferation and apoptosis. These genetic predispositions may therefore affect an individual’s susceptibility to diseases where vitamin C plays a protective or modulatory role.[8]
There is no information about the management, treatment, or prevention of ‘vitamin c’ in the provided context.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs33972313 | SLC23A1 | serum creatinine amount glomerular filtration rate vitamin c measurement glycerate measurement oxalate measurement |
| rs13028225 | SLC23A3 | vitamin c measurement |
| rs2559850 | CHPT1 | blood protein amount vitamin c measurement glycosyltransferase-like protein LARGE1 measurement protein measurement cathepsin L2 measurement |
| rs9895661 | TBX2-AS1, BCAS3 | hematocrit chronic kidney disease, serum creatinine amount urinary system trait glomerular filtration rate chronic kidney disease |
| rs117885456 | SNRPF-DT | vitamin c measurement |
| rs6693447 | RER1 | alkaline phosphatase measurement vitamin c measurement fatty acid amount omega-3 polyunsaturated fatty acid measurement degree of unsaturation measurement |
| rs56738967 | LINC01229, MAFTRR | vitamin c measurement alkaline phosphatase measurement platelet crit |
| rs7740812 | GSTA11P | vitamin c measurement |
| rs10051765 | RGS14 - SLC34A1 | vitamin c measurement nephrolithiasis fibroblast growth factor 23 amount phosphate measurement inflammatory bowel disease |
| rs10136000 | AKT1 | vitamin c measurement |
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Antioxidant Defense and Disease Modulation
Section titled “Antioxidant Defense and Disease Modulation”Antioxidant vitamins and minerals play a role in biological processes by contributing to the body’s defense mechanisms. These compounds were investigated in primary prevention trials, such as the SU.VI.MAX study, for their potential health effects in relation to cardiovascular diseases and cancers.[1]The trial involved nutritional doses of these antioxidants and aimed to assess their impact on disease incidence in a general population.[1]While the specific molecular interactions of individual components were not detailed, the collective action of antioxidant vitamins is generally understood to involve neutralizing free radicals, thereby mitigating oxidative stress that can contribute to the pathogenesis of various chronic conditions.[1]
References
Section titled “References”[1] Hercberg, S., et al. “The SU.VI.MAX Study: a randomized, placebo-controlled trial of the health effects of antioxidant vitamins and minerals.”Archives of Internal Medicine, vol. 164, no. 21, 2004, pp. 2335–2342.
[2] Benjamin, E. J. et al. (2007). Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med Genet.
[3] Sabatti, C. et al. (2009). Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat Genet.
[4] Pare, G. et al. (2008). 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.
[5] O’Donnell, C. J. et al. (2007). Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study. BMC Med Genet.
[6] Willer, C. J. et al. (2008). Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet.
[7] Hwang, S. J. et al. (2007). A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study. BMC Med Genet.
[8] Burkhardt R. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol. 2008;28(10):1858-1865.