Dihydroferulic Acid Sulfate
Background
Section titled “Background”Dihydroferulic acid sulfate is a phenolic metabolite, a compound derived from the breakdown of other substances within the body. It is primarily recognized as a product of the metabolism of dietary polyphenols, particularly ferulic acid and chlorogenic acid. These precursor compounds are commonly found in a wide variety of plant-based foods and beverages, including coffee, fruits, and vegetables. Upon ingestion, these dietary polyphenols are processed by the gut microbiota and subsequently undergo further modification in human tissues, often through a process called sulfation, which leads to the formation of compounds like dihydroferulic acid sulfate.
Biological Basis
Section titled “Biological Basis”The formation of dihydroferulic acid sulfate involves a multi-step metabolic pathway. Initially, gut bacteria convert dietary phenolic acids, such as ferulic acid, into dihydroferulic acid. Following this microbial transformation, dihydroferulic acid is absorbed and undergoes phase II metabolism, predominantly sulfation, within the liver and intestinal cells. This sulfation process involves the addition of a sulfate group, which generally increases the compound’s water solubility, facilitating its transport and excretion from the body. Biologically, dihydroferulic acid sulfate is believed to contribute to the overall antioxidant and anti-inflammatory properties associated with diets rich in polyphenols. It may also influence various cellular signaling pathways, contributing to its potential health effects.[1]
Clinical Relevance
Section titled “Clinical Relevance”Dihydroferulic acid sulfate serves as a valuable biomarker for both the dietary intake of specific polyphenols and the metabolic activity of the gut microbiome. Its concentrations in biological fluids, such as urine and plasma, can indicate an individual’s exposure to ferulic acid-rich foods and reflect variations in their gut microbial composition and metabolic capacity. Emerging research suggests that higher levels of this metabolite may be linked to beneficial health outcomes, including a reduction in risk factors for cardiovascular disease and improvements in overall metabolic health. These potential benefits are often attributed to the compound’s antioxidant and anti-inflammatory characteristics.[2]
Social Importance
Section titled “Social Importance”The study of dihydroferulic acid sulfate holds significant social importance in the fields of public health and personalized nutrition. As a quantifiable marker of dietary polyphenol consumption and gut health, it offers insights into how diet influences health and disease prevention. This understanding can inform evidence-based dietary guidelines, encourage healthier eating behaviors focused on plant-rich foods, and potentially aid in developing strategies for preventing chronic illnesses by optimizing both dietary choices and gut microbial balance.[3]
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”Many studies evaluating genetic associations are constrained by sample size, which can limit statistical power and increase the risk of false negative findings.[4] For instance, some analyses, such as within-family association tests, have inherently limited power as they only utilize data from individuals with heterozygous parents. [5] Furthermore, the reported statistical significances and estimated effect sizes often require careful interpretation, particularly when unadjusted for multiple comparisons, as this can lead to an inflation of perceived significance. [5] The ultimate validation of genetic associations frequently necessitates independent replication in diverse cohorts, and a lack of such replication can indicate that initial findings may be false positives, highlighting the need for further confirmatory studies. [4]This challenge is compounded by the fact that non-replication can stem from differences in study design, statistical power, or even variations in linkage disequilibrium patterns between studies, where different single nucleotide polymorphisms (SNPs) might be associated with a trait but not with one another, potentially reflecting multiple causal variants within the same gene. [6]
Population Specificity and Generalizability
Section titled “Population Specificity and Generalizability”A significant limitation across many genetic association studies is the restricted generalizability of findings due to cohort characteristics. Often, study populations are predominantly composed of individuals of a specific ancestry, such as white Europeans, and are frequently concentrated within particular age ranges, like middle-aged to elderly individuals. [4] This demographic homogeneity means that the results may not be directly applicable or transferable to younger populations or individuals of different ethnic or racial backgrounds. [4] While some efforts are made to include multiethnic samples or account for population stratification, such as through principal component analysis, the primary findings often remain anchored to the initial, less diverse cohorts, making broad generalizations challenging without extensive cross-population validation. [7] Additionally, the timing of DNA collection in relation to a cohort’s enrollment can introduce survival bias, further impacting the representativeness of the sample. [4]
Phenotype Assessment and Environmental Confounders
Section titled “Phenotype Assessment and Environmental Confounders”The accuracy and consistency of phenotype assessment are critical for reliable genetic association studies, yet they often present limitations. Variability in biomarker levels can be influenced by transient factors such as the time of day blood samples are collected or an individual’s physiological status, like menopausal status. [5] Such environmental or physiological confounders can obscure true genetic effects, necessitating careful adjustment or standardized collection protocols to mitigate their impact. [5] Additionally, the choice of specific biomarkers and the methods used for their assessment can introduce limitations; for example, certain markers may reflect broader physiological states beyond their primary intended function, or established transforming equations might be inappropriate for diverse or large population cohorts. [8] Studies that focus exclusively on multivariable models might also miss important bivariate associations between SNPs and specific phenotypes, highlighting remaining knowledge gaps in understanding complex trait architecture. [8]
Variants
Section titled “Variants”The SLC17A4 gene encodes a protein that is a member of the Solute Carrier family 17, primarily known for its function as an organic anion transporter. These transporters are vital for moving various metabolites and xenobiotics across cell membranes, particularly in organs like the kidneys, where they contribute to the body’s detoxification and waste removal processes. The genomic region containing SLC17A4 on chromosome 6 shows extensive linkage disequilibrium with neighboring genes, SLC17A3 and SLC17A1. This broader genetic segment has been consistently linked to variations in serum uric acid levels, strongly suggestingSLC17A4’s involvement in the renal processing or systemic regulation of organic anions. [9]Research indicates that causal genetic variants influencing uric acid levels may be located within or downstream ofSLC17A3, potentially including SLC17A1 or SLC17A4, due to this observed extensive linkage disequilibrium. [9]
The single nucleotide polymorphism (SNP)rs11754288 is situated within the SLC17A4 gene. While the precise functional outcome of this variant can differ depending on its location and type, SNPs within gene regions, such as introns or regulatory sequences, can influence gene expression levels, protein structure, or overall stability, thereby affecting the transporter’s efficiency. Any such alteration in SLC17A4activity could modify the transport rates of its various physiological substrates. Given the gene’s strong association with uric acid regulation,rs11754288 may contribute to individual differences in how the body processes organic anions, potentially impacting their concentrations in the blood and urine. [9] The extensive linkage disequilibrium observed across the SLC17A3/SLC17A1/SLC17A4 region further highlights how variants like rs11754288 can be part of a complex genetic influence on diverse metabolic traits. [9]
As an organic anion transporter, SLC17A4is hypothesized to play a role in the cellular uptake or efflux of various sulfated metabolites, including dihydroferulic acid sulfate. Dihydroferulic acid sulfate is a phenolic acid metabolite, often derived from dietary sources and influenced by gut microbial metabolism, which can contribute to the body’s antioxidant defenses. Variations likers11754288 in SLC17A4could therefore impact the bioavailability, systemic levels, or urinary excretion of dihydroferulic acid sulfate, potentially affecting its physiological roles.[9]These changes might have implications for an individual’s antioxidant status and overall metabolic health, demonstrating a broader influence of this gene on the metabolism of diverse organic compounds beyond just uric acid.[9]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs11754288 | SLC17A4 | uric acid measurement dihydroferulic acid sulfate measurement X-11469 measurement |
Biological Background
Section titled “Biological Background”Genetic Regulation of Lipid Metabolism
Section titled “Genetic Regulation of Lipid Metabolism”The efficiency of lipid metabolism, particularly the synthesis of polyunsaturated fatty acids (PUFAs), is significantly influenced by genetic factors. For instance, the fatty acid delta-5 desaturase enzyme, encoded by the FADS1 gene, plays a crucial role in converting eicosatrienoyl-CoA (C20:3) to arachidonyl-CoA (C20:4). [10] Polymorphisms within the FADS1 gene or its regulatory elements can reduce the catalytic activity or protein abundance of this enzyme, thereby altering the availability of these key fatty acids. [10] Such genetic variations in the FADS1 FADS2 gene cluster are associated with the overall fatty acid composition found in phospholipids. [11]
Molecular Pathways of Glycerophospholipid Homeostasis
Section titled “Molecular Pathways of Glycerophospholipid Homeostasis”Variations in the efficiency of the delta-5 desaturase reaction have cascading effects on the profiles of various glycerophospholipids in human serum. A reduced FADS1 activity, for example, leads to increased concentrations of specific phospholipids with fewer double bonds, such as PC aa C36:3, and decreased concentrations of those with more double bonds, like PC aa C36:4. [10] This imbalance extends to other lipid classes, with observed positive associations for phosphatidylcholines (e.g., PC aa C34:2, PC ae C34:2), phosphatidylethanolamines (e.g., PE aa C34:2), and phosphatidylinositol (e.g., PI aa C36:2) that contain fewer double bonds in their PUFA side chains. [10]Furthermore, altered phosphatidylcholine homeostasis can indirectly affect sphingomyelin concentrations, as sphingomyelin is produced from phosphatidylcholine via sphingomyelin synthase activity, and can also lead to changes in lyso-phosphatidylethanolamine levels.[10]
Urate Transport and Metabolic Regulation
Section titled “Urate Transport and Metabolic Regulation”Uric acid homeostasis is tightly regulated by specific transport proteins, with theSLC2A9 gene (also known as GLUT9) identified as a key determinant of serum uric acid concentrations.[12] SLC2A9functions as a facilitative glucose transporter-like protein, but it is also a critical renal urate anion exchanger that regulates blood urate levels and excretion.[13] Genetic variants within the SLC2A9gene are strongly associated with serum uric acid levels, often exhibiting sex-specific effects, and are implicated in conditions such as gout.[12]Uric acid itself serves as an antioxidant defense in humans but can also be a risk marker for cardiovascular disease, metabolic syndrome, and type 2 diabetes mellitus.[14]
Systemic Consequences of Metabolite Dysregulation
Section titled “Systemic Consequences of Metabolite Dysregulation”Metabolomics provides a functional readout of the physiological state, offering insights into how genetic variants influence the homeostasis of critical biomolecules like lipids, carbohydrates, and amino acids. [10]Genetic polymorphisms that directly impact metabolite conversion or modification are expected to have significant effects on metabolite concentrations and can elucidate underlying molecular mechanisms of disease.[10] For instance, the ratio between concentrations of substrate-product pairs in an enzymatic reaction can serve as a strong indicator of the enzyme’s efficiency. [10]Disruptions in these metabolic balances, as seen with altered lipid profiles or dysregulated uric acid transport, highlight the interconnectedness of cellular pathways and their broad systemic consequences on health and disease risk.[10]
References
Section titled “References”[1] Del Rio, Daniele, et al. “Consumption of Coffee Is Associated with Higher Plasma Concentrations of Dihydroferulic Acid Sulfate, a Major Metabolite of Hydroxycinnamic Acids.”Molecular Nutrition & Food Research, vol. 59, no. 12, 2015, pp. 2482-2489.
[2] Khera, Amit V., et al. “Metabolite Profiles of Plant-Based Dietary Patterns and Their Association with Cardiometabolic Risk Factors.”Journal of the American College of Cardiology, vol. 77, no. 1, 2021, pp. 60-72.
[3] Slavin, Joanne L. “Dietary Fiber and Body Weight.”Nutrition Reviews, vol. 69, no. 2, 2011, pp. 100-110.
[4] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. 62.
[5] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 83, no. 6, 2008, pp. 758-65.
[6] Sabatti, Chiara, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 40, no. 12, 2008, pp. 1398-406.
[7] Kathiresan, Sekar, et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nature Genetics, vol. 40, no. 2, 2008, pp. 189-97.
[8] 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, 2007, p. 61.
[9] Dehghan, Abbas, 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. 1959-65.
[10] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 5, no. 11, 2009, e1000282.
[11] Schaeffer, L., et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet, vol. 15, no. 10, 2006, pp. 1745–1756.
[12] Döring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430–436.
[13] Augustin, R., et al. “Identification and characterization of human glucose transporter-like protein-9 (GLUT9): alternative splicing alters trafficking.”J Biol Chem, vol. 279, no. 16, 2004, pp. 16229–36.
[14] Ames, Bruce N., et al. “Uric acid provides an antioxidant defense in humans against oxidant- and radical-caused aging and cancer: a hypothesis.”Proc Natl Acad Sci U S A, vol. 78, no. 11, 1981, pp. 6858-6862.