Omentin
Background
Section titled “Background”Omentin is a protein primarily secreted by visceral adipose tissue, which is fat surrounding internal organs. It functions as an adipokine, a type of signaling molecule produced by fat cells that plays diverse roles in metabolic regulation.
Biological Basis
Section titled “Biological Basis”Omentin is understood to influence glucose and lipid metabolism, enhance insulin sensitivity, and exhibit anti-inflammatory and anti-atherogenic properties. These functions suggest a potential protective role in maintaining metabolic health and cardiovascular well-being.
Clinical Relevance
Section titled “Clinical Relevance”Given its biological roles, omentin levels have been investigated in various metabolic disorders. Altered omentin levels are associated with conditions such as insulin resistance, type 2 diabetes, obesity, and cardiovascular diseases.[1] Research into genetic variants influencing metabolic traits, including lipid levels and diabetes-related markers, is crucial for understanding the predisposition to these conditions. [1]Genome-wide association studies (GWAS) have been instrumental in identifying genetic loci linked to such metabolic biomarkers and disease risks, providing insights into the complex interplay of genetics and metabolism.
Social Importance
Section titled “Social Importance”The study of adipokines like omentin contributes to a broader understanding of metabolic health and disease, which has significant public health implications. Identifying the genetic and molecular basis of metabolic disorders can inform strategies for prevention, diagnosis, and treatment of widespread conditions such as obesity, diabetes, and heart disease.[1] This research holds promise for developing personalized medicine approaches to improve public health outcomes.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genome-wide association studies (GWAS) often face challenges related to cohort size and statistical power, which can impact the reliability of findings for biomarker traits. The moderate size of some cohorts can lead to inadequate statistical power, increasing the likelihood of false negative findings and limiting the ability to detect genetic effects of modest size.[2] Furthermore, the issue of replication remains a significant hurdle; only a fraction of previously reported associations have been successfully replicated, with potential explanations ranging from initial false positive findings to differences in study populations or insufficient power in replication efforts. [2]
The extensive number of tests performed in GWAS necessitates stringent statistical thresholds, which can obscure true associations or lead to moderately strong associations being false positives, despite biological plausibility. [3] To mitigate the multiple testing burden, some studies employ strategies like sex-pooled analyses, which may inadvertently miss sex-specific genetic associations. [4] Additionally, the reliance on genotype imputation to increase SNP coverage introduces a degree of uncertainty, with reported error rates per allele potentially impacting the accuracy of association calls. [5]
Generalizability and Phenotype Assessment
Section titled “Generalizability and Phenotype Assessment”The demographic characteristics of study cohorts significantly influence the generalizability of findings. Many studies are predominantly composed of individuals of white European descent, often middle-aged to elderly, which limits the applicability of the results to younger populations or those of diverse ethnic and racial backgrounds. [2] This demographic homogeneity, sometimes reinforced by the exclusion of non-European ancestry individuals during quality control, means that observed genetic associations may not hold true across different ancestral groups. [6] Furthermore, DNA collection at later examination points can introduce a survival bias, as only individuals who lived long enough to participate in those examinations are included . Specific data transformations, such as log-transforming triglycerides or standardizing residuals after age adjustment, are necessary for statistical analysis but represent choices that can affect the interpretation and comparability of findings across different research contexts. [6]
Unexplored Genetic and Environmental Influences
Section titled “Unexplored Genetic and Environmental Influences”A critical limitation in current GWAS is the often-unaddressed role of gene-environment interactions. Genetic variants can influence phenotypes in a context-specific manner, with their effects modulated by environmental factors such as diet or lifestyle.[3] The absence of comprehensive investigations into these complex interactions means that significant genetic influences may be overlooked, and the full picture of how genes contribute to biomarker traits remains incomplete. [3]
Current GWAS platforms, by using a subset of all available SNPs, may not provide complete coverage of genetic variation, potentially missing causal genes or variants due to gaps in genotyping or imputation. [4] This partial coverage also means that GWAS data are often insufficient for a comprehensive study of candidate genes without additional targeted sequencing. The ultimate validation of identified associations requires not only replication in independent cohorts but also functional studies to elucidate the biological mechanisms by which these genetic variants influence biomarker traits, addressing the fundamental challenge of prioritizing and interpreting findings from large-scale genetic screens. [2]
Variants
Section titled “Variants”The ITLN1gene encodes Intelectin-1, more commonly known as Omentin-1, an adipokine primarily secreted by visceral adipose tissue. Omentin-1 plays a crucial role in maintaining metabolic health, particularly in glucose homeostasis, insulin sensitivity, and anti-inflammatory processes. It is recognized for its beneficial effects, with lower circulating levels often observed in individuals with obesity, insulin resistance, type 2 diabetes, and cardiovascular disease[7]. [8] As a protective factor against metabolic dysfunction, variations within the ITLN1 gene, such as rs34067571 , can influence its expression or function, thereby impacting its role in these complex physiological pathways.
The single nucleotide polymorphism (SNP)rs34067571 in the ITLN1gene represents a genetic alteration that could modulate the production or activity of Omentin-1. Depending on its location, this variant might affect gene transcription, mRNA stability, or the resulting protein’s structure and function. For instance, a variant in a regulatory region could alterITLN1expression levels, leading to either increased or decreased omentin-1 secretion, which would subsequently influence systemic insulin sensitivity and inflammatory responses[9]. [2]Given omentin’s involvement in glucose metabolism, such genetic variations are of significant interest in understanding individual predisposition to metabolic disorders.
Beyond direct effects on ITLN1, genetic variations in other genes also contribute to the complex landscape of metabolic and inflammatory traits, often overlapping with omentin’s functions. For example, variants in genes likeGLUT9(Glucose Transporter 9) are associated with serum uric acid levels and glucose metabolism, impacting cellular transport of hexoses and renal uric acid regulation[10]. [11] Similarly, associations have been found for inflammatory biomarkers like Interleukin-6 (IL6) and C-reactive protein (CRP) with variants in their respective gene regions, highlighting the interconnectedness of metabolic and inflammatory pathways that omentin-1 also influences[2]. [9] Understanding how rs34067571 interacts with these broader genetic factors can provide insights into the comprehensive genetic architecture of metabolic syndrome and related conditions.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs34067571 | ITLN1 | omentin measurement |
Biological Background
Section titled “Biological Background”Regulation of Lipid Metabolism and Glucose Homeostasis
Section titled “Regulation of Lipid Metabolism and Glucose Homeostasis”The intricate balance of lipid metabolism and glucose homeostasis is central to overall physiological health, with disruptions contributing to various metabolic disorders. Research highlights the role of several key biomolecules and their associated pathways in maintaining these processes. For instance, theADIPONUTRINgene is crucial, as its expression in human adipose tissue is regulated by insulin and glucose, directly impacting metabolic function.[12]Variations within this gene have been observed to influence its expression and are associated with obesity.[13] Furthermore, other essential proteins like Angiopoietin-like 3 (ANGPTL3) and Angiopoietin-like 4 (ANGPTL4) are known regulators of lipid metabolism, with ANGPTL3 influencing lipid profiles in mice and variations in ANGPTL4linked to reduced triglycerides and increased high-density lipoprotein (HDL) levels in humans[14]. [15] The interplay of these molecules underscores a complex regulatory network governing the body’s energy balance and lipid transport.
Genetic Influences on Metabolic Traits
Section titled “Genetic Influences on Metabolic Traits”Genetic mechanisms play a significant role in shaping an individual’s metabolic profile, with numerous studies identifying specific genetic variants that contribute to common metabolic traits. Genome-wide association studies have revealed multiple loci influencing plasma levels of liver enzymes, as well as blood low-density lipoprotein (LDL) cholesterol, HDL cholesterol, and triglycerides[1], [5], [16], [17]. [18] For example, variation in the MLXIPL gene has been associated with plasma triglycerides [18] and a null mutation in human APOC3 has been shown to confer a favorable plasma lipid profile and apparent cardioprotection. [19] These findings demonstrate how specific gene functions and regulatory elements can impact metabolic processes, highlighting the genetic underpinnings of conditions like dyslipidemia and type 2 diabetes [8]. [20]
Inflammatory Pathways and Systemic Consequences
Section titled “Inflammatory Pathways and Systemic Consequences”Inflammation is a critical pathophysiological process intertwined with metabolic health, and its dysregulation can lead to systemic consequences. Biomolecules such as C-reactive protein (CRP) serve as key indicators of inflammation, with genetic loci related to metabolic-syndrome pathways, includingLEPR, HNF1A, IL6R, and GCKR, associating with plasma CRP levels. [20] The carboxypeptidase N enzyme also plays a significant role as a pleiotropic regulator of inflammation. [21]These findings illustrate how genetic variations can influence inflammatory responses, which are often disrupted in metabolic diseases, potentially impacting homeostatic mechanisms and contributing to the development of conditions like type 2 diabetes and cardiovascular disease[20]. [2]
Cellular and Tissue-Specific Mechanisms
Section titled “Cellular and Tissue-Specific Mechanisms”Biological processes at the cellular and tissue levels are fundamental to metabolic function, involving specific enzymes, structural components, and cellular interactions. For instance, hexokinase 1 (HK1) is an enzyme involved in glycolysis, with its red blood cell-specific isozyme being identified [22]. [23] Genetic variations in HK1have been associated with glycated hemoglobin in non-diabetic populations, demonstrating its role in glucose metabolism.[22] Furthermore, the patatin-like phospholipase family, which includes ADIPONUTRIN, is critical for lipid processing. [24] Proteins like Erlin-1 and Erlin-2 are novel members of the prohibitin family that define lipid-raft-like domains of the endoplasmic reticulum, indicating their involvement in cellular membrane organization and function. [25] These examples highlight the diverse cellular functions and tissue interactions that collectively contribute to systemic metabolic health.
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Clinical Relevance
Section titled “Clinical Relevance”References
Section titled “References”[1] Kathiresan, S. et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008.
[2] Benjamin, E. J. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 65.
[3] Vasan, R. S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 66.
[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, 2007, p. 64.
[5] Willer, C. J. et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, 2008.
[6] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 40, no. 12, 2008, pp. 1412-20.
[7] Sabatti, C. et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2008.
[8] Saxena, R. et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, 2007.
[9] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[10] McArdle, P. F. et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.”Arthritis Rheum, 2008.
[11] Li, S. et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, 2007.
[12] Moldes, M., et al. “Adiponutrin gene is regulated by insulin and glucose in human adipose tissue.”Eur J Endocrinol, vol. 155, no. 2, 2006, pp. 317–327.
[13] Johansson, A., et al. “Variation in the adiponutrin gene influences its expression and associates with obesity.”Diabetes, vol. 55, no. 3, 2006, pp. 826–833.
[14] Koishi, R., et al. “Angptl3 regulates lipid metabolism in mice.” Nat Genet, vol. 30, no. 2, 2002, pp. 151–157.
[15] Romeo, S., et al. “Population-based resequencing of ANGPTL4 uncovers variations that reduce triglycerides and increase HDL.” Nat Genet, vol. 39, no. 4, 2007, pp. 513–516.
[16] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56–65.
[17] Yuan, X. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, 2008.
[18] Kooner, J. S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149–151.
[19] Pollin, T. I., et al. “A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection.” Science, vol. 326, no. 5957, 2009, pp. 1234–1238.
[20] Ridker, P. M., et al. “Loci related to metabolic-syndrome pathways including LEPR, HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”Am J Hum Genet, vol. 82, no. 5, 2008, pp. 1116–1128.
[21] Matthews, K. W., et al. “Carboxypeptidase N: A pleiotropic regulator of inflammation.” Mol Immunol, vol. 40, no. 12, 2004, pp. 785–793.
[22] 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, vol. 4, no. 7, 2008, p. e1000118.
[23] Murakami, K., and Piomelli, S. “Identification of the cDNA for human red blood cell-specific hexokinase isozyme.” Blood, vol. 89, no. 3, 1997, pp. 762–766.
[24] Wilson, P. A., et al. “Characterization of the human patatin-like phospholipase family.” J Lipid Res, vol. 47, no. 9, 2006, pp. 1940–1949.
[25] Browman, D. T., et al. “Erlin-1 and erlin-2 are novel members of the prohibitin family of proteins that define lipid-raft-like domains of the ER.” J Cell Sci, vol. 119, no. 15, 2006, pp. 3149–3160.