Cystic Fibrosis Related Diabetes
Introduction
Section titled “Introduction”Background
Section titled “Background”Cystic Fibrosis Related Diabetes (CFRD) is a significant comorbidity of cystic fibrosis (CF), a severe genetic disorder that affects multiple organ systems, predominantly the lungs and pancreas. CFRD is a distinct form of diabetes, sharing characteristics with both type 1 and type 2 diabetes but possessing its own unique pathological mechanisms and clinical course. Its emergence is often associated with advanced CF disease and increasing patient longevity.
Biological Basis
Section titled “Biological Basis”Cystic fibrosis is caused by mutations in theCFTRgene, which encodes the Cystic Fibrosis Transmembrane Conductance Regulator protein. This protein functions as an ion channel, essential for the transport of chloride and bicarbonate ions across epithelial cell membranes. In the pancreas,CFTRdysfunction leads to the production of thick, sticky mucus that obstructs pancreatic ducts. This obstruction causes progressive damage to the pancreatic exocrine tissue and, subsequently, the insulin-producing beta cells, resulting in impaired insulin secretion. This impairment is a primary characteristic of CFRD.[1] Research into other forms of diabetes also highlights the critical role of impaired pancreatic beta-cell function in the development of hyperglycemia. [2] Many patients with CF are characterized by pancreatic insufficiency. [1]
Clinical Relevance
Section titled “Clinical Relevance”The development of CFRD has a profound impact on the clinical outcomes of individuals with CF. It is associated with accelerated decline in lung function, increased risk of malnutrition, and a higher mortality rate. Early detection and aggressive management, typically involving insulin therapy, are crucial to mitigate these adverse effects, preserve lung health, and improve overall survival and quality of life for CF patients. Studies on diabetes treatment underscore the importance of intensive intervention in preventing the development and progression of long-term complications.[3]
Social Importance
Section titled “Social Importance”As therapeutic advancements have significantly extended the life expectancy of individuals with cystic fibrosis, the prevalence of CFRD has concurrently risen, positioning it as a growing challenge in CF care. Understanding the genetic and environmental factors contributing to CFRD is vital for developing effective screening protocols, preventive strategies, and personalized treatment plans. Such knowledge is essential for reducing the burden of disease and enhancing the well-being of the CF population. The global health impact of diabetes, with type 2 diabetes alone affecting a large portion of the world’s population and its prevalence projected to increase, emphasizes the broader social importance of addressing all its forms, including CFRD.[2]
Limitations
Section titled “Limitations”Methodological and Statistical Challenges
Section titled “Methodological and Statistical Challenges”Genetic studies for complex traits such as cystic fibrosis related diabetes often encounter significant methodological and statistical challenges. A primary limitation is typically the relatively small sample sizes available, particularly when compared to the vast cohorts utilized in large-scale meta-analyses for other common diseases ([4]). This constraint can lead to insufficient statistical power, making it difficult to detect genetic variants that exert only modest effects on disease susceptibility, thereby potentially missing many true associations and providing an incomplete picture of the genetic architecture of cystic fibrosis related diabetes ([5]). The smaller sample sizes inherent to studies of conditions within specific patient populations, such as cystic fibrosis, can further exacerbate these power issues ([1]).
Furthermore, the replication of initial genetic findings remains a substantial hurdle. A lack of consistent replication across studies can stem from various factors, including the inherent under-powering of individual studies to detect subtle effects, or the presence of false-positive associations in discovery phases ([5]). The “winner’s curse” phenomenon can also contribute, where early studies reporting a significant association may overestimate the true effect size of a genetic variant, complicating subsequent replication attempts ([5]). Additionally, observed heterogeneity in the strength of association across different studies, possibly reflecting genuine biological differences or variations in subject ascertainment, can confound meta-analyses and hinder a clear interpretation of genetic influences on cystic fibrosis related diabetes ([6]). While genomic control adjustments are employed to manage inflation of test statistics, residual inflation can still occur, necessitating careful evaluation of reported associations ([7]).
Population and Phenotypic Heterogeneity
Section titled “Population and Phenotypic Heterogeneity”The generalizability of genetic findings for cystic fibrosis related diabetes can be limited by significant population and phenotypic heterogeneity. Genetic associations identified in one population may not be directly transferable or have the same magnitude of effect in other ancestral groups, largely due to differences in the allele frequencies of risk variants or varying patterns of linkage disequilibrium (LD) between causal variants and genotyped markers ([5]). For example, variants strongly associated in European-derived populations might show lower frequencies or different LD structures in Asian or African populations, potentially explaining why some established associations fail to replicate or exhibit weaker effects in diverse cohorts ([7]). This highlights the critical need for genetic studies across a broader spectrum of populations to fully unravel the genetic underpinnings of cystic fibrosis related diabetes ([5]).
Moreover, the specific methods used to define and ascertain cases of cystic fibrosis related diabetes, along with the selection of control groups, can introduce significant bias and influence the genetic signals detected. Variations in diagnostic criteria, inclusion thresholds (e.g., for BMI), or the presence of co-morbidities can alter the observed patterns of genetic association ([8]). For instance, intentionally recruiting individuals from families with multiple affected members might increase statistical power but can also lead to an overestimation of effect sizes compared to studies of unselected populations ([5]). Such differences in phenotypic characterization and recruitment strategies contribute to the heterogeneity observed across studies and complicate the consistent interpretation of genetic susceptibility to cystic fibrosis related diabetes ([6]). While methods like Principal Component Analysis are used to account for population substructure, the potential for residual stratification or cryptic relatedness can still subtly affect study results ([7]).
Unaccounted Factors and Research Gaps
Section titled “Unaccounted Factors and Research Gaps”Current genetic research on cystic fibrosis related diabetes, like other complex diseases, faces limitations in fully accounting for the intricate interplay between genetic and non-genetic factors. The etiology of this condition is likely influenced by complex gene-environment interactions, where the impact of a specific genetic variant might vary significantly depending on environmental or lifestyle factors unique to a particular population ([9]). These interactions are often population-specific and challenging to fully capture with current methodologies, contributing to the “missing heritability” and indicating that much remains unknown about the complete genetic contribution to cystic fibrosis related diabetes.
Despite advancements, substantial knowledge gaps persist in understanding the comprehensive genetic basis of cystic fibrosis related diabetes. The observed inconsistencies in findings across studies and populations underscore the necessity for continued research, including the development of novel study designs and analytical approaches, particularly within high-risk groups ([5]). Moving beyond mere statistical association to elucidate the biological mechanisms of identified genetic variants requires integrating genomic data with functional studies, such as expression quantitative trait loci (eQTL) analyses ([5]). Ongoing exploration of genetically diverse populations and the collection of comprehensive phenotypic data are essential to identify additional genetic risk factors and refine our understanding of this complex condition ([5]).
Variants
Section titled “Variants”The genetic landscape of cystic fibrosis related diabetes (CFRD) involves a complex interplay of genes that influence pancreatic function, insulin secretion, and glucose metabolism. Variants within genes such likeSLC26A9, CEBPB, and RAB7Bare of particular interest due to their roles in ion transport, transcriptional regulation, and cellular trafficking, respectively, which can collectively impact the development and progression of CFRD. Type 2 diabetes, a condition sharing some pathophysiological mechanisms with CFRD, is a chronic metabolic disorder characterized by defects in both insulin secretion and its peripheral actions.[10] Understanding the specific genetic contributions from these loci can shed light on the unique challenges in managing CFRD.
The SLC26A9gene encodes a chloride channel that plays a crucial role in epithelial ion transport, particularly in the gastrointestinal tract and lungs, where it interacts with the cystic fibrosis transmembrane conductance regulator (CFTR) protein. Variants such as rs1874361 and rs2036100 in SLC26A9may alter the function or expression of this chloride channel, potentially affecting the pancreatic environment and contributing to the impaired insulin secretion seen in CFRD. Given thatCFTRdysfunction is the root cause of cystic fibrosis, any genetic variation that modifies ion transport in pancreatic beta cells could influence their ability to respond to glucose and release insulin effectively, thereby impacting diabetes risk. Heritability values for diabetes-related traits vary, with many estimates suggesting a significant genetic predisposition.[10]
The CEBPB gene, encoding CCAAT/enhancer-binding protein beta, is a transcription factor involved in various cellular processes, including immune responses, inflammation, and metabolic regulation. In the context of CFRD, variants like rs2869963 , rs6095829 , and rs11699802 in CEBPBcould influence pancreatic inflammation, beta-cell stress, and insulin production.CEBPBplays a role in adipogenesis and glucose homeostasis, and its altered activity may contribute to the metabolic dysregulation characteristic of diabetes. For instance, the regulation of beta-cell processes can be influenced by various factors, including the activity of certain kinases and the overall cellular environment.[11]Dysfunction in these pathways can exacerbate the existing pancreatic damage in cystic fibrosis patients, increasing their susceptibility to diabetes.
Finally, the RAB7B gene, often discussed in relation to SLC26A9 due to potential functional interactions, encodes a small GTPase involved in regulating vesicular trafficking and lysosomal degradation pathways. The variant rs4077468 in RAB7Bcould affect intracellular transport processes within pancreatic beta cells, potentially impacting the synthesis, storage, or secretion of insulin. Efficient insulin granule formation and release are critical for maintaining glucose homeostasis, and defects in these processes are a hallmark of diabetes. Genetic variations that influence gene expression or protein function can lead to altered molecular processes, which in turn can affect disease susceptibility.[12] Therefore, variations in RAB7Bcould modulate the severity of beta-cell dysfunction in individuals with cystic fibrosis, influencing their risk of developing CFRD.
The provided research context does not contain specific information regarding the causes of cystic fibrosis related diabetes. Therefore, a “Causes” section for this specific trait cannot be generated based solely on the given materials.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1874361 rs2036100 | SLC26A9 | cystic fibrosis-related diabetes |
| rs2869963 rs6095829 rs11699802 | CEBPB - PELATON | cystic fibrosis associated meconium ileus cystic fibrosis-related diabetes |
| rs4077468 | SLC26A9 - RAB7B | cystic fibrosis, type 2 diabetes mellitus cystic fibrosis associated meconium ileus cystic fibrosis-related diabetes |
Biological Background
Section titled “Biological Background”Genetic Predisposition and Regulatory Mechanisms
Section titled “Genetic Predisposition and Regulatory Mechanisms”Diabetes is characterized as a complex trait, arising from the intricate interplay between genetic predispositions and environmental factors. Most cases are considered polygenic disorders, meaning that multiple genetic variants each contribute a partial and additive effect to an individual’s susceptibility. However, a smaller proportion of cases, such as maturity-onset diabetes of the young (MODY) and neonatal diabetes, are attributed to single gene defects. [2]Genome-wide association studies (GWAS) have been instrumental in identifying numerous susceptibility loci for various forms of diabetes across diverse populations, providing critical insights into the genetic architecture of the disease.[13]These identified genomic regions often contain genes that play vital roles in cellular functions related to insulin secretion, insulin action, and immune system regulation.
Variations in specific genes, frequently in the form of single nucleotide polymorphisms (SNPs), have been directly linked to diabetes susceptibility. For instance, variants within theKCNQ1gene, which encodes a potassium channel, are consistently associated with type 2 diabetes in both East Asian and European populations.[14] Other genes that have been strongly implicated in type 2 diabetes include PPARG (peroxisome proliferator-activated receptor-gamma), KCNJ11(potassium inwardly-rectifying channel J11), andTCF2 (transcription factor 2 isoform b). [2] For type 1 diabetes, beyond the major histocompatibility complex (MHC) region, studies have highlighted the significance of non-MHS loci such as the IL2 gene, reflecting regulatory variation within the IL2 pathway, and a region on chromosome 12p13 that includes CD69 and multiple CLEC (C-type lectin domain family) genes, pointing to the importance of immune system modulation. [15]The precise expression patterns of these genes, influenced by regulatory elements and potentially epigenetic modifications, contribute to the cumulative genetic risk and the progression of the disease.
Pathophysiology of Glucose Dysregulation
Section titled “Pathophysiology of Glucose Dysregulation”A fundamental pathophysiological process in diabetes involves the pancreatic beta cells, which are specialized endocrine cells responsible for the synthesis and secretion of insulin. Impaired beta cell function is a defining characteristic of type 2 diabetes, leading to insufficient insulin release relative to metabolic demand.[2]Many of the genetic variants identified through GWAS are associated with genes expressed in pancreatic islets, underscoring their critical role in beta-cell physiology and the maintenance of glucose homeostasis.[16]The proper functioning of these cells is paramount for regulating blood glucose levels, and their dysfunction invariably leads to hyperglycemia.
Beyond the primary failure of beta-cell function, other critical pathophysiological components of diabetes include decreased insulin action at target tissues, a condition known as insulin resistance, and an increased output of glucose by the liver.[2]Insulin resistance disrupts normal metabolic processes, as peripheral tissues like muscle and fat fail to adequately take up glucose in response to insulin, thereby contributing to elevated blood glucose. While compensatory responses, such as an initial increase in insulin production, may temporarily manage hyperglycemia, prolonged metabolic stress can eventually exhaust beta cells, leading to more severe and persistent dysfunction.
Key Molecular Players and Signaling Pathways
Section titled “Key Molecular Players and Signaling Pathways”A diverse array of critical biomolecules and intricate signaling pathways are central to the development and progression of diabetes. Insulin, a peptide hormone, is secreted by beta cells and is the primary regulator of glucose metabolism.[2]Disruptions in its production or action are directly implicated in the disease. Key proteins such asKCNQ1 and KCNJ11are potassium channels that play a crucial role in regulating the electrical activity of beta cells and, consequently, insulin secretion.[14] Transcription factors, including PPARG and TCF2, are vital for controlling the expression of genes involved in lipid and glucose metabolism, as well as beta-cell development and function.[2]
Signaling pathways involving IRS1(insulin receptor substrate 1) are essential for mediating insulin action within target tissues, and genetic variants affectingIRS1expression or function can contribute significantly to insulin resistance.[16] In the context of type 1 diabetes, the IL2 pathway is implicated in the autoimmune processes that lead to the selective destruction of pancreatic beta cells. [15] Furthermore, research has identified other genes associated with type 2 diabetes susceptibility, such as TP53INP1, CCNE2, ZBED3, CENTD2, HNF1A, and PRC1, suggesting their involvement in fundamental cellular processes like cell cycle regulation within pancreatic islets. [16] These biomolecules and their associated pathways collectively form an elaborate regulatory network, and disturbances within this network are key contributors to the onset and progression of diabetes.
Tissue-Specific Effects and Systemic Consequences
Section titled “Tissue-Specific Effects and Systemic Consequences”The biological mechanisms underpinning diabetes manifest with significant tissue- and organ-specific effects, extending beyond the primary dysfunction in the pancreas. The liver plays a critical role in glucose homeostasis, and in diabetes, increased glucose output by the liver contributes substantially to the hyperglycemic state, particularly in type 2 diabetes.[2]Peripheral tissues such as skeletal muscle and adipose tissue are primary targets for insulin action, and their reduced responsiveness to insulin, characteristic of insulin resistance, has profound systemic consequences for glucose uptake and utilization.[2]
The complex interplay between these various organs and their cellular functions ultimately gives rise to a systemic metabolic disorder. For example, the combined impact of dysfunctional beta cells, insulin resistance in peripheral tissues, and dysregulated glucose production by the liver collectively drives the persistent hyperglycemic condition seen in diabetes. The long-term effects of chronic hyperglycemia are widespread, leading to microvascular and cardiovascular diseases, which underscore the broad systemic consequences of these metabolic disruptions.[13] A comprehensive understanding of these intricate inter-organ communications and their underlying molecular mechanisms is therefore essential for grasping the full scope and impact of diabetes.
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Genetic Architecture and Regulatory Mechanisms
Section titled “Genetic Architecture and Regulatory Mechanisms”The development of diabetes, particularly type 2 diabetes, is understood as a complex trait influenced by an intricate interplay of genetic factors and environmental elements. Genome-wide association studies have been instrumental in identifying genetic variations associated with diabetes-related traits, revealing the polygenic nature of the disease.[17] These genetic predispositions often impact regulatory mechanisms that control gene expression and protein function, ultimately leading to pathway dysregulation. For instance, a common polymorphism in the PPAR-gammagene has been identified as being associated with a decreased risk of type 2 diabetes, highlighting how specific genetic variants can modulate disease susceptibility.[18] Such genetic variations can alter the efficiency of gene regulation, impacting the levels or activity of critical proteins involved in metabolic processes.
Transcriptional Control of Metabolic Pathways by PPAR-gamma
Section titled “Transcriptional Control of Metabolic Pathways by PPAR-gamma”Peroxisome proliferator-activated receptor gamma (PPAR-gamma) functions as a nuclear receptor and a crucial transcription factor involved in regulating various metabolic pathways, including adipogenesis, lipid metabolism, and glucose homeostasis. Activation ofPPAR-gamma typically involves ligand binding, which then enables it to bind to specific DNA sequences, thereby modulating the transcription of target genes. The presence of a common polymorphism in the PPAR-gamma gene, which is associated with a reduced risk of type 2 diabetes, suggests that this variant may alter the receptor’s activity or its interaction with co-regulators, leading to more favorable metabolic outcomes. [18] This genetic influence on PPAR-gamma function provides a mechanism through which transcriptional control can impact metabolic regulation and, consequently, an individual’s susceptibility to diabetes.
Pancreatic Beta-Cell Signaling and Ion Channel Dynamics
Section titled “Pancreatic Beta-Cell Signaling and Ion Channel Dynamics”The proper functioning of pancreatic beta-cells is essential for maintaining glucose homeostasis, primarily through the regulated secretion of insulin. A key component in this process is the adenosine triphosphate (ATP)-sensitive potassium (KATP) channel, which consists of two subunits: Kir6.2 (encoded by KCNJ11) and SUR1 (encoded by ABCC8). [19]These channels are critical for coupling glucose metabolism to electrical activity and insulin release; when glucose levels rise, ATP production increases, closing theKATPchannels, depolarizing the beta-cell membrane, and triggering insulin secretion. Genetic variants, such as theKCNJ11E23K variant, have been confirmed to be associated with type 2 diabetes, indicating that alterations in these ion channel subunits can lead to dysfunctional beta-cell signaling and impaired insulin release.[19] This dysregulation in ion channel dynamics represents a direct mechanistic link between genetic variation, protein modification, and the pathogenesis of diabetes.
Network Interactions and Systems-Level Dysregulation
Section titled “Network Interactions and Systems-Level Dysregulation”Diabetes arises from a complex interplay of multiple genetic and environmental factors, making it a classic example of a complex trait. [20]At a systems level, various pathways do not operate in isolation but rather engage in intricate crosstalk and network interactions. Genetic studies, including genome-wide association studies, reveal that the inherited basis of diabetes involves contributions from numerous genes, each potentially influencing different aspects of glucose metabolism, insulin signaling, and beta-cell function.[17] The collective impact of these genetic variations, some of which may affect PPAR-gamma mediated transcription or KATP channel activity, leads to emergent properties of dysregulation across interconnected biological networks. Understanding these hierarchical regulations and the extensive pathway crosstalk is crucial for deciphering the full spectrum of diabetes pathology and identifying potential therapeutic targets that can address these multifaceted dysregulations.
Frequently Asked Questions About Cystic Fibrosis Related Diabetes
Section titled “Frequently Asked Questions About Cystic Fibrosis Related Diabetes”These questions address the most important and specific aspects of cystic fibrosis related diabetes based on current genetic research.
1. My CF friend doesn’t have diabetes; why do I?
Section titled “1. My CF friend doesn’t have diabetes; why do I?”Even though you both have CF, the development of CFRD isn’t guaranteed for everyone with the condition. While CFTR gene mutations are the primary cause, other genetic and environmental factors can influence who develops CFRD and when. This variation makes each person’s experience unique, even among those with CF.
2. Can my breathing problems make my diabetes worse?
Section titled “2. Can my breathing problems make my diabetes worse?”Yes, they are closely linked. CFRD is associated with an accelerated decline in lung function in CF patients. Managing your diabetes well, typically with insulin therapy, is crucial for preserving lung health and improving your overall well-being and longevity.
3. If I eat really healthy, can I avoid insulin for my CFRD?
Section titled “3. If I eat really healthy, can I avoid insulin for my CFRD?”For most people with CFRD, insulin therapy is crucial, even with a healthy diet. CFRD is primarily caused by progressive damage to the pancreas from CF, which impairs the insulin-producing cells. While a good diet is important for overall health, it usually cannot overcome this fundamental issue of insulin deficiency.
4. Will my kids with CF definitely get CFRD too?
Section titled “4. Will my kids with CF definitely get CFRD too?”Not necessarily. While CF is the underlying genetic cause, the development of CFRD is complex and influenced by additional genetic and environmental factors. Early screening for CFRD is important for all individuals with CF to detect it promptly if it does develop.
5. Does my CFRD get worse as I get older with CF?
Section titled “5. Does my CFRD get worse as I get older with CF?”CFRD often emerges with more advanced CF disease and as patients live longer. The progressive damage to the pancreatic beta cells over time can lead to increasingly impaired insulin secretion. Regular monitoring and proactive management are key to addressing its progression as you age.
6. Does having CFRD mean I’ll feel sicker all the time?
Section titled “6. Does having CFRD mean I’ll feel sicker all the time?”Untreated CFRD can indeed lead to increased risk of malnutrition and a higher mortality rate, making you feel sicker. However, with early detection and aggressive management, typically involving insulin therapy, you can significantly mitigate these adverse effects and improve your quality of life.
7. Can regular exercise help control my CFRD?
Section titled “7. Can regular exercise help control my CFRD?”Regular exercise is generally beneficial for managing blood sugar and overall health, which can support your CFRD management. However, CFRD primarily stems from pancreatic damage affecting insulin production. While exercise can help, it won’t replace the need for insulin therapy, which is crucial for managing this specific type of diabetes.
8. Could a special test predict my CFRD risk early?
Section titled “8. Could a special test predict my CFRD risk early?”Understanding the genetic and environmental factors contributing to CFRD is vital for developing effective screening and preventive strategies. While the article doesn’t name a specific “special test,” ongoing research aims to identify genetic markers that could help predict your risk for CFRD, allowing for earlier intervention.
9. Does my family background affect my CFRD risk?
Section titled “9. Does my family background affect my CFRD risk?”Yes, your ancestral background can play a role. Genetic associations identified in one population may not be directly transferable or have the same effect in other ancestral groups due to differences in allele frequencies or genetic patterns. This highlights the need for diverse genetic studies to fully understand CFRD risk across different populations.
10. Is my CF diabetes different from my friend’s type 2?
Section titled “10. Is my CF diabetes different from my friend’s type 2?”Yes, CFRD is a distinct form of diabetes. While it shares characteristics with both type 1 and type 2 diabetes, its unique cause is the damage to your pancreas due to the CFTRgene dysfunction, leading to impaired insulin secretion. Your friend’s type 2 diabetes has different underlying pathological mechanisms.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
Section titled “References”[1] Wright, F. A., et al. “Genome-wide association and linkage identify modifier loci of lung disease severity in cystic fibrosis at 11p13 and 20q13.2.”Nat Genet, vol. 43, no. 6, 2011, pp. 539-546.
[2] Tsai, F. J., et al. “A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese.” PLoS Genet, vol. 6, no. 2, 2010, p. e1000847.
[3] Grant, S. F., et al. “Follow-up analysis of genome-wide association data identifies novel loci for type 1 diabetes.” Diabetes, vol. 57, no. 12, 2008, pp. 3367-3371.
[4] Sim, X., et al. “Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia.” PLoS Genetics, vol. 7, no. 4, 2011, p. e1001362.
[5] Below, J. E., et al. “Genome-wide association and meta-analysis in populations from Starr County, Texas, and Mexico City identify type 2 diabetes susceptibility loci and enrichment for expression quantitative trait loci in top signals.” Diabetologia, vol. 54, no. 10, 2011, pp. 2568-77.
[6] Timpson, N. J., et al. “Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data.” Diabetes, vol. 58, no. 2, 2009, pp. 502-10.
[7] McDonough, C. W., et al. “A genome-wide association study for diabetic nephropathy genes in African Americans.” Kidney International, vol. 79, no. 3, 2011, pp. 327-36.
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[9] Salonen, J. T., et al. “Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium.” The American Journal of Human Genetics, vol. 81, no. 2, 2007, pp. 268-76.
[10] Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature; PMID: 17554300
[11] Scott LJ. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science; PMID: 17463248
[12] Voight BF. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet; PMID: 20581827
[13] Bradfield, J. P., et al. “A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci.” PLoS Genetics, vol. 7, no. 9, 2011, p. e1002293.
[14] Yasuda, Kenshi, et al. “Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus.”Nat Genet, vol. 40, no. 8, 2008, pp. 1025-29.
[15] Wellcome Trust Case Control Consortium. “Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls.” Nature, vol. 447, no. 7145, 2007, pp. 661-78.
[16] Voight, Benjamin F., et al. “Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.” Nat Genet, vol. 42, no. 7, 2010, pp. 579-89.
[17] Meigs, James B. “Genome-wide association with diabetes-related traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 64.
[18] Altshuler, David, et al. “The common PPAR-polymorphism associated decreased risk of type 2 diabetes.” Nat Genet, vol. 26, no. 1, 2000, pp. 76-80.
[19] Gloyn, Anna L., et al. “Large-scale association studies of variants in genes encoding the pancreatic b-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes.” Diabetes, vol. 52, no. 2, 2003, pp. 568-572.
[20] Florez, Jose C., et al. “The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits.”Annu Rev Genomics Hum Genet, vol. 4, 2003, pp. 257-291.