Peptide Yy
Background
Section titled “Background”Peptide YY (PYY) is a peptide hormone primarily produced by L-cells, a type of enteroendocrine cell found predominantly in the ileum and colon of the gastrointestinal tract. Its release into the bloodstream is stimulated by food intake, with levels typically rising post-prandially in proportion to the caloric content of a meal.
Biological Basis
Section titled “Biological Basis”PYY belongs to the neuropeptide Y family, which also includes neuropeptide Y (NPY) and pancreatic polypeptide. It exerts its physiological effects by interacting with specific G protein-coupled receptors known as Y receptors (Y1, Y2, Y4, Y5). The most prominent function of PYYis its role as an anorexigenic hormone, meaning it signals satiety and helps to reduce appetite. This is achieved through several mechanisms, including slowing gastric emptying, decreasing gut motility, and inhibiting pancreatic exocrine secretion, all of which contribute to a prolonged feeling of fullness after eating.
Clinical Relevance
Section titled “Clinical Relevance”Due to its crucial involvement in regulating appetite and energy balance, PYYis a subject of significant interest in the clinical context, particularly concerning obesity and metabolic disorders. Studies have explored the relationship between circulatingPYYlevels and body weight, with some research suggesting alteredPYYresponses in individuals with obesity. Consequently,PYY has been investigated as a potential therapeutic target for weight management strategies and as a factor in understanding the pathophysiology of conditions like type 2 diabetes. Beyond metabolic health, PYY also contributes to overall digestive function and may have implications for various gastrointestinal conditions.
Social Importance
Section titled “Social Importance”The rising global prevalence of obesity, metabolic syndrome, and related health issues highlights the social importance of understanding hormones likePYY. Research into PYY’s biological mechanisms and its impact on human physiology can contribute to the development of new pharmacological treatments, more effective dietary interventions, and personalized approaches to health. By offering insights into how the body regulates hunger and satiety, PYY plays a vital role in addressing major public health challenges related to nutrition and metabolic well-being.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Statistical power and the burden of multiple testing are significant challenges in identifying genetic associations. Many studies acknowledge limited power to detect modest genetic effects, especially given the vast number of statistical tests performed in genome-wide association studies (GWAS). [1] This can lead to false negative findings, where true associations remain undetected, or conversely, an increased risk of false positive findings due to chance associations across numerous comparisons. [2] The necessity of replication in independent cohorts is frequently highlighted as crucial for validating initial findings, as many associations often fail to replicate across different studies, potentially due to initial false positives, cohort-specific factors, or differing statistical power. [2]
Furthermore, aspects of study design such as sample ascertainment and phenotype measurement can introduce constraints. For instance, some studies derive phenotypic data from means of repeated observations or from specific populations like monozygotic twins, which might influence the generalizability of findings. [3] While rigorous quality control is applied to biomarker assessments in many cohorts, variations in measurement protocols or the inherent variability of the trait itself can impact the precision and comparability of results across different research settings. The decision to perform only sex-pooled analyses, rather than sex-specific ones, might also obscure genetic associations that are unique to one sex. [4]
Population and Generalizability Challenges
Section titled “Population and Generalizability Challenges”A significant limitation lies in the generalizability of findings, as many large-scale genetic studies are predominantly conducted in cohorts of European ancestry. [2] This demographic bias means that genetic associations identified may not be directly transferable or possess the same effect sizes in populations of different ethnic or racial backgrounds, limiting their broader applicability to global health. The underrepresentation of diverse ancestries in initial discovery cohorts restricts the ability to identify population-specific variants and understand the full spectrum of genetic architecture influencing traits across human populations.
While various statistical methods, such as genomic control and principal component analysis, are employed to account for population stratification within cohorts, residual substructure can still inflate Type I error rates or obscure true associations. [5] Although studies often report minimal stratification effects after adjustment, the inherent complexity of human population structure suggests that some subtle biases may persist. The reliance on self-reported ancestry, even when confirmed by genetic clustering, may not fully capture the nuanced genetic diversity relevant to complex trait etiology.
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”Current genetic studies, particularly those employing GWAS, may not fully capture the complex interplay between genetic predispositions and environmental factors. While the provided texts do not explicitly detail gene-environment confounders, the acknowledgment that various factors can modify phenotype-genotype associations suggests that unmeasured environmental influences or gene-environment interactions could explain a portion of the unexplained trait variance. [2] This incomplete understanding contributes to the challenge of “missing heritability,” where identified genetic variants only account for a fraction of the total heritable variation for a given trait, indicating that significant genetic and environmental determinants remain undiscovered.
Furthermore, limitations in genomic coverage and imputation quality can lead to remaining knowledge gaps. Early GWAS often utilized a subset of available SNPs, potentially missing important genetic variants not tagged by the array or poorly imputed, thus hindering the comprehensive study of candidate genes or the discovery of novel loci. [4] While imputation methods improve coverage, their accuracy depends on reference panels like HapMap, which themselves may not fully represent all genetic variation, especially in diverse populations. [6] This incomplete genomic picture, coupled with the complexity of polygenic traits, means that many genetic influences, including those with pleiotropic effects, are yet to be fully elucidated.
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing an individual’s physiology, including the regulation of appetite and metabolism, often through their effects on hormones like Peptide YY (PYY). The PYY gene itself is subject to variation, with rs8074783 and rs111390042 representing single nucleotide polymorphisms that can potentially influence the production, secretion, or receptor binding affinity of thePYYhormone.PYYis a vital gut hormone released after meals, signaling satiety to the brain and thereby impacting food intake and energy balance. Alterations inPYY function due to these variants could contribute to differences in appetite control and susceptibility to metabolic conditions, as broadly observed in genome-wide association studies exploring metabolic profiles. [7] Adjacent to the PYY gene, LINC01976 is a long intergenic non-coding RNA, whose precise function is still being elucidated, but lncRNAs are known to regulate gene expression, potentially influencing PYY activity. Furthermore, the FUT2 (Fucosyltransferase 2) gene, with its variant rs516316 , plays a significant role in determining secretor status, which impacts the composition of the gut microbiome; a healthy and balanced gut microbiome is increasingly recognized for its indirect influence on metabolism and the regulation of gut hormones, includingPYY, affecting satiety and overall health.
Several other genes encoding transcription factors also feature variants that could indirectly modulate metabolic pathways and hormone regulation.POU2AF2 (POU Class 2 Homeobox Associating Factor 2), with variants rs3087967 and rs7116087 , acts as a transcriptional coactivator, modulating the activity of POU domain transcription factors essential for various developmental and cellular processes, including immune responses. Changes in POU2AF2 function could alter gene expression networks, potentially affecting inflammatory pathways or metabolic control, which can then have downstream effects on hormones like PYY. [8] Similarly, ETV1 (ETS Variant 1), a transcription factor with the variant rs749723594 , is involved in cell proliferation, differentiation, and development, including neuronal and endocrine cell differentiation. Variations in ETV1could impact the development or function of cells critical for metabolic regulation or the production of gut hormones. Another zinc finger transcription factor,ZNF91 (Zinc Finger Protein 91), featuring the rs399348 variant, is involved in gene regulation during stress responses and cell growth. Alterations in these broad regulatory genes can have cascading effects on cellular pathways, some of which may converge on metabolic health and the intricate signaling networks governing appetite-regulating hormones.
Beyond direct hormone regulation and transcription, other genes with diverse cellular functions also carry variants of interest.CAST(Calpastatin), with variantsrs56092644 and rs546750808 , encodes an inhibitor of calpain proteases, which are involved in various cellular processes including muscle protein turnover and cell signaling. Imbalances in the calpain-calpastatin system could affect cellular protein degradation and signaling, indirectly influencing metabolic processes that might impactPYY secretion or sensitivity. [4] LSM12 (LSM12 homolog, U6 snRNA associated), a component of the LSM protein complex with variant rs537092999 , plays a role in RNA processing and decay, suggesting its involvement in regulating gene expression at the post-transcriptional level. Such broad impacts on gene expression could encompass metabolic regulators or PYY production. The BCL2 (B-cell CLL/lymphoma 2) gene, featuring rs72943036 , is a critical anti-apoptotic protein that promotes cell survival. While not directly involved in PYY signaling, dysregulation of apoptosis can contribute to metabolic disorders and inflammation, indirectly affecting hormonal balance. Lastly, CFH (Complement Factor H), a key regulator of the complement system with variant rs10922098 , influences innate immunity and inflammatory responses. Chronic low-grade inflammation, often linked to complement dysregulation, is associated with metabolic dysfunction and can influence the regulation of appetite hormones like PYY, thus creating an indirect link between CFH variants and metabolic health. [9]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs8074783 | LINC01976 - PYY | blood protein amount peptide yy measurement |
| rs56092644 rs546750808 | CAST | blood protein amount level of gastrin in blood promotilin measurement protein measurement chromogranin-A measurement |
| rs3087967 rs7116087 | POU2AF2 | colorectal cancer, colorectal adenoma colorectal cancer rectum cancer peptide yy measurement polyp of colon |
| rs516316 | FUT2 | susceptibility to mumps measurement C-reactive protein measurement secreted and transmembrane protein 1 measurement polypeptide N-acetylgalactosaminyltransferase 3 measurement level of 3-galactosyl-N-acetylglucosaminide 4-alpha-L-fucosyltransferase FUT3 in blood, level of 4-galactosyl-N-acetylglucosaminide 3-alpha-L-fucosyltransferase FUT5 in blood |
| rs749723594 | RBMX2P4 - ETV1 | peptide yy measurement |
| rs537092999 | LSM12 | peptide yy measurement |
| rs399348 | VN1R91P - ZNF91 | peptide yy measurement |
| rs72943036 | BCL2 | peptide yy measurement benign colon neoplasm polyp of colon |
| rs111390042 | PYY | peptide yy measurement |
| rs10922098 | CFH | protein measurement blood protein amount uromodulin measurement probable G-protein coupled receptor 135 measurement g-protein coupled receptor 26 measurement |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”References
Section titled “References”[1] 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 Medical Genetics, vol. 8, suppl. 1, 2007, S2.
[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, S10.
[3] 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. 758-766.
[4] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, suppl. 1, 2007, S9.
[5] 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 Genetics, vol. 4, no. 7, 2008, e1000118.
[6] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” American Journal of Human Genetics, vol. 83, no. 5, 2008, pp. 520-528.
[7] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genetics, vol. 4, no. 11, 2008, e1000282.
[8] Reiner, Alexander P., et al. “Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein.”The American Journal of Human Genetics, vol. 82, no. 5, 2008, pp. 1193-1201.
[9] 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, no. S1, 2007, S10.