Caffeic Acid
Introduction
Section titled “Introduction”Caffeic acid is a naturally occurring phenolic compound widely found in plants, where it plays a role in defense mechanisms and structural support. As a derivative of cinnamic acid, it is abundant in common dietary sources such as coffee, fruits, vegetables, and wine. Caffeic acid is part of a broader class of phytochemicals known for their diverse biological activities.
Biological Basis
Section titled “Biological Basis”In humans, caffeic acid is a metabolite whose levels in serum can be influenced by various factors, including genetics. Research has demonstrated the ability to conduct genome-wide association studies (GWAS) to identify genetic variants that associate with profiles of metabolites like caffeic acid in human serum.[1]These studies aim to understand the genetic architecture underlying individual metabolic differences, providing insights into how genes interact with diet and environment to shape an individual’s metabolome.
Clinical Relevance
Section titled “Clinical Relevance”The presence and concentration of caffeic acid in human serum are of interest due to its potential health implications. As a metabolite, its levels can serve as biomarkers reflecting dietary intake, metabolic status, and potential disease risk. While specific clinical outcomes directly linked to caffeic acid levels are complex and multifactorial, its role in metabolic profiles suggests it may be relevant in studies investigating cardiometabolic health and other conditions influenced by diet and metabolism.[1]
Social Importance
Section titled “Social Importance”Caffeic acid holds social importance primarily through its widespread presence in the human diet and its potential contribution to the health benefits associated with consuming plant-based foods. Public interest in the health effects of dietary compounds drives research into understanding how substances like caffeic acid interact with human biology. This knowledge can inform dietary recommendations and contribute to strategies for disease prevention and health promotion.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Research into traits like caffeic acid, especially through genome-wide association studies (GWAS), is often constrained by study design and statistical power. Many studies, even those involving moderate-sized cohorts, may be susceptible to false negative findings due to inadequate statistical power, limiting the detection of genuine associations.[2] The extensive number of statistical tests performed in GWAS necessitates stringent p-value cut-offs, which, while reducing Type I errors, can lead to the oversight of potentially important associations, such as those that might be sex-specific. [3] Furthermore, the challenges in replicating findings across different cohorts are substantial; only a fraction of reported associations are consistently replicated, possibly due to initial false positives, differences in study cohorts, or insufficient power in replication studies. [2] This highlights the critical need for independent validation to confirm genetic associations and prevent the propagation of spurious results.
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”The applicability of findings from genetic studies can be limited by the demographic characteristics of the study populations. Many cohorts are predominantly composed of individuals of specific ancestries, such as those of European descent, which restricts the generalizability of the results to younger populations or individuals from other ethnic and racial backgrounds. [2] Population stratification, even within seemingly homogenous groups, can introduce spurious associations if not adequately addressed through methods like principal component analysis or genomic control. [4] Additionally, the precise measurement and definition of phenotypes are crucial; reliance on proxy indicators or existing equations developed in different populations or using alternative methodologies can introduce bias or inaccuracies in trait assessment, potentially obscuring true genetic influences. [5] The timing of data collection, such as DNA sampling at later examinations, can also introduce survival bias, further impacting the representativeness of the study population. [2]
Unexplained Genetic Variance and Future Research Directions
Section titled “Unexplained Genetic Variance and Future Research Directions”Despite the advancements in identifying genetic variants, a significant portion of the heritability for many complex traits remains unexplained, pointing to considerable knowledge gaps. Current GWAS, even with extensive SNP panels, may not fully capture all genetic variation due to incomplete coverage of the genome or specific gene regions, potentially missing causal variants or genes that contribute to the trait. [6] The complexity of genetic architecture means that observed associations might be with SNPs in linkage disequilibrium with an unknown causal variant, or multiple causal variants within the same gene, making the precise identification of underlying biological mechanisms challenging. [7] Addressing these gaps requires not only denser genetic arrays but also comprehensive functional studies and investigations into gene-by-environment interactions, which are critical for a complete understanding of the genetic and environmental factors influencing complex traits. [2]
Variants
Section titled “Variants”The genetic variants rs11776694 , rs857445 , and rs1186859 are located in genomic regions associated with genes involved in fundamental cellular processes, which can influence an individual’s biological responses, including those to dietary compounds like caffeic acid. These single nucleotide polymorphisms (SNPs) and their associated genes play roles in diverse pathways ranging from cell signaling and immune function to gene regulation and protein synthesis. Understanding these connections helps to elucidate how genetic predispositions might modulate the effects of bioactive molecules.
The variant rs11776694 is found near the ST3GAL1 gene, which encodes an enzyme crucial for adding sialic acid to various molecules, a process known as sialylation. This biochemical modification is fundamental for cell-to-cell communication, immune responses, and the regulation of cellular adhesion. [8] For instance, ST3GAL1 is involved in synthesizing the sialyl Lewis X antigen, which plays a key role in mediating leukocyte rolling and adhesion during inflammation. Variations like rs11776694 may influence the expression or enzymatic activity of ST3GAL1, potentially altering the overall pattern of sialylation in cells. [9]Such alterations could impact how cells respond to various stimuli, including bioactive compounds like caffeic acid, a natural phenolic compound known for its antioxidant and anti-inflammatory properties.
The genomic region containing rs857445 is associated with several non-coding RNA genes, including LINC01108, a long intergenic non-coding RNA, and the pseudogenes RNU6-793P and RNU6-1145P. Long non-coding RNAs like LINC01108 are increasingly recognized for their diverse regulatory roles, which can include modulating gene expression, influencing chromatin structure, and participating in post-transcriptional control. [10] Similarly, while RNU6 pseudogenes are non-coding copies of the essential U6 small nuclear RNA involved in splicing, some pseudogenes can exert regulatory effects, such as acting as microRNA sponges or influencing the expression of their functional counterparts. A variant like rs857445 could affect the transcription, stability, or processing of these non-coding RNAs, thereby subtly altering cellular regulatory networks. [11]These genetic influences might modify cellular responses to environmental factors, including the anti-inflammatory and antioxidant effects of caffeic acid.
The variant rs1186859 is located near RPS24P7, a pseudogene of RPS24, which encodes a ribosomal protein. Ribosomal proteins are crucial components of ribosomes, the cellular machinery responsible for synthesizing all proteins in a cell. While pseudogenes like RPS24P7 are often considered non-functional copies, they can sometimes play regulatory roles by influencing the expression of their functional counterparts or by sequestering regulatory molecules . A genetic variant such as rs1186859 could potentially affect the expression or stability of RPS24P7, which might, in turn, have indirect effects on the overall efficiency of protein synthesis or cellular stress responses. The broad biological activities of caffeic acid, including its ability to modulate cell growth and antioxidant pathways, could therefore be subtly influenced by variations that affect fundamental cellular processes like protein synthesis.[10]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs11776694 | ST3GAL1 | serum metabolite level caffeic acid measurement caffeine measurement |
| rs857445 | LINC01108 - RNU6-793P | caffeic acid measurement |
| rs1186859 | RNU6-1145P - RPS24P7 | caffeic acid measurement body height |
References
Section titled “References”[1] Gieger, Christian, et al. “Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum.”PLoS Genetics, vol. 4, no. 11, 2008, p. e1000282.
[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007.
[3] 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 Genet, 2007.
[4] Dehghan, A., et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, 2008.
[5] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, 2008.
[6] O’Donnell, C. J., et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet, 2007.
[7] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2008.
[8] Wallace, Cathryn, 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.
[9] Wilk, J. B., et al. “Framingham Heart Study Genome-Wide Association: Results for Pulmonary Function Measures.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S8.
[10] Gieger, Christian, et al. “Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum.”PLoS Genet, vol. 5, no. 2, 2009, p. e1000373.
[11] Melzer, David, et al. “A Genome-Wide Association Study Identifies Protein Quantitative Trait Loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, p. e1000072.